The Democratic Benefits of Centralized Institutions in Ghana

THE DEMOCRATIC BENEFITS OF CENTRALIZED INSTITUTIONS IN GHANA

By JENNIFER C. BOYLAN

A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2016 ⃝c 2016 Jennifer C. Boylan To Adventures. ACKNOWLEDGMENTS During my graduate school career, I received institutional, funding, academic and research support from a wide variety of organizations and individuals.

First, I would like to thank the institutional support I received from the Department of

Political Science and the Center for African Studies at the University of Florida (UF) for their formative roles in my graduate training, for inviting leading-edge scholars to present their research on campus, and for seeking out funding opportunities from which graduate students like myself have beneﬁted. My doctoral education and research was funded by the David L.

Boren Fellowship, UF Department of Political Science, UF Center for African Studies, UF

Center for International Business Education & Research (CIBER), and the African Studies Center at Michigan State University. Through these opportunities I was able to complete over

16 months of research across 3 trips to Ghana.

For my academic training, I would like to thank my dissertation advisor, Michael Bernhard, and my other committee members (Ben Smith, Staffan Lindberg, Brenda Chalfin and Badredine Arfi) for their encouraging, detailed, critical, and innovative remarks throughout the development and production of my dissertation project. I also need to thank my Akan-Twi teachers, particularly James Essegbey, Levi Ofoe, Patience Asare, Forster Asare Kena, and

Emmanuel Koﬁ Amo Ofori, for over 4 years of language training.

Many, many thanks to all those who assisted me in the completion of my research in Ghana. The following institutions were of particular help: Ghana Statistical Services, the

Center for Democratic Development (Ghana-CDD), the Parliament of Ghana, the Electoral

Commission (EC), the Ministry of Local Government and Rural Development (MLGRD), the

M/M/DCE offices and staff in each of the six districts in which surveys were conducted, as well as the Members of Parliament, traditional leaders, assemblypersons, unit committee persons, and any other individuals who allowed me to interview them as part of this research. I would also like to thank the following individuals for their help in the completion of this project: my survey project manager JoJo Baiden, David Kombat, Patrick Adzovor, Juliana Ama Kplorfia,

4 Prof. E. Gyimah-Boadi, Franklin Oduro, James Adimah, George Kagya-Agyemang, Benedict Fiiﬁ Appiah, Yaw Opoku, Edward Takyi, James Atikpo, Anthony Agboga, Godwin Keteku,

Chester Fiagbe, George Atta Quainoo, and each of the other surveyor assistants.

5 TABLE OF CONTENTS page

ACKNOWLEDGMENTS ...... 4

LIST OF TABLES ...... 10

LIST OF FIGURES ...... 14 ABSTRACT ...... 17

CHAPTER

1 INTRODUCTION ...... 19

1.1 The Problem ...... 20 1.2 The Proposed Solution: National and sub-National Competition ...... 21 1.3 Dominant Party Politics, Neopatrimonialism, and Ethnic Voting ...... 22 1.3.1 Elections and the Development of Dominant Party Politics ...... 22 1.3.2 Addressing Neopatrimonial Logics ...... 24 1.3.3 The Durability of Ethnic Voting ...... 26 1.3.3.1 The rationality of voting ethnically ...... 27 1.3.3.2 But why is ethnic voting bad? ...... 29 1.4 How Eﬀective National-Level Competition Develops in Divided Societies .... 30 1.4.1 Credible Oppositions ...... 30 1.4.2 Majoritarian Electoral Systems ...... 32 1.5 Taking the Eﬀects of Institutionalized National and Sub-National Competition to Ghana ...... 33 1.5.1 Majoritarian Electoral Systems in Ghana ...... 33 1.5.2 Ghana’s Centralized System of Local Government ...... 34 1.6 Competing Explanations ...... 36 1.6.1 Democratization-via-Elections ...... 36 1.6.2 Democratization-via-Economic Growth ...... 38 1.7 Outline of Chapters to Come ...... 41

2 GHANA’S HISTORY OF CENTRALIZATION AND ETHNIC POLITICS ...... 45

2.1 The Argument ...... 46 2.2 Pre-Colonial Ethnicity and Colonial Rule ...... 47 2.2.1 Ethnicity, Chiefs and Regional Identities ...... 47 2.2.2 The Educated Elite Response in the 1940’s and 1950’s ...... 49 2.3 Post-Colonial Centralization and the Ethnic Response ...... 51 2.3.1 CPP versus NLM in the Post-1951 Election Period ...... 51 2.3.2 The Post-Independence CPP Regime ...... 56 2.3.3 The NLC and the 1966 Coup ...... 57 2.3.4 Busia and the Progress Party (PP) ...... 59 2.3.5 Acheampong, the NRC and SMC-I ...... 62

6 2.3.6 Akuﬀo and the SMC-II ...... 64 2.3.7 Rawlings-I and Limann ...... 66 2.3.8 Rawlings-II ...... 69 2.4 Ethnic Voting in the Fourth Republic ...... 72 2.5 Discussion ...... 74

3 GHANA’S CENTRALIZED SYSTEM OF LOCAL GOVERNMENT & THE POWER OF THE DISTRICT CHIEF EXECUTIVE ...... 76

3.1 An Overview of Ghana’s System of Local Government ...... 77 3.2 District Assembly Authority & Revenue Sources ...... 82 3.2.1 District Assembly Authority ...... 83 3.2.2 District Assembly Revenue ...... 86 3.3 The Relationship Between MPs and DCEs ...... 89 3.4 Discussion ...... 92

4 ECOLOGICAL INFERENCE ...... 95 4.1 The Application of Ecological Inference Tools to Ghana ...... 97 4.2 Ethnic Voting in Ghana ...... 101 4.3 The Model ...... 103 4.4 Data ...... 106 4.5 Results ...... 109 4.5.1 Method of Bounds ...... 109 4.5.2 Multinomial-Dirichlet Models ...... 110 4.5.2.1 Core political party supporters ...... 110 4.5.2.2 Peripheral political party supporters ...... 111 4.5.2.3 Unincorporated groups with mixed voting patterns ...... 115 4.6 Presidential Kingmakers ...... 116 4.7 Discussion ...... 119

5 THE INSTITUTIONALIZATION OF LOCAL-LEVEL COMPETITION IN GHANA . 155 5.1 Hypotheses ...... 157 5.2 Model Overview ...... 160 5.2.1 Dependent Variable and Primary Independent Variable ...... 160 5.2.2 Controlling for Structural Conditions Impacting MP-DCE Relationships 161 5.2.3 Controlling for Structural Conditions Impacting Local Politics ..... 162 5.3 Results ...... 164 5.3.1 2000 Elections ...... 165 5.3.2 2000 Presidential Runoﬀ Elections ...... 166 5.3.3 2004 Elections ...... 166 5.3.4 2008 Elections ...... 167 5.3.5 2008 Presidential Runoﬀ Elections ...... 168 5.3.6 2012 Elections ...... 169 5.4 Alternative Explanations ...... 170 5.5 Discussion ...... 174

7 6 SURVEY ANALYSIS OF INDIVIDUALS’ VOTES ...... 191 6.1 General Conclusions from Chapter 6 ...... 193 6.2 The Survey ...... 195 6.3 The District Pairs ...... 197 6.3.1 NPP Strongholds: Bosome Freho & Birim South ...... 197 6.3.2 NDC Strongholds: Adaklu Anyigbe & Ketu South ...... 198 6.3.3 Mfantsiman & Asikuma Odoben Brakwa ...... 199 6.4 Qualitative Explanation of District-Level Politics ...... 199 6.4.1 Bosome Freho ...... 199 6.4.2 Birim South ...... 201 6.4.3 Adaklu Anyigbe ...... 203 6.4.4 Ketu South ...... 205 6.4.5 Mfantsiman ...... 208 6.4.6 Asikuma Odoben Brakwa ...... 210 6.5 Survey Analysis: Political Knowledge and Behavior ...... 212 6.5.1 Biggest Reasons for Your Vote and Votes in the Community ...... 213 6.5.1.1 Overall results ...... 213 6.5.1.2 Within district pairs ...... 215 6.5.1.3 District-by-district analysis ...... 216 6.5.2 Identifying NDC and NPP Ideologies ...... 217 6.5.2.1 The NDC ideology ...... 218 6.5.2.2 The NPP ideology ...... 219 6.5.2.3 Within district pairs ...... 220 6.5.2.4 District-by-district analysis ...... 222 6.6 Discussion ...... 224 7 PREDICTING RESPONDENTS’ VOTES AND SWING-VOTING ...... 261

7.1 Predicting Votes ...... 263 7.1.1 2004 Presidential and Parliamentary Elections ...... 265 7.1.2 2008 Presidential and Parliamentary Elections ...... 268 7.1.3 2012 Presidential and Parliamentary Elections ...... 271 7.2 Who Are The Swing Voters? ...... 273 7.2.1 Demographic Trends ...... 273 7.2.2 Logit Models Predicting Swing Voters ...... 275 7.3 Conclusion ...... 280

8 SURVEY EXPERIMENTS ...... 310

8.1 Identity Bias Voting Experiment ...... 312 8.1.1 Linear Regressions Predicting Candidate Ratings ...... 315 8.1.2 Logistic Regressions Predicting Candidate Votes ...... 319 8.1.3 Categorical Analysis ...... 321 8.2 List Experiments to Hide Undemocratic Beliefs/Behaviors ...... 327 8.3 Discussion ...... 331

8 9 CONCLUSION ...... 354 9.1 The Mixed-Methods Research Design ...... 355 9.2 Contributions to the Literature ...... 358 9.2.1 Contributions to Democratization Theory ...... 359 9.2.2 Contributions to Ethnic Politics ...... 362 9.3 Moving Forward ...... 365

APPENDIX

A VOTING PATTERNS BY TRIBE AND ETHNO-LINGUISTIC GROUP ...... 368

B METHODS OF BOUNDS ...... 375

C ECOLOGICAL INFERENCE RESULTS ...... 419

D SURVEY: MISSING RESPONSE BIAS CHECK ...... 444 BIBLIOGRAPHY ...... 445

BIOGRAPHICAL SKETCH ...... 462

9 LIST OF TABLES Table page

1-1 Ghana’s income share held by what population percentage ...... 44

3-1 District types (1996-2012) ...... 94

4-1 Ghana’s ethno-linguistic groups and tribes ...... 121 4-2 Asante bounds - Amansie West District ...... 122

5-1 Constituencies under analysis ...... 176

5-2 Changes in party votes: 2000 - 1996 ...... 177

5-3 Changes in party votes: 2000 Pres. Runoﬀ - 2000 Pres. Election ...... 179 5-4 Changes in party votes: 2004 - 2000 (reg. election) ...... 181

5-5 Changes in party votes: 2008 - 2004 ...... 183

5-6 Changes in party votes: 2008 Pres. Runoﬀ - 2008 Pres. Election ...... 185

5-7 Changes in party votes: 2012 - 2008 (reg. election) ...... 187

5-8 Number of constituency-level political party strongholds (over 65% of the vote) ... 189 5-9 Competitive and uncompetitive constituencies ...... 190

6-1 Survey population stats vis-a-vis the 2010 Ghana Census ...... 227

6-2 Bosome Freho & Birim South structural characteristics ...... 228

6-3 Bosome Freho & Birim South presidential vote patterns ...... 229 6-4 Bosome Freho & Birim South parliamentary vote patterns ...... 230

6-5 Adaklu-Anyigbe & Ketu South structural characteristics ...... 231

6-6 Adaklu-Anyigbe & Ketu South presidential vote patterns ...... 232

6-7 Adaklu-Anyigbe & Ketu South parliamentary vote patterns ...... 233

6-8 Mfantsiman* & Asikuma-Odoben-Brakwa structural characteristics ...... 234 6-9 Mfantsiman* & Asikuma-Odoben-Brakwa presidential vote patterns ...... 235

6-10 Mfantsiman* & Asikuma-Odoben-Brakwa parliamentary vote patterns ...... 236

6-11 Q9: Three biggest reasons for your vote for President ...... 237

6-12 Q13: Three biggest reasons for your vote for MP ...... 238

10 6-13 Q10: Biggest reason driving presidential votes within this community? ...... 239 6-14 Q20: Do Ghana’s political parties have diﬀerent ideologies? ...... 240

6-15 Q21: Components of the NDC’s political ideology ...... 241

6-16 Q22: Components of the NPP’s political ideology ...... 242

7-1 Predicting 2004 presidential votes ...... 283 7-2 Predicting 2004 parliamentary votes ...... 285

7-3 Predicting 2008 presidential votes ...... 287

7-4 Predicting 2008 parliamentary votes ...... 289

7-5 Predicting 2012 presidential votes ...... 291

7-6 Predicting 2012 parliamentary votes ...... 293 7-7 Swing Voters ...... 295

7-8 Swing Voters2 ...... 296

7-9 Swing Voters3 ...... 297

7-10 Swing Voters4 ...... 298 7-11 Skirt-and-blouse swing voters ...... 299

7-12 Presidential election-to-election swing voters* ...... 300

7-13 Parliamentary election-to-election swing voters* ...... 301

7-14 Logit model odds ratios- predicting swing voters across elections ...... 302

7-15 Logit model odds ratios- predicting swing voters across elections ...... 303 8-1 Average candidate ratings t-test per district ...... 333

8-2 Vote for candidate? t-tests ...... 334

8-3 Linear regression predicting candidate rating ...... 335

8-4 Linear regression predicting candidate rating ...... 336

8-5 Linear regression predicting candidate rating ...... 337 8-6 Logistic regressions (odds ratios) predicting votes for the candidate ...... 338

8-7 Logistic regressions (odds ratios) predicting votes for the candidate ...... 339

8-8 Logistic regressions (odds ratios) predicting votes for the candidate ...... 340

11 8-9 Q29: Muslim president list experiment ...... 341 8-10 Q29: Muslim president list experiment- by district ...... 342

8-11 Clientelism list experiments ...... 343

8-12 Clientelism list experiments- 2012 Presidential election by district ...... 344

8-13 Clientelism list experiments- 2012 Parliamentary elections by district ...... 345 8-14 Q34: Choose the Statement Which Is Closest To Your View ...... 346

A-1 Presidential Vote Margins by Tribe (1996-2012) ...... 369

A-2 Parliamentary Voting Margins by Tribe (1996-2012) ...... 371

A-3 Presidential Vote Margins by Ethnic Group (1996-2012) ...... 373

A-4 Parliamentary Vote Margins by Ethnic Group (1996-2012) ...... 374 B-1 District-Level Bounds of Votes by Tribe - 1996 Presidential ...... 375

B-2 District-Level Bounds of Votes by Tribe - 1996 Parliamentary ...... 378

B-3 District-Level Bounds of Votes by Tribe - 2000 Presidential ...... 381

B-4 District-Level Bounds of Votes by Tribe - 2000 Parliamentary ...... 383 B-5 District-Level Bounds of Votes by Tribe - 2000 Pres. Runoﬀ ...... 387

B-6 District-Level Bounds of Votes by Tribe - 2004 Presidential ...... 389

B-7 District-Level Bounds of Votes by Tribe - 2004 Parliamentary ...... 393

B-8 District-Level Bounds of Votes by Tribe - 2008 Presidential ...... 398

B-9 District-Level Bounds of Votes by Tribe - 2008 Parliamentary ...... 403 B-10 District-Level Bounds of Votes by Tribe - 2008 Presidential Runoﬀ ...... 408

B-11 District-Level Bounds of Votes by Tribe - 2012 Presidential ...... 413

B-12 District-Level Bounds of Votes by Tribe - 2012 Parliamentary ...... 416

C-1 2012 Presidential Vote Estimates by Tribe (urban covariate, ﬂat priors ...... 420

C-2 2012 Parliamentary Results by Tribe (urban covariate, flat priors) ...... 422 C-3 2008 Presidential Runoff Votes by Tribe (urban covariate, flat priors) ...... 424

C-4 2008 Presidential Votes by Tribe (urban covariate, ﬂat priors) ...... 426

C-5 2008 Parliamentary Votes by Tribe (urban covariate, ﬂat priors) ...... 428

12 C-6 2004 Presidential Votes by Tribe (urban covariate, ﬂat priors) ...... 430 C-7 2004 Presidential Votes by Tribe (urban covariate, ﬂat priors) ...... 432

C-8 2000 Presidential Runoﬀ Votes by Tribe (urban covariate, ﬂat priors) ...... 434

C-9 2000 Presidential Votes by Tribe (urban covariate, ﬂat priors) ...... 436

C-10 2000 Parliamentary Votes by Tribe (urban covariate, ﬂat priors) ...... 438 C-11 1996 Presidential Votes by Tribe (urban covariate, ﬂat priors) ...... 440

C-12 1996 Parliamentary Votes by Tribe (urban covariate, ﬂat priors) ...... 442

D-1 Logit Models Missing Response Bias Check ...... 444

13 LIST OF FIGURES Figure page

3-1 Overview of the system of local government in Ghana ...... 93

4-1 Asante and Akyem presidential voting statistics ...... 123

4-2 Ewe presidential and parliamentary voting statistics ...... 124 4-3 Asante and Akyem parliamentary voting statistics ...... 125

4-4 Ga and Mole Dagbani presidential voting statistics ...... 126

4-5 Akan presidential and parliamentary voting statistics ...... 127

4-6 Ga and Mole Dagbani parliamentary voting statistics ...... 128 4-7 Bimoba and Sefwi presidential voting statistics ...... 129

4-8 Dangme and Ga presidential voting statistics ...... 130

4-9 Dagarte and Dagomba presidential voting statistics ...... 131

4-10 Nankansi and Kusasi presidential voting statistics ...... 132

4-11 Bimoba and Sefwi parliamentary voting statistics ...... 133 4-12 Dangme and Ga parliamentary voting statistics ...... 134

4-13 Dagarte and Dagomba parliamentary voting statistics ...... 135

4-14 Nankansi and Kusasi parliamentary voting statistics ...... 136

4-15 Akuapem and Boron presidential voting statistics ...... 137 4-16 Denkyira/Twifo and Ahanta presidential voting statistics ...... 138

4-17 Asen and Kwahu presidential voting statistics ...... 139

4-18 Akuapem and Boron parliamentary voting statistics ...... 140

4-19 Denkyira/Twifo and Ahanta parliamentary voting statistics ...... 141

4-20 Asen and Kwahu parliamentary voting statistics ...... 142 4-21 Guan, Gruma, and Grusi presidential voting statistics ...... 143

4-22 Mande and Ethnic Others’ presidential voting statistics ...... 144

4-23 Guan, Gruma, and Grusi parliamentary voting statistics ...... 145

4-24 Mande and Ethnic Others’ parliamentary voting statistics ...... 146

14 4-25 Chokosi, Kasena, and Builsa presidential voting statistics ...... 147 4-26 Guan3, Wasa, and Sisala presidential voting statistics ...... 148

4-27 Fante, Nzema, and Guan5 presidential voting statistics ...... 149

4-28 Mamprusi and Kokomba presidential voting statistics ...... 150

4-29 Chokosi, Kasena, and Builsa parliamentary voting statistics ...... 151 4-30 Guan3, Wasa, and Sisala parliamentary voting statistics ...... 152

4-31 Fante, Nzema, and Guan5 parliamentary voting statistics ...... 153

4-32 Mamprusi and Kokomba parliamentary voting statistics ...... 154

6-1 Your vote for President- Bosome Freho and Birim South ...... 243

6-2 Your vote for President- Adaklu Anyigbe and Ketu South ...... 244 6-3 Your vote for President- Mfantsiman and Asikuma Odoben Brakwa ...... 245

6-4 Your vote for MP- Bosome Freho and Birim South ...... 246

6-5 Your vote for MP- Adaklu Anyigbe and Ketu South ...... 247

6-6 Your vote for MP- Mfantsiman and Asikuma Odoben Brakwa ...... 248 6-7 Pres. votes within the community- Bosome Freho and Birim South ...... 249

6-8 Pres. votes within the community- Adaklu Anyigbe and Ketu South ...... 250

6-9 Pres. votes within the community- Mfantsiman and Asikuma Odoben Brakwa ... 251

6-10 Do parties have diﬀerent ideologies- Bosome Freho and Birim South ...... 252

6-11 Do parties have diﬀerent ideologies- Adaklu Anyigbe and Ketu South ...... 253 6-12 Do parties have diﬀerent ideologies- Mfantsiman and Asikuma Odoben Brakwa ... 254

6-13 NDC ideology- Bosome Freho and Birim South ...... 255

6-14 NDC ideology- Adaklu Anyigbe and Ketu South ...... 256

6-15 NDC ideology- Mfantsiman and Asikuma Odoben Brakwa ...... 257

6-16 NPP ideology- Bosome Freho and Birim South ...... 258 6-17 NPP ideology- Adaklu Anyigbe and Ketu South ...... 259

6-18 NPP ideology- Mfantsiman and Asikuma Odoben Brakwa ...... 260

15 7-1 Change in probabilities for voting for NPP(0), NDC(1), or a third party (3) in the 2004 Pres. race ...... 304

7-2 Change in probabilities for voting for NPP(0), NDC(1), or a third party (3) in the 2004 Parl. races ...... 305

7-3 Change in probabilities for voting for NPP(0), NDC(1), or a third party (3) in the 2008 Pres. race ...... 306

7-4 Change in probabilities for voting for NPP(0), NDC(1), or a third party (3) in the 2008 Parl. races ...... 307

7-5 Change in probabilities for voting for NPP(0), NDC(1), or a third party (3) in the 2012 Pres. race ...... 308 7-6 Change in probabilities for voting for NPP(0), NDC(1), or a third party (3) in the 2012 Parl. races ...... 309

8-1 Vote for candidate- all districts ...... 347 8-2 Vote for candidate- Bosome Freho ...... 348

8-3 Vote for candidate- Birim South ...... 349

8-4 Vote for candidate- Adaklu Anyigbe ...... 350

8-5 Vote for candidate- Ketu South ...... 351

8-6 Vote for candidate- Mfantsiman ...... 352 8-7 Vote for candidate- Asikuma Odoben Brakwa ...... 353

16 Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulﬁllment of the Requirements for the Degree of Doctor of Philosophy THE DEMOCRATIC BENEFITS OF CENTRALIZED INSTITUTIONS IN GHANA

Jennifer C. Boylan

December 2016

Chair: Michael Bernhard Major: Political Science

Decentralization is widely believed to have positive democratic beneﬁts in new democracies, while centralized institutions are characterized as a nasty remnant of prior authoritarian regimes. Using Ghana as a case study, this dissertation explores the contradiction in Ghana’s famed democratic success despite its highly centralized political system. The ﬁndings show that

Ghana’s majoritarian electoral rules encourages turnover of power while its centralized system introduces political competition at the local-level. Both institutional dynamics encourages more responsive behavior on the part of politicians and oﬀers citizens the opportunity to consider information outside of ethnic identities when voting.

The historical background of Ghana’s institutions and their eﬀects on ethnic politics is presented as a backdrop to the current system of centralized government. Within the current system, particular emphasis is placed on the relationship between the Presidentially-appointed

District Chief Executives and locally-elected Member(s) of Parliament. Ecological Inference models are then used to prove increasing volatility in ethno-linguistic and tribal group voting patterns. OLS regressions next demonstrate that vote volatility in Presidential and

Parliamentary elections signiﬁcantly increased in areas with institutionally-promoted (i.e.

Unfriendly District Chief Executive-MP Pairs) high levels of competition as compared to low levels of competition (Friendly District Chief Executive-MP Pairs). Finally, survey evidence investigating individual vote motivations suggests that voters, and particularly swing voters,

17 increasingly rely on evaluative rationales in comparison to ethnic identity when making vote decisions.

This work demonstrates the positive outcomes of centralization in the case of Ghana. In making this argument, this dissertation also makes contributions to the study of ethnic politics by investigating the political behavior of both ethno-linguistic and tribal groups, as well as to research methods by combining a wide range of analytic tools (e.g., archival research, in-depth interviews, survey research, Ecological Inference models, and other quantitative tools) to ascertain historical, ethnographic, qualitative, and statistical aspects of the research question.

18 CHAPTER 1 INTRODUCTION

The democratic record of African1 nations since the independence period has been irregular at best. Promising democratic transitions in the 1950’s and 60’s were coupled with strongly centralized governments and powerful individual heads of state. It was popularly believed that African nations needed strong direction and tough leadership to drive out structural divides the colonial governments had largely perpetuated and did little to remedy.

After independence was achieved, Africa’s founding fathers were typically reluctant to leave oﬃce. In some instances, founding leaders eﬀectively maintained their positions (e.g., Kenyatta in Kenya, Houphouet-Boigny in Cote D’Ivoire, Kaunda in Zambia, Ahidjo in

Cameroon), while instability and power struggles quickly developed in others (e.g., Benin,

Democratic Republic of the Congo, Republic of the Congo, Sierra Leone, Ghana). Quite distinct from the initial democratic hopes, the 1970’s and 1980’s were generally the African decades of authoritarian or military rule and economic disaster. The eﬀects of these democratic and economic deﬁcits were widespread and many nations’ economies still have not recovered.

Indeed, by 1999 Collier and Gunning (1999) found that 32 African countries were poorer than they had been in 1980.

The breakup of the Soviet Union and the end of the Cold War in the late 1980’s and early 1990’s led to a great deal of political change across the globe. African nations were swept up in the third wave’s current and many democratic transitions were initiated. According to Bratton and van de Walle (1994), “Between 1990 and 1993 more than half of Africa’s

ﬁfty-two governments responded to domestic and international pressures by holding competitive presidential or legislative elections” (453). Though the results of these democratic openings were mixed, there have been important cases of persistent democratic progress: By 2011,

22 African states had held 3 to 5 uninterrupted rounds of multiparty elections (LeBas 2011,

1 Throughout the text I am primarily referring to sub-Saharan Africa when I reference Africa

19 8). Democratic institutions are taking hold in many African nations. But is good governance following?

1.1 The Problem

African democracies are plagued by dominant party politics, neopatrimonialism, and

ethnic voting- all of which erode the quality of a democracy. These enduring problems for

African governments are inherently diﬃcult to tackle. Still they are under-addressed by Western scholars, development agencies and aid practitioners who are instead principally concerned

with the construction of democratic institutions, and particularly electoral institutions, when

promoting democratic governance.

The politicization of ethnicity is intimately related to these persistent obstacles to improved democratic governance in African states. The development of ethnic identities

has roots in the trans-Atlantic slave trade. Though Africans had participated in global trade

networks prior to contact with Europeans, the introduction of guns and the high demand for

slaves revolutionized Africans’ role in this trade system. This dynamic and competitive trade

with Europeans, ﬁrst dealing in slaves and later valuable natural resources and agricultural products, made strict delineations of ‘insiders’ and ‘outsiders’ within African communities

politically useful.2 A perverse manipulation of ethnic group identities led to a diﬀerentiation between ‘sons of the soil’ and outsiders at the local level (Geshiere 2009). These divides have persisted, at least in part because the absence of formal-legal institutions of the state, to use Adida’s (2014) term, forced populations to rely on ethnic systems of rule and trade networks as the primary form of protection from unpredictable forces of the international market. This perversion now manifests itself in an intense rivalry between a primordial and civic public, Ekeh’s (1975) words, or between a personalized familial and ethnic-oriented morality which dominates an impersonal and bureaucratic market-oriented ethic. As primordial

2 See Greene (1996), for instance, for a historical study of the development of insider/outsider statuses among the Anlo Ewe in southeastern Ghana.

20 morality overtakes civic morality, the resulting biased and particularistic politics have resulted in dominant party politics, neopatrimonial political logics, and ethnic voting across Africa’s democracies.

• Dominant Party Politics: Dominant party systems develop when one political party consistently (usually across 3 or more elections (Bogaards 2004)) wins the Presidency or a majority in Parliament and thereby selects the President/Prime Minister. In the context of Africa’s ethnically divided nations, political parties typically base their support on ethnic group membership rather than programmatic diﬀerences. Dominant party systems thus develop when one political party’s ethnic base makes up a majority of the population or consistently aligns with other ethnic groups to obtain a majority.

• Neopatrimonialsm: Characterized, “as an informal political system based on personalized rule and organized through clientelistic networks of patronage, personal loyalty and coercion” (Lindberg 2003, 123), neopatrimonial politics in African democracies tend to follow ethnic, hometown, and familial networks. The general public loses as government funds are directed toward biased and unproductive clientelistic networks rather than eﬀective development strategies.

• Ethnic Voting: when the politicization of ethnicity dominates the politics of a nation such that voters use ethnic rationales as the basis for their vote decisions. Ethnic voting, as opposed to ideological, programmatic, retrospective and prospective voting, brings less qualiﬁed politicians to power and does not hold politicians accountable for their performance in oﬃce.

1.2 The Proposed Solution: National and sub-National Competition

The central argument of this dissertation holds that Ghana has been able to either dodge or signiﬁcantly limit the problems of dominant party politics, neopatrimonial political logics, and ethnic voting through institutionally-induced political competition. Ironically, the centralized nature of Ghana’s institutions has been instrumental in generating legitimate political competition at both the national level and particularly the sub-national level.

Democracy is a system for the management of conﬂict. Grounded in debates in political theory, scholars interested in democratic institutions in multicultural settings theorize about the impacts of institutions which incorporate diversity, through decentralized, federal, parliamentary, and proportional systems of governance, as compared to centralized, unitary, presidential, and majoritarian institutions which de-emphasize pluralism. However, I maintain that an important additional condition is that democratic institutions also regulate ethnic

21 divisions. Regulation ensures that politicized cleavages become less potentially volatile and disruptive of democracy over time. But regulation can also help to move a nation’s politics away from ethnic politics and toward issue-based politics.

In the work that follows I will demonstrate how one case has been able to signiﬁcantly regulate the politicization of ethnicity through national and sub-national political competition. Though Ghana’s highly centralized system bucks the democratization-via-decentralization trend, the institutionalization of competitive political environments at both the national and sub-national levels contribute to the de-politicization of national-level ethnic identities.

How do Ghana’s institutions undermine dominant party politics, neopatrimonial political logics, and ethnic voting? Far from the norm for new democracies in Africa, Ghana’s national level institutions compel political parties to appeal to cross-ethnic bases, while, at the local level, well-funded appointees from the center strategically compete against locally-elected

Members of Parliament. By requiring political parties to seek support from multiple ethnic groups in Presidential elections and exposing citizens to political competition at the local level, Ghana’s democratic institutions weaken the cycle of dominant party politics, neopatrimonalism, and ethnic voting that is so characteristic of African politics. While these problems are not yet entirely resolved, the longevity of Ghana’s democratic regime, the absence of dominant party politics at the national-level, and, as I will show, a depreciation of automatic voting based on national-level ethnic groups is testimony to the success of its institutions. Furthermore, the unusual way in which Ghana has achieved such democratic success is important. The evidence oﬀers proof that centralized institutions are an alternative option for fomenting healthy political competition without the coordination struggles inherent within plurality systems which increase the numbers of decision-makers.

1.3 Dominant Party Politics, Neopatrimonialism, and Ethnic Voting

1.3.1 Elections and the Development of Dominant Party Politics

A major component of the Democratization literature considers the factors which lead to institutional democracies. The focus on initial democratic transitions and electoral

22 institutions, such that even democratic consolidation is largely defined by electoral factors, biases explanations away from the factors which lead to effective democratic governance. One problematic result of this focus on elections is the ability of dominant party politics to co-opt a democratic regime. Political parties are crucial for the most effective functioning of modern democratic regimes. As Kuenzi and Lambright (2001) write, “parties allow diverse groups to pursue their interests in a peaceful, systematic fashion within a political system” (438). Indeed, many scholars use between 1 and 3 peaceful transitions of power as a defining characteristic of a consolidated democracy (Przeworski 1988; Huntington 1991), which inherently presupposes the existence of multiple political parties. Dominant party political systems, however, develop when a singular political party dominates national power through consecutive election victories over time.

Dominant party political systems originate in different ways (Schedler 2013, 4) and vary in terms of the free and fairness of their electoral environments. At best, the absence of legitimate inter-party competition stifles the development of diverse competing political platforms for consideration by the electorate when voting. At worst, freedoms of the press and association are restricted, opposition leaders and their followers may be harassed and/or imprisoned, and electoral processes come with voter suppression, unfair access to the media on the part of the incumbent, and egregious instances of electoral fraud. Some examples of dominant party politics, such as is the case within Botswana and South Africa, are actual functioning democracies3 , but others merely offer the facade of democratic governance with no real chance of an opposition leader or challenger to the President winning a national election (e.g. Uganda, Rwanda, Zimbabwe, Tanzania, Cameroon).

3 Though these cases are not without their critiques: Botswana in terms of inclusion of minorities (see Solway 2002; Nyamnjoh 2007) and South Africa in terms of corruption and the electoral dominance of the ANC (Herbst 2005; Bassett and Clarke 2008; Gumede 2008).

23 In the context of African nations, dominant party politics develop by way of historical and ethnic traditions which concentrate large portions of a country’s population within particular party traditions. When one ethnic group makes up a sizable majority of the population in a nation, it can be difficult for opposition parties without access to government coffers to foment a campaign strong enough to overturn the incumbent party. Between ethnic voting, neopatrimonial politics and the use of clientelistic networks to pay for votes, and the unfair advantages incumbent parties derive from their access to power and money when campaigning, it has proven difficult for many new African democracies to see a peaceful alternation of power.

To sum up, when we assume that democratic outcomes stem from democratic institutional features, we ignore the ways in which democratic institutions, including free and fair elections, fail to produce wholly democratic results. Put diﬀerently, this work questions the extent to which elections can foment meaningful political competition, particularly in new democracies.

Along these same lines, democratization eﬀorts should be oriented towards overcoming these historical relationships between the state, political elites, and the citizenry in order to improve the quality of democratic governance in African contexts.

1.3.2 Addressing Neopatrimonial Logics

A radicalization of Weber’s arbitrary, traditional, and personalistic concept of patrimonialism

(Weber [1922] 1978, chap 12-13), neopatrimonialism is the hybridization of both patrimonial and legal-rational bureaucratic rule. As Erdmann and Engel (2007) explain, neopatrimonial rule stems from a deeply rooted insecurity in the political system, as actors use both patrimonial personal relations and legal-rational bureaucratic rules to overcome this insecurity. Similar to both Ekeh’s (1975) presentation of the bifurcated primordial and civic publics and Mamdani’s

(1996) understanding of ethnicity as a form of anticolonial and anti-centralized state revolt

(184-185), for Erdmann and Engel (2007) a neopatrimonial system is one where “the patrimonial penetrates the legal-rational system and twists its logic, functions, and output, but does not take exclusive control over the legal-rational logic” (105).

24 Not only do neopatrimonial logics not abruptly end after a democratic transition, the deregulation and privatization reforms pushed on African states by the Washington Consensus create new opportunities for the continuation of corrupt practices (Szeftel 1998). The granting of governmental positions or contracts as patronage (Arriola 2009) and political elite’s personal use of public funds both undermine, “the continuity, trustworthiness and objectivity of the legal order and the rational, predictable functioning of legal and administrative agencies”

(Weber [1922] 1978, 1095), which Weber sees as so necessary for the emergence of industrial capitalism and stable economies. Further, the resource and information hoarding on which these networks depend can further deprive citizens of sustainable community development

(Auyero 2000). Rather than resulting in a redistribution of wealth, the clientelistic politics of neo-patrimonial systems instead encourages predatory rent-seeking (Kurer 2007), in-turn used to establish a new wealthy indigenous elite (Szeftel 1982). African leaders have proven themselves adept at manipulating international support to fund political networks (Reno 1998; Van de Walle 2001) and have also intervened into economic markets, such as through the manipulation of marketing boards, to accumulate political resources (Bates 1981; Reno 1998, 21). Indeed, the development literature is intensely concerned about granting recipient governments opportunities to tailor funding projects to country needs while still maintaining oversight to prevent misappropriation of funds (Collier 2007). And the slow growth rates in Africa have been explained as the result of neo-patrimonial rulers’ subversion of state rules, increasing inequality rates, and ineﬀective international aid that may even worsen African economies rather than improve them (Easterly 2007). While the strategies leaders utilize to direct resources from the state to their personal control are not necessarily linked to ethnicity, the high politicization of ethnicity and ethnic-voting in African states means leaders’ support networks are intimately entangled with ethnic politics.

25 1.3.3 The Durability of Ethnic Voting

Elites manipulate ethnic appeals in order to acquire or maintain access to state resources.

Originating in the colonial era, the concept of ethnic representation is used by elites to

justify the allocation of state resources to their group, after it passes through their hands

as the ethnic representative (Bayart 1993). When ethnic elites do not feel the current

institutional makeup allows for adequate representation of their ethnic group at the top, ethnic representatives have proved adept at mobilizing publics to violent ends (Wilkinson 2004). Elites

in power can also mobilize ethnic publics, and this typically takes the form of mobilization

against minority scapegoats as a distraction from government failures (Panggabean and Smith

2011; Adida 2014). In newly democratized states, publics can also become mobilized to vote based on identity

and not substantive policies. In African politics, where the state is ‘soft’ and cannot be relied

on to provide consistent and even development across the country, politicians eﬀectively

campaign on promises to develop and direct resources back to their ethnic home regions should

they win the election: “A cabinet minister in Africa is considered ‘a kind of superrepresentative’ who is expected to speak for the interest of co-ethnics, as well as channel resources to them”

(Arriola 2009, 1346).4 Delivering goods back to ethnic communities or networks may contain a moral dimension which acts as a coercive force pushing politicians to dole out state, and sometimes personal, resources to co-ethnics (Ekeh 1975; Lindberg 2003, 127). Indeed, once politicians engage in ethnic politicking, it can quickly slip out of their control (Bates 1974, 471-473).

Finally, in societies where political parties are organized along ethnic lines, and electoral results take the shape of an ‘ethnic census’ (Horowitz 1985, 84-85, 324-326), stable ethnic

4 The colonial alignment of within-country administrative units to ethnic boundaries (Bates 1974, 464), which were then adopted in post-independence regimes, also generally contributed to the understanding of elected oﬃcials as ethnic superrepresentatives.

26 population figures equates to pre-determined electoral outcomes and weak opposition parties. A credible opposition provides citizens with the opportunity to engage with government and effect political change (LeBas 2011, 13) and can generate accountability, responsiveness, and compliance with time in office laws (Arriola 2012, 21-22). In ethnically-divided contexts, then, it is important to foment elite agreement across politicized ethnic groups and cleavages, but, as Kenya’s politics of collusion demonstrates (Cheeseman 2011), even cross-ethnic elite agreement may not be enough to produce good democratic governance.

1.3.3.1 The rationality of voting ethnically

Democratization scholars attempt to prove that individuals engage in evaluative voting, using retrospective and prospective rationales, as opposed to merely relying on candidates’ ethnic backgrounds. It is argued that, after experiencing several electoral iterations with alternations in power, voters will have the experience and incentives available to make both retrospective and prospective voting decisions (Key Jr. 1955; Anderson and Dodd

2005; Lindberg and Morrison 2005). But conceptualizing ethnic and democratic voting as mutually exclusive processes, however, suggests voters cannot be inﬂuenced both by programmatic decision-making processes and subconscious identity factors simultaneously.

And, in democratizing contexts, it will be increasingly diﬃcult to measure the impact of identity on votes as voters are routinized to justify their voting preferences using democratic rhetorics, rather than ethnic rationales. Whether because they are embarrassed to admit their increasingly socially-unacceptable preferences for politicians based on identity, or that voters are not aware of their suppressed identity-related biases, respondents in exit-poll or other surveys are increasingly unlikely to mention ethnicity when justifying their votes. The well-known ‘Bradley’ or ‘Wilder’ eﬀect in American politics shows how hidden ethnic biases in voting can make vote polling unreliable (Finkel, Guterbock and Borg 1991). Indeed, survey evidence presented in this dissertation, as well as survey evidence presented in other scholars’ publications, show that Ghanaians rarely say that ethnicity played a part in their vote decision

(Lindberg and Morrison 2008).

27 However, correlational analyses show that ethnic backgrounds clearly have an impact on voting in African states. Though economic and democratic factors also matter, voting analyses in African countries consistently show that ethnicity remains the best predictor of votes, (Bratton and Kimenyi 2008; Keefer 2010). Three explanations are commonly used to explain ethnic voting. First, some emphasize the psychological benefits voters receive by supporting their group (Chandra 2004), a benefit likely driven by some mix of uncertainty reduction and self-enhancement (Lieberman and McClendon 2013, 676). Second, it is argued that voters can use identity cues as a cognitive shortcut for politicians’ future decisions. These voter rationales have sometimes been explained as predictions about future policy implementations (Ferree 2006), but more commonly are explained as predictions about future regional, community or personal goods likely to be distributed from elected leaders. With regard to the former, when policy aims and differences between candidates are ill-defined, as is the case in most

African democracies (Van de Walle 2003; Weghorst and Lindberg 2011, 1194-1195; Whitfield 2009, 630), ethnic cues may be used to infer some of this information. With regard to the latter explanation, in the absence of strong institutions and effective governance, voters might feel that voting for a co-ethnic politician is their best shot at receiving some benefit for their community.

A third explanation suggests that a link exists between ethnic or cultural background and policy preferences (Rabushka and Shepsle 1972; Bates 1974). Ishiyama (2012) ﬁnds higher rates of ethnic bloc voting within more concentrated ethnic groups, an outcome he explains is the result of common social, economic, and political interests. Lieberman and McClendon

(2013) come to similar ﬁndings but conclude that though public policy preferences vary by ethnic group membership, political relevance and intergroup wealth diﬀerentials better explain this outcome than do cultural accounts.

28 1.3.3.2 But why is ethnic voting bad?

Voting on the sole basis of ethnicity, as mentioned in Section 1.1, brings politicians to office based on their identities rather than their qualifications and makes these same elected politicians less accountable to the electorate. When elected officials can rely on their ethnic bases of support for re-election, they may exhibit poor job performance and still be re-elected.

In other words, neopatrimonial systems based on ethnic voting take away the democratic incentives of performance review by citizens that otherwise should come with elections. Such a system also stymies electoral competition from anyone outside of the dominant ethnic group.

However, I argue that ethnic voting is merely symptomatic of a larger issue facing new democracies: the absence of real political competition. Competitive party politics within new democratic systems are supposed to combat against non-evaluative voting because citizens have the opportunity to evaluate a party’s time in power and decide whether they will continue to vote for the party or will switch their vote to another party. But the lack of policy differentiation between political parties means that voters are deciding about the quality of the politician based on other cues. Similarly, voters realistically need to experience different political parties’ governments in order to make informed decisions about what the parties have to offer. And the competitive nature of democracy will be significantly hindered, particularly in terms of accountability and responsiveness of elected representatives, if voters do not have realistic opportunities to vote for a viable opposition party with an actual shot at winning.

In new democracies, this can be a slow process. The democratic regimes in Botswana (Botswana Democratic Party in power since 1966) and post-apartheid South Africa (African

National Congress in power since 1994) have never experienced an alternation of power.

The polity has to take a big leap of faith when they collectively decide to bring a first-time government to power. This gamble becomes even more significant in the absence of distinct policy differentiations separating candidates and political parties.

Generating sub-national political competition is even more diﬃcult than the national-level in part because local-level party politics tend to be less developed and because constituencies

29 tend to be more ethnically homogeneous. When parties derive the bulk of their support from co-ethnics, parties will have difficulty fielding candidates in localities outside of their ethnic base. Local-level political competition is all the more important because locally elected representatives are significantly more likely to impact constituents’ lives, with elections based on issues directly relevant for the locality, as compared to the head of state. The development of competitive politics in local communities in African states has been extremely slow to develop and is still largely absent across the continent.

1.4 How Eﬀective National-Level Competition Develops in Divided Societies

Overcoming dominant party politics, neopatrimonial state logics, and ethnic voting are the greatest tests for new African democracies. A recent surge of publications has indeed emphasized the importance of national-level competition, through credible opposition parties, to counter the development of dominant party politics in African democracies. This work oﬀers a contribution to this literature by pointing out how tailored institutional designs, most likely taking the form of a 50% +1 majoritarian electoral rule, also substantially contributes to increased national-level political competition by requiring a wider support base on the part of the political parties. Yet, as I will introduce in the next section, the coupling of a majoritarian electoral rule at the national level with the way in which Ghana’s centralized government institutionalizes sub-national political competition addresses all three of the common democratic ills facing new African democracies.

1.4.1 Credible Oppositions

A recent literature on democratization in African states emphasizes the development of a strong opposition as necessary to challenge the authoritarian regime, push for democracy, and stamp out one-party dominance in the new democratic regime. African nations’ oppositional groups typically have an ethnic-orientation, usually made up of those groups which the authoritarian leader failed to incorporate into his political fold. African nations’ authoritarian governments inherently enjoy cross-ethnic support because of the patronage resources at their disposal, and will work hard to fragment the opposition (LeBas 2011). When an authoritarian

30 government faces a serious challenge, such as an economic crisis, that challenge is more likely to result in a democratic transition if the opposition has a cross-ethnic base. A cross-ethnic opposition is significantly less likely to alienate potential supporters who might otherwise fear that new leaders would simply replace the authoritarian regime with another exclusive government. Scholars emphasize the development of strong oppositions as the result of historical economic liberties which allowed oppositional elites to develop economic bases independent of the state (Arriola 2012); as the result of weak authoritarian governments without high levels of popular support who did not have the opportunity to shape democratic rules to their own interests (Riedl 2014); because of a country’s ethnic make-up which determines the development of ethnic versus programmatic parties (Elischer 2013); and finally because strong opposition parties have been able to utilize the mobilizing structures of the former authoritarian government and are able to maintain their strength when they use strategies that escalate conflict along partisan lines of affiliation (LeBas 2011). The running similarity throughout these strong cross-ethnic oppositional explanations of democratization is the deterministic and context-heavy nature of their arguments. These works, like traditional democratization literatures which emphasize strong opposition parties, are successful in identifying paths through which African nations have attained democracy but, unfortunately, all of these paths suggest that strong oppositions take significant periods of time, or the right contextual features, to develop. Works that show how institutional features affect the expression and nature of politicized ethnic cleavages (Wilkinson 2004; Posner

2005), however, suggest that diﬀerent types of democratic institutions can encourage a more rapid development of strong cross-ethnic parties. I argue that tailored institutional rules and legitimate competition at the national and local level are one way to undercut dominant party politics and neopatrimonialism and stimulate programmatic voting in new African democracies.

31 1.4.2 Majoritarian Electoral Systems

One institutional mechanism with great potential for encouraging national-level political competition is a majoritarian electoral rule. Scholars working in ethnically-divided societies have long debated about the political eﬀects of electoral institutions on the nature of ethnic divides.

Horowitz (1985), Sartori (2000), and Reilly (2001) all argue that majoritarian systems, with a 2nd round option, are important for diﬀusing mono-ethnic parties. In a 50% + 1 electoral game, parties are required to broaden their appeals to wide-swaths of the population and incumbent governments cannot risk alienating large groups from their list of supporters. As

Briggs (2012) has shown, the absence of widely-distributed political goods in such an electoral context can prove electorally costly for incumbent governments. With more constituent support necessary for national-level electoral victories, the overall electoral competition naturally becomes more competitive. In most cases no one ethnic group makes up greater than 50% of a population, so even if a political party represented a dominant ethnic group, it would be forced to oﬀer an ethnically-inclusive campaign platform

(or at least avoid an ethnically-exclusive campaign platform) if it was in serious contention for national power. While an increasingly number of African governments have turned to the

50% + 1 electoral rules, adopting these rules can be controversial. In Zambia, for example, the implementation of such a rule has been contentious and, until recently, was a major factor contributing to a stalled reform of the national constitution despite several constitutional review committees recommending a 50% + 1 change (Motsamai 2014). In another case, Sierra Leone’s 1991 Constitution enforces a particularly stringent supermajoritarian electoral system in which the presidential winner must secure greater than 55% of the vote in order to avoid a run-oﬀ election. Still, it is surprising how rarely majoritarian electoral rules have been adopted in the history of democratic trials in African states.

32 1.5 Taking the Eﬀects of Institutionalized National and Sub-National Competition to Ghana

A vacuum in political competition challenges democratic principles of self-government, accountability, and responsiveness. I argue that the implementation of institutions which generated legitimate party competition at both the national and local level in Ghana have gone a long way in preventing dominant party politics and regulating patrimonialism and ethnic politics. In particular, no one ethnic group in Ghana makes up over 50% of the population meaning the 50% + 1 majoritarian electoral rules at the national level requires political parties to appeal to voters outside of their traditional ethnic bases. Secondly, the presidential appointments of well-funded politicians (Metropolitan, Municipal, & District Chief

Executives (referred to as DCEs throughout)) to the local-level to compete for support against locally-elected Members of Parliament (MPs) has increased evaluative voting on the part of citizens and politicians’ appeals to voters on the basis of policy platforms. Though personal ethnic backgrounds still have an impact on many vote decisions, citizens are increasingly less likely to engage in automatic, knee-jerk co-ethnic voting so common in prior regimes in Ghana.

Ghana proves that institutional engineering and tailoring institutions to country context can signiﬁcantly increase the pace at which quality governance deepens in a new democracy.

1.5.1 Majoritarian Electoral Systems in Ghana

First adopted in the 1979 Constitution, Ghana had used a 50% + 1 electoral rule only once in the 1979 Presidential elections before Flight Lieutenant John Jerry Rawlings staged a second coup d’´etatand installed a 10-year military government beginning in 1982. Once democracy was restored, the 1992 Constitution again implemented a 50% + 1 electoral requirement for Presidential elections. Now six Presidential elections have been held since the adoption of the 1992 Constitution, with run-oﬀs held in 2000 and 2008. A majoritarian electoral rule is crucial in the context of Ghana’s ethnic population makeup.

Even when linguistically-deﬁned, no ethnic group in Ghana has a population proportion of greater than 50%. The closest linguistic group are the Akan-Twi speakers, whose population

33 proportion is 47.5% as of the 2010 Census. Historically the Akan-Twi linguistic group has never been entirely uniﬁed, but even if a particularly charismatic leader was interested in uniting all

Akan-Twi speakers behind her or his candidacy, the majoritarian requirement still makes an automatic victory diﬃcult to accomplish.

The majoritarian electoral rule may also make alternations in power more likely. While others have pointed out that economic (Arriola 2012) and historical/contextual factors (LeBas

2011; Elischer 2013; Riedl 2014) contribute to the development of strong oppositions, the simple fact that both governmental and opposition parties are realistically required to appeal to voters outside of their base, coupled with voters learning that votes for narrow regional or ethnic political parties are likely to be wasted, is part of the appeal of plurality, and by extension majoritarian, electoral rules tending toward two-party systems (Duverger 1954). In the context of plurality electoral rules, the largest ethnic group’s party is most likely to win the election (a la Horowitz’s (1985) ‘ethnic census’). In a 50% + 1 majoritarian system, a second round voting option habituates politicians and voters to think in broad-based political logics. Nonetheless, majoritarian electoral rules, and competitive electoral politics in general, are not at odds with clientelistic logics driving political goods distributions (Lindberg 2006, 20).

Electoral competition in new democracies does not automatically equate to programmatic and ideological based competition. Other than strong opposition parties, something further is needed to encourage programmatic politics. I argue that Ghana’s uniquely centralized democratic system ﬁlls the void by institutionalizing political competition at the local level.

1.5.2 Ghana’s Centralized System of Local Government

The inability of any single ethnic group to win national elections under Ghana’s majoritarian electoral rule is particularly crucial in the context of Ghana’s local institutions.

In each of Ghana’s districts, the 1992 Constitution authorizes the President to appoint DCEs who exist alongside locally-elected MPs. Both DCEs and MPs control portions of the national budget, distributed through the Common Fund, to construct development projects within the district/constituency. MPs and DCEs are in a natural competition to provide more eﬀective

34 development. Two crucial factors are at play in this development competition. First, DCEs control a much larger portion of the Common Fund than do MPs. Second, DCEs reside in the community, while MPs reside in the capital and have to travel home to visit their constituents. While DCEs have a ‘face-time’ advantage as they are much more accessible by their constituents, MPs have a lobbying advantage because residing in Accra puts them in close proximity to the bureaucratic ministries which proactive MPs can pressure for their constituency’s inclusion within upcoming development projects.

In theory, DCEs and MPs can maintain a cordial and effective working relationship, particularly if they are members of the same political party. However, even when these two officials are of the same political party, it is assumed that the DCE aspires to become the future MP of the constituency. This is for several reasons. First, DCEs face two term limits whereas MPs can be elected indefinitely. Further, MPs are national figures, reside in state-furnished housing in Accra, have larger incomes and can even receive Presidential appointments to simultaneously serve as ministry heads. Overall, MPs have the more coveted position.

When DCEs and MPs are not of the same political party, which happens when the national government is of a diﬀerent political party than the locally-dominant party, the DCE functions as the local representative of the President and is speciﬁcally instructed to implement development projects in order to convince voters away from the MP’s party. One trick at the DCE’s disposal, for instance, is to hold founding or opening ceremonies for new development projects without informing the Accra-based MPs. If you or your representatives are not present at such an opening ceremony, citizens will assume you had no part in that project.

The level of competition between DCEs and MPs thus ranges from moderate (intra-party

Friendly Pairs) competition to high (inter-party Unfriendly Pairs) competition. When alternations in national-level power occur, which has happened with two runoﬀ elections in both 2000 and 2008, the political party in power replaces the prior regime’s DCEs with their own party-member appointees to the local level. Every district in Ghana has now had

35 both NDC (1992-2000 and 2008-2016) and NPP (2000-2008) appointments to the DCE positions. Though the national-level appointment of local representatives is a far cry from the democratization - via - decentralization theme of the development community popular since the

1990’s, I argue that this highly-centralized system of local government has actually been quite eﬀective in generating legitimate political competition at the sub-national level. 1.6 Competing Explanations

This dissertation argues that Ghana’s speciﬁc national-level and local institutions introduce legitimate political competition which prevents dominant party politics, undermines neopatrimonial political logics and encourages programmatic voting. In other words, I point to

Ghana’s institutions as the primary factor leading to the increased local-level competition and weakening of ethnic voting in Ghana. Two other arguments which alternatively may explain the increase in political competition and changes in ethnic voting are that (1) the occurrence of elections sets in motion processes which lead to a greater supply and demand for democracy and (2) economic growth increases the ability of candidates/parties to implement universal development/wide-ranging appeals as well as makes votes from a wealthier citizenry more expensive to buy. I address these two alternative explanations below.

1.6.1 Democratization-via-Elections

The most important competing explanation that explains democratic deepening is the idea that holding multiparty elections pushes democracy forward. Famously argued by Lindberg

(2006), the implementation of even faulty democratic elections is important for re-orienting elite calculations and citizen perceptions toward democratic rules of political competition.

Citizens now have a mechanism through which they can voice their displeasure with politicians and become empowered by their newly granted authority. Politicians fear this new authority and reconsider their political calculations to account for public perceptions of their eﬀectiveness in oﬃce. Further, these new institutions, and actors’ socialization to democratic structures also leads to the deepening of civil liberties as measured by Freedom House’s civil liberties scores.

Lindberg ﬁnds that once African nations held two elections, they were signiﬁcantly more likely

36 to remain democratic. After three elections, a country’s democratic quality improves radically (Lindberg 2006, 74).

Though Lindberg’s argument is intuitive and his evidence through 2003 convincing, this argument has been challenged in other parts of the world. The application of this argument to Eastern Europe originally looked promising as protests surrounding faulty elections proved important for initiating democratic transitions. But the initially successful color revolutions in Yugoslavia, Georgia, Ukraine, and Kyrgyzstan in the early 2000’s have not led to stabilized democracy and democratic progress has even arguably backslide in these states since. In other cases civil society mobilization around elections to push for democratic change was less successful (Belarus, Moldova, and Romania). Scholarly work that applies Lindberg’s argument to the broader postcommunist states have found that elections do not necessarily promote democratization (Kaya and Bernhard 2013) and can actually strengthen authoritarianism in some contexts (Brownlee 2009). This is a similar conclusion found in a hybrid regimes literature which emphasizes election manipulations as a tool used by authoritarian governments to retain power (e.g., Levitsky and Way 2010).

Further, in both Latin America and the Middle East, scholars have found that elections do not have as democratizing an eﬀect as in Lindberg’s argument (Lust-Okar 2009; McCoy and Hartlyn 2009). Finally, some Africanists have suggested that the early nature of Lindberg’s dataset accounts for his robust results. Well-summarized by Lynch and Crawford (2011), depreciations in Freedom House’s civil liberties scores for African nations in 2006 as well as worse democratic ratings assigned to previously promising democratic regimes in Kenya,

Nigeria, Ethiopia and Senegal (Lynch and Crawford 2011, 280) have made the Africanist community increasingly skeptical of the democratizing promise of elections.

In Ghana’s case, increased political competition at the local level could be the result of the long practice with democratic institutions, across 6 elections and 2 peaceful transitions of power. The real reason behind a lessening of ethnic voting could be that democratic institutions and repeated elections have incentivized programmatic behavior on the part of

37 campaigning politicians and voting citizens. Though viable, in Chapter 5 I show that vote differences significantly increased in crucial elections after local institutions altered the nature of political competition at the constituency level. If vote differentials were not significantly altered in the wake of changes in local competition, then Lindberg’s explanation might hold more weight in explaining deepening democracy in Ghana. Instead I argue that it is not just the routinization of democracy via elections that results in programmatic voting, but an increase in real political competition at the local level is what is driving programmatic political logics and nullifying clientelistic vote-buying.

1.6.2 Democratization-via-Economic Growth

First, one of the arguments for economic growth’s contribution to the lessoning of ethnic voting and neopatrimonial politics is that it potentially supports the funding of far-reaching programmatic political strategies because governments can now aﬀord to appeal to voters on a wide-reaching programmatic basis. Indeed, in Arriola’s (2012) work, the ability of potential opposition leaders to amass the political and economic resources they would need to generate wide-reaching political coalitions that could challenge the government in power was dependent on the closed or open nature of the economy. In Cameroon’s closed economy, for instance, only individuals with ties to the government could gain wealth through the country’s heavily-regulated avenues. Potential opposition leaders were thus restricted in their ability to independently gain massive amounts of wealth necessary for coalition-building. In Ghana’s case, it could be argued that economic growth has meant that both political parties have been able to spread their campaigns across Ghana, oﬀering monetary support and/or gifts in trade for citizens’ votes, and thus creating greater vote volatility across Ghana’s localities.

For one, Ghana has experienced strong economic growth recently, but it’s strongest economic growth only occurred after the discovery of oil in 2007. The World Bank lists Ghana’s annual GDP growth at between 3.7% and 6.4% between 2000 and 2006. It was not until after 2007 when Ghana’s annual GDP growth grew to the impressive levels of 9.1% in

2008, 4.8% in 2009, 7.9% in 2010, 14.0% in 2011, 9.3% in 2012, and 7.3% in 2013 (The

38 World Bank 2016b). My analysis starts tracing the eﬀects of central appointments of DCEs in opposition areas to 2000, prior to the discovery of oil.

Second, assuming universalistic programmatic political strategies did result from increased economic growth, the power of the district-level DCEs would be even greater as they would have control over an even larger amount of district-level development. In other words, if Ghana’s increased development funding did filter through the DCE, as it likely would given this individual’s crucial knowledge of the needs of local communities within their district, economic growth would not contradict my theory but would actually heighten the degree of competition between the DCE and MP(s). Indeed, the District Assembly Common Fund (DACF) did increase from 5% to 7.5% of Ghana’s budget in 2008. Third, Ghana’s increased economic growth does not explain the variation in vote volatility as presented in Chapter 5. In particular, I provide evidence that constituencies with MPs of the same political party as the DCE, as compared to constituencies with MPs of different political parties from the DCE, have stable or decreased votes for the MP’s/DCE’s party. When constituencies elect MP’s of different political parties from the DCE, votes for the DCE’s party increase in the subsequent election. If economic growth led to more inclusive development distributions and this in-turn was increasing vote volatility, we should not see this variation in vote volatility based on friendly (same party) or unfriendly (different parties) MP-DCE pairs.

Next, it is also possible that economic growth in Ghana has led to increased citizen wealth, which makes it more expensive to rely on an ethnic-based neopatrimonial system to guarantee one’s power. But this assumes that national-level economic growth leads to increased wealth across society. In other words, that economic growth could make it more expensive and thus unfeasible to rely upon a system of co-ethnic vote buying is dependent upon decreasing inequality.5

5 Theoretically, I argue that neopatrimonial politics functions so well in African states because of the high degree of inequality which exists (e.g. Markussen 2011; Robinson and

39 However, the application of this argument to Ghana cannot account for an increase in programmatic voting because Ghana has actually become more unequal over time

(Obeng-Odoom 2012). As Table 1-1 shows, the total income value earned by Ghana’s richest 20% income earners has increased, while the income earned by every other income sub-population, save the 60-80% income earners, has decreased over time. Though the data available does not cover all the years of Ghana’s Fourth Republic, the dataset does span prior to the democratic transition which occurred in 1991, and extends to 2005, which is two years prior to the discovery of oil off the Ghanaian coast. And preliminary evidence suggests that the discovery of oil has only led to further increases in inequality. In other words, if economic growth were making it more expensive to rely on a neopatrimonial system and thus forcing politicians to appeal to voters with programmatic policies, then we should see decreasing inequality over time. As it appears now, everyday citizens in Ghana are not becoming wealthier relative to the top echelons of society, so buying votes should not have become more expensive and thus cannot be driving the break down of ethnic voting in Ghana. Finally, as Chapter 8 demonstrates, the list experiments testing for the impact of clientelistic-inducements on voting were only found to have an impact in the competitive districts, and not the NPP or NDC strongholds. If clientelistic payoffs were increasingly turned to as a result of the greater availability of funds in Ghana’s growing economy, clientelistic-inducements would have been effective across the NDC and NPP strongholds as well.

Verdier 2013), and which neopatrimonialism may very well perpetuate (Van de Walle 2009, 320-321). This is primarily because paying for votes on a mass scale is only eﬀective when payoﬀs per individual or household are relatively low. While we might expect the opposite, that inequality produces calls for redistributive programmatic policies, empirically clientelistic payouts continues to function very well in African cases with high levels of inequality. However, I would argue that, if economic growth leads to a decrease in inequality, paying the masses for their votes becomes more expensive and clientelistic political strategies become less viable. More unequal societies, according to this logic, would tend toward particularistic logics where rich political elites use the resources at their disposal to engage in rentier politics.

40 1.7 Outline of Chapters to Come

In the chapters that follow, Chapter 2 demonstrates how politicized ethnic divides contributed to unstable democratic and authoritative regimes prior to Ghana’s Fourth Republic.

This chapter also contextualizes the process of institutional change and how past institutions were modiﬁed as a response to the nature of politicized ethnic divides. Though Ghana was a notoriously diﬃcult colonial possession to manage, with the imposition of indirect rule facing strong resistance (Simensen 1975), decades of institutional reform in response to politicized ethnic group challengers to the state eventually led to the current centralized system of power in the Fourth Republic.

In comparison to past regimes’ centralized institutions which tried and failed to address ethnic divisions, Chapter 3 shows how the centralized institutions of Ghana’s Fourth Republic diﬀer in a way that is crucial to de-politicizing national-level ethnic divides. In particular, the

Fourth Republic institutionalizes political competition at the local-level. The chapter begins by explaining the current local government system, specifying where the central government retains control and where the District Assembly can exercise its authority. I then situate the DCE and MP within this system, highlighting how the incentives and motivations of these two political actors results in a competitive relationship. I show how the level of competition between the DCE and MP is moderate when they are of the same political party (friendly pairs) and signiﬁcantly stronger when they are of diﬀerent political parties (unfriendly pairs). I argue that this level of competition is the driving force behind the break down of ethnic voting, particularly in the post-2000 elections, in Ghana’s Fourth Republic.

To estimate the impact of political competition on vote outcomes, I use three methodological tools. Chapter 4 introduces the ﬁrst of these tools: Ecological Inference (EI) Models. These models estimate individual vote outcomes using aggregate election and demographic data. Two general hypotheses are tested in this chapter. First, though the literature on ethnic voting highlights the importance of linguistic-group deﬁnitions of ethnicity, I argue that Ghana’s tribal group identities are also politically relevant. Tribal group relevance is, as I argue, particularly

41 on the rise given the politicized nature of Ghana’s system of local government. I predict, then, that analysis of tribal-group voting patterns will show that tribes within the same linguistic group diﬀer dramatically in their vote patterns and are thus more accurate depictions of

‘ethnic’ voting. Second, I predict that votes by core and peripheral political party supporter groups will increase for the opposition party from 2004 through 2012. These changes, I argue, are due to the competitive political environments at the local-level engendered by Ghana’s centralized system of local government.

Though EI allows us to estimate vote choices along ethnic identity groups, Chapter

5 introduces national-level regression analyses to parse out evidence of actual changes in partisan votes. Using the percent change in the diﬀerence between both Presidential and Parliamentary votes for the NPP and NDC at the constituency level as my outcome variable,

I show that votes for the DCE’s political party significantly increased, in both Presidential and Parliamentary races since 2000, in constituencies which had voted in a MP was of the opposition party in the prior term. In other words, votes for the NPP increased in 2004 at significantly higher rates for districts with NDC MPs as compared to NPP MPs. Though one might argue that votes for the NPP could only go up in the NDC strongholds who had voted in NDC MPs in 2000, the effect holds for 2008. Keeping in mind that 2008 was an electoral turnover rate where the NDC won the Presidency, votes for the NPP again significantly increased in districts with NDC MPs as compared to NPP MPs. Finally, in 2012 votes for the NDC increase at significantly higher rates in districts which had elected NPP MPs in 2008 as compared to NDC MPs.

Finally, in Chapters 6-8, I analyze the results of an N=1,932 survey conducted across 6 districts in Ghana (September 2013 December 2013) to better pinpoint the factors which actually contributed to individual vote choices. I introduce the district pairs in Chapter 6, detailing the structural similarities and voting differences within the NPP strongholds, NDC strongholds, and competitive district pairs. I explain the stories behind the voting differences, where possible, and then begin to evaluate three different hypotheses for voting rationales

42 across the entire sample and within/between the district pairs. The three hypotheses are (1) Identity-Based Voting, (2) Economic or Policy-Based Voting, and (3) Clientelistic-Based

Voting.

In Chapter 7 I use multinomial logistic regressions to test for respondents’ self-report votes and logistic regressions to predict swing voters from stable voters. In Chapter 8 I test for predictors of swing voting, analyze a tribal survey experiment, and evaluate two diﬀerent list experiments on bias towards Muslim politicians and whether clientelistic gifts aﬀected respondents’ votes.

The overall analysis ﬁnds strong evidence for Hypothesis 2: Policy or Economic-Based

Voting in direct questions but also when testing for respondents’ votes as well as for the factors which inﬂuence swing voters. Outside of Hypothesis 2, Hypothesis 1: Identity-Based

Voting is somewhat supported throughout the analysis, while evidence for Hypothesis 3:

Clientelistic-Based Voting was only found using list experiments.

Chapter 9 concludes the work with a summary of the manuscript and an overview of its major arguments. In particular, this work has argued that Ghana’s democratic success and democratic deepening is largely the result of both the country’s national-level majoritarian electoral rules and uncharacteristically centralized democratic system of local government. This centralized system greatly aﬀects the levels of local competition at the constituency level in ways that other decentralized systems do not allow. The politically competitive environments at both the national-level and local-level in Ghana address the democratic ills of dominant party politics, neopatrimonial political logics, and ethnic voting which plague many other democracies in Africa.

43 Table 1-1. Ghana’s income share held by what population percentage Year Top 20% 20-40% 40-60% 60-80% Bottom 20% 1987 42.7% 11.7% 16.3% 22.3% 7.0% 1988 43.4% 11.5% 16.0% 22.1% 7.0% 1991 45.6% 10.9% 15.2% 21.7% 6.6% 1998 46.2% 10.3% 15.0% 22.7% 5.8% 2005 48.6% 09.9% 14.6% 21.7% 5.2% Source: The World Bank 2016a

44 CHAPTER 2 GHANA’S HISTORY OF CENTRALIZATION AND ETHNIC POLITICS

In this dissertation I argue that national-level electoral rules and the centralization of institutions in the Fourth Republic encouraged competitive national elections and competitive local-level politics, and thus prevented dominant party politics at the national-level and led to a lessening of neopatrimonialism and ethnic voting at the sub-national level. In prior regimes, however, ethnic mobilization was high and contributed to both authoritarian and democratic regime instability. The centralization of institutions was, historically, the primary mechanism used to address ethnic divides, but these reforms only further provoked ethnic-based opposition and contributed to overall instability. It is only with the imposition of political competition at the local-level in Ghana’s Fourth Republic that neopatrimonial political logics and ethnic voting have begun to subside.

From institutional choice literatures we know that historical cleavages and past institutions heavily inﬂuence latter institutional design. Within this same vein, the argument presented in this chapter identiﬁes both the development of ethnic politics and centralized institutions as parallel processes in Ghana’s history. After introducing this argument in greater detail below, I then systematically present the history of centralization and ethnic divisions in

Ghana, pointing out the ways in which regimes’ centralized institutions provoked particular ethnic responses, which in turn became instrumental in determining the political outcomes of the economic-motivated coups which ended each of Ghana’s past regimes. In other words, though economic contractions typically were the last straw that broke the back of diﬀerent regimes, the nature of the prior regime’s institutions and the ethnic-based opposition to those institutions greatly determined the justiﬁcation of coups as well as the post-coup regime. Centralization and issues of ethnic representation, including the relative power of ethnic chieftaincies, were thus fundamental to prior regime instability in Ghana. Finally, at the end of the chapter I establish that ethnic voting patterns at the beginning of the Fourth Republic were reminiscent of voting behavior in prior regimes. The overall argument of this work is

45 that neopatrimonial political logics and ethnic voting are diminishing and the sub-national political competition instigated by the Fourth Republic’s centralized system is the primary causal mechanism behind this outcome.

2.1 The Argument

With origins in the colonial period, the politicization of ethnicity was intricately linked to the imposition of centralized rule in each of Ghana’s past regimes. To begin, the centralization of British power in Ghana was linked to the creation and/or re-definition of ethnic group boundaries under an artificial and hierarchical chieftaincy structure. The dual aim was both to rule over society and divide the colonial population. Chiefs became empowered by the British, making them beholden to colonial rulers rather than their own communities. As indigenous institutional checks and balances on chieftaincy power faded away, the colonial authorities favored the traditional chiefs over educated elites who sought a share of political power. When ethnic entrepreneurs would later use ethnicity as a tool for political mobilization, they implicitly relied on the concept of an ethnic identity and intra-ethnic unity promoted by colonial-era centralization.1 After independence, successive regimes perpetuated colonial-era centralized control, justified to protect or defend against Akan power. On the one hand, some regimes, including

Nkrumah’s and Acheampong’s, argued that centralized rule was necessary to combat both the inadequacies of traditional chiefs and the ethnic divisions which they reinforced, which

1 Though rarely acknowledged, chiefs have consistently been central to ethnic politics in Ghana. As the ultimate arbiters of tradition and culture, chieftaincy institutions provide ethnic entrepreneurs with a ready-made discourse through which ethnicized publics are mobilized. First, chiefs continuously deﬁne and reinforce ethnicity, and use tradition to maintain their role as the arbiters of the community and its interests. Second, chiefs also are patrons of community-level development. In Ghana, chieftaincy approval is informally necessary for the implementation of any public development project, particularly because most of the land available for development projects is communal land. Beginning in the colonial period, and extending into the post-colonial era, chiefs in Ghana were consistently incorporated into institutions of rule. This perpetuated their local authority and guaranteed the continued relevance of tradition and ethnicity for politics.

46 also served as a thinly-veiled assault on Akan power. On the other, the second justiﬁcation emphasized by Busia and Akuﬀo, used centralized rule in an exclusionary fashion, which guarded the interests of particularly powerful ethnic groups and their chiefs, namely the

Akans. Whether by de-emphasizing ethnicity or privileging particular ethnic groups, centralized institutions produced politicized ethnic cleavages either in the form of a politics of grievance by powerful groups not receiving ‘their fair share’ of the national pie (e.g. Akans under Nkrumah,

Acheampong, and, later, Rawlings) or from groups excluded from positions of privilege (e.g. non-Akans under Busia and Akuﬀo). Furthermore, the provocation of politicized ethnic divides resulted in strong ethnic-based oppositions and unstable authoritarian rule.

Though the centralizing reforms of the Fourth Republic were more a continuation of past authoritarian control than they were changes made for the sake of democracy, for the ﬁrst time the political system encouraged real political competition at the grassroots level. Historically under-served citizens, particularly those residing in rural areas, now have programmatic alternatives to voting for the locally dominant party. This has contributed to a lessening of the inﬂuence of neopatrimonial political logics and ethnicity on vote decisions and is crucial for understanding Ghana’s democratic stability.

2.2 Pre-Colonial Ethnicity and Colonial Rule

2.2.1 Ethnicity, Chiefs and Regional Identities

Ethnicity in Ghana developed out of both pre-colonial and colonial institutional structures.

Ethnicity was neither wholly created by colonial powers nor was it static in response to changing political and institutional circumstances. Historically the deﬁning features of an ethnic community were determined by chiefs and elders, and rules/boundary lines determining who belonged were manipulated over time to serve community needs (e.g., Greene 1996). As the extension of economic markets and migration patterns put greater strains on local community resources, community leaders began to diﬀerentiate who counted as autochthonous ‘sons of the soil’ and who was labeled a stranger.

47 This distinction between insiders and outsiders was exacerbated by colonial rule which promoted particular groups within a largely artiﬁcial hierarchical chieftaincy structure imposed for administrative purposes. Missionary groups also selected particular languages and dialects over others for translation of the Bible, reinforcing ethnic unity on the basis of particular dialects. Later, after the British took sole control of the Gold Coast, they imposed Lugardian Indirect Rule using traditional leaders and customary law. The British employed anthropologists to codify customary law which was then enforced by the Native Authority and

Native Courts, headed by chiefs and backed by the British; “Thus, custom became transformed from a political resource for re-negotiation of social status and access to resources to a set of enforceable rules that froze status and restricted access” (Spear 2003, 14). Based on codiﬁed customs, ethnic boundaries solidiﬁed and became convenient political commodities ready for ethnic elite manipulation in the post-colonial era.

Throughout the colonial period, chieftaincy power waxed and waned but was consistently incorporated into the formal institutions of the state. Rule through chiefs was administered in ad-hoc fashion, with different powers allowed to different chiefs and a continual re-codification of official ethnic customs. In addition, colonial institutions intentionally empowered chieftaincy institutions at the expense of educated elites. At the same time, the British also ruled the Gold

Coast as three separately administered colonial territories: “the Gold Coast Colony since the

1830’s, Ashanti colony since 1874 and the Northern Territories since 1891” (Massing 1994, 2-3)2 Inconsistencies between and within British colonial administrations were common and, as

2 The Gold Coast colony, consisting of control over coastal territories, was established ﬁrst. The extension of British control over the Ashanti Empire was completed after a series of Anglo-Ashanti Wars (in which the British utilized Fante (another Akan tribe) soldiers from the Gold Coast colony against the hinterland Ashanti Empire). British control of the Northern Protectorate was established after ‘treaties of friendship’ were signed with the majority of Northern chiefs.

48 different groups and regions were administrated differently, an overall effect of indirect rule was an emphasis of regional and chieftaincy-based ethnic identities.3

By the 1940’s, the divide between chiefs, whom relied on colonial authority for power, and the educated elites, who began to push for independence, was cemented. Relatedly, ethnic traditions and customs, with chiefs as the custodians, were essentially codiﬁed. And, with greater bureaucratization of political rule, chieftaincy power was limited, but still formally incorporated, alongside greater centralized British control. This set the stage for the formation of oﬃcial elite-based opposition to both British colonial rule and, as their allies, traditional chiefs.

2.2.2 The Educated Elite Response in the 1940’s and 1950’s

In reaction to the unrepresentative nature of colonial rule, a group of lawyers, merchants

and academics formed the United Gold Coast Convention (UGCC) in Saltpond, Gold Coast

in 1947. They were led by the ‘Big Six’ - Ebenezer Ako-Adjei, Edward Akufo-Addo, Joseph

Boakye Danquah, Emanuel Obetsebi-Lamptey, William Ofori Atta, and the newly-invited

3 Beginning in 1932, after the successful introduction of Lugardian Indirect Rule first in Tanzania and then in Northern Nigeria, official indirect rule was brought to the Northern Territories, with an emphasis on the regularization of taxes and the creation of a budget to control spending (Crowder and Ikime 1970, xx-xxi). By 1936, Lugardian Indirect Rule proved very successful in the Northern Territories (e.g., Saaka 1978), partially because of its status as an underdeveloped region far from the coasts and with fewer educated citizens to protest the new measures. But indirect rule was not formally introduced in the Gold Coast colony until 1944 (Simensen 1975, 297) when the establishment of official government treasuries finally placed local budgets in the hands of the colonial bureaucracy as opposed to chiefs (Owusu 1970, 2000). A uniform policy for the entire country (no longer separated into distinct territories and colonies) was not adopted until 1951, a mere six years prior to independence (Saaka 1978, 21). Legislation introduced in 1944 further increased British control. Now the tribunals which had been controlled by chiefs became under control of the Governor, and political control was also transferred away from State and Provincial Councils and back to the British Government (Simensen 1975, 304). As Simensen (1975) describes, the general public was extremely frustrated that the reforms in both the 1944 legislation and the subsequent Burns Constitution of 1946 failed to democratize traditional councils and that neither the educated elite nor the masses were formally represented in government.

49 Kwame Nkrumah. The UGCC was a conservative capitalist-minded political party which worked for self-government, but not at a rapid rate. An elitist organization, the UGCC had an interest in inheriting political rule, but it was also interested in the maintenance of the colonial economic system upon which their wealth depended (Owusu 1970, 188).4

Partially in response to the elitist orientation of the UGCC5 , the slow time-table for independence, and the awakening mobilization of the masses, Kwame Nkrumah famously broke away from the party and founded the Conventional People’s Party (CPP) in June 1949. The

CPP was a mass-oriented populist and socialist-leaning political party whose principal slogan was ‘Self-Government Now’ (Saaka 1978, 28).6

4 Though the UGCC pushed for independence, it was willing to follow the colonial timeline on when it would be granted. Other segments of the population, the new urban classes, and those “generally dissatisfied with their economic lot” (ibid, 188), were becoming increasingly frustrated with price inflation and their prospects under colonial rule (Simensen 1975, 314). Disturbances in 1948 included extensive riots after colonial forces opened fire on World War II veterans peacefully protesting the failure of the colonial government to disburse promised pensions.

5 Allman (1993) describes the political disposition of J.B. Danquah of the UGCC and a representative of the Asante old-guard intelligentsia: “Mass support was both cumbersome and irrelevant to the assumption of political oﬃce. As the noted African-American novelist, Richard Write, concluded after interviewing J.B. Danquah, the personiﬁcation of the old guard: ‘He was of the old school. One did not speak for the masses; one told them what to do”’ (48).

6 About this same time, the Coussey Constitutional Commission formed to address the Watson Commission’s findings that the lack of self-government and the antiquated and inefficient Native Authority system operating in the rural areas were major issues requiring attention.The status of the chiefs in this pre-1951 election era was continually renegotiated. Chieftaincy power was severely diminished from the early colonial period, but it also remained institutionally protected. The 1949 Coussey Report rejected the Watson Report’s recommendation that chiefs be removed from local government (Saaka 1978, 30). Traditional Councils thus remained in existence alongside State Councils, though now Traditional Councils only handled customary functions. During this time it was established that communal land and profits from its production belonged to the public as opposed to the chiefs. Similarly, “the total replacement of the Native Courts by a full-fledged professional local court system” (Simensen 1975, 317), though not finally enacted until 1959, meant usurping judicial power from Traditional Councils. Local Councils were also re-institutionalized such that their constituencies coincided with native state territory and one-third of local council members

50 Leading up to the 1951 elections, the major contending parties were the UGCC and Nkrumah’s CPP. Nkrumah had been arrested by the British authorities for instigating strife within the colony and he began campaigning from his jail cell. The arrest only increased

Nkrumah’s popularity and, realizing they had created a martyr, the British were compelled to release him. The CPP handily won the 1951 Parliamentary elections, leading to Nkrumah’s appointment to Prime Minister, and it subsequently won over 90 percent of the seats in the Local Council elections of 1952 (Owusu 1970, 196). Two major campaign issues helped secure CPP victory. First, Nkrumah had promised to help cocoa farmers in the face of the

British-created Cocoa Marketing Board (Owusu 1970, 191). Second, Nkrumah played upon chieftaincy disputes, and particularly appealed to the grievances of sub-chiefs who were disempowered by colonial centralization eﬀorts of the 1940s and 1950s (Simensen 1975, 316).

2.3 Post-Colonial Centralization and the Ethnic Response

2.3.1 CPP versus NLM in the Post-1951 Election Period

As Prime Minister, Nkrumah argued that increased centralized control was necessary to combat ethnic divisions. Yet the speciﬁc policies pursued by the CPP government particularly targeted elite cocoa wealth and Ashanti power, co-opted Akan sub-tribes to disunite an Akan-based opposition, and provoked an ethnic response given voice by the Ashanti

Region-based National Liberation Movement (NLM). Finally, the NLM resorted to a call for

were to be nominated by the State Council. The Paramount Chief would act as ceremonial president of the Local Council, but the chairman would be an elected citizen (ibid). Further, to some extent traditional interests were also implicitly present in the bureaucracy and in local councils as relatives of chiefs were often the privileged in society. Not only would relatives of chiefs become representatives in government or the bureaucracy, but, “frequently, young men risen in the modern system as clerks, educators, administrators or political activists enter the traditional system at a later age by competing for chieftaincy or eldership” (Massing 1994, 37). The revolving door between traditional and formal power complicates the incorporation of traditional power in modern rule, particularly in the late pre- and early post-independence period when formal opportunities were scarce and limited to society’s elites.

51 a federal system of government, demonstrating the initiate association between institutional structures and ethnic politics at the time.

One argument oﬀered to explain the development of regional and ethnic opposition from

the NLM and later the ethnic-coalition United Party (UP) is because “it had proven impossible,

as evidenced by the results of the 1951 election, to compete ideologically with the CPP as an alternative Gold Coast-wide nationalist party” (Allman 1993, 20). Yet, as Allman agrees, this does not explain why the strongest form of opposition came from the Ashanti Region.

At a basic level, the historical dominance of the Ashanti Empire, the region’s endowments in cocoa and gold, and the thriving commercial environment in Kumasi, the capital of the region, produced a formidable elite whose interests the UGCC had promised to protect. However, with Nkrumah’s massively popular CPP government in power, an ethno-nationalist Ashanti movement sprung up largely in response to two policies: the seat allocation in the 1954

Legislative Assembly and the freezing of the price of cocoa at 72 shillings.

Beginning in 1953, the Report of the Commission of Inquiry into Representational and Electoral Reform had allocated the Ashanti Region with roughly 20% of the seats in the

1954 Legislative Assembly. This was a drop from the 25% share the Ashanti Region had held in the 1951 Legislative Assembly. This immediately visible loss of power became the ﬁrst major rallying point for an Ashanti-regional opposition (ibid, 23). The youngmen of the NLM movement then sought an alliance with the Asantehene and other Ashanti chiefs, despite having themselves rallied against chieftaincy power prior to the NLM formation, in order to gain both economic and cultural resources at the chiefs’ disposal (ibid, 40-48).

The other controversial CPP initiative which solidiﬁed the NLM-Asantehene coalition was the CPP’s decision to not only continue the Cocoa Marketing Board (CMB)7 but also to

7 The colonial powers had created the Cocoa Marketing Board to protect farmers from exposure to the ﬂuctuations of the international price of cocoa. By setting a price, and empowering the CMB as the only purchaser of cocoa in the country (what Bates (1981) refers to as a monsopony), the colonial government would become the regulator of the

52 freeze the price of cocoa at 72 shillings per load. Though the CMB did provide some level of protection for peasant farmers, large commercial farmers were particularly against the CMB.

The issue of cocoa regulation was very politicized at this time. As Allman (1993) explains,

“the economic welfare of Asante, as a whole, was inextricably tied to cocoa. Approximately 51 percent of the cocoa exported from the Gold Coast in 1954-55 was produced in Asante at a time when the Gold Coast was the largest producer of cocoa in the world. Cocoa accounted for over 80 percent of the total value of domestic exports” (36-37).

In response, Nkrumah began a campaign to disunite the Ashanti-based opposition. As a

ﬁrst step, he widely publicized the idea that the 72 shillings price set by the CMB was accepted by poor farmers and that it was only elites represented in the NLM which sought to double the price to 150 shillings per load (Herbst 1993, 79). Soon after, the CPP rolled out a propaganda campaign to emphasize the degree to which historical ethnic empires, namely the Ashanti

Empire, wanted to again dominate other ethnic groups as they had done throughout the 18th,

19th and early 20th centuries.8 Finally, the major initiative to disunite the Ashanti-based opposition was Nkrumah’s strategy of co-optation of potential co-ethnic allies. First, Nkrumah began to chip away

cocoa industry. As it were, however, the price at which cocoa was set remained fairly low in comparison to the international market price, and Ghanaians began to view the CMB as illegitimate when surplus funds were not re-directed back to the localities (Allman 1993, 38-39).

8 For instance, an October 16, 1954 article in the pro-NLM Ashanti Pioneer cites a ruling in Accra Market that Ashanti cloth sellers in Accra could only sell through Ga cloth sellers. The author of the article was of the opinion that the ruling was intended to incite a tribalistic rivalry between the Gas and the Ashantis, by establishing Ga ownership priority in their traditional Accra homeland (“Baﬀoe’s Death Steps up Liberation’s Support” 1954). In the colonial period, the Ashantis had been allies with and adversaries of the Gas at diﬀerent points in time. Alternatively, a pro-government newspaper reported that the Ashanti Federation Movement aimed to re-institute Ashanti autocratic rule in Ghana and that it represented elite interests as opposed to the rural cocoa farmers which had agreed with the CPP government’s cocoa price (“Federation Movement Aims At The Revival of Autocratic Rule in Ashanti” 1954).

53 at chieftaincy hierarchies by catering to sub-chiefs who had lost power under the colonial creation of now strong paramount chieftaincies, including Akan chiefs who now reported to

the Asantehene. Nkrumah co-opted these sub-chiefs by promoting them, putting them on

government payroll and demoting other chiefs deemed problematic for CPP rule.9

Secondly, in February 1955 the CPP made moves to co-opt the Brong and Ahafo tribes by considering a petition to create a Brong-Kyempim Traditional Council as separate

from the Asanteman Traditional Council within the CPP-dominated National Assembly.

The Brong-Ahafo territory had long been a part of the Ashanti Empire and that the

CPP-government considered this issue in 1955, as Allman (1993) puts it, “was a clear

indication to Asantes that the nation was about to be assaulted on all fronts” (99).10 In reaction, the NLM Movement began to advocate for a federal system of government.

The NLM, though ﬁercely defensive of Ashanti interests in rhetoric used in Kumasi-area

newspapers11 , elsewhere tried to present itself as a non-ethnic party vying for strong regional

institutions which would serve as a protective barrier to Nkrumah’s increasingly centralized CPP government. The NLM thus made direct appeals to other regional and/or ethnic political

parties and organizations to encourage them to join the movement for regional power.

9 Printed in the Ashanti Pioneer on December 9, 1954, the Ashanti empire thus began making direct political appeals for a federal government for Ghana in order to protect the pro-federation Ashanti movement and the need to return to chieftaincy; “Secondly, Nana Ofori Atta said, the Ashantis wanted to regain our traditional institutions and usages and respect for Chieftaincy which were gradually being snatched away by the introduction of party politics in the country. Even the whites attached great importance to our traditional institutions and they consulted the Chiefs before issues of vital importance aﬀecting the country were enacted” (“J.P.C. Debates Asanteman Council...” 1954). 10 The petition was eventually approved in 1959, creating a separate Brong-Kyempim Council and Brong-Ahafo Region.

11 For instance, the Ashanti Pioneer printed a story about the Asantehene’s endorsement of the National Liberation Movement’s quest for a federation. After the Asantehene stated ‘Ashanti knows no retreat’ the crowd responded with the Ashanti War Cry (“Otumfuo Backs Federation Demand...” 1954).

54 The ethno/regional organizations associated with the Northern territories12 and the present-day Volta Region13 were particularly prominent at this time. The parties meeting with the NLM included the Northern People’s Party (NPP), the Togoland Congress (TC), the Ghana Action Party (GAP), the Muslim Association Party (MAP), the Ghana Congress

Party (GCP), and the Anlo Youth Organization (AYO). Further, the Daily Echo reported on September 3, 1955 that the GCP, GAP, AYO, and Ghana Youth Federation had oﬃcially merged with the NLM, while the MAP was an NLM aﬃliate (“Colony Parties Merge Into

NLM” 1955). In fact, though an Ashanti-Ewe rivalry is widely acknowledged in Ghana’s Fourth

Republic, the Ashanti Pioneer was reporting on the details of the Ewe Uniﬁcation Movement as it mattered for the NLM’s own federal negotiations (“Anlo Chiefs For Uniﬁcation” 1954). In November 1956, the CPP government appointed the Greenwood Commission to study the system of local government. The subsequent Greenwood report described two plans

12 Northern interests were concerned with the fast-paced nature of the Gold Coast’s advancement to independence. Historically under-developed due to its great distance from the coast as well as its separate administration under British rule, Northern interests were primarily concerned that the North should be developed prior to independence so that it would not face political disadvantages in a newly-independent nation (Massing 1994, 38-39). The number of Western-educated citizens were fewer in the North as compared to the southern areas, and the well-founded fear was that early self-government would essentially translate into rule by non-Northerners. 13 The Ewe organizations of the present-day Volta Region represented at NLM meetings were the Togoland Congress and Anlo Youth Organization (AYO). Previously a German colonial possession, the Togoland colony was split into British and French protectorates after WWI. An area dominated by Ewes, local leaders of these protectorates saw three options before them in the approaching independence era: (1) the protectorates could each become independent on their own; (2) they could join together and become independent; or (3) they could join the Gold Coast. The Togoland Congress ultimately decided that it would (unsuccessfully) vie for uniﬁcation of British Togoland with French Togoland separate from the Gold Coast. The Anlo Youth Organization represented the southern-most tip of the present-day Volta Region - an area dominated by Ewes but always part of the Gold Coast colony. The AYO eventually decided it wanted the British trans-Volta Togoland territory with Ghana. A 1956 Plebiscite held in the British Togoland Protectorates saw voters choose to separate from the French Protectorate and to join the Gold Coast (Amenumey 1968; 1989).

55 of local government institutions it saw as viable for the Gold Coast. The CPP government rejected Plan B of the report, which would have established strong regional governments, and instead adopted Plan A whereby the central government would maintain authority in local government through district councils (Saaka 1978, 37; Amegashi-Viglo 2014, 14). However, given the signiﬁcant aﬀront Plan A was to Ashanti authority as well as British concerns about leaving Ghana in the hands of a CPP government which had not granted the opposition any concessions, a compromise had to be made. In early 1957, just prior to independence,

Nkrumah’s CPP government agreed to create semi-independent regional governments. As reported on February 11, 1957 in The Liberator, ﬁve regions would be created, each with its own Assembly, House of Chiefs, and Head of State, and no provision was made for a separate Brong Ahafo region (“Kumasi Rejoices Over Contents of White Paper...” 1957). Celebrations in the Ashanti Region would not last long, however, as this concession would be eliminated soon after independence.14

2.3.2 The Post-Independence CPP Regime

After independence, oppositional, regional and chieftaincy power continued to suﬀer at the hands of Nkrumah. The Avoidance of Discrimination Act in 1957 prevented any political party from associating along sectional identities, thereby outlawing the NLM. The United Party

(UP) was thereby formed, made of the NLM, NPP, TC, AYO, and MAP (Owusu 1970, 278).

Also during this initial independence period, Nkrumah “handpicked Regional Commissioners as representatives of government, even though these were to be chosen by the Regional Council (House of Chiefs) [and] without awaiting local government elections, he declared district

14 Already suspecting that the agreement on regional governments would not be enforced, the President of the Asante Youth Association made a speech on February 26, 1957 threatening Ashanti secession if the agreement was not implemented (“Ashanti Bent Upon Secession If..” 1957). On March 1, 1957, it was reported that Cabinet Ministers were delivering speeches suggesting that the semi-independent regional governments would not be implemented (“Any Breaches Of The Constitution Will Destroy Bonds Between Component Regions.” The Libera- tor, March 1, 1957). The Gold Coast achieved independence on March 6, 1957.

56 councils the main units of local government, thereby abolishing and dissolving the regional councils” (Massing 1994, 4). The compromise on regional governments Nkrumah had used to guarantee independence was abolished early in the post-independence era.

Matters quickly headed downhill as Nkrumah sought to secure his regime through authoritarian measures to disrupt the original Ashanti-based opposition. First, the opposition was crippled by arrests and imprisonment of its members under the 1958 Preventive Detention and Deportation Acts (Allman 1993).15 Soon after, the Stool Lands Control Act of 1959

(1961) prevented chiefs from receiving revenue from communal lands (Owusu 1970, 281), the 1961 Local Government Act forbade chiefs from serving as members on local councils

(Massing 1994, 56) and the 1960 Constitution, which removed from the Queen of England as the Head of State, gave Nkrumah absolute veto power over legislation (Schwelb 1960). By

1964 chieftaincy power was at an all-time low and the CPP was the only legal party in the country. On February 24, 1966 a successful coup carried out by the military and the police ousted Nkrumah from power while on a diplomatic mission in Asia. Nkrumah remained in exile in Guinea thereafter, never returning to Ghana.

2.3.3 The NLC and the 1966 Coup

Though the National Liberation Council (NLC) came to power backed by a cross-ethnic coalition, it became associated with Akan interests and helped to deliver the election of an

Akan-centric Progress Party (PP) regime. Under Lt .Gen. Joseph Ankrah, the NLC took over a Ghana bankrupted by the Nkrumah regime’s heavy state-spending. The NLC dismissed the CPP government and made membership in the CPP illegal. While espousing anti-centralization and anti-Nkrumah rhetoric, the NLC proved quite committed to return Ghana to democratic rule. An 18-member Constitutional Commission headed by Chief Justice Edward Akufo-Addo

15 The 1958 Preventive Detention and Deportation Acts empowered the government to “arrest and detain for ﬁve years anybody suspected or found acting in a manner prejudicial to the defense of Ghana, to her relations with other states and to state security” (Boahen 1975, 194). In practice, this Act was used to quiet any and all forms of opposition.

57 (one of the ‘Big Six’ UGCC founders) was formed on November 18, 1966 to gather public opinion about a new constitution (Frempong 2007).16 Later, a 150-member Constituent

Assembly also met and adopted a constitution that barred a one-party state, prevented MPs from ‘crossing carpet’ (i.e., switching parties, as Nkrumah had forced many opposition MPs to do during his reign) and created a Prime Minister alongside a (semi-ceremonial) President. As advisors to the Prime Minister and President, the Council of State was composed of the

Prime Minister, the Speaker of the National Assembly, the Leader of the Opposition, and the

President of the National House of Chiefs (Owusu 1979).

Chiefs and regional interests were explicitly supported by the NLC.17 With the deposal of Nkrumah’s CPP government, the original chieftaincies were restored and many of the chiefs that Nkrumah had promoted or made into Paramount Chiefs were demoted (Massing 1994,

5). During this time the Chieftaincy Act of 1971 created the National and Regional House of

Chiefs institutions, still politically important in Ghana today (Pul 2003, 45-46). Traditional interests were also enshrined into local government as local councils were elected partly by traditional authorities and partly by the public (ibid, 64). Above local councils sat district councils, whose members were half-elected and half-appointed by these local councils (i.e.

50% traditional interests). Finally, above District Councils sat the Regional Councils originally created, but immediately dismissed, in the pre-independence compromise between Nkrumah and the NLM.

16 Though the NLC and Busia’s regime were associated with traditional interests/chiefs, during this Constitutional Commission chiefs had supported the creation of a no-party system. This suggestion was strongly opposed by the intellectuals and middle-class elite which dominated by the Constitutional Commission and subsequent Constituent Assembly (Owusu 1979, 101).

17 Though regional and chieftaincy interests were supported, the North particularly suffered at the barring of CPP members from office as well as the replacement of administrative officers. As Massing (1994) explains, “In the North an entire generation of politicians was retired after the coup: most of them were barred from holding office for 5-10 years” (59). The historical disadvantages faced by the North would continue.

58 Parliamentary elections held in August 1969, saw Koﬁ A. Busia’s Progress Party (PP) and Komla A. Gbedemah’s National Alliance of Liberals (NAL) as the top seat contenders. The

Progress Party won the election with 105 out of 140 seats and voted Busia as Prime Minister, while the NAL won 29 seats (Frempong 2007). Edward Akufo-Addo (the late father of Nana

Akufo-Addo) was subsequently appointed to the largely-ceremonial Presidency. 2.3.4 Busia and the Progress Party (PP)

Rather than justifying centralization as protection against ethnic divisions a la Nkrumah,

Busia’s PP government centralized institutions in the name of alignment with the chiefs and in order to perpetuate an exclusionary pro-Akan regime. Between enforcing strict interpretations of institutional rules to exclude political opponents while failing to enforce laws against his own allies, issuing the 1969 Alien Compliance Order and allowing ethno-political biases to enter in bureaucratic downsizing policies, Busia’s regime was very clearly understood as a continuation of the exclusionary and elitist pre-colonial UGCC party, but perhaps with an even stronger pro-Akan ethnic bent.

While the NLC had emphasized decentralized government, tradition, and chieftaincy power, Busia’s PP government retained a high degree of centralized power and instigated ethnic ﬂare-ups. For instance, Busia’s regime continued to play ideological and ethnic games by persisting with anti-Nkrumah policies previously instituted by the NLC, and in ﬁlling cabinet posts with ethnic Akans (Brown 1983, 443). However, three ethnic-fueled events in particular cemented the public’s interpretation of Busia’s government as exclusionary. First, the PP government did not allow the opposition leader, Gbedemah, to take his

Parliamentary seat as representative of the Keta Constituency because, as a former CPP party member, he was barred from holding oﬃce for 10 years. This was a controversial ruling because Gbedemah had been dismissed from Nkrumah’s government in 1961 and remained in exile for some time. That the NLC and Busia’s Progress Party were associated with Akan, and

59 particularly Ashanti, interests and were now barring a prominent Ewe opposition leader from oﬃce provided some groundwork for a major ethnic divide.18

Frempong (2007) argues that Gbedemah’s presence in Parliament would have helped to smooth over ethnic politics because this experienced politician would have had a moderating influence on his younger opposition colleagues (14). This issue would become politicized to an even greater extent when Busia’s Finance Minister, J.H. Mensah, was found to be in violation of Article 61 which stated that ministers could not hold any other office of profit while in office. Though calls were made for Mensah’s resignation, Busia refused to sack him, creating an appearance of a double standard (Frempong 2007).

Second, due to perceptions about Nigerian dominance over Ghanaian markets in Accra and other city-centers, the PP government used this issue to scape-goat foreigners for Ghana’s economic troubles. Busia issued the Alien Compliance Order on November 18, 1969 forcing the expulsion of at least 100,000 foreigners from Ghana. Partially because they did not blend

18 Compounding this Ewe issue, an important ethnic matter arose early in the NLC regime which has had a lasting effect on the politicization of an Ashanti-Ewe rivalry. Two military officers who had participated in the NLC coup were Brigadier Akwasi A. Afrifa, an Ashanti, and Lieutenant General Emmanuel K. Kotoka, an Ewe. To hear the story from an Ewe perspective, Kotoka was known as a fierce and undefeatable warrior who used traditional religious magic to protect himself on the battlefield. Kotoka and Afrifa became good friends during their time stationed in Kumasi. It is believed that Kotoka let his guard down and visited his shrine in the accompaniment of Afrifa, thus exposing his only weakness in his invisible armor: the back of his ankle. In an abortive coup attempt by junior officers, this information was used to kill Kotoka. In defense of Afrifa, an Ashanti perspective emphasizes that Afrifa was in the northern part of Ghana at the time of the murder and thus could not have participated in Kotoka’s death. Still, many Ewes contend that Afrifa was the mastermind behind the murder. It is still possible to hear individuals in Eweland warn that though Ewes can be close to Ashantis, they should never trust them with their life because there is the great likelihood that they can betray you as Afrifa betrayed Kotoka. Indeed, I will later discuss the ethnic bias in hiring practices by the Rawlings’ regime in the 1980’s. These biased hiring practices were largely restricted to military employment. Some defend these hiring decisions, citing Afrifa’s murder of Kotoka, because Rawlings could not absolutely trust ethnic-Akan soldiers because of the possibility of betrayal. The Kotoka International Airport in Accra is located at the site of Kotoka’s murder and thus named in his honor.

60 in as the Nigerian Hausas could in Hausa-speaking communities in Ghana, the Yorubas were particularly affected by these expulsions (Adida 2014). Still, Hausa-speaking communities throughout Ghana were harassed as government officials combed through communities trying to find foreigners. Northerners living in the south often reside in segregated Zongo communities where Hausa is the lingua-franca. Many ‘foreigners’ were born in Ghana and had never been to Nigeria before. Others had integrated into Ghanaian society through marriage and had children born in Ghana. The expulsion of these individuals, as well as the harassment of Zongo-area residents, created a long-lasting division between a great deal of Northerner votes and the PP and its successor parties. Still today rumors about the expulsion of foreigners are used to rouse anti-NPP (New Patriotic Party) sentiment during election time. Finally, because Nkrumah had over-extended the nation’s budget in adopting massively inclusive-employment policies and sinking money into protecting nationalized industries, making them unproductive and causing their failure, Busia’s government was forced to rationalize expenses. As part of these efforts, the sacking of 568 public servants in 1970, termed Apollo 568, created controversy after the PP government was accused of selecting opposition sympathizers to be fired (Frempong 2007). Overall, this move also alienated the civil service.

The exclusionary and ethnic interpretation of Busia’s PP government thus became widespread. Massing (1994) summarizes the state of aﬀairs at this time:

“Non-Akan people and particularly Ewe and Ga felt excluded from the ruling

coalition; the Volta and Northern regions which had manifested their opposition in the elections, reduced contact with government; by 1970, even the Akan and Fante

broke the traditional Akan alliance. The urban professional bias of the Party excluded

other upwardly mobile groups, e.g. traders, farmers, entrepreneurs. The civil service

and the military were hostile to the government and demanded the rectiﬁcation of

inequitable policies” (66).

61 Other than ethno-regional sectional issues induced by the PP government, Busia’s regime also retained a signiﬁcantly high degree of centralized control despite Busia having been outspoken about decentralized government during the NLC tenure. For one, while the Regional

Councils were reinstated, Busia retained the right to appoint the chairmen of the district and regional councils and could dissolve any Regional Council as he pleased (Massing 1994, 5). In another case, and very reminiscent of Nkrumah’s meddling in the judiciary during his time in power, Busia refused to accept a Supreme Court ruling which found that a public servant had illegally been forced into early retirement, saying that in this case the court had ‘exceeded its competence’ (Frempong 2007).

However, in addition to the stirring of ethnic issues, the alienation of the civil service as well as the military (both of which experienced funding cuts), and the hypocritical grasp on centralized control, the straw that really broke the camel’s back was the 1971 devaluation of the cedi. Cedi devaluations are extremely politicized in Ghana and are associated with harming urban interests because food and other imported goods become more expensive. As Massing (1994) writes,“The grave economic crisis made regionalism and ethnicity even more important at the end of the Busia period than at the end of the Nkrumah regime” (66). On January

13, 1972, a military coup put Colonel Ignatius K. Acheampong and the National Redemption

Council (NRC) in power.

2.3.5 Acheampong, the NRC and SMC-I

Like Nkrumah, Acheampong argued that centralized control was necessary to rid the nation of ethnic divisions as well as to put the country back on sound economic footing.

Also like Nkrumah, Acheampong issued policies co-opting chiefs and suppressing ethnicity.

The suppression of ethnicity and a contraction in the economy resulted in an environment of heightened ethnic politicization, with particular opposition stemming from Akan-dominated regions, and resulted in an overall unstable authoritarian regime overthrown in a palace coup in

1978.

62 Acheampong’s National Redemption Council (NRC) came to power in 1972 citing the need to re-unify the nation. The NRC regime’s ﬁrst step was to arrest more than

1,300 politicians and limit the freedom of speech (Massing 1994, 5-6). Actions taken by

Acheampong’s government, such as banning the use of the word ‘tribe’, avoidance of ethnic bias in political appointments, and increasing centralized control over local government institutions, was intended to quell ethnic dissention. In reality, attempts to stamp out the politicization of ethnicity only reinvigorated ethnic mobilization (ibid, 68).

Acheampong paired the anti-ethnic ﬂavor of his regime with highly centralized institutions.

This was done to de-emphasize participatory politics and thus regional and local inﬂuence: “In eﬀect, local government in Ghana from 1974 [had] ceased paying even lip service to the idea of local autonomy. The new system made local government, in essence, the local agency of central government” (Saaka 1978, 43-43). As an example, now the Regional Commissioners and the District Chief Executives were both political appointees from the center (ibid, 45-46).

Acheampong’s regime also promoted a ‘return to tradition’, co-opting chiefs support while de-emphasizing ethnicity. The District Councils were now composed of two-thirds members nominated by the government and one-third representative of traditional bodies. Below

District Councils sat Municipal, Urban, Area, and Local Councils which would also consist of appointments made by traditional councils and the government (ibid, 45). Chiefs were directly employed under Acheampong’s regime and received regularized payment from the government (Massing 1994, 68). By empowering the central government and traditional leaders while excluding regional-based and educated elite voices, Acheampong’s regime was reminiscent of the centralized strategies used under colonial rule.

Though Acheampong attempted to increase national unity, regional and ethnic issues grew and created more structural problems. As happened during the Nkrumah and Busia regimes, economic diﬃculties ﬁnally exacerbated the situation: “In 1975 opposition and strikes by unions, students, professionals, churches and regional traditional authorities against the government grew so strong that the military purged the NRC of civilians and placed

63 government in the hands of an Acheampong-led Supreme Military Council (SMC) which promised a return to constitutional rule and civilian government for 1978/79 under a so-called

non-partisan Union Government” (Massing 1994, 6).

Acheampong had vowed that power would not be handed over to a civilian regime until

the Ghanaian economy was on sound footing (Frempong 2007), but continued economic failure forced action.19 The NRC was modiﬁed into the SMC and pushed a Union Government concept which would implement a no-party democratic government where the army and police would share power with civilians (Hitchens 1979). An ad hoc committee was formed to survey public opinion about ‘Uni-Gov’ and a highly-questionable March 30, 1978 referendum claimed that Uni-Gov was favored 54% to 46% (Frempong 2007). Three regions, the Ashanti, Brong-Ahafo, and Eastern Regions (all with Akan-dominant populations), had a majority opposing the Referendum, despite Acheampong’s Ashanti-regional roots.

After the Uni-Gov referendum passed, a Constitutional Commission was formed in April to submit a draft constitution by October. By this time, widespread discontent existed amongst the Akan regions, the Ewes of the Volta Region, and increasingly the Fanti and Ga (Chazan and Le Vine 1979, 189). Further, the chiefs were demanding public pay-raises and “regional leaders demanded more equitable distribution, [meaning] that the depoliticization attempts of the early years were thwarted by growing ethnic discontent and regional disparities” (Massing

1994, 68). A palace coup removed Acheampong from oﬃce on July 5, 1978. 2.3.6 Akuﬀo and the SMC-II

Paralleling Busia’s reaction to Nkrumah’s regime, Lt. Gen. Fred Akuﬀo’s short-lived

SMC-II regime reacted against the suppression of ethnicity under Acheampong’s rule and

19 Chazan and Le Vine (1979) describe the 1977 situation as a time of sharply fallen export prices, commonplace smuggling, a staggering rate of inﬂation, and a cedi which had dropped in real terms to 1/6 of its oﬃcial rate (181). Herbst (1993) similarly describes how Acheampong’s elimination of most of the devaluation implemented by the Busia regime led to massive overvaluation of the cedi, crippling the Ghanaian economy (23).

64 became aligned with Akan and elite interests. The SMC–II regime was in many ways a continuation of the highly-centralized SMC-I, except with more guarantees for Akans. Akuﬀo also attempted to implement economic and anti-smuggling reforms, but this would not be enough to dissuade the successful AFRC coup led by junior oﬃcers on June 4, 1979.

Akuffo’s reform efforts were noble, but had limited success, and it became clear that Akuffo was not fully in control of the SMC-II regime. While Acheampong’s NRC regime had proven unable “to cope with the economic crisis and to restore at least creditworthiness and an air of credibility in the economic domain” (Massing 1994, 66), Akuffo’s SMC-II regime implemented reforms to try and remedy the economic and political situation in Ghana. For instance, Chazan and Le Vine (1979) credit Akuffo with attempting to reverse the decline in cocoa revenues by addressing smuggling and mismanagement within the Cocoa Marketing

Board, by deporting two Lebanese merchants for tax evasion, and by devaluing the cedi

(something which Acheampong had refused to do) (202). The anti-smuggling efforts were only somewhat successful (Amamoo 2000, 174) and, importantly the reform efforts did not do much in the way of alternating either the overall distribution of power or the system for the accumulation of resources. When Akuffo decided not to prosecute Acheampong and released him from prison, this was taken as a clear sign that former leaders were safe and Akuffo would not make any significant changes to the power distribution guaranteeing elite wealth and privilege (Goldschmidt 1980). The Akuffo regime began taking steps to transition to civilian constitutional rule. In

November 1978, the Mensah Commission published its proposals for a new Constitution.

Non-partisan local council elections were also held. These elections prompted the SMC-II regime to lift the political party ban, in eﬀect since the NRC coup in 1972, because local council candidates were well-known former members of either the CPP or PP and citizens overwhelmingly voted along party lines. The political party ban was lifted on January 1, 1979, and a Constituent Assembly established to discuss the new Constitution completed its work in

May 1979 (Goldschmidt 1980). Nonetheless, on June 4, 1979, Jerry John Rawlings and other

65 junior military oﬃcers in the Armed Forces Revolutionary Council (AFRC) overthrew the Akuﬀo government.

2.3.7 Rawlings-I and Limann

The AFRC takeover was economically-motivated, like past coups, but also diﬀered in that it speciﬁcally aimed to change the mechanisms guaranteeing elite wealth and power. Though critical of traditional power, the AFRC did not espouse an ethnic-orientation and was more concerned with economic power distributions than it was with ethnic divisions. During the three-month long time in power, the AFRC conducted a ‘house-cleaning exercise’, supervised

Presidential and Parliamentary elections, and oversaw the transfer of power from military to civilian rule on October 1st, 1979. Prior to June 4th, Flight Lieutenant Rawlings and several other members of the air force had been arrested and tortured for participating in a failed coup attempt several weeks prior.

On June 3rd, a small group of junior officers released Rawlings from a military prison, and on June 4th the coup was publicly announced. The stated goal of the June 4th coup was to clean-up the failures that Akuffo’s regime had refused to address. The house-cleaning exercise that commenced included the arrest and charging of military officers, former and current officials, and wealthy businesspeople with crimes related to corruption and embezzlement.

Famously, eight senior military oﬃcers, including three former heads of state (Afrifa20 ,

Acheampong, Akuﬀo), were accused of corruption and embezzlement and were executed on

June 16th and June 26th, 1979. The AFRC also sent hundreds of oﬃcers and civilians to long prison sentences on accusations of corruption, blew up Makola market (the major trading center in Accra), and sacked hundreds of top civil servants and police oﬃcers for corrupt

20 Afrifa had only been in power for a little over a year during the NLC tenure, after Joseph Arthur Ankrah was forced to resign amidst a bribery scandal. Afrifa won the Parliamentary seat for the Mampong North constituency as a candidate for the United National Convention (UNC) in the June 18th, 1979 elections, but was executed on June 26th, 1979. That Afrifa was singled out for execution, given his short tenure as head of state, is popularly believed as punishment for Afrifa’s involvement in Kotoka’s murder in 1967.

66 practices (Hanson and Collins 1980, 3; 18). The blowing up of Makola was part of a broader agenda to end hoarding by forcing traders to sell at controlled prices which the poor could aﬀord. Those found to be uncooperative faced public discipline and other sanctions (Ahiakpor

1991, 587-588). Rawlings used populist rhetoric and promised to hold elections to legitimize the coup.21 Despite the on-going house-cleaning exercise, the Presidential and Parliamentary elections were still held on June 18th and the handover to civilian government was only postponed from

July 1st to October 1st. The new 1979 Constitution, based on the proposals by the Mensah

Commission and ﬁnalized by the Constituent Assembly, was still implemented. The Mensah

Commission had emphasized local government institutions and the 1979 Constitution made the district the basic local government unit. Local and traditional power thereby increased, as traditional members would hold a maximum of one-third of the district council seats, and because “an amendment of the constitution [had to] be accepted by two-thirds of all local government councils, and [as] the chiefs [were now] represented in the Lands Commission and the regional police committees” (Goldschmidt 1980, 56-57).

In the 1979 elections, the major parties were the PNP, whose candidate was Professor

Hilla Limann (a Northerner and the eventual winner), the Popular Front Party (PFP), whose candidate was Victor Owusu (a prominent member of the Ashanti-based National Liberation

Movement in the pre-independence period), the United National Convention (UNC), whose candidate was Paa Willie Ofori-Atta (a UGCC founding member and ex-Foreign Minister in

Busia’s government), and the Action Congress Party (ACP) whose candidate was Colonel

Bernasko (a Fante who worked as the Commissioner for Agriculture under Acheampong’s

NRC). Massing (1994) describes the broad-based nature of the PNP as compared to the PFP:

21 See Hansen and Collins (1980) on the importance of coup legitimation in Ghana.

67 “the PNP represented local combinations of class, ethnic and other group factors rather than presenting a homogenous Akan cum elite-professional alliance as the PFP;

and the PNP stressed the centrality of local community and regional issues (in the

North) in politics. Presidential candidate Limann, being Imoru Egala’s nephew and

political successor, campaigned on Nkrumah’ist issues like Egala himself who as an ex-CPP member was barred from running for oﬃce” (77).

Goldschmidt (1980) explains that though many former PP members joined the social democratic PFP “embracing Busia’s ideals of liberalism and human rights” (50-51), not all former PP members joined the PFP. Some were attracted away to the UNC, led by

Ofori Atta, a former head of state during General Afrifa’s NLC regime. For the first time, the 1979 elections saw the major Akan ethno-linguistic groups split their votes. The PFP relied on Asante and Brong votes, the UNC relied on an Ewe-Ga alliance with Akan votes, while the ACP relied on southern Fante votes, leaving PNP’s Limann to secure a narrow base of Northern and Nkrumah’ist votes. The PFP and PNP each secured enough votes to push the election to a runoff. In the subsequent election the Akan-Ewe-Ga votes which had supported the UNC and the ACG’s Fante votes mostly transferred to PNP’s Limann (Jeffries 1980), making him the first Northerner President in Ghana’s history. Importantly, Limann’s government seemed unaware of its narrow power base (Massing 1994, 79) and quickly made moves to alienate itself from the military.

Limann began to publicly separate himself from Rawlings and the latter was forced into early retirement after he refused to sit on the State Council (Massing 1994, 79). Unlike past regimes, Limann’s government was not wide-reaching enough to achieve national support, and its ethno-regional base was too narrow to guarantee the regime. Further, Rawlings and his associates began to openly criticize Limann’s government after it failed to end corruption and revitalize the economy (Brown 1983, 456). Limann came to understand Rawlings as a threat, attempted to label the June 4th Revolution as tribalistic in nature and accused Rawlings of tribal aggrandizement. As Brown (1983) writes, “at the core of the accusations during 1980

68 and 1981 was that Rawlings and his associates were Ewes who were seeking to use the 4 June ‘revolution’ and their earlier domination of the Armed Forces Revolutionary Council (AFRC) in order to further their own narrow interests of Ewe domination” (ibid, 457). However, there is more evidence that it was actually Limann’s own government which was stirring the ethnic-pot:

“When the government appointed representatives from the regions to the 27-member Council of State it chose chiefs, academics and professionals without roots among the Akan and Ewe”

(Massing 1994, 79).

The increasingly divisive tactics used by the Limann regime were not backed by a broad-based constituency that cut across regional and ethnic power bases. As Massing

(1994) writes, “Despite Northern support for the victorious PNP, it had a smaller and more fragmented power base than earlier parties. Notwithstanding, the victorious presidential Hilla

Limann used heavy-handed tactics towards opposition groups, warned of another military takeover and personally attacked Flight Lieutenant J.J. Rawlings. During the 26 months of its existence the Limann government was neither able to correct the economic problems inherited from its predecessors nor reform the political process” (6). The Limann regime’s inability to change the economic system, where wealth was concentrated in the hands of a few rich and well-connected individuals while the poor purchased goods at expensive and uncontrolled prices on the black market, prompted Rawlings’ second coup on December 31, 1981 (Ahiakpor 1991,

593). 2.3.8 Rawlings-II

Rawling’s highly-centralized military authoritarian regime, the Provisional National Defense

Council (PNDC), relied on a narrow political base, surviving through the violent enforcement of the regime. During PNDC rule, Rawlings appealed to other ethnic groups, notably the cocoa farmers in the Akan-dominated Ashanti, Eastern, and Central regions, but was still pegged as ethnically-biased towards Ewes, particularly in regime hiring practices. When Rawlings led the transition from the PNDC regime to Ghana’s Fourth Republic in 1991, he implemented a

69 highly-centralized democratic system reminiscent of Acheampong’s NRC institutional designs. As before, opposition coalesced around Akan interests.

When Rawlings’ PNDC staged the 1981 coup, he initially held fast to a populist

Nkrumah-esque rhetoric, with direct appeals made to workers and rural-dwellers. The major issue of concern was the economic and political distribution of power, with particular focus on the black market and hoarding. The overvalued cedi had made basic goods very cheap and abundant. In response, traders hoarded goods to force consumers to pay at high black market prices. Rawlings saw this behavior as the epitome of everything that was wrong with Ghanaian society: the rich had access to basic goods while the poor lost the ability to purchase bread, sugar, milk, and other basic needs. The PNDC response to traders was harsh and the regime was accused of publicly stripping and beating market women and those accused of hoarding.

The ‘second coming of Rawlings’ was characterized by heavy government interference in the economic market and a furthering economic decline (Jeﬀries 1982; Ahiakpor 1991), but Rawlings quickly realized the extent of economic problems and in 1983 turned to the International Monetary Fund (IMF) to ease the worsening situation. It did not take long for

Rawlings’ socialist ideology to be replaced by market capitalist principles, per IMF stipulations.

Beginning in 1983, Rawlings instituted tough IMF restructuring policies with the help of repressive tactics (Herbst 1993, 46). The cedi was devalued in such a way that the allocation of foreign exchange was altered dramatically. In essence, this meant that the PNDC would be unable to easily inﬂuence the exchange rate in the future, making the rate externally reliable and thereby encouraging foreign investment (ibid, 51).

The Rawlings’ military regime certainly exhibited high levels of centralized control, but this was not expressly justiﬁed in the name of national unity nor for the protection of particular interests (though the regime was certainly also weary of Akan challenges to Rawlings’ rule). Instead centralized control was necessary, it was argued, so that Ghanaians would accept the tough restructuring of society, power, and economic aﬀairs with which prior regimes had miserably failed. The brutal enforcement of power made these restructuring reforms possible

70 under Rawlings (Herbst 1993). Additionally, some also point to the fact that the economic situation at the time was so terrible that citizens largely withdrew from the state, allowing the regime to push through economic reforms that otherwise would have faced strong resistance

(Haynes 1999, 107; Herbst 1993, 32).

Though not a major issue pushed by the PNDC, ethnic divisions long in the making became a polarizing issue for the regime. First, Rawlings is half-Scottish, half-Ewe, and though resources were not particularly delivered to the Ewe-dominated Volta Region, certain

Ewe-preferences in hiring practices did lead to an overall public perception of the PNDC as

Ewe-biased (Herbst 1993, 87). Importantly, anti-PNDC ethnic sentiment came most strongly from the Ashanti and other Akan groups. That mobilized Ashantis and Akan groups in general have positioned themselves in opposition to Nkrumah’ist logics, which the Rawlings regime was associated with, is not surprising. Rather, what is surprising is that Akan groups made up the strongest source of opposition even though the primary benefactors of Rawlings’ 1983

Economic Recovery Program were the predominantly Akan cocoa farmers. As Herbst (1993) describes, the devaluation of the cedi increased the price of cocoa and reforms pushed through the Cocoa Marketing Board meant cocoa farmers were receiving higher prices for cocoa (81).

Herbst even points to the cocoa farmers as the big winners in Ghana’s structural adjustment program while acknowledging that the PNDC never really received credit for this amongst Akan constituents. Finally, the PNDC regime also began explicitly developing the historically-ignored northern regions.

As part of his attack on the well-to-do, Rawlings’ continued his critique of chieftaincy power. The AFRC’s 1979 populist coup had been based on heavy critique of traditional rulers, particularly chiefs, but the 1979 elections brought Limann’s pro-chief civilian government to power (Pul 2003, 46). When Rawlings’ PNDC regime took power again in 1981, he continued to openly question the authority of the chiefs. This led to the passage of a 1985 amendment to the Chieftaincy Act of 1971 which empowered the government’s discretion to recognize

71 or withdraw recognition from chiefs (ibid, 47). It would not be until the passage of the 1992 Constitution and transition to democracy that this amendment was revoked.

By 1987, a policy of local government (misleadingly termed ‘decentralization’) was announced and, via the Local Government PNDC Law 207, the District Assembly was established. Two-thirds of assembly representatives were directly elected in the subsequent local government elections of 1988 and 1989, while one-third of assembly members were appointed by the PNDC. The PNDC also appointed the District Secretary who sat as the head of the District Assembly. This local government system was reminiscent of the reforms implemented during the Acheampong years, but, notably, traditional authorities’ inﬂuence was no longer formally institutionalized within the assembly structure.22 Two years after the establishment of the District Assemblies, two delegates from each assembly and civic and business organizational reps were sent to the National Consultative

Assembly to discuss a new constitution in April 1991. The new constitution was approved in

March 1992 and elections were held in November (Presidential) and December (Parliamentary). Under newly-elected NDC President Rawlings, Ghana’s Fourth Republic was initiated on

January 1st, 1993.

2.4 Ethnic Voting in the Fourth Republic

The response to the Rawlings’ NDC regime was ethnic in nature, as had happened with the highly-centralized regimes of the past. The top two presidential contenders in the 1992 elections were Rawlings’ Progressive Alliance, itself a coalition of the National Democratic Congress (NDC), National Convention Party (NCP), and the Every Ghanaian

22 Chiefs are still powerful political players in Ghana’s Fourth Republic. Massing (1994) found that the majority of those elected into the assembly were close relatives of established traditional powers, particularly in the North (7). Similarly, traditional disputes also creeped into the system when the PNDC “reorganized the districts on the basis of the former Local Councils” such that “the creation of new districts reﬂected ethno-political divisions and ethnic conﬂicts of previous years, as well as the demand by local groups to be represented in the various representative bodies” (Massing 1994, 7).

72 Living Everywhere (EGLE) movement, against A. Adu Boahen’s New Patriotic Party (NPP). A college professor of history, Boahen “was ideologically in the conservative-liberal tradition of J.B. Danquah and K. Busia” (Jeﬀries and Thomas 1993, 334-335). Rawlings espoused a political rhetoric critical of intellectuals and elites who sought to take advantage of the common people, similar in ideology to the prior CPP regime. Boahen’s NPP won 30.3% of the Nov. 1992 Presidential elections, as compared to Rawlings’ 58.4% and, citing electoral irregularities (largely, though not wholly, refuted by Jeﬀries and Thomas (1993)), the party boycotted the December 1992 Parliamentary elections.

The two major political parties which remained after the 1992 election, the New Patriotic

Party (NPP) and the National Democratic Congress (NDC), are widely understood as representing the Akan and Ewe peoples, respectively. Fridy’s (2007a) article uses GIS maps to demonstrate that the NPP and NDC party strongholds have consistently been located in the respective Ashanti (Akan-dominated) and Volta (Ewe-dominated) regions. Determining the ways in which Ghanaians have voted, however, has been a great challenge for scholars. This is mostly due to the fact that the Electoral Commission has never publicly released polling station-level election data. Analyses of district level election data generates ecological fallacy concerns, as researchers cannot be sure which voters in electoral districts are turning out for the election and casting a vote.

Complicating matters is that both political parties are also associated with political ideologies, somewhat distinct from ethnic concerns. The ﬁrst tradition, known as the

Danquah-Busia-Dombo ideology, supports capitalism, business interests, federalism and/or restrictions on the power of the central state. The second tradition, Nkrumahism, has traditionally been in favor of socialist policies, concerned about the rural and poor population sectors, and supportive of a strongly centralized state (Fridy 2007b, 57-58). While the

73 Danquah-Busia-Dombo ideology was linked to the Akans23 from its inception, Nkrumahism was more associated with ‘ethnic others’, underrepresented ethnic groups and ethnic groups fearful of Akan hegemony in Ghana. Through the NDC, Nkrumahism did become associated with Ewes over time, but this connection was only really solidified when half-Ewe J.J. Rawlings staged his second coup d’étatin 1981. In summary, ethnic divides in Ghana have historically existed primarily between Akans and those likely to lose out from pro-Akan regimes. It is well known that Ashantis and Akyems have always been prominent tribes within the Akan-based Danquah-Busia-Dombo tradition but, beyond these two groups, it is more difficult to determine the voting patterns of smaller

Akan tribes. Though Northerners and Gas also historically leaned toward Nkrumahism, the combination of the Trans-Volta Togoland Protectorate history, the belief that Afrifa betrayed

Kotoka, and the authoritarian-turned-democratic regime under J.J. Rawlings all served to thrust Ewes forward as the most incorporated Nkrumahi’st ethnic group in the Fourth Republic.

2.5 Discussion

Centralized institutions and ethnic politics must be studied in conjunction to understand regime change and historical institutional design in Ghana. The implementation of centralized control has consistently provoked aggregate ethnic responses, either from Akans whose interests are not protected by an anti-ethnicity centralized regime (i.e. Nkrumah, Acheampong, and to some extent Rawlings) or from groups excluded from the particularistic nature of the centralized regime (i.e. Busia and Akuﬀo), and consistently created unstable authoritarian regimes. That the centralized nature of the democratic institutions in Ghana’s Fourth Republic, inclusive of Presidential-appointments of very powerful heads of district assemblies (DCEs), should produce a relatively uniﬁed Akan opposition front is not surprising. The only time in

Ghana’s history that the Akan vote had fractured was under Rawlings and the AFRC’s watch

23 Akan refers to the overall ethno-linguistic Twi-speaking group, while the major politicized sub-groups strongly associated with the NPP are the Ashantis and Akyems.

74 in the 1979 elections. And in that case the AFRC regime had speciﬁcally encouraged voters to elect a responsible government which the AFRC would hold accountable (Jeﬀries and Thomas

1993), giving sub-Akan groups the opportunity to rise against the elitist and Ashanti-dominated

Danquah-Busia-Dombo political tradition.

What is surprising is that the centralized system in Ghana’s Fourth Republic has endured without reform across six national elections. Further, unlike many other new democratic regimes in sub-Saharan Africa, the correlation between party votes and ethnic groups have continually decreased since the 2000 elections. Some might say that is a normal progression under democratic elections as voters have greater opportunities to make retrospective and prospective vote decisions with each passing election (e.g., Lindberg 2009). But other perspectives point to the enduring nature of structural cleavages and party systems generated by historical critical junctures that would be diﬃcult to simply dissipate ( Lipset and Rokkan

1967; Collier and Collier 2002). The next chapter of this work details Ghana’s centralized system of government, the fourth chapter uses Ecological Inference models to show that ethnic voting has decreased across subsequent Fourth Republic elections, and Chapter 5 provides evidence that it is Ghana’s centralized system of government which is producing these signiﬁcant vote changes.

75 CHAPTER 3 GHANA’S CENTRALIZED SYSTEM OF LOCAL GOVERNMENT & THE POWER OF THE DISTRICT CHIEF EXECUTIVE The centralized nature of past regimes in Ghana continues into the Fourth Republic, yet this chapter details the new mechanisms at the sub-national level which generates political competition at the local level. It is this political competition which has directly contributed to the lessening of the eﬀects of neopatrimonial logics and ethnicity on individual vote decisions.

While regimes had appointed their own oﬃcials as their local-level representatives in the past, now centrally-appointed DCEs exist alongside locally-elected Members of Parliament (MPs).

The most powerful political player in local government, the DCE retains a lot of authority as the head of the District Assembly and thus has a great deal of control over development within the district. Though the independence of the district assembly is restricted by the central government, the District Assembly Common Fund allows for some independent planning of development projects. Further, even when the central government dictates development initiatives, the district assembly still retains a great deal of control in the placement of those projects.

After providing an overview of Ghana’s system of local government and detailing the role of the District Assembly, the third part of the chapter characterizes the relationship between the DCE and MP, emphasizing the enhanced degree of competition between these two officials when they are of different political parties. While these officials do not compete in the same electoral contests, they both strive to increase support for their respective parties. When the

DCE and MP are of the same political party (Friendly pairs), the level of competition between them is moderate as it is often assumed that the DCE is aiming to become the next MP.

But when the DCE and MP are of diﬀerent political parties (Unfriendly Pairs), that naturally competitive relationship is heightened. In the case of Unfriendly Pairs, DCEs are still presumed to covet the MP position, but now the DCE and MP also compete to increase support for their respective parties which plays out in the competitive installment of development projects.

As DCEs and MPs compete for the public’s support, citizens get a ﬁrst-hand opportunity to

76 compare both oﬃcials’ performance within their own localities. I argue that this mechanism is causually linked to the depreciation of neopatrimonial political logics and ethnic voting in

Ghana’s Fourth Republic.

3.1 An Overview of Ghana’s System of Local Government

Ghana’s current system of local government is technically a five-tiered system which exists alongside the network of locally-elected MPs who sit in Parliament. The five-tiered system at first appears quite complicated. However, apart from the Central Government and the

Metropolitan, Municipal, & District Assemblies (shortened to District Assemblies throughout), the other three tiers are comprised of councils with largely unspeciﬁed roles and which only operate as consultative bodies providing advice to the Central Government and District Assemblies.

Debrah (2014) describes Ghana’s local government structures as, “a fused or mixed type in which institutions extending from the central government and deconcentrated departments and agencies as well as grassroots institutions are aggregated in a single unit at the local level”

(55). Essentially, the institutions extending from the Central Government are the Regional Coordinating Councils (RCCs) and the District Assemblies, the deconcentrated departments and agencies refer to the bureaucratic civil-service backdrop of the District Assemblies, and the grassroots institutions refer to the sub-district councils and unit committees (see Figure

3-1). Within these ﬁve tiers, evidence of the central government’s control is seen in the

Presidential-appointment of Regional Ministers (heads of the RCCs), DCEs (heads of District Assemblies), 30% of District Assembly Members, 100% of Town/Zonal/Urban, Town, Area

Council Members, and 5 of the 15 members of each village-level Unit Committee. Furthermore,

District Assembly Members, Council Members, and Unit Committee Members are all unpaid positions, though District Assembly Members do receive marginal sitting fees. Created by the 1993 Local Government Act 462, the 10 Regional Coordinating Councils

(RCCs) are charged with coordinating and supervising local assemblies within their region.

Each RCC is chaired by a Presidentially-appointed Regional Minister. The duties and functions

77 of the Regional Minister are largely unspeciﬁed (Ahwoi 2010, 14), though Regional Ministers often play important informal roles at the level of the District Assemblies.1 The rest of the council is composed of the Deputy Regional Minister (also Presidentially-appointed), the DCE and Presiding Member from each District Assembly, and two chiefs from the Regional House of

Chiefs. Additionally, the regional heads of decentralized ministries sit as non-voting members (Crawford 2004, 12).

Below the Regional level sits the District Assemblies. In response to the 1992 Constitutional mandate that Parliament devolve power and resources to the local level, the above mentioned

1993 Local Government Act 462 also shifted functions to the District Assemblies (Debrah

2014, 49). The District Assemblies have both a political head, in the DCE, and an apolitical bureaucratic head, in the Metro./Municipal/District Coordinating Director (DCD). The DCE is appointed by the President of Ghana, with the Assembly’s approval2 , while the occupant of

1 From my field research it is clear that Regional Ministers often become directly involved in the Assembly Members’ voting process to approve of nominated DCEs. In particular, Regional Ministers are often present during the first and especially the second District Assembly vote, to encourage the approval of the President’s nominee. Regional Ministers can offer money to secure the support of disaffecting assembly members, they can remind the District Assemblies that it will take months for another nominee to come, or they can coerce appointed assembly members to vote to approve by facilitating the immediate rejection of Assembly Members’ appointment to the District Assembly. In cases where a DCE is not yet nominated or when a nominated DCE does not receive the 2/3 vote of approval necessary to confirm the President’s appointment, Regional Ministers, or perhaps their deputies, temporarily assume the responsibilities of DCEs.

2 The appointment of DCEs requires a vote of approval by 2/3 of Assembly members. An appointed DCE has two chances to receive a 2/3 approval vote. It is not uncommon for an appointee to require two votes, as assembly members have an interest in holding out for the first vote in order to receive some payoff or benefit approving the DCE the second time. The 2/3 approval vote requirement acts as a check on Presidential appointments, though this check is significantly limited in three ways. First, 30% of Assembly members are themselves Presidential appointees, and are thus unlikely to vote to reject the DCE. In cases where Presidentially-appointed Assembly members are suspected of having voted against the DCE, the Assembly Members appointment can and have been immediately withdrawn and the member replaced prior to the second vote. Second, when DCE appointees do receive two votes of no confidence, it typically takes several months if not years for the President to

78 the DCD position has climbed up the bureaucratic ranks to arrive at their position. The DCE is the head of the Assembly, and also serves as the Chairman of the Executive Committee, the

most powerful and important committee within the Assembly.3 On the other hand, the DCD

is allegiant to both the central government, via the Ministry of Local Government and Rural

Development (MLGRD), and to the DCE.4 Other than the DCE, the District Assemblies are composed of anywhere between 54 to

upwards of 130 members (USAID 2003, 9), 70% of which have been elected by the public

and 30% of which have been appointed by the President. The justiﬁcation for this mixed

elected/appointed system within the District Assemblies is that such a system creates

nominate a new DCE. This means districts have to operate a considerable amount of time without an Assembly head. In the absence of a DCE, for one, the district’s development funds are administered by the Regional Minister, who is not likely to engage in any significant development planning or begin any major projects. As districts development plans halt and community projects suffer, Assembly Members realize the full effects of rejecting the initial DCE appointee. Finally, a recent development now occurring under President John Mahama’s time in power is that, after the two votes rejection of the Presidential DCE appointee, the central government has waited several months only to re-appoint the same individual to the DCE position. To my knowledge this has occurred on at least one occasion (Akim Swedru). This move has obviously generated some controversy, but the Mahama government is defending it as a legal re-interpretation of the Local Government Act. For each of these reasons, the 2/3 Assembly member vote of approval requirement is not nearly as important a check on centralized power as it might initially appear. 3 The Member(s) of Parliament within a district are also non-voting members of the District Assembly.

4 A fault commonly identiﬁed in analyses of Ghana’s decentralization system is that bureaucratic departments operating at the district level are not decentralized. These departments are still appointed by and responsible to their parent ministries in Accra. This is despite legislation within the 1992 Constitution (Article 240[2][d]) which stipulates that the District Assemblies were to assume control over the deconcentrated bureaucratic departments. In December 2009, however, the 2003 Local Government Service Act (Act 656), which implements the 1992 Constitution’s directive regarding decentralizing bureaucratic departments, became operational. The result was that the management of these civil service employees operating at the local level was somewhat shifted from the central government to the local government (Debrah 2014, 61). Yet, these deconcentrated departments are still not under the full control of the District Assemblies.

79 a balance between national and local interests (Debrah 2014, 57-58) and it allows for the representation of special groups (i.e. traditional authorities), underrepresented groups

(e.g., women5 , Muslims, occupational groups, etc.) and individuals with special skills (e.g., engineers, etc.). However, as one interviewee, who had served on the consultative committee for the 1992 Constitution, complained, “At Constitution time, we were thinking local politics would be exempt from national politics. That’s why we saved 1/3 [of the] seats for [the] government to appoint specialists and experts. But appointed members are now just party boys who contribute poorly to the assembly” (Interview, 12/06/2013). Similarly, the appointment of

30% of the District Assembly members is supposed to be done in consultation with important social and economic groups, as well as the traditional authorities, within the District Assembly. Yet, in practice, this directive is often overlooked, causing great consternation for traditional authorities who worry about the continuous erosion of their power and inﬂuence.

Every member of the District Assembly must be a member of at least one legislative committee within the assembly. The most powerful committee, the Executive Committee, is not supposed to contain more than 1/3 of the total number of Assembly Members (Ayee

1996, 37). Related, the Presiding Member and Member(s) of Parliament are excluded from the

Executive Committee. This is intended to provide a check on the DCE when they report on the activity of the executive committee to the District Assembly (Crook 1994, 17). In reality, however, removing other powerful political players from the Executive Committee only further bolsters the DCE’s independence and power.

The Presiding Member leads 3 to 4 general assemblies a year, while the rest of the District

Assemblies yearly activities take place through committee structures. Other than the Executive

Committee, the other permanent committees consist of development planning, social services,

5 A 1998 government directive instructs that at least 30% of appointment members should be women. As for elected members, Crawford (2004) writes that women made up only 5% of elected DA members in 2000.

80 works/technical infrastructure, justice and security, and ﬁnance and administration. Still the Executive Committee reigns supreme. For instance, one regular critique from Assembly

Members was that decisions made on the ﬂoor of the District Assembly were often later

changed and implemented by the Executive Committee without consulting the general

assembly. Similarly, other Assembly Members inherently acknowledged the power of the DCE when they described their lobbying eﬀorts to convince the DCE to implement some

project within their electoral area. Assembly Members known as opposition sympathizers

appeared to be less likely to have development projects bestowed on their electoral areas, as

compared to Assembly Members whose party is in government.

Finally, the distinction between a Metropolis, Municipality, and a District is based on population size and economic activity (see Table 3-1). In particular, per Act 462 of the

1993 Local Government Act, Metropolitan Districts are to have populations of more than

250,000 residents, Municipal Districts should have between 75,000 and 250,000 residents,

while Districts have less than 75,000 residents (Hoﬀman and Metzroth 2010, 6-7). These qualiﬁcations are not always met in actuality (see Ahwoi 2010) as more districts are created or

upgraded by the party in power in order to appease local populations.6

Third, below the District Assemblies sit the sub-district structures, namely the sub-Metropolitan councils, the Town, Zonal, and Urban/Town/Area councils, and Unit Committees, which are closely attached to the District Assemblies. They carry out functions delegated to them, have no independent source of power, and largely exist to promote community self-help development projects and meet on complex local issues, including those dealing with the eﬀects of urbanization (Crook 1994, 5; Ayee 2008; Debrah 2014). Crawford (2004) writes there were

6 Though the creation of districts is used as a political tool in order to gain votes, it is also the case that the creation of new administrative areas can create political chaos. For instance, see Lentz (2006) for insight into the exacerbation of land ownership and citizenship conﬂict in Ghana that occurs whenever administrative and political units are drawn more narrowly.

81 roughly 1300 Town, Zonal, and Urban/Town/Area councils as well as 16,000 Unit Committees in Ghana in 2000 (11).

The Town, Zonal, and Urban/Town/Area council members are not elected. They are composed of 25-30 representatives from the District Assembly, from the Unit Committees, and from DCE appointments made on behalf of the President (Crawford 2009). The Unit Committees, on the other hand, are composed of 10 elected members and 5 DCE-appointees.

Both the Town, Zonal, and Urban/Town/Area Councils and Unit Committees act essentially as implementing agencies of the District Assemblies. A powerful indicator of the (in)eﬀectiveness of these sub-district structures, in October 2002 over 10,000 Unit Committee elections were canceled because of an insuﬃcient number of candidates (USAID 2003, 8). 3.2 District Assembly Authority & Revenue Sources

As we have seen, the District Assembly is the most powerful institution of local government in Ghana. Similarly, we have seen the extent to which the District Assembly is dominated by the Executive Committee, headed by the Presidentially-appointed DCE. In such a highly-centralized system it should come as no surprise that the central government has devolved little independent authority to the District Assemblies.

However, that little independent power is devolved to the local level should not obscure the fact that this system of local governance in Ghana’s Fourth Republic has transferred more power, and has resulted in greater attention to local development, than had previously ever existed in Ghana’s history (Owusu 2005). Importantly, those living outside of the capital have greater access to central government resources. In the past, rural residents had to travel all the way to Accra before they could reach central government officials. Similarly, the creation of more districts in 2004, 2008, and 2012 mean rural communities become bigger fish in their political representatives’ constituency pond and thus receive greater concentrated attention from their political representatives. This section will outline the extent of devolution of authority and revenue to the District Assemblies, emphasizing the areas in which the DCE can most effectively implement change in local communities.

82 3.2.1 District Assembly Authority

The District Assemblies are generally understood as the principle institution in charge of development activities at the local level, including coordinating development eﬀorts from both governmental and non-governmental sources. Crawford (2004) divides District Assembly responsibilities into three categories: deconcentrated public services, delegated public services, and devolved public services. Deconcentrated public services refer to those services provided by the central government which the District Assembly coordinates but does not actively participate in the provision of that service. These deconcentrated public services include police, customs and excise, immigration, and the ﬁre service. Delegated public services are those which the District Assemblies are assigned to by a parent government ministry or agency. Crawford (2004) gives the example of the provision of public lighting in conjunction with the Electricity Corporation or the provision of public health in consultation with the Ministry of Health. Finally, devolved public services refer to those services over which the District

Assembly maintains the most authority and these projects tend to be related to improving electoral results in favor of the President and DCE’s political party. As Crawford (2009) describes, “DA activities are concentrated on small-scale construction projects such as rural health posts, nurses’ and teachers’ accommodation, classroom blocks and boreholes, favoured for their high visibility to the local electorate” (72-73).

Within the realm of devolved public services, every District Assembly is required to draw up three-year Medium Term Development Plans, subject to Ministry of Local Government and Rural Development approval. According to one DCE, the process is as follows: First, the DCE and the entire bureaucratic/departmental staﬀ move to the grassroots to assess local needs.7

7 This DCE does not go to the Assembly Members to ask for community needs because every Assembly Member represents several communities or villages, only one of which the Assembly Member hails from. As a result, the Assembly Member will naturally want to push development projects to his/her home village and will provide a biased assessment of local needs.

83 Once the needs of his District are assessed, the DCE and bureaucratic/departmental staﬀ prioritize projects and draw up a proposed development plan. This plan is debated within the

Executive Committee and its modiﬁed form is presented to the District Assembly. After ﬁnal revisions, the plan is sent to the Regional Department where the DCE has to go and defend it. If the District wants to complete any development project, it must be in the Medium Term Development Plan. Unplanned development spending is only allowed in times of emergency

(Interview with a DCE, 10/13/2013). In another District Assembly, however, the MCE8 explained that,

“for the Medium Term Development Plan, we call all the assembly members to bring

input. We put together the plan and then have a public hearing. We assemble some

public opinion leaders to air the plan and get their input and approval. We then bring

it back to the assembly for the general house to approve. After the plan is approved, we have to arrange projects in terms of priority. Sometimes the priority plan has to be

re-arranged. In that case you have to complete an Action Plan and a Supplementary

Action Plan” (Interview with a MCE, 11/11/2013).

In reference to the drawing up of the Medium Term Development Plan, one Assembly

Member, emphasized the degree of DCE discretion over the plans: “Politics comes in. The

MCE at times wants to favor some people as a thank you for voting for the government. [The Medium Term Development Plan] is at the discretion of the MCE and the Executive

Committee” (Interview, 11/04/2013). Though it is clear that the DCE has a large hand in the creation of Medium Term Development Plans, other respondents placed greater emphasis on the ability of the DCE to implement emergency spending outside of the Medium Term

Development Plan. For instance, upon being prompted about this issue a Senior District

8 Note that a MCE is the equivalent of a DCE, only they are in charge of a Metropolis or Municipality rather than a District.

84 Planning Oﬃcer retorted that emergency funding is done at the DCE’s discretion, and is only tracked within the Quarterly Progress Reports (Interview, 10/23/2013). Though some restraints are in place which restrict DCE power, the system still allows for a great amount of DCE inﬂuence over development planning, and loopholes allow the DCE to circumvent the

Medium Term Development Plans when necessary. Finally, in addition to control over development initiatives, DCEs also have some leeway over the awarding of contracts. Per the Local Government Act 1993 and the Public

Procurement Law 2003 (Act 663), publicly-awarded contracts must be publicly announced and

ﬁrms can bid for contracts.9 Contracts are typically awarded within the District Assembly.

Two crucial stipulations to this rule increase the DCE’s power in this process. First, the DCE can create service contracts on their own, as long as they stay under 50,000 GHc.10

Second, contracts between 50,000 and 200,000 GHc must be reviewed by the District Tender

Board, of which the DCE is the chairman. As the chairman of both the Executive Committee and District Assembly which award contracts and the District Tender Board which reviews contracts, the DCE has a good deal of influence in this process. Both of these stipulations offer the DCEs significant opportunities to influence the system of awarding contracts.

Through this system of local government, an extensive range of public services is provided at the local level. At each level of public services (deconcentrated, delegated, and devolved), the central government maintains signiﬁcant, if not almost complete, authority. Accountability

9 Another category of contract awards is single-source or sole-source procurement. These awards apply when goods or services are only available from one particular supplier. In these cases, the contract does not have to be advertised publicly and the district can hire the supplier outright. In order for this to happen, the DCE must ﬁrst seek approval from the National Procurement Authority, empowered by the Public Procurement Act 663. The National Procurement Authority will do a background check to ensure this is the only provider before the contract can be awarded.

10 Since 2007, the value of the Ghana Cedi vis-a-vis the U.S. Dollar has continuously depreciated. While the exchange rate was roughly 1 GHc : 1 USD in 2008, the rate has varied between 3.2-3.8 GHc : 1 USD in 2015.

85 thus ﬂows upward to the central government, instead of downward to the public. As the centrally-appointed head of the District Assembly, the DCE obviously acquires a great deal of individual authority from this system and is, “undoubtedly the most powerful person in the

DA system” (Crawford 2009, 62). The authority of the DCEs is constrained, however, by a structure which limits the amount of unrestricted transfers from the central government and in a context where local revenue raising ventures (i.e. taxes) are insuﬃcient for district spending needs.

3.2.2 District Assembly Revenue

The District Assemblies’ three sources of revenue include revenue ceded from the central government (typically with strings attached), the District Assembly Common Fund (DACF), and through the District Assemblies own revenue-raising powers (primarily through the issuance of business licenses and local taxation).

The revenue ceded from the central government to the District Assemblies is tightly controlled and makes up about 85% of District Assembly budgets (Hoﬀman and Metzroth

2010, 7). It includes money from international donor grants, transfers from diﬀerent funds including the Highly Indebted Poor Country (HIPC) initiative, and ministerial funds to cover the salaries of bureaucrats working within the deconcentrated bureaucratic departments at the

District Assembly.

All of Ghana’s revenues and international sources of funding are housed in the Consolidated

Fund. A portion of the Consolidated Fund is diverted to Ghana’s Development Budget, which is the annual installment of the Public Investment Programme (PIP). The National

Development Planning Commission (NDPC), which is tasked with creating ﬁve-year development plans, guides the generation of the three-year PIP. The NDPC, Ministry of

Finance, and Ministry of Local Government and Rural Development (MLGRD) all coordinate national and district-level development projects. Some of the more prominent development funds disbursed to the district-level include the District Wide Assistance Project (DWAP), the

District Development Facility (DDF), the GETFUND, and the Road Fund.

86 The District Wide Assistance Project (DWAP) was funded from 2004 to 2012 by the Canadian International Development Agency (CIDA). It supported districts in the northern three regions of Ghana in the creation of their development plans and served as the template for the later District Development Facility (DDF). The DDF grants districts an additional source of revenue according to their performance on the Functional and Organisation Assessment Tool (FOAT). The FOAT is a general performance tool to determine the eﬀectiveness of the District Assembly in terms of administrative, organizational and ﬁnancial indicators. The DDF is joint-funded by the AFD (France), CIDA, DANIDA (Denmark), KfW

(Germany), and the Government of Ghana. Donors impose restrictions on the ways in which the District Assemblies can spend DDF funds. International-donor restrictions mandate that DDF funds cannot be used for consumables (i.e. office materials, the construction of office buildings, the purchase of vehicles), but can be used for infrastructure projects, such as the building of schools and hospitals, and for internal capacity building (i.e. training programs for the bureaucratic staffs or the assembly members). Additionally, these infrastructure and capacity building projects still have to get approved by the central government via the Medium

Term Development Plan.

Because the DWAP and the DDF are donor-sponsored programs, there are no delays in the transfer of funds, in important contrast to domestic funding sources, and the District

Assemblies plan their most important projects with these funds. In one conversation which took place with both an Assembly Member and a District Planning Oﬃcer, the Assembly

Member wanted ‘covereds’ or simple bridges to help with ﬂooding in his electoral area. The

District Planning Oﬃcer replied that the project has already been planned for, prompting the

Assembly Member to react, “But we need DDF money to pay for it because DDF money is cash! Government of Ghana money? When will that come?” (Interview 10/23/2013). Clearly the differences in the sources of development funding are significant enough to even effect the way Assembly Members lobby for development in their electoral areas.

87 Other prominent development funds are under strict central government control. The Ghana Education Trust Fund (GETFund), established in 2000 by the Ghana Education Trust

Fund Act 581, disburses money to the districts for targeting projects including the construction of classroom blocks, sending classroom materials to schools, and the provision of buses for secondary schools. The District Assembly can appeal to the GETFund to sponsor a particular project, but this project must be approved by the GETFund agency, whose head administrator is appointed by the President. Similarly, the Road Fund disburses resources to ensure the management and upkeep of Ghana’s roads networks. It is administered by the Road Fund

Management Board, whose chairman is the Presidentially-appointed Minister of Roads and

Highways. Apart from these development funds, the revenue which is intended to consistently support the District Assemblies is the District Assembly Common Fund (DACF). Established by Act 455 in 1993, the DACF is a block grant administered to the District Assemblies based on a guarded Revenue Sharing Formula created by Parliament. Previously 5% of total national revenues were transferred to the DACF, but this stipulation was recently increased to 7.5% by the 2008 Local Government Instrument (LI) 1961 (Hoﬀman and Metzroth 2010,

9). DACF funds account for 40% of District Assembly revenue (Debrah 2014) and are administered quarterly by the Oﬃce of the Administrator of the District Assemblies Common

Fund, though these transfers can be signiﬁcantly delayed as much as a year (King et al. 2003). The DACF funds are technically discretionary, but in practice DACF transfers often come with conditionality constraints (ibid, 64). Estimates of the percentage of DACF funding fully discretional to District Assemblies ranges from 15% to 25% (Crawford 2004; Hoﬀman and

Metzroth 2010).

88 Ten percent of the DACF budget goes to the ‘Reserve Fund’.11 The remaining 65%-75% of funds are determined by Ministry of Local Government and Rural Development

(MLGRD), NDPC, and Ministry of Finance guidelines. Still, even when DACF funds come with conditionality speciﬁcations attached, the District Assembly typically gets to decide which communities or individuals in particular will beneﬁt from DACF spending. If funds are provided to construct 10 primary schools, for instance, the District Assembly typically retains the authority to decide where these schools will be built. This is the arena in which the authority of the DCE comes into play, and local politics greatly determine where development projects are placed.

Finally, the extent to which District Assemblies can raise local revenue on their own is stipulated by the 64 revenue authorities devolved through the Sixth Schedule of the 1993

Local Government Act 462, though only some of the rates are determined by the District

Assemblies (Debrah 2014, 63). While increasing the revenue-raising powers of the District

Assemblies technically increases its authority, the low economic base of most districts means little revenue is actually generated (Hoﬀman and Metzroth 2010) and the imposition of taxes from deprived areas has at times seriously damaged the relationship between the district and local communities (Ayee 1996, 42).

3.3 The Relationship Between MPs and DCEs

In low-development/rural contexts, prominent government positions are hard to come by. In Ghana, the DCE and MP are the most prominent politicians in any locality, though the MP spot is more coveted. Many DCEs aim to use their position as a stepping stone to becoming the next MP. MPs are aware of this aim and the relationship between DCEs and

11 Within the Reserve Fund, 50% of this fund (or 5% of the total DACF) is allocated to the MPs and is referred to as the MP’s Common Fund. The RCCs are allocated 25% of this fund. Finally, the remaining 25% is allocated for activities on sanitation, maintenance of rural/feeder roads, and for rural health, housing, telecommunication, and emergencies (King et al. 2003, 12).

89 MPs is naturally competitive. Still, when DCEs and MPs are of the same political party, they have to work together to appease party executives and to present a united political front to the public. When DCEs and MPs are of diﬀerent political parties, they may have a working relationship but DCEs explicitly try to implement development projects to attract voters away from the MP’s party. The MPs have to respond with development initiatives of their own, which creates a development race in full-view of the voters. As voters have the opportunity to compare the eﬀectiveness of these two politicians, they bring that information with them to the voting booth. I argue that the competition between the MPs and DCEs drives vote volatility and a breakdown of ethnic voting in Ghana.

Alongside Ghana’s system of local government exists Members of Parliament (MPs) popularly elected per constituency. As the constituency-level representative in Ghana’s

House of Parliament, the MPs’ primary role is to participate in the legislative process. Yet, service to the nation as a good legislator is not enough to ensure re-election. In addition to frequent visits back to the constituency from their residence in Accra, Ghana’s MPs must implement development projects in their communities if they wish to remain competitive in the next election. MPs face several diﬃculties in the never-ending pursuit of development initiatives. First, though the MP is a national-level politician who is better positioned to lobby

Accra-based ministries for development, Ghanaian voters want to see their representative and are very sensitive about feeling forgotten while their MP enjoys life in Accra. This requires MPs to travel back to their constituencies frequently, though a busy schedule and Ghana’s poor roads network makes visits back home quite arduous. Secondly, though constituents expect development projects, MPs are allocated a comparatively smaller portion of the District

Assemblies Common Fund (DACF), with which to pay for these projects, and which they are quick to complain about.12

12 Several calls for the creation of an independent Member of Parliament Constituency Development Fund (CDF) have been raised during Ghana’s Fourth Republic. Former President

90 When lobbying the ministries, MPs of the opposition party are at a disadvantage. This is because Presidential-appointed heads of the ministries tend to not want to prop-up opposition

MPs’ re-election chances. In addition to the ministries, MPs can also lobby international NGOs for development. But there is no guarantee that such an opportunity will be found. Given the limited funding opportunities, in many cases MPs spend large portions of their personal wealth within their constituencies. Finally, MPs face a particularly formidable challenger for local popularity in the DCE. In contrast to the Accra-based MPs with little automatic access to development funding, the DCEs reside within the community and have signiﬁcant control over

District Assembly development planning, the awarding of contracts13 , and the placement of development projects. As the two most prominent oﬃcials in a given area, the relationship between MPs and

DCEs is often competitive (Debrah 2014, 51; Ahwoi 2010). The position of DCE is less prestigious and the position faces two term limits. As such, MPs typically perceive DCEs as gearing up to challenge the MP for the Parliamentary seat in a future election (Ayee 1999, 60). When the MP and DCE are of the same political party, this level of competition is typically moderate (ibid, 58). After all, both actors have an interest in improving the party’s support at the local level. For instance, I know of one case where the DCE had previously served as the MP’s campaign manager. When asked about development in the district, the DCE replied that (s)he does not complete any project without ﬁrst getting the MP’s approval (Interview, 11/15/2013). In another example, another district’s DCE and MP were of the same political party, but the MP was also preoccupied as a Minister in Accra. In this case, the MP gave the

Atta Mills gave this suggestion the most serious thought when he promised that such a CDF would be put in place by 2009. As of 2015, however, no MP CDF exists in Ghana.

13 Though DCEs serve as the Chairman of the District Tender Board, one of the MPs within the district also serves on that board. When the DCE and MP are of diﬀerent political parties, this likely serves as a check on the DCEs inﬂuence, assuming that the MP is fully aware of, and present within, District Tender Board meetings.

91 DCE power of attorney to administer the MP’s Common Fund on his/her behalf (Interview 11/17/2013).

But when the MP and DCE are of different political parties, the relationship between these two actors can be quite competitive, even fierce. As an appointed District Assembly Member described, “Any project that needs financial support, the DCE and MP work together [if] they are from the same party. If the DCE is of a party other than the MP, they can’t always work together; they will try to thwart each other’s projects” (Interview, 10/23/2013). Almost ensuring a degree of animosity, the central government sometimes appoints the unsuccessful

Parliamentarian candidate of the last election as DCE, causing predictable problems within

MP-DCE working relationships. Further, the DCE controls the disbursement of the MP’s portion of the Common Fund, and every MP has heard stories of DCEs who have refused to disburse money to their corresponding MP(s). A common strategy used by DCEs to ‘sabotage’

MPs is to implement development projects with MP support while forgetting to notify the MP of the project’s opening ceremony: “ , as DCE, would not invite the MP to inaugurate projects so (s)he became popular” (Interview with constituency-level political party executives,

11/15/2013). If the MP is not present at the ceremony, the public will assume the MP is not involved.

Particularly when the MP and DCE are of diﬀerent political parties, then, they compete against one another for constituent support. If the MP builds a school in one community, for instance, the DCE feels tough pressure to respond with a corresponding development project in another.

3.4 Discussion

The chapter has gone into detail about the organization of Ghana’s centralized system of local government and made the argument that the DCE is the most powerful political player at the sub-national level. As the head of the District Assembly and the Executive

Committee, the DCE has a large amount of power in determining local development initiatives and the placement of DACF funding projects. The combination of this power along with the

92 DCE’s full-time residence in the district makes him/her a formidable opponent against the incumbent MP. When the DCE and MP are of different political parties, the competition can become fierce. The artificial introduction of political competition in this way has opened up the opportunity for voters to compare prominent local politicians against one another, thus encouraging retrospective/prospective voting bases. In the following chapter I use EI models to demonstrate a decline in ethnic voting in Ghana since 2004 which I attribute to the heightened levels of local competition in Ghana’s Fourth Republic.

Figure 3-1. Overview of the system of local government in Ghana Source: Crawford 2009, 61.

93 Table 3-1. District types (1996-2012) Year Metropolitan Municipal District N 1996 3 4 103 110 2000 3 4 103 110 2004 3 4 131 138 2008 6 39 125 170 2012 6 49 161 216

94 CHAPTER 4 ECOLOGICAL INFERENCE

The central argument of this dissertation is that Ghana’s centralized system of local government contributes to a lessening of neopatrimonialism and ethnic voting, and thus contributing to deepened democratic governance. The mechanism whereby democracy is deepened is through the insertion of the president’s party member into districts as DCEs, granting them charge of district-level development funding, and generating a competitive political environment between the DCE and MP(s). This level of competition allows voters the chance to compare the dominant party in the area against the president’s appointee, thus opening up opportunities for evaluative voting outside of neopatrimonial or ethnic voting cues.

I argue that volatility in Ghana’s levels of ethnic voting, as presented in this chapter, is unusual for a new democracy and is fueled by Ghana’s centralized system of local government.

In order to identify ethnic voting patterns, I use Ecological Inference (EI) models to estimate votes by ethno-linguistic and tribal groups in Ghana’s 1996 through 2012 Presidential and Parliamentary elections. I ﬁnd that since 2004, core party supporters and particularly peripheral party supporters from both sides are increasingly willing to vote against their ethnic group’s voting tradition.1

When comparing the NDC and NPP’s core support groups (the Ewes for the NDC and the Asantes and Akyems for the NPP), against peripheral party supporters (i.e. NDC or NPP

- leaning tribes)2 , the core groups are more stable in their voting traditions. Importantly,

1 See Table 4-1 for a list of Ghana’s Ethno-Linguistic Groups and Tribes, the tribes included in the EI analysis in this chapter, and the overall political leanings of the tribe-encompassing ethno-linguistic groups.

2 Peripheral party supporters is used to refer to tribes which clearly have a preference for one party or the other, but are outside either party’s core support groups. The NDC-leaning tribes are the Bimoba, Sefwi (an Akan tribe), Dangme, Ga, Dagarte, Dagomba, Nankansi, and Kusasi. The NPP-leaning tribes are all Akan tribes and consist of the Akuapem, Boron, Denkyira/Twifo, Ahanta, Asen, and Kwahu.

95 however, both the Ewes and Asantes do increase their support for the opposing party in recent Presidential and Parliamentary races, and particularly the 2012 Parliamentary election. On the other hand, Akyem support of the NPP has increased relative to the NDC, in both the

Presidential and Parliamentary races since 2004. This observation corresponds to the fact that since 2008, the NPP Presidential Candidate Nana Akufo Addo is a member of the Akyem tribe.

Of the peripheral party supporters, the vote margin of diﬀerence separating the NDC and NPP has narrowed since 2004 for 2 out 8 NDC-leaning groups in both Presidential and

Parliamentary races, 4 out of 6 NPP-leaning groups in Presidential races, and 5 out of 6

NPP-leaning groups in Parliamentary races. The evidence suggests that a greater number of NPP-leaning groups are more willing to vote for the opposition as compared to NDC-leaning groups. Similarly, there is some support that ethnic voting is more volatile in Parliamentary races as compared to Presidential races.

Finally, we can pinpoint the groups whose changes in votes critically aﬀected changes in power at the Presidential level. The 2000 and 2000 runoﬀ elections saw increased support for the NPP across NPP and NDC-leaning tribes, but I maintain that this result has more to do with the end of Rawlings’ 19-year long rule and the public’s interest in a peaceful turnover of power. A popular President, Kufuor won re-election in 2004, but no longer with as broad a coalition of voters as NDC-leaning groups, and even some NPP-leaning groups, began to withdraw their prior support.

In the second peaceful alternation of power in 2008, Atta Mills (NDC) won after depreciated support for the NPP from NPP-leaning groups and increased support for the NDC among NDC-leaning groups forced a runoff election. In the runoff election, 5 of 8 NDC-leaning groups and 2 of 6 NPP-leaning groups again increased their support for the NDC in the runoff as compared to the 2008 regular election. Lastly, in the 2012 race, Mahama (NDC) won the election helped in part by the 125% estimated increase in NDC votes, as compared to the

28.9% increase in NPP votes, by Asante voters from 2008 to 2012. Other than the Asantes,

96 the Nankansi (NDC-leaning) were the only other group to increase their votes for the NDC in 2012 as compared to the 2008 general election.

That some groups important for the political parties are increasingly turning out for the opposing party is an important observation from the Ghanaian political landscape. In the next chapter I provide evidence that these changes in vote volatility are due to increased local levels of competition as the result of Ghana’s centralized system of government.

4.1 The Application of Ecological Inference Tools to Ghana

Establishing a link between ethnic identity and vote outcomes is a problem for researchers when the data is in ecological form. At the most basic level, researchers cannot easily use vote outcomes to determine levels of ethnic voting because voter identities are secret. It is a potential fallacy to assume that citizens turn out to vote in the same proportions that ethnic groups make up the local population. Some ethnic groups might be more politically motivated and thus more likely to vote as compared to other ethnic groups.

Without ecological inference tools, researchers have had to rely on survey data, which depend on truthful and accurate memories of past voting behavior, and exit polls to draw links between individual identities and vote choices. And while these tools may establish general links between ethnic identities and vote behavior, national-level analyses would rarely collect enough data from enough respondents to make any assertions about voting behavior from any ethnic or tribal groups other than the largest groups in the country. By incorporating deterministic information within a three-stage hierarchical Bayesian model, the Ecological Inference models presented in this chapter provide unbiased estimates, with reported conﬁdence intervals, of vote decisions by ethno-linguistic and tribal group members in Ghana.

This is not the ﬁrst time scholars have applied EI models to African Politics. Most prominently, Ferree’s (2002) dissertation on racial voting in South Africa incorporated King’s (1997) R x C ecological inference approach. At this stage, King’s approach was still based on dichotomous data, but R x C estimates could be produced in iterations, or to diﬀerent subsets of the data at a time (Ferree 2004, 144). Later, Horowitz (2012), a student of Ferree,

97 incorporated Ecological Inference models to estimate ethnic bloc voting in Kenya into his dissertation. In that work Horowtiz used the frequentist approach to multinomial-dirichlet methods of ecological inference developed from Rosen et al. (2001) for use in the ei.RxC package in R by Wittenberg et al. (2007) (Horowitz 2012, 59).

The analysis presented in this dissertation relies on the latest improvements in the EI model series, found within ei.MD.bayes, a multinomial-dirichlet model using a hierarchical

Bayesian model ﬁt. In comparison to past work, I use a more advanced EI model to predict both ethno-linguistic groups and tribal groups vote patterns in Ghana. The analysis makes several important contributions to vote analysis in Ghana. First, the EI models compare ethno-linguistic groups against tribal groups voting patterns3 for Presidential and Parliamentary races over time. To my knowledge this is the ﬁrst ever comprehensive analysis of voting on the basis of tribal group delineation in Ghana. Second, by looking at ethnic vote patterns over time I can pinpoint not only core party supporters but also NDC- and NPP-leaning groups.

Other than the Asantes and Akyems, for instance, a host of other tribes belong within the Akan ethno-linguistic group, yet their voting behavior has not been systematically analyzed.

As this analysis will show, it is a mistake to assume all Akans vote alike because they all speak derivatives of Twi. Several Akan tribes (e.g., the Sefwi, Chokosi, Fante, Builsa, Wasa, Nzema) have competitive voting traditions and/or at times voted strongly for the NDC. Challenging researchers’ automatic analysis of political behavior at the ethno-linguistic group level, as opposed to the tribal group level, is an intentional objective of this work.

3 Ethnicity is generally defined by groups’ shared language, culture, customs, and so on. Linguistic differences have become the paramount criteria distinguishing one ethnic group from another. Tribal groups are found within ethno-linguistic groups and they are distinguished by variations in their language, culture, history, and/or customs. Ghana’s Census distinguishes between 9 ethno-linguistic group categories, and 63 tribal group categories. According to the 2010 Census, “The classification of ethnic groups in Ghana is that officially provided by the Bureau of Ghana Languages and has been in use since the 1960 census” (Ghana 2010 Census, xi).

98 I use Ecological Inference models to analyze ethno-linguistic and tribal voting trends for all Presidential and Parliamentary elections in Ghana’s Fourth Republic, save the founding

1992 Presidential and boycotted Parliamentary races.4 However, I do not expect ethnic voting trends in the 1996 and 2000 elections, including the 2000 runoﬀ, to follow the same ethnic voting patterns in the 2004-2012 elections. This distinction is warranted for several reasons. First, both the 1996 and 2000 elections were held while Rawlings’ NDC government was still in power. At the very least, Rawlings’ government utilized incumbent advantages, including lifting the ban on party politics only a few months prior, in securing the 1992 election (Jeﬀries and Thomas 1993, 339; Nugent 1998, 8; Gyimah-Boadi 2001a; 2001b).5 In the 1996 elections, still capitalizing on incumbent advantages, appealing to it’s everyday citizen/rural voter base, and playing up fears about the Ashanti-hegemony background of the NPP, the NDC won a majority of the votes in every region except for the Ashanti Region.6 The 1992 and 1996

Presidential elections were far from competitive, as compared to the future Fourth Republic elections. Next, the 2000 elections were the ﬁrst contests in which Rawlings was not running and in which the NPP had a legitimate chance of taking the Presidency. Many forces came

4 As mentioned in chapter two, the NPP held that electoral irregularities de-legitimized the 1992 Presidential results, which declared J.J. Rawlings the winner, and thus boycotted the subsequent Parliamentary elections. (The 1992 Presidential and Parliamentary elections were held almost two months apart. In every election thereafter, the Presidential and Parliamentary contests were held on the same day.) The contested nature of the presidential contest and the NPP’s boycott of the parliamentary contests led me to exclude these elections from the analysis.

5 The NPP refers to the 1992 Presidential Election as ‘The Stolen Verdict’. Some scholars agree with this interpretation (Oquaye 1995), while others suggest that the NPP’s claim of massive vote-rigging was greatly overstated (Jeﬀries and Thomas 1993, 349; Nugent 1998, 15-16. 6 As Nugent (1998) writes, “If Kufuor [NPP] had managed to sweep the Akan board in the manner of Busia in 1969, he would almost certainly have made it to a second round of voting. As it happened, he fell well short of the target” (19).

99 together to push the NPP through to win the 2000 Presidential election7 and today many NDC members acknowledge that the NPP victory in 2000 was necessary to keep opposition supporters satisﬁed with the regime and to prove that Ghana’s government was indeed democratic. In many ways, the 2000 elections were the Fourth Republic’s founding elections.8

For instance, though the Presidential race went to a runoﬀ because no candidate received more than 50% of the vote, John A. Kufuor (NPP), helped by the six opposition parties who

threw their support behind the NPP in the runoﬀ election (Gyimah-Boadi 2001a, 108) easily

won with 56.9% to Atta Mills’ (NDC) 43.1%. The politics surrounding the 2000 elections

were paramount to Ghana’s overall democratic transition and its step away from the prior

authoritarian regime. Third, the overall argument made in this dissertation is that the centralized nature of

Ghana’s government counter-intuitively oﬀers voters a competitive political environment

and incentivizes voters’ reliance on evaluative voting rather than historical ethnic-based

voting. Though this centralized system of local government was in place at the beginning of the Fourth Republic, the mechanism that introduced competition at the local level was

absent during Rawlings’ presidency. As Chapter 5 will explain in greater detail, I argue that

the appointment of local-level DCEs by the President leads to more competitive political

environments in opposition strongholds. However, the appointment of NDC DCEs by President

Rawlings after the 1992 and 1996 elections were unlikely to be interpreted as anything but

7 For instance, “All ﬁve minor opposition parties came together to support and campaign for the NPP and its presidential candidate in the runoﬀ. For once, the left-of-center Nkrumahist parties and the right-of-center NPP, which have been feuding since the early 1950s, seemed to have found common ground” (Gyimah-Boadi 2001a, 113). 8 Gyimah-Boadi (2001a) underscores the importance of the 2000 elections: “Even the 1996 elections, however, failed to remove fundamental doubts about the prospects for democratic consolidation. Ghana appeared to be developing a ‘party-state’ political system in which the NDC was permanently entrenched in power; the opposition parties, civil society, independent media, and other key democratic institutions rooted in the 1992 Constitution were constrained by severe handicaps” (104).

100 the continuation of authoritarian-era interference in local politics. Even if Rawlings’ DCEs were eﬀective local development agents, they would likely be resented by local populations, particularly in opposition strongholds, just as the District Secretaries had been resented under the authoritarian PNDC regime. It is only after the 2000 NPP presidential candidate John

Kufuor won the Presidency in the 2000 runoﬀ elections that I would expect the presidential appointment of DCEs to genuinely contribute to a competitive local political environment.

The Ecological Inference models presented in this chapter estimate votes by ethnic group members, compare and contrast ethno-linguistic and tribal groups’ voting behavior, and show how the relationship between ethnicity and vote choice has diminished over time. Chapters 5,

6, 7 and 8 help us understand these changes in voting behavior ﬁrst by showing that they occur in relation to changes in levels of local competition and second by using survey analysis to test for programmatic versus ethnic inputs in vote decisions.

4.2 Ethnic Voting in Ghana

As introduced in Chapter 2, ethnic divides have dominated Ghana’s political scene since independence. Yet when the secret vote prevents us from knowing the ethnic backgrounds of voters, we cannot easily know the relationship between ethnicity and votes based on correlational analyses alone. In the case of Ghana, NPP and NDC voting blocs in the

Ashanti and Volta Regions, respectively dominated by the Asantes and Ewes (Fridy 2007a), corroborates contextual information we know about the existence of an Asante-Ewe rivalry long in the making. But we cannot know how many Asantes and Ewes, in comparison to ethnic minorities, turned out to vote in those block regions and, further, this tells us nothing about how ethnic biases impact other Ghanaians’ votes.

A related concern is that ethno-linguistic groups may not be the ethnic identities most relevant for political analysis in Ghana. Certainly ethnic entrepreneurs would prefer to lead broader uniﬁed ethnic groups when vying for political power. But the historical rivalries and dialect diﬀerences within ethno-linguistic groups in Ghana make the study of tribal voting similarly worthy of analysis.

101 A growing literature tests for politically relevant identity group boundaries or conﬁgurations (Laitin and Posner 2001; Fearon 2003; Posner 2004; Desmet, Ortuno-Ortin, and Weber 2009;

Wimmer, Cederman, and Min 2009; Baldwin and Huber 2010). But these studies pair linguistic data with new information, rather than reconsider the relevance of ethno-linguistic boundaries for social conflict within their cases. That the political relevance of other ethnic boundaries, such as cultural differences, is not explored disregards valuable contextual information. Rather than assume that linguistic differences are the causal mechanism behind identity-driven political behavior, it might be that linguistic group boundaries do not define the set of politically relevant groups in a given area. Politically relevant identities refer to those identities which are made salient at the group level and can be mobilized to achieve some political aim. Different identities can be made salient within the same communities at different times, and for different issues. In some cases, ethno-linguistic groups are the most politically relevant identity groups.

In other cases, however, other identity categories are the most politically relevant groups, such as caste in India (Banerjee, Iyer and Somanathan 2005). Political institutions go a long way in determining the politically relevant groups, particularly when political goods are at stake during elections (Posner 2005).9

9 By way of example, a recent article published on Ghana in the American Political Science Review uses advanced geocoded polling station-level election results to test for the voting behaviors of rural voters. The sample is split between rural voters whose geographical area is dominated by members of another ethnic group as compared to rural voters residing in areas where their group makes up a larger portion of the population (Ichino and Nathan 2013). Relying on election and census data, these authors find “that the vote share of a political party identified with a particular ethnic group is significantly greater for polling stations in areas where that ethnic group makes up a larger share of the surrounding population” (ibid, 1). Second, combining census data with Afrobarometer data, they, “show that rural survey respondents are significantly more likely to support an ethnically affiliated political party when living in an area where the ethnic group affiliated with that party makes up a larger proportion of the population, even when this means voting against the party affiliated with the respondent’s own ethnic group” (ibid, 2). Even though the analysis uses Presidential election results, by using ethno-linguistic groups Ichino and Nathan do not account for the divisions within the ethno-linguistic groups, which are particularly prominent for the Akans. As this analysis will show, the major Akan group which dominates the Brong-Ahafo Region,

102 4.3 The Model

At the most basic level, the ecological inference problem is one concerned with the reconstruction of individual behavior from aggregate data. When King developed his EI program (1997), he was interested in solving the distribution of white and black voters (2 categories) in U.S. elections. King’s work combined 2 previous approaches to ecological inference problems: a deterministic methods of bounds approach and a two-stage hierarchical model to ﬁt the data and then estimate the quantities of interest. The two-stage hierarchical stage modeled the racial turnout distribution as if they were generated by a truncated bivariate normal distribution, conditional on the proportion of the voting-age population who belonged to the white and black racial categories. Within R, there have been a number of extensions to this EI program, primarily because of the original program’s limitation to two demographic categories (i.e. black and white voters).

The ﬁrst model, ei.reg, uses Goodman’s 1953 ecological regression, which uses regressions based on row and column marginals to estimate population proportions, and is provided in the eiPack module. Second, ei.reg.bayes uses the same principles in Goodman’s models, but implements Bayesian normal regression. The unfortunate part about Goodman’s approaches in general, however, is that they usually provide at least some out of bounds point estimates

(i.e. turnout estimates under 0% or over 100%) (Rosen et al. 2001, 135; Lau, Moore, and

Kellermann 2007). Another extension, ei.RxC, provides a frequentist approach using the

Hierarchical Multinomial-Dirichlet Ecological Inference Model for RxC tables, as discussed in Rosen et al. (2001). Finally, the models used in this paper, also presented in Rosen et al.

(2001), is ei.MD.bayes, a Multinomial-Dirichlet model which uses a three-stage hierarchical

Bayesian model ﬁt using a Metropolis-within-Gibbs algorithm.

the Borons, are themselves not fully incorporated within the New Patriotic Party (NPP) (see Figures 4-15 and 4-18). My research has shown that local political traditions, uncontrolled for in their analysis, are highly determinative of ethnic voting patterns and turnout rates by dominant and minority groups in a given locality.

103 First, prior to the implementation of ei.MD.bayes, I present Method of Bounds output made available by the eiPack. King’s (1997) implementation of the method of bounds

combines information from two deterministic processes, capitalizing on the amount of

deterministic information available in order to provide more speciﬁc parameter bounds with

greater precision (see Duncan and Davis 1953 for reference). For determining the upper bound of the number of, say, Asante voters who turned out to vote in a heterogeneous district, the

following algebraic expressions are used. First, the proportion of voting-age Asantes who voted

b bT (βi ) is equal to the number of Asantes who turned out to vote (Ni ) divided by the number

b of Asantes of voting age (Ni ). The number of Asantes of voting age is known, but the number of Asantes turning out to vote is unknown. Further, the number of Asantes turning out to vote cannot be larger than the total number of people (Asantes + everyone else) who turned out

to vote. Similarly the number of Asantes turning out to vote cannot be larger than the total

number of Asantes of voting age.

bT Thus, the maximum number of Asantes turning out to vote (max(Ni )) is equal to

T b min(Ni ,Ni ) where min(a, b) equals a if a is less than or equal to b and b otherwise. King

b divides the equation by Ni (the number of Asantes of voting age) to arrive at the upper bound

b of βi (the proporition of voting-age Asantes who voted). ( ) ( ) bT T b b Ni Ni Ni max(βi ) = max b = min b , b (4–1) Ni N(i Ni ) T = min i (4–2) Xi, 1

A similar, though slightly diﬀerent procedure, is used to ﬁnd the minimum value of the proportion of Asantes who voted and also the proportions of Asantes who voted for a particular political party or candidate (see King 1997, Appendix B).

Relying heavily on Rosen et al. (2001) here, the ﬁrst stage of the hierarchy assumes that the column totals representing the number of voting-age people who turnout to vote for each political party follows a multinomial distribution, under the constraint that the sum of all the

104 voters and non-voters = 1. The contribution of a polling station i to the likelihood is:

′ ′ ∑ − ∑ − T T − C 1 − C 1 ′ 1i × × C 1,i × − Ni c=1 Tci θ1i ... θ − (1 θci) . (4–3) C 1,i c=1

The second stage of the hierarchical model assumes that the vectors containing the vote estimates by ethnic group follow independent Dirichlet distributions, which means that the proportion of members of, say, the Asante tribe who showed up to vote is not related to the number of Asantes in any given district or precinct. This is a problem if, for instance, Asantes in highly homogenous Asante districts were more likely to turn out to vote than Asantes in heterogeneous districts or in districts where they make up the minority.10 The second-stage

i means of the βrc’s are

( drexp(γrc + δrcZi) ) exp(γrc + δrcZi) ∑ = ∑ − , (4–4) C−1 C 1 1 + exp(γrj + δrjZi) dr 1 + j=1 exp(γrj + δrjZi) j=1 for i = 1, ..., p, r = 1, ..., R and c = 1, ..., C − 1, which implies the following log odds

i E(βrc) log i = γrc + δrcZi. (4–5) E(βrC )

11 In these equations, the log odds depend linearly on the covariate Zi .

At the third stage, the regression parameters (the γrc’s and the δrc’s) are assumed to be a priori independent, putting a ﬂat prior on these regression parameters. This assumption means that voting turnout rates are independent of each other, after taking into account the proportion of Asantes who turn out to vote. In other words, there should not be geographical or spatial or other patterns in voter turnout. Finally, the parameters dr, r = 1, ..., R, are assumed to follow exponential distributions with means 1/λ.

10 This could be a problem given theories suggesting that ethnicity is ‘realized’ in urban areas, where individuals share city spaces with members of other groups. I address this concern by running the EI models with urban covariates, or the proportion of citizens residing in urban areas within each given district (the unit of analysis). 11 I use a covariate for urban districts in my analysis.

105 Markov chain Monte Carlo (MCMC) models rely on samples taken from the posterior distribution, where estimation events depend on the current state of the process and the probability of changing to another state (Young 2005). As Rosen et al. (2001) write, “By

Bayes’ theorem, the posterior distribution is proportional to the likelihood times the prior”

(138). In order to estimate the marginals of the posterior distribution, Rosen et al. (2001) rely on the Metropolis-within-Gibbs sampler, which uses iterations to determine if a value sampled from the univariate normal distribution is representative of the stationary distribution in which we are interested.

4.4 Data

To construct the dataset used in the Methods of Bounds and Ecological Inference analyses, I paired 2010 district-level census data with constituency-level 1996-2012 election results. Rather than apply ethnic population information at the district level to each constituency contained within the district, I instead maintained districts as the level of analysis and, where necessary, aggregated constituency-level electoral information up to the district-level. Next, the EI programs require that the total ethnic population in each district is equal to the total population of voters and non-voters. Since the 2010 ethnic data needed to match with 1996 through 2012 electoral data, I calculated ethnic group population proportions per district based on the 2010 data. However, I needed population totals for each election year upon which these district-level proportions would be applied.12 Rather than use the total number of voters in each election as the district population total, I instead applied the ethnic

12 Keep in mind that, outside of electoral information, the only district-level population information available was through the 2010 census information. District populations would be diﬀerent in the election years (1996-2012) as compared to the year in which the census was collected (2010).

106 proportions to the registered voter population at the district level.13 In using this method, I risk making a fallacy by assuming that ethnic populations registered to vote in the same proportion as their population total. However, this risk is (1) much less severe than using voter population totals and assuming Ghanaians voted in the same proportion as their ethnic population proportion and (2) there are substantial reasons to believe that registering to vote would have much less of an ethnic bias as compared to actual voting.

In Ghana, voter registration cards are the most common form of national identification used by the populace. This is for several reasons. First, a national identification card system, the ‘Ghana Card’, was slow to become established in Ghana’s Fourth Republic.14 Pilot mass registrations for the Ghana Card, issued by the National Identification Authority (NIA), were not begun until 2007. Then, with the introduction of a biometric identification system in

Ghana, the Ghana Card collection and issuing process was reformed and the new cards were not available for re-issue until 2011.

Second, voter registration cards have been more available in Ghana. Prior to the initiation of the Ghana Card system, the Electoral Commission had been issuing low-technology voter registration cards since 1992. Unlike national identiﬁcation cards which required traveling to the district capital on two occasions (to submit an application and then to pick up the card),

13 Since the ethnic population totals are equal to the registered population totals, the datasets upon which the Methods of Bounds and Ecological Inference models are therefore based on the following categories: NDC, NPP, Third Party, Rejected Ballots, and No Vote. In order to calculate political party vote estimates by ethnic group, presented in Figures 4-1 - 4-32, I take the Registered Total minus the No Vote estimate to obtain a new Voted total. I then divide the NDC and NPP respective estimates by the new Voted total to arrive at NDC and NPP estimates as a proportion of total votes rather than registered voters.

14 One casual conversation during my fieldwork led to an anecdotal story where the individual told me that when he tried to cross into Burkina Faso from Ghana and showed his Ghanaian national identification card, the guard stopped him, presented him in front of the entire line, and admonished the other would-be border crossers that this man was proof that Ghanaian national identification cards exist! The guard knew the card to exist but cited that almost no Ghanaian crossing the border ever seemed to have one.

107 the Electoral Commission travels to voters’ polling stations to register voters and, until the new 2012 biometric registration cards, issued registration cards within minutes. Further, until the new 2012 biometric system was established, voters’ registration cards did not expire. Though the capture of biometric information and issuing of registration cards was more complicated in the 2012 registration process (the EC still travels to polling stations to register voters but cards are issued on a diﬀerent day), Ghanaians were already routinized to use voter registration cards as national IDs.

Even before the biometric registration process, voter registration cards were accepted when making bank transactions, engaging with public institutions, registering a business, picking up international money transfers, and so on. Further ECOWAS (Economic Community of West African States) stipulations allow citizens of West African nations to cross borders without passports15 , making the registered voters cards the most common ID used at border crossings. That registered voter cards are widely accepted, and that locally both voter information is collected and registered voter cards distributed, make the registered voter card the preferred form of ID in Ghana. And now with the new biometric fingerprint capture included in the 2012 registration process, these identification cards are more reliable than ever. As a result, even if one is not politically active and does not intend to vote, it still is very rational to register to vote in order to receive a registered voter identification card.

Unlike voting, interest in receiving an ID card is signiﬁcantly less likely to depend on structural characteristics such as ethnicity.

15 Obtaining a Ghanaian passport is its own challenge. It requires two trips to the district capital (for application and for pick-up), anywhere from weeks to months of waiting to receive it, and, until recently, expired every 5 years. And, ﬁnally, you cannot use a passport to vote. Unless one is traveling outside of the ECOWAS territories, getting a passport is not the preferred form of ID for the vast majority of Ghanaians.

108 4.5 Results

4.5.1 Method of Bounds

As discussed previously, the Method of Bounds analysis provides deterministic information about the minimum and maximum proportion of voters, by ethnic group, for any given political party. Usually the boundaries estimated by the Method of Bounds are too wide to be of any analytic use. But the information provided by the Method of Bounds is theoretically important because EI models generally use districts with strong ethnic-political party correlations to estimate the voting behavior of ethnic members in other districts.

The Method of Bounds tables show the deterministic information about tribal groups for each of Ghana’s districts. By way of example, I showcase bounds information in Table 4-2 about voting patterns in Presidential elections by Asante voters in the Amansie West District in the Ashanti Region. What this information shows is that, deterministically, at most 18.5% of

Asante constituents voted for the NDC, while at least 58.0% of voted for the NPP, in the 2012

Presidential race in Amansie West. Amansie West is an NPP stronghold and it is clear that

Asante voters make up a great part of the party’s success in that district. Overall, I provide the Method of Bounds results16 to show which groups in which districts are contributing more information to the EI modeling process.17

16 See Appendix B. 17 It is important to keep in mind that Method of Bounds estimates, like the EI models, are predicting vote patterns based on the registered population. So when between 58.0% and 78.0% of Asantes in Amansie West deterministically voted for the NPP, that is based on the total possible number of voters (i.e. registered voters) rather than the total number of voters. In other words, between 58.0% and 78.0% of registered Asante voters voted for the NPP in the 2012 election, while the rest of the Asante population either voted for the NDC party, a third party, had their ballot rejected, or did not vote in the election.

109 4.5.2 Multinomial-Dirichlet Models

Next, I provide EI results for Presidential and Parliamentary races in Ghana from

1996-2012. I estimate results for both ethno-linguistic groups as well as tribal group delineations18 , with error bars representing 95% conﬁdence intervals19 20

4.5.2.1 Core political party supporters

As previously introduced, the ethno-linguistic groups most associated with political party support are the Akans (NPP) and the Ewes (NDC). Within the Akan group, the two tribes most notorious for their support of the NPP are the Asantes and the Akyems. While there are divisions within the Ewe ethno-linguistic group, these diﬀerences are not captured by the census. Asante, Akyem, and Ewe Presidential and Parliamentary voting patterns are presented in Figures 4-1 - 4-3.21 Beginning with Presidential voting trends (Figures 4-1 and 4-2), we see that Asante and Akyem support for the NPP and Ewe support for the NDC is consistently high. There is a minimum of about 75 percentage points consistently separating NPP and NDC votes by

Asantes, and a consistent minimum of 54 percentage points separating NPP and NDC votes by

Ewes. Both Asante support for the NPP and Ewe support for the NDC dipped slightly in 2012: at 87.1%, the Asante-NPP vote dropped below 90% for the ﬁrst time since 1996 while the

Ewe-NDC vote slightly dropped to 85.2% from 87.7% and 92.6% in the respective 2008 and

2008 Runoﬀ elections. The degree of NPP and NDC vote separation by Akyem voters has also been wide with no signs of diminishing. That the Akyem-NPP vote levels have continuously

18 The full EI results are provided in Appendix C.

19 Figures 4-1 - 4-32

20 Also, see Appendix A for a table presentation of tribes and ethnic groups’ estimated vote percentages for the NDC and NPP in Presidential and Parliamentary elections.

21 In order to derive the party support percentages used in these graphs, I take the EI estimate of the number of ethnic voters who have not voted and subtract that from the total registered amount. I then divide NDC and NPP vote estimates by this new total.

110 increased since the 2004 Presidential race is likely related to the fact that Nana Akufo-Addo, the NPP Presidential candidate beginning in 2008, is a member of the Akyem tribe.

Turning to Parliamentary trends by the Asantes, Akyems, and Ewes (Figures 4-2 and 4-3), there is a greater degree of volatility in Asante votes in Parliamentary races than Presidential races, particularly after 2004. At ﬁrst Asante support for NPP MPs has been high, and support for NDC MPs low, but the margin between the parties dipped to about 60% in 2008 from a roughly 90% margin in 2004. This margin slightly widened to about 70% in 2012, but Asante votes for the NDC remained at about 13% (up from 5.5% in 2004).22 As for the Akyems, like the presidential races the parliamentary trends show increasing NPP support and decreasing

NDC support since 1996, the largest margin at about 66% in 2012. Finally, Ewe votes in Parliamentary races are high for NDC MPs and low for NPP MPs as expected. Yet 2012 showcased a dip in Ewe-NDC support to 64.3% of the vote with Ewe-NPP support at 23.8%, the lowest point spread in the Fourth Republic elections. Like the Asante-NPP Parliamentary trends, the Ewe-NDC vote peaked in 2004 and was lower in subsequent elections. The groups encompassing the Core Party Supporters were relatively stable in their NPP or NDC support. Yet two groups, the Asantes and Ewes, decreased the vote margin separating the two political parties in recent Presidential and particularly Parliamentary races. For both groups, these depreciations occurred in the 2008 and 2012 elections as compared to prior races.

4.5.2.2 Peripheral political party supporters

Outside of the Ewe core-NDC group, the Ga-Dangme and Mole Dagbani ethno-linguistic groups usually lean toward the NDC, while the Akans are the broader NPP-leaning ethno-linguistic group which encompasses Twi-speaking groups including and beyond the Asante and Akyem.

22 As mentioned elsewhere, several NPP-gone-Independent candidates were successful in the 2008 Parliamentary elections, taking away votes from the NPP candidates. While this may explain the particularly low turnout for the NPP in 2008 among Asante voters, this does not explain the still comparatively low Asante-NPP turnout in 2012 nor the increases in Asante-NDC votes over time.

111 The Presidential and Parliamentary voting trends for these groups are presented in Figures 4-4 - 4-6.

First, the Ga-Dangme and Mole Dagbani groups have been relatively consistent in their support for the NDC and NPP in Presidential elections since 2004. Mole Dagbani support for the NDC is comparatively higher than that of the Ga-Dangme group.23 Ga-Dangme and Mole Dagbani NPP votes have been very stable as well, such that a dip in Mole Dagbani NDC votes in 2012 did not translate into an increase for the NPP Presidential candidate.

The Akan Presidential voting trends are more interesting. Since the 2000 runoﬀ elections,

Akan votes for the NPP Presidential candidate have steadily decreased while Akan votes for the NDC Presidential candidate have steadily increased. Given that we have seen Asante and Akyem Presidential voting trends alone cannot account for this overall Akan-NPP Presidential vote downtrend, it appears some Akan tribes are not as driven in their support of the NPP as the Asante and Akyem Twi language speakers.

In the Parliamentary races (Figures 4-5 and 4-6), the diﬀerence between NDC and NPP votes by Ga-Dangmes has increasingly narrowed since 1996, from about a 60 percentage point spread to a less than 10 percentage point diﬀerence in 2012. This is in stark contrast to the very consistent Ga-Dangme Presidential voting trends. Mole Dagbani Parliamentary votes, on the other hand, have consistently largely gone to the NDC, though support for the

NPP did reach about 35% in 2008. The vote margins between the NDC and NPP since the 2000 elections are also narrower in Parliamentary races than in the Presidential races for Mole

Dagbani voters. Whereas the NDC-NPP vote margin had ranged from 30.3 to 36.6 percentage points in the Presidential races, the NDC-NPP vote margin was signiﬁcantly narrower in the

Parliamentary races, ranging from 18.7 to 25.5 percentage points.

23 The conﬁdence intervals are relatively wide for the Ga-Dangme estimates, probably because of the Ga dominance in Accra where it is diﬃcult to parse out voting patterns in concentrated, diverse, and urban settings.

112 Finally, like the Akan Presidential voting trends, Akan votes for NPP MPs were lower in 2008 and 2012 as compared to 2000 and 2004, while support for the NDC has continuously risen and was up to 33% in the 2012 races. Overall, the Presidential and especially the

Parliamentary estimates demonstrate vote volatility within these peripheral ethnic supporters of the respective NDC and NPP parties. Excluding the Ewe ethno-linguistic group, each of Ghana’s 8 other ethno-linguistic group categories can be divided into a number of encompassing tribal groups. My analysis of tribal voting patterns is restricted to those tribes which make up at least 50% of the population in one district, or at least 35% of the population in 2 or more districts. As explained above,

EI relies on deterministic information and voting patterns from administrative areas in which groups are dominant as part of its estimation technique to determine overall ethno-linguistic or tribal group voting patterns. This cutoﬀ is used to ensure the results from the Ecological

Inference models are reliable.24

First, in Figures 4-7 - 4-14, we consider the NDC-leaning tribes. We have already looked at Ewe (the NDC core group) Presidential and Parliamentary voting patterns, but here we also consider vote patterns of peripheral-NDC supporters: Bimobas, Sefwis, Dangmes, Dagartes,

Dagombas, Nankansis, Kusasis, and Gas. For these tribes, the diﬀerences between NDC and

NPP support in Presidential races since 2000 remained about the same or widened in 6 out of 8 groups (the Bimoba, Dangme, Dagarte25 , Dagomba, Nankansi and Ga) (Figures 4-7 - 4-10). It was only the Sefwis and Kusasis whose NDC-NPP vote margins did not widen. For

24 I make a couple of exceptions to this rule by providing results for the following tribes which narrowly miss the cutoﬀ: Ga, Asen, and Denkyira/Twifo. First, the Ga somewhat narrowly miss the cutoﬀ in making up 38.1% and 22.1% of the population in two districts, as well as over 10% of the population in three others. The Asen make up about 48.4% of the population in one district and 31.9% of the population in a second district. Finally, the Denkyira/Twifo make up 47.2%, 32.3%, and 29.5% of the population in three respective districts.

25 The Dagarte Presidential vote trend did initially narrow in 2004 and 2008 only to signiﬁcantly widen in the 2008 runoﬀ and 2012 elections.

113 the Sefwis, the vote margin narrowed beginning after the 2000 elections. For the Kusasis, the NDC-NPP margin, decreased from a 66.9 percentage point diﬀerence in 2004 to a

46.8 percentage point diﬀerence in 2012. Likewise, Kusasi support for the NPP opposition continuously increased since 2004, from 12.3% to 23.1% in 2012.

The picture is similar for the Parliamentary voting patterns of the NDC-leaning groups (Figures 4-11 - 4-14). Now the margin between NDC and NPP votes since 2004 narrowed for two groups: Sefwi and Dagomba. Since 2000, the vote margin of diﬀerence widened for two other groups, Bimoba and Dangme, and was either too close to call or waxed and waned for the four other groups (Dagarte26 , Nankansi, Kusasi, and Ga). Overall for the NDC-leaning groups, then, two of the eight groups narrowed their vote margins in Presidential (Sefwi and Kusasi) and Parliamentary (Sefwi and Dagomba) elections. But only one group (Dagarte) waxed and waned in its support in the Presidential contest as compared to four groups in the Parliamentary elections (Dagarte, Nankansi, Kusasi, and Ga). By way of comparison, diﬀerences in the vote margin separating the NDC and NPP decreased for the majority of NPP-leaning tribes in both Presidential and Parliamentary elections.

As for NPP-leaning tribes, the Presidential elections (Figures 4-15 - 4-17) were slightly less competitive for more groups overall than the Parliamentary races (Figures 4-18 - 4-20).

First, in presidential contests since at least 2004, four NPP-leaning tribes, the Akuapem,

Boron, Denkyira/Twifo, and Asen, increasingly narrowed the vote margin separating the NDC and NPP. The Ahanta had stable vote margins separating the NDC and NPP while the Kwahu generally supported the NPP over the NDC but the estimates had very wide margins of error.

Switching to the Parliamentary races, now 5 out of 6 NPP-leaning tribes (Akuapem,

Boron, Denkyira/Twifo, Ahanta, and Asen) narrowed the margin between NPP and NDC

26 Dagarte Parliamentary votes in 2008 were particularly close between the NDC and NPP in 2008. And, though the margin of diﬀerence did widen again in 2012, votes for the NPP in 2012 were still higher than they had been since 1996.

114 votes, and particularly so since at least 2004. Even the Ahanta which had maintained high levels of support for the NPP Presidential candidate over the years have seen their party support wane in Parliamentary races. Again, the Kwahu appear to consistently favor the NPP, but the margins of error are too wide in the 2008 and 2012 races to make ﬁrm conclusions.

Overall, the NPP-leaning tribes appear more willing to vote for the NDC opposition than NDC-leaning tribes.

4.5.2.3 Unincorporated groups with mixed voting patterns

Figures 4-21 through 4-24 show Presidential and Parliamentary voting trends for ethno-linguistic groups not known to outright support one party or the other. Gruma votes in Presidential races have been increasingly competitive since 2004, while Grusi votes have increasingly tended toward the NDC. Guan votes in Presidential elections are consistently competitive, save for the 1996 race, while Mande and Other votes in Presidential elections are erratic and/or too close to call. In the Parliamentary races, Guan, Gruma and Grusi votes have been very close since at least 2004.

Finally, of the tribes with mixed voting patterns (Figures 4-25 - 4-32), three tribes (Kasena, Builsa, Sisala) favored the NDC in Presidential voting but exhibited competitive voting or favored the NPP in Parliamentary elections. Two other tribes (Wasa, Nzema) favored the NPP in Presidential voting. The Wasa in particular gradually depressed their support for the NPP over time in both Presidential and Parliamentary elections, whereas the Nzema somewhat leaned to the NDC in Parliamentary elections, except in 2004. Finally, six tribes (Chokosi, Guan3, Fante, Guan5, Mamprusi, Kokomba) displayed mixed or competitive voting patterns in Presidential elections. Two of these tribes, the Chokosi and Guan3, largely favored the NDC in Parliamentary elections. The Fante strongly favored the NDC in the 2008 and

2012 Parliamentary elections, while the Kokomba strongly favored the NPP in these same election years. Finally, the Guan5 and Mamprusi tribes exhibited competitive voting patterns in both Presidential and Parliamentary races.

115 Notably, three groups narrowed the diﬀerence between NDC and NPP support since 2004 (Wasa, Sisala, and Mamprusi) while two other groups widened their vote margins (Builsa and

Kokomba).

4.6 Presidential Kingmakers

Results from the EI models also highlight the tribes whose changes in voting patterns were particularly inﬂuential in determining the winners of presidential elections. Kufuor (NPP) won the 2000 and 2000 runoﬀ elections with support from a wide range of groups, but this wide-based support narrowed in his still successful re-election bid in 2004. In 2008,

Atta Mills (NDC) forced a runoﬀ, against then ahead NPP candidate Akufo-Addo, based on increased support from both NDC-leaning and NPP-leaning groups. The 2008 runoﬀ election was secured for the NDC with increased votes from NDC-core and leaning groups and two

NPP-leaning groups. Finally, Mahama’s (NDC) victory in 2012 was largely built upon the coalitions established in Atta Mills’ 2008 victory, except for two notable increases in NDC votes from an NPP-core group (Asantes) and an NDC-leaning group (Nankansi).

First, the 2000 and 2000 runoﬀ results demonstrate the strong nation-wide push for the ﬁrst alternation of power, from the NDC to the NPP, in Ghana’s Fourth Republic. One of the

NPP-core groups, the Asantes, increased their votes for the NPP Presidential candidate in

2000, as compared to 1996, but so did the Ewes (an NDC core group), 4 of 6 NPP-leaning tribes (Boron, Denkyira/Twifo, Ahanta, and Asen), and 6 of 8 NDC-leaning tribes (Bimoba,

Sefwi, Dangme, Dagarte, Nankansi, and Kusasi). Further, the Asantes and Akyems increased their NPP vote percentages in the 2000 runoﬀ as compared to the 2000 election, but again so did the Ewes, Boron, Denkyira/Twifo, Ahanta, Asen, Kwahu, Bimoba, Dangme, Dagarte, and

Nankansi. Interestingly, four NDC-leaning tribes (Dangme, Dagomba, Nankansi, and Kusasi) strongly increased their support of the NPP in either the 2000 or 2000 runoﬀ elections, but then switched back to a majority voting for the NDC in 2004 and every election thereafter.

Clearly the national momentum for the ﬁrst change in power in 2000 was so strong that it sometimes meant voting against the home party.

116 Though Kufuor (NPP) successfully won re-election in 2004 (52.5% to 44.6%), overall Akan support for the NPP began to fall in 2004 as compared to the 2000 runoﬀ. The Akyems

(NPP-core) and Borons (NPP-leaning) in particular faltered significantly in their support for the NPP as compared to the 2000 runoff. On the other hand, the Akuapems (NPP-leaning) dramatically increased their support for the NPP in the 2004 Presidential election as compared to the 2000 runoff. As for NDC-leaning tribes, the Bimoba and Dagarte significantly increased their support for the NPP in 2004 at the expense of the NDC, as compared to 2000, while other tribes, including the Dangme, Dagomba, Nankansi, and Kusasi began to systematically decrease their levels of NPP support and increase their NDC Presidential votes beginning in

2004. One notable unincorporated group, the Fantes, did also strongly increase their votes for the NPP in 2004 as compared to the 2000 runoﬀ. The percentage of Fantes voting for the

NPP increased from 44.9% in 2000 and 57.7% in the 2000 runoﬀ to 64.9% in 2004 (Figure

4-27), resulting in an estimated 244,363 more votes for the NPP (and only 51,656 estimated more votes for the NDC) as compared to the 2000 runoﬀ election. In the second alternation of power, from the NPP to the NDC in 2008, the Ewes, Bimoba,

Dangme, Ga, and Nankansi increased their relative support for the NDC from the 2004

Presidential race. While the number of Bimoba, Dangme, Ga, and Nankansi voters who turned out for the 2008 Presidential election were fewer as compared to 2004, votes for the NDC increased by 24.7%, 12.9%, 25.1%, and 41.1% for each of these respective groups. That meant a total of 139,973 estimated more votes for the NDC, and an estimated 65,578 fewer votes for the NPP, from these four peripheral groups in 2008 as compared to 2004.

Further, all 6 NPP-leaning tribes decreased their NPP votes in the 2008 Presidential election as compared to 2004. Though a fewer number of voters from these groups turned out to vote in 2008 as compared to 2004 (except for Kwahu), NDC votes increased by 45.4%, 78.0%, 78.0%, 11.5%, 6.5% and 294.3% by the Ahanta, Akuapem, Asen, Boron,

Denkyira/Twifo, and Kwahu, respectively. Votes for the NPP for these tribes changed by 0.5%,

-12/3%, -25.0%, -10.7%, -7.7%, and -7.5%, respectively. In total, the NPP-leaning tribes

117 provided 78,093 more votes for the NDC, and 81,221 fewer votes for the NPP, in the 2008 Presidential election as compared to 2004. Finally, Fante (unincorporated) votes for the NPP dropped by an estimated 220,708 votes while votes for the NDC increased by an estimated

135,755 votes, as compared to the 2004 election.

Neither NPP candidate Akufo-Addo (49.15%) nor NDC candidate Atta Mills (47.92%) received greater than 50% of the vote, thus forcing a runoﬀ election. Akufo-Addo only missed the 50% cutoﬀ point by 73,478 votes. Had the increased 139,973 votes for the NDC by the

NDC-leaning tribes, the 78,093 more votes for the NDC by the NPP-leaning tribes, or the

135,755 more NDC votes by the Fantes not come, Akufo-Addo would have won the 2008 election outright. Signiﬁcantly, this also would also have reversed Ghana’s second transfer of power at the national level.

In the subsequent 2008 runoﬀ election, the Ewes (NDC-core), Bimoba, Sefwi, Dagarte,

Dagomba, and Nankansi (each NDC-leaning) increased their relative support for the NDC as compared to the 2008 regular Presidential race. As mentioned above, only the Akyems increased their NPP Presidential support in both the 2008 and 2008 runoff elections. Of the NPP-leaning tribes, 3 of 6 groups (Ahanta, Asen, Kwahu) did increase their levels of support for the NPP in the 2008 runoff elections as compared to the regular election, but two others, the Akuapems and particularly the Denkyira/Twifo, actually increased their votes for the NDC in the 2008 runoff. Finally, four other unincorporated groups (Builsa, Sisala, Fante, Mamprusi)(Figures 4-25 - 4-28) strongly increased their votes for the NDC in the 2008 runoff, resulting in an estimated 123,564 more votes for the NDC (and 50,271 fewer votes for the NPP), in comparison to the regular election, in a runoff only decided by 40,586 out of

9,001,478 valid votes cast (0.5%).

In 2012, when John Mahama (NDC) won as the replacement for the recently deceased President Atta Mills (NDC), the Asantes (NPP-core) and Nankansi (NDC-leaning) were the only two tribes to increase their level of support for the NDC from the 2008 regular election. Estimates show that, while turnout was about 35% higher for Asantes in the 2012

118 Presidential Election as compared to the 2008 regular Presidential Election, Asante votes for the NPP increased by 28.9% while Asante votes for the NDC increased by 125.0%. With the NDC only winning the 2012 Presidential Election by 977,589 votes, the 131,180 more estimated votes for the NDC by the Asantes in 2012 as compared to 2008 makes up a sizeable

13.4% of the difference separating the NDC from the NPP in the final tally. Further, Ghana’s first-past-the-post electoral system means the NDC only narrowly avoided a runoff election by exceeding the 50% mark in 2012 by 231,390 votes, of which the increase in Asante votes for the NDC from 2008 to 2012 makes up 56.7%. Had the Asantes not increased their votes for the 2012 NDC Presidential candidate, as compared to their 2008 numbers, Mahama would only have passed the 50% mark by 100,210 votes out of 32,985,786 (0.3%) valid votes cast. Conversely, the Akyems (NPP-core), Ewes (NDC-core) and Kusasi (NDC-leaning) actually increased their support for the NPP Presidential candidate in 2012 as compared to

2008. These increases were not enough to hold John Mahama back from reaching the 50%

ﬁrst-past-the-post threshold. 4.7 Discussion

The application of Ecological Inference models to estimate votes by ethnic groups can greatly contribute to voting analyses in countries dominated by ethnic divides. Much of what we know about ethnic voting in Africa relies on generalities because of the focus on regional-level voting results or because researchers use national-level analyses of individual behavior which are forced to collect individuals’ ethno-linguistic identities to ensure a large enough N, ignoring their tribal backgrounds. As EI estimation techniques become more reliable and well-known, and as data on sub-Saharan African populations becomes more available, ecological inference oﬀers a lot of potential to political analysis at the sub-national level in

African countries. My application of Ecological Inference models to Ghana analyzed national-level voting trends while maintaining individuals’ tribal group identities vis-a-vis their ethno-linguistic group identities. Outside of Asantes, Akyems, and Ewes, I presented data on voting trends

119 for 8 other NDC-leaning tribes, 6 other NPP-leaning tribes, as well as 11 other tribes who do not outright support one political party over the other. This analysis demonstrates that

Ghanaians are increasingly willing to vote against their ethnic voting tradition. The propensity to support the opposition is overall stronger for peripheral support groups rather than core party supporters. But both the Asantes and Ewes did increase support for the opposition in recent Presidential and Parliamentary elections. Additionally, it is also clear that NPP-leaning groups have been more volatile in their votes as compared to NDC-leaning groups. Overall, for all of the peripheral party supports, 6 out of 14 decreased the vote margins separating the NDC and NPP in Presidential elections, while 7 out of 14 decreased these same vote margins in

Parliamentary elections. And while all three core party support groups maintained or increased the vote margins separating the NDC and NPP in Presidential elections, two of three (Asantes and Ewes) showed some willingness to decrease this vote margin in elections after 2004.

This chapter has demonstrated an increasing level of vote volatility in elections since

2004 for ethnic groups in Ghana. The following chapter provides evidence that this volatility is directly related to the increased political competition at the local level imposed by Ghana’s centralized system of government.

120 Table 4-1. Ghana’s ethno-linguistic groups and tribes

Ethno- Akan Ga-Dangme Ewe Guan1 Gruma Mole- Grusi Mande Other linguistic Dagbani group Tribes Agona Dangme* Guan1 Bimoba* Builsa* Kasena* Busanga inside Ahafo Ga* Guan2 Kokomba* Dagarte* Mo Wangara outside Ahanta* Other Ga- Guan3* Basare Wali Sisala* Other Dangme Mande Akuapem* Guan4 Pilapila Dagomba* Vagala Akwamu Guan5* Salfalba Kusasi* Other Grusi1 Akyem* Guan6 Kotokoli Mamprusi* Other Grusi2 Aowin Guan7 Chamba Namnam Asante* Guan8 Other Nankansi,

121 Gruma Tal., Gur.* Asen* Other Guan Nanumba Boron* Mosi Chokosi* Other Mole- Dagbani Denkyira/Twifo* Evalue Fante* Kwahu* Nzema* Sefwi* Wasa* Bawle Other Akan Party Affili- NPP NDC- NDC mixed mixed NDC- mixed mixed mixed ation leaning leaning *Refers to tribes included in the Ecological Inference analyses. 1Guan tribal categories are made up of several tribes. Guan1=Akpafu, Lolobi, Likpe, Bowiri, Buem, Santrokofi, Akposo; Guan2=Avatime, Ny- ongbo, Tafi, Logba; Guan3=Awutu, Efutu, Senya, Breku; Guan4=Cherepong, Larteh, Anum-Boso; Guan5=Gonja; Guan6=Nkonya; Guan7=Yeji, Nchumuru, Krachi, Nawuri, Bassa Achode; Guan8=Nkomi, Wiase, Dwan. Table 4-2. Asante bounds - Amansie West District

Year NDC NPP

1996 Pres.: ndc (.126, .268) npp (.660, .840) 2000 Pres.: ndc(.000, .175) npp (.825, 1.00) 2000 Runoﬀ: ndc(.000, .145) npp (.855, 1.00) 2004 Pres.: ndc (.000, .108) npp (.752, .894) 2008 Pres.: ndc (.000, .097) npp (.594, .744) 2008 Runoﬀ: ndc (.000., .143) npp (.661, .860) 2012 Pres.: ndc (.000, .185) npp (.580, .780)

122 Figure 4-1. Asante and Akyem presidential voting statistics

123 Figure 4-2. Ewe presidential and parliamentary voting statistics

124 Figure 4-3. Asante and Akyem parliamentary voting statistics

125 Figure 4-4. Ga and Mole Dagbani presidential voting statistics

126 Figure 4-5. Akan presidential and parliamentary voting statistics

127 Figure 4-6. Ga and Mole Dagbani parliamentary voting statistics

128 Figure 4-7. Bimoba and Sefwi presidential voting statistics

129 Figure 4-8. Dangme and Ga presidential voting statistics

130 Figure 4-9. Dagarte and Dagomba presidential voting statistics

131 Figure 4-10. Nankansi and Kusasi presidential voting statistics

132 Figure 4-11. Bimoba and Sefwi parliamentary voting statistics

133 Figure 4-12. Dangme and Ga parliamentary voting statistics

134 Figure 4-13. Dagarte and Dagomba parliamentary voting statistics

135 Figure 4-14. Nankansi and Kusasi parliamentary voting statistics

136 Figure 4-15. Akuapem and Boron presidential voting statistics

137 Figure 4-16. Denkyira/Twifo and Ahanta presidential voting statistics

138 Figure 4-17. Asen and Kwahu presidential voting statistics

139 Figure 4-18. Akuapem and Boron parliamentary voting statistics

140 Figure 4-19. Denkyira/Twifo and Ahanta parliamentary voting statistics

141 Figure 4-20. Asen and Kwahu parliamentary voting statistics

142 Figure 4-21. Guan, Gruma, and Grusi presidential voting statistics

143 Figure 4-22. Mande and Ethnic Others’ presidential voting statistics

144 Figure 4-23. Guan, Gruma, and Grusi parliamentary voting statistics

145 Figure 4-24. Mande and Ethnic Others’ parliamentary voting statistics

146 Figure 4-25. Chokosi, Kasena, and Builsa presidential voting statistics

147 Figure 4-26. Guan3, Wasa, and Sisala presidential voting statistics

148 Figure 4-27. Fante, Nzema, and Guan5 presidential voting statistics

149 Figure 4-28. Mamprusi and Kokomba presidential voting statistics

150 Figure 4-29. Chokosi, Kasena, and Builsa parliamentary voting statistics

151 Figure 4-30. Guan3, Wasa, and Sisala parliamentary voting statistics

152 Figure 4-31. Fante, Nzema, and Guan5 parliamentary voting statistics

153 Figure 4-32. Mamprusi and Kokomba parliamentary voting statistics

154 CHAPTER 5 THE INSTITUTIONALIZATION OF LOCAL-LEVEL COMPETITION IN GHANA

Features of Ghana’s democratic institutions break the cycle of dominant party politics, neopatrimonialism, and ethnic voting without the coordination struggles inherent in decentralized institutional configurations which increase the number of political decision makers. I argue that the central appointment of officials in Ghana’s system of local government actually institutionalizes political competition at the local-level and has thus contributed to increased responsiveness on the part of politicians and increased programmatic influences on citizen votes. The co-existence of both a centrally-appointed district-level District Chief Executive

(DCE) and a constituency-level elected Member of Parliament (MP) within each constituency is signiﬁcant.1 The relationship between DCEs and MPs is naturally competitive, but the

nature of this competition intensifies when these two officials are of different political parties (‘Unfriendly Pairs’). As power has changed hands at the national-level, the partisan

appointments of DCEs also alternate. Informed by 16 months of ﬁeldwork in Ghana, I use

OLS regressions to model voting eﬀects when the MP and DCE are of the same political party

versus when these politicians are of diﬀerent political parties. I ﬁnd that competition produces

increased electoral volatility in constituencies with ‘Unfriendly (DCE-MP) Pairs’ as compared to those with ‘Friendly Pairs’. In short, even institutionally crafted local-level competition can

contribute to deepened democratic progress.

1 Constituencies fall within Administrative Districts such that one constituency may correspond to one district, as is the case in rural areas, one constituency may be paired with another under a single Administrative District, or two or more constituencies may fall under a single (Metropolitan) Administrative District. As of 2012, in addition to the centrally-appointed DCE, districts had as little 1 up to as many as 13 MPs (Accra Metropolis District) elected within a district’s boundaries.

155 Antithetical to decentralized systems where locally oﬃcials are solely accountable to the public, the presidential appointee (DCE) is almost wholly non-accountable to the public.2

The benefits of decentralized systems are widely touted, while centralized local government structures are condemned as undemocratic and as evidence of a central leader’s interest in maintaining control at the local level. Less explored, however, are the potential benefits of the simultaneous institutionalization of both locally-elected and centrally-appointed officials. There may be good reason for this. Central governments may want to signal democratic progress by implementing local government reform when, in actuality, such a hybrid system allows power to remain in the hands of the central government vis-a-vis their centrally appointed officials.

However, in order for a system of Presidential appointments to function at its best capacity, an alternation of power at the national level is required to induce alternations in local-level appointments. If local government reform is merely intended to signal democratic progress, then such a transfer of power at the top is unlikely. Similarly, if central-appointments of local oﬃcials were primarily used to channel patronage (rather than development) down to the local level to secure the President’s grip on power, then we would not have seen turnovers in the 2000 and 2008 elections. It is important that Ghana’s centralized system also incorporates majoritarian electoral rules at the national level, to encourage alternations in national power-holders and thus alternations in sub-national appointments.

The beneﬁts of decentralization are widely understood, but in this chapter I argue that the presence of central appointments in Ghana’s system of local government increased local-level political competition at a surprisingly quick rate for such a new democracy. Local-level political

2 The DCE requires a confirmation vote in the District Assembly prior to his/her appointment. As discussed in Chapter 3, assembly members are heavily pressured to approve of DCE appointments and district development suffers in the absence of a DCE. After a DCE is confirmed, the assembly can technically issue a disapproval vote dismissing the DCE, but, given the political and developmental problems this entails, a vote for the dismissal of a DCE rarely occurs.

156 competition has directly contributed to a lessening of neopatrimonial political logics and ethnic voting in Ghana.

5.1 Hypotheses

As illustrated in Chapter 3, the relationship between DCEs and MPs can be quite tenuous, particularly if the pair come from different political parties. In this chapter I argue that the appointment of an DCE of a different political party from the local MP(s) heightens political competition at the local level. Voters in Ghana, like voters in other African democracies, expect and respond to provisions of development goods by their politicians. As each of these officials seek greater constituent support for their parties, this competition is played out in development goods, and particularly those development goods which are both cheap and highly visible to the electorate (e.g., water boreholes, connections to the electric grid, primary and secondary school blocks, etc.). This leads to my principal hypothesis:

Hypothesis 1: Unfriendly3 MP-DCE pairs will cause a greater turnover of votes in the subsequent Presidential and Parliamentary elections as compared to Friendly MP-DCE pairs. My research has shown that voters exposed to an Unfriendly MP-DCE pair can compare

the eﬀectiveness of each political party to a greater extent than if an MP and DCE are of the

same political party. That alternations of power have occurred in both 2000 and 2008 means

that both the NDC and the NPP have appointed their own party oﬃcials in every district

across the country. The combination of the positioning of DCEs from political traditions which oppose the locally dominant party as well as the fact that the Fourth Republic’s

local government system is more eﬀective than any past regime, have had big impacts on

individuals’ experience with government. Since voters in African countries are more concerned

with politicians’ abilities to deliver public goods provisions than they are policy agendas

3 Unfriendly Pairs means, in the term prior to the election under analysis, the MP and DCE were of diﬀerent political parties. Third party and Independent MPs are excluded from the analysis because we cannot be sure with whom the MP sat in Parliament.

157 (Wantchekon 2003; Baldwin 2013), Unfriendly MP-DCE Pairs are more responsive to citizen needs and promote greater development initiatives as they work to upstage one another.

Further, in the elections following an Unfriendly MP-DCE Pair, I also expect the direction of the increased vote volatility to favor the DCE’s party. Not only are some communities experiencing effective governance from the opposing party (through the DCE) often for the first time in their localities, but DCEs are also better equipped to provide more effective development vis-a-vis the MPs. DCEs reside within the communities year-round and are armed with a significantly greater portion of the District Assembly Common Fund (DACF) as compared to the Accra-based MPs. As for the MPs, technically they do have the advantage of being able to lobby ministries to include their constituency within the next planned national-level development project, but these opposition MPs’ political party membership puts them at a lobbying disadvantage and they also often have a hard time taking credit for such national-level initiatives. DCEs should win the unfriendly MP-DCE competition, and, if so, the evidence will be born out in increased DCE party votes and decreased MP party votes in elections immediately following the unfriendly pairing. This prediction is captured in Hypothesis

1a:

– Hypothesis 1a: The direction of the increased vote total should favor the DCE’s political party Alternatively, it might be the case that voters actually resent the Presidential appointment of such a powerful individual in the DCE within their communities. There exists a great deal of debate in Ghana about whether DCEs should continue to be appointed by the President.

Indeed, both NPP and NDC politicians have encouraged the election of DCEs when they have been in opposition, but these same oﬃcials quickly quiet their tune after their party wins the Presidential election and hence the right to appoint DCEs. Thus, the presence of an Unfriendly

MP-DCE Pair prior to an election might actually mobilize voters behind their locally-elected

MP and his/her political party, in comparison to areas with relaxed Friendly MP-DCE Pairs.

158 Though the analysis covers the 2000-2012 Presidential and Parliamentarian elections, I expect the eﬀect of Unfriendly MP-DCE Pairs to be less operative in both the elections prior to

2004 and in the Presidential Runoﬀ elections in 2000 and 2008. First, the reason for qualifying the 2000 elections is because this electoral period was heavily inﬂuenced by the longevity of NDC rule up to that time. President J.J. Rawlings had been the democratically-elected President since 1992, but he had also ruled as an authoritarian leader of the country since

1982. As demonstrated in Chapter 4, when Rawlings announced that he would abide by the

Presidential two-term limit as stipulated in the 1992 Constitution, Ghana’s voters were heavily inﬂuenced by the possibility of a democratic transfer of power. As such, the eﬀect of Unfriendly

MP-DCE Pairs was less of a driving force in determining votes as compared to the motivations associated with achieving the Fourth Republic’s ﬁrst turnover of presidential power with John

Kufuor’s (NPP) victory.4 We thus might expect to see an increase in NPP votes across each of the districts, but particularly those districts whose constituencies had elected an NPP MP to power in the prior (1996) election.

Hypothesis 2: The effect of Unfriendly MP-DCE Pairs on increasing support in favor of the DCE’s political party should not begin until after the NDC faces its first defeat in the 2000 elections. Instead, the 2000 elections should show us that districts with NPP MPs, and thus Unfriendly MP-DCE pairings in the prior term, rallied voters behind the NPP party in order to force the Fourth Republic’s first transfer of power.

Secondly, as pertains to Presidential Runoﬀ elections, the mechanisms which guide votes during Presidential Runoﬀs are less about competition between the MP and DCE over the last four years and more about each party’s surge of national resources and patronage distribution to increase both voter turnout and votes for the respective parties. As such, Hypothesis 3 states:

4 Indeed, this was the first time in 21 years that someone of the Danquah-Busia-Dombo political tradition, in Kufuor, could be elected President. The last head of state belonging to the Danquah-Busia-Dombo political tradition was arguably the Supreme Military Council under Fred Akuffo. Akuffo was deposed by the Armed Forces Revolutionary Council, of which J.J. Rawlings was a leading member, in 1979.

159 Hypothesis 3: The eﬀect of Unfriendly MP-DCE Pairs on changes in votes will be muted in the Presidential Runoﬀ elections where parties’ national resources mobilize their respective political bases. 5.2 Model Overview

5.2.1 Dependent Variable and Primary Independent Variable

In this paper, I use OLS regressions with robust standard errors to predict changes in party votes in communities where the MP and DCE are of diﬀerent political parties (Unfriendly

Pairs), as compared to communities where these actors are of the same political party (Friendly

Pairs). To assess the eﬀect of Unfriendly versus Friendly Pairs, my dependent variable is the constituency-level diﬀerences in respective Presidential and Parliamentary party votes from one election to the next. So, using the 2012 races as an example, four outcome variables are predicted: 2012 minus 2008 NDC Presidential votes, 2012 minus 2008 NDC Parliamentary votes, 2012 minus 2008 NPP Presidential votes, and 2012 minus 2008 NPP Parliamentary votes. The primary independent variable under investigation is whether Unfriendly MP-DCE

Pairs, as compared to Friendly Pairs, result in increased DCE party votes, increased MP party votes, or no signiﬁcant diﬀerence in party votes, in the subsequent election. Unfriendly

Pairs are coded dichotomously, where 1 represents an Unfriendly Pair in the prior term and 0 represents a Friendly Pair in the prior term. For instance, NPP candidate John Kufuor won the

2000 Presidential election and subsequently dismissed all of former-President Rawlings’ DCEs.

Kufuor then appointed NPP DCEs in each district.5 If an MP elected in 2000 belongs to

5 Even within opposition party strongholds, the President goes to great ends to find a party-affiliated individual who would qualify as a DCE. One former DCE explained that (s)he had been working in Accra prior to her/his appointment. One day this individual received a call from the President’s Chief of Staff who explained that (s)he was needed at the Office of the President for a meeting the following day. This individual did not attend party meetings or openly affiliate with the President’s party but it turns out that her/his name and place of birth were discovered on an old party members list from when the individual had attended the University of Legon several years prior. This individual was appointed DCE of her/his hometown district after this meeting (Interview 11/07/2013).

160 the NPP political party, this will be coded as a Friendly Pair (coded as 0), considering all the DCEs under Kufuor were also NPP members. This variable is then used in the 2004 elections analysis.6 Constituencies where NDC MPs were elected in 2000 were coded as ‘Unfriendly

Pairs’ (coded as 1). Finally, I excluded constituencies which had elected 3rd party/Independent

MPs because these MPs may have either remained independent or may have sat with one or the other party in Parliament (Table 5-1).

5.2.2 Controlling for Structural Conditions Impacting MP-DCE Relationships

Three structural conditions impacting MP-DCE relationships are (1) the Number of MPs within the District, (2) the Type of District, and (3) whether the district was newly created.

First, as explained in footnote 1 in this chapter, multiple constituencies sometimes fall within any given Metropolis, Municipality, or District. Since MPs are elected at the constituency-level, multiple MPs are sometimes elected within one District. In these cases, a single DCE has to manage relationships with multiple MPs and this may impact the nature of each MP-DCE relationship. In particular, the competitive nature of an Unfriendly MP-DCE pair may be dulled or enhanced in the presence of other either Unfriendly or Friendly MP-DCE pairs. The variable, Multiple MPs, is coded as 1 for constituencies whose MPs are not the only

MP in the district, and 0 for constituencies whose MPs are the sole MPs within the district.

Second, the three types of districts are Metropolises (coded as 3), Municipalities (coded as 2), and Districts (coded as 1). These district types have decreasing population sizes and economic bases, and are assigned diﬀerent weights in the sharing formula used to determine the DACF. These diﬀerences could have an impact on the nature of the relationship between

MPs and DCEs, including the development opportunities available to the DCEs.

6 Remember, we are controlling for the presence of an Unfriendly, as opposed to Friendly, Pair in the prior term. This is in order to test for impact of an Unfriendly Pair in the following election.

161 Third, when new districts are created it takes time to set up the District Assembly and to appoint and orient bureaucratic department heads to the new district’s terrain and particularities. New districts may be less eﬃcient as compared to older districts, and this may hinder the DCE’s ability to initiate development projects and sway voters. Further, the creation of narrower administrative units has been known to instigate local conﬂicts (Lentz 2006), which may preoccupy the attention of the DCE and/or MP(s) and reduce the provision of development goods. New District⟨04⟩⟨08⟩ is coded dichotomously, where newly created districts in the prior term are coded as 1 and all other districts are coded as 0.7

5.2.3 Controlling for Structural Conditions Impacting Local Politics

I also control for 8 structural conditions which generally aﬀect local politics and voter perceptions leading up to an election. First, I use a dummy variable to control for the eﬀect that the announcement of a new constituency or district might have on voters. Ghana’s politicians use the creation and upgrading of constituencies/districts to appeal for constituent votes, even when the structural population and economic activity requirements spelled out in

Local Government Act, 1993 (Act 462) are not met (Ahwoi 2010). The announcement of a new constituency or district (coded as 1), it is predicted, will correlate with increased votes for the President’s party in the upcoming election.

Second, I control for the overall competitive nature of the elections within each constituency. Naturally more competitive constituencies may generally result in greater vote volatility, outside of MP-DCE pairings. To control for level of competition in the prior election, I use the Parliamentary winner’s share of votes, in decimal form, at the constituency

7 No new districts were created until 2004, so this variable is not included in Tables 5-2 - 5-4. For Tables 5-5 - 5-7, a high degree of collinearity is introduced in the models when controlling for both Multiple MPs and whether the district was newly created (ex: New District08). Including either Multiple MPs or New District did not substantially change the results. Models controlling for New District are presented in Tables 5-5 through 5-7.

162 level in the prior Parliamentary race. An increase in this variable means fewer votes were ‘up for grabs’ and thus the election was less competitive.

Next, I control for ﬁve diﬀerent demographic characteristics derived from Ghana’s 2010

census which may impact the functioning of local politics.8 These are (a) linguistic diversity,

(b) percentage of agricultural households, (c) education rates, (d) religious demographics, and (e) ethnic group population percentages.

(a) In global settings, diversity has been linked to lower levels of trust and can thus negatively impact economic success (Knack and Keefer 1997), the provisions of public goods (Alesina, Baqir, and Easterly 1999; Vigdor 2004), and encourage rent-seeking behavior by politicians (Knack and Keefer 1997; Franck and Rainier 2012). Because speaking the same language can play an important role in generating understanding and trust within ethnically-diverse communities (Banerjee, Iyer, and Somanathan 2005, 639), I control for Linguistic Diversity, captured as the inverse of the Simpson’s/Herﬁndahl-Hirschman Index based on 10 ethno-linguistic categories within the Ghana 2010 Population and Housing Census. This index measures the probability that any two individuals selected at random belong to the same ethno-linguistic group. I take the inverse of this index so that increased measures of this variable refer to increased levels of diversity.9

(b) I use the fraction of households engaged in agricultural practices to control for degree of ‘ruralness’. Ghana’s 2010 Census does produce a rural and urban indicator, but this measure is only based on population size (localities with 5,000 or more persons are automatically classiﬁed as urban) and does not take into account the level of development of the district. ‘Agric rate’ is coded such that a 0.4 means 40% of the

8 The linear regression models presented in Tables 5-2 -5-7 use constituencies as the level of observation. Ghana’s 2010 census reports data at the district level. As a result, these 5 demographic controls are imputed from the district level onto the constituency observation. For instance, if a district has more than one constituency within it, the district-level data is applied to both constituencies. Though this imputation implements an assumption of homogeneity which may bias the results, it was deemed more important that these controls be included within the analysis.

9 Alternatively, I also control for Tribal Diversity in case the politically salient groups exist within language groups (Fearon 1999, 5). Alternating between Ethno-Linguistic and Tribal measures of diversity did not have substantive impacts on the models and only the models controlling for Ethno-Linguistic Diversity are presented in Tables 5-2 - 5-7.

163 households are engaged in agricultural production activities. These activities include crop farming, tree planting, ﬁsh farming, or animal rearing.10

(c) The more educated a population, the more likely it is that voters are informed about local and national politics. Alternatively, educational attainment can also signify wealth and individuals with higher educational attainment are sometimes associated with the NPP. This may be because the NPP espouses an ‘elitist’ political tradition or that the party historically championed a pro-capitalist, rather than socialist, rhetoric. Either way, to control for education, the models include the respective fraction of the population who have attended primary school (excluded as the reference category), secondary school, post-secondary school, and who have not had any formal education.

(d) In Ghana, Muslims in the southern half of the country often reside in segregated Zongo, or ‘stranger’, settlements within the community. In the North, Muslims often make up the majority of the population and are well-integrated into society. Similarly, the historical forced expulsion of Nigerians by President Busia’s government in 1969 particularly terrorized Zongo communities, because they also housed a large percentage of Muslim Nigerians resident in Ghana. This history has trickled its way into politics of Ghana’s Fourth Republic and Muslims are, on average, more favorable of the NDC and often shy away from the NPP whose political tradition is associated with the Busia government. I thus control for the fraction of residents who are Muslim as derived from the 2010 Population and Housing Census.

(e) In both African and Ghanaian politics, ethnicity is consistently the best predictor of citizen voting behavior (Fridy 2007a; Bratton, Bhavnani, and Chen 2012). Ghana’s Statistical Services collects tribal data in conducting the 2010 census and collates those tribes into major ethno-linguistic groups when reporting ethnicity population ﬁgures. The fraction of the population falling within these 9 categories (Akan, Ga-Dangbe, Ewe, Guan, Gurma, Mole Dagbani, Grusi, Mande, Others) are controlled for in the models, where Ga-Dangbe serves as the reference category.

5.3 Results

When Jerry John Rawlings transitioned the authoritarian Provisional National Defense Council (PNDC) to democratic rule (Ghana’s Fourth Republic) in 1992, Rawlings contested

the 1992 elections as the nominee of the National Democratic Congress (NDC) political party.

Rawlings won the Presidential elections in 1992 and 1996 and stepped down from power at the

end of his second term in 2000. The 2000 elections marked the ﬁrst time a peaceful transfer

10 According to the 2010 Census Report, only about 1% of the households engaged in agricultural activities were involved in ﬁsh farming.

164 of power via the ballot box had occurred in Ghana’s history. John Kufuor (NPP) was elected President in two rounds of voting. Kufuor was re-elected in 2004 and stepped down at the conclusion of his second term in 2008. The 2008 elections marked the second time a peaceful transfer of power occurred, as John Atta Mills (NDC) won the Presidency in two rounds of voting. Atta Mills passed away just before the 2012 elections and his Vice President, John Mahama, was elected President in 2012.

To reiterate, with reference to Hypothesis 1a, I expect that the presence of Unfriendly

Pairs in the prior term should increase votes for the President’s party in the 2004-2012 elections. Speciﬁcally, in the presence of Unfriendly Pairs, I expect NPP votes to increase in

2004 and 2008 (when the NPP had appointed all the DCEs in the prior term) and decrease in 2012 (because the NDC won in 2008 and appointed all the DCEs for the 2008-2012 term).

Similarly, I also expect NDC votes to decrease in 2004 and 2008 and increase in 2012, in constituencies which have Unfriendly Pairs. Per Hypothesis 2, I do not expect the presence of

Unfriendly MP-DCE Pairs to aﬀect voting until after the 2000 elections. Finally, per Hypothesis 3, the presence of Unfriendly MP-DCE Pairs in the 2000 and 2008 Presidential run-oﬀ elections are not expected to impact vote decisions in the same manner as regular Presidential and

Parliamentary elections.

5.3.1 2000 Elections

Across the elections, the impact of Unfriendly MP-DCE Pairs as compared to Friendly

Pairs, has a signiﬁcant impact on changes in party votes. The eﬀect is generally weaker for the restricted models, which only control for the structural conditions impacting MP-DCE

Relationships, as compared to the full models which additionally control for the structural conditions aﬀecting local politics. Beginning with the 2000 Elections, as predicted in

Hypothesis 2, constituencies which had NPP MPs alongside NDC DCEs had signiﬁcantly decreased NDC Presidential (-5.2%, Model 2) and Parliamentary (-8.7%, Model 4) votes in 2000 from 1996, as compared to constituencies with Friendly Pairs. Similarly, districts with Unfriendly Pairs had increased NPP Presidential (+3.0%, Model 6) and Parliamentary

165 (+11.4%, Model 8) votes in 2000 from 1996, as compared to constituencies with Friendly Pairs. Other substantive variables signiﬁcantly correlated with votes were Multiple MPs, Level of Competition, Secondary School Attendance, Muslims (%) and a few ethnic variables (Table

5-2).

5.3.2 2000 Presidential Runoﬀ Elections

In the 2000 Presidential Runoff election (Table 5-3), the effect of having a NPP MP in the prior term is correlated with increased NPP Presidential votes by 3.1% (Model 12). The effect of having a NPP MP in the prior term also corresponds to increased NDC Presidential votes in the restricted model (Model 9) but this effect was nullified by the introduction of several structural factors impacting local politics, including level of competition and select religious and ethnic variables (Model 10). Just as Models 1-8 showed that constituencies with NPP MPs in the prior term had increased their votes for the NPP, particularly in the Parliamentary race

(+11.4% in Model 8), constituencies with NPP MPs (i.e. Unfriendly Pairs) also increased their support for the NPP in the runoﬀ election, as compared to constituencies with NDC MPs (i.e.

Friendly Pairs). 5.3.3 2004 Elections

The 2004 elections are the ﬁrst time we see a shift in the voting pattern (Table 5-4). As compared to the 2000 elections, votes for the NDC Presidential (-3.7% in Model 14), though not Parliamentary, race decreased in constituencies which had elected NDC MPs in the prior term (Unfriendly Pairs). Conversely, votes in the NPP Presidential (+5.7% in Model 18) and Parliamentary (+7.9% in Model 20) races increased in constituencies which had elected NDC

MPs in 2000 (Friendly Pairs).

With reference to other controls, the structural variables impacting the nature of the MP-DCE relationship (i.e. Multiple MPs within one district and District Type) were signiﬁcant predictors across almost all the restricted models (Models 13, 15, 17, & 19) but lost signiﬁcance in the full models (Models 14, 16, 18, & 20). As a constituency’s prior

Parliamentary election became less competitive, votes for the 2004 NDC Presidential and

166 Parliamentary candidates decreased. Put diﬀerently, increased constituency-level competition in the 2000 Parliamentary races is correlated with increased votes for the 2004 NDC Presidential and Parliamentary candidates. As the percentage of agricultural households increased by 10%, there was a corresponding negative correlation with NDC Presidential votes (-0.91% in Model

14) and a positive correlation with NPP Presidential votes (+0.70% in Model 18). A 10% increase in Secondary school rates, as compared to primary rates, is correlated with increased 2004 NDC Presidential votes by 0.78% (Model 14) and decreased NPP Presidential votes (-1.01% in Model 18). Islam was also a major predictor of votes in 2004. A 10% increase in the rate of Muslim residents is associated with increased NDC Presidential votes (+1.58%,

Model 14) and NDC Parliamentary votes (+1.86%, Model 16) and decreased NPP Presidential votes and Parliamentary votes by -1.31% and -1.32%, respectively (Models 18 & 20). Finally, the most relevant ethnic category is Akan, as a 10% increase in Akan residents correlates with depreciated NDC Presidential and Parliamentary votes and increased NPP Presidential, though not Parliamentary, votes. 5.3.4 2008 Elections

In the 2008 Presidential Elections, John Atta Mills (NDC) faced oﬀ against Nana Akufo

Addo (NPP). The first round election was very competitive, with both candidates within 3% of the 50% first-past-the-post mark, forcing a run-off. The NPP had just enjoyed 8 years of power under President John Kufuor and the NDC was looking to again take the presidency.

In this context, then, it is very curious that the same trends in 2004 continue on into 2008 (Table 5-5). In particular, the Unfriendly Pairs variable is significant across each of the models and, like 2004, is correlated with depreciated NDC votes and appreciated NPP votes. Those constituencies which had Unfriendly MP-DCE pairings, meaning they had elected a NDC MP into office in the prior term, again decreased their NDC Presidential votes (-4.4%, Model 22) and Parliamentary votes (-7.0%, Model 24) as compared to constituencies which had elected a NPP MP into office in 2004. We would rather expect constituencies which had both an NPP MP and NPP DCE to decrease their NDC votes in the subsequent election.

167 Similarly, constituencies which had elected a NDC MP into oﬃce in 2004, increased their NPP Presidential (+3.7%, Model 26) and Parliamentary votes (+5.3%, Model 28) as compared to constituencies which had a Friendly Pair (NPP MP + NPP DCE).

For the 2008 elections, District Type was signiﬁcantly correlated with votes in each of the models except Model 24. Constituency votes increased for the NDC President by 1.0% as the type of district in which the constituency was located changed from a District’ to a

Municipality’, and again increased by 1.0% as the district was changed from a Municipality’ to a Metropolis’ (Model 22). Changes in district types were also correlated with decreased

NPP Presidential (-1.4%, Model 26) and Parliamentary (-2.7%, Model 28) votes. For political competition, a 10% increase in the winner’s share of the vote in the prior Parliamentary race (i.e. a decrease in the level of political competition), is correlated with decreased

2008 NDC Presidential (-1.39%, Model 22) and Parliamentary (-3.98%, Model 24) votes and decreased NPP Parliamentary votes (-1.79%, Model 28). In other words, an increased level of competition in the prior 2004 Parliamentary races is correlated with increased NDC Presidential, NDC Parliamentary and NPP Parliamentary votes in the 2008 races.

Ethno-Linguistic Diversity is signiﬁcantly correlated to vote outcomes in Models 22, 24, and 26. A 0.1 increase in a constituency’s ethno-linguistic diversity measure is associated with decreased NDC Presidential (-0.11%, Model 22) and Parliamentary (-0.25%, Model 24) votes and increased NPP Presidential (+0.10%, Model 26), though not Parliamentary votes. Finally, a 10% increase in the percentage of agric. households in a constituency is negatively correlated with NDC Parliamentary votes, and positively correlated with NPP

Presidential votes, though this eﬀect was most substantial in the NDC Parliamentary races

(-1.87%, Model 24).

5.3.5 2008 Presidential Runoﬀ Elections

In the party results within the 2008 Presidential Runoﬀ as compared to the 2008 Regular elections, constituencies with Unfriendly MP-DCE Pairs in 2004-2008 were signiﬁcantly correlated with increased NDC votes (+1.7%, Model 30) and decreased NPP votes (-1.2%,

168 Model 32), as compared to constituencies with Friendly MP-DCE Pairs. In other words, constituencies which had elected a NDC MP to office in 2004 increased their NDC votes during the 2008 Presidential Runoff and decreased their NPP votes. As predicted in Hypothesis 3, this is reflective of both the NDC and NPP’s attempts to mobilize their respective bases. NDC areas increased their NDC votes as more voters were mobilized to the cause, while NPP areas increased their NPP votes. This dynamic is less affected by the nature of the MP-DCE Pair over the prior 4 years.

Other substantive variables signiﬁcantly correlated with votes were District Type, Level of

Competition, Linguistic Diversity, and Muslims (%) (Table 5-6).

5.3.6 2012 Elections

The Unfriendly Pair trends established in the 2004 and 2008 Elections are conﬁrmed in 2012 (Table 5-7). Now, however, constituencies which had elected a NPP MP in 2008 are signiﬁcantly correlated with increased NDC votes (Presidential: +2.1% (Model 34);

Parliamentary: +7.1% (Model 36)) and decreased NPP votes (Presidential: -2.0% (Model

38); Parliamentary: -2.9% (Model 40)) as compared to the prior 2008 general elections. Like the 2004 and 2008 elections, the eﬀect of Unfriendly Pairs is stronger for the Parliamentary races than for the Presidential race. Consistent across the 2004-2012 general elections, then, constituencies which had an opposing MP and DCE increased their votes for the DCE’s party signiﬁcantly more than constituencies which enjoyed a Friendly MP-DCE Pair.

Though constituencies which had voted NPP MPs in oﬃce in 2008 increased their NDC Presidential and Parliamentary votes in 2012, NDC DCEs were apparently less eﬀective within the 2008 newly-created districts, as NPP Presidential (+1.2%, Model 38) and Parliamentary

(+2.6%, Model 40) votes increased in the 2012 elections as compared to the 2008 races.

A 10% increase in the Parliamentary winner’s share of votes in the 2008 Parliamentary races is correlated with decreased 2012 NDC Presidential (-0.92%, Model 34) and Parliamentary votes (-3.38%, Model 36) and decreased NPP Parliamentary votes (-2.06%, Model 40). Put

169 diﬀerently, increased competition in the prior 2008 Parliamentary races is correlated with increased NDC Presidential and Parliamentary votes and NPP Parliamentary in 2012.

An increase in the Linguistic Diversity measure is correlated with a signiﬁcant, though small, depreciation in NDC Parliamentary (Model 36) and NPP Parliamentary (Model 40) votes. Finally, increases in the proportion of Muslim residents is correlated with decreased NDC Presidential and increased NPP Presidential votes, but this variable is not signiﬁcantly correlated with either of the Parliamentary races.

5.4 Alternative Explanations

The evidence presented consistently shows that constituencies which had Unfriendly

MP-DCE Pairs were signiﬁcantly more likely to increase their votes for the DCE’s party, in both the Presidential and Parliamentary Elections, in the subsequent races. In one sense, this is a strange result. Why would NPP votes increase in the 2004 and 2008 elections and then decrease in the 2012 elections in constituencies which had elected NDC MPs in the prior term?

Similarly, why should NDC votes decrease in the 2004 (Presidential only) and 2008 elections and then increase in the 2012 elections in constituencies which had elected NDC MPs in the prior term?11 Why do votes for the locally-elected MPs’ party diminish in the next election when that MP is in an Unfriendly Pair as compared to increased votes for locally-elected MPs’ parties when that MP is in a Friendly Pair? As I have argued, the increased DCE party votes are due to the eﬀects of local competition engendered by the presence of a MP and DCE of diﬀerent political parties, as compared to those districts which had a Friendly MP-DCE pairing in the prior term. However, it is necessary to address several alternatives to this interpretation of the regression results.

First, one might argue that constituencies which had voted in an opposition MP were more likely to have high levels of opposition party votes in both the Presidential and

11 Note that the reverse relationship also holds for constituencies which had elected NPP MPs in the prior term.

170 Parliamentary elections and thus less room to increase support for this party. In other words, the only direction that the constituency’s level of vote changes could go, in these cases, was down. Conversely, assuming these constituencies also had low levels of government party votes, the only direction that the constituency’s level of vote changes could go was up. This interpretation suggests that the dependent variable is problematic because, though it controls for changes in party votes, it does not capture party vote starting points.

I highlight a number of points in response to this alternative explanation. First, just because strongholds have high levels of votes for one particular party does not automatically mean constituents will increase votes for the opposing party. Second, the vast majority of

Ghana’s constituencies display rather competitive voting patterns. On average, 59.6% of constituencies in Presidential elections and 74.1% of constituencies in Parliamentary elections have competitive voting patterns, meaning no party receives more than 65% of the vote

(Tables 5-8 and 5-9). Further, a large portion of party strongholds are located in the Volta and

Ashanti Regions, respectively. Though ideally I would have been able to control for NDC or NPP votes in each election, this variable correlates too highly with Unfriendly Pairs because constituencies with high (or low) NDC votes in the 1996 Presidential or Parliamentary election for example, are less (or more) likely to have voted in an NPP MP (i.e. an Unfriendly Pair).

However, given the territorial concentration of party strongholds, I inserted a dummy variable for both the Volta and Ashanti Regions within the regressions. If the inability of party votes to increase in party strongholds really was driving the results, then the inclusion of these regional dummy variables should have altered the effect of Unfriendly Pairs on party votes. As it were, the inclusion of these control variables did not substantially effect the significance of Unfriendly

Pairs.

Third, that strongholds already have high levels of voting for one party and can only decrease their votes for the DCE’s party, in Unfriendly Pairs, does not adequately explain the systematic pattern of changes in voting results. In the 2004 elections, as compared to the

2000 elections, Unfriendly Pair constituencies increased their NPP votes by 5.7% and 7.9%

171 in the Presidential and Parliamentary races, while decreasing their NDC votes by 3.7% in the Presidential race (Table 5-4). Next, in 2008 (Table 5-5) Unfriendly Pairs further increased their NPP votes by 3.7% and 5.3% and decreased their NDC votes by 4.4% and 7.0% in the

Presidential and Parliamentary races, despite the NDC winning the Presidential election in

2008. Finally, in 2012 (Table 5-7) Unfriendly Pair constituencies decreased their NPP votes by 2.0% and 2.9% and increased their NDC votes by 2.1% and 7.1% in the respective Presidential and Parliamentary races. Though ceiling effects might affect votes by a percentage point or two as it is not the case that the entire voting population of one election carried over to the next election, ceiling effects cannot explain the systematic vote changes in Unfriendly Pair constituencies as compared to Friendly Pair constituencies. A second alternative interpretation of the results is that perhaps what voters really want is to elect MPs who are of the same political party as the President. As I have argued earlier,

MPs do not receive very much outright development funding and instead have to lobby at the ministries for their constituency’s inclusion in national development projects. If MPs belong to the opposition party, however, they may face a harder time gaining access to the

Presidentially-appointed Minister’s ear as compared to MPs of the government’s party. Baldwin

(2013) shows how voters in Zambia pay close attention to the relationship between their local chiefs and potential Members of Parliament when voting and it is not unreasonable to expect

Ghanaian voters to take into account their MP’s relationship with the President when voting. Two reasons, however, make it diﬃcult for voters to award or strip the MP of their title based on his/her belonging to the President’s political party. First, Presidential and

Parliamentary elections are held at the same time in Ghana and, given the overall close nature of Ghana’s elections in general, voters may have a hard time predicting who will win the Presidential election. Though constituents have access to the radio and other forms of media, residents of rural areas are somewhat insulated within their communities and regions and receive biased news about the state of the nation. For instance, during the course of my ﬁeldwork, it was not uncommon for residents in NPP areas to explain that they had

172 attempted to think about who would win the Presidential election when casting their votes in the MP election, but that all the information they had received pointed to the (incorrect) fact that Akufo-Addo (NPP) was going to win the 2012 Presidential election. Similarly, it is not uncommon to hear constituents back-up their theories of electoral fraud harming their candidate(s) in past elections by saying something like ‘Everyone knows that no one voted for [the opposing candidate]’. I have found that these voters’ location and information networks greatly aﬀect the type of news they receive regarding the generally highly competitive nature of

Ghana’s elections.

Second, while predictions about who is likely to win the Presidency might impact votes for MP, this mechanism cannot fully explain the outcomes as presented in Tables 5-4 - 5-7, particularly the switch in voting patterns in the 2012 elections as compared to 2004 and 2008.

First, after John Kufuor (NPP) won the Presidential election in 2000, we could reasonably expect voters to predict that Kufuor, a popular president with incumbent advantage, would win his re-election bid in 2004. That constituencies which had voted in NDC MPs in 2000 were correlated with decreased NDC (Presidential only) and increased NPP votes in 2004, according to this coattails logic, might not be surprising.

Turning to the 2008 elections (presented in Table 5-5), President Kufuor’s two term mandate had expired meaning a sitting President was not running in the election. In the battle for the Presidency, the close election between Akufo-Addo (NPP) and Atta-Mills (NDC) forced a run-oﬀ, which Atta-Mills won. When we look at the 2008 election analysis in Table 5-5, we see that constituencies which had elected a NDC MP in 2004 decreased their NDC votes and increased their NPP votes as compared to constituencies which had elected a NPP MP in 2004. Not only would voters have had a harder time predicting the Presidential winner in an election without an incumbent candidate, it does not then follow that voters which had previously elected a NDC MP would be correlated with decreased NDC votes and increased

NPP votes in 2008, as compared to voters which had voted in NPP MPs in 2004. If voters are making vote choices based on who they think is likely to win the Presidential election, it seems

173 unlikely that voters under NDC MPs would incorrectly predict the future 2008 Presidential winner as compared to voters under NPP MPs. That voters beneﬁt from the development works completed by their NPP DCE in 2004, particularly in the context of areas that vote

NDC, is a more plausible explanation.

Finally, after the unexpected death of President Atta Mills in July 2012 barely 4 months prior to the 2012 elections, no incumbent candidate was again running in the election. Though it was widely expected that the unpopular Atta Mills’ NDC government was going to be voted out of power in 2012, the transfer of Presidential authority to the Vice President, John

Mahama, perhaps re-energized the NDC campaign and Mahama narrowly beat Akufo-Addo

(NPP) in the 2012 Presidential elections. As you recall from the 2008 analysis presented in Table 5-5, constituencies which had Unfriendly Pairs (NDC MP + NPP DCE) were correlated with decreased NDC votes and increased NPP votes in the subsequent election as compared to Friendly Pairs (NPP MP + NPP DCE). Now, in 2012 (Table 5-7), constituencies with

Unfriendly Pairs in the prior term (NPP MP + NDC DCE) are correlated with increased NDC votes and decreased NPP votes in the 2012 elections. With the death of the former President, and in the context of an, up until then, unpopular NDC government, it would have been diﬃcult for voters to assume the NDC would again win the Presidency and that they should thus vote-in a NDC MP. What better explains the correlation of a 2.1% (Pres.) and 7.1%

(Parl.) increase in NDC votes (Table 5-7) in areas with Unfriendly Pairs (NPP MPs + NDC DCE) is that voters now had a NDC local politician engaging in development works in their area for the ﬁrst time since the Rawlings NDC government stepped down in 2000.

5.5 Discussion

Inefficient decentralization systems, where locally-elected politicians’ independence and authority have been hindered as a result of the severe under-funding of local governments by central state coffers, have often stifled the extent to which democratic progress has been made in new democracies. The argument made in this dissertation, that the centralized system of local government in Ghana, where elected MPs exist alongside centrally-appointed (and

174 well-funded) DCEs, increases political competition at the local level, shows how a theoretically less democratic institutional framework actually stimulated democratic progress in this case. As the election of MPs concurs with the central-appointment of DCEs, the political competition generated when these two oﬃcials are of opposing political parties has contributed to deepened democratic governance and a lessening of neopatrimonial political logics and ethnic voting in Ghana.

175 Table 5-1. Constituencies under analysis Year Total constituencies 3rd N party/independent MPs (#) 1996 200 constituencies 5 195 2000 200 constituencies 5 195 2004 230 constituencies 10 220 2008 230 constituencies 8 221* 2012 275 constituencies 7 267* *Note: The 2008 Parliamentary Elections were postponed in Akwatia constituency and is thus an additional constituency missing from the analysis.

176 Table 5-2. Changes in party votes: 2000 - 1996

NDC Pres1 NDC Parl NPP Pres NPP Parl

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8

Unfriendly96 -0.040∗∗∗ -0.052∗∗∗ -0.068∗∗∗ -0.087∗∗∗ -0.005 0.030∗∗∗ 0.054∗∗∗ 0.114∗∗∗ (0.007) (0.009) (0.013) (0.017) (0.008) (0.008) (0.015) (0.023) Mult.MPs 0.023∗∗ 0.029∗∗ 0.001 0.003 -0.028∗∗∗ -0.017∗ 0.004 0.006 (0.010) (0.011) (0.021) (0.022) (0.011) (0.009) (0.019) (0.017) District -0.001 -0.002 0.007 -0.005 0.012∗∗ -0.004 0.013 -0.007 Type (0.005) (0.007) (0.011) (0.014) (0.006) (0.006) (0.010) (0.012) parl96winner -0.069∗ -0.426∗∗∗ 0.013 -0.079 (0.041) (0.087) (0.029) (0.077) Volta 0.043 0.056 (0.028) (0.075)

177 Ashanti 0.004 -0.007 (0.009) (0.022) Ling. Div. -0.006 -0.009 0.017∗∗∗ 0.006 (0.005) (0.012) (0.004) (0.013) Agric rate -0.051∗ -0.008 0.045 0.030 (0.028) (0.063) (0.033) (0.058) Secondary -0.163∗ -0.187 0.198∗∗ 0.481∗∗∗ (0.085) (0.165) (0.078) (0.151) Post Sec -0.037 -0.086 0.054 0.060 (0.114) (0.215) (0.123) (0.273) Muslims 0.040 -0.102 -0.082∗ 0.130 (0.061) (0.122) (0.044) (0.093) No Relig 0.047 -0.123 0.158 0.650 (0.203) (0.600) (0.173) (0.419) Trad -0.071 -0.217 -0.040 0.169 (0.117) (0.213) (0.074) (0.134) other -0.487 -0.319 -1.003 -0.770 (1.361) (1.976) (1.096) (2.491) Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01 1Incumbent President in 1996. Table 5-2. Continued

NDC Pres1 NDC Parl NPP Pres NPP Parl

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8

Akan 0.039 -0.043 -0.029 0.012 (0.027) (0.063) (0.033) (0.049) Ewe 0.103∗∗ 0.055 -0.109∗∗∗ -0.104∗ (0.046) (0.111) (0.032) (0.054) Guan 0.096∗ 0.037 -0.086∗∗ 0.043 (0.052) (0.110) (0.043) (0.137) Gurma -0.032 -0.094 -0.003 -0.025 (0.072) (0.139) (0.042) (0.101) Mole -0.027 0.025 -0.022 0.021 Dagbani

178 (0.048) (0.092) (0.036) (0.076) Grusi 0.084 -0.017 -0.024 0.042 (0.086) (0.102) (0.071) (0.118) Mande 0.294 -0.115 0.438∗ 0.613 (0.354) (0.524) (0.256) (0.706) others -0.379 1.112 -0.546 0.140 (0.652) (1.276) (0.513) (1.541) Non-Ghanaians 0.012 -0.366 0.185 -0.234 (0.455) (1.008) (0.337) (1.313) Constant -0.102∗∗∗ 0.034 -0.076∗∗∗ 0.380∗∗ 0.069∗∗∗ -0.061 0.034∗ -0.252 (0.008) (0.096) (0.015) (0.178) (0.009) (0.088) (0.018) (0.159) Obs. 195 195 195 195 195 195 195 195 Adj. R2 0.106 0.271 0.072 0.175 0.028 0.467 0.036 0.177 Res. Std. 0.059 0.053 0.108 0.102 0.062 0.046 0.109 0.100 Error (df) (191) (172) (191) (172) (191) (172) (191) (172) Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01 1Incumbent President in 1996. Table 5-3. Changes in party votes: 2000 Pres. Runoff - 2000 Pres. Election NDC Pres Runoff NPP Pres Runoff Model 9 Model 10 Model 11 Model 12 Unfriendly96 0.014∗∗∗ -0.003 0.053∗∗∗ 0.031∗∗∗ (0.004) (0.004) (0.009) (0.010) Multiple MPs 0.010∗ -0.003 -0.002 -0.013 (0.006) (0.005) (0.013) (0.012) District Type -0.006∗ -0.005 -0.006 0.008 (0.004) (0.003) (0.006) (0.006) parl96winner 0.038∗∗∗ -0.071∗ (0.015) (0.041) Volta 0.020 (0.013) Ashanti -0.009

179 (0.008) Ling Div. 0.004 -0.016∗∗ (0.003) (0.007) Agric rate -0.026∗ 0.014 (0.014) (0.027) Secondary -0.044 -0.042 (0.044) (0.078) Post Sec 0.021 -0.019 (0.057) (0.122) Muslims 0.004 0.007 (0.024) (0.062) No Relig -0.067 -0.462∗∗ (0.093) (0.186) Trad 0.109∗∗ 0.195∗ (0.052) (0.103) Other 0.268 2.113 (0.557) (1.417) Table 5-3. Continued NDC Pres Runoﬀ NPP Pres Runoﬀ Model 9 Model 10 Model 11 Model 12 Akan 0.010 -0.014 (0.014) (0.030) Ewe 0.038∗ -0.104∗∗∗ (0.021) (0.031) Guan -0.005 -0.045 (0.024) (0.051) Gurma -0.005 -0.034 (0.025) (0.072) Mole Dagbani 0.043∗ 0.039 (0.023) (0.058)

180 Grusi -0.029 0.151 (0.026) (0.157) Mande 0.320 -1.038∗∗∗ (0.200) (0.390) Others 0.566 1.243 (0.348) (0.823) Non-Ghanaians -0.341 -0.476 (0.272) (0.570) Constant -0.023∗∗∗ -0.030 0.073∗∗∗ 0.188∗∗ (0.005) (0.037) (0.008) (0.073) Observations 195 195 195 195 Adjusted R2 0.050 0.565 0.077 0.549 Residual Std. Error 0.034 0.023 0.082 0.057 (degrees of freedom) (191) (172) (191) (172) Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01 Table 5-4. Changes in party votes: 2004 - 2000 (reg. election)

NDC Pres NDC Parl NPP Pres1 NPP Parl Model 13 Model 14 Model 15 Model 16 Model 17 Model 18 Model 19 Model 20 Unfriendly00 -0.021∗∗ -0.037∗∗∗ 0.0001 -0.021 0.069∗∗∗ 0.057∗∗∗ 0.099∗∗∗ 0.079∗∗∗ (0.009) (0.009) (0.015) (0.018) (0.009) (0.009) (0.013) (0.015) Mult.MPs 0.039∗∗∗ 0.004 0.053∗∗∗ 0.021 -0.028∗∗∗ 0.006 -0.017 0.015 (0.009) (0.011) (0.015) (0.018) (0.009) (0.011) (0.014) (0.014) District 0.023∗∗∗ -0.002 0.030∗∗∗ -0.014 -0.019∗∗∗ 0.004 -0.024∗∗∗ 0.006 Type (0.008) (0.008) (0.011) (0.014) (0.006) (0.007) (0.009) (0.010) New AA04 0.004 0.004 -0.013 -0.019 (0.010) (0.018) (0.009) (0.015) parl00winner -0.098∗∗∗ -0.273∗∗∗ 0.012 -0.027 (0.035) (0.078) (0.036) (0.073) Volta 0.060∗∗∗ 0.054 (0.022) (0.056) 181 Ashanti -0.005 0.021 (0.011) (0.020) Ling Div 0.0004 0.013 -0.005 0.006 (0.005) (0.010) (0.006) (0.009) Agric rate -0.091∗∗∗ -0.035 0.070∗∗ 0.026 (0.032) (0.055) (0.031) (0.035) Secondary 0.078∗ 0.110 -0.101∗∗ -0.114∗ (0.045) (0.094) (0.044) (0.065) Post Sec 0.075 0.346 -0.114 -0.492∗∗∗ (0.195) (0.314) (0.169) (0.182) Muslims 0.158∗∗∗ 0.186∗∗∗ -0.131∗∗∗ -0.132∗ (0.039) (0.055) (0.045) (0.067) No Relig 0.392∗∗ 0.357 -0.540∗∗∗ -0.802∗∗∗ (0.180) (0.373) (0.174) (0.260) Trad -0.089 -0.082 0.237∗∗ 0.204 (0.072) (0.126) (0.098) (0.140) other -1.028 -2.935 -0.653 -3.597∗ (0.971) (2.239) (1.221) (2.037) 1Incumbent President in 2000. Table 5-4. Continued

NDC Pres NDC Parl NPP Pres1 NPP Parl Model 13 Model 14 Model 15 Model 16 Model 17 Model 18 Model 19 Model 20 Akan -0.107∗∗∗ -0.169∗∗∗ 0.094∗∗∗ 0.056 (0.029) (0.042) (0.033) (0.045) Ewe -0.057 -0.088 -0.035 0.008 (0.043) (0.087) (0.038) (0.063) Guan -0.108∗∗∗ -0.166∗ 0.091∗ 0.097 (0.039) (0.089) (0.047) (0.091) Gurma -0.064 -0.104 -0.006 -0.0001 (0.052) (0.075) (0.069) (0.098) Mole 0.045 -0.081 -0.022 -0.050 Dagbani (0.040) (0.066) (0.049) (0.067)

182 Grusi -0.012 -0.150 -0.072 -0.023 (0.069) (0.131) (0.071) (0.134) Mande 0.715∗∗∗ 0.830∗ -1.088∗∗∗ -1.465∗∗ (0.275) (0.445) (0.322) (0.589) others -1.881∗∗∗ -2.896∗∗ 2.299∗∗∗ 2.211∗ (0.583) (1.159) (0.676) (1.265) Non-Ghanaians 0.129 0.227 -0.458 -1.761∗∗ (0.458) (1.055) (0.585) (0.893) Constant -0.042∗∗∗ 0.116∗∗ -0.077∗∗∗ 0.209∗∗ 0.055∗∗∗ 0.026 0.039∗∗∗ 0.153∗∗ (0.011) (0.046) (0.015) (0.083) (0.009) (0.047) (0.014) (0.063) Obs 220 220 220 220 220 220 220 220 Adj R2 0.180 0.538 0.095 0.262 0.319 0.563 0.271 0.377 Res Std 0.064 0.048 0.105 0.095 0.063 0.050 0.092 0.085 Error (df) (216) (196) (216) (196) (216) (196) (216) (196) Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01 1Incumbent President in 2000. Table 5-5. Changes in party votes: 2008 - 2004

NDC Pres NDC Parl NPP Pres1 NPP Parl Model 21 Model 22 Model 23 Model 24 Model 25 Model 26 Model 27 Model 28 Unfriendly04 -0.056∗∗∗ -0.044∗∗∗ -0.051∗∗∗ -0.070∗∗∗ 0.059∗∗∗ 0.037∗∗∗ 0.070∗∗∗ 0.053∗∗∗ (0.007) (0.008) (0.013) (0.015) (0.008) (0.008) (0.013) (0.014) District Type 0.017∗∗∗ 0.010∗∗ 0.023∗∗∗ 0.004 -0.021∗∗∗ -0.014∗∗∗ -0.013∗ -0.027∗∗∗ (0.004) (0.005) (0.008) (0.010) (0.004) (0.005) (0.007) (0.010) NewDist.04 -0.009 -0.006 -0.013 -0.005 0.010 0.009 0.018 0.021 (0.007) (0.007) (0.018) (0.015) (0.008) (0.008) (0.013) (0.015) New AA08 0.002 -0.009 0.007 0.003 (0.006) (0.014) (0.007) (0.015) Parl04Winner -0.139∗∗∗ -0.398∗∗∗ 0.036 -0.179∗∗ (0.025) (0.061) (0.029) (0.074) Volta -0.023 0.021 (0.024) (0.048) 183 Ashanti 0.038∗∗∗ 0.007 (0.008) (0.023) Ling Div -0.011∗∗ -0.025∗∗∗ 0.010∗∗ 0.004 (0.005) (0.008) (0.005) (0.009) Agric rate -0.059∗∗ -0.187∗∗∗ 0.109∗∗∗ 0.072 (0.023) (0.051) (0.027) (0.048) Secondary -0.150∗∗ -0.181∗ 0.179∗∗∗ 0.265∗∗ (0.062) (0.099) (0.062) (0.125) Post Sec -0.175∗∗ -0.451∗∗∗ 0.312∗∗∗ 0.496∗∗∗ (0.086) (0.171) (0.094) (0.192) Muslims -0.037 -0.092 0.036 0.081 (0.047) (0.080) (0.049) (0.074) No Relig -0.321∗∗∗ 0.047 0.326∗∗ 0.861∗∗∗ (0.122) (0.277) (0.129) (0.287) Trad -0.146∗ -0.332∗∗ 0.170∗ 0.231 (0.083) (0.136) (0.091) (0.146) Other 2.209∗ -1.543 -2.084∗∗ -5.207∗∗∗ (1.194) (1.954) (1.050) (1.949) 1Incumbent President in 2004. Table 5-5. Continued

NDC Pres NDC Parl NPP Pres1 NPP Parl Model 21 Model 22 Model 23 Model 24 Model 25 Model 26 Model 27 Model 28 Akan 0.033∗ -0.011 -0.061∗∗∗ -0.100∗∗ (0.018) (0.038) (0.022) (0.047) Ewe 0.052 0.122∗ -0.020 -0.061 (0.034) (0.074) (0.024) (0.068) Guan -0.005 0.039 -0.029 -0.012 (0.046) (0.068) (0.040) (0.077) Gurma -0.048 -0.080 0.026 -0.068 (0.052) (0.089) (0.055) (0.084) Mole -0.041 0.012 0.058 0.011 Dagbani (0.037) (0.065) (0.041) (0.075)

184 Grusi -0.050 -0.054 0.018 0.011 (0.063) (0.129) (0.061) (0.099) Mande -0.111 0.454 -0.322 0.222 (0.188) (0.317) (0.236) (0.418) Others -0.616 -1.197 1.532∗∗∗ 1.202 (0.507) (0.749) (0.568) (0.936) Non-Ghanaians 0.556 1.161 -0.659 -0.833 (0.430) (0.750) (0.407) (0.748) Constant 0.029∗∗∗ 0.255∗∗∗ 0.017 0.573∗∗∗ -0.026∗∗∗ -0.243∗∗∗ -0.033∗∗ -0.089 (0.007) (0.050) (0.015) (0.107) (0.008) (0.055) (0.015) (0.110) Obs 221 221 221 221 221 221 221 221 Adj R2 0.298 0.523 0.084 0.341 0.274 0.603 0.130 0.245 Res Std 0.047 0.039 0.095 0.080 0.054 0.040 0.093 0.086 Error (df) (217) (197) (217) (197) (217) (197) (217) (197) Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01 1Incumbent President in 2004. Table 5-6. Changes in party votes: 2008 Pres. Runoﬀ - 2008 Pres. Election

NDC Pres NPP Pres Model 29 Model 30 Model 31 Model 32 Unfriendly04 0.029∗∗∗ 0.017∗∗∗ -0.016∗∗∗ -0.012∗∗ (0.006) (0.006) (0.006) (0.006) District Type -0.001 0.010∗∗∗ -0.003 -0.005∗∗∗ (0.003) (0.003) (0.003) (0.002) New District04 -0.009 -0.003 0.006 0.005 (0.006) (0.005) (0.006) (0.004) New AA08 0.0005 0.003 (0.004) (0.003) Parl 04 Winner -0.079∗∗∗ -0.001 (0.019) (0.019) Volta 0.001

185 (0.014) Ashanti 0.019∗∗∗ (0.004) Ling Div -0.004∗ 0.00002 (0.002) (0.001) Agric rate 0.005 -0.018 (0.015) (0.018) Secondary -0.128∗∗ 0.020 (0.051) (0.049) Post Sec -0.041 -0.052 (0.048) (0.054) Muslims -0.103∗∗∗ 0.052∗∗∗ (0.020) (0.019) No Relig -0.156∗ -0.055 (0.088) (0.064) Trad 0.056 0.043 (0.070) (0.038) Other 0.084 1.164∗∗ (0.844) (0.584) Table 5-6. Continued

NDC Pres NPP Pres Model 29 Model 30 Model 31 Model 32 Akan 0.022 0.007 (0.015) (0.015) Ewe 0.001 0.016 (0.021) (0.011) Guan 0.018 -0.020 (0.025) (0.019) Gurma -0.087∗∗ 0.023 (0.040) (0.024) Mole Dagbani 0.040∗ -0.008 (0.022) (0.018) 186 Grusi 0.016 0.006 (0.044) (0.030) Mande -0.054 -0.239∗∗ (0.106) (0.108) Others 0.614∗∗ -0.090 (0.288) (0.310) Non-Ghanaians -0.204 -0.052 (0.234) (0.209) Constant 0.022∗∗∗ 0.139∗∗∗ 0.008 -0.008 (0.007) (0.044) (0.007) (0.039) Obs 221 221 221 221 Adj R2 0.096 0.385 0.046 0.089 Res Std Error 0.043 0.035 0.033 0.032 (df) (217) (197) (217) (197) Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01 Table 5-7. Changes in party votes: 2012 - 2008 (reg. election)

NDC Pres1 NDC Parl NPP Pres NPP Parl Model 33 Model 34 Model 35 Model 36 Model 37 Model 38 Model 39 Model 40 Unfriendly08 0.015∗∗ 0.021∗∗∗ 0.063∗∗∗ 0.071∗∗∗ -0.019∗∗∗ -0.020∗∗∗ -0.025∗∗∗ -0.029∗∗ (0.007) (0.008) (0.013) (0.014) (0.007) (0.007) (0.007) (0.012) District -0.012∗∗ -0.004 -0.024∗∗∗ 0.003 0.009∗∗ -0.001 0.001 -0.009 Type (0.005) (0.007) (0.009) (0.015) (0.005) (0.007) (0.005) (0.011) NewDis.08 -0.017∗∗ -0.009 0.003 0.026 0.020∗∗∗ 0.012∗ 0.024∗∗∗ 0.026∗ (0.007) (0.007) (0.014) (0.016) (0.007) (0.007) (0.007) (0.013) New AA12 0.003 0.007 -0.002 -0.003 (0.007) (0.012) (0.007) (0.010) parl08winner -0.092∗∗∗ -0.338∗∗∗ 0.031 -0.206∗∗∗ (0.030) (0.067) (0.042) (0.061) Volta 0.041∗ 0.058 (0.021) (0.059) 187 Ashanti -0.015 0.040∗∗ (0.017) (0.019) Ling Div -0.001 -0.025∗∗ -0.003 -0.014∗ (0.005) (0.011) (0.004) (0.008) Agric rate -0.0002 -0.010 -0.034 0.002 (0.024) (0.051) (0.026) (0.044) Secondary -0.022 0.018 0.009 0.064 (0.067) (0.128) (0.062) (0.106) Post sec 0.009 -0.068 -0.039 0.198 (0.085) (0.164) (0.092) (0.125) Muslims -0.089∗∗ -0.057 0.078∗ 0.069 (0.035) (0.073) (0.046) (0.076) No relig 0.083 0.560∗ -0.085 0.157 (0.164) (0.313) (0.167) (0.235) Trad -0.005 -0.019 0.001 0.029 (0.059) (0.123) (0.075) (0.135) Other -1.624 -0.412 1.935∗ 3.290 (1.061) (1.979) (1.135) (2.135) 1Incumbent President in 2008. Table 5-7. Continued

NDC Pres1 NDC Parl NPP Pres NPP Parl Model 33 Model 34 Model 35 Model 36 Model 37 Model 38 Model 39 Model 40 Akan 0.016 0.024 -0.006 -0.038 (0.025) (0.048) (0.026) (0.052) Ewe -0.011 0.057 -0.018 -0.045 (0.035) (0.085) (0.026) (0.058) Guan 0.056 0.230∗∗∗ -0.049 0.016 (0.048) (0.078) (0.045) (0.067) Gurma 0.005 0.127 0.005 -0.024 (0.035) (0.090) (0.049) (0.076) Mole 0.076∗∗ 0.047 -0.052 -0.031 Dagbani (0.031) (0.058) (0.036) (0.071) Grusi 0.229∗∗∗ 0.231∗∗ -0.145∗∗ -0.105 188 (0.049) (0.110) (0.063) (0.094) Mande 0.177 1.036∗∗∗ -0.165 -0.198 (0.229) (0.369) (0.251) (0.478) Others 0.456 -0.460 -0.182 -0.742 (0.568) (0.851) (0.636) (1.151) non-Ghanaians -0.215 0.202 -0.180 1.004 (0.375) (0.968) (0.386) (0.750) Constant 0.053∗∗∗ 0.091∗ 0.035∗∗ 0.149 -0.035∗∗∗ 0.001 -0.009 0.071 (0.007) (0.054) (0.015) (0.113) (0.008) (0.050) (0.008) (0.094) Obs 267 267 267 267 267 267 267 267 Adj R2 0.065 0.213 0.088 0.211 0.072 0.123 0.033 0.056 Res Std 0.058 0.053 0.102 0.095 0.057 0.055 0.083 0.082 Error (df) (263) (243) (263) (243) (263) (243) (263) (243) Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01 1Incumbent President in 2008. Table 5-8. Number of constituency-level political party strongholds (over 65% of the vote) 1996 2000 2004 2008 2012 Pres. Parl. Pres. Parl. Pres. Parl. Pres. Parl. Pres. Parl. NDC 76 55 41 22 39 27 36 26 65 30 Volta 19 15 19 13 21 14 18 16 22 19 Region 189 NPP 18 15 40 28 58 39 39 25 43 35 Ashanti 16 14 24 22 33 26 29 21 34 29 Region

Total 200 200 200 200 230 230 229* 229* 275 275 constit. *Note: 2008 Elections were postponed in the Akwatia constituency and are not included Table 5-9. Competitive and uncompetitive constituencies 1996 2000 2004 2008 2012 Pres. Parl. Pres. Parl. Pres. Parl. Pres. Parl. Pres. Parl. Comp. 106, 130, 119, 160, 133, 164, 154, 178, 167, 210, % (53%) (65%) (59.5%) (80%) (57.8%) (71.3%) (67.2%) (77.7%) (60.7%) (76.4%) 190 Uncomp. 94, 70, 81, 40, 97, 66, 75, 51, 108, 65, % 47% 35% 40.5% 20% 42.2% 28.7% 32.5% 22.3% 39.3% 23.6%

Total 200 200 200 200 230 230 229* 229* 275 275 constit. *Note: 2008 Elections were postponed in the Akwatia constituency and are not included CHAPTER 6 SURVEY ANALYSIS OF INDIVIDUALS’ VOTES

Thus far I have traced how the development of Ghana’s centralized system of government was created with the ethnic conﬁguration of Ghanaian society in mind. The guiding principle, historically used to justify authoritarian control in Africa, is that centralized control is necessary to combat the inﬂuence of chiefs and ethnic leaders on segmented populations which is detrimental to national unity. As I argue in Chapters 4 and 5, the particular way in which the system of centralization was implemented in Ghana’s Fourth Republic contributes to a depreciation of neo-patrimonial politics and ethnic voting. Put simply, the implementation of political competition in sub-national districts across Ghana has increased the number of viable candidate or party options available to Ghanaians in making their vote choices.

I have discussed the ways in which sub-national political competition has increased the vote opportunities for Ghanaians, but I have not yet tested for the factors which contribute to individual-level vote decisions. Though the politicization of ethnicity in Ghana is certainly acknowledged by scholars, studies completed mid-way through the Fourth Republic emphasize that ideological voting has just as big an impact as ethnic voting in Ghana’s Fourth Republic

(Fridy 2007a; Whitﬁeld 2009). While sometimes explained as an information problem constraining local voters and resulting in the appearance of ethnic voting (Lindberg and Morrison 2005; 2008), more recent explanations emphasize that voters have become more mature democratic citizens over time and now engage in retrospective or prospective voting

(Weghorst and Lindberg 2011; Hoﬀman and Long 2013). Further, Weghorst and Lindberg

(2011) ﬁnd that clientelistic inducements are losing their eﬀect in Ghana as voters are now equipped with retrospective information about both political parties and apply that knowledge in their voting decisions. Finally, while survey and exit poll evidence points to critical voting rationales by Ghanaian voters, it is also important to consider that respondents would be unlikely to admit that ethnicity or clientelistic inducements impacted their votes.

In my analysis I consider three core hypotheses about Ghanaian voting behavior:

191 1) Identity-Based Voting: Ethno-linguistic, tribal, hometown, and/or religious identities impact individuals’ votes.

– Identity-Based Voting refers to ethnic, religious and/or particularistic influences which might impact someone’s vote. Ethnic and Religious categories are the same as those used by Ghana Statistical Services (GSS) in capturing the 2010 census. Particularistic influences are conceptualized as factors related to hometown (e.g. how likely would you be to vote for an MP who was not born in this constituency?) or family influences (e.g. Do members of your immediate family support the same political party as yourself?).

2) Policy-Based or Economic-Based Voting: Political platforms, candidate performance, and perceptions about the state of the economy impact individuals’ votes.

– Policy-Based Voting is conceptualized as votes aﬀected by social and economic policies and/or political party ideologies. Social and economic policies encompass both voters’ prospective beliefs and retrospective judgments about candidate performance. Distinct from economic policies, economic-based voting refers to the impact of past and present perceptions about the state of the economy, or future predictions about Ghana’s economic well-being, on individuals’ vote decisions.

3) Clientelistic-Based Voting: Individual-level inducements aﬀect individuals’ votes.

– Clientelistic inﬂuences on vote decisions are strictly deﬁned as ‘payouts’ or gifts provided by the political party, candidate, or ‘party boys’ to an individual or individual’s family in trade for an individual’s vote. Community-level development ‘gifts’, such as the building of a water bore-hole, are not included within this category.

Throughout the district-level analysis (Chapters 6-8), I find evidence supporting all three hypotheses.Yet different analytic tools emphasize different factors. First, the qualitative explanation of district-level politics emphasized Identity-Based Voting (Hypothesis 1), albeit limited to local-level identities, Policy or Economic Based Voting (Hypothesis 2), and Clientelistic-Based Voting (Hypothesis 3), the last of which was heightened in the competitive districts. Within the survey data, respondents emphasized Policy-Based or Economic-Based Voting rationales when explaining their reason for voting for President or

MP, or community-members’ top reason for voting for President. Identity-Based factors, and particularly national-level cleavages, became relevant, while Policy and Economic-Based factors remained important, when respondents were asked about party ideologies. Further, models

192 predicting respondents’ votes also found respondent tribe/ethnic group signiﬁcantly predicted vote decisions though, the strength of the ethnic variables waxed and waned for diﬀerent groups over time. Finally, as Chapters 7 and 8 will show, respondents do not readily admit to ethnic or clientelistic impacts on their votes and instead support for these hypotheses is gathered from indirect questions and list experiments. 6.1 General Conclusions from Chapter 6

In this chapter I first introduce the 3 district pairs (6 districts in total) in which surveys were collected. Within the district pairs, the districts are demographically similar NPP strongholds, NDC strongholds, and competitive districts. Though similar in terms of party strongholds or competitive districts, the voting patterns within district pairs still differ in some significant way. In the analysis that follows, I first rely largely on interview data to provide a qualitative explanation of district-level politics and what local factors drive citizen votes. I then turn to my survey data, analyzing general political behavior via direct survey questions about political and economic values and vote behavior. First I consider questions where respondents report reasons for their votes for (1) President and (2) MP as well as (3) their perception about the top reason for vote decisions within their community. Second, I consider respondents’ ideological knowledge about the political parties. I ask respondents whether the party ideologies are different from one another (4) and to identify components of the (5)

NDC’s ideology and the (6) NPP’s ideology.

From the qualitative analysis, identity-based voting (Hypothesis 1) features prominently in the political explanation in the NPP and NDC strongholds, though not the competitive districts. However, the identity political cleavages that are galvanized are based on local-level identities (i.e. tribes, towns, district zones, etc.) rather than historically prominent ethno-linguistic cleavages. In the competitive districts, however, the absence of a single dominant ethno-political tradition (i.e. the Fantes are neither exclusively tied to the NDC or NPP) meant politics were less overtly motivated by identity cleavages and rather featured through the political parties in terms of citizen approval of campaign promises or perceived government eﬀectiveness

193 (Hypothesis 2). Still, local-level identities do play into political party support in these competitive districts as it is not uncommon to ﬁnd chiefs publicly aligned with particular political party traditions.1 Further, there is some suggestion that the degree of Fantes chiefs’ proximity to the Asantehene is related to an increased likelihood of supporting the NPP on the part of themselves and members of their community. Turning to the survey data, I generally ﬁnd support for Hypothesis 2: Policy or Economic-Based

Voting when respondents explain their or their community members’ votes. However, this portion of the analysis is based on self-report data, which is not likely to pick up on

Identity-Based Voting (Hypothesis 1)2 or Clientelistic-Based Voting (Hypothesis 3).

When explaining the components of the NDC and NPP ideologies, however, support for Hypothesis 1 increased as respondents were more willing to identify prominent ethnic groups or religious groups with party ideologies.3 Support for Hypothesis 2: Policy or Economic-Voting was again high in the political ideology responses, though diﬀerent districts cited diﬀerent versions of a party’s economic and policy ideologies. Finally, there was no evidence of clientelistic gifts as central to a political party’s ideology.

In the next chapter I use respondents’ self-report voting history (7) to predict votes for the 2004-2012 Presidential and Parliamentary races using standard demographic variables as well as variables related to the three core hypotheses. I also analyze swing voting patterns and use logistic regressions to predict swing voters. Finally, in Chapter 8 I use three diﬀerent survey

1 In the NPP and NDC strongholds in which I worked, it would be frowned upon to have chiefs come out publicly in favor of one party or another. 2 Support for Hypothesis 1 was low when respondents explained their votes, though respondents in Adaklu Anyigbe and Ketu South, the NDC strongholds, tended to cite particularistic/ethnic reasons at greater rates than respondents from other districts.

3 Respondents from the NDC strongholds again cited ethnic or religious groups with party ideologies at a greater rate than other districts, though Birim South ranked second in the proportion of respondents who identiﬁed Ewes/Muslims/Northerners with the NDC ideology.

194 experiments to test for the impact of tribal bias (8), religious bias (9), and clientelistic gifts (10-11) on vote decisions.

6.2 The Survey

The data analyzed in Chapters 6 through 8 comes from an original survey (N=1,932) collected in October-December 2013. The survey sample consisted of six purposefully-selected districts in the southern half of Ghana, whereby three sets of pairs were selected using Mill’s method of difference (i.e. similar demographic/structural characteristics but differing voting patterns between pairs) (Lijphart 1971). First, district pairs were selected controlling for similar demographic and ethnic characteristics and differing levels of electoral competition. One pair are NPP strongholds with majority Asante/Akyem populations (Bosome Freho and Birim South); the second pair are NDC strongholds with majority Ewe populations (Adaklu Anyigbe and Ketu South); and the third pair are electorally competitive districts with majority Fante populations (Mfantsiman and Asikuma Odoben Brakwa (AOB)).4

Second, the six districts were chosen with an emphasis on variation in voting patterns, despite their dominant party and structural similarities. In particular, why did one district in each of the NPP and NDC strongholds elect or narrowly elect an Independent MP while the

4 The unit of analysis for the surveys is the district in order for the analysis to correspond to the 2010 Ghana Census. As discussed in previous chapters, districts are the site of administrative local government bodies while constituencies, which either correspond to district boundaries or ﬁt within districts, are the local-level electoral units. DCEs, for example, govern at the district-level while MPs are elected at the constituency-level. Out of the six districts in the sample, only Mfantsiman had two constituencies within its district boundaries as of 2008. Also relevant, Birim South, Adaklu Anyigbe, and Mfantsiman Districts were split into two districts in 2012. Because Birim South and Adaklu Anyigbe both existed as a single district and constituency prior to 2012, surveys were conducted according to the 2008 Birim South and Adaklu Anyigbe district boundaries. As for Mfantsiman, a clerical error meant surveys were only collected in the 2008 Mfantsiman West constituency (now Mfantsiman District), rather than for the entire 2008 Mfantsiman District. Though census information is generalized to the entire 2010 Mfantsiman District, surveys were only conducted in Mfantsiman West constituency rather than Mfantsiman West constituency and Mfantsiman East/Ekumﬁ constituency.

195 member of the pair never swayed from the dominant party? Similarly, given the demographic similarities, why does one of the districts in the competitive district pair favor the NDC while the other favors the NPP?

Third, when collecting surveys, a signiﬁcant eﬀort was made to generate a randomly-selected representative sample from each district.5 For this purpose the survey team employed Ghana Statistical Services (GSS) to randomly select 8 enumeration areas (EAs), 4 urban and 4 rural, within each district.6 Enumeration Areas are the most basic unit of organization (i.e. the lowest unit above the individual-level) when collecting the census. The average population of an enumeration area in our sample was 553 residents.7

5 Though random selection procedures were used within each district, it is useful to verify whether the respondents’ demographic characteristics matched those of the district at-large. Table 6-1 presents comparisons of demographic data collected from the survey and the census in each district. Overall the survey samples matched the average age, gender distribution, and ethnic group population percentages very closely. Some areas of concern, however, include the under-sampling of female respondents in Ketu South and general, though moderate, over-sampling of those with higher levels of education across the districts. Finally, signiﬁcantly greater portions of the survey respondents own cell phones and use the internet than is reported in the census. However, I point out that the increasingly digitally-connected nature of Ghana’s population likely accounts for some of the diﬀerences between the census counts of Census Night, September 26, 2010, and the survey collected three years later in October-December 2013. Overall, the respondent demographics appear to generally match the census demographics.

6 Bosome Freho is a 100% rural district meaning no urban enumeration areas exist. Remember, the 2010 Ghana census classifies any enumeration area with a population greater than 5,000 as automatically urban. By necessity, surveys were only collected in rural enumeration areas in Bosome Freho. 7 The goal was to collect 40 surveys within each Enumeration Area. When the survey team arrived at the field, we utilized maps to walk the boundary of the entire EA and then survey enumerators were staggered at different starting points around the boundary. In dense urban areas we always stuck to the EA boundaries. In rural areas we sometimes found it difficult to randomly select respondents in low-population EAs. When the EA encompassed an entire village, we often split the surveys between the selected EA and a neighboring village (i.e. another EA). Still only 40 surveys were distributed in any given site (EA + a neighboring village, where applicable).

196 6.3 The District Pairs

6.3.1 NPP Strongholds: Bosome Freho & Birim South

Though the dominant tribal population in Bosome Freho and Birim South are diﬀerent, both districts’ respective Asante and Akyem groups are strongly associated with the NPP.

Bosome Freho and Birim South are relatively comparable in the presence of a core NPP tribe in the district, in terms of English literacy rates (48.3% vs. 61.5%) and the percentage of the population engaged in agriculture (81.6% vs. 78.2%). Bosome Freho is more rural8 , however, and has fewer cell phone owners and internet users as compared to Birim South (Table 6-2).

Electorally speaking (see Volatility Rates in Table 6-3), both of these areas have voted for the NPP Presidential candidate with consistency, though the NPP receives between

7-11% fewer votes in Birim South than Bosome Freho. Both districts are relatively stable in their Presidential voting patterns. In Parliamentary elections, however, Birim South votes have been very consistent while those in Bosome Freho ﬂuctuated greatly after an

NPP-turned-Independent candidate was elected in 2008 (Table 6-4). Between these two NPP

In order to ensure the random selection of households in each EA, survey enumerators used a ‘daycode’ method whereby the day of the month determined the number of residences separating survey respondents. If the day was a double-digit number, the two numbers were added together. So, October 13th meant 4 residences separated each respondent, making sure to count residences on both sides of the path/road. To select a respondent, survey enumerators first alternated the gender of the respondent in each household. In a household which required a male respondent, for instance, the survey enumerator then collected the first names of all the male individuals over the age of 18 who resided in the residence. Each individual was assigned a number and a member of the household was randomly selected. The selected individual would then be given the survey. If the individual selected was not available, one return visit was made. If the individual was still unavailable, the next household on the right would be substituted. If the household or selected respondent refused to participate, the daycode interval was then used to select another household. 8 That the area is rural is demonstrated by the high degree of familial connections between local politicians. The NPP Constituency Chairman is the cousin of the 2008-2016 NDC DCE, while the 2004 and 2008 NPP, and later Independent, MP is the brother-in-law to the 2000 NDC MP, who is himself also first cousins with both the NPP Constituency Chairman as well as the 2012 NPP MP.

197 strongholds, why was Bosome Freho able to buck the NPP party choice for MP in 2008 while the voters in Birim South (Akim Swedru) did not follow their NPP Parliamentarian who also tried to run as an Independent in 2012?

6.3.2 NDC Strongholds: Adaklu Anyigbe & Ketu South

Adaklu Anyigbe and Ketu South are both dominated by Ewe populations and both are heavily involved in the NDC voting tradition. English literacy rates are also comparable between the two districts (Table 6-5). While the proportion of households engaged in agriculture differ for Adaklu Anyigbe (76.7%) and Ketu South (21.4%), Adaklu Anyigbe is located in the interior of the Volta Region where farm land is more abundant while Ketu South is along the coast, has sandier soils, and more of its population fishes. Finally, Ketu South contains the Aflao border town, is less rural, and has a greater proportion of cell phone owners and internet users than Adaklu Anyigbe, though the former’s numbers are still below national averages.

Presidential voting patterns in both districts are very consistent over time, with low volatility rates for both districts (0.98 and 1.08, respectively) (Table 6-6). But Adaklu Anyigbe is a great deal more volatile in its Parliamentary voting patterns than is Ketu South (14.2 and

7.66, respectively) (Table 6-7). Third party votes were particularly high in Adaklu Anyigbe in

2004 (47.8%) and 2008 (41.4%) but returned to normal levels in 2012, though NDC candidate

Juliana Azumah-Mensah was able to win in all three of the 2004-2012 elections.9 Akin to the

Bosome Freho - Birim South pairing, why were the 2004-2008 MP races in Adaklu Anyigbe competitive while voters in Ketu South continuously toe the party line?

9 Azumah-Mensah won in Adaklu Anyigbe (Ho East constituency) in 2004 and 2008. After the district and constituency were split in 2012, Azumah-Mensah won in the Agotime-Ziope constituency.

198 6.3.3 Mfantsiman & Asikuma Odoben Brakwa

About 86% of the population in both the Mfantsiman and Asikuma Odoben Brakwa

(AOB) Districts are Fante speakers (Table 6-8). English literacy rates, proportion of rural communities and cell phone ownership are also similar between the districts. Mfanstiman is located on the coast, so more people are engaged in ﬁshing, while AOB is located in the hinterland and primarily consists of farming communities. Though both districts are structurally very similar and electorally competitive, voters in

Mfantsiman lean toward the NDC while voters in AOB lean toward the NPP. For Presidential races (Table 6-9), the NPP won in Mfantsiman in 2004 but lost in 2008 and 2012, while the

NPP won in AOB in 2004 and 2008, but lost in 2012. The Presidential volatility rates for these two districts are roughly comparable (8.45 (MF) vs. 5.49 (AOB)). In the Parliamentary races (Table 6-10), Mfantsiman elected an NPP MP in 2004 and an NDC MP in 2008 and

2012 while AOB elected an NPP MP in 2004 and 2008 and an NDC MP in 2012. Again, the volatility rates were similar, except this time Mfantsiman was lower than AOB (6.05 vs. 8.61, respectively). In the context of similar demographic characteristics, why do Mfanstiman voters lean toward the NDC while AOB voters lean toward the NPP?

6.4 Qualitative Explanation of District-Level Politics

In order of relevance, the qualitative explanations emphasize locally-relevant identity cleavages (Hypothesis 1), though less emphasized in the competitive districts, perceptions about incumbent performance (Hypothesis 2), and, particularly in the competitive districts, clientelistic incentives to vote (Hypothesis 3) as drivers of district-level politics.

6.4.1 Bosome Freho

The politically relevant identities in Bosome Freho revolved around four informal ‘zones’ which organized the district population into sectors of political behavior. In order of decreasing strength of the NPP, the four zones are Abosam Zo/Sunsu Freho Zone, Lake Zone, Bosome Zone, and Nsuta Zone. Tribal identities play into the politicization of these four zones. For instance, one interviewee posited that Abosam Zo Zone was the strongest for the NPP because

199 it consists of over 90% Ashanti residents (Interview 10/09/2013), while other interviewees identiﬁed the Nsuta Zone as the NDC stronghold because most of the residents are migrant

farmers (Interview 10/14/2013; Interview 11/06/2013). A majority of Bosome Freho’s

population is Asante (72.5%), but diﬀerent tribes have also migrated to the area for farming

over time, including Fantes, Krobo and Ewes.10 Second, after electing NPP MPs from 1996 to 2004, in 2008 Bosome Freho constituency elected an Independent MP to oﬃce. The primary reasons behind supporting the Independent candidate over the NPP nominee in 2008 have to do with positive perceptions about the incumbent’s performance, despite his failure to get re-nominated by the NPP, mixed with perhaps rural-urban or even class divides. After the NPP MP elected in 2004, Edward Ofori-Kuragu, failed to get enough NPP Executives’ votes in the 2008 NPP primary to secure the nomination, he followed the lead of Joseph Osei of Bekwai in opting to leave the party in order to run as an Independent candidate. Kuragu was successful, along with three other

NPP-turned-Independent MPs in other constituencies in 2008, including Osei.11 Essentially, Kuragu lost favor with the constituency-level NPP executives but the general public still

approved of him as a candidate and rejected the local NPP party’s decision to back another

candidate, Kwadwo Kyei Frimpong. Kuragu was re-elected in 2008 as an Independent, but

ultimately lost the 2012 race to Frimpong (NPP).

10 The status of migrant farmers is a politicized issue, particularly during election time, because migrants typically occupy land which is owned by the community and maintained by the chief. In essence, migrant farmers’ claim to the land is tenuous, even if their families had lived in the area for several generations. As economic resources become tight, the politicization of insider/outsider diﬀerences become more acute.

11 This was not the ﬁrst time an NPP politician had left the party in Bosome Freho. The 1996 NPP Parliamentary candidate, Professor Osei Kweku Agyemang, left the party to become the 2000 NDC Parliamentary candidate. Now a chief in Asiwa, Ageymang’s switch left a serious stain on the NPP party within Bosome Freho. Indeed, when Kuragu (Ind.) beat Frimpong (NPP) in the 2008 Parliamentary race, many NDC members opted to vote for Kuragu partially because they believed he might join the NDC in Parliament.

200 Finally, though local politicians need to curry favor, often with gifts, with the local-level NPP executives as well as the chiefs, clientelistic quid-pro-quo vote buying did not feature as a prominent factor determining citizen votes in Bosome Freho.

6.4.2 Birim South

Birim South is a somewhat more diverse district as compared to Bosome Freho, and politicized identity cleavages exist as allegiance to one of the three Akyem traditional areas (Bosome, Kotoku, and Abuakwa12 ), between the major towns in the district (Achiase, Swedru, and Aperade 13 ), and between the majority Akyem population and migrant farmers (e.g.,

Ekumﬁs, Fantes, Ewes and Krobos), the latter of which tend to support the NDC. All three of these local identities interact and the political parties make very conscientious decisions about spreading important government and party posts to candidates from diﬀerent backgrounds.

Second, like Bosome Freho, Birim South also has a strong tradition of electing NPP MPs but, unlike Bosome Freho, voters did not follow incumbent MP Joseph Ampomah Bosompem when he left the NPP party to run as an Independent candidate in 2012. Like Kuragu in

Bosome Freho, Bosompem was not re-elected in the NPP Primary largely because he lost the support of the local NPP executives, but unlike in Kuragu’s case, the voters instead supported the NPP candidate, Robert Kwesi Amoah in the 2012 MP race.14

12 Abuakwa is arguably the strongest Akyem paramountcy, as it owns land far from its base in Kyibi, capital of the East Akim Municipal District. Some say that the settlers of towns belonging to the Kotoku traditional area, which has strong ties to the Asantehene in Kumasi, came to the area prior to the Abuakwas, while others hold that the Kotokus came and begged for land from Abuakwas. Locally, this issue causes some tension, particularly as relates to the local division of power and distribution of development.

13 Previously the major town rivalry existed between Achiase and Swedru, but in 2012 the Akim Swedru constituency (the constituency that matched Birim South district boundaries) was split into two constituencies, Achiase and Akim Swedru. Now an up-and-coming townal division in Achiase constituency exists between Achiase and Aperade.

14 In the new Akim Swedru constituency, a fresh-faced Kennedy Osei Nyarko won in an uncontested NPP primary and won the subsequent MP race.

201 Relatedly, however, though Birim South District is a NPP stronghold, local politics between the NDC and NPP are still divisive. This divisiveness was clearly demonstrated in a

local controversy surrounding the failed conﬁrmation of President John Mahama’s appointment

of Baﬀour Takyi to DCE on at least 3 occasions.15 That the District went 2.5 years (Jan

2012 - July 2014) without an active DCE, and that it still took two votes to conﬁrm a second nominee, is suggestive of the level of inter-party contention within the area.

Finally, given the level of political contention both within the NPP and between the NPP and NDC, clientelistic politics, and particularly paying for individual students’ school fees, is a dominant political strategy. For instance, very few respondents had negative words to say about Bosompem as MP, but most people were aware of the development projects and individual support that Amoah had implemented prior to running in the NPP MP primary.

15 Baffour Takyi, who had run on the NDC ticket for Member of Parliament in 1996 and 2004, was President Mahama’s original DCE appointment in 2010. After serving for 2 years, Takyi’s re-nomination failed to receive a 2/3 majority approval vote in the District Assembly on three occasions. Failing to receive a 2/3 approval vote on two occasions typically means the President would have to nominate a new candidate. Yet, a new interpretation of a clause in the Constitution by the 2012 NDC government was taken to mean that the President could continue to nominate the same candidate. As Deputy Eastern Regional Minister Mavis Ama Frimpong put in her own words about the situation after the second vote failed to pass in July 2013: “I regretted coming to this meeting. If I knew you were going to vote this way and reject Baffour Takyi the second time, I would not have come at all. But I say to you that the President will renominate and renominate and renominate him. No one else will be nominated so if you continue to reject him, the President will ask him to continue acting as your DCE” (Today’s Ghana 2013). Baffour Takyi was nominated a third time, and again failed to receive a 2/3 majority vote. Finally, in June 2014, another candidate was nominated who also failed to receive a 2/3 majority in his first vote. It was not until a successful second vote in July 2014 that the Birim South District finally had a DCE. That Takyi was still rejected after his 3rd nomination is explained by members of the NDC community as NPP members’ fear of empowering an effective NDC politician. Indeed, Takyi had garnered 35.3% and 31% of the vote in the respective 1996 and 2004 MP races. Those in the NPP camp point to a conflict between the Achiase chief, who is a divisional chief under the Abuakwa traditional state, and Takyi actively campaigned to destool the chief. Finally, one last argument is that assembly members rejected Takyi because he is from Achiase and ‘everything’, meaning development and political appointments, has gone to Achiase.

202 When Amoah announced his NPP candidacy, the public strongly believed that these ‘good works’ would continue under his watch as MP.

Party politics are more contentious in Birim South than Bosome Freho, yet Bosome Freho

was willing to elect an Independent MP over the NPP MP in 2008. Outside of these qualitative

factors, is there something fundamentally diﬀerent about allegiance to the NPP among voters in Bosome Freho as compared to Birim South?

6.4.3 Adaklu Anyigbe

The political cleavages in Adaklu Anyigbe District are based on the three dominant

traditional areas: Agotime (Kpetoe-area), Ziope, and Adaklu. The traditional areas are

generally made up of Ewe-speakers, though the Agotime people have historical Ga-Adangbe roots but married freely with Ewes in the area since pre-colonial times.16

The competitive MP elections in Adaklu Anyigbe from 2004-2012 were directly linked to these traditional area rivalries. These divisions laid the groundwork over the creation of the

2004 Adaklu Anyigbe district (Nugent 2010, 142-43) and the placement of the new district capital in particular. In 2004, the new NDC MP nominee, Juliana Azumah-Mensah, hailed from Kpetoe and her nomination became entangled with a decision to place the district

16 Ethnically speaking, the Agotime traditional area was settled by Ga-Adangbe traders and merchants in the early eighteenth century. While some argue that Agotimes were ﬁrst in the area, this is considered unlikely by area experts (Nugent 2010, 131). Relying heavily on Nugent (2010) here, Agotimes quickly established their dominance in the area, supported by their active role in the slave trade as allies of the Akwamu (an Akan) state. In order to increase their population numbers in Kpetoe, the present-day capital of Agotime Ziope District, Agotimes incorporated Ewe slaves into their families and particular rules were followed for Ewe absorption into kin groups (ibid, 132). After the Akwamu state fell out with the Volta Region states, the Asante invaded the trans-Volta area in 1869. During this invasion, Adaklu Ewes collaborated with the Asantes and supposedly directed them straight to Kpetoe. After the Asante and their Anlo allies withdrew from the area some three years later, Agotime exacted revenge on Adaklu. Later, after the British defeated the Asante in 1874, Anlo traders became regular visitors to the area and requested permission to settle, despite having allied with the Asante not long before (ibid, 133). And this is how Ziope came to be settled by ‘strangers’ (i.e. Ewes from the South including Anloga, Akatsi, Klikor, etc.).

203 capital at Kpetoe instead of Adaklu Waya. A lecturer from Accra, Dr. Steve Buame entered the scene as an Independent candidate stoking the tribal diﬀerences between Agotime and

Adaklu and campaigning that he would return the district capital to Adaklu Waya.17 So, in a traditional NDC stronghold, Azumah-Mensah (NDC) only won the 2004 Parliamentary election by 536 votes out of 25,173 total votes and governance thereafter was diﬃcult.18 In 2008, Azumah-Mensah won by a 3,856 vote margin (with 55.5%), but Buame still received 39.2% of the vote. There was ﬁnally peace when Adaklu and Agotime-Ziope became their own separate districts in 2012.19

Third, though clientelistic political strategies are generally used in the area, the strong dominance of the NDC deemphasizes the need to pay for votes. The high levels of competition between Azumah-Mensah and Buame were stoked by identity-based cleavages and did not

17 When President Kufuor announced that the Ho East constituency would receive its own district in 2004, he also announced that Kpetoe would be the new district capital. Rumor has it that the then MP, Akorli, and some regional oﬃcials conspired to switch the district capital to Adaklu Waya. Kufuor overturned this decision but the way it was understood in the Adaklu traditional area was that Juliana Azumah-Mensah, who had just arrived on the scene to begin campaigning for the MP position, had conspired to move the district capital back to her hometown, Kpetoe.

18 After her election, Adaklu residents boycotted the district assembly, refused to meet with the MP or dressed in war gear to meet her, and sent back or destroyed development goods sent from the MP or the district. Kufuor wisely appointed a DCE from Adaklu who fortunately was the nephew of the most senior divisional chief in Adaklu as well as the son of the second-in-command warlord chief. Adaklu never officially declared war on Agotime, but it was certainly threatened and the central government often sent soldiers to the area to keep the peace. The district’s budget was forced to cover the costs of housing and feeding the soldiers, and development in the area was delayed. The conflict began to subside because Azumah-Mensah, also a Cabinet Minister, promised to get Adaklu its own district. President Atta Mills confirmed her promises when he invited Adaklu chiefs and officials to Osu Castle.

19 With the creation of the new districts, the overall Adaklu Anyigbe budget was simply split down the middle, half going to Agotime Ziope and half to Adaklu, without any additional funds for paying for the districts’ administration. Now two sets of DCE salaries, administration salaries, costs of district assembly and upkeep, sitting fees for assembly members, car and fuel expenditures and so on have to come from the same amount of money originally delegated to one district, Adaklu Anyigbe, in 2004.

204 require additional clientelistic payouts to fuel it. Still, the dominance of the NDC means there are NDC informants in every town which report to the party oﬃcials whenever the NPP tries to

rally support via development projects or handouts in even remote villages. It is then that the

NDC is particularly forced to respond with their own project or clientelistic gifts.

Since Agotime-Ziope and Adaklu received their districts in 2012, voting for the NDC is again very high. Politicized divides between towns/tribes are now developing in both districts,

with Agotime competing against Ziope for development and with the river size Tordzenu

people against the mountainside Tonu people in Adaklu. Land ownership and sons of soil

status conﬂates the Agotime versus Ziope controversy, as Ziope’s ‘stranger’ status is now

often brought up in decisions about development allocations. As I will demonstrate, however, Ketu South has similar tribal-level disputes that are instead ﬁltered through intra-NDC party

competition instead of resulting in competition from independent MP candidates.

6.4.4 Ketu South

Ketu South is known as the ‘NDC World Bank’, and is possibly the most formidable

NDC stronghold in Ghana. Though the area is a very stable NDC stronghold, internal NDC politics in the area can also be described as intense. The area cuts across three, politicized,

Ewe-speaking traditional areas: Aﬂao, Klikor, and Some.20

Politicians’ performance is certainly a driver of votes in the area. But the dominance of the NDC means factions within the party combined with local traditional area rivalries heighten the sense of competition within Ketu South. The current MP, Fiﬁ Fiavy Kwetey, won the NDC

20 The citizens of each of the traditional areas are all considered Anlo-Ewe, one way or another. Aflao, the capital of Aflao traditional area, is the largest town in the municipality. It is a border town, and on the other side of the border sits Lomé,the capital of Togo. It is also ethnically diverse while the Some and Klikor traditional areas are more homogeneous in their Anlo-Ewe background. While the Some and Klikor are close and they intermarry freely, for instance, the Klikor Paramount Chief and the Some Paramount Chief are cousins, there is a growing divide between Some and Klikor on the one end and Aflao on the other. These differences are now enmeshed in local politics.

205 primary over the sitting candidate primarily because of his resume, his history working with the NDC party’s founder, and his background which spreads across the three traditional areas.21

Like the Birim South NPP stronghold, local politics created signiﬁcant controversy over the appointment and conﬁrmation of the MCE of Ketu South Municipality in 2013.

Eventually Pascal Lamptey of Aflao, and close ally of MP Kwetey, was confirmed in 2013. But members of the Klikor and Some traditional areas interpreted Kwetey’s push for Lamptey as a mechanism through which the already power-hungry Aflao traditional area would be favored at the expense of their areas.22

21 Kwetey’s surname is Adangbe (a Ga tribe), his parents are from the Some traditional area, his maternal grandmother is from Klikor traditional area, and he was raised in Aﬂao. In addition to his well-spread indigenous background, Kwetey is nationally prominent as a former spokesperson for J.J. Rawlings and as the Deputy Minister of Finance and Economic Planning from 2009-2012. After his term as Parliamentarian began in 2013, he was appointed as Minister of State in charge of Financial and Allied Institution, Minister of Food and Agriculture in 2014, and Minister of Transport in 2016.

22 First, Kwetey had been Lamptey’s ‘school father’ at Ho Polytechnic and Lamptey subsequently married Kwetey’s cousin. Prior to Lamptey’s nomination, at least two other individuals were announced as the government’s nominee. But nomination letters from the President were missing and these nominees were later denied by the government. According to several sources, at least one of these individuals was a close ally of the former MP, who Kwetey had defeated in the 2012 NDC constituency primary. Rumor has it that Kwetey worked to block this nominee. With Lamptey’s nomination, several sources suggested that members of the former MP’s inner circle were working to block Lamptey as retribution and in hopes of getting their own candidate into the position. Second, Lamptey previously served as the NDC youth coordinator within the constituency and it is suggested that he burned bridges with NDC constituency executives when he publicized the fact that the local NDC office was not paying members of the youth who had worked for the party during the election. Third, members of the Klikor and Some traditional areas feared Aflao domination and complained that they were not consulted about the nomination (consulting traditional authorities about DCE nominations, though vague, is stipulated in the Constitution), and they felt Lamptey was being forced on them. These three levels of controversy (Kwetey vs. the former MP, NDC Executives vs. Lamptey, Klikor/Some vs. Aflao) surrounding the nomination of Pascal Lamptey all combined to make for a very eventful nomination process. Like the prior announced nominees, it was difficult to get hold of a nomination letter for Lamptey and when one was finally produced it was printed on ‘Draft’ paper and was addressed to the Regional Minister rather than the Municipality. At

206 Finally, though clientelistic campaign strategies are used as a political strategy in Ketu South, the traditional area rivalries are the primary driver of political cleavages and local vote decisions. At the national level, the vast majority vote for the NDC regardless.

Again, is there something fundamentally diﬀerent about allegiance to the NDC among voters in Adaklu Anyigbe as compared to Ketu South? Does Adaklu Anyigbe’s apparent willingness to vote against an NDC candidate in favor of an Independent MP suggest something about their overall attachment to the NDC as compared to Ketu South whose competition is more strictly within the NDC?

Lamptey’s first confirmation vote he needed 37 votes but only received 33 votes out of 55 present members. When Lamptey was not approved, ‘Aflao boys’ arrived on the scene and began harassing assembly members who sought refuge in the second floor of the assembly building. There the Minister of Trade scolded the assembly members and tried to force them to take a second vote that very day. Meanwhile, an elected assembly member who is in Kwetey’s inner circle calculated how likely it was that any of the 18 appointed assembly members were part of the 22 no-votes for Lamptey. It was determined that some appointed members must have voted against Lamptey. Ten days later, on the last day the second vote was eligible to take place, the majority of government appointees were revoked and new appointees were sworn-in just prior to a vote which approved Lamptey’s nomination, 47 to 8. The Some and Klikor paramountcies had sought injunctions to stop the vote, but these were not approved. The distribution of money to district assembly members was also widely acknowledged to have had an impact on this second vote. Finally, after Pascal’s second vote, it came out that the appointment letters for the new nominees was dated October 24th, but the termination letters for the prior appointees was dated the 28th. The terminated appointees were then given letters that were backdated to the 21st. This information became public and was announced on the radio, further suggesting political manipulations were behind the events. Though some challenged the second confirmation vote as invalid on the basis of the mis-matching letter dates, Lamptey was quickly whisked away to M/M/DCE orientation and he currently serves as the MCE under the Mahama administration. These events made national news and the district assembly votes were attended by prominent government and NDC officials alike.

207 6.4.5 Mfantsiman

The major political cleavages in Mfantsiman exist along political party lines, rather than

identity. Ethnically, the constituency is made up of a majority of Fante speakers, though there

is some diversity in terms of the origins of these Fante speakers. 23

Candidate performance, mixed with partisan preferences, is a driver of vote decisions.

Regionally, the district strongly favored the NPP in the 2004 Presidential election24 and then increasingly switched to favor the NDC in the 2008 and, even more so, in the 2012 Presidential

contests.25 As far as MP elections, Steven Asamoah Boateng (NPP) was elected in 2004 but lost in 2008 and 2012 to Aquinas Quansah (NDC). That Boateng lost in 2008 and thereafter is explained as his ineﬀective time in oﬃce and, more importantly, divisions within the NPP which Boateng exacerbated.26

23 The word ‘Fante’ itself translates to ‘the part that has moved’ and most Fante speakers originate from the Ashanti or Brong Ahafo Regions.A number of communities are made up of people who are originally Gonjas, Guans and Bonos from Techiman in the Brong Ahafo Region. The story goes that people migrated east and then south when the Bono-Techiman Kingdom was at war with the Asante Kingdom. Denkyiras, also known as Agonas, also originated adjacent to the Asante Kingdom and after a war with the Asantes, became a tributary state to the Asante Empire in the early 1700’s. Later, Denkyiras joined with the Fante Confederacy and the British to ﬁght against the Asantes and Dutch in the mid 1800’s. Denkyira, or Agona, communities are generally located along the north side of Mfantsiman constituency.

24 Yet, Mfantsiman was the only Central Region district in 2004 to not surpass the 50% threshold for votes for NPP’s Kufuor.

25 In 2004, the Mfantsiman District was made up of Mfantsiman West and Ekumﬁ constituencies. As presented in Table 6-9, Mfantsiman West (where the surveys took place) did pass the 50% mark in voting for Kufuor in 2004, but at 54.6% the constituency was still low in support for the NPP as compared to other Central Region constituencies.

26 Boateng, aﬀectionately known as ‘Asabee’, was a most controversial candidate for the constituency. Though he was the only NPP candidate to win the MP spot, he also was and continues to be the local NPP branch’s greatest liability. For instance, Boateng is ﬁrmly entrenched in the camp of former NPP President John Kufuor. Under Kufuor’s administration he was appointed Deputy Minister for Information and National Orientation in 2004, Minister for Local Government and Rural Development in 2006 and Minister for Tourism and Diasporean Relations in 2007. Yet, Asabee also supported Alan Kyeremantan as Kufuor’s

208 That the district leans toward the NDC more than some other Central Regional Districts can partially be explained as a result of the proximity of former President Atta Mills’ (NDC) hometown. The present-day Mfantsiman Municipal District was once combined with

Mfantsiman East, known today as Ekumfi, constituency but Ekumfi received its own district status in 2012. That former President Atta Mills hails from neighboring Ekumfi certainly had an impact on the perceptions of the NDC in Mfantsiman, particularly when the two areas belonged to the same district.27

Finally, clientelistic incentives to vote were widely reported during our work in Mfantsiman.

It appears the political parties hold rallies or go door-to-door and oﬀer cash incentives for voting, and to a much greater degree in Mfanstiman (and Asikuma Odoben Brakwa) than the other four party stronghold districts. Several interviewees suggested that the NPP engaged in political rallies in city centers, which were less eﬀective than the NDC’s door-to-door campaign style. Further, Mfantsiman is known as a ‘prominent’ district and individuals from the area are typically appointed to high-level positions. As one source put it, “If you don’t give [the] Mfantsiman area a minister, you don’t understand Ghanaian politics” (Interview 11/17/2013).

This fact is also suggestive of neopatrimonial politicking (i.e. awarding ministerial positions for party support) in this area.

successor and some sources say he actively campaigns against Akufo-Addo’s presidential candidacy. Internal constituency rifts about supporting Akufo-Addo versus Kyeremantin were compounded by Asabee’s decision to schedule his own constituency primary in 2008. He did not inform some crucial constituency-level NPP executives of this plan as well as a prospective candidate who therefore missed the vote. The absent candidate took the matter to court, and the local NPP branch had to spend time and resources sorting out the matter during the 2008 election year, time which otherwise would have been spent on campaigning. In that election Asabee, a Minister, was voted out of oﬃce. Asabee (unsuccessfully) challenged the results of both the 2008 and 2012 parliamentary election results in court.

27 That Ekumfi, and thus Mfantsiman, received their own districts is locally understood as the result of Atta Mills’ election in 2008, especially considering Ekumfi did not have a large enough population or the significant amount of commercial activity necessary to become a district.

209 6.4.6 Asikuma Odoben Brakwa

Though a predominantly Fante district along with Mfanstiman, political cleavages in

Asikuma Odoben Brakwa (AOB) largely revolve around inter-town rivalries coupled with an urban-rural divide. The name Asikuma Odoben Brakwa refers to the names of the three urban towns in the district. Ethnically, the area is technically Fante. However, the Breman traditional state, itself a hybrid of Asante and Fante, identiﬁes as an independent tribe with its own language, Breman.28 Other tribes in the area include Gomoas, whose people formed one of the states in the historical Fante Confederacy and are now typically considered Fantes,

Agonas, Akumo/Asantes, Akyems, Ewes, etc, who have largely come to the area for cocoa farming purposes. Traditional area and town rivalries coincide and, like Ketu South though with much less intensity, aﬀect local politics. Great eﬀorts have to be taken by the NPP and

NDC to spread the number of government posts and party positions from across the three towns. Additionally, the NPP is favored within the more urban towns, while the NDC is favored in rural areas, particularly among migrant farmers.

Electorally, party politics are competitive in the district. AOB was more typical of northern Central Regional Districts in its hesitation to switch from the NPP in the 2004 Presidential contest to the NDC in 2008. From a nearly 30% point spread in the 2004 Presidential election,

AOB switched to a less than 2% vote margin in the 2008 and 2012 Presidential elections.

Within MP races, P.C. Appiah-Ofori (NPP) won in 2000 with a 7 point margin, in 2004 with

28 The term Breman is a German-derived word. One person described it as “Bremans are Fantes that are from Kumasi. Originally [they are] Asantes from Kumasi but they intermarried so now we are Fantes” (Interview 11/14/2013). The Ghana Census does not recognize Breman as a distinct tribe and rather groups it in with Fante. Asikuma-Odoben-Brakwa is also traditionally under the Breman paramountcy, but at some point a local controversy caused the Odoben and Brakwa chiefs to secede from the Breman traditional area to join the Ajumako paramountcy based in the neighboring Ajumako-Enyan-Esiam district.

210 a 18 point margin, and in 2008 with a 2 point margin.29 Though she lost in the 2004 and 2008 races, Georgina Nkrumah Aboah (NDC) was appointed DCE in 2009 and won in the subsequent MP race in 2012. Like Mfantsiman, intra-party rivalries are used to explain why the NPP was not able to continue it’s hold on the MP seat after Appiah-Ofori retired in 2012.

As Anthony Eﬀah (NPP) has worked systematically to shore up support with the local NPP executives, the 2016 MP race is likely to be very competitive.

Inter-party rivalries were exempliﬁed in a controversy over the appointment of the DCE, similar to that of Ketu South but with much less intensity. Out of 15 individuals who applied for the DCE position locally, three were shortlisted by the Region and their names sent to

Accra. But, instead a name (Samuel Adom Botchway) was sent back which was not on the original short list.30 Still, unlike the dramatic result of a similar controversy in Ketu South,

Botchway did not get conﬁrmed during the ﬁrst vote but, after some standard pressure from the Central Region Deputy Regional Minister, was approved in a second vote held several months later. Finally, like Mfanstiman, clientelistic payouts as a political strategy were heightened in

AOB. As interviewees described in Mfanstiman, part of the reason the NDC won the MP spot

29 Though nationally prominent as an anti-corruption crusader, Appiah-Ofori remained a controversial ﬁgure within the NPP and partially explain why he was never appointed to any Minister post. His forthrightness in announcing corruption led him at times to be critical of both the NPP and NDC, including in 2009 when he prominently accused NPP MPs of accepting $5,000 bribes to approve of telecommunications giant Vodaphone’s 70% acquisition of state-owned Ghana Telecom.

30 This moved caused great consternation for the local NDC executives who strongly favored a locally-popular Zenith Bank Operations Manager who had resigned his post knowing he would get the nod. How Botchway’s name came to be returned when he did not even apply is unknown, but it is widely believed to have to do with Botchway’s connections and string-pulling from big wig NDC ﬁgures. After all, Botchway was funded to go to Cuba for education during Rawlings’ time, is the former administrative manager of Kobina Fosu’s law ﬁrm in Tema, and was the campaign coordinator for Aboah’s 2012 MP campaign.

211 in 2012 was due to it’s door-to-door campaign style as compared to the NPP which more commonly holds town hall-type rallies.31

What explains Mfantsiman’s preference for the NDC as compared to AOB’s preference for the NPP? I rule out Mfantsiman’s proximity to former President Atta Mills’ hometown as the sole causal factor because only 43.9% of Mfantsiman West voters voted for Atta Mills (NDC) for President in 2004 and because other districts in the southern portion of the Central Region also display similar NDC-preference voting patterns as Mfantsiman. What explains AOB’s preference for the NPP, given that the district is also dominated by Fantes? And why does

AOB’s preference for the NPP mimic the voting preferences of other districts in the northern half of the Central Region? 6.5 Survey Analysis: Political Knowledge and Behavior

As we have seen in the qualitative explanation of politics in each district, each of the three core hypotheses are supported to varying degrees. Following up on the qualitative analysis,

I test for the three core hypotheses using 11 diﬀerent survey questions throughout Chapters

6-8. In the reminder of this chapter I begin the survey analysis by analyzing respondents’ reasons for their votes as well as knowledge about party ideologies. In the subsequent chapter

I predict respondents’ votes in multinomial logistic regression models and analyze swing voting behaviors. Finally in Chapter 8 I separately test for tribal, religious, and clientelistic impacts on citizen votes through the use of survey experiments.

Overall, I ﬁnd ample evidence for Hypothesis 2: Policy-Based or Economic-Based Voting, both when respondents are directly asked for their reasons for their votes, when using multinomial logistic regressions to test for respondent votes, and logistic models to test for swing voters in Chapters 6 and 7. I do also ﬁnd some evidence of Hypothesis 1:

31 One interviewee described the swing-voting tendencies of the area as because “party executives come and don’t tell the truth so the youth switch their votes” (Interview 11/14/2013).

212 Identity-Based Voting and Hypothesis 3: Clientelistic-Based Voting from indirect questions and list experiments. Though respondents might not be keen to suggest that identity or clientelistic factors also impact their vote decisions, it is increasingly clear that each of the three hypotheses combine with one another to influence citizen votes. This conclusion is drawn from the presence of all three hypotheses in the qualitative analysis of district-level politics as well as the support for each hypothesis in different types of survey questions. Finally, given the unwillingness of voters to share ethnic or clientelistic vote biases, implicit or hidden survey tests should be used in sub-Saharan African democracies, as they are used in developed democracies, to test for these biases. Otherwise we risk underestimating and/or ignoring the effect of identity or clientelsitic inducements on votes. 6.5.1 Biggest Reasons for Your Vote and Votes in the Community

6.5.1.1 Overall results

When asked to report the three biggest reasons for their vote for President or Member of Parliament in the 2012 contests, without saying whom they voted for, the Candidate’s

Social Policy (76.7%- Pres., 68.0%- MP), Candidate’s Economic Policy (71.2%- Pres., 62.8%-

MP), and Candidate Approval (55.01%- Pres., 70.49%- MP) were the three most popular responses for both questions (Tables 6-11 & 6-12). Similarly, when asked for the biggest reason driving Presidential votes within this community (Table 6-13), respondents again pointed to the

Candidate’s Social Policy (41.7%), the Candidate’s Economic Policy (26.5%), and Candidate

Approval (20.16%) (Tables 6-11 to 6-13).32

32 Categories: 1) Candidate’s Economic Policy (ex: commodity prices will be moderated, keep inﬂation down, etc.) 2) Candidate’s Social Policy (ex: youth employment, jobs for women, education, etc.) 3) Party Legacy (ex: political party performed well here in the past, party legacy, past leaders, etc.) 4) Candidate Approval (ex: candidate traits/capabilities, believe candidate will help my community, etc.)

213 In all three of these sets of questions identity-based factors (Hypothesis 1) are signiﬁcantly downplayed. Only 5.98% (8.07%) of respondents identiﬁed a Particularistic or Ethnic reason as

one of their three biggest reasons for voting for President (MP). And, whereas we might expect

respondents to blame ‘un-democratic’ voting habits on others rather than themselves, only

1.1% of respondents said a Particularistic or Ethnic factor was the biggest reason for driving Presidential votes within the community. Relatedly, less than 1% of respondents identiﬁed

clientelistic reasons (Hypothesis 3) for either their votes for President or MP or others’ votes

for President.

It is important to note that less than 1% of respondents cited their disapproval of the

current government or candidate as a reason driving their own or others’ votes. Responses instead were predominantly forward-looking such as “[to] improve health insurance”, “[for

the] building of more schools in the constituency”, and “to better Ghana”.33 Even those respondents who referred to the state of the economy as a reason phrased their responses in forward-thinking ways: “[for the] economic well-being of the community” and “[for] someone to manage the economy”. Overall, respondents understood their votes and their community members’ votes as driven by policy and/or economic assessments (Hypothesis 2), with particular emphasis on prospective social and economic policies as opposed to the social or economic downfalls of prior candidates/governments.

5) Disapproval of Gov./Candidate (ex: to bring change, gov. didn’t continue past projects, etc.) 6) Particularistic/Ethnic (ex: candidate’s tribe/ethnicity, candidate’s religion, candidate’s home area, etc.) 7) Clientelistic (ex: personal help or promise of personal help, landlord asked me to vote) 8) Voting as a Right (ex: voting is a constitutional right, vote to be a good citizen, vote to get voter’s card)

33 Questions 9, 10, and 13 were open-ended response items that were coded by the surveyor at the time of the interview. If surveyors were even slightly unsure of how to code an item, they were instructed to write the response down verbatim. These written responses provide a partial view of the nature of responses overall.

214 6.5.1.2 Within district pairs

Turning to the six districts in the sample, there are some interesting points of diﬀerentiation

separating Bosome Freho from Birim South, Adaklu Anyigbe from Ketu South, and Mfantsiman

from Asikuma-Odoben-Brakwa when giving reasons for their votes for President and MP, as

well as community members votes for President (Figures 6-1 - 6-9).

First, within the Bosome Freho-Birim South pair, party legacy and candidate approval were more common explanations of respondents or community members’ votes in Bosome

Freho than Birim South. In Birim South, particularistic/ethnic rationales were cited more

in explanations for presidential votes (Figure 6-1), while economic and social policies were

generally cited more in explanations of votes for MP and community members’ votes for President (Figures 6-4 and 6-7).34

Next, when comparing the NDC strongholds, Adaklu Anyigbe and Ketu South, against

each other in terms of respondent explanations for their votes, economic and social policies,

party legacy, and particularistic/ethnic reasons were generally cited by more respondents in

Adaklu Anyigbe than Ketu South, across the vote explanation questions. Particularistic/ethnic reasons for voting for MP were highest in Adaklu Anyigbe (13.5%) and Ketu South (10.4%) in

comparison to the rest of the districts. Candidate approval and voting as a right were generally

higher in Ketu South than Adaklu Anyigbe (Figures 6-2, 6-5, and 6-8).35

Third, among the competitive districts, Mfantsiman and Asikuma Odoben Brakwa, the districts were particularly similar across the questions. First, in responses explaining their votes for President and MP, Mfanstiman respondents cited Particularistic/Ethnic rationales with

34 Note that a greater percentage of Birim South respondents (82.5%) cited the Candidate’s Economic Policy more than any other district in explaining their Presidential votes, while a greater percentage cited both Economic Policy (75.9%) and Social Policy (81.6%) than any other district when explaining their votes for MP.

35 In Ketu South, however, Candidate’s Social Policy was only given as a reason by 43.8% of respondents, far below the sample-wide average of 76.7%.

215 slightly greater frequency while candidate approval was cited slightly more in AOB (Figures 6-3 and 6-6). In explaining community members’ votes for President, economic policy was cited with greater frequency in Mfantsiman while social policy and, to a slight degree, party legacy was cited more in AOB (Figure 6-9).

6.5.1.3 District-by-district analysis

First, support for Hypothesis 2: Policy-Based or Economic-Based Voting is strong across the districts, with Social Policy tending to be more cited than Economic Policy. In Ketu

South, however, Candidate’s Social Policy was only given as a reason by 43.8% of respondents, far below the sample-wide average of 76.7%. Also a greater percentage of Birim South respondents (82.5%) cited the Candidate’s Economic Policy more than any other district. Second, though 5.98% of the total sample’s respondents cited a Particularistic/Ethnic reason for their vote for President, that average was certainly raised by the 13.5% of respondents in Adaklu Anygibe who gave a Particularistic/Ethnic reason. Ketu South (6.6%) and Birim South (6.3%) were also above the 5.98% sample average. Finally, clientelistic reasons for votes (Hypothesis 3) were never cited by more than 1% of respondents in any district.

Respondents’ explanations of the reasons behind their vote for MP (Figures 6-4 - 6-6) also provide a lot of support for Hypothesis 2: Policy-Based or Economic-Based Voting with

Candidate’s Economic Policy and Social Policy consistently among the top three answer choices per district. However, variation across districts exists. For instance, Economic Policy and Social Policy are very high in Birim South (75.9%, 81.6%) and Adaklu Anyigbe (65.3%,

74%), but are comparatively lower in the other four districts. Yet, only 42.6% of Bosome Freho respondents cited Economic Policy and 44.5% of Ketu South respondents cited Social Policy, which are the lowest respective rates across the 6 districts in each case. Finally, about 52% of respondents cited Candidate’s Economic Policy and Social Policy in both Mfantsiman and

AOB.

216 The sample-wide percentage of respondents who cited an Ethnic/Particularistic reason for their vote for MP (Hypothesis 1) was 8.07%, yet this was again driven by the 13.5% of respondents from Adaklu Anygibe and 10.4% of respondents from Ketu South who gave an

Ethnic/Particular reason. Again almost no respondent gave a Clientelistic reason for their vote for MP. Finally, in comparing respondents’ answers to the question, ‘What Is The Biggest

Reason Driving Presidential Votes within this Community?’ (Figures 6-7 - 6-9), Social Policy dominates in Bosome Freho, Birim South, Adaklu Anyigbe and AOB. In Mfantsiman, Social

Policy is the third highest category and in Ketu South it is fourth. Economic Policy is the most cited category in Mfantsiman (31.7%), cited the second most in Bosome Freho, Birim South, Adaklu Anyigbe, and Ketu South, and the third most cited category in AOB. Overall, respondents focused on policies in the Bosome Freho and Birim South NPP Strongholds, whereas Party Legacy and Candidate Approval factored more in the other four districts.

Identity-Based Voting (Hypothesis 1) accounts for very few responses across the districts, though Particularistic/Ethnic reasons for community-members’ votes were again cited more in

Adaklu Anyigbe and Ketu South than any other district. Finally, almost no respondents cite

Clientelistic Reasons for Community Votes (Hypothesis 3).

6.5.2 Identifying NDC and NPP Ideologies

Respondents were next asked whether Ghana’s political parties have diﬀerent ideologies

(Q20)36 and to identify components of the NDC’s Political Ideology (Q21) and the NPP’s Political Ideology (Q22). When respondents cite ethnicity as a part of either party’s ideology, this suggests that identity information (Hypothesis 1) does matter in the minds of respondents in terms of how they perceive the political parties. When respondents cite Policy-Based Criteria

36 Respondents were asked if Ghana’s political parties have diﬀerent ideologies and then were cued with ‘Are the political parties known for diﬀerent political platforms or policies?’

217 of a party’s ideology, this provides evidence for Hypothesis 2. Third, Clientelistic components of a party’s ideology provides support for Hypothesis 3.

To begin, though political parties in new democracies are characteristically known for their weak party platforms, 82.48% of the respondents in our sample said that Ghana’s political parties’ ideologies were ‘Very Different’. Less than 18% of the sample responded that Ghana’s political parties are ‘Somewhat Different’ or that there is ‘No Difference [between them]’ (Table

6-14).

6.5.2.1 The NDC ideology

The NDC has traditionally been associated with Socialist ideals, ﬁrst propagated by

Rawlings early in the PNDC regime, and then converted into the concept of Social Democracy in Ghana’s Fourth Republic. The purging of corrupt political elites in the ‘June 4th Revolution’ by the AFRC regime, including the death by firing squad sentences handed out to several prominent individuals including three former heads of state, as well as the latter PNDC regimes’ emphasis on ending corruption and hoarding, both aligned Rawlings with the working class and the poor. That Rawlings and his comrades were junior officers overthrowing corrupt senior officers provided symbolism for the movement. The present-day NDC still draws on these traditions in the Fourth Republic.

As discussed in prior chapters, the NDC is associated with Ewe and Northerner interests, largely due to the fact that Rawlings is half-Ewe and that Rawlings’ PNDC regime implemented policies which directly beneﬁted the grossly underdeveloped North of the country (present-day Northern, Upper East, and Upper West Regions). Finally, the NDC has also been associated with a development platform called the ‘Better Ghana Agenda’. The term was ﬁrst used by the

2008 Atta Mills administration and, according to the 2008 and 2012 NDC Manifestos, covers a wide range of topics including corruption, women’s representation in government and public service, promotion of lending to businesses, and strengthening of environmental regulations, among other topics. As explained in these NDC manifestos, the Better Ghana Agenda is a very

218 general platform with little eﬀort made to explain how the agenda goals will be accomplished (National Democratic Congress 2008; 2012).

When asked about the components of the NDC’s Political Ideology (Table 6-15), 72.1% of

respondents cited the NDC’s broad stroke policy agenda, the Better Ghana Agenda. Another

3.72% cited speciﬁc social policy/development projects, such as building of roads or schools, that they said the NDC was known for.37 The second most-cited component was Development

(43.81%) and the third was Socialism/Social Democracy (38.14%). I generally understand these responses as related to Hypothesis 2: Policy-Based or Economic-Based Voting, because of their emphasis on social/developmental policies and performance.

In reference to Hypothesis 1: Identity-Based Voting, interestingly, 16.9% of respondents said that the NDC was for the Ewes and/or for the Muslims/Northerners. This is double the high of 8.07% who cited a Particularistic/Ethnic reason within the top three biggest reasons for their vote for MP. This might suggest that respondents are more willing to blame ethnic galvanizing on the politicians and political parties rather than on themselves or their fellow community-members.

Finally, only 8.02% of respondents cited the Working Class, Poor, or the Masses when asked about the NDC’s Political Ideology. This is less than half of the proportion who cited

Ewes, Muslims and/or Northerners. Apparently, ethnicity/region/religion have stronger associations with the NDC ideology than economic classes. 6.5.2.2 The NPP ideology

While the NDC is sometimes understood as a party born out of the CPP, it is widely

agreed that the present-day NPP is within the same political tradition as the original United

Gold Coast Convention (UGCC), which transformed into the United Party (UP), Progress Party

37 These responses did not converge on any one or two types of development projects. Instead the projects cited were very diverse, even from respondents within the same district.

219 (PP), and the Popular Front Party (PFP), before becoming the New Patriotic Party (NPP) in the Fourth Republic.

With strong ties to the historical UGCC and UP political traditions, the NPP has traditionally be associated with capitalism and market-oriented economics, elites, the protection of cocoa farmers interests, and Asante and Akyem interests. In comparison to the NDC’s Better Ghana Agenda, and rather than push a general platform, the 2012 NPP national campaign instead focused on one particular policy: Free Secondary School Education.

In responding to the question asking about the components of the NPP’s Political

Ideology, 77.21% of respondents identiﬁed the NPP national platform of Free Secondary

High School Education (Table 6-16). This is by far the most cited category, while the next is Development with 29.27% of respondents citing it. A close third category is that the NPP is known for pushing a Market-Oriented or Laissez-Faire Economy (23.58% of respondents).

Respondents are certainly aware of the Policy Platforms for the NPP.

In comparison to the 16.9% of respondents who said the NDC was for the Ewes, Muslims and/or Northerners, only 11.03% said that the NPP is for the Asantes and/or Akans. Also interesting is that 13.64% of respondents said the NPP was for the Working Class, Poor and/or the Masses, in comparison to the 8.02% who cited the NDC. The NPP are historically associated with educated and elite interests, yet only 8.0% cited the Rich and/or Elite as associated with the NPP ideology. Clearly the NPP has put in some impressive legwork to alter its historical associations in the Fourth Republic.

6.5.2.3 Within district pairs

Again, comparing Bosome Freho to Birim South, Adaklu Anyigbe to Ketu South, and

Mfantsiman to Asikuma Odoben Brakwa shows some interesting diﬀerences within district pairs

(Figures 6-10 - 6-18). First, within the Bosome Freho-Birim South pair, a greater proportion of Bosome Freho respondents said the political parties’ ideologies were Somewhat Diﬀerent, as compared to Birim South, while a greater proportion of Birim South respondents reported there

220 was No Diﬀerence between the parties’ ideologies (Figure 6-10). In describing the NDC ideology, Bosome Freho residents were more likely to cite Socialism/Social Democracy while Birim South respondents associated the Better Ghana Agenda, Development, and

Ewes/Muslims/Northerners with the NDC with greater frequency (Figure 6-13). Finally, in describing the NPP, Bosome Freho respondents cited the working class/poor and development more while Birim South respondents cited Asantes/Akans and the Free SHS Policy more than those in Bosome Freho (Figure 6-16).

Next, in comparing whether Ghana’s political parties have diﬀerent ideologies within the

NDC strongholds, Adaklu Anyigbe respondents cited that party ideologies were Somewhat

Diﬀerent at a higher rate while Ketu South respondents cited Very Diﬀerent at a higher rate (Figure 6-11). When describing the NDC ideology, the Working Class/Poor and

Ewes/Muslims/Northerners were slightly cited more in Adaklu Anyigbe while Socialism/Social

Democracy and development were somewhat higher in Ketu South (Figure 6-14). Finally,

Adaklu Anyigbe respondents were somewhat higher in citing the Working Class/Poor, Rich/Elite, and Asantes/Akans as associated with the NPP ideology while Ketu South respondents were somewhat more likely to cite Market-Oriented/Laissez-Faire policies and

Development (Figure 6-17). Adaklu Anyigbe and particularly Ketu South respondents had some of the highest percentage of respondents cite both Socialism/Social Democracy for the

NDC ideology and Market-Oriented/Laissez Faire economic policies for the NPP ideology. Third, the competitive district respondents were very similar in whether or not they felt

Ghana’s political parties had diﬀerent ideologies (Figure 6-12). When explaining the NDC ideology, responses were again similar, though Ewes/Muslims/Northerners and the Better

Ghana Agenda were cited with slightly higher frequency in Mfantsiman while Socialism/Social

Democracy and Development was cited with slightly greater frequency in AOB (Figure 6-15). Finally, when describing the NPP ideology, the Rich/Elite were cited somewhat more in

Mfantsiman while the Working Class/Poor and Development were somewhat higher in AOB

(Figure 6-18).

221 6.5.2.4 District-by-district analysis

Next I consider the district-level analysis of respondents’ answers about Ghanaian political party ideologies. In the overall sample we had seen that 82.5% of respondents said that the political parties were Very Different, 13.3% had said they were Somewhat Different and 4.2% said there was No Difference. Now considering the district-level responses, respondents from

Mfantsiman (92.6%) and AOB (90.7%), the competitive districts, were more likely to cite the political party ideologies as Very Diﬀerent (Figure 6-12), while fewer respondents within Adaklu

Anyigbe (65.7%) said the party ideologies were Very Diﬀerent (Figure 6-11). Turning to the

No Diﬀerence response, Birim South respondents stand out for having the highest percentage of respondents (11.04%) reporting that there was No Diﬀerence in Ghana’s political party ideologies (Figure 6-10). On the other end of the scale, only between 1-2% of respondents in

Bosome Freho, Mfantsiman, and AOB said there was No Diﬀerence between the political party ideologies.

Analyzing what respondents say about the party ideologies provides an insight into the information respondents bring with them to vote. In terms of Hypothesis 2: Policy-Based or Economic-Based Voting, on average 72.1% of respondents in the entire sample cited the

Better Ghana Agenda as part of the NDC’s ideology. The proportion of respondents who cited the Better Ghana Agenda in Birim South (71.5%), Bosome Freho (68.1%), and Ketu South

(55.6%) were below average while the proportion of respondents citing the Better Ghana

Agenda in Mfantsiman (81.1%), AOB (78.3%), and Adaklu Anyigbe (75.5%) were above average (Figures 6-13 - 6-15). That Ketu South, one of the NDC strongholds, had the lowest proportion of respondents cite the Better Ghana Agenda again suggests that respondents in the ‘NDC World Bank’ are so committed to the party that either politicians do not emphasize policy platforms or that voters do not pay policy platforms as much heed. It is also interesting that respondents in the competitive districts had the highest proportion of respondents identify the Better Ghana Agenda, as well as way above average proportion of respondents who cited particular social policy projects with the NDC. Clearly the competitive electoral environment in

222 these districts contributes to enhanced campaign strategies to diﬀerentiate the parties from one another.

Further, the identiﬁcation of Socialism or the Working Class as a component of the NDC ideology can also be considered as support for Hypothesis 2. Here respondents from the NDC strongholds were more likely to cite Socialism (63.4%- Adaklu Anyigbe, 59.0%- Ketu South), though 42.0% of Bosome Freho respondents also cited it. Relatedly, 18.5% of Adaklu Anyigbe respondents identiﬁed the Working Class with the NDC’s ideology, while about 7% cited the working class in Ketu South, Mfantsiman and AOB. Only 0.5% and 3.2% of respondents in

Bosome Freho and Birim South (NPP strongholds) cited the working class as a component of the NDC ideology. There is also some evidence supporting Hypothesis 1: Identity-Based Voting in the identiﬁcation of the Ewes, Muslims, and/or Northerners with the NDC ideology. The districts with the highest proportion of respondents identifying these groups with the NDC ideology are

Adaklu Anyigbe (29.6%) and Birim South (22.8%). At 11.1% and 4.9% respectively, Bosome Freho and AOB respondents had the lowest percentage identifying these identity groups with the NDC ideology.

In the district-level responses identifying the NPP’s political ideology (Figures 6-16 -

6-18), the NPP’s Free Secondary High School (SHS) is the most commonly cited response in every district. While an average of 77.2% of respondents in the entire sample gave Free SHS as a NPP ideological component, about 90% of respondents in both Mfantsiman and AOB, 69 -

84% of respondents in Bosome Freho, Birim South, and Adaklu Anyigbe identiﬁed the NPP’s

Free SHS policy. The district with the lowest proportion of respondents identifying the Free

SHS policy was Ketu South at 49.8%.

Laissez-faire economics and elite interests are also historical components of the NPP’s ideology, or at least the ideologies of past parties which the NPP is a continuation.

Interestingly, a very large 53.4% and 45.% of respondents in Adaklu Anyigbe and Ketu South, the NDC strongholds, identiﬁed laissez-faire or free market economics as a component of the

223 NPP ideology. This is in comparison to about 14% of respondents in both Bosome Freho and Birim South, and less than 7% in both Mfantsiman and AOB. Recall that large proportions of the NDC strongholds had identified Socialism as a component of the NDC ideology. For whatever reason, it appears Adaklu Anyigbe and Ketu South residents are well-versed on the historical economic principles of both parties as compared to other districts. Finally, a greater proportion of Adaklu Anyigbe respondents (21.2%), as compared to every other district, identified elite interests as a component of the NPP ideology. In general, a higher proportion of respondents in each district identified the Working Class, as opposed to the Elite, as a central component of the NPP ideology.

Finally, a relatively high proportion of respondents in Adaklu Anyigbe (32.5%), Ketu South (16.5%), and Birim South (11.4%) identiﬁed Asantes and/or Akans as a central component of the NPP ideology. Outside of these three districts, very few respondents in the remaining three districts cited these identity groups as part of the NPP ideology.

6.6 Discussion

Thus far in the analysis, there is varying yet consistent qualitative evidence for all three hypotheses. Within the survey analysis conducted thus far, there is some support for

Hypothesis 2: Policy-Based or Economic-Based Voting when respondents explain their own and other community-members’ rationales for voting. Identity-Based Voting (Hypothesis 1) and Clientelistic-Based Voting (Hypothesis 3) are signiﬁcantly downplayed in respondents’ explanations. However, even though policies are the dominant explanation for votes, Particularistic/Ethnic explanations for votes were higher in Adaklu Anyigbe and Ketu South

(the NDC strongholds) than the other four districts. For all districts, Particularistic/Ethnic explanations were generally cited more when respondents’ explained their own votes for

President, except for Bosome Freho which cited this explanation more in votes for MP. Finally, it should be kept in mind that the opinions respondents share are unlikely to be sensitive or embarrassing, making it inherently diﬃcult to ﬁnd support for Identity-Based Voting or

Clientelistic-Based Voting in forthright survey questions.

224 In assessing respondents’ opinions about political party ideologies, there is a great deal of evidence that respondents view party ideologies in Ghana as very diﬀerent. The NDC’s

Better Ghana Agenda was the top response for the NDC’s ideology (though it was beat out by the Socialism response in Ketu South), while the NPP’s Free Secondary High School

Education policy was the most common response for NPP ideology. Ideological backgrounds of both parties, such as Socialism in the case of the NDC and Laissez-Faire/Market-Oriented

Economy in the case of the NPP, were also common responses. The ideological responses, whether put in terms of current policy agendas or background ideological principles, provide support for policy-based voting (Hypothesis 2). However, support for Hypothesis

1: Identity-Based Voting is still present as a respective 16.9% and 11.0% of respondents cited the Ewes/Muslims/Northerners as for the NDC and the Asantes/Akans as for the NPP. A higher proportion of respondents identify ethnic/religious/regional groups with party ideologies than do respondents that explain vote decisions on the basis of particularistic/ethnic rationales.

There is also a great deal of evidence that respondents from the NPP strongholds, NDC strongholds, and competitive districts explain their votes in diﬀerent ways and understand the

NDC and NPP party ideologies differently from one another. Further, there is some evidence that responses differ within the district pairs and that these differences may be linked to the variation in voting patterns between district pairs. Interestingly, Bosome Freho and Ketu

South appear to be the strongholds with the most uncriticized allegiance to the respective NPP and NDC parties, partially because of respondents’ lack of emphasis on policies and greater emphasis on party legacy and candidate approval. Yet of the NPP strongholds Bosome

Freho was the district that voted in an Independent candidate in 2008, while of the NDC strongholds Ketu South never considered a party other than the NDC. I interpret this as a strong commitment to the NPP in Bosome Freho that was not challenged when their NPP MP lost the NPP primary and switched to Independent status. In other words, NPP voters in

Bosome Freho did not switch their votes to an Independent candidate for ideological reasons, but rather felt they were still voting for the NPP. Ketu South, on the other hand, is more

225 committed to the NDC than it’s NDC stronghold counterpart, Adaklu Anyigbe, whose residents were willing to let their local tribal rivalries supersede their love for the NDC. The strong tribal rivalries that do exist in Ketu South become subsumed in politics leading up to the NDC primary, rather than translating to tribal support of an Independent candidate or another party.

Further, small discernible differences between responses from the Competitive Districts make it difficult to pinpoint how respondents from these districts differ. But one notable difference is that when describing the NPP ideology, respondents from Mfantsiman were more likely to associate the party with the Rich/Elite while respondents from AOB were more likely to associate the party with the Working Class/Poor. These perceptions likely explain some of attachment to the NPP in AOB as compared to Mfantsiman. Moving forward, the next chapter uses multinomial logistic regression models to predict respondents’ vote choices and logistic regressions to predict swing voters.

226 Table 6-1. Survey population stats vis-a-vis the 2010 Ghana Census District Data Avg. Female Ethnic No Primary Sec. Post-sec. Cell own Internet type age group formal rate rate rate use (SD.)* school B.F. Survey 39.5 52.68% Asante, 16.04% 26.73% 53.46% 3.77% 69.40% 7.89% (14.8) 76.90% B.F. Census 37.03 50.74% Asante, 30.85% 12.18% 53.53% 3.44% 36.91% 1.90% (16.15) 72.50% B.S. Survey 41.5 50.78% Akyem, 20.82% 23.66% 49.84% 0.63% 82.08% 11.64% (15.2) 55.38% B.S. Census 37.01 51.59% Akyem, 24.99% 14.16% 57.09% 3.76% 47.91% 6.02% (17.17) 62.85% A.A. Survey 40.6 49.55% Ewe, 11.75% 12.65% 65.96% 9.64% 79.03% 14.16% (16.3) 94.24% A.A. Census 40.13 51.40% Ewe, 25.80% 16.07% 53.73% 4.40% 39.20% 2.00%

227 (17.87) 88.54% K.S. Survey 42.6 42.63% Ewe, 13.74% 15.02% 61.98% 9.27% 89.71% 25.81% (15.8) 96.81% K.S. Census 40.13 52.94% Ewe, 33.62% 17.39% 44.92% 4.07% 53.52% 4.25% (17.87) 96.89% Mf.** Survey 37.6 53.16% Fante, 14.20% 33.40% 55.84% 7.57% 78.55% 14.70% (14.0) 93.08% Mf. Census 38.82 55.02% Fante, 33.11% 10.91% 50.51% 5.47% 49.91% 4.82% (17.27) 90.82% AOB Survey 40.3 51.13% Fante, 13.38% 24.20% 55.10% 7.32% 72.35% 7.72% (15.2) 67.31%*** AOB Census 38.82 51.83% Fante, 27.81% 13.81% 54.34% 4.04% 42.04% 2.09% (17.27) 86.02% *Census Average Age and Std. Dev. is calculated at the Regional level due to data availability. **The Mfantsiman survey was only distributed in Mfantsiman West, and not the entire Mfantsiman District. ***Many respondents in the AOB District identify as Breman, which GSS does not consider as distinct from Fante. If we add Bremans and Fantes survey respondents together, the population percentage increases to 84.6%. Source: 2010 Census, Ghana Statistical Services Table 6-2. Bosome Freho & Birim South structural characteristics District Tribe, % Eng. Agric. Rural Cell phone Internet literacy households ownership use Bosome Asante, 48.3% 81.6% 100% 36.9% 1.9% Freho 71.5% Birim Akyem, 61.5% 78.2% 52.5% 47.9% 6.0% South 61.6% National 62.0% 45.8% 49.1% 56.2% 8.4% Avg. Source: 2010 Census, Ghana Statistical Services

228 Table 6-3. Bosome Freho & Birim South presidential vote patterns 2012 Presidential 2008 Presidential 2004 Presidential Volatility District NDC NPP 3rd NDC NPP 3rd NDC NPP 3rd 229 Bosome 24.9% 73.4% 1.68% 19.3% 78.1% 2.58% 16.8% 82.1% 1.04% 3.75 Freho Birim 32.7% 66.0% 1.26% 31.6% 67.2% 1.25% 25.4% 73.5% 1.11% 0.80 South Source: Ghana Electoral Commission Table 6-4. Bosome Freho & Birim South parliamentary vote patterns 2012 Parliamentary 2008 Parliamentary 2004 Parliamentary Volatility District NDC NPP 3rd NDC NPP 3rd NDC NPP 3rd 230 Bosome 22.1% 57.8% 20.1% 11.2% 41.4% 47.3% 17.3% 81.9% 0.84% 24.6 Freho Birim 31.0% 59.8% 9.18% 35.7% 63.6% 0.76% 31.0% 67.4% 1.6% 4.38 South Source: Ghana Electoral Commission Table 6-5. Adaklu-Anyigbe & Ketu South structural characteristics District Tribe, % Eng. Agric. Rural Cell phone Internet literacy households ownership use Adaklu Ewe, 56.9% 76.7% 89.4% 39.2% 2.0% Anyigbe 83.3% Ketu Ewe, 57.1% 21.4% 53.4% 53.5% 4.3% South 90.8% National 62.0% 45.8% 49.1% 56.2% 8.4% Avgs. Source: 2010 Census, Ghana Statistical Services

231 Table 6-6. Adaklu-Anyigbe & Ketu South presidential vote patterns 2012 Presidential 2008 Presidential 2004 Presidential Volatility District NDC NPP 3rd NDC NPP 3rd NDC NPP 3rd 232 Adaklu 91.5% 6.80% 1.68% 91.1% 5.91% 2.99% 90.7% 7.53% 1.76% 0.98 Anyigbe Ketu 93.1% 5.88% 0.98% 93.8% 4.67% 1.56% 92.4% 6.70% 0.92% 1.08 South Source: Ghana Electoral Commission Table 6-7. Adaklu-Anyigbe & Ketu South parliamentary vote patterns 2012 Parliamentary 2008 Parliamentary 2004 Parliamentary Volatility District NDC NPP 3rd NDC NPP 3rd NDC NPP 3rd 233 Adaklu 82.7% 8.22% 9.10% 55.5% 3.11% 41.4% 45.1% 7.17% 47.8% 14.2 Anyigbe Ketu 88.9% 4.71% 6.37% 89.7% 5.65% 4.66% 68.4% 6.82% 24.8% 7.66 South Source: Ghana Electoral Commission Table 6-8. Mfantsiman* & Asikuma-Odoben-Brakwa structural characteristics District Tribe, % Eng. Agric. Rural Cell Phone Internet Literacy Households Ownership Use Mfantsiman Fante, 60.9% 37.9% 49.6% 49.9% 4.8% 88.6% AOB Fante, 62.7% 83.0% 51.9% 42.0% 2.1% 84.2% National 62.0% 45.8% 49.1% 56.2% 8.4% Avgs. Source: 2010 Census, Ghana Statistical Services *Note: Due to a clerical error, surveys were only collected in Mfanstiman West, whereas Mfanstiman District consists of both Mfantsiman West and Mfanstiman East/Ekumﬁ.

234 Table 6-9. Mfantsiman* & Asikuma-Odoben-Brakwa presidential vote patterns 2012 Presidential 2008 Presidential 2004 Presidential Volatility

235 District NDC NPP 3rd NDC NPP 3rd NDC NPP 3rd Mfantsiman54.7% 42.9% 2.41% 56.5% 40.6% 2.98% 43.9% 54.6% 1.55% 8.45 AOB 49.8% 48.6% 1.67% 47.7% 49.5% 2.73% 34.5% 63.9% 1.56% 5.49 Source: Ghana Electoral Commission *The electoral data presented here refers to Mfantsiman West, and not the entire Mfantsiman District. Table 6-10. Mfantsiman* & Asikuma-Odoben-Brakwa parliamentary vote patterns 2012 Parliamentary 2008 Parliamentary 2004 Parliamentary Volatility

236 District NDC NPP 3rd NDC NPP 3rd NDC NPP 3rd Mfantsiman51.0% 47.2% 1.35% 52.1% 45.6% 2.27% 41.4% 56.6% 2.0% 6.05 AOB 51.9% 45.8% 2.27% 47.8% 49.0% 3.22% 40.1% 58.3% 1.63% 8.61 Source: Ghana Electoral Commission *The electoral data presented here refers to Mfantsiman West, and not the entire Mfantsiman District. Table 6-11. Q9: Three biggest reasons for your vote for President Response Frequency Percent Percent of N category cases1 Economic Policy 1,285 28.86% 71.19% 1,805 Social Policy 1,385 31.11% 76.73% 1,805 Candidate/VP 993 22.30% 55.01% 1,805 Approval Political 601 13.50% 33.30% 1,805 Party/Legacy Particularistic/ 108 2.43% 5.98% 1,805 Ethnic Voting for 61 1.37% 3.38% 1,805 voting’s sake Clientelistic 11 0.25% 0.61% 1,805 Disapproval of 8 0.18% 0.44% 1,805 Current Gov. Total 4,452 100% 246.64% 1Respondents were allowed to give more than one answer for this question. Percent of Cases refers to the percentage of individuals in the sample who gave that response category.

237 Table 6-12. Q13: Three biggest reasons for your vote for MP Response Frequency Percent Percent of N Category Cases1 Economic Policy 1,113 36.22% 62.81% 1,772 Social Policy 1,205 39.21% 68.00% 1,772 Candidate 1,249 40.64% 70.49% 1,772 Approval Political 569 18.52% 32.11% 1,772 Party/Legacy Particularistic/ 143 4.65% 8.07% 1,772 Ethnic Voting for 35 1.14% 1.98% 1,772 voting’s sake Clientelistic 2 0.07% 0.11% 1,772 Disapproval of 6 0.20% 0.34% 1,772 Current Gov. Total 3,073 100% 243.91% 1Respondents were allowed to give more than one answer for this question. Percent of Cases refers to the percentage of individuals in the sample who gave that response category.

238 Table 6-13. Q10: Biggest reason driving presidential votes within this community? Response Frequency Percent percent of N category cases1 Economic Policy 482 26.00% 26.48% 1,820 Social Policy 758 40.88% 41.65% 1,820 Candidate/VP 367 19.80% 20.16% 1,820 Approval Political 193 10.41% 10.60% 1,820 Party/Legacy Particularistic/ 20 1.08% 1.10% 1,820 Ethnic Voting for 25 1.35% 1.37% 1,820 voting’s sake Clientelistic 6 0.32% 0.33% 1,820 Disapproval of 3 0.16% 0.16% 1,820 Current Gov. Total 1,854 100% 101.85% 1Respondents sometimes gave more than one answer for this question. Percent of Cases refers to the percentage of individuals in the sample who gave that response category.

239 Table 6-14. Q20: Do Ghana’s political parties have diﬀerent ideologies? Response category Frequency Percent

Very different 1,478 82.48% Somewhat different 239 13.34% No difference 75 4.19% Total 1,792 100%

240 Table 6-15. Q21: Components of the NDC’s political ideology Response category Frequency Percent Percent of cases1 N

Socialism/ social 585 19.95% 38.14% 1,534 democracy Working class/ poor/ 123 4.19% 8.02% 1,534 masses Rich/elite 39 1.32% 2.54% 1,534 Ewes/ Muslims/ 259 8.86% 16.88% 1,534

241 Northerners Better Ghana Agenda 1,106 37.71% 72.10% 1,534 Development 672 22.91% 43.81% 1,534 Non-policy negative 70 2.39% 4.56% 1,534 General party descriptor 18 0.61% 1.17% 1,534 Social policy/ particular 57 1.94% 3.72% 1,534 dev. projects Founding leaders/ 4 0.14% 0.26% 1,534 traditions Total 2,933 100% 191.20% 1Respondents were allowed to give more than one answer for this question. Percent of Cases refers to the percentage of individuals in the sample who gave that response category. Table 6-16. Q22: Components of the NPP’s political ideology Response category Frequency Percent Percent of cases1 N

Market-oriented/ 389 14.19% 23.58% 1,650 laissez-faire economy Working class/ poor/ 225 8.21% 13.64% 1,650 masses Rich/ elite 132 4.82% 8.00% 1,650 Asantes/ Akans 182 6.64% 11.03% 1,650

242 Free SHS education 1,274 46.48% 77.21% 1,650 Development 483 17.62% 29.27% 1,650 Non-policy negative 16 0.58% 0.97% 1,650 General party descriptor 10 0.36% 0.61% 1,650 Social policy/ particular 23 0.84% 1.39% 1,650 dev. projects Founding leaders/ 2 0.01% 0.12% 1,650 traditions Economic policy 5 0.18% 0.30% 1,650 Total 2,741 100% 166.12% 1Respondents were allowed to give more than one answer for this question. Percent of Cases refers to the percentage of individuals in the sample who gave that response category. Figure 6-1. Your vote for President- Bosome Freho and Birim South

243 Figure 6-2. Your vote for President- Adaklu Anyigbe and Ketu South

244 Figure 6-3. Your vote for President- Mfantsiman and Asikuma Odoben Brakwa

245 Figure 6-4. Your vote for MP- Bosome Freho and Birim South

246 Figure 6-5. Your vote for MP- Adaklu Anyigbe and Ketu South

247 Figure 6-6. Your vote for MP- Mfantsiman and Asikuma Odoben Brakwa

248 Figure 6-7. Pres. votes within the community- Bosome Freho and Birim South

249 Figure 6-8. Pres. votes within the community- Adaklu Anyigbe and Ketu South

250 Figure 6-9. Pres. votes within the community- Mfantsiman and Asikuma Odoben Brakwa

251 Figure 6-10. Do parties have diﬀerent ideologies- Bosome Freho and Birim South

252 Figure 6-11. Do parties have diﬀerent ideologies- Adaklu Anyigbe and Ketu South

253 Figure 6-12. Do parties have diﬀerent ideologies- Mfantsiman and Asikuma Odoben Brakwa

254 Figure 6-13. NDC ideology- Bosome Freho and Birim South

255 Figure 6-14. NDC ideology- Adaklu Anyigbe and Ketu South

256 Figure 6-15. NDC ideology- Mfantsiman and Asikuma Odoben Brakwa

257 Figure 6-16. NPP ideology- Bosome Freho and Birim South

258 Figure 6-17. NPP ideology- Adaklu Anyigbe and Ketu South

259 Figure 6-18. NPP ideology- Mfantsiman and Asikuma Odoben Brakwa

260 CHAPTER 7 PREDICTING RESPONDENTS’ VOTES AND SWING-VOTING

In this chapter I use respondents’ self-report voting history to predict individual-level votes and swing voting across the 2004, 2008 and 2012 Presidential and Parliamentary elections. My overall argument is that the level of political competition at Ghana’s sub-national level is increasing due to the presence of centrally-appointed DCEs and locally-elected MPs of different political parties. When these officials are of different political parties, which occurs anytime the President, and by extension the centrally-appointed DCEs, are of a different political party than the locally-elected MP, the DCE and MP compete for constituency support. When these officials are of the same political party, that level of competition is diminished.

Generally speaking, local-level competition is higher in Ghana’s Fourth Republic, and particularly since the end of Rawling’s presidency in 2000, than in past regimes. Five of the six districts in the survey have experienced an Unfriendly DCE-MP pair.1 As political competition increases locally, voters have the opportunity to use contextual candidate and party evaluations, as opposed to relying on party tradition and ethnic backgrounds, when casting their votes. Further, when faced with real experiential information about two viable candidates, increased political competition should also theoretically lessen the extent to which clientelistic-inducements are eﬀective at persuading citizens for their votes. The models I present systematically test for Identity-Based (Hypothesis 1), Policy or

Economic-Based (Hypothesis 2), and Clientelistic-Based (Hypothesis 3) voting rationales.

To test for individual votes I use multinomial logistic regressions, where votes for the NPP

1 It is only in Mfantsiman, a competitive district which elected a NPP MP in 2000 and 2004 and a NDC MP in 2008 and 2012 which has not experienced a competitive DCE-MP pair since the 2000 elections. However, unlike the NPP and NDC strongholds in the sample, local-level competition is likely to be inherently high given the close elections in the constituency and the election of MPs of diﬀerent political parties.

261 (reference category), NDC, and Third party are predicted.2 To analyze swing voting, I ﬁrst provide descriptive analysis of the demographic characteristics linked to swing voting.

I then use Logit Models to predict swing voters as compared to stable voters. Overall, the analysis presented in this chapter provides support for Hypothesis 1 and Hypothesis 2, but not

Hypothesis 3. The general ﬁndings for individual vote predictions are that respondent ratings of the current and past government’s handling of the economy and success at bringing development to the area are strong predictors of respondent votes. Additionally, particular tribes, namely

Asante, Akyem, Other Akan, and Ewe, were consistent predictors of respondent votes, providing support for Hypothesis 1: Identity-Based Voting. However, when comparing these variables against one another in terms of changes in predicted probabilities of having voted for a particular political party, economic and developmental evaluations generally had a larger effect on vote choice than did tribal or ethno-linguistic group membership. Further, the effect of tribal group membership on predicted probabilities of voting for the NPP versus the NDC was significantly diminished for Parliamentary races as compared to Presidential races. The effect of economic or developmental evaluations on changes in predicted probabilities of voting for particular political parties was consistent over time.

Of the tribes which did have a large effect on predicted probabilities of having voted for the NPP versus the NPP were limited to the Asante and Akyem tribes, while the effect of Fante, Other Akan, Ewes, and Mole Dagbani group membership were much smaller. This might suggest that the effects of political competition differ across ethnic groups. Finally, respondents who identified the NDC as the political party most known for giving out more gifts (i.e. a test for Hypothesis 3: Clientelistic-Based Voting) were significantly associated with

2 Third party votes have become very scarce in Ghana’s two-party dominant system. Throughout the analysis I focus on the likelihood of having voted for the NDC, as compared to the NPP reference category, rather than the likelihood of having voted for a Third party.

262 increased NPP over NDC votes across elections. This suggests that respondents understand that giving out gifts has negative implications for political parties, thus refuting evidence for

Clientelistic-Based Voting.

The general results for the swing voting analysis shows that overall swing voting occurred least among respondents in the NDC strongholds. Election-to-election swing voting was overall more common than skirt and blouse swing voting (i.e. votes for diﬀerent parties in the

Presidential and Parliamentary elections within the same year). Finally, in the logit models predicting swing voters, there is some small evidence that identity and clientelistic-inducements impact swing voting (Hypotheses 1 and 3), and more evidence that Policy and Economic considerations impact swing voters (Hypothesis 2). 7.1 Predicting Votes

For each election, in Model 1 I test for standard demographic indicators including age, gender, religion, and three development/class indicators: Internet use3 or cell phone ownership

(cell own)4 , whether water is available inside the home compound (water inside)5 , and whether farming is a respondents’ primary occupation. I also add dummy variables for the 6 districts in which the surveys were collected. Asikuma Odoben Brakwa is used as the reference category because, as a competitive district, it was less skewed to one party as were the party strongholds. Asikuma Odoben Brakwa is slightly more NPP-leaning than its competitive pair counterpart, Mfantsiman. Note that because districts were selected with an eye to their ethnic

3 For this question, respondents were asked if they ever used the Internet. This variable is coded dichotomously where 1 refers to Yes and 0 refers to No. 4 In this indicator, 1 refers to respondents who use a mobile phone that they own. 0 refers to respondents who either do not use a mobile phone or use a mobile phone owned by someone else. Internet use and Cell Phone Ownership were never included within the same model because they are too highly correlated.

5 Coded dichotomously, 1 refers to respondents whose main source of water is inside the compound, either from a polytank or pipes, while 0 refers to those whose main source of water is found outside the home compound.

263 population makeup, the district dummy variables are too correlated with ethnic groups to keep both within the same model.

In the second model (Model 2) of each election analysis, I add ethnic group information to test for Hypothesis 1: Identity-Based Voting. In particular, I break the Akan ethno-linguistic group into ‘Asante’, ‘Fante’, ‘Akyem’, and ‘Other Akan’ tribes, so that I can control for dominant tribal groups in the Bosome Freho (Asante), Birim South (Akyem), Mfantsiman

(Fante), and AOB (Fante) districts. I also include Ewe, the dominant ethno-linguistic group in

Adaklu Anyigbe and Ketu South, Guan, Mole Dagbani, and Others (i.e. Gruma, Grusi, Mande, and Other Tribes (as captured by the 2010 Ghana Census)). The reference category used is

Ga-Dangme, a group that sometimes leans toward the NDC in its voting patterns, but is not known for overwhelmingly supporting one party or another.

In Model 3 I added two political behavior controls: whether respondents vote for the same party all the time (1) or sometimes/always change their votes (0) (vote stays the same) and whether or not respondents believe Ghana’s political parties have diﬀerent ideologies (diﬀ.ideo). In the 2012 models, I also added a political activism measure in whether or not the respondent watched either of the two Presidential Debates prior to the 2012 election (debate6 ).7

Finally, in Model 4 I add economic perceptions about the present and past regimes’ handling of the economy (2000 NPP Econ.; 2008 NDC Econ.; 2012 NDC Econ.) and evaluations of how likely the 2012 NDC government would bring development (2012 NDC

6 It is important to note that the phrasing of the question was, “Did you watch either of the Presidential debates just before the 2012 election?”. It came to the attention of the survey team that respondents may have listened to the debates on the radio, rather than watched them on TV. Surveyors were thus instructed to count the question as Yes if the respondent listened to one of the debates. Overall, about 43.0% of the entire sample said they watched or listened to at least one of the two Presidential debates.

7 A variable that was excluded from the analysis was whether or not an individual was a member of the NDC or NPP. This variable was excluded because the correlations ran too high (i.e. >0.75).

264 Dev.) or how successful past regimes have been at bringing development to the respondents’ area (2000 NPP Dev.; 2008 NDC Dev.). These sets of questions are the closest to estimating respondent perceptions about the successes or failures of past governments

In addition to the evaluative questions, Model 4 also tests for the impact of family influences on voting (family votes the same), and whether one party has a bigger reputation for giving out gifts (NDCgifts, coded as 1 if the respondent identified the NDC and 0 if another party or no party was identified).

7.1.1 2004 Presidential and Parliamentary Elections

Results from the analysis of respondent votes in the 2004 Presidential (Table 7-1) and

Parliamentary (Table 7-2) elections provide support for Hypothesis 1: Identity-Based voting, with significant ethnic and family vote predictors. Similarly, evaluations of regimes’ economic and developmental success were also significantly related to respondents’ votes. Finally, responses for a question asking about the political party with the biggest reputation for giving out gifts finds that respondents report the political parties which they do not vote for. Not only does this fail to provide evidence for Hypothesis 3: Clientelistic-Based Voting, but it suggests that clientelistic-inducements may even harm a party’s chances on Election Day.

The ﬁrst two models for both the Presidential and Parliamentary sets of analysis test for demographic control variables, district controls, and ethnic identities. In both tables, cell phone ownership is the only signiﬁcant predictor of votes across both models, with ownership decreasing the relative risk ratio of having voted for the NDC over the NPP by a factor of .762 (.677) in the 2004 Presidential (Parliamentary) election.8 In both of the 2004 races (Tables

7-1 and 7-2) the district variables in Model 1 show that respondents from the NPP strongholds

(NDC strongholds) were less likely to (more likely to) vote for the NDC over the NPP, in comparison to the Asikuma Odoben Brakwa reference category. Similarly, the competitive yet

8 Relative risk ratios are calculated by exponentiating the multinomial logit coeﬃcients (ecoef ).

265 NDC-leaning Mfantsiman District respondents were more likely to vote for the NDC over the NPP as compared to the competitive yet NPP-leaning Mfantsiman District reference category.

In Model 2, I evaluate ethnic predictors of vote choice. In the 2004 Presidential election, the relative risk ratio of having voted for the NDC over the NPP would be expected to decrease by a factor of 0.059, 0.142, and 0.165 for members of the Ashanti, Akyem, and Other Akan tribes, and increase by a factor of 7.39 for members of the Ewe ethno-linguistic group, as compared to the Ga-Dangbe reference category. In the 2004 Parliamentary election, the relative risk ratio decreased by a factor of 0.057, 0.144, and 0.196 for members of the Ashanti, Akyem, and Other Akan tribes and increase by a factor of 7.10 for members of the Ewe ethno-linguistic group, again as compared to Ga-Dangbe respondents. The risk ratios are very similar in both races.

In Model 3, I add variables for whether respondents say they vote for the same political party always and whether respondents believe the political party ideologies in Ghana are diﬀerent. In both races, Models 3 and 4 show that respondents who say they vote for the same political party over time are signiﬁcantly more likely to vote for the NDC over the NPP.

In Model 3, two ethnic variables in the Presidential and Parliamentary analyses became significantly related to respondent vote choice. First, Fante tribe members were significantly less likely to vote for the NDC over the NPP in 2004, in comparison to the Ga-Dangbe reference category, though the coefficient was much smaller than the other Akan tribes. The effect of this variable dropped out after controlling for additional variables in Model 4.

Members of the Mole Dagbani ethno-linguistic group were also more likely to vote for the NDC over the NPP, in comparison to the reference category.

Finally, to evaluate the diﬀerent hypotheses against one another, in Model 4 I test the ethnic variables (Hypothesis 1) against economic and developmental performance evaluations (Hypothesis 2) and whether any political party had a reputation for giving out more gifts

(Hypothesis 3). Except in the case of Fantes, the ethnic variables which were significant in Model 3 were also significant in Model 4, but the strength of the coefficients generally

266 lessened. In addition to the ethnic variables I include a family vote variable for whether members of a respondent’s family vote the same as the respondent. Table 7-1 Model 4 shows that if a respondent’s family does vote the same as there respondent, the relative risk ratio of having voted for the NDC over the NPP would be expected to decrease by a factor of 0.631.

This provides some evidence that politics is aﬀected by familial ties, though this variable is not signiﬁcant in the 2004 Parliamentary vote analysis.

Second, when asked to rate the past 2000-2008 NPP government’s handling of the economy, a one-unit increase towards a more positive evaluation decreased the relative risk ratio of having voted for the NDC over the NPP by a factor of 0.292 in the Presidential election and 0.304 in the Parliamentary election. Similarly, a one-unit increase in the positive evaluation of how successful the 2000-2008 NPP government was at bringing development to the respondent’s area translated to a decreased relative risk ratio of voting for the NDC over the NPP in 2004 by a factor of 0.497 (Presidential) and 0.487 (Parliamentary), a less significant effect as compared to the economic evaluation. Third, when asked if any political party had a reputation for giving out more gifts, if respondents selected the NDC, they were significantly less likely to have voted for the NDC over the NPP in the 2004 Presidential elections. In other words, when respondents identified a political party as giving out more gifts this was more an accusation of corruption than a positive evaluation of the political party identified. Finally, Figures 7-1 and 7-2 displays the change in predicted probabilities for having voted for the NPP (0), NDC (1) or a third party (3) in the 2004 Presidential and Parliamentary elections using the data from Model 4 in Tables 7-1 and 7-2. The variables being compared are the significant tribal/ethnic variables and the economic and developmental regime evaluations.

In -1, for instance, moving from a non-Asante respondent to an Asante respondent changed the predicted probability of having voted for the NPP in the 2004 Presidential election by about 0.40. Similarly, moving from a ‘Very Badly’ evaluation of the 2000-2008 NPP government’s handling of the economy to a ‘Very Well’ evaluation, or 0 to 3, changed the

267 predicted probability that a respondent voted for the NPP in the 2004 Presidential race by about 0.65. In general, as pertains to the 2004 Presidential election analysis, the distance between the NPP and the NDC was is greatest for the economic evaluation. There is a similar predicted probability distance separating the NPP from the NDC for the developmental evaluation variable and the Asante, Akyem, and Mole Dagbani tribal variables. The distance between the NPP and NDC is smallest for Ewe and other Akan respondents. This overall suggests that at least the economic evaluation had a greater impact on determining respondent votes than did ethnicity, while the developmental evaluation was about even with three diﬀerent tribes.

Turning to the change in predicted probabilities for the 2004 Parliamentary election (Figure 7-2), however, shows that a respondents’ economic evaluation and developmental evaluation had a larger impact on votes than did any of the tribal/ethno-linguistic groups.

Only the Mole Dagbani variable comes close to the same distance separating the NPP from the NDC as the developmental evaluation. Moving from a ‘Very Badly’ evaluation of the 2000-2008 NPP regime’s handling of the economy to a ‘Very Well’ evaluation increased the probability of a respondent having voted for the NPP by about 0.63 and decreased the probability of a respondent having voted for the NDC by about -0.66.

Overall, Hypothesis 2 is supported in the multinomial regression models and the predicted probabilities ﬁgures show that developmental and particularly economic evaluations of a regime are a greater determinant of vote choice than ethnicity. Still, tribal and ethno-linguistic variables have relationship to vote decisions (Hypothesis 1). Finally, Hypothesis 3: Clientelistic-Based

Voting is challenged in that respondents were signiﬁcantly more likely to identify the political parties which they did not vote for as opposed to the parties they favored.

7.1.2 2008 Presidential and Parliamentary Elections

The district variables in Tables 7-3 and 7-4, Model 1 are similar to those predicting NDC over NPP votes in the 2004 Presidential and Parliamentary elections (Tables 7-1 and 7-2,

Model 1), except the sizes of the coeﬃcients when predicting NDC over NPP votes in the

268 2008 Presidential election were generally smaller. The only exception to this is the decreased likelihood that respondents in Birim South voted for the NDC over the NPP in the 2008 elections as compared to the 2004 elections. Similarly, testing for ethnic variables in Tables 7-3 and 7-4, Model 2 provides similar results as compared to the 2004 Presidential election (Tables

7-2, Model 2), but again the coeﬃcients are smaller. In Models 3 and 4, for the 2008 Presidential election (Table 7-3) cell-phone owners were less likely and individuals who vote the same were more likely to vote for the NDC over the

NPP. These two variables are also signiﬁcant for the 2008 Parliamentary race (Table 7-4), except now older respondents, those who practice Islam, and those who believe the political party ideologies in Ghana are very diﬀerent are also more likely to have voted for the NDC over the NPP.

Finally, Model 4 tests for Hypotheses 1, 2, and 3 against one another. First, the ethnic variables in Tables 7-3 and 7-4, Model 1 are similar to those in the 2004 Presidential and

Parliamentary analyses, except now the strength of the coeﬃcients is generally increased. Now, the relative risk ratio of voting for the NDC over the NPP in 2008 decreased by a factor of

0.047, 0.326, 0.103, and 0.112 (0.045, 0.295, 0.100, and 0.095) for members of the respective

Ashanti, Fante, Akyem, and Other Akan tribes, decreased by a factor of 0.177 (0.144) for members of the Mole Dagbani ethno-linguistic group, and increased by a factor of 3.22 (3.25) for members of the Ewe ethno-linguistic groups, in comparison to the reference category, for the 2008 Presidential (Parliamentary) election. For both elections, in comparison to 2004 the size of the ethnic coeﬃcients increased in 2008 for Ashantis, Fantes, Akyems, and Other

Akans, but decreased for Ewes and Mole Dagbanis. This provides evidence that identity-based voting is still in eﬀect in 2008, and possibly that tribal identities are becoming more relevant.

Now in Model 4, family voting no longer signiﬁcantly aﬀects the 2008 vote choices. A one-unit increase in the rating of the 2008 NDC government’s handling of the economy and success at bringing development to the area increased the relative risk ratio of having voted for the NDC over the NPP by a factor of 2.01 and 2.39 in the Presidential elections and 2.23

269 and 2.27 in the Parliamentary elections, respectively. Finally, identifying the NDC as the party with the biggest reputation for giving out gifts decreased the relative risk ratio of having voted for the NDC over the NPP by a factor of 0.458 and 0.477 in the respective Presidential and

Parliamentary races.

Finally, Figures 7-3 and 7-4 show the change in predicted probabilities for having voted for a political party in the 2008 Presidential and Parliamentary elections. Now, in the 2008

Presidential election analysis, the developmental evaluation of the 2008 NDC regime has a larger impact on the change in predicted probability of having voted for the NPP vs. the NDC than does the economic evaluation. Further, one tribe, the Asantes, has just as big an impact on the predicted probability of having voted for the NPP vs. the NDC as the developmental evaluation. Akyems were the tribe with the second largest impact, and about as large an impact as the economic evaluation, while the eﬀect of Fantes, Other Akans, and Ewes were smaller than the economic and developmental evaluations.

For the 2008 Parliamentary election analysis (Figure 7-4), the eﬀect of tribal and ethno-linguistic groups is smaller than both the economic and developmental evaluations.

The impact of being Akyem still has a large eﬀect on the predicted probability of voting for the NPP (about 0.50) and the NDC (about -0.46). The distances separating the predicted probabilities of voting for NPP from the NDC is much smaller for members of the Asante,

Fante, Other Akan and Ewe groups as compared to the economic and developmental evaluations.

In the 2008 voting models, increased support for Hypothesis 1: Identity-Based Voting comes in the way of increased sizes of the coeﬃcients for the pro-NPP Akan tribal groups and decreased sizes of the coeﬃcients for the Ewe and Mole Dagbani ethno-linguistic groups. Support for evaluation-based impacts on voting, per Hypothesis 2: Policy-Based and Economic-Based Voting, again has support in the 2008 Presidential and Parliamentary vote models, except now development-evaluations has a larger impact on votes than economic-evaluations. However, when we look at the change in predicted probabilities for

270 these variable simultaneously, we see that the economic, and particularly the developmental, evaluations of the 2000-2008 NPP government had just as big an impact, and often a larger impact, than did tribal/ethno-linguistic group membership. Finally, identifying the NDC as the party with the reputation for giving out the most gifts again decreases the relative risk ratio of having voted for the NDC over the NPP in 2008, further diminishing support for Hypothesis 3. 7.1.3 2012 Presidential and Parliamentary Elections

Finally, in Model 1 of Tables 7-5 (2012 Presidential) and 7-6 (2012 Parliamentary), the district-level variables were in the same direction as prior models. In both Models 1 and 2 of the 2012 Presidential election voting analysis (Table 7-5), cell-phone owners are again less likely to have voted for the NDC over the NPP, while internet users in Models 1 and 2 of the Parliamentary election (Table 7-6) also decreased the likelihood of a voting for the NDC over the NPP. Models 3 and 4 of both the 2012 Presidential and Parliamentary elections also found that internet users were signiﬁcantly less likely to vote for the NDC over the NPP in the 2012

Presidential elections.9 Another change is that Model 4 in Table 7-5 (Table 7-6) now shows that a unit increase in whether a respondent believes Ghana’s political parties to have diﬀerent ideologies increases the relative risk ratio of having voted for the NDC over the NPP by a factor of 1.65 (1.99).

The ethnic variables in the 2012 Presidential and Parliamentary Models 2-4 are again significant in the expected directions. However, now in Model 4 the size of the tribal coefficients decreased from the 2008 Presidential and Parliamentary coefficient sizes, while the Ewe ethno-linguistic variable coefficient increased in both of the 2012 models as compared to 2008. The Mole Dagbani ethno-linguistic variable is now an insignificant predictor of votes. Overall, the relative risk ratio of having voted for the NDC over the NPP in the 2012

Presidential (Parliamentary) election decreased by a factor of 0.124, 0.214, and 0.336 (0.

9 The variable ‘Internet Users’ was not signiﬁcant in past election models, so ‘Cell Phone Ownership’ was instead used.

271 145, 0.183, and 0.326) for Ashantis, Akyems and Other Akans and increased by a factor of 4.53 (4.81) for Ewes, as compared to the Ga-Dangbe reference category. In both of the 2012 elections (Tables 7-5 and 7-6), family-voting now longer signiﬁcantly predicted respondents’ vote choices.

A unit increase toward more positive evaluations of the 2012 NDC government’s handling of the economy and success at bringing development to the respondent’s area increased the relative risk ratio of having voted for the NDC over the NPP in the 2012 Presidential race by a factor of 2.66 and 2.10, and in the 2012 Parliamentary race by a factor of 2.77 and 2.14, respectively. Finally, naming the NDC as the political party with the biggest reputation for giving out more gifts decreased the relative risk ratio of having voted for the NDC over the NPP by a factor of 0.445 in the Presidential race and by a factor of 0.436 in the Parliamentary race.

Finally, Figures 7-5 and 7-6 use Model 4 from Tables 7-5 and 7-6 to analyze the change in predicted probability for having voted for a political party in the 2012 Presidential and Parliamentary elections. In both the Presidential and Parliamentary election analysis, economic and developmental evaluations had a bigger impact on the change in predicted probability for having voted for the NPP vs. the NDC than did any tribal or ethno-linguistic group. The tribe with the largest eﬀect in the 2012 Presidential election was the Asantes, while there was no tribe or ethno-linguisitc group even close to the degree of predicted probability change in voting for the NPP vs. the NDC as the economic and developmental evaluators.

Again, Hypotheses 1 and 2 are supported in the 2012 Presidential voting analyses, but Figures 7-5 and 7-6 suggest that the economic and developmental evaluations of party performance actually had greater inﬂuence on voting for the NPP versus the NDC as compared to the tribal and ethno-linguistic groups. Finally, evidence is again lacking for Hypothesis 3.

272 7.2 Who Are The Swing Voters?

About 16% of the sample10 voted for two or more diﬀerent parties some time during the

1996-2012 Fourth Republic elections. Swing voting is defined as votes for different parties in the Presidential or Parliamentary races in different years (i.e. election-to-election swing voting) as well as votes for different parties in the Presidential and Parliamentary elections within the same year (i.e. skirt and blouse swing voting). In this section I analyze identity trends for these self-admitted swing voters (Hypothesis 1), as well as whether policy or economic performance

(Hypothesis 2) or Clientilestic inducements (Hypothesis 3) impacted a respondents’ decision to swing their vote.

7.2.1 Demographic Trends

First, as shown in Table 7-7, the district sample with the greatest proportion of swing voters is Mfantsiman with 21.6%. This makes logical sense in that Mfantsiman is a competitive electoral constituency. However, AOB is also a competitive electoral constituency yet only

16.8% of respondents from AOB reported swing voting behavior. Indeed this 16.8% is behind the 18% and 17.8% portion of the Bosome Freho and Birim South respective respondents who engaged in swing voting behavior. The respondents least likely to report swing voting behavior were the NDC strongholds, Adaklu Anyigbe with 11.5%, and Ketu South with 9.9%.

Also in Table 7-7, though the vast majority of swing voters are not political party members (89.6%) but a surprising 10.4% of the self-declared party members did vote for

2 diﬀerent political parties at some point in their personal voting histories. Further, voters who only vote for one party do not necessarily consider themselves party members; 61.4% of non-party members have only voted for 1 political party in their voting history. The largest

10 Respondents were excluded from the swing voter analysis if they had only voted in one election or if their voting data was missing. 221 respondents were excluded, leaving 1,711 out of 1,932 respondents for the analysis. 273 of 1,712 (15.96%) reported swing voting.

273 percentage of swing voters lies in the non-political party member sample, with 38.6% of non-party members also being swing voters.

There are no signiﬁcant diﬀerences between gender (Table 7-7) and education (Table 7-8) groups and swing-voting. And though we might expect swing voters to be more critical of the overall functioning of democracy in Ghana, swing voters appear only slightly less likely to rate Ghana a Full Democracy or a Democracy with Minor Problems and slightly more likely to rate

Ghana a Democracy with Major Problems. These diﬀerences are signiﬁcant at the p<0.1 level.

In Table 7-8, while 68.5% of swing voters said their vote Sometimes Changed or

Diﬀered Every Election, a surprising 31.5% of swing voters said that they Vote for One Party.

Unsurprisingly, 93.9% of stable voters said they only Vote for One Party, while an interesting 6.1% of stable voters reported that their vote Sometimes Changed or Diﬀered Every Election.

Swing Voters from our sample also appear to be less trusting of others. When asked “How

Much Do You Trust Your Relatives?”, 37.3% of Swing Voters selected ‘Not at All’ or ‘Just a Little’, as compared to 25.8% of stable voters. Similarly, 62.6% of Swing Voters selected ‘Somewhat’ or ‘A Lot’ as compared to 74.3% of stable voters (p<0.00, Table 7-9). Swing

Voters were also somewhat less likely to say they trust their neighbors (p<0.1, (Table 7-10).

Finally, when respondents were asked if they trusted people who do not speak their same local dialect, swing voters were both more likely to respond ‘Not at all’ and ‘A Lot’ as compared to stable voters. Finally I consider the timing of swing votes in the sample: skirt and blouse voting as compared to election-to-election swing voting. Overall, a greater percentage of respondents engaged in election-to-election swing voting than skirt and blouse voting. First, Table 7-11 shows the number of skirt and blouse swing voters in our sample by district and by year.

For three districts, Bosome Freho, Mfantsiman, and Asikuma-Odoben-Brakwa, the highest proportion of respondents who engaged in skirt-and-blouse swing voting was in 2012. For two other districts, Adaklu Anyigbe and Ketu South, the highest proportion of skirt-and-blouse swing voters was in 2004. Finally, for Birim South, the highest proportion of skirt-and-blouse

274 swing voting was in 1996. The average rate of skirt-and-blouse swing voting across the districts was a high of 3.67% in 2012 and a low of 2.41% in 2008. Overall, the district with the highest average proportion of respondents was AOB at 4.65% while the lowest was Ketu South at

1.65%.

Looking at election-to-election swing voters in Tables 7-12 and 7-13, the 2008 Presidential and Parliamentary elections saw the highest average proportion of swing voters in the sample, with 7.27% and 7.14% respective averages. The district with the highest average proportion of election-to-election swing voters over time was Mfantsiman (8.83% Presidential, 8.58%

Parliamentary), with Birim South (8.07% Presidential, 8.27% Parliamentary) at a close second.

Ketu South was again the district with the lowest proportion of election-to-election swing voters (3.81% Presidential, 3.46% Parliamentary).

7.2.2 Logit Models Predicting Swing Voters

Using logit models to predict binary-coded Swing Voters, I test for Identity-Based

(Hypothesis 1), Policy and Economic-Based (Hypothesis 2), and Clientelistic-Based (Hypothesis

3) contributions to vote decisions.11

11 In the course of this analysis you will notice that the original sample of swing voters/stable voters has 1,712 observations, yet the full logit models’ observation numbers fall to a low of 1,200. With about 500 observations missing, I was somewhat concerned about missing observation bias. I generated binary variables for whether or not a response was missing for the swing voter dependent variable as well as three independent variables with the most missing variables (Table D-1 in Appendix D)(Note that the district reference variable for each model was selected based on which district had a median number of missing observations for the outcome in question.). Beginning with testing for missing-ness within swing voter variable (Model 1), age is negatively correlated with missing swing voter values. In other words, younger respondents had a greater likelihood of missing for the swing voter variable. But this makes sense considering respondents were excluded from the swing voter analysis if they did not have a chance to swing vote and this especially impacted first-time (i.e. younger) voters. Next, respondents whose main source of water was found inside the home as opposed to outside the home (water inside) was also positively correlated with missing swing voter data. This finding is perplexing as it suggests that those who are more well-off or live in urban areas are more likely to be excluded from the swing voter analysis, either because the did not respond or only voted in one election in the Fourth Republic. Finally, respondents in Adaklu Anyigbe

275 First, Tables 7-14 and 7-15 show the Odds Ratios for the Logit Models predicting Swing Voters. In Table 7-14, the models test for demographic controls and respondents’ districts in

Model 1, demographic controls and ethnicity in Model 2, and demographic controls, ethnicity, regime performance indicators, and clientelistic voting indicators in Model 3. In Table 7-15,

Models 4-6 test for demographic controls, ethnicity, reasons for a respondent’s vote for President (Model 4), for MP (Model 5), and for the community’s vote for President (Model

6), as well as clientelistic voting indicators. Overall, the analysis points to developmental and economic evaluations of past regimes, as well as whether or not the NDC was identiﬁed as the

are more likely to be missing for swing voter and the effect is large. Overall 55 of 220 (25.0%) respondents with missing swing voter data were from Adaklu Anyigbe. Turning to the the party member variable analysis (Model 2) older respondents are less likely to be missing from the party member variable. Those who attended a political party rally are more likely to be missing from party member. Third, in Model3, those who attended a political party rally are also significantly less likely to be missing from Question 24 which asked if members of the respondents’ family voted for one political party or altered their votes across elections (family votes the same). This might mean that respondents with strong political party inclinations also come from families with strong party ties, making the respondent more open about talking about their families’ votes. ‘Water inside’ is again positively associated with missing for Question 24/’family votes the same’. Finally, respondents from Birim South are less likely to be missing while respondents from Ketu South are more likely to be missing for this question. Lastly, in Model 4, respondents who attended a political party rally are less likely to be missing for Question 35: How likely is it that the current 2012 NDC government will bring development to this area? (2012 NDC Dev.). A district bias also exists for this question in that those from Bosome Freho are more likely to be missing while those from Adaklu are less likely to be missing. Perhaps this can be taken to mean that NPP voters in Bosome Freho did not want to admit that the NDC government might develop their area while respondents in Adaklu Anyigbe, an NDC stronghold, had no problem answering the question. Overall, the swing voter models likely suffer some bias for greater inclusion of politically active respondents, though those who attended a political rally are more likely to be missing from the party member question. Still, attending a political party rally had no significant effect on respondents’ missing from the swing voter data. Similarly it appears that those with access to water from within their homes are more likely to be missing from the swing voter analysis. This is a surprising result, but perhaps those with greater water access and personal wealth are more likely to withhold information about their or their families voting patterns (Models 1 and 3). Finally, respondents from the NDC strongholds are more likely to be missing in the swing voter logit models because of their greater missing-ness in the swing voter data and family votes data (Models 1 and 3).

276 party with the biggest reputation for gifts, as the biggest determinants of swing voting outside of demographic controls.

Beginning with Table 7-14, respondents who report that they vote for one party, party members, and respondents who report that they vote the same as their family are each significantly decrease the odds that a respondent is a swing voter. Further, respondents who don’t trust their neighbors or who believe that their vote is not secret (e.g. ‘big’ men and women can find out how they voted) also significantly decrease the odds that the respondent is a swing voter. Finally, using Ketu South (e.g. the district with the lowest amount of swing voting) as the reference category, it was only respondents from the NPP strongholds, Bosome

Freho and Birim South, that were signiﬁcantly more likely to be swing voters. And while Bosome Freho was selected for having voted in an Independent MP in 2008, its respondents were less likely to be swing voters as compared to those in Birim South. This could be because those who voted for Kuragu as an Independent MP in 2008 in Bosome Freho felt they were voting for the NPP or because, though Birim South is a NPP stronghold, a sizeable population sometimes votes for the NDC.

Switching out district variables for ethnic variables in Model 212 shows that, in comparison to the Ga-Dangbe reference category13 , only Guan ethno-linguistic group

12 It is important to note that ethnicity is too highly correlated with district to include both in the same model. That ethnicity and district are highly correlated is actually somewhat by design.Recall that district pairs were chosen such that they were similar in demographic characteristics, including ethnic population make-up, but one of the districts had an unusual voting history. The Bosome Freho-Birim South pairing particularly matters for this case because this was the only district pair in which the dominant tribe was not the same (Asantes dominated in Bosome Freho while Akyems dominated in Birim South). It then makes sense that Asantes should be more likely to be swing voters as compared to Akyems. However, outside of the Bosome Freho-Birim South pair, the same ethno-linguistic group (Ewes, Fantes) makes up the majority of the district populations, such that each district’s ethnic swing voters cancel out or do not make that ethnic or tribal group more likely to swing vote in general.

13 Though they may slightly favor the NDC, this reference category was chosen because Ga-Dangbe’s voting patterns tend to be more middle-of-the-road as compared to other groups.

277 members were signiﬁcantly more likely to be swing voters.14 However, after adding regime performance and clientelistic voting indicators in Model 3, the Guan ethno-linguistic variable

loses its signiﬁcance. Also within Model 3, a one-unit increase in rating of the 2000-2008

NPP government’s success at developing the respondent’s area increased the odds that a

respondent is a swing voter by 1.43. Further, a one-unit increase in the rating of the 2008 NDC government’s handling of the economy also increased the odds that a respondent is

a swing voter by 1.416. Finally, also in Model 3 the variable measuring whether or not a

respondent identiﬁed the NDC as the political party with the biggest reputation for giving out

the most gifts as a signiﬁcant predictor of a swing voter.

So far, the analysis provides little evidence of ethnicity (Hypothesis 1) having an impact on swing voting. There is some evidence that swing voters do have more positive performance

ratings of the 2000-2008 NPP government’s development success and the 2008 NDC

government’s economic management success as compared to stable voters. Finally, identifying

the NDC as the political party with the biggest reputation for giving out gifts decreased the odds that a respondent was a swing voter. This is probably because strong supporters of the

NPP were most likely to identify the NDC as having this gift-giving reputation and would

also be less likely to swing vote. That respondents identify parties which they do not vote

for, and that there was not signiﬁcant diﬀerence in whether respondents felt gifts actually

impacted voters’ vote decisions (gifts voting) provides little overall evidence for Hypothesis 3: Clientelistic-Induced Voting.

In Table 7-15, Models 4-6 each replicate Model 3 in Table 7-14 except the regime performance indicators are substituted out for respondents’ reasons for their 2012 Presidential,

2012 Parliamentary and their community members’ votes. In Models 4 through 6, the

14 Though voting data is only available for 12 Guan respondents in the entire sample, 5 of them (41.7%) report swing voting. Also important, all 12 of these Guan respondents come from the Birim South District.

278 demographic and ethnic variables’ signiﬁcance is similar to that of Table 7-14, except now Guan ethno-linguistic group members again are more likely to be swing voters as compared to the Ga-Dangbe reference category.

In Model 4, when explaining their reasons for their vote for President in the 2012 election, respondents who cited the candidate’s Economic Policy, disapproval of the past candidate/regime, and voting for voting’s sake each significantly increased the odds that the respondent was a swing voter at some point in their voting history. In Model 5, where respondents explain their reason for their vote for MP, no explanation significantly predicted swing voters as compared to stable voters. Finally, in Model 6 respondents’ explanations for presidential votes in general within the community were included in the model. Respondents who explained community votes as impacted by the candidate’s economic policy and social policy increased the odds that the respondent was a swing voter. Finally, also in Model 6, now a one-unit increase towards the belief that when individuals take gifts from political parties they still vote the way they want and not necessarily with the party that gave the gift, as opposed to the belief that individuals vote the way they want regardless of gift inducements, significantly decreased the odds that a respondent was a swing voter.

In Models 4-6 then, there is some evidence for ethnic impacts on voting (Hypothesis 1) as Guan voters are more likely to be swing voters throughout. But there is stronger evidence for Hypothesis 2, Policy or Economic-Based Voting, in that swing voters were more likely to explain their 2012 Presidential vote with the candidate’s economic policy and disapproval of the past candidate/regime. Similarly, swing voters were also more likely to explain community members’ votes for president as the result of the candidate’s economic and social policy. It appears swing voters are somewhat more critical of policies and past government performance than stable voters. Finally, as pertains to Hypothesis 3, Clientelistic-Based Voting, swing voters were more likely to say that voters vote for the party which gives them gifts in Model 6. This might suggest that swing voters are critical of the voting process in Ghana, such that political parties can simply pay for votes rather than earn votes, or that swing voters themselves might

279 be more likely to have been induced by clientelistic gifts for their votes. Either way, this variable provides some small support for Hypothesis 3.

7.3 Conclusion

Using respondents’ self-report data about their voting history to test for predictors of vote decisions ﬁnds support for Hypothesis 1: Identity-Based Voting but more so for Hypothesis

2: Policy or Economic-Based Voting, with little evidence for Hypothesis 3: Clientelism-Based Voting. Further, there is some evidence that economic or policy-based voting impacts swing voters more than stable voters, with less evidence of identity or clientelistic-inducements of swing voting.

Throughout the 2004-2012 Presidential and Parliamentary elections, ethnic identities were consistently strong predictors of vote choice but not swing voters. Using a mix of tribal and ethno-linguistic groups, the vote choice analysis shows that Ashantis, Akyems, and Other Akan

Tribes, as well as the Mole Dagbani ethno-linguistic group, consistently favored the NPP over the NDC, while the Ewe ethno-linguistic group consistently favored the NDC over the NPP, in comparison to the Ga-Dangbe reference category. Fante group membership also signiﬁcantly predicted NPP over NDC votes in the 2008 Presidential and Parliamentary elections. In the swing voting analysis, only members of the Guan ethno-linguistic group were more likely to be swing voters as compared to the Ga-Dangbe reference category.

However, it should be remembered that the survey sample is not a national representative sample but is rather a random sample within the 6 purposely selected districts. While the voting patterns of the Ashantis, Akyems and Ewes are generally in-line with the nation-wide voting behavior of these groups, Fantes, Other Akan tribes, and Mole Dagbanis’ signiﬁcance in the model is not representative of these groups competitive nationwide voting traditions.

Similarly, I also tested for the relationship between respondents’ votes and their immediate family members’ votes. Respondents who reported that their family votes the same way as they do were signiﬁcantly less likely to vote for the NDC over the NPP in the 2004 Presidential election alone. Yet, respondents who reported that their family members are stable voters were

280 signiﬁcantly less likely to be swing voters. Perhaps swing voting is a trait passed down within families or perhaps swing voters are more likely to think of their family as swing voters because they themselves are one.

Second, to test for Hypothesis 2: Policy or Economic-Based Voting, I use respondents’ evaluations of regime’s handling of the economy and success at bringing development to the respondents’ area in both the vote predictions and swing voter models. Economic evaluations were a stronger predictor of votes than development evaluations in the 2004 and 2012 Presidential and Parliamentary elections. But, development evaluations were stronger predictors of votes than economic evaluations in the 2008 Presidential and Parliamentary elections. When comparing economic and developmental evaluations of regimes against ethnic coefficients in the changes in predicted probabilities figures, economic and developmental tended to have stronger effects on the probability of voting for the NPP versus the NDC than did ethnicity.

In the swing voter analyses, swing voters were more likely to rate both the 2000-2008 NPP government’s development initiatives and the 2008-2012 NDC government’s handling of the economy in a positive light as compared to stable voters. Further, in the swing voting analysis, swing voters were also more likely to justify their 2012 presidential votes with the candidate’s economic policy and as the result of their disapproval with the prior candidate/regime. Finally, swing voters also believe that presidential votes within their community are driven by candidates’ economic and social policies. It might be that swing voters are naturally more critical of policies and performance but it may also be the case that swing voting is encouraged by the increased policy diﬀerentiation between Presidential candidates in Ghana.

Finally, responses asking about the political party with the biggest reputation for handing out gifts found that respondents reported political parties which they did not vote for. The vote choice models show that respondents who identiﬁed the NDC as having the biggest reputation for handing out gifts were signiﬁcantly less likely to vote for the NDC over the NPP

281 in each of the elections. In the swing voter models, if respondents identiﬁed the NDC as giving out the most gifts, they were less likely to be swing voters, again suggesting that partisan

NPP members pointing out the NDC led to this result. That respondents are reporting the political parties which they do not vote for, suggests either that clientelistic-incentives do not usually aﬀect voting or that respondents are unwilling to report their or their political party’s undemocratic behavior.

Overall this analysis within this chapter provides a great deal of support for Hypothesis

2 and some support for Hypothesis 1. However, some constraints exist when testing for predictors of retrospective vote decisions. Respondents may not remember their votes accurately or may not be willing to accurately report them to the survey enumerator. Respondents opinions about past regimes certainly change over time and their current evaluations about a past regime’s performance might not be the same mindset they had when deciding which party to vote for in past elections. Finally, direct questions in surveys may not be very accurate in obtaining sensitive information, such as clientelistic pay-for-votes behavior.

For these reasons, we cannot solely rely on respondents’ self-report data to predict their voting decisions. In the next chapter I analyze self-reported swing votes and three survey experiments to further identify patterns in respondents’ votes.

282 Table 7-1. Predicting 2004 presidential votes Model 1 Model 2 NDC Third party NDC Third party

age 0.004 -0.03 0.004 -0.04 (0.01) (0.03) (0.01) (0.03) female -0.04 -2.02∗ -0.04 -2.01∗ (0.15) (1.09) (0.15) (1.09) islam 1.13∗∗∗ 1.46 0.43 1.23 (0.29) (1.14) (0.37) (1.14) otherrelig -0.37 -13.80∗∗∗ -0.17 -13.34∗∗∗ (0.41) (0.0000) (0.38) (0.0000) cellown -0.28 0.21 -0.32∗ -0.02 (0.18) (1.12) (0.18) (1.14) water inside -0.63∗∗ 0.58 -0.26 0.46 (0.28) (0.99) (0.26) (0.96) farmer 0.40∗∗ 0.71 0.03 0.17 (0.18) (0.86) (0.16) (0.86) Bosome Freho -1.63∗∗∗ 12.09∗∗∗ (0.26) (1.03) Birim South -0.60∗∗∗ 12.14∗∗∗ (0.21) (1.01) Adaklu 3.21∗∗∗ 15.51∗∗∗ (0.31) (0.80) Ketu South 3.86∗∗∗ 14.88∗∗∗ (0.37) (1.11) Mfantsiman 0.73∗∗∗ 14.07∗∗∗ (0.22) (0.63) ashanti -2.83∗∗∗ -3.44∗∗∗ (0.53) (0.0000) fante -0.68 11.12∗∗∗ (0.48) (0.64) akyem -1.95∗∗∗ -2.58∗∗∗ (0.51) (0.0000) otherakan -1.80∗∗∗ 11.75∗∗∗ (0.52) (0.79) ewe 2.00∗∗∗ 12.92∗∗∗ (0.50) (0.75) guan -0.17 -8.61∗∗∗ (0.93) (0.00) moledagbani -1.12 -12.60∗∗∗ (0.85) (0.00) other ethnicity 0.11 -8.50∗∗∗ (0.65) (0.00) Constant -0.68∗ -16.04∗∗∗ 0.61 -13.26∗∗∗ (0.38) (1.58) (0.58) (1.39)

N 1279 1277 AIC 1,290.20 1,320.76 Pseudo R2 0.3292 0.3212 Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

283 Table 7-1. Continued Model 3 Model 4 NDC Third party NDC Third party islam 0.58 1.37 0.51 1.22 (0.38) (1.13) (0.45) (1.22) otherrelig -0.13 -14.84∗∗∗ -0.94∗ -29.84∗∗∗ (0.38) (0.0000) (0.50) (0.00) cellown -0.45∗∗ -0.22 -0.35 -0.19 (0.18) (0.84) (0.23) (0.87) vote stays the 0.55∗∗∗ -0.83∗∗ 0.84∗∗∗ -0.87∗ same (0.13) (0.38) (0.18) (0.51) diﬀ.ideo 0.02 0.28 0.19 0.59 (0.16) (0.76) (0.20) (0.83) ashanti -3.25∗∗∗ -7.56∗∗∗ -1.98∗∗∗ -0.70 (0.54) (0.0000) (0.76) (135.32) fante -0.92∗ 9.51∗∗∗ -0.27 9.92 (0.48) (0.61) (0.70) (34.56) akyem -2.16∗∗∗ -9.54∗∗∗ -1.60∗∗ -1.73 (0.52) (0.00) (0.74) (145.96) otherakan -2.02∗∗∗ 10.61∗∗∗ -1.52∗∗ 11.11 (0.53) (0.64) (0.75) (34.56) ewe 1.69∗∗∗ 11.40∗∗∗ 1.38∗ 10.72 (0.50) (0.67) (0.72) (34.56) guan -0.33 11.22∗∗∗ 0.42 12.23 (0.93) (1.29) (1.29) (34.58) moledagbani -1.69∗ -11.19∗∗∗ -2.90∗∗ -14.14∗∗∗ (0.90) (0.00) (1.27) (0.0000) other ethnicity -0.01 -7.67∗∗∗ 0.51 -6.32∗∗∗ (0.69) (0.00) (0.96) (0.03) family votes the -0.46∗ -0.94 same (0.24) (0.85) 2000 NPP Econ. -1.23∗∗∗ -1.18∗∗∗ (0.13) (0.38) 2000 NPP Dev. -0.70∗∗∗ -1.10∗∗∗ (0.11) (0.39) NDCgifts -0.78∗∗∗ 0.41 (0.22) (0.79) Constant 0.64 -13.55∗∗∗ 4.38∗∗∗ -8.81 (0.52) (0.80) (0.78) (34.57)

N 1288 1129 AIC 1,314.87 914.85 Pseudo R 2 0.3331 0.4940 Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01 284 Table 7-2. Predicting 2004 parliamentary votes Model 1 Model 2 NDC Third party NDC Third party

age 0.002 0.01 0.002 0.01 (0.01) (0.03) (0.01) (0.03) female -0.04 -0.49 -0.05 -0.67 (0.15) (0.66) (0.15) (0.68) islam 1.05∗∗∗ 0.92 0.23 0.04 (0.29) (1.10) (0.37) (1.39) otherrelig -0.30 -15.29∗∗∗ -0.12 -22.05∗∗∗ (0.40) (0.00) (0.38) (0.00) cellown -0.35∗ 0.03 -0.39∗∗ -0.06 (0.18) (0.84) (0.18) (0.84) water inside -0.39 0.08 -0.05 -0.11 (0.27) (1.11) (0.26) (1.12) farmer 0.51∗∗∗ 0.04 0.14 -0.45 (0.18) (0.77) (0.16) (0.74) Bosome Freho -1.70∗∗∗ 26.53∗∗∗ (0.27) (0.64) Birim South -0.60∗∗∗ 26.08∗∗∗ (0.21) (0.74) Adaklu 3.12∗∗∗ 28.48∗∗∗ (0.31) (0.75) Ketu South 3.72∗∗∗ 15.79∗∗∗ (0.35) (0.0000) Mfantsiman 0.69∗∗∗ 27.13∗∗∗ (0.22) (0.59) ashanti -2.86∗∗∗ 9.59∗∗∗ (0.54) (0.71) fante -0.64 9.60∗∗∗ (0.48) (0.62) akyem -1.94∗∗∗ 9.18∗∗∗ (0.51) (0.96) otherakan -1.63∗∗∗ 10.35∗∗∗ (0.52) (0.72) ewe 1.96∗∗∗ 10.18∗∗∗ (0.50) (0.99) guan 0.08 -10.69∗∗∗ (0.93) (0.00) moledagbani -0.87 -13.17∗∗∗ (0.85) (0.00) other ethnicity 0.39 11.85∗∗∗ (0.66) (1.23) Constant -0.56 -30.72∗∗∗ 0.65 -13.62∗∗∗ (0.38) (1.25) (0.58) (1.34)

N 1277 1176 AIC 1,321.17 1,356.15 Pseudo R2 0.3209 0.3229 Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

285 Table 7-2. Continued Model 3 Model 4 NDC Third party NDC Third party islam 0.48 0.15 0.27 0.39 (0.38) (1.34) (0.46) (1.32) otherrelig -0.15 -35.45∗∗∗ -1.18∗∗ -9.58 (0.38) (0.00) (0.48) (116.04) cellown -0.51∗∗∗ -0.52 -0.47∗∗ -0.95 (0.18) (0.72) (0.22) (0.75) vote stays the 0.61∗∗∗ -0.70∗ 0.83∗∗∗ -0.72 same (0.13) (0.37) (0.18) (0.50) diﬀ.ideo 0.08 0.43 0.23 0.30 (0.16) (0.72) (0.20) (0.74) ashanti -3.25∗∗∗ 9.50∗∗∗ -2.01∗∗∗ 9.58∗∗∗ (0.54) (0.76) (0.76) (1.00) fante -0.97∗∗ 9.13∗∗∗ -0.33 9.73∗∗∗ (0.48) (0.75) (0.71) (0.80) akyem -2.22∗∗∗ 9.27∗∗∗ -1.67∗∗ 9.42∗∗∗ (0.52) (0.98) (0.74) (0.98) otherakan -1.92∗∗∗ 10.69∗∗∗ -1.38∗ 11.37∗∗∗ (0.52) (0.67) (0.75) (0.75) ewe 1.68∗∗∗ 10.31∗∗∗ 1.37∗ 10.17∗∗∗ (0.50) (0.99) (0.72) (1.00) guan -0.19 12.54∗∗∗ 0.67 13.09∗∗∗ (0.94) (1.49) (1.30) (1.58) moledagbani -1.56∗ -11.24∗∗∗ -2.67∗∗ -7.74∗∗∗ (0.90) (0.00) (1.27) (0.0000) other ethnicity -0.01 11.84∗∗∗ 0.49 11.64∗∗∗ (0.70) (1.13) (0.96) (1.19) family votes the -0.34 0.06 same (0.24) (0.85) 2000 NPP Econ. -1.19∗∗∗ -0.90∗∗ (0.12) (0.38) 2000 NPP Dev. -0.72∗∗∗ -0.79∗∗ (0.11) (0.39) NDCgifts -0.84∗∗∗ 0.87 (0.22) (0.72) Constant 0.60 -13.45∗∗∗ 4.38∗∗∗ -10.00∗∗∗ (0.52) (0.79) (0.78) (1.16)

N 1277 1127 AIC 1,328.92 926.14 Pseudo R2 0.3209 0.4863 Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01 286 Table 7-3. Predicting 2008 presidential votes Model 1 Model 2 NDC Third party NDC Third party

age 0.003 0.003 0.004 0.002 (0.01) (0.03) (0.01) (0.03) female -0.13 -0.16 -0.15 -0.18 (0.14) (0.73) (0.14) (0.73) islam 1.21∗∗∗ -17.91∗∗∗ 0.32 -21.54∗∗∗ (0.27) (0.00) (0.33) (0.00) otherrelig -0.35 0.40 -0.06 0.31 (0.39) (1.30) (0.35) (1.16) cellown -0.33∗ 0.23 -0.36∗∗ 0.13 (0.17) (1.12) (0.17) (1.12) water inside -0.36 -13.47∗∗∗ -0.02 -14.73∗∗∗ (0.25) (0.0000) (0.23) (0.0000) farmer 0.19 -1.26 -0.19 -1.36 (0.16) (1.17) (0.15) (1.13) Bosome Freho -1.36∗∗∗ 24.81∗∗∗ (0.22) (0.93) Birim South -0.81∗∗∗ 24.67∗∗∗ (0.19) (0.96) Adaklu 2.43∗∗∗ 27.56∗∗∗ (0.26) (0.67) Ketu South 3.70∗∗∗ 28.09∗∗∗ (0.38) (0.93) Mfantsiman 0.73∗∗∗ 24.96∗∗∗ (0.20) (0.92) ashanti -2.22∗∗∗ -1.27∗∗∗ (0.46) (0.0000) fante -0.47 10.77∗∗∗ (0.42) (0.71) akyem -1.89∗∗∗ -9.18∗∗∗ (0.46) (0.00) otherakan -1.39∗∗∗ 11.24∗∗∗ (0.46) (0.90) ewe 1.81∗∗∗ 13.26∗∗∗ (0.44) (0.69) guan 1.39 2.48∗∗∗ (1.18) (0.00) moledagbani -0.20 -3.39∗∗∗ (0.81) (0.00) other ethnicity -0.01 -10.84∗∗∗ (0.60) (0.00) Constant -0.14 -29.46∗∗∗ 0.86∗ -14.96∗∗∗ (0.33) (1.45) (0.51) (1.30)

N 1435 1431 AIC 1,515.23 1,550.12 Pseudo R2 0.2844 0.2714 Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

287 Table 7-3. Continued Model 3 Model 4 NDC Third party NDC Third party islam 0.44 -10.39∗∗∗ 0.53 -13.34∗∗∗ (0.34) (0.0000) (0.39) (0.0000) otherrelig -0.06 0.75 0.08 1.03 (0.36) (1.15) (0.47) (1.20) cellown -0.39∗∗ 0.44 -0.49∗∗ 0.35 (0.16) (1.09) (0.20) (1.10) vote stays the -0.50∗∗∗ -1.18∗∗∗ -0.49∗∗∗ -0.89∗ same (0.12) (0.44) (0.16) (0.53) diﬀ.ideo 0.09 -0.10 0.30∗ 0.17 (0.14) (0.69) (0.17) (0.73) ashanti -2.75∗∗∗ -12.32∗∗∗ -3.06∗∗∗ -16.09∗∗∗ (0.47) (0.00) (0.61) (0.00) fante -0.68 11.73∗∗∗ -1.12∗∗ 10.09∗∗∗ (0.43) (0.64) (0.56) (0.70) akyem -2.07∗∗∗ -6.96∗∗∗ -2.27∗∗∗ -9.86∗∗∗ (0.46) (0.00) (0.59) (0.00) otherakan -1.66∗∗∗ 12.68∗∗∗ -2.19∗∗∗ 10.91∗∗∗ (0.47) (0.65) (0.60) (0.74) ewe 1.74∗∗∗ 14.11∗∗∗ 1.17∗∗ 12.25∗∗∗ (0.45) (0.57) (0.58) (0.68) guan 0.52 2.32∗∗∗ 0.91 4.66∗∗∗ (0.94) (0.00) (1.28) (0.00) moledagbani -0.77 -1.27∗∗∗ -1.73∗ -1.31∗∗∗ (0.85) (0.00) (0.97) (0.00) other ethnicity -0.19 -4.63∗∗∗ -0.61 -2.97∗∗∗ (0.63) (0.00) (0.79) (0.0000) family votes the 0.18 -1.03 same (0.20) (0.87) 2008 NDC Econ. 0.70∗∗∗ 0.45 (0.10) (0.47) 2008 NDC Dev. 0.87∗∗∗ 0.41 (0.11) (0.47) NDCgifts -0.78∗∗∗ -0.27 (0.18) (0.88) Constant 1.38∗∗∗ -15.91∗∗∗ -0.96 -15.11∗∗∗ (0.47) (0.86) (0.64) (1.28)

N 1444 1263 AIC 1,512.18 1,149.49 Pseudo R2 0.2969 0.4107 Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

288 Table 7-4. Predicting 2008 parliamentary votes Model 1 Model 2 NDC Third party NDC Third party

age 0.01 0.05∗∗ 0.01 0.05∗ (0.01) (0.03) (0.01) (0.02) female -0.13 -0.64 -0.16 -0.60 (0.14) (0.71) (0.14) (0.70) islam 1.38∗∗∗ -20.64∗∗∗ 0.41 -14.83∗∗∗ (0.28) (0.00) (0.34) (0.0000) otherrelig -0.12 -20.22∗∗∗ 0.06 -37.77 (0.39) (0.00) (0.36) cellown -0.28∗ -1.24 -0.32∗ -1.15 (0.17) (0.76) (0.17) (0.74) water inside -0.39 -13.90∗∗∗ -0.08 -19.02∗∗∗ (0.25) (0.0000) (0.23) (0.00) farmer 0.25 -1.63∗ -0.12 -1.39∗ (0.16) (0.87) (0.15) (0.82) Bosome Freho -1.41∗∗∗ 26.56∗∗∗ (0.23) (0.55) Birim South -0.80∗∗∗ 24.91∗∗∗ (0.19) (0.91) Adaklu 2.72∗∗∗ 27.35∗∗∗ (0.28) (0.74) Ketu South 3.52∗∗∗ 27.59∗∗∗ (0.36) (1.02) Mfantsiman 0.71∗∗∗ 12.14∗∗∗ (0.20) (0.0000) ashanti -2.35∗∗∗ 10.16∗∗∗ (0.46) (0.56) fante -0.50 8.14∗∗∗ (0.42) (0.90) akyem -1.88∗∗∗ -9.35∗∗∗ (0.46) (0.00) otherakan -1.48∗∗∗ 9.73∗∗∗ (0.46) (0.90) ewe 1.87∗∗∗ 11.12∗∗∗ (0.45) (0.71) guan 1.32 1.95∗∗∗ (1.18) (0.00) moledagbani -0.31 -3.93∗∗∗ (0.81) (0.00) other ethnicity 0.25 -9.83∗∗∗ (0.61) (0.00) Constant -0.36 -30.23∗∗∗ 0.67 -14.03∗∗∗ (0.34) (1.20) (0.51) (1.16)

N 1432 1318 Akaike Inf. Crit. 1,489.75 1,524.96 Pseudo R2 0.2984 0.2994 Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

289 Table 7-4. Continued Model 3 Model 4 NDC Third party NDC Third party age 0.01 0.03 0.01∗∗ 0.03 (0.01) (0.02) (0.01) (0.02) islam 0.63∗ -23.07∗∗∗ 0.73∗ -14.58∗∗∗ (0.35) (0.69) (0.40) (0.0000) otherrelig -0.04 -18.50∗∗∗ 0.18 -19.10∗∗∗ (0.37) (0.00) (0.48) (0.00) cellown -0.41∗∗ -0.97 -0.45∗∗ -1.15 (0.17) (0.71) (0.21) (0.83) vote stays the -0.48∗∗∗ -1.14∗∗∗ -0.55∗∗∗ -1.39∗∗ same (0.12) (0.41) (0.16) (0.54) diﬀ.ideo 0.21 0.46 0.33∗ -0.004 (0.15) (0.77) (0.18) (0.82) ashanti -2.82∗∗∗ 9.28∗∗∗ -3.10∗∗∗ 9.45∗∗∗ (0.47) (0.62) (0.61) (0.75) fante -0.74∗ 7.95∗∗∗ -1.22∗∗ 8.45∗∗∗ (0.43) (0.89) (0.56) (0.92) akyem -2.06∗∗∗ -10.77∗∗∗ -2.30∗∗∗ -19.85∗∗∗ (0.46) (0.00) (0.60) (0.00) otherakan -1.82∗∗∗ 9.69∗∗∗ -2.35∗∗∗ 10.36∗∗∗ (0.47) (0.70) (0.61) (0.75) ewe 1.73∗∗∗ 11.13∗∗∗ 1.18∗∗ 11.63∗∗∗ (0.45) (0.67) (0.58) (0.84) guan 1.05 36.59∗∗∗ 0.77 1.91∗∗∗ (1.18) (0.69) (1.30) (0.00) moledagbani -0.93 -8.62∗∗∗ -1.94∗∗ -4.12∗∗∗ (0.86) (0.00) (0.99) (0.00) other ethnicity -0.09 -11.66∗∗∗ -0.48 -9.04∗∗∗ (0.64) (0.00) (0.82) (0.00) family votes the 0.23 0.39 same (0.21) (0.93) 2008 NDC Econ. 0.80∗∗∗ 0.52 (0.11) (0.48) 2008 NDC Dev. 0.82∗∗∗ -0.17 (0.11) (0.45) NDCgifts -0.74∗∗∗ 0.88 (0.19) (0.78) Constant 1.07∗∗ -13.81∗∗∗ -1.59∗∗ -14.53∗∗∗ (0.52) (1.17) (0.72) (1.60)

N 1435 1258 AIC 1,502.91 1,124.53 Pseudo R2 0.3050 0.4240 Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

290 Table 7-5. Predicting 2012 presidential votes Model 1 Model 2 NDC Third party NDC Third party

age 0.002 -0.02 0.003 -0.02 (0.005) (0.02) (0.005) (0.02) female -0.12 -0.22 -0.14 -0.32 (0.13) (0.56) (0.13) (0.57) islam 1.12∗∗∗ -14.22∗∗∗ 0.11 -18.14∗∗∗ (0.26) (0.0000) (0.33) (0.00) otherrelig -0.46 1.73∗ -0.28 1.27 (0.37) (0.93) (0.34) (0.90) cellown -0.34∗∗ -0.18 -0.34∗∗ -0.11 (0.16) (0.81) (0.16) (0.82) water inside -0.26 -0.53 0.03 -0.64 (0.22) (1.08) (0.22) (1.08) farmer 0.37∗∗ -1.80∗ 0.03 -1.99∗ (0.16) (1.09) (0.15) (1.08) Bosome Freho -1.58∗∗∗ -13.37∗∗∗ (0.22) (0.0000) Birim South -0.91∗∗∗ 0.38 (0.19) (0.89) Adaklu 2.60∗∗∗ 1.39 (0.24) (1.04) Ketu South 3.13∗∗∗ 0.32 (0.30) (1.38) Mfantsiman 0.72∗∗∗ 0.83 (0.19) (0.87) ashanti -2.08∗∗∗ -4.52∗∗∗ (0.45) (0.0000) fante 0.09 10.50∗∗∗ (0.40) (0.48) akyem -1.37∗∗∗ 10.21∗∗∗ (0.44) (0.65) otherakan -1.08∗∗ 11.20∗∗∗ (0.44) (0.51) ewe 2.30∗∗∗ 11.08∗∗∗ (0.42) (0.62) guan 0.77 -0.77∗∗∗ (0.79) (0.00) moledagbani 0.39 -1.49∗∗∗ (0.75) (0.0000) other ethnicity 0.89 -8.62∗∗∗ (0.57) (0.00) Constant -0.19 -2.72∗∗ 0.20 -13.04∗∗∗ (0.29) (1.31) (0.48) (0.96)

N 1612 1606 Akaike Inf. Crit. 1,728.85 1,751.93 Pseudo R2 0.2886 0.2816 Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

291 Table 7-5. Continued Model 3 Model 4 NDC Third party NDC Third party islam 0.23 -15.48∗∗∗ 0.23 -15.99∗∗∗ (0.33) (0.55) (0.39) (0.65) otherrelig -0.005 0.64 -0.13 0.92 (0.34) (1.11) (0.46) (1.20) internetuse -0.82∗∗∗ 0.05 -0.90∗∗∗ 0.14 (0.20) (0.69) (0.26) (0.81) vote stays the -0.15 -1.56∗∗∗ -0.16 -1.81∗∗∗ same (0.12) (0.31) (0.17) (0.41) diﬀ.ideo 0.03 0.26 0.50∗∗∗ 0.41 (0.14) (0.58) (0.18) (0.64) ashanti -2.26∗∗∗ -14.69∗∗∗ -2.09∗∗∗ -16.16∗∗∗ (0.45) (0.00) (0.64) (0.00) fante -0.09 10.42∗∗∗ 0.09 10.98∗∗∗ (0.40) (0.47) (0.58) (0.52) akyem -1.53∗∗∗ 10.17∗∗∗ -1.54∗∗ 10.20∗∗∗ (0.44) (0.64) (0.62) (0.68) otherakan -1.22∗∗∗ 11.37∗∗∗ -1.09∗ 11.56∗∗∗ (0.44) (0.51) (0.63) (0.63) ewe 2.22∗∗∗ 11.48∗∗∗ 1.51∗∗ 10.72∗∗∗ (0.42) (0.56) (0.60) (0.73) guan 0.73 28.40∗∗∗ 0.03 28.09∗∗∗ (0.79) (0.55) (1.00) (0.65) moledagbani -0.01 -2.90∗∗∗ -1.12 -2.63∗∗∗ (0.77) (0.00) (1.00) (0.00) other ethnicity 0.82 -7.05∗∗∗ 0.36 -13.42∗∗∗ (0.60) (0.00) (0.84) (0.00) family votes the 0.21 0.15 same (0.21) (0.70) 2012 NDC Econ. 0.98∗∗∗ 0.76∗∗ (0.11) (0.38) 2012 NDC Dev. 0.74∗∗∗ 0.71∗∗ (0.08) (0.33) NDCgifts -0.81∗∗∗ 0.35 (0.19) (0.69) debate -0.10 0.09 (0.17) (0.67) Constant 0.30 -13.84∗∗∗ -1.93∗∗∗ -15.95∗∗∗ (0.42) (0.46) (0.62) (0.88)

N 1433 1329 AIC 1,686.20 1,147.39 Pseudo R2 0.2962 0.4547 Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

292 Table 7-6. Predicting 2012 parliamentary votes Model 1 Model 2 NDC Third party NDC Third party

age 0.001 0.02 0.001 0.03 (0.005) (0.02) (0.005) (0.02) female -0.07 0.98∗ -0.09 0.94∗ (0.13) (0.56) (0.13) (0.56) islam 1.22∗∗∗ 1.04 0.22 0.19 (0.26) (0.83) (0.33) (1.15) otherrelig -0.23 1.08 -0.05 0.46 (0.37) (1.09) (0.35) (1.11) internetuse -0.61∗∗∗ 0.66 -0.64∗∗∗ 0.71 (0.22) (0.73) (0.21) (0.73) water inside -0.11 0.11 0.18 0.06 (0.23) (0.83) (0.22) (0.83) farmer 0.39∗∗ 0.13 0.06 0.03 (0.16) (0.57) (0.15) (0.56) Bosome Freho -1.59∗∗∗ 0.39 (0.22) (0.88) Birim South -1.07∗∗∗ 0.55 (0.19) (0.83) Adaklu 2.65∗∗∗ 2.06∗∗ (0.25) (0.96) Ketu South 3.22∗∗∗ -10.74∗∗∗ (0.31) (0.0000) Mfantsiman 0.61∗∗∗ 0.29 (0.19) (1.04) ashanti -1.93∗∗∗ 10.79∗∗∗ (0.45) (0.62) fante 0.16 10.43∗∗∗ (0.40) (0.61) akyem -1.46∗∗∗ 10.45∗∗∗ (0.44) (0.72) otherakan -0.98∗∗ 11.78∗∗∗ (0.44) (0.52) ewe 2.46∗∗∗ 11.31∗∗∗ (0.42) (0.77) guan 0.77 -12.01∗∗∗ (0.80) (0.00) moledagbani 0.31 13.16∗∗∗ (0.76) (1.30) other ethnicity 1.05∗ 12.24∗∗∗ (0.59) (1.06) Constant -0.34 -6.05∗∗∗ -0.04 -16.49∗∗∗ (0.25) (1.14) (0.44) (0.87)

N 1597 1590 AIC 1,713.03 1,736.63 Pseudo R2 0.2944 0.2869 Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

293 Table 7-6. Continued Model 3 Model 4 NDC Third party NDC Third party islam 0.38 -0.01 0.44 -0.17 (0.33) (1.08) (0.40) (1.08) otherrelig 0.004 -20.94∗∗∗ -0.11 -31.61∗∗∗ (0.35) (0.00) (0.47) (0.00) internetuse -0.74∗∗∗ -0.12 -0.77∗∗∗ 0.23 (0.20) (0.69) (0.26) (0.74) vote stays the -0.22∗ -0.95∗∗∗ -0.16 -0.91∗∗ same (0.12) (0.30) (0.17) (0.39) diﬀ.ideo 0.16 0.86 0.69∗∗∗ 1.28∗ (0.14) (0.69) (0.18) (0.74) ashanti -2.17∗∗∗ 10.78∗∗∗ -1.93∗∗∗ 11.40∗∗∗ (0.45) (0.60) (0.64) (0.62) fante -0.11 10.12∗∗∗ 0.05 10.76∗∗∗ (0.40) (0.65) (0.59) (0.68) akyem -1.65∗∗∗ 10.28∗∗∗ -1.70∗∗∗ 10.47∗∗∗ (0.44) (0.75) (0.63) (0.77) otherakan -1.22∗∗∗ 11.63∗∗∗ -1.12∗ 11.58∗∗∗ (0.44) (0.57) (0.64) (0.70) ewe 2.30∗∗∗ 11.54∗∗∗ 1.57∗∗∗ 11.35∗∗∗ (0.42) (0.76) (0.60) (0.78) guan 0.60 13.32∗∗∗ -0.17 12.95∗∗∗ (0.79) (1.29) (1.01) (1.37) moledagbani -0.25 13.00∗∗∗ -1.49 12.19∗∗∗ (0.79) (1.24) (1.02) (1.29) other ethnicity 0.72 12.41∗∗∗ 0.27 12.19∗∗∗ (0.60) (1.02) (0.84) (1.11) family votes the 0.25 -0.06 same (0.21) (0.63) 2012 NDC Econ. 1.02∗∗∗ 0.92∗∗∗ (0.11) (0.32) 2012 NDC Dev. 0.76∗∗∗ 0.62∗∗ (0.08) (0.29) NDCgifts -0.83∗∗∗ -0.07 (0.19) (0.55) debate -0.16 -0.71 (0.17) (0.59) Constant 0.23 -14.59∗∗∗ -2.16∗∗∗ -16.53∗∗∗ (0.42) (0.68) (0.63) (0.95)

N 1445 1326 AIC 1,713.46 1,174.17 Pseudo R2 0.2919 0.4484 Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

294 Table 7-7. Swing Voters District1 Party member2 Female3 Swing Bosome Birim Adaku Ketu Mfant. AOB Party Non-party Women Men voter Freho South Anyigbe South member member

295 1 52, 53, 32, 28, 60, 48, 135, 113, 126, 145, 17.99% 17.79% 11.47% 9.89% 21.58% 16.84% 10.41% 38.57% 14.77% 17.01% 0 237, 245, 247, 255, 218, 237, 1,162, 180, 727, 703, 82.01% 82.21% 88.53% 90.11% 78.42% 83.16% 89.59% 61.43% 85.23% 82.90% Total 289 298 279 283 278 285 1,297 293 853 848 1Pearson chi2 (5): = 20.3209; Pr = 0.001 2Pearson chi2 (1): = 143.957; Pr = 0.000 3Pearson chi2 (1): = 1.7202; Pr = 0.190 Table 7-8. Swing Voters2 Education1 Democracy rating2 Swing Low edu. Med. edu. High edu. Total Full Dem. Dem. Not a Total voter democracy w/minor w/major democracy problems problems 296

1 99, 147, 27, 273, 83, 118, 66, 3, 270, 36.3% 53.8% 9.9% 100% 30.74% 43.7% 24.4% 1.1% 100% 0 553, 780, 98, 1,431, 498, 638, 253, 26, 1,415, 38.6% 54.5% 6.8% 100% 35.2% 45.1% 17.9% 1.8% 100% 1Pearson chi2 (2): = 3.2496; Pr = 0.197 2Pearson chi2 (3): = 7.2573; Pr = 0.064 Table 7-9. Swing Voters3 Vote stays the same1 Trust relatives2 Swing Vote stays Sometimes Diﬀers Total Not at all Just a Somewhat A lot Total voter the same changes every little election 297

1 85, 101, 84, 270, 48, 51, 48, 118, 265, 31.5% 37.4% 31.1% 100% 18.1% 19.2% 18.1% 44.5% 100% 0 1,314, 47, 38, 1,399, 200, 163, 237, 811, 1,411, 93.9% 3.4% 2.7% 100% 14.2% 11.6% 16.8% 57.5% 100% 1Pearson chi2 (2): = 650.7776; Pr = 0.000 2Pearson chi2 (3): = 19.6563; Pr = 0.000 Table 7-10. Swing Voters4 Trust neighbors1 Trust other dialects2 Swing Not at Just a Somewhat A lot Total Not at Just a Somewhat A lot Total voter all little all little 298 1 86, 51, 93, 31, 261 127, 48, 61, 25, 261, 33.0% 19.5% 35.6% 11.9% 100% 48.7% 18.4% 23.4% 9.6% 100% 0 372, 312, 475, 234, 1,393, 623, 319, 360, 79, 1,381, 26.7% 22.4% 34.1% 16.8% 100% 45.1% 23.1% 26.1% 5.7% 100% 1Pearson chi2 (3): = 7.3867; Pr = 0.061 2Pearson chi2 (3): = 8.5630; Pr = 0.036 Table 7-11. Skirt-and-blouse swing voters

District 2012 2008 2004 2000 1996 District Avg.

Bosome Freho 13, 9, 5, 7, 6, 4.68%* 3.32% 1.99% 3.17% 3.37% 3.31% Birim South 12, 5, 9, 7, 9, 4.78% 2.05% 4.17% 3.83% 5.59% 4.08%

299 Adaklu Anyigbe 11, 11, 12, 9, 8, 4.09% 4.21% 5.45% 4.76% 4.88% 4.68% Ketu South 6, 3, 6, 2, 2, 2.24% 1.13% 2.63% 1.04% 1.20% 1.65% Mfantsiman 13, 8, 6, 2, 3, 4.76% 2.97% 2.45% 0.93% 1.48% 2.52% AOB 4, 2, 1, 2, 2, 1.49% 0.77% 0.42% 0.96% 1.01% 4.65%

Yearly avg. 3.67% 2.41% 2.85% 2.45% 2.92 *Total number of swing votes are divided by the total number of eligible swing voters per district per year. Table 7-12. Presidential election-to-election swing voters* District 2012** 2008 2004 2000 District Avg.

Bosome Freho 15, 15, 9, 17, 5.40%*** 5.54% 3.59% 7.69% 5.56% Birim South 19, 22, 15, 16, 7.57% 9.02% 6.94% 8.74% 8.07% Adaklu Anyigbe 12, 13, 13, 10, 4.46% 4.98% 5.91% 5.29% 5.16% 300 Ketu South 12, 10, 10, 5, 4.48% 3.77% 4.39% 2.60% 3.81% Mfantsiman 25, 36, 13, 16, 9.16% 13.38% 5.31% 7.48% 8.83% AOB 18, 18, 16, 18, 6.72% 6.90% 6.69% 8.65% 7.24%

Yearly average 6.30% 7.27% 5.47% 6.74% *Respondents’ votes were collected from 2012 through 1996 **Data in table reﬂects the year the vote change was made ***Total number of swing votes are divided by the total number of eligible swing voters per district per year. Table 7-13. Parliamentary election-to-election swing voters* District 2012** 2008 2004 2000 District Avg.

Bosome Freho 25, 17, 12, 18, 8.99%*** 6.27% 4.78% 8.14% 7.05% Birim South 20, 23, 15, 16, 7.97% 9.43% 6.94% 8.74% 8.27% Adaklu Anyigbe 8, 11, 14, 8, 2.97% 4.21% 6.36% 4.23% 4.44% 301 Ketu South 10, 11, 10, 3, 3.73% 4.15% 4.39% 1.56% 3.46% Mfantsiman 25, 33, 11, 18, 9.16% 12.27% 4.49% 8.41% 8.58% AOB 18, 17, 16, 17, 6.72% 6.51% 6.69% 8.17% 7.02%

Yearly average 6.59% 7.14% 5.61% 6.54% *Respondents’ votes were collected from 2012 through 1996 **Data in table reﬂects the year the vote change was made ***Total number of swing votes are divided by the total number of eligible swing voters per district per year. Table 7-14. Logit model odds ratios- predicting swing voters across elections Dependent variable: swing voter (0 or 1) Model 1 Model 2 Model 3 age 1.008(0.994,1.022) 1.009(0.995,1.023) 1.006(0.990,1.022) vote stays the same 0.154∗∗∗ (0.110, 0.213) 0.163∗∗∗ (0.117, 0.223) 0.167∗∗∗ (0.114, 0.238) partymember 0.271∗∗∗ (0.171, 0.427) 0.291∗∗∗ (0.185, 0.458) 0.260∗∗∗ (0.152, 0.444) family votes the same 0.555∗∗∗ (0.411, 0.756) 0.569∗∗∗ (0.426, 0.769) 0.596∗∗∗ (0.431, 0.835) trust neighbors 0.728∗∗∗ (0.593, 0.890) 0.742∗∗∗ (0.604, 0.907) 0.826(0.656,1.037) vote isn’t secret 0.823∗∗ (0.694, 0.970) 0.838∗∗ (0.707, 0.989) 0.789∗∗ (0.650, 0.950) Bosome Freho 2.182∗ (1.014, 4.887) Birim South 2.290∗∗ (1.061, 5.155) Adaklu 0.521(0.217,1.258) Mfantsiman 1.440(0.663,3.226) AOB 1.703(0.779,3.856) ashanti 4.609(0.655,101.643) 3.300(0.420,76.868) fante 4.415(0.655,95.723) 2.837(0.386,64.676) akyem 3.810(0.516,85.780) 2.540(0.316,60.310)

302 otherakan 5.340(0.733,119.536) 3.148(0.378,75.419) ewe 1.819(0.263,39.759) 1.290(0.172,30.040) guan 17.902∗∗ (1.313, 510.189) 6.319(0.258,230.695) moledagbani 5.362(0.209,178.233) 2.857(0.084,103.763) other ethnicity 2.754(0.229,75.240) 1.900(0.143,56.016) 2012 NDC Dev. 1.192(0.910,1.567) 2008 NDC Dev. 0.774(0.558,1.074) 2000 NPP Dev. 1.430∗∗ (1.061, 1.952) 2012 NDC Econ. 1.174(0.843,1.629) 2008 NDC Econ. 1.416∗∗ (1.036, 1.954) 2000 NPP Econ. 1.117(0.823,1.531) MP Election change 1.349(0.815,2.225) NDCgifts 0.606∗ (0.349, 1.026) gifts voting 0.941(0.738,1.209) Constant 1.790(0.643,4.952) 0.624(0.028,4.689) 0.289(0.009,3.799)

Obs. 1,358 1,354 1,122 Log Likelihood -360.021 -359.785 -284.200 AIC 744.042 749.569 616.400 Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01 Table 7-15. Logit model odds ratios- predicting swing voters across elections Dependent variable: swing voter (0 or 1) Model 4 Model 5 Model 6 2012 Pres. 2012 Parl. Pres. votes by community age 1.012(0.996,1.027) 1.011(0.996,1.027) 1.009(0.993,1.024) vote stays the same 0.138∗∗∗ (0.093, 0.200) 0.181∗∗∗ (0.125, 0.256) 0.148∗∗∗ (0.100, 0.214) partymember 0.222∗∗∗ (0.129, 0.380) 0.231∗∗∗ (0.137, 0.387) 0.211∗∗∗ (0.122, 0.362) family votes the same 0.631∗∗∗ (0.452, 0.896) 0.573∗∗∗ (0.415, 0.802) 0.662∗∗ (0.472, 0.945) trust neighbor 0.790∗∗ (0.628, 0.989) 0.793∗∗ (0.636, 0.985) 0.787∗∗ (0.629, 0.980) vote isn’t secret 0.784∗∗ (0.646, 0.945) 0.823∗∗ (0.682, 0.987) 0.789∗∗ (0.649, 0.952) ashanti 5.502(0.616,147.404) 4.740(0.614,111.221) 6.138(0.667,168.023) fante 5.415(0.631,143.234) 4.083(0.552,94.496) 5.779(0.658,155.789) akyem 3.615(0.383,99.029) 3.465(0.431,82.873) 3.575(0.372,99.552) otherakan 5.587(0.593,153.100) 5.179(0.643,124.022) 5.547(0.576,154.809) ewe 1.734(0.194,46.376) 1.802(0.237,41.960) 2.131(0.236,57.865) guan 14.947∗ (0.814, 551.638) 14.865∗ (0.957, 468.313) 26.285∗∗ (1.302, 1, 054.050)

303 moledagbani 2.930(0.070,131.630) 3.946(0.116,142.015) 4.179(0.107,178.182) other ethnicity 3.506(0.217,121.375) 2.521(0.165,77.783) 4.280(0.264,149.648) Econ Policy 3.827∗∗∗ (2.014, 7.647) 1.617(0.870,3.025) 2.417∗∗∗ (1.410, 4.260) Social Policy 1.725(0.893,3.454) 1.100(0.587,2.068) 1.711∗ (0.982, 3.073) Party Legacy 1.364(0.822,2.251) 0.928(0.394,2.101) 0.980(0.587,1.622) Gov Disapproval 596.646∗∗∗ (19.928, 18, 024.530) Partic Ethnic 1.465(0.498,3.973) 0.613(0.024,5.534) 0.737(0.274,1.830) Clientelistic 1.436(0.048,17.036) Votings Sake 5.128∗∗ (1.016, 19.870) 3.973(0.525,22.083) 1.149(0.123,6.194) NDCgifts 0.607∗ (0.356, 1.011) 0.636∗ (0.380, 1.042) 0.697(0.411,1.155) gifts voting 0.826(0.651,1.053) 0.904(0.720,1.142) 0.814∗ (0.642, 1.039) Constant 0.263(0.008,3.403) 0.839(0.032,8.766) 0.536(0.017,6.550)

Obs. 1,197 1,184 1,183 Log Likelihood -293.840 -309.493 -294.118 AIC 635.681 662.985 634.237 Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01 In Table 7-15, Models 4-6 test for reasons for a respondent’s vote for President (Model 4), for MP (Model 5), and for the community’s vote for President (Model 6). Figure 7-1. Change in probabilities for voting for NPP(0), NDC(1), or a third party (3) in the 2004 Pres. race

304 Figure 7-2. Change in probabilities for voting for NPP(0), NDC(1), or a third party (3) in the 2004 Parl. races

305 Figure 7-3. Change in probabilities for voting for NPP(0), NDC(1), or a third party (3) in the 2008 Pres. race

306 Figure 7-4. Change in probabilities for voting for NPP(0), NDC(1), or a third party (3) in the 2008 Parl. races

307 Figure 7-5. Change in probabilities for voting for NPP(0), NDC(1), or a third party (3) in the 2012 Pres. race

308 Figure 7-6. Change in probabilities for voting for NPP(0), NDC(1), or a third party (3) in the 2012 Parl. races

309 CHAPTER 8 SURVEY EXPERIMENTS

If the development of a competitive political environment at the local level actually has had an impact on citizens’ vote decisions, which I argue is the reason behind the break down of national-level ethnic voting since the 2000 elections, then we should see more evidence of voting based on performance or economic evaluations rather than identity or clientelistic-based voting in the survey sample. Further, given the diﬀerences in voting records within the 3 district pairs (NPP strongholds, NDC strongholds, and competitive districts), we should also see diﬀerences in support for Hypothesis 2: Policy or Economic-Based Voting as compared to Identity-Based Voting (Hypothesis 1) or Clientelistic-Based Voting (Hypothesis 3) between district pairs.

Thus far in the survey analysis there has been strong support for Hypothesis 2: Policy or

Economic-Based Voting when respondents explain their reasons for their votes. There is also some evidence for Hypothesis 2 in respondents’ rating of past regimes success at development or handling the economy, though opinions about past regimes have no doubt been altered by time. Aside from Hypothesis 2, there is also some support for Hypothesis 1: Identity-Based

Voting, and less support for Hypothesis 3: Clientelistic-Based Voting. Correlations between ethnicity and vote choice are somewhat high, but few explain community votes for President with ethnic/particularistic reasons and even fewer explain their own vote with these rationales.

Further, almost no respondent explained either their or their community members votes as clientelistic-induced, except perhaps for one respondent who said she voted for the candidate because her landlord asked her to.

Within district pairs, diﬀerences separating Bosome Freho from Birim South and Ketu South from Adaklu Anyigbe appear to revolve around the former districts’ respondents focusing on party legacy and candidate approval while the latter districts’ respondents focused on ethnicity and policies in their vote decisions. The more homogeneous ethnic populations in both Bosome Freho and Ketu South likely have something to do with the strong respective

310 attachment to the NPP and NDC. However, it is also the case that political competition in terms of MP races is heightened in Birim South and Adaklu Anyigbe, as compared to their counterparts (and despite Bosome Freho having elected an NPP-turned-Independent candidate in 2008), which likely is related to the focus on policies in these districts.

In this chapter, I turn to survey experiments to test for the isolated eﬀects of identity and clientelism on political behavior. In all, a total of three survey experiments were run. The

first experiment presents each of the respondents with an identical description of a political candidate for Member of Parliament, while altering the candidate’s and the candidate’s parent’s names to reflect differing tribal backgrounds. Respondents were then asked to rate the candidate, whether or not they would vote for the candidate, and an open-ended response to explain their reason to vote/not vote for the candidate.

The second experiment is a list experiment testing for religious biases on the part of respondents. Respondents were asked how many of the following items upset them and then half were given a list of four items and half were given a list of five items. The fifth experimental item said ‘Having a Muslim as President of Ghana’. If the average number of items selected for the five item respondents was significantly higher than the average number of items selected for the four item respondent, we would know that experimental item had an impact on the number of items selected.

Finally, the third experiment was also a 2 question list experiment asking about Presidential and Parliamentary votes. Respondents were asked to select the number of items from the list which aﬀected their Presidential (Question 31) and Parliamentary (Question

32) votes in 2012. The fifth experimental item was ‘Payouts, in the form of money or other gifts, provided by the candidate or his party boys’. Again, if the average number of items selected was higher to a statistically significant degree in the 5-item version of the question as compared to the 4-item version of the question, we would know that payouts affected respondents votes.

311 As presented in greater detail below, evidence of a tribal bias was mixed, evidence of religious bias against Muslims was mixed, while evidence of clientelistic-based voting

was supported in the competitive districts. Within the district pairs, none of the district

respondents favored the insider candidate over the outsider candidate in terms of candidate

ratings. Further, in one district, Bosome Freho, the outsider candidate was given higher ratings than the insider candidate. But when asked if they would vote for the candidate, respondents

from both of the NDC strongholds’ respondents reported that they were more likely to vote for

the insider candidate than the outsider candidate. As far as the list experiments, three districts

(one from each pair) did show statistically signiﬁcant results suggesting that their respondents

were upset at the thought of a Muslim President of Ghana. Finally, in the clientelism list experiments, it was only respondents from the competitive districts, Mfantsiman and AOB,

which signiﬁcantly selected more items on the 5-item version of the question, suggesting

clientelistic-inducements did have an impact on their votes in both the 2012 Presidential and

Parliamentary races. 8.1 Identity Bias Voting Experiment

Since respondents may be either unwilling to admit or are unaware of ethnic or tribal

biases in their voting habits, I employ an experimental survey question to test for these biases

in perceptions of a hypothetical candidate for MP. Every respondent was read the same

ﬁctional description of a parliamentary candidate for their constituency1 , and then were asked

1 The description of the candidate reads as follows: “ is a school teacher and district assembly member who is interested in running for Parliamentary Oﬃce in your constituency. He was born on September 15, 1968 to , a farmer and carpenter, and , a seamstress. He is the third of his parent’s ﬁve children. Mr. was an excellent student in school and dazzled his audiences with his superb debating skills as a member of his secondary school’s debating team. He was awarded the prize of Best Overall Student in Form 3. Mr. also had a keen interest in sport ans was team captain of his Football team in SS. After Secondary School, Mr. attended university at the University of Ghana- Legon. He undertook political and economic studies, and completed his degree in 1993. Having been inspired by his teachers growing up, Mr. has always wanted to be a teacher. When his

312 in Questions 2-4 to rate the candidate, whether they would vote for the candidate, and an open-ended question asking why they would or would not vote for the candidate. Though

each respondent was read the same description of the candidate, the candidate’s name was

changed to reﬂect diﬀerent tribal backgrounds. Half of the respondents in each district were

read the candidate description with a name indigenous to the dominant tribal group in the area, while the other half were read the same description but for a candidate whose name

referenced a tribe which is not indigenous to the area. One shortcoming is that respondents

were not tested to see if they were aware of the candidate’s ethnic background. However,

to ensure that respondent’s understood the candidate’s ethnic background, the candidate’s

mother’s and father’s names were also given and also reﬂected the same tribal background as the candidate.2

mother fell sick, he returned to his mother’s house to care for her, and began working as a teacher. taught senior secondary school in this constituency for fifteen years. During that time Mr. ’s interest in politics led him to run for the position of District Assemblyman. He has served in that position for five years, and now believes he is qualified to run for the position of Member of Parliament in 2016.” 2 Names used for each district are as follows: (1) Bosome Freho - Insider (Asante): Joseph Kwabena Opokuware, Mother: Sarah Akua Prempeh, Father: John Kweku Opokuware; - Outsider (Akyem): Joseph Kwabena Attafuah, Mother: Sarah Akua Akyea, Father: John Kweku Attafuah; (2) Birim South - Insider (Akyem) : Joseph Kwabena Kwakye, Mother: Sarah Akua Akyea, Father: John Kweku Kwakye; - Outsider (Asante): Joseph Kwabena Opokuware, Mother: Sarah Akua Prempeh, Father: John Kweku Opokuware; (3) Adaklu Anyigbe - Insider (Vedome Ewe): Joseph Etornam Agbesi, Mother: Sarah Esinam Kwashie, Father: John Senyo Agbezuge; - Outsider (Anlo Ewe): Joseph Etornam Dogbatse, Mother: Sarah Esinam Amegashie, Father: John Senyo Dogbatse; (4) Ketu South - Insider (Anlo Ewe): Joseph Etornam Dogbatse, Mother: Sarah Esinam Amegashie, Father: John Senyo Dogbatse; - Outsider (Vedome Ewe): Joseph Etornam Agbesi, Mother: Sarah Esinam Kwashie, Father: John Senyo Agbezuge;

313 Names were chosen to emphasize tribal diﬀerences between the two candidates. The candidate, the candidate’s mother, and the candidate’s father were each given an English

name, a Day name3 , and a Tribal Surname. For most of the districts, the candidate’s and candidate’s parent’s day names conveyed the individuals’ ethno-linguistic group, while the tribal surname conveyed the tribe and/or part of the country the individuals are from. For instance, in Bosome Freho the insider candidate was Asante and the outsider candidate was

Akyem. Asantes and Akyems are both part of the Akan ethno-linguistic group, and as such their day names are virtually the same. In this case the candidate’s and mother’s surnames convey the tribe of the candidate, while the candidate’s, mother’s and father’s day names convey membership in the Akan ethno-linguistic group. The same is true of Ewe candidates in Adaklu Anyigbe and Ketu South where the candidate’s and mother’s surname conveyed either

Vedome-Ewe or Anlo-Ewe membership while the candidate’s, mother’s and father’s day names connoted membership to the broader Ewe ethno-linguistic group.

In the case of Mfantsiman and AOB, the tribal diﬀerences separating Fantes (the insider) from Asantes (the outsider) also allowed me to manipulate the individuals’ day names to reﬂect their tribal identities. Thus, for Mfantsiman, the insider candidate is Joseph Ebo Robertson

while the outsider candidate is Joseph Kwabena Opokuware. While the other districts’

candidates and family names convey tribal information with two names, the candidate and the

(5) Mfantsiman - Insider (Fante): Joseph Ebo Robertson, Mother: Sarah Kukua Eshun, Father: John Kweku Robertson; - Outsider (Asante): Joseph Kwabena Opokuware, Mother: Sarah Akua Prempeh, Father: John Kweku Opokuware; (6) Asikuma Odoben Brakwa - Insider (Fante): Joseph Ebo Robertson, Mother: Sarah Kukua Eshun, Father: John Kweku Robertson; - Outsider (Asante): Joseph Kwabena Opokuware, Mother: Sarah Akua Prempeh, Father: John Kweku Opokuware.

3 A Day name is a name given to a child based on the day of the week on which they were born. Day names generally diﬀer for males and females, but are often phonetically related (e.g. Kwabena (male) and Abena (female) Akan day names for those born on Tuesday). Day names diﬀer by ethno-linguistic group and sometimes tribe in Ghana.

314 mother’s respective surnames, in Mfantsiman and AOB the candidate and family convey tribal information with ﬁve of their names: all three individuals’ day names and the candidate’s and mother’s surnames.

The results from this test are mixed and surprising. When comparing average candidate rating, Question 2, for insider and outsider candidates, the outsider candidate received a higher average rating than the insider candidate for every single district. Further, in Bosome Freho,

Agotime Ziope, and AOB, which also happen to be the three districts selected for their unusual voting behaviors, the diﬀerences between candidate ratings were signiﬁcant at a p<0.1 level or better (Table 8-1).

Probing further to Question 3, whether or not the respondent says they would vote for the candidate, the treatment eﬀect on the candidate’s name no longer signiﬁcantly predicts the response except for in one district. Ketu South respondents now strongly say they will vote for the Anlo Ewe (insider) candidate (0.927) as compared to the Vedome Ewe (outsider) candidate

(0.688) (Table 8-2). To better understand what is happening here, I use linear regressions to predict respondents’ ratings of candidates, logistic regressions to predict respondents’ answers about whether or not they will vote for the candidate, and district-level bar charts to decipher patterns in respondents’ open-ended explanations about why they will or will not vote for the candidate. 8.1.1 Linear Regressions Predicting Candidate Ratings

In Tables 8-3 - 8-5 I present linear regressions predicting candidate rating within each district. I run two models for each district, rather than an overall model for the entire sample, because the diﬀerent candidate names paired with the unique tribal histories and circumstances in each district requires separate tests to determine why a candidate was or was not favored in each place. In each model, outsider candidates are controlled for, as well as an array of controls relating to political participation, trust, xenophobia, ethnicity, and political and economic perceptions.

315 First, I control for age because of the possibility that respondents of diﬀerent ages might respond to the experiment diﬀerently, though I do not have any particular expectations about

the direction of this potential eﬀect. I next control for whether respondents’ attended a

political rally (rally) in the 2012 campaign cycle to control for level of political participation.

Those respondents who are more interested in politics might be more critical or more accepting of the candidate as compared to those who are less interested. Third, I control for whether

the respondent is a member of the NDC because this was likely to impact respondents’

impressions of a NPP candidate in the NPP strongholds, and a NDC candidate in the NDC

strongholds and competitive districts. Fourth, I control for whether respondents view their

living conditions as better or worse than other Ghanaians across the country as both an individual wealth indicator as well as a control for the extent to which the respondent feels

government intervention and/or eﬀective leaders are necessary.

Fifth, I control for whether or not the respondent feels Ghana’s political parties have

different ideologies (diff.ideo), partially because the fictional candidate is attached to a particular political party and because this question is reflective of the degree of cynicism with

which the respondent engages with Ghana’s political system. Whether or not the respondent

views themselves as a consistent or swing voter (vote stays the same) could potentially impact

whether or not the respondent would be open-minded about supporting the ﬁctional candidate.

The degree of trust of speakers of diﬀerent dialects is controlled for because the outsider candidate speaks the same language as the insider candidate, but a diﬀerent local dialect.

Eighth, I control for whether or not respondents feel they are likely to vote for a MP not born

in the area (MP born) as a control for the willingness of the respondent to accept outsiders.

Education is controlled for next because individuals with higher levels of education tend to be more politically informed and/or engaged with politics. The tribal groups dominant in the districts under analysis are controlled for to test for whether it is respondents of the insider group who have particular opinions about the insider versus outsider candidate. Finally, a last control is whether or not the respondent agrees that the election of the MP brings change

316 within the community (MP Election change). The more the respondent disagrees with that sentiment, the higher the response on a 1 to 4 scale.

What the results generally show is that, after controlling for other factors, a candidate’s outsider status is not a significant predictor of candidate rating in five of the six districts. It is only in Bosome Freho where the respondents who received the outsider candidate significantly increased their ratings of the candidate. That a candidate’s outsider tribal background did not result in lower candidate ratings in any of the districts, and that the outsider identity resulted in higher candidate ratings in one of the districts, runs counter to the expected effect of outsider candidates.

In addition to the eﬀect of candidate outsider on Bosome Freho respondents’ candidate ratings, older respondents, those who believe the political parties have diﬀerent ideologies

(diff.ideo), those who vote for one political party (vote stays the same), those who are more likely to vote for a MP who was not born in the area (MP born), medium educated respondents (as opposed to low education respondents) (mededu), Asantes, and those who do not believe the election of the MP brings change within the community (MP Election change) are all significantly more likely to have given the candidate a higher rating. Variables associated with significantly lower ratings in Bosome Freho include those from NDC members, those whose living conditions is worse than other Ghanaians, and respondents who do not trust speakers of different dialects (Table 8-3). As for Birim South respondents, while outsider candidates were not a significant driver of respondent candidate ratings, older respondents, those with self-described worse living conditions, those who admit to being less likely to vote for a MP who was not born in the area (MP born), Akyems, and respondents who do not believe MP elections bring change

(MP Election change) were all signiﬁcantly associated with higher candidate ratings (Table 8-3).

In Adaklu Anyigbe and Ketu South, outsider candidates were not a signiﬁcant driver of candidate ratings (Table 8-4). As a respondent gave a more positive rating for the 2008

317 NDC government’s handling of the economy, the respondent was signiﬁcantly associated with increased candidate ratings in Adaklu Anyigbe and decreased candidate ratings in Ketu South.

Respondents who negatively rated the 2000 NPP government’s handling of the economy,

Ewes, and those who only vote for one political party (vote stays the same), are all signiﬁcantly associated with higher candidate ratings in Adaklu Anyigbe but not in Ketu South. Finally, respondents who were more likely to say they would vote for a MP who was not born in the area (MP born) were signiﬁcantly associated with lesser candidate ratings in Adaklu Anyigbe but higher candidate ratings in Ketu South.

Finally, outsider candidates were not significantly associated with candidate ratings in either Mfantsiman or AOB (Table 8-5). First, in Mfantsiman’s full model, those with better living conditions and who vote for one party were significantly associated with decreased candidate ratings, while those who gave positive ratings of the state of the current economy, and those who believe MP elections do not bring change (MP Election change) were significantly associated with increased candidate quality ratings. As for AOB, NDC members and those who believe MP elections do not bring change (MP Election change) were also significantly associated with higher candidate ratings. Respondents who trust speakers of different dialects, and those who are more likely to vote for an MP who was not born in the area were significantly associated with depreciated candidate ratings.

In conclusion, because an outsider candidate was only a signiﬁcant predictor of candidate ratings in Bosome Freho, there is likely something to be said about Asantes feelings about

Akyems in Bosome Freho. I cannot easily explain why Asantes in Bosome Freho would favor

Attafuah for MP over Opokuware for MP. Opokuware is a very prominent Asante name in

Ghana, and it was the surname of the Asantehene who ruled from 1970 to 1999. Perhaps there is some underlying relationship with Bosome Freho residents and the name Opokuware that biases respondents against the insider candidate. Or perhaps, for whatever reason, Bosome

Freho residents are more trusting of outsiders and/or Akyems than Asantes.

318 Still, it is amazing that no district favored a ‘son of the soil’ in terms of candidate ratings. That the candidate description depicted a person who had lived and worked in the constituency for 15 years as a teacher and an assemblyman was apparently enough to surpass any eﬀect the candidate’s tribal background had on respondents’ assessments of him as a resident of the district. 8.1.2 Logistic Regressions Predicting Candidate Votes

Though t-tests of difference showed higher candidate ratings for outsider candidates rather than insider candidates (Table 8-1), only in Ketu South did t-tests of difference significantly show a strong preference for the insider candidate when respondents were asked if they would vote for the candidate (Table 8-2). In Tables 8-6 through 8-8 I present odds ratios for logit models which predict whether respondents would vote for the candidate within each district.

Across all the districts, it is only in Adaklu Anyigbe and Ketu South that outsider candidates change the odds of a respondent voting for the candidate. In both of these districts, outsider candidates decreased the odds of a respondent saying they would vote for the candidate by 0.259 in Adaklu Anyigbe and 0.313 in Ketu South. In Bosome Freho (Table 8-6), NPP members and Asantes were signiﬁcantly more likely to increase the odds of voting for the candidate, while respondents with better living conditions and those who suggested it was likely the 2012 NDC government would develop the area signiﬁcantly decreased the odds of the respondent having voted for the candidate. For Birim

South, Model 4, older respondents and NPP members increased the odds of saying they would vote for the candidate, while those with better living conditions, whose vote stays the same, who is likely to support an MP not born in the area, and who believes Ghana’s political parties ideologies are more diﬀerent decreased the odds of a respondent saying they would vote for the candidate. The odds of voting for the candidate decreased when a respondent was exposed to an outsider candidate and if the respondent attended a political rally in 2012 in both Adaklu

Anyigbe and Ketu South. Respondents with better living conditions and Ewes increased the

319 odds of a respondent voting for the candidate in Adaklu Anyigbe while only those who said they would vote for an MP not born in the constituency signiﬁcantly increased the odds of a respondent voting for the candidate in Ketu South (Table 8-7).

Finally, Table 8-8 displays the odds ratios for respondents saying they would or would not vote for the candidate in Mfantsiman and AOB. Outsider candidates were not a significant driver of changes in the odds ratios for either Mfantsiman or AOB. For Mfantisman, those who had attended a rally, those who would vote for an MP not born in the area, and those who feel it is likely that big men and women can find out how they voted significantly decreased the odds of a respondent voting for the candidate, while saying that MP elections do not bring change significantly increased the odds of voting for the candidate. For AOB, respondents whose votes stay the same and those who would not vote for an MP not born in the area significantly decreased the odds that a respondent would vote for the candidate, while Fantes, those who feel it is likely that big men and women can find out how they voted, and those who say MP elections do not bring change significantly increased the odds of voting for the candidate.

Overall, though candidate ratings may have been in the favor of outsider candidates in Bosome Freho and AOB, these eﬀects faded away when respondents were asked if they would vote for the candidate. In the logit models presented it is only respondents in Adaklu

Anyigbe and Ketu South whose odds of having voted for the candidate significantly decrease when the candidate is an outsider. That Ewes are often said to be more ethnic-oriented than other groups in Ghana may help explain this result. But it is interesting that tribal differences between candidates was only significant in the two districts dominated by the Ewe ethno-linguistic group whose tribal differences are not captured by the Ghana census.

320 8.1.3 Categorical Analysis

Finally, in this last section I present bar charts depicting the categories of open-ended

responses used by respondents to explain why they would or would not vote for the candidate.4

Question 4 open-ended responses are divided into 8 categories:

(1) I will vote for him because of what the candidate can do; his qualities, past work, development evidence, etc. (2) I will vote for him because he is my party member (3) I will not vote for him because he is not my party member (4) I will vote for him because of who he is/he knows us/he came back and stayed here (5) I will not vote for him because he is doesn’t know us/hasn’t stayed long enough/we don’t know him (6) I will vote for him because I know the candidate5 (7) I will vote for him for some other reason (8) I will not vote for him for some other reason

4 I also used Word Tag Clouds (not shown) to qualitatively analyze the open-ended responses. The overall results show that respondents were generally interested in what the candidate can do. Party membership matters more in the NPP strongholds. Within the NDC strongholds, ‘community’ and ‘development’ were stated with greater frequency in Adaklu Anyigbe, whereas fewer words stood out from responses from Ketu South, though the prominence of ‘educated’ was relatively unique to the two NDC strongholds. Finally, whether the candidate can ‘help’ is of greater concern in the competitive districts, Mfantsiman and AOB, with ‘community/area’ and ‘develop’ not lagging far behind. The word tag clouds are interesting, ﬁrst, because they are generally positive in their outlook. ‘Can’ is a dominant word used across the districts as opposed to can’t or can not. As the districts whose residents were signiﬁcantly less likely to Vote for candidate outsiders, Adaklu Anyigbe and Ketu South’s word tag clouds were also largely positive in their outlook. Adaklu Anyigbe respondents focused on community and development, while Ketu South respondents were more varied in their word choices (i.e. not many words stood out for their common usage). Prior work in the Volta Region has alerted me to the fact that Ewes generally value education and it is thus not surprising that respondents in both of these districts more often noticed and reverberated that the candidate was educated.

5 A number of respondents within each district felt that, after the candidate description was read, that they knew the imaginary candidate. Respondents were only told after the survey that the candidate was imaginary. Still, this was an unexpected result. That some respondents felt they knew the candidate may be because there are only so many elites in districts outside of major cities, because small district populations mean particularly prominent residents are rarely ‘unknown’, or because the candidate’s name or description was similar enough to an existing person that respondents decided they probably knew him.

321 From Figure 8-1 above, it is clear that most respondents (59.5%) said they would vote for the candidate because of his potential eﬀectiveness/what he can do (i.e. his qualities, work and educational background, past developmental work, etc.). The next most commonly cited response (17.8%) cited something about the fact that the candidate is living in the area or is from the area as making him an ideal candidate. 9.9% said they liked the candidate because he is their party member, 3.7% believed they personally knew the candidate, and 4.4% made another sort of positive statement about the candidate. Overall only 13.1% said they would not vote for the candidate because he was not their party member (6.9%), is unknown in the community (1.8%), or some other sort of negative statement about the candidate (4.4%).

In Bosome Freho (Figure 8-2), respondents who received the candidate insider (control) treatment were less likely to say they would vote for the candidate based on his qualifications and more likely to say they would vote for the candidate because of his political party, despite the fact that an equal number of self-identified NPP members received both the candidate insider and candidate outsider groups in Bosome Freho. Perhaps something about the heavy Asante traditions associated with the name Opokuware (insider candidate) made people more likely to cite the party legacy. Similarly, a greater proportion of respondents receiving the insider treatment were more likely to say they would not vote for the candidate because the candidate is not in my party. However, looking back at the sample, 23 of 35 (65.7%) of self-identified NDC members happened to receive the candidate insider treatment in Bosome Freho, which may account for this difference.

As far as the candidate’s identity, here’s where things get interesting. 26.2% of those receiving the candidate insider treatment said that the candidate would be eﬀective because of who he is (i.e. he lives in the area and/or is from the area) as compared to 31.8% of respondents receiving the candidate outsider treatment. This suggests two possibilities. First, the respondents may not have recognized the tribal identities of the candidates. While it is virtually impossible for a Ghanaian not to know that Opokuware is an Asante name, other

Akan names are perhaps less famous and possibly less recognizable for that reason. So it

322 is possible that Bosome Freho respondents did not recognize Attafuah as an Akyem name or, though much less likely, as a non-Asante name. However, 5.2% of respondents receiving the candidate outsider treatment said they would not vote for the candidate because ‘we don’t know him’, ‘he hasn’t stayed here long enough’, etc, as compared to 0% of respondents receiving the candidate insider treatment. This leads to a second possibility: that respondents may or may not have recognized

Attafuah as an Akyem, but the fact that he had stayed in the community for a substantial period of time and that his mother lived there were more important than the candidate’s particular tribal background.

In comparison to Bosome Freho, respondents in Birim South (Figure 8-3) were more likely to justify their support of the candidate using the candidate’s qualiﬁcations (i.e. what he can do). Interestingly, despite the fact that both self-described NPP members and NDC members were virtually split between the two treatments (95 respondents versus 97 respondents; 45 and

40 respondents), a greater proportion of respondents receiving the candidate insider treatment said they wouldn’t vote for the candidate because he was not in their party (13.1% versus

10.0%) while almost twice the proportion of respondents receiving the candidate outsider treatment said they would vote for the candidate because he was their party member. Again, for NPP members, that the outsider candidate is named Opokuware might emphasize the importance of the Asante-NPP party legacy, even for non-Asantes. Alternatively, it might be that Birim South residents have to use his party status to justify their support of the outsider candidate since they cannot justify their support based on his being a native of the area.

Finally, that the candidate stays in the area and/or is from the area is of comparatively less importance to Birim South respondents. Still, at 11.9%, a greater proportion of respondents receiving the candidate outsider treatment said they would vote for the candidate because of where he stayed/was from, as compared to 8.1% receiving the insider treatment.

Again this suggests that the candidate stayed in the community for such a long time, and worked as a teacher, overcame the outsider candidate’s Asante background.

323 Responses were even less varied in Adaklu (Figure 8-4) as compared to Birim South. The vast majority of respondents in either treatment group cited the candidate’s abilities as the reason for their vote for him. And, despite the fact that 12 out of 21 NPP members in

Adaklu received the candidate insider treatment, that the candidate was not a member of the respondent’s political party was only cited twice (1.3%) by respondents receiving the candidate outsider treatment. That the candidate was a member of the NDC was not given as a reason for supporting him by any respondent in either treatment group. Third, that the candidate lived in the area or was from the area was cited by even fewer responses in Adaklu Anyigbe, with virtually no diﬀerence in the proportion of responses in either the candidate insider or candidate outsider treatment group. Further, a similar proportion of respondents in both groups said they wouldn’t vote for the candidate because the community didn’t know him or he had stayed outside for too long (5.2%- candidate insider; 4.6%- candidate outsider).

Of all the districts, Ketu South (Figure 8-5) respondents were the most likely to be missing in their explanation for why they would or would not vote for the candidate. Whereas 39 respondents in all 5 other districts were missing for Question 4, another 39 respondents within Ketu South alone were also missing for Question 4. Of these, 26 of 39 responses

(66.6%) were missing from the candidate outsider treatment.

In Ketu South, a greater proportion of respondents in the candidate insider treatment justiﬁed their support of the candidate based on his capabilities (78.8% vs. 51.5%) as well as on the fact that he lives in the area and/or is from the area (9.6% vs. 2.3%). Finally, a relatively high proportion of respondents gave positive-other and negative-other rationales for voting or not voting for the candidate. The majority of the ‘positive-other’ responses in Ketu

South are statements qualifying the respondent’s support, such as I will vote for him ‘if he can develop the community’, ‘if he will deliver’, ‘provided his mother’s character’, etc. Similarly, the ‘negative-other’ responses were largely made up of respondents who said they wouldn’t vote for the candidate because they personally do not know him and/or haven’t seen him as well as respondents who expressed preference for the current MP or another candidate.

324 In comparison to the other districts discussed thus far, Mfantsiman respondents’ rationales for supporting or not supporting the candidate (Figure 8-6) are almost as varied as Bosome

Freho. About 50% of respondents in the treatment and control group cited the candidate’s capabilities as their reason for voting. A higher proportion of respondents in the candidate outsider treatment group said they would vote for the candidate because he is in their political party. But, for Mfantsiman, 61 out of 114 (53.5%) self-declared NDC members happened to be exposed to the outsider treatment, as compared to 46.5% of the candidate insider control group, which probably accounts for some of that diﬀerence.

Importantly, Mfantsiman respondents who cited the candidate living in the area or being from the area as their reason for their vote for him is about 10% higher for the candidate insider control group than it is for the candidate outsider treatment group. Of all the candidates’ identities who could be mis-identiﬁed, the Robertson (Fante) - Opokuware

(Asante) candidate pairing in Mfantisman and AOB are the least likely to be confused.

But, having voted in an Asante MP (Asamoah Boateng) in the recent past, it perhaps also makes sense that almost no respondents challenged the outsider candidate as unknown in the community.

The responses from AOB (Figure 8-7) are very similar in proportion to those from

Mfantsiman. Around 50% of respondents in both the candidate insider control group and the candidate outsider treatment group cited the candidate’s eﬀectiveness/capabilities as their reason for voting for him. The next largest category were respondents who would vote for the candidate because he would be eﬀective because of who he is (i.e. he lived in and/or was from the area). Like Mfantsiman, about 10% more respondents in the candidate insider control group cited this reason as compared to the candidate outsider treatment group. Again, like Mfantsiman, no respondent challenged the candidate as being unknown in the area. For AOB, about 40% of each group’s respondents were self-declared NPP members and a similar proportion of respondents cited the candidate’s party as the reason for voting for him. About

30% of each group’s respondents were self-declared NDC members yet about 9.0% of the

325 candidate insider control group respondents cited the candidate’s political party as the reason for not voting for him as compared to 14.2% for the candidate outsider treatment group.

In conclusion, that Bosome Freho respondents were more likely to give the outsider candidate higher ratings in comparison to the insider candidate, even after holding for other factors in the linear regressions, is difficult to understand. Further, that no districts’ respondents were more likely to give the insider candidate higher ratings is surprising. But, when we turn to whether or not a district’s respondents would vote for the candidate, two districts respondents stand out for being more likely to vote for the insider candidate. That these two districts, Adaklu Anyigbe and Ketu South, are dominated by Ewes would initially be unsurprising to some who consider the Ewes more ethnically-oriented than other tribes. However, the candidate names being tested were both Ewe names as the test was rather a tribal test than an ethno-lingusitic bias test. If Ewes were more ‘inward-looking’ as is sometimes said, then we might expect that these two districts’ respondents would be the least likely to differentiate between an insider and outsider candidate so long as both candidates are Ewes. Instead what we see is that Adaklu Anyigbe residents are less likely to vote for a candidate from the south of the Volta Region while Ketu South residents are less likely to vote for a candidate from the middle of the Volta Region/northern Ewe territory. That tribal differences within the Ewe ethno-linguistic group are not captured by the Ghana census is surprising because, as is clear from my research and as this test result emphasizes, there are politicized differences between Ewe tribes.

Finally, it was clearly important to respondents that the candidate had such a robust education and work background, as answers like these were used by 59.5% of respondents to explain why they would vote for the candidate. However, the second category used by 17.8% of the sample to explain why they would vote for the candidate was that the candidate would be eﬀective because of his identity (i.e. that he was from the area, lived in the area, knew the people and their problems, etc.). So the candidate’s status as an insider or outsider did matter, but apparently respondents in four of the districts did not deﬁne that insider/outsider

326 status solely based on the candidate’s tribal background. In the two districts where the candidate’s name did matter, it was not insider/outsider status deﬁned based on Ewe-ness but rather intra-Ewe tribal identities which are not captured in Ghana’s Census and are largely misunderstood by Ghanaians from other ethno-linguistic group backgrounds.

8.2 List Experiments to Hide Undemocratic Beliefs/Behaviors

To further test for Hypothesis 1: Identity-Based Voting and Hypothesis 3: Clientelistic-Based Voting, I employed three list experiments. List experiments are used such that the response being tested is not immediately clear to the respondent when answering a question. Respondents are ideally supposed to feel more comfortable sharing sensitive beliefs or behaviors in list experiments since neither the survey administrator delivering the questionnaire nor anyone else will know whether any individual respondent identiﬁed the sensitive item of interest on the list as their own belief/behavior. The list experiments I ran tested for (a) Religious Biases, against

Muslims in particular, in voting for the President of Ghana and (b) Clientelism impacts on respondents’ 2012 votes for President or Vice President.

The entire survey sample was split into two, such that half received the control and half received the experimental manipulation. Version A delivered 5 items in the list experiment on religious bias (the manipulation) and 4 items each in the two clientelism list experiments (the control). Version B presented 4 items in the religious bias list experiment (the control) and 5 items on the two clientelism list experiments (the manipulation). For the Religious Bias List

Experiment, respondents were told: “I’m now going to read a list of ﬁve things that sometimes make people angry or upset. After I read all ﬁve statements, just tell me how many of them upset you. I don’t want to know which ones, just how many.” Version A respondents were then read a list with 5 items:

a The way gasoline prices keep going up b The amount of money Parliamentarians receive c People’s preference for hospitals over traditional medicine d That policemen and women carry guns

327 e Having a Muslim as President of Ghana6 Respondents who received Version B were read the same information, excluding item e,

“Having a Muslim as President of Ghana”. Testing for biases against Muslim politicians is an increasingly relevant question in Ghanaian politics. The politicization of religious divides has consistently been a close secondary to ethnic or class divides. But, with the election of Vice President Alhaji Aliu Mahama, who is Muslim, during Kufuor’s NPP regime, and the nomination of Mahamudu Bawumia as the VP on the NPP Presidential ticket in 2008 and

2012, have meant the possibility of having a Muslim President of Ghana is increasingly likely.

However, comments made by politicians in the media suggest that this possibility is still controversial.7 If having a Muslim as President of Ghana upset some respondents, then we should see a signiﬁcant diﬀerence between the average number of items selected in sub-samples Version

A and Version B. When comparing the average number of items selected that upset the

6 It is important to note that the items on the list were selected such that at least one item would be very unlikely to upset a respondent. This is done to increase the chances that no respondent is upset by all 4 or 5 items, thus canceling out their anonymity in answering the question. In this case, the two questions that were not expected to make respondents upset are Item c: people’s preference for hospitals over traditional medicine and Item d: That policemen and women carry guns. That people prefer hospitals over traditional medicine is not such a taboo subject in Ghana that respondents would be likely to be upset about it. Secondly, while respondents in the United States might say they are upset that police carry guns due to the politicization of police violence against citizens, this topic is not politicized in Ghana as there are very few events where police use their guns against citizens. As such, that police men and women carry guns is unlikely to make respondents upset.

7 For instance, in the 2011 Wikileaks release, Fiiﬁ Fiavi Kweetey, then Deputy Minister of Finance and Economic Planning, was exposed for having explained to oﬃcials of the US embassy that a Mulsim could never become the President of Ghana. Kwetey later defended his comments explaining that the NPP would use Muslim VPs to attract Muslim voters but would never nominate a Muslim/Northerner VP to succeed to the Presidential nomination. Further, since holding the VP slot on the NPP Presidential ticket, Bawumia receives a great deal of criticism from fellow NPP members, and derogatory comments about his Muslim/Northerner background are sometimes made.

328 respondent, the average for Version B (4 items) was 1.771 while the average for Version A (5 items) was 1.777 (Table 8-9). The difference between these two means is not statistically significant and thus suggests that including item e on the Question 29’s List Experiment did not have a statistically significant effect on how many items were selected. Put simply, this sample’s respondents did not appear to get upset at the thought of having a Muslim as President of Ghana.

However, Table 8-10 presents the district-level results from the Muslim bias list experiment. The diﬀerences in mean item responses selected were signiﬁcant and in the correct direction for Bosome Freho (p<.01), Adaklu Anyigbe (p<0.1), and Asikuma Odoben

Brakwa (p<0.05). In the case of Mfantsiman, the average number of items selected was actually higher for the 4-item questions versions than the 5-item versions. So it appears that the having a Muslim as President of Ghana did have a statistically signiﬁcant eﬀect in Bosome

Freho, Adaklu Anyigbe and AOB.8

Turning to the Clientelism List Experiments (Table 8-11), respondents who received Version A of the survey were read four items for Questions 31, how many items affected the respondents’ vote in the 2012 Presidential election, and Question 32, how many items affected the respondents’ vote in the 2012 Parliamentary election. Respondents who received Version B of the survey were read five items:

a The height of the Presidential (Parliamentary) candidate b The policies of the Presidential (Parliamentary) candidate c The likelihood of the Presidential (Parliamentary) candidate winning d Your family’s opinion about the Presidential (Parliamentary) candidate

8 These three districts, Bosome Freho, Adaklu Anyigbe, and Asikuma Odoben Brakwa, each represent a NPP stronghold, a NDC stronghold, and a competitive district, respectively. As my research was primarily concerned with politicized ethnicity rather than religion, I do not have a clear sense about why respondents in these three districts would have a greater sensitivity to a Muslim President as compared to the other three districts. More tests should be done to verify this result and identify the causal link between respondents’ sensitivity to this idea in some districts rather than others.

329 e Payouts, in the form of money or other gifts, provided by the Presidential (Parliamentary) candidate or his party boys9

If Item e: payouts provided by the candidate or his party boys, affected respondents’ votes for President or MP, we should see a statistically different average number of items selected in Question 31 and Question 32. As Table 8-11 demonstrates, the difference in the average number of responses selected for Version A and B is significant for Question 31 (p<0.05) and Question 32 (p<0.1). In Question 31, on average, respondents who received Version A

(4 items) selected 1.543 items as compared to 1.615 items in Version B (5 items). Similarly, in Question 32, respondents with Version A (4 items) selected an average of 1.507 items as compared to 1.570 items selected by respondents with Version B (5 items). It appears then that Ghanaians may not hold secret politicized biases against Muslims, but that they will admit to clientelistic payouts having impacted their votes when it is indirectly asked in an anonymous list experiment. However, in Tables 8-12 and 8-13 I consider the district-level responses for the Clientelism

List Experiments. The district-level results pertaining to the 2012 Presidential election shows that the significantly different mean number of responses presented in Table 8-11 is actually triggered by the statistically significant greater number of responses for the 5-item versions in Mfantsiman (p<0.000) and AOB (p<0.000), the competitive districts. Though this result could mean that respondents in the competitive districts are simply more honest, what is more likely is that clientelistic payments or gifts are more common in these areas where the MP seat is more up for grabs. Secondly, a very similar result is presented in Table 8-13, but this time for the 2012 Parliamentary races. Here again it is only the competitive districts (Mfantsiman-

9 As was the case with the Religious Bias List Experiment, one of the responses should have aﬀected very few respondents’ votes. In this case the unlikely item was Item a: The height of the candidate. Though candidate height may play a part in peoples’ perceptions about politicians in some contexts, candidate height appears unpoliticized in Ghana, particularly since the 2008 and 2012 NPP Presidential candidate, Nana Akufo Addo, is relatively short.

330 p<0.000; AOB- p<0.001) whose respondents are signiﬁcantly more likely to select more items on the 5-item version than the 4-item version of the list experiment.

This list experiment bolsters the findings from Question 34, where respondents were asked if they more strongly agreed that ‘When individuals take gifts from party officials and party ‘boys’, they vote for that political party during the election’ as compared to ‘When individuals take gifts from party officials and party ‘boys’, they sill vote the way they want, and not necessarily with the political party that gave the gift’. As presented in Table 8-14, about

81% of respondents in the sample agreed with Statement 2, that individuals still voted the way they wanted even if they took gifts from a political party. Yet about 19% of the sample still agreed that gifts during election time aﬀected people’s votes. Between the list experiment and this indirect question, it does seem that clientelistic gifts have somewhat of an eﬀect on voting behavior, though this may be limited to areas with competitive MP races.

8.3 Discussion

In conclusion, even when testing for the isolated effects of ethnic or religious biases, or the effects of clientelistic-inducements, the results are mixed. Within the tribal experiment, one district favored the ethnic outsider candidate in candidate ratings, while all the others did not show a difference in candidate ratings when controlling for other factors. But when asked if the respondent would vote for the candidate, two districts’ respondents, the NDC strongholds, responded that they would vote for the insider candidate by a statistically significant margin over the outsider candidate. Second, there appeared to be no support for biases against Muslim candidates by the overall sample, but when broken down to the district level, three districts’ respondents did appear to be upset by the idea of a Muslim President of Ghana. Finally, the list experiments testing for the effects of clientelistic-inducements on respondents’ votes found a significant difference in both the Presidential and Parliamentary questions. However, when broken down to the district level, it was actually only respondents from the competitive districts which fueled this outcome.

331 Given that it was difficult to ascertain support for Hypothesis 1 (Identity-Based Voting) even via indirect means or hidden list experiments, it is difficult to tell whether competitive local politics is having a positive or negative effect on tribal/ethnic or religious biases. The results suggest that further implicit tests should be conducted to verify the effect of these factors on voting. That the candidate experiment employed in this survey did not have the expected effect might mean that respondents are not biased against candidates who are not

‘sons of the soil’ or that the fact that the hypothetical candidate had lived and worked in the areas as a teacher and assemblyman for 15+ years allowed respondents to largely overlook the candidate’s tribal background. Further, it is interesting but unclear why bias against a Muslim

President would appear to be a factor in only half of the districts: one NPP stronghold, one NDC stronghold, and one competitive district.

Finally, the results from the clientelistic list experiment suggests that clientelistic inducements are more common in constituencies which have a history of voting in MPs of diﬀerent political parties. This ﬁnding demonstrates the importance of testing for sensitive behavior with list experiments, considering the very small support for Clientelistic-Based Voting

(Hypothesis 3) in the analysis of more direct survey questions in Chapters 6 and 7. Though this result needs to be veriﬁed, if true on a national-level, it also suggests that the level of political competition between DCEs and MPs does not drive up clientelistic-inducements as compared to constituencies with hotly contested battles for MP and Presidential votes. Of course, each of the results from the tests showcased in this chapter should be veriﬁed with further implicit tests of tribal/ethnic voting bias, religious bias, and clientelistic-inducements in

Ghana and other sub-Saharan African nations.

332 Table 8-1. Average candidate ratings t-test per district

District x, y mean of x mean of y t df p-value

Bosome outsider, 7.369 6.755 -2.159 298 0.032** Freho insider Birim South outsider, 7.145 6.816 -1.203 315 0.230 insider Agotime outsider, 7.637 7.147 -1.798 260 0.073* Ziope insider Ketu South outsider, 6.877 6.641 -1.052 306 0.294 insider Mfantsiman outsider, 7.255 7.497 1.044 316 0.298 insider Asikuma outsider, 7.570 7.031 -1.829 317 0.068* Odoben insider Brakwa Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

333 Table 8-2. Vote for candidate? t-tests

District x, y mean of x mean of y t df p-value

Bosome outsider, 0.903 0.852 -1.371 314 0.172 Freho insider Birim South outsider, 0.875 0.855 -0.513 317 0.609 insider Agotime outsider, 0.919 0.877 -1.137 261 0.257 Ziope insider Ketu South outsider, 0.688 0.927 3.509 287 0.001*** insider Mfantsiman outsider, 0.861 0.890 0.780 319 0.436 insider Asikuma outsider, 0.859 0.880 0.545 318 0.586 Odoben insider Brakwa

Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

334 Table 8-3. Linear regression predicting candidate rating Dependent variable: q2 Bosome Freho Birim South (1) (2) (3) (4) candidateoutsider 0.846∗∗∗ 0.496∗ 0.252 0.282 (0.258) (0.270) (0.276) (0.293) age 0.008 0.018∗∗ 0.024∗∗ 0.023∗ (0.008) (0.008) (0.011) (0.012) rally 0.733∗∗∗ 0.284 (0.248) (0.296) NDCmember -2.133∗∗∗ -1.643∗∗∗ -0.415 -0.231 (0.395) (0.374) (0.316) (0.353) living condition -0.700∗∗∗ -0.729∗∗∗ -0.559∗∗∗ -0.557∗∗∗ (0.171) (0.174) (0.123) (0.148) diﬀ.ideo 0.707∗∗ -0.168 (0.277) (0.250) vote stays the 0.555∗∗ -0.177 same (0.256) (0.274) trust diﬀdialect -0.317∗∗ -0.104 (0.138) (0.150) MP born 0.436∗∗∗ -0.289∗ (0.095) (0.152) highedu 0.580 -0.428 (0.608) (0.714) mededu 0.777∗∗∗ -0.364 (0.246) (0.327) asante 1.047∗∗∗ (0.291) akyem 0.730∗∗ (0.306) MP Election change 1.373∗∗∗ 0.685∗ (0.330) (0.364) Constant 5.665∗∗∗ 2.942∗∗∗ 5.688∗∗∗ 6.212∗∗∗ (0.415) (0.591) (0.543) (0.733) Observations 62 205 282 253 Adjusted R2 0.271 0.515 0.093 0.121 Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

335 Table 8-4. Linear regression predicting candidate rating Dependent variable: q2 Adaklu Anyigbe Ketu South (1) (2) (3) (4) candidateoutsider 0.059 0.004 0.335 0.384 (0.307) (0.319) (0.286) (0.289) age -0.005 0.001 0.011 -0.0001 (0.009) (0.010) (0.009) (0.009) rally -0.003 0.183 (0.317) (0.283) NDCmember 0.446 0.340 -0.327 -0.103 (0.375) (0.399) (0.373) (0.392) living condition 0.233 0.364∗ 0.080 0.066 (0.202) (0.215) (0.144) (0.157) trust diﬀdialect -0.220 -0.036 0.196 0.127 (0.230) (0.237) (0.144) (0.151) econ current -0.051 -0.097 (0.139) (0.122) ewe 2.167∗∗∗ 0.178 (0.778) (0.824) vote stays the 0.425 -0.264 same (0.278) (0.233) MP born -0.221 0.566∗∗∗ (0.149) (0.130) Constant 7.350∗∗∗ 4.990∗∗∗ 6.237∗∗∗ 5.629∗∗∗ (0.592) (0.900) (0.514) (0.925) Observations 211 200 250 227 R2 0.021 0.082 0.027 0.120 Adjusted R2 -0.008 0.038 0.003 0.083 Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

336 Table 8-5. Linear regression predicting candidate rating Dependent variable: q2 Mfantsiman AOB (1) (2) (3) (4) candidateoutsider -0.337 -0.209 0.412 0.527 (0.253) (0.253) (0.343) (0.330) age -0.001 -0.013 -0.001 0.004 (0.009) (0.009) (0.011) (0.011) rally -0.269 -0.395 (0.259) (0.320) NDCmember 0.312 0.298 1.121∗∗∗ 0.895∗∗ (0.267) (0.266) (0.367) (0.360) living condition -0.217 -0.295∗ -0.023 -0.086 (0.137) (0.156) (0.180) (0.210) trust diﬀdialect -0.347∗∗ -0.060 -0.642∗∗∗ -0.451∗∗∗ (0.140) (0.150) (0.159) (0.161) econ current 0.427∗∗∗ 0.320 (0.129) (0.228) fante 0.104 0.290 (0.481) (0.318) vote stays the -0.286 -0.396 same (0.181) (0.286) MP born -0.015 -0.364∗∗∗ (0.108) (0.127) MP Election change 1.595∗∗∗ 1.308∗∗∗ (0.274) (0.351) Constant 7.558∗∗∗ 7.535∗∗∗ 7.345∗∗∗ 7.356∗∗∗ (0.446) (0.678) (0.570) (0.717) Observations 272 244 255 232 R2 0.044 0.206 0.107 0.250 Adjusted R2 0.022 0.172 0.085 0.216 Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

337 Table 8-6. Logistic regressions (odds ratios) predicting votes for the candidate q3 Bosome Freho Birim South (1) (2) (3) (4) candidateoutsider 2.384∗(0.944, 6.514) 1.828 (0.462, 7.987) 0.812 (0.346, 1.871) 0.583 (0.212, 1.522) age 0.986 (0.959, 1.014) 1.007 (0.970, 1.049) 1.034∗(1.000, 1.072) 1.035∗(0.996, 1.080) female 1.478 (0.596, 3.783) 0.912 (0.244, 3.303) 0.679 (0.292, 1.555) 0.648 (0.241, 1.695) rally 1.490 (0.613, 3.736) 1.261 (0.368, 4.500) 0.623 (0.248, 1.515) 0.848 (0.292, 2.415) NPPmember 6.700∗∗∗(2.550, 21.029) 4.767∗∗(1.158, 22.406) 11.256∗∗∗(4.531, 32.585) 14.215∗∗∗(5.024, 47.065) living condition 0.420∗∗∗(0.247, 0.691) 0.376∗∗(0.168, 0.776) 0.619∗∗(0.393, 0.952) 0.543∗∗(0.310, 0.913) 2012 NDC Dev. 0.377∗∗∗(0.176, 0.718) ∗∗ 338 vote stays the same 0.813 (0.204, 2.598) 0.205 (0.033, 0.674) MP born 0.421∗∗∗(0.251, 0.668) akyem 0.966 (0.357, 2.558) trust diﬀdialect 1.298 (0.611, 2.853) 0.795 (0.516, 1.222) 0.823 (0.492, 1.378) vote isn’t secret 0.914 (0.469, 1.755) MP Election change 0.845 (0.135, 6.017) Asante 3.620∗∗(1.105, 12.476) diﬀ.ideo 0.457∗(0.182, 1.000) Constant 1.844 (0.441, 7.901) 2.348 (0.152, 42.323) 1.620 (0.246, 10.983) 38.110∗∗(2.700, 797.811)

Observations 273 196 275 253 Log Likelihood -73.098 -45.222 -77.064 -60.338 Akaike Inf. Crit. 160.195 116.445 170.127 144.676 Note: ∗p < 0.1;∗∗ p < 0.05;∗∗∗ p < 0.01 Table 8-7. Logistic regressions (odds ratios) predicting votes for the candidate q3 Adaklu Anyigbe Ketu South (1) (2) (3) (4)

candidateoutsider 0.818 (0.294, 2.241) 0.340∗(0.091, 1.166) 0.294∗∗∗(0.131, 0.639) 0.313∗∗(0.124, 0.749) age 0.986 (0.956, 1.017) 0.989 (0.955, 1.027) 1.007 (0.985, 1.032) female 1.807 (0.640, 5.613) 1.666 (0.527, 5.752) 1.490 (0.758, 2.986) rally 0.330 (0.072, 1.096) 0.251 (0.034, 1.092) 0.513 (0.215, 1.158) 0.367∗∗(0.142, 0.881) NDCmember 2.109 (0.693, 6.012) 1.898 (0.452, 7.403) 0.526 (0.162, 1.451) 0.757 (0.191, 2.466) ∗ ∗ 339 living condition 2.141 (1.027, 4.996) 2.236 (0.981, 5.701) 1.013 (0.693, 1.480) 0.942 (0.587, 1.516) trust diﬀdialect 1.094 (0.511, 2.769) 1.380 (0.507, 4.905) 1.388 (0.890, 2.329) 1.237 (0.756, 2.154) diﬀ.ideo 1.219 (0.420, 3.328) 0.404 (0.095, 1.202) vote stays the same 1.284 (0.419, 3.437) 0.743 (0.298, 1.558) MP born 1.482 (0.824, 2.929) 1.804∗∗∗(1.243, 2.663) ewe 15.216∗∗(1.591, 126.200) 0.729 (0.030, 7.652) econ current 1.087 (0.652, 1.820) 1.081 (0.752, 1.575) Constant 30.356∗∗∗(4.117, 288.502) 2.486 (0.142, 55.507) 10.813∗∗∗(2.496, 54.363) 22.632∗(1.327, 845.097)

Observations 213 192 236 203 Log Likelihood -57.182 -47.762 -107.740 -89.935 Akaike Inf. Crit. 130.365 121.524 231.481 201.870 Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01 Table 8-8. Logistic regressions (odds ratios) predicting votes for the candidate q3 Mfantsiman AOB (1) (2) (3) (4)

candidate outsider 0.797 (0.390, 1.612) 1.011 (0.421, 2.433) 0.735 (0.336, 1.626) 1.013 (0.365, 2.910) age 0.992 (0.968, 1.018) 0.988 (0.954, 1.024) 0.973∗∗(0.946, 1.000) 0.974 (0.939, 1.008) female 0.695 (0.333, 1.415) 0.477 (0.189, 1.142) 0.671 (0.307, 1.443) rally 0.426∗∗(0.196, 0.884) 0.269∗∗∗(0.101, 0.658) 0.453∗∗(0.207, 0.963) 0.575 (0.197, 1.620) NDCmember 1.105 (0.535, 2.343) 1.472 (0.586, 3.883) 7.489∗∗∗(2.461, 29.624) 1.873 (0.509, 8.645) living condition 1.004 (0.677, 1.526) 1.039 (0.651, 1.704) 0.664∗∗(0.442, 1.000) 0.947 (0.513, 1.758) ∗∗∗ ∗∗∗ 340 trust diﬀdialect 0.591 (0.401, 0.862) 0.689 (0.412, 1.158) 0.572 (0.380, 0.840) 0.649 (0.352, 1.154) diﬀ.ideo 0.605 (0.101, 2.162) 1.530 (0.242, 6.603) vote stays the same 0.620 (0.244, 1.298) 0.284∗(0.045, 0.941) MP born 0.700∗(0.484, 1.000) 0.576∗∗(0.365, 0.881) fante 2.132 (0.407, 9.165) 2.6724∗(0.990, 7.911) econ current 1.506 (0.533, 5.927) vote isn’t secret 0.621∗∗(0.411, 0.917) 1.481∗(0.990, 2.286) MP Election change 3.177∗∗(1.146, 10.045) 5.259∗(1.182, 38.778) Constant 30.418∗∗∗(7.339, 142.037) 101.291∗∗∗(7.157, 2, 037.215) 34.337∗∗∗(7.066, 193.741) 96.651∗∗(3.074, 5, 918.055)

Observations 273 224 255 217 Log Likelihood -105.606 -74.563 -91.313 -59.206 Akaike Inf. Crit. 227.211 177.125 198.625 146.412 Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01 Table 8-9. Q29: Muslim president list experiment Obs Mean SD 95% CI 341 Q29 4 items 888 1.771 0.753 (1.722, 1.821) 5 items 951 1.777 0.785 (1.727, 1.827) t = -0.1582, df = 1837, p-value = 0.8743 Table 8-10. Q29: Muslim president list experiment- by district Obs Mean SD 95% CI Bosome Freho1 4 items 155 1.548 0.704 (1.437, 1.660) 5 items 106 1.802 0.653 (1.676, 1.928)

Birim South2 4 items 158 1.835 0.647 (1.734, 1.937) 5 items 154 1.708 0.840 (1.574, 1.841)

Adaklu Anyigbe3 4 items 163 1.926 0.920 (1.784, 2.069) 5 items 164 2.079 0.726 (1.967, 2.191)

Ketu South4 342 4 items 155 2.116 0.756 (1.996, 2.236) 5 items 149 2.121 0.744 (2.000, 2.241)

Mfantsiman5 4 items 165 1.885 0.768 (1.767, 2.003) 5 items 155 1.432 0.603 (1.337, 1.528)

AOB6 4 items 155 1.335 0.617 (1.238, 1.433) 5 items 160 1.5 0.624 (1.403, 1.598)

1t = 2.9399, df = 259, p-value = 0.0036 2t = -1.5065, df = 310, p-value = 0.1330 3t = 1.6685, df = 325, p-value = 0.0962 4t = 0.0544, df = 302, p-value = 0.9567 5t = -5.8360, df = 318, p-value = 0.0000 6t = 2.3521, df = 313, p-value = 0.0193 Table 8-11. Clientelism list experiments

Obs Mean SD 95% CI Q31- President1 4 items 961 1.543 0.772 (1.494, 1.592) 343 5 items 894 1.615 0.746 (1.566, 1.664)

Q32- MP2 4 items 961 1.507 0.720 (1.461, 1.552) 5 items 892 1.570 0.705 (1.523, 1.616) 1t = 2.0398, df = 1853, p-value = 0.0415 2t = 1.8928, df = 1851, p-value = 0.0585 Table 8-12. Clientelism list experiments- 2012 Presidential election by district

Obs Mean SD 95% CI Bosome Freho1 4 items 105 1.790 0.631 (1.668, 1.913) 5 items 157 1.089 0.414 (1.024, 1.154)

Birim South2 4 items 154 1.494 0.669 (1.387, 1.600) 5 items 157 1.287 0.520 (1.205, 1.369)

Adaklu Anyigbe3 4 items 167 1.976 0.768 (1.859, 2.093) 5 items 165 1.6 0.861 (1.468, 1.732)

344 Ketu South 4 items 156 2.045 0.806 (1.917, 2.172) 5 items 158 1.829 0.839 (1.697, 1.961)

Mfantsiman5 4 items 154 1.123 0.367 (1.065, 1.182) 5 items 166 1.687 0.659 (1.586, 1.788)

AOB6 4 items 158 1.291 0.611 (1.195, 1.387) 5 items 158 1.753 0.922 (1.608, 1.898)

1t = 10.8662, df = 260, p-value = 0.0000 2t = 3.0490, df = 309, p-value = 0.0025 3t = 4.2006, df = 330, p-value = 0.0000 4t = 2.3241, df = 312, p-value = 0.0208 5t = -9.3431, df = 318, p-value = 0.0000 6t = -5.2519, df = 314, p-value = 0.0000 Table 8-13. Clientelism list experiments- 2012 Parliamentary elections by district

Obs Mean SD 95% CI Bosome Freho1 4 items 104 1.75 0.570 (1.639, 1.861) 5 items 157 1.089 0.398 (1.026, 1.152)

Birim South2 4 items 153 1.471 0.618 (1.372, 1.569) 5 items 158 1.278 0.516 (1.197, 1.360)

Adaklu Anyigbe3 4 items 166 1.898 0.719 (1.787, 2.008) 5 items 165 1.606 0.881 (1.471, 1.742)

345 Ketu South 4 items 155 1.942 0.766 (1.820, 2.064) 5 items 159 1.818 0.786 (1.694, 1.941)

Mfantsiman5 4 items 154 1.110 0.405 (1.046, 1.175) 5 items 164 1.652 0.661 (1.551, 1.754)

AOB6 4 items 160 1.288 0.618 (1.191, 1.384) 5 items 158 1.582 0.707 (1.471, 1.693)

1t = 11.0212, df = 259, p-value = 0.0000 2t = 2.9793, df = 309, p-value = 0.0031 3t = 3.2986, df = 329, p-value = 0.0011 4t = 1.4182, df = 312, p-value = 0.1571 5t = -8.7500, df = 316, p-value = 0.0000 6t = -3.9599, df = 316, p-value = 0.0001 Table 8-14. Q34: Choose the Statement Which Is Closest To Your View Response category Frequency Percent

Agree very strongly w/statement 1 153 8.54% Agree w/statement 1 189 10.55% 346 Agree w/statement 2 576 32.16% Agree very strongly w/statement 2 873 48.74% Agree with neither 0 0.00% Total 1,791 100% Statement 1: when individuals take gifts from party oﬃcials and party ‘boys’, they vote for that political party during the election. Statement 2:when individuals take gifts from party oﬃcials and party ‘boys’, they still vote the way they want, and not necessarily with the political party that gave the gift. Figure 8-1. Vote for candidate- all districts

347 Figure 8-2. Vote for candidate- Bosome Freho

348 Figure 8-3. Vote for candidate- Birim South

349 Figure 8-4. Vote for candidate- Adaklu Anyigbe

350 Figure 8-5. Vote for candidate- Ketu South

351 Figure 8-6. Vote for candidate- Mfantsiman

352 Figure 8-7. Vote for candidate- Asikuma Odoben Brakwa

353 CHAPTER 9 CONCLUSION

This work has made the argument that Ghana’s national-level institutions have encouraged competitive national elections while its centralized system of local government induces real political competition at the grassroots level, and thus contributes to a lessening of neopatrimonialism and ethnic voting. The ﬁrst chapter introduced the overall argument, and the second chapter traced the historical development of centralized institutions and the ethnic reactions they provoked. The third chapter shows how the institutions of the

Fourth Republic continue the centralized reforms of past regimes, except now political competition is institutionalized at the local level via the competitive relationship between the centrally-appointed DCE and locally-elected MP(s).

Chapters 4 and 5 use Ecological Inference models and OLS regressions to analyze vote volatility in the Fourth Republic. In particular, the EI models in Chapter 4 show that ethno-linguistic groups, and particularly the Akans, are not unified in their voting patterns, and that ethno-linguistic and tribal votes for opposition parties have been on the rise. Chapter 5 then showed statistically significant differences in voting patterns in districts with high levels of local competition as compared to those with low levels of local competition. When the party of the centrally-appointed District Chief Executive differed from the party of the locally-elected Member of Parliament, votes for the DCE’s party increased in the next election as compared to districts where the DCE and MP were of the same political party. This effect was significant across presidential administrations.

Finally Chapters 6-8 presented the analysis of a survey conducted in 6 purposefully-selected districts in Ghana. Three competing factors are thought to contribute to citizens’ votes: (1) Identity-Based Voting; (2) Policy or Economic-Based Voting; and (3) Clientelistic-Based

Voting. These hypotheses are tested in the 3 district pairs (a NPP stronghold, a NDC stronghold, and a competitive district pair), where evidence supporting each hypothesis is

354 found via qualitative analysis of district-level politics and survey analysis of self-report vote data and survey experiments across Chapters 6-8.

9.1 The Mixed-Methods Research Design

This dissertation utilized a number of analytic tools to understand political and vote dynamics in Ghana’s Fourth Republic. The combination of these tools (historical analysis, over

140 interviews of formal and informal political elites, census and electoral data, qualitative data, and survey data) has many beneﬁts in terms of the type of analytic inferences it allows.

The data I use include historical data (including archival research), national-level data on institutions and vote behavior, sub-national-level data on institutions and vote behavior, and

finally individual-level data on vote behavior. By engaging in a mixed-methods research design, I intend to acknowledge that each method offers different potential contributions to achieving causal inference. By using historical data, I tease out the ways in which centralized institutions and ethnic politicization developed in conjunction with one another throughout Ghana’s history. In the past, Ghana’s centralized institutions both produced and responded to broad-based national-level ethno-linguistic cleavages. Knowing this history then highlighted the notable ways in which the current system of centralization in Ghana was instead producing divisions and complexities within national-level ethno-linguistic groups. In Chapter 3 I detail the dynamics of the Fourth Republic institutions and theorize how the centralized appointment of a DCE coupled with local-level elections of

MPs creates a competitive dynamic previously missing from the past centralized democratic and authoritarian regimes in Ghana. Essentially, by granting districts local representation in

Parliament and well-funded district assemblies headed by Presidential appointees, local-level politics is of greater importance and relevance in terms of tangible outcomes than it had been in prior times. In Chapter 4, I then test that theoretical framework using Ecological Inference models for a national-level analysis of ethno-linguistic and tribal level voting patterns. That analysis shows that core party supporters and particularly peripheral party supporters are increasingly willing to

355 vote against their ethnic group voting tradition. In Chapter 5 I then tie that volatility in ethnic votes to the institutional mechanism of Unfriendly DCE-MP pairs using constituency-level voting data, essentially ﬁnding that vote volatility increases in the elections after the presence of an Unfriendly DCE-MP pair as compared to Friendly Pairs.

The analysis through Chapter 5 has focused on historical, national, district, and constituency-level trends of group behavior. If competitive subnational political environments are lessening neopatrimonial and ethnic voting incentives then on-the-ground research should bear some of this out. In Chapter 6 I present a qualitative analysis of district-level politics in 6 purposefully selected districts in southern Ghana. Districts were selected on the basis of similar population, including ethnic, demographics and electoral voting patterns but with signiﬁcant diﬀerences in volatility in at least one election. The qualitative analysis, based on interviews with district political elites at both the national and local level, as well as local-level traditional elites and community leaders, demonstrated that explanations of Identity-Based factors,

Economic or Policy-Based factors, and Clientelistic-Based factors each had some traction at the local level. Interestingly, however, the identity-based cleavages which were relevant were not based on ethno-linguistic cleavages but rather existed on the basis of tribe, town, or traditional area.

Finally, the survey analysis of individual-level vote incentives also provided varying degrees of support for identity-based, economic or policy-based, and clientelistic-based voting. Diﬀerent survey questions provided evidence for diﬀerent voting rationales. Economic or Policy-Based

Voting received the strongest support, particularly when individuals were actively describing

356 their or their community-members’ reasons for voting. It was only within the less direct or hidden questions that identity-based1 or clientelistic-based voting incentives were isolated.2

Overall, the mixed-methods research design was used to provide evidence of the underlying causal mechanism that Ghana’s centralized system had increased political competition and

1 The tribal experiment which manipulated candidate names to represent different tribal identities did not provide consistent results either for or against Hypothesis 2. However, when asked if they would vote for the fictional candidate, respondents in the NDC strongholds did favor the insider candidate to a statistically significant degree more than the outsider candidate. It is interesting that the only districts in which the insider candidate was favored were dominated by Ewes, whose tribal difference are not captured by the Ghanaian census. These experimental results go some way in suggesting that Ewe tribal identities are politicized. However, the overall lack of bias in favor of the insider candidate across the districts was surprising. That the insider did not receive consistent partial treatment might be because the candidate’s ties to the constituency were otherwise so strong that respondents considered him a ‘son of the soil’ regardless of his tribal background. While it is possible that respondents are not biased of local candidates in terms of their ethnic backgrounds, it is also certainly the case that local identities are very politicized within each district. Analysis of the open-ended responses about why respondents would or would not vote for the candidate show that respondents do care a great deal about the candidate’s living in the area/being from the area. But perhaps name indicators of tribal identity are not enough to capture the complexity of local politicized identities. More testing is required to rule out political bias against candidates from outsider tribes. Perhaps an alternative experiment in the future would test for ethno-linguistic candidate differences against tribal differences. 2 Finally, the list experiments used to test for the effect of clientelistic inducements on respondent vote decisions are the only questions which provide evidence for Hypothesis 3: Clientelism-Based Voting. That such evidence was produced only after hiding the clientelism question in a list experiment suggests the great extent to which Ghanaian voters either do not consider clientelistic inducements as having a big impact on their vote decisions or that they do not want to admit it. Either way, hidden experimental question types were the only questions providing the most prominent evidence for this hypothesis. Of course, it is also extremely telling that, once broken down to the district-level, evidence of clientelistic impacts on voting were only found in the competitive districts (i.e. the districts which have voted in MPs of both the NDC and NPP in the Fourth Republic), Mfantsiman and Asikuma Odoben Brakwa. This finding suggests that close elections do fuel clientelistic payouts to individual voters. However, by extension, this suggests that the increased political competition between centrally-appointed DCEs and locally-elected MPs of different political parties is not fueling patronage battles. This interpretation is backed by the research I have done which points to escalating development initiatives as the avenue through which the DCE and MP increase their parties’ local levels of support.

357 contributed to a lessening of neopatrimonial and ethnic voting incentives. Diﬀerent methods provided critical insights into the validity of causal logic. While the historical and current institutional data provided the framework for the causal argument, and the national-level and constituency-level statistical analyses tied numerical trends to the proposed causal mechanism, the qualitative analysis and survey data took the analysis to the individual level. Overall the qualitative evidence suggests the decreasing relevance of national-level ethno-linguistic identities is plausible while showing that now ethnic mobilization takes the form of local-level identities (tribe, town, traditional area). The system of local government both empowers local-level decision makers and makes local-level politics extremely relevant for individual livelihoods. However, though the survey evidence does overwhelmingly support voters’ consideration of economic or policy-related issues, it provides mixed-evidence of the relevance of local-level identities.3 That this evidence does not perfectly align with the qualitative evidence requires a reconsideration of the survey tools used, particularly since the tests for the politicization of local-level identities were non-exhaustive. Simply put, there is a great deal of room for more research on identity politicization at the individual level.

9.2 Contributions to the Literature

Outside of Research Methods, this dissertation also makes several contributions to the broad literatures on (1) Democratic Theory and (2) Ethnic Politics.

3 In the candidate experiment in Chapter 8, the overall lack of bias in favor of the insider candidate across the districts was surprising. That the insider did not receive consistent partial treatment might be because the candidate’s ties to the constituency were otherwise so strong that respondents considered him a ‘son of the soil’ regardless of his tribal background. While it is possible that respondents are not biased of local candidates in terms of their ethnic backgrounds, it is also certainly the case that local identities are very politicized within each district. Analysis of the open-ended responses about why respondents would or would not vote for the candidate show that respondents do care a great deal about the candidate’s living in the area/being from the area. But perhaps name indicators of tribal identity are not enough to capture the complexity of local politicized identities. More testing is required to rule out political bias against candidates from outsider tribes. Perhaps an alternative experiment in the future would test for ethno-linguistic candidate diﬀerences against tribal diﬀerences.

358 9.2.1 Contributions to Democratization Theory

First, this research makes a contribution to Democratization Theory in arguing that both sub-national institutional design and the regulation of mobilizable politicized cleavages are important considerations when setting up institutions in new democracies. I have made the argument that sub-national institutional design is as important, if not more important, for ensuring the overall quality of a democracy, particularly at the sub-national level. This work looks at the important eﬀects of local political competition, pointing out that national-level democratic transitions, even in the context of stable political competition, does not mean that citizens are also getting the opportunity to make retrospective and prospective vote choices in local elections. By far the largest contingent of scholars studying local government institutions tout the democratic beneﬁts of decentralized institutions for local communities. Assuming power is politically, administratively and economically devolved (e.g., Burki et al 1997; Falleti 2005;

Willis, Garman, and Haggard 1999; Filippetti and Sacchi 2013), decentralization is assumed to offer a wide range of democratic benefits. Yet many scholars are less specific about these benefits as compared to their attention to the problems associated with incomplete decentralization. It can generally be discerned that decentralization is supposed to increase local autonomy, which can act as a buffer against the state. Similarly, decentralization is supposed to provide a system of democratic governance at the local level which comes with the presumed benefits of increased community involvement and voter turnout (never mind that American voters need to be continuously reminded that local elections have a greater impact on their lives than do national elections).

The local government reform that was instead implemented in Ghana, as Rondinelli (1990) warns against, is that the central government deconcentrated its bureaucracies, installing local-level versions of national-level departments in the districts without also promoting political or economic decentralization. This system thereby allows deeper penetration of power and control by the central state in localities. Yet, what my argument also emphasizes, and

359 which is of particular concern within developing nations, is that the generalized public may not feel the eﬀects of decentralized institutions in new democracies, particularly in terms of local development within their communities. In low development contexts, local revenue cannot solely be raised from local tax bases and instead have to rely on transfers from the central government which, particularly in sub-Saharan Africa, is itself monetarily constrained. Further, when political competition does not extend down to the sub-national level, as is common in SSA countries where historical legacies, political traditions, and ethnic politics create binding political strongholds, citizens do not hold their politicians or political parties accountable, opposition parties do not compete where a loss is guaranteed, political parties do not develop strong party platforms, and politicians are not incentivized to be responsive to their constituents.

Even where citizens want to punish their politicians for poor behavior, in the context of the ethnically-politicized landscapes of African countries, members of the voting public are dis-incentivized to vote against the dominant political tradition and/or their co-ethnic politician because of the assumption that an oppositional or non-co-ethnic politician will exclusively distribute resources to their own co-ethnic constituents. The devil you know is better than the angel you don’t know.

In this on-going cycle, local politicians are not held accountable and citizens lose out on the beneﬁts real local competition brings. If, as Ghana has, a system can be institutionalized where opposition leaders are positioned in dominant party strongholds to compete against locally-elected politicians for party support, then voters have the opportunity to see both parties in action in their localities. Voters can compare the quality and eﬀectiveness of the two political parties and make informed decisions in the next election. If local political competition is not institutionalized, then voters in party strongholds become stuck in the political tradition, not having a reason to vote for an opposition party which has never won a local election and which probably does not even bother to campaign in the area. The institutionalization of

360 real local competition generates greater democratic opportunities at the local level at a much quicker rate than would have otherwise naturally progressed.

Second the argument presented in this work also emphasizes that the incorporation of politicized cleavages does not go far enough to ensure democratic stability. The democratization community needs to be additionally concerned about the regulation or de-politicization of national-level ethnic cleavages. The origins of the cleavage incorporation argument have deep roots from democratization scholars working in much older democracies (Lipset and Rokkan

1967; Luebbert 1991; Collier and Collier 2002). Yet the cleavage incorporation theory is still a prominent feature of today’s democratization theories, including its permeation into recent arguments about the development of strong opposition parties and democratic transitions in sub-Saharan Africa.

For instance, a recent African Politics scholarship emphasizes the importance of the development of a credible opposition for the prospects of a stable democratic transition (LeBas

2011; Arriola 2012; Elischer 2013; Riedl 2014). This literature presumes that the development of an opposition strong enough to challenge authoritarian rule and force a democratic regime change will need to be composed of ethnic-alliances. Their major question investigates under what conditions strong ethnic opposition movements develop. Yet, by incorporating ethnic cleavages into a new democracy’s institutions, the danger is that these divides will be frozen into the political landscape for a signiﬁcant time, and thus increase the possibility that politicized ethnic divides stabilize and can be mobilized for violent or non-violent political means.

My argument challenges this narrow focus on ethnic incorporation in further asking, given a democratic transition, which political institutions contribute to the de-escalation and de-politicization of national-level ethnic divides. Unfortunately, nation-building is an exhaustive process and African nations face several structural impediments (i.e. insecure borders, weak budgets, particularistic political traditions, and pre-bureaucratic moral economies) which diminishes the likelihood of a national identity overcoming communal ethnic identities (e.g.,

361 Ekeh 1975; Weber 1978; Herbst 2000, etc). By implementing centralized control over localities, combined with national-level alternations in power, Ghana’s political parties have each been able to inﬁltrate opposition strongholds, creating meaningful political competition at the local levels which also combats national-level ethnic divides.

9.2.2 Contributions to Ethnic Politics

As a scholar whose first love was the study of race, ethnicity, and identity politics, an important focus of this work is an informed and sophisticated treatment of ethnicity. As statistical procedures increasingly dominate the research methods used by political scientists, ethno-linguistic categories have become the staple level at which ethnicity is captured as a variable and tested in statistical models. This is justified because linguistic differences, it is argued, are representative of cultural differences and it is these cultural differences which are used to mobilize communal groups for political action. But how can we be sure that linguistic differences automatically proxy the politically relevant cultural differences for any given society?

Sometimes, for instance, institutional or social structures foment politically salient divisions within language groups (Fearon 1999, 5). Well known and tragic examples of conﬂicts between members of the same ethno-linguistic group include violence between the Hutus and Tutsi in Rwanda and Burundi as well as the deadly intra-Dagbon chieftaincy conﬂicts in Northern

Ghana (Weiss 2005).

A growing literature attempts to test for politically relevant identity group boundaries or configurations (Laitin and Posner 2001; Fearon 2003; Posner 2004; Desmet, Ortuno-Ortin and Weber 2009; Wimmer, Cederman, and Min 2009; Baldwin and Huber 2010). But these studies pair linguistic data with new information, as opposed to a reconsideration of the relevance of ethno-linguistic boundaries for social conflict within their cases. That the political relevance of other ethnic boundaries, such as cultural or religious differences, is not explored underutilizes valuable contextual information.

Rather than assume that linguistic diﬀerences are the causal mechanism behind politicized ethnic behavior, it might be that linguistic group boundaries do not deﬁne the set of politically

362 relevant groups in a given area. Politically relevant identities refer to those identities which are made salient at the group level and can be mobilized to achieve some political aim.

Different identities can be made salient within the same communities at different times, and for different issues. In some cases, ethno-linguistic groups are the most politically relevant identity groups. In other cases, however, other identity categories, such as caste in India, are the most politically relevant groups (Banerjee, Iyer and Somanathan 2005). Political institutions can greatly impact the politicization of different groups, particularly when political goods are at stake during elections (Posner 2005).

Because of these epistemological concerns, this work takes substantial strides to treat ethnicity in a sophisticated way. But I also address the shortcomings of my methods, which naturally accompany any social science research on identity politics and which particularly need to be acknowledged when the capture of identity for statistical procedures is used.

The research methods used in this work included Ecological Inference models to estimate the relationships between citizens’ identities and vote outcomes, an experimental survey question which primes respondents to test for biases in perceptions about political candidates, and captures survey respondents’ own ethnic identities by asking for their tribe, the ﬁrst language they learned as a child, and their mother’s and father’s tribes.

First, Chapter 4 presents Ecological Inference models which utilize ethno-linguistic and tribal group categories to test for the politicized cleavage groups as well as to analyze the vote behaviors of these groups across Fourth Republic elections. As such, this work assesses the often untested assumption that ethno-linguistic group diﬀerences are an African nation’s politicized groups. While some ethno-linguistic group estimates show strong associations with vote outcomes, the tribal analysis shows that not all of an ethno-linguistic groups’ tribes vote according to the ethno-linguistic group’s political tradition. To my knowledge, this is the ﬁrst statistical analysis of Ghana’s tribal groups, and the results show that the political behavior of tribal group members is more dynamic than an analysis of ethno-linguistic groups would demonstrate.

363 Two methodological concerns arise out of Chapter 4. First, an obvious concern is that my analysis relies on the census capture of identity information. Many facets of identity cannot be captured by censuses and surveys. The ethno-linguistic and tribal group information captured by the Ghanaian census serve as proxies for concepts of much greater complexity, and thus the statistical analysis of these variables is prone to conceptual shortcomings. Still, I argue that the analysis of tribal captures of ethnicity alongside ethno-linguistic variables is an important improvement from past work.

Relatedly, there is also the concern that the differences between tribes are not significant or are not politically relevant and that an analysis based on tribes thus creates higher degrees of fractionalization than otherwise exists (Fearon 2003). I defend my use of tribe on two accounts. First, unlike works which use fractionalization measures to predict political outcomes, such as conflict, and thus have an incentive to increase or decrease a nation’s fractionalization measure to suit the theory, my research tests for the political relevance of tribal categories in terms of political party vote estimates. In my case, proving that Ghanaian tribes perfectly align with the political traditions of their encompassing ethno-linguistic group would also have been an interesting finding. This, however, was not what the analysis showed. Second, this work argues that tribal differences have been historically important in Ghana, but that both colonial and past regimes’ centralized institutions provoked unified ethno-linguistic responses. Though there was a breakdown of ethno-linguistic voting in the 1979 elections, which were supervised by a military junta which specifically attacked the political elite and opened up space for tribal political contention, the centralized institutions of the Fourth Republic de-escalate national-level ethnic mobilization by introducing political competition at the local levels.

Beyond the Ecological Inference chapter, I also employ an experimental question which tests for tribal bias in political perceptions and vote decisions. When testing for ethnic voting’ in Africa, researchers tend to either ask respondents what factors inﬂuenced their votes, if ethnicity inﬂuenced their votes, or researchers infer ethnic voting took place if a voter’s ethnicity matches the ethnicity known for supporting the politician or political party. In

364 Western democracies, we accept that voters’ identities will impact their vote decisions as a rational behavior while acknowledging that voters are also simultaneously capable of making calculated and informed decisions when voting (Abrajano, Nagler, and Alvarez 2005). Further, researchers in Western democracies also realize that it is socially unacceptable to admit that you are voting for or against a candidate because of their race/gender or that many voters may be unaware of the extent to which racial or gender biases impact their vote decisions.

In response, researchers instead hide’ the race/gender information in a question, in order to subconsciously prime respondents to think about race or gender and then asking questions of a political nature (e.g. rate the electability of this political candidate or the popularity of this public policy) (Terkildsen 1993; Matland 1994; Sanbonmatsu 2002). Research of this nature is seldom conducted in sub-Saharan Africa. Political research experiments have only come into popularity for research in SSA in the last 15 years and very few studies test for subconscious or hidden ethnic or gender biases. The analysis of the experimental survey question within this work oﬀers an innovative contribution to the ﬁeld of African Politics.

9.3 Moving Forward

While this dissertation has made use of innovative tools to analyze new ethnic data in

Ghana, there are some areas where further tests and more data would solidify some of the arguments made in this work. First, the models presented in Chapter 5 provide evidence that there is greater vote volatility in constituency elections with an Unfriendly DCE-MP pairing in the prior term, as compared to constituencies with Friendly Pairs. This data is used to argue that Unfriendly DCE-MP pairs generate particularly competitive political environments as these oﬃcials compete to increase their party’s share of the votes in the next election.

However, as is the case in many African democracies, the way in which Ghanaian voters judge the performance of their public oﬃcials is the extent to which they successfully implement development projects. I argue the presence of an Unfriendly Pair in a constituency generates a development race between the DCE and MP, as compared to Friendly Pairs where the DCE

365 and MP work together to implement development projects which happen at a slower rate in the absence of a heightened competitive environment.

In order to shore up the claims made in Chapter 5, I require data on constituency-level development projects in order to show that development increased in constituencies with

Unfriendly Pairs as opposed to Friendly Pairs. Development project data is diﬃcult to come by in sub-Saharan Africa. Even if one gains access to a comprehensive project dataset, such as the years and location of school projects, an analysis based on this data would probably fall short because not every Unfriendly Pair competes on school projects. Perhaps some Unfriendly Pairs, for instance, compete in the construction of water boreholes and markets rather than schools.

The analysis would fall short in this area. Still, I have a couple of leads on some development datasets and I hope to incorporate them as an extension of this work in the future.

Second, this work applies a number of innovative methods to Ghana which would be useful in studies of other sub-Saharan African nations. For one, I use Ecological Inference models to analyze ethno-linguistic and tribal voting behavior in Ghana. While this is not the ﬁrst time EI models have been used to analyze elections in African nations, this is the ﬁrst use

(to my knowledge) of the Multinomial-Dirichlet EI model using a hierarchical Bayesian model

ﬁt to an election in an African nation. As EI models become more advanced, and as ethnic and election data becomes more readily available in African nations, we will increasingly know more about ethnic voting habits in sub-Saharan African nations. Similarly, I also use two types of experiments in my survey. List experiments were used to test for religious discriminatory beliefs and for the eﬀect of clientelistic gifts on voting.

List experiments are increasingly being used to test for embarrassing or sensitive behavior in

African nations. I expect this trend will continue. The experiment I used to test for tribal discriminatory beliefs, however, is not often applied in African nations. First, reading the description of a ﬁctional candidate and manipulating the candidate’s gender or race is a common tool used to identify gender or racial discriminatory beliefs in the US. However, other than this dissertation, I am unaware of any similar experiments tested in an African

366 country. Relatedly, I also used tribal instead of ethno-linguistic identity as the manipulated information in the experiment. Rather than assume that ethno-linguistic group diﬀerences are the politically salient diﬀerences in a society, more experimental testing needs to be done to measure the extent of politicized identity bias in African states. My own test found contradictory information in that one district’s respondents gave higher candidate ratings to the outsider candidate while two other districts’ respondents preferred voting for the insider candidate. More testing needs to be done to determine if politicians’ tribal identities do have an impact on voter perceptions, with the very distinct possibility that tribal identities matter in some districts more than others.

367 APPENDIX A VOTING PATTERNS BY TRIBE AND ETHNO-LINGUISTIC GROUP

368 Table A-1. Presidential Vote Margins by Tribe (1996-2012) NDC NPP Election >10% <10% >10% <10%

2012 Pres sefwi(53.4%), dan- fante(40.7%) ahanta(63.4%), boron(44.8%) gme(52.3%), ewe(58.4%) akuapem(71.6%) bimoba(74.9%), akyem (72.9%), as- denkyira(54.8%) builsa(67.1%), da- ante(76.2%) garte(50.4%) dagomba(53.3%), kwahu(66.6%) kusasi(66.3%), nankansi(53.5%) 2008 Runoﬀ sefwi(47.3%), ewe(58.4%) dangme(46.5%) ahanta(70.5%), boron(34.5%) akuapem(61.9%) bimoba(67.4%), akyem(67.5%, as- chokosi(55.7%)

369 builsa(72.0%) ante(79.3%) dagarte(45.6%), asen(68.4%), kwahu(53.1%) dagomba(56.6%) denkyira(58.3%) kusasi(49.6%), mamprusi(51.8%) 2008 Pres dangme(47.3%), sefwi(44.2%) ahanta(59.3%), wasa(41.6%) ewe(44.5%) akuapem(67.3%) bimoba(58.6%), nankansi(40.2%) akyem(58.2%), as- builsa(47.7%) ante(66.4%) dagomba(48.9%), denkyira(62.0%), kusasi(60.1%) sisala(55.6%) kasena(57.0%) 2004 Pres sefwi(59.5%), ga(64.0%) dangme(49.8%) ahanta(71.4%), boron(47.7%) akuapem(90.3%) ewe(63.5%), bi- akyem(70.3%), as- moba(56.8%) ante(83.7%) dagarte(42.3%), asen(73.8%), dagomba(62.0%) denkyira(79.3%) Table A-1. Continued NDC NPP Election >10% <10% >10% <10%

2004 Pres cont’d kusasi(64.7%), fante(51.4%), kasena(68.1%) kwahu(43.7%) sisala(52.1%) wasa(66.5%) 2000 Runoﬀ sefwi(41.4%), ewe(44.0%) kokomba(29.2%) agona(83.2%), ahanta(43.0%) akyem(59.0%) bimoba(53.5%), dagarte(26.5%) asante(61.3%), asen(71.9%) sisala(57.1%) boron(34.0%) denkyira(70.3%), kwahu(51.3%) wasa(53.3%) kasena(54.9%) nankansi(34.9%) 2000 Pres sefwi(47.0%), ewe(36.9%), kokomba(26.2%) agona(81.4%), boron(31.9%) bimoba(57.1%) ahanta(66.2%) builsa(33.7%) akuapem(61.5%), wasa(55.4%) 370 akyem(52.3%) dagarte(21.6%) asante(59.0%), asen(53.5%) sisala(37.1%) denkyira(67.3%) 1996 Pres aowin(49.2%), asen(59.1%) agona(58.2%) ahanta(59.6%), nzema(33.8%) akyem(64.2%) chokosi(70.6%), se- boron(36.7%) asante(63.8%), wasa(49.4%) fwi(70.9%), dan- kwahu(61.0%) gme(49.9%) ewe(73.1%), bi- denkyira(55.5%) moba(96.8%), kokomba(38.2%) builsa(67.9%), ga(51.7%) dagarte(51.1%), kusasi(63.8%) , mamprusi(48.6%), nankansi(52.2%), sisala(47.0%) Table A-2. Parliamentary Voting Margins by Tribe (1996-2012)

NDC NPP Third Party Election >10% <10% >10% <10% <10%

2012 Parl chokosi(59.4%), nzema(36.8%), akyem(72.3%), ahanta(55.7%) ewe(40.1%) sefwi(46.8%) asante(71.3%) bimoba(63.4%), dangme(49.5%), builsa(51.2%) akuapem(51.1%) kusasi(55.3%) dagarte(40.7%) dagomba(47.4%), boron(44.1%) nankansi(45.0%) kasena(50.7%) denkyira(49.0%) kokomba(34.9%)

371 2008 Parl ga(68.5%), chokosi(53.2%), akuapem(71.3%), ahanta(55.4%) ewe(42.1%) sefwi(46.4%) akyem(63.4%) bimoba(47.8%) guan3(48.3%), asante(55.6%), kokomba(37.7%) dagomba(47.2%) asen(63.1%) builsa(49.5%) 2004 Parl sefwi(59.7%), dangme(47.6%), ahanta(83.1%), fante(46.1%) ga(64.6%) nankansi(39.5%) akuapem(81.9%) ewe(65.5%), kasena(59.0%) akyem(73.4%), kwahu(36.3%) bimoba(62.4%) asante(84.5%) dagarte(48.6%), asen(87.8%), nzema(43.6%) dagomba(62.3%) boron(48.2%) kusasi(67.5%) chokosi(54.5%), builsa(40.6%) denkyira(83.4%) wasa(70.3%), guan3(62.0%) sisala(60.3%) Table A-2. Continued

NDC NPP Third Party Election >10% <10% >10% <10% <10%

2000 Parl sefwi(46.0%), dagarte(26.2%), ahanta(64.4%), boron(31.7%) nzema ga(47.5%) nankansi(24.5%) akuapem(58.5%) ewe(32.0%), sisala(51.3%) akyem(56.9%), kasena(48.9%) (39.7%) builsa(50.2%) asante(57.7%) asen(63.2), kwahu(61.0%) wasa(45.4%), guan5(52.8%) 1996 Parl agona(63.5%), dangme(34.5%), asante(67.2%) akuapem(51.9%) kokomba

372 ahafo(98.0%) dagarte(33.4%) aowin(69.5%), dagomba(31.9%), akyem(54.6%) (35.0%) asen(88.1%) mamprusi(40.5%) chokosi(70.9%), fante(38.1%) denkyira(71.6%) nzema(44.0%), sefwi(71.0%) wasa(48.4%), ga(80.8%) ewe(47.9%), guan5(60.8%) bimoba(99.8%), builsa(76.3%) kasena(81.4%), sisala(73.9%) Table A-3. Presidential Vote Margins by Ethnic Group (1996-2012)

NDC Vote Margin NPP Vote Margin Election >10% <10% >10% <10% 2012 Pres ewe(65.2%) gadangme(45.9%) akan(55.4%) mole dagbani(49.3%) grusi(59.8%)

2008 Runoﬀ ewe(63.9%) gadangme(41.9%) akan(51.7%) mole dagbani(50.2%)

2008 Pres ewe(55.6%) gadangme(42.0%) akan(46.7%) mole dagbani(45.7%) mande(83.3%)

2004 Pres ewe(69.6%) gadangme(53.9%) akan(65.3%)

373 mole dagbani(49.8%) others(49.3%)

2000 Runoﬀ ewe(59.8%) akan(48.1%) gruma(39.0%) mande(61.5%)

2000 Pres ewe(48.4%) mole dagbani(26.5%) akan(45.7%) gruma(42.7%) mande(70.3%) others(57.1%)

1996 Pres ewe(74.2%) guan(47.3%) akan(48.0%) gadangme(57.5%) others(47.5%) gruma(64.2%) mole dagbani(42.2%) grusi(48.0%) mande(80.1%) Table A-4. Parliamentary Vote Margins by Ethnic Group (1996-2012)

NDC Vote Margin NPP Vote Margin Election >10% <10% >10% <10%

2012 Parl ewe(51.7%) akan(49.9%) mole dagbani(44.5%) others(74.2%)

2008 Parl ewe(45.4%) mole dagbani(39.1%) akan(42.9%) gadangme(48.9%) mande(61.3%)

374 2004 Parl ewe(53.1%) akan(61.2%) gadangme(53.2%) mole dagbani(45.3%)

2000 Parl ewe(38.3%) gadangme(29.8%) akan(43.5%) mande(82.7%) gruma(27.6%) mole dagbani(28.1%)

1996 Parl ewe(59.9%) akan(45.8%) gadangme(57.8%) gruma(53.1%) mole dagbani(33.3%) grusi(83.0%) mande(99.9%) others(81.1%) APPENDIX B METHODS OF BOUNDS

Table B-1. District-Level Bounds of Votes by Tribe - 1996 Presidential tribe (lower bound, upper bound) District NDC NPP Jomoro nzema, ndc (0, .536) Ellembelle nzema, ndc (.187, .360) nzema, npp (.327, .650) Ahanta West ahanta, ndc (0, .538) ahanta, npp (.102, 1) Sekondi Takoradi Metro. fante, ndc (0, .568) fante, npp (.064, 1) Shama fante, ndc (.300, .524) fante, npp (.286, .592) Tarkwa Nsuaem Mun. wasa, ndc (0, .782) Wassa Amenﬁ East wasa, ndc (0, .908) Sefwi Wiawso sefwi, ndc (.275, 1) sefwi, npp (0, .484) Sefwi Bibiani-Ahwiaso B. sefwi, ndc (.354, .765) sefwi, npp (0, .480) Juabeso sefwi, ndc (.202, 1) sefwi, npp (0, .483) KEEA fante, ndc (.424, .534) fante, npp (.283, .431) Cape Coast Metro. fante, ndc (.107, .569) fante, npp (.262, .860) Abura-Asebu-Kwamankese fante, ndc (.420, .498) fante, npp (.329, .433) Mfantsiman Mun. fante, ndc (.357, .458) fante, npp (.332, .471) Ajumako-Enyan-Essiam fante, ndc (.334, .371) fante, npp (.470, .519) Gomoa West fante, ndc (.352, .397) fante, npp (.378, .444) Gomoa East guan3, ndc (0, .741) guan3, npp (0, .955) Eﬀutu Mun. fante, ndc (.122, .628) fante, npp (0, .804) Agona East fante, ndc (0, .957) Agona West Mun. fante, ndc (0, .901) Asikuma-Odoben-Brakwa fante, ndc (.387, .550) fante, npp (.276, .477) Assin South asen, ndc (0, .905) Upper Denkyira East Mun. denkyira, ndc (0, .966) Dangbe West dangme, ndc (.485, .959) dangme, npp (0, .181) Dangbe East dangme, ndc (.665, .880) dangme, npp (0, .053) South Tongu ewe, ndc (.844, .884) ewe, npp (0, .023) Keta Mun. ewe, ndc (.876, .889) ewe, npp (0, .008) Ketu South ewe, ndc (.735, .767) ewe, npp (0, .027) Ketu North ewe, ndc (.766, .784) ewe, npp (.012, .035) Akatsi ewe, ndc (.817, .831) ewe, npp (0, .012) North Tongu ewe, ndc (.766, .807) ewe, npp (0, .031) Adaklu Anyigbe ewe, ndc (.836, .965) ewe, npp (0, .035) Ho Mun. ewe, ndc (.795, .893) ewe, npp (0, .053) South Dayi ewe, ndc (.564, .628) ewe, npp (0, .058) North Dayi ewe, ndc (.776, .850) ewe, npp (0, .032) Hohoe Mun. ewe, ndc (.707, 1) ewe, npp (0, .061) Biakoye ewe, ndc (.363, 1) ewe, npp (0, .308) Jasikan ewe, ndc (.448, 1) ewe, npp (0, .128) guan1, ndc (.274, 1) guan1, npp (0, .270) Kadjebi ewe, ndc (.284, 1) ewe, npp (0, .279) Nkwanta South kokomba, ndc (.445, 1) kokomba, npp (0, .307)

375 Table B-1. Continued tribe (lower bound, upper bound) District NDC NPP Birim South akyem, ndc (0, .563) akyem, npp (.219, 1) Birim Mun. akyem, ndc (0, .695) Yilo Krobo dangme, ndc (.539, .762) dangme, npp (0, .244) Asuogyaman dangme, ndc (.376, .746) dangme, npp (0, .485) Lower Manya ewe, ndc (.169, 1) ewe, npp (0, .608) Upper Manya dangme, ndc (.585, .760) dangme, npp (0, .140) East Akim Mun. akyem, ndc (0, .775) Atiwa akyem, ndc (0, .622) Kwaebibirem akyem, ndc (0, .860) Birim North akyem, ndc (0, .875) Kwahu West Mun. kwahu, ndc (0, .442) kwahu, npp (.320, 1) Kwahu South kwahu, ndc (0, .482) kwahu, npp (.257, 1) Kwahu East kwahu, ndc (0, .437) kwahu, npp (.285, 1) Kwahu North ewe, ndc (.492, 1) Atwima Mponua asante, ndc (0, .689) Amansie West asante, ndc (.083, .283) asante, npp (.610, .885) Amansie Central asante, ndc (.126, .268) asante, npp (.660, .840) Obuasi Mun. asante, ndc (0, .576) asante, npp (.094, 1) Adansi North asante, ndc (0, .515) asante, npp (.215, 1) Bekwai Mun. asante, ndc (0, .182) asante, npp (.751, 1) Bosome Freho asante, ndc (.043, .422) asante, npp (.329, .932) Asante Akim South asante, ndc (0, .808) Asante Akim North Mun. asante, ndc (0, .398) asante, npp (.437, .981) Ejisu Juaben Mun. asante, ndc (0, .259) asante, npp (.643, 1) Bosumtwi asante, ndc (0, .268) asante, npp (.566, .992) Atwima Kwanwoma asante, ndc (0, .192) asante, npp (.718, 1) Kumasi Metro. asante, ndc (0, .327) asante, npp (.447, 1) Atwima Nwabiagya asante, ndc (0, .299) asante, npp (.607, 1) Ahafo Ano South asante, ndc (0, .821) Offinso Mun. asante, ndc (0, .502) asante, npp (.250, 1) Afigya Kwabre asante, ndc (0, .353) asante, npp (.505, 1) Afigya Sekyere asante, ndc (.005, .396) asante, npp (.447, .976) Kwabre East asante, ndc (0, .249) asante, npp (.644, 1) Mampong Mun. asante, ndc (0, .323) asante, npp (.549, 1) Sekyere East asante, ndc (.052, .291) asante, npp (.603, .929) Sekyere Afram Plains asante, ndc (0, .473) asante, npp (.331, 1) Sekyere Central asante, ndc (0, .527) asante, npp (.046, 1) Offinso North asante, ndc (0, .950) Dormaa Mun. boron, ndc (.294, .607) boron, npp (.087, .558) Dormaa East boron, ndc (.236, .418) boron, npp (.302, .607) Sunyani Mun. boron, ndc (0, .695) Sunyani West boron, ndc (.031, .589) boron, npp (.119, .952)

376 Table B-1. Continued tribe (lower bound, upper bound) District NDC NPP Berekum Mun. boron, ndc (.275, .487) boron, npp (.200, .537) Jaman South boron, ndc (.241, .387) boron, npp (.119, .452) Tain boron, ndc (.227, .540) boron, npp (0, .480) Wenchi Mun. boron, ndc (0, .786) Techiman Mun. boron, ndc (0, .900) Nkoranza South boron, ndc (.149, .897) boron, npp (0, .646) Sawla-Tuna-Kalba dagarte, ndc (.265, 1) dagarte, npp (0, .245) West Gonja guan5, ndc (0, .777) Gonja Central guan5, ndc (.184, .780) guan5, npp (0, .663) East Gonja guan5, ndc (0, .814) Kpandai kokomba, ndc (.449, 1) kokomba, npp (0, .287) Nanumba South kokomba, ndc (.048, .629) kokomba, npp (0, .902) Nanumba North kokomba, ndc (0, .530) Zabzugu Tatali kokomba, ndc (.061, 1) kokomba, npp (0, .585) Yendi Mun. kokomba, ndc (0, .831) dagomba, npp (.104, 1) dagomba, ndc (0, .481) Tamale Metro. dagomba, ndc (.199, .454) dagomba, npp (.339, .710) Tolon Kumbugu dagomba, ndc (.452, .490) dagomba, npp (.353, .404) Savelugu Nanton dagomba, ndc (.469, .562) dagomba, npp (.215, .334) Karaga dagomba, ndc (.076, .564) dagomba, npp (.049, .805) Saboba kokomba, ndc (.619, .722) kokomba, npp (.062, .195) Chereponi chokosi, ndc (.225, .715) chokosi, npp (0, .482) Bunkpurugu Yonyo bimoba, ndc (.408, 1) bimoba, npp (0, .216) kokomba, npp (0, .862) Mamprusi East mamprusi, ndc (.107, 1) mamprusi, npp (0, .324) Mamprusi West mamprusi, ndc (.391, .669) mamprusi, npp (0, .259) Builsa builsa, ndc (.508, .635) builsa, npp (0, .071) Kasena Nankana West kasena, ndc (.200, .808) kasena, npp (0, .480) Kasena Nankana East nankansi, ndc (0, .986) Bolgatanga Mun. nankansi, ndc (.287, .596) nankansi, npp (0, .266) Talensi Nabdam nankansi, ndc (.326, .872) nankansi, npp (0, .270) namnam, npp (0, .871) Bongo nankansi, ndc (.609, .636) nankansi, npp (.064, .101) Bawku West kusasi, ndc (.355, .655) kusasi, npp (0, .155) Garu Tempane kusasi, ndc (.224, 1) kusasi, npp (0, .350) Bawku Mun. kusasi, ndc (.062, .963) kusasi, npp (0, .771) Wa East dagarte, npp (0, .864) Sissala East sisala, ndc (.311, .564) sisala, npp (0, .130) Nadowli dagarte, ndc (.450, .735) dagarte, npp (0, .167) Jirapa dagarte, ndc (.581, .632) dagarte, npp (0, .075) Lambussie Karni dagarte, ndc (.267, 1) dagarte, npp (0, .278) Lawra dagarte, ndc (.480, .545) dagarte, npp (0, .080)

377 Table B-2. District-Level Bounds of Votes by Tribe - 1996 Parliamentary tribe (lower bound, upper bound) District NDC NPP Jomoro nzema, ndc (0, .527) Ellembelle nzema, ndc (.181, .354) Nzema East evalue, ndc (0, .987) Ahanta West ahanta, ndc (0, .457) Sekondi Takoradi Metro. fante, ndc (0, .532) Shama fante, ndc (.263, .487) fante, npp (.005, .463) Tarkwa Nsuaem Mun. wasa, ndc (0, .799) Wassa Amenﬁ East wasa, ndc (0, .810) Sefwi Wiawso sefwi, ndc (.241, 1) sefwi, npp (0, .596) Sefwi-Bibiani-Ahwiaso sefwi, ndc (.328, .738) sefwi, npp (0, .539) Juabeso sefwi, ndc (.178, 1) sefwi, npp (0, .558) KEEA fante, ndc (.443, .553) Cape Coast Metro. fante, ndc (.112, .574) fante, npp (.271, .857) Abura-Asebu-Kwamankese fante, ndc (.398, .476) fante, npp (.325, .436) Mfantsiman Mun. fante, ndc (.305, .406) fante, npp (0, .080) Ajumako-Enyan-Essiam fante, ndc (.279, .315) fante, npp (.471, .528) Gomoa West fante, ndc (.327, .372) fante, npp (.324, .401) Gomoa East guan3, ndc (.004, .756) guan3, npp (0, .940) Eﬀutu Mun. fante, ndc (.138, .645) fante, npp (.016, .789) Agona East fante, ndc (0, .948) Agona West Mun. fante, ndc (0, .881) Asikuma-Odoben-Brakwa fante, ndc (.390, .553) fante, npp (.280, .484) Assin South asen, ndc (0, .891) Upper Denkyira East Mun. denkyira, ndc (0, .939) Dangbe West dangme, ndc (.416, .891) dangme, npp (0, .214) Dangbe East dangme, ndc (.486, .701) South Tongu ewe, ndc (.709, .750) Keta Mun. ewe, ndc (.856, .869) ewe, npp (.008, .023) Ketu South ewe, ndc (.679, .711) ewe, npp (0, .041) Ketu North ewe, ndc (.738, .756) ewe, npp (.060, .082) Akatsi ewe, ndc (.683, .697) ewe, npp (0, .014) North Tongu ewe, ndc (.522, .563) Adaklu Anyigbe ewe, ndc (.792, .921) ewe, npp (0, .055) Ho Mun. ewe, ndc (.756, .854) ewe, npp (0, .037) South Dayi ewe, ndc (.430, .494) ewe, npp (.015, .141) North Dayi ewe, ndc (.732, .807) ewe, npp (0, .025) Hohoe Mun. ewe, ndc (.582, .918) ewe, npp (0, .079) Biakoye ewe, ndc (.185, 1) ewe, npp (0, .417) Nkwanta South kokomba, ndc (.114, 1) kokomba, npp (0, .550) Birim South akyem, ndc (0, .562) akyem, npp (.181, 1)

378 Table B-2. Continued tribe (lower bound, upper bound) District NDC NPP Birim Mun. akyem, ndc (0, .662) Akwapem South Mun. akuapem, ndc (0, .976) Yilo Krobo dangme, ndc (.381, .604) dangme, npp (0, .292) Asuogyaman dangme, ndc (.335, .705) dangme, npp (0, .441) Lower Manya ewe, ndc (.116, 1) ewe, npp (0, .721) Upper Manya dangme, ndc (.334, .509) dangme, npp (0, .208) East Akim Mun. akyem, ndc (0, .755) Atiwa akyem, ndc (0, .628) Kwaebibirem akyem, ndc (0, .954) Birim North akyem, ndc (0, .830) Kwahu West Mun. kwahu, ndc (0, .413) kwahu, npp (.143, 1) Kwahu South kwahu, ndc (0, .496) kwahu, npp (.188, 1) Kwahu East kwahu, ndc (0, .435) kwahu, npp (.046, 1) Kwahu North ewe, ndc (.109, .991) ewe, npp (0, .474) Atwima Mponua asante, ndc (0, .707) Amansie West asante, ndc (.084, .283) asante, npp (.574, .861) Amansie Central asante, ndc (.142, .284) asante, npp (.648, .824) Obuasi Mun. asante, ndc (0, .520) asante, npp (.038, 1) Adansi North asante, ndc (0, .527) asante, npp (.192, 1) Bekwai Mun. asante, ndc (.010, .193) asante, npp (.745, .987) Bosome Freho asante, ndc (.023, .402) asante, npp (.299, .960) Asante Akim South asante, ndc (0, .869) Asante Akim North Mun. asante, ndc (0, .312) asante, npp (.494, 1) Ejisu Juaben Mun. asante, ndc (0, .248) asante, npp (.661, 1) Bosumtwi asante, ndc (0, .280) asante, npp (.357, .973) Atwima Kwanwoma asante, ndc (0, .204) asante, npp (.696, 1) Kumasi Metro. asante, ndc (0, .299) asante, npp (.410, 1) Atwima Nwabiagya asante, ndc (0, .299) asante, npp (.596, 1) Ahafo Ano South asante, ndc (0, .821) Offinso Mun. asante, ndc (0, .469) asante, npp (.192, 1) Afigya Kwabre asante, ndc (0, .340) asante, npp (.541, 1) Afigya Sekyere asante, ndc (.000, .391) asante, npp (.393, .999) Kwabre East asante, ndc (0, .251) asante, npp (.635, 1) Mampong Mun. asante, ndc (0, .351) asante, npp (.521, 1) Sekyere East asante, ndc (.067, .306) asante, npp (.590, .910) Sekyere Afram Plains asante, ndc (0, .471) asante, npp (.330, 1) Sekyere Central asante, ndc (0, .551) asante, npp (.048, 1) Offinso North asante, ndc (0, .911) Dormaa Mun. boron, ndc (.247, .561) boron, npp (.116, .598) Dormaa East boron, ndc (.244, .426) boron, npp (.273, .572) Sunyani Mun. boron, ndc (0, .676)

379 Table B-2. Continued tribe (lower bound, upper bound) District NDC NPP Sunyani West boron, ndc (.040, .587) boron, npp (.095, .907) Berekum Mun. boron, ndc (.289, .501) boron, npp (.211, .539) Jaman South boron, ndc (.251, .398) boron, npp (.124, .434) Tain boron, ndc (.234, .547) boron, npp (0, .506) Wenchi Mun. boron, ndc (0, .720) Techiman Mun. boron, ndc (0, .878) Nkoranza South boron, ndc (.121, .869) boron, npp (0, .668) Sawla-Tuna-Kalba dagarte, ndc (.213, 1) West Gonja guan5, ndc (0, .873) Gonja Central guan5, ndc (.154, .751) guan5, npp (0, .652) East Gonja guan5, ndc (0, .818) Kpandai kokomba, ndc (.182, .828) kokomba, npp (0, .499) Nanumba South kokomba, ndc (0, .295) Nanumba North kokomba, ndc (0, .280) Zabzugu Tatali kokomba, ndc (0, .861) Yendi Mun. kokomba, ndc (0, .617) dagomba, ndc (0, .357) Tamale Metro. dagomba, ndc (.142, .397) dagomba, npp (.071, .667) Tolon Kumbugu dagomba, ndc (.446, .485) dagomba, npp (.227, .288) Savelugu Nanton dagomba, ndc (.456, .548) dagomba, npp (.179, .315) Karaga dagomba, ndc (.040, .528) dagomba, npp (.022, .863) Saboba kokomba, ndc (.623, .726) kokomba, npp (.098, .226) Chereponi chokosi, ndc (.100, .589) chokosi, npp (0, .495) Bunkpurugu Yonyo bimoba, ndc (.357, 1) Mamprusi East mamprusi, ndc (.023, 1) mamprusi, npp (0, .462) Mamprusi West mamprusi, ndc (.316, .593) Builsa builsa, ndc (.482, .610) builsa, npp (0, .065) Kasena Nankana West kasena, ndc (.168, .776) Kasena Nankana East nankansi, ndc (0, .991) Bolgatanga Mun. nankansi, ndc (.252, .560) nankansi, npp (0, .210) Talensi Nabdam nankansi, ndc (.327, .873) nankansi, npp (0, .282) namnam, npp (0, .952) Bongo nankansi, ndc (.588, .614) Bawku West kusasi, ndc (.292, .592) kusasi, npp (0, .117) Garu Tempane kusasi, ndc (0, .994) kusasi, npp (0, .764) Bawku Mun. kusasi, ndc (0, .803) Wa East dagarte, npp (0, .847) Sissala East sisala, ndc (.299, .552) sisala, npp (0, .098) Nadowli dagarte, ndc (.397, .682) dagarte, npp (0, .233) Jirapa dagarte, ndc (.576, .626) dagarte, npp (.009, .086) Lambussie Karni dagarte, ndc (.224, 1) dagarte, npp (0, .444) Lawra dagarte, ndc (.395, .459) dagarte, npp (0, .094)

380 Table B-3. District-Level Bounds of Votes by Tribe - 2000 Presidential tribe (lower bound, upper bound) District NDC NPP Ellembelle nzema, ndc (.214, .695) nzema, npp (.305, .786) Shama nzema, ndc (.114, .604) fante, npp (.396, .886) Sefwi-Bibiani-Ahwiaso sefwi, ndc (.224, .961) sefwi, npp (.039, .776) KEEA fante, ndc (.506, .755) fante, npp (.245, .494) Cape Coast Metro. fante, ndc (0, .783) fante, npp (.217, 1) Abura-Asebu-Kwam. fante, ndc (.436, .589) fante, npp (.411, 564) Mfantsiman Mun. fante, ndc (.536, .766) fante, npp (.234, .464) Ajumako-Enyan-Essiam fante, ndc (.402, 472) fante, npp (.528, .598) Gomoa West fante, ndc (.463, .562) fante, npp (.438, .537) Eﬀutu Mun. fante, ndc (0, .960) fante, npp (.040, 1) Asikuma Odoben Brakwa fante, ndc (.307, .586) fante, npp (.414, .693) Dangbe West dangme, ndc (.570, 1) dangme, npp (0, .430) Dangbe East dangme, ndc (.823, 1) dangme, npp (0, .177) South Tongu ewe, ndc (.963, 1) ewe, npp (0, .037) Keta Mun. ewe, ndc (.975, .996) ewe, npp (.004, .025) Ketu South ewe, ndc (.941, 1) ewe, npp (0, .059) Ketu North ewe, ndc (.922, .957) ewe, npp (.043, .078) Akatsi ewe, ndc (.965, .992) ewe, npp (.008, .035) North Tongu ewe, ndc (.943, 1) ewe, npp (0, .057) Adaklu Anyigbe ewe, ndc (.955, 1) ewe, npp (0, .045) Ho Mun. ewe, ndc (.915, 1) ewe, npp (0, .085) South Dayi ewe, ndc (.907, 1) ewe, npp (0, .093) North Dayi ewe, ndc (.920, 1) ewe, npp (0, .080) Hohoe Mun. ewe, ndc (.825, 1) ewe, npp (0, .175) Birim South akyem, ndc (0, .831) akyem, npp (.169, 1) Yilo Krobo dangme, ndc (.447, .974) dangme, npp (.026, .553) Asuogyaman dangme, ndc (.228, 1) dangme, npp (0, .772) Upper Manya dangme, ndc (.687, 1) dangme, npp (0, .313) Kwahu West Mun. kwahu, ndc (0, .678) kwahu, npp (.322, 1) Kwahu South kwahu, ndc (0, .857) kwahu, npp (.143, 1) Kwahu East kwahu, ndc (0, .885) kwahu, npp (.115, 1) Amansie West asante, ndc (0, .217) asante, npp (.783, 1) Amansie Central asante, ndc (0, .175) asante, npp (.825, 1) Adansi North asante, ndc (0, .555) asante, npp (.445, 1) Bekwai Mun. asante, ndc (0, .145) asante, npp (.855, 1) Bosome Freho asante, ndc (0, .488) asante, npp (.512, 1) Asante Akim North Mun. asante, ndc (0, .460) asante, npp (.540, 1) Ejisu Juaben Mun. asante, ndc (0, .250) asante, npp (.750, 1) Bosumtwi asante, ndc (0, .264) asante, npp (.736, 1) Atwima Kwanwoma asante, ndc (0, .165) asante, npp (.835, 1) Kumasi Metro. asante, ndc (0, .556) asante, npp (.444, 1)

381 Table B-3. Continued tribe (lower bound, upper bound) District NDC NPP Atwima Nwabiagya asante, ndc (0, .274) asante, npp (.726, 1) Offinso Mun. asante, ndc (0, .715) asante, npp (.285, 1) Afigya Kwabre asante, ndc (0, .371) asante, npp (.629, 1) Afigya Sekyere asante, ndc (0, .407) asante, npp (.593, 1) Kwabre East asante, ndc (0, .242) asante, npp (.758, 1) Mampong Mun. asante, ndc (0, .360) asante, npp (.640, 1) Sekyere East asante, ndc (0, .305) asante, npp (.695, 1) Sekyere Afram Plains asante, ndc (0, .619) asante, npp (.381, 1) Dormaa Mun. boron, ndc (.097, .747) boron, npp (.253, .903) Dormaa East boron, ndc (.060, .451) boron, npp (.549, .940) Sunyani West boron, ndc (0, .839) boron, npp (.161, 1) Berekum Mun. boron, ndc (.055, .491) boron, npp (.509, .945) Jaman South boron, ndc (.293, .751) boron, npp (.249, .707) Kpandai kokomba, ndc (.149, 1) Tamale Metro. dagomba, ndc (.111, .856) dagomba, npp (.144, .890) Tolon Kumbugu dagomba, ndc (.639, .713) dagomba, npp (.287, .361) Savelugu Nanton dagomba, ndc (.574, .741) dagomba, npp (.259, .426) Karaga dagomba, ndc (.048, 1) dagomba, npp (0, .952) Saboba kokomba, ndc (.657, .874) kokomba, npp (.126, .343) Mamprusi West mamprusi, ndc (.381, 1) mamprusi, npp (0, .619) Builsa builsa, ndc (.681, 1) builsa, npp (0, .319) Kasena Nankana West kasena, ndc (.008, 1) kasena, npp (0, .992) Bongo nankansi, ndc (.851, .920) nankansi, npp (.080, .149) Bawku West kusasi, ndc (.958, 1) kusasi, npp (0, .042) Sissala East sisala, ndc (.589, 1) sisala, npp (0, .411) Nadowli dagarte, ndc (.729, 1) dagarte, npp (0, .271) Jirapa dagarte, ndc (.936, 1) dagarte, npp (0, .064) Lawra dagarte, ndc (.858, 1) dagarte, npp (0, .142)

382 Table B-4. District-Level Bounds of Votes by Tribe - 2000 Parliamentary tribe (lower bound, upper bound) District NDC NPP Jomoro fante, ndc (0, .871) nzema, ndc (0, .225)

Ellembelle nzema, ndc (.085, .258) Nzema East evalue, ndc (0, .778) nzema, ndc (0, .901) Ahanta West ahanta, ndc (0, .294) Sekondi Takoradi Metro. fante, ndc (0, .300) Shama fante, ndc (.009, .233) fante, npp (.181, .969) Mpohor-Wassa East fante, ndc (0, .716) wasa, ndc (.056, .798) Tarkwa Nsuaem Mun. fante, ndc (0, .621) wasa, ndc (0, .358) Prestea/Huni Valley fante, ndc (0, .906) wasa, ndc (0, .645) Wassa Amenfi East wasa, ndc (0, .519) Wassa Amenfi West wasa, ndc (0, .779) Aowin/Suaman aowin, ndc (0, .716) Sefwi Wiawso sefwi, ndc (0, .834) Sefwi-Bibiani-Ahwiaso sefwi, ndc (.112, .522) sefwi, npp (.055, .798) Juabeso sefwi, ndc (0, .754) KEEA fante, ndc (.196, .306) fante, npp (0, .243) Cape Coast Metro. fante, ndc (0, .423) fante, npp (.201, 1) Abura-Asebu-Kwam. fante, ndc (.215, .293) fante, npp (.395, 556) Mfantsiman Mun. fante, ndc (.209, .310) fante, npp (.236, .485) Ajumako-Enyan-Essiam fante, ndc (.212, .248) fante, npp (.515, .586) Gomoa West fante, ndc (.210, .255) fante, npp (.408, .512) Gomoa East guan3, ndc (0, .411) Effutu Mun. fante, ndc (0, .497) Agona East fante, ndc (0, .618) agona, ndc (0, .932) Agona West Mun. fante, ndc (0, .555) agona, ndc (0, .706) Asikuma-Odoben-Brakwa fante, ndc (.180, .343) fante, npp (.419, .695) Assin South fante, ndc (0, .782) asen, ndc (0, .529) Assin North Mun. fante, ndc (0, .782) asen, ndc (0, .796) Twifo-Heman-Lower-Denkyira fante, ndc (0, .790) Upper Denkyira East Mun. denkyira, ndc (0, .544) Ga East Mun. ewe, ndc (0, .861) Ledzokuku/Krowor Mun. ga, ndc (0, .586)

383 Table B-4. Continued tribe (lower bound, upper bound) District NDC NPP Ashaiman Mun. dangme, ndc (0, .852) ewe, ndc (0, .360) Tema Metro. fante, ndc (0, .947) ewe, ndc (0, .788) Dangbe West dangme, ndc (.091, .565) dangme, npp (0, .600) Dangbe East dangme, ndc (.113, .328) dangme, npp (0, .173) South Tongu ewe, ndc (.599, .639) ewe, npp (0, .026) Keta Mun. ewe, ndc (.326, .339) ewe, npp (.002, .040) Ketu South ewe, ndc (.437, .469) ewe, npp (.020, .087) Ketu North ewe, ndc (.303, .321) ewe, npp (.052, .105) Akatsi ewe, ndc (.443, .457) ewe, npp (0, .015) North Tongu ewe, ndc (.475, .516) ewe, npp (0, .032) Adaklu Anyigbe ewe, ndc (.554, .684) ewe, npp (0, .048) Ho Mun. ewe, ndc (.452, .550) ewe, npp (0, .082) South Dayi ewe, ndc (.313, .377) ewe, npp (0, .097) North Dayi ewe, ndc (.371, .446) ewe, npp (0, .055) Hohoe Mun. ewe, ndc (.203, .539) ewe, npp (0, .490) Biakoye ewe, ndc (0, .978) Jasikan ewe, ndc (0, .636) guan1, ndc (0, .836) Kadjebi ewe, ndc (0, .542) Nkwanta South kokomba, ndc (0, .656) Birim South akyem, ndc (0, .386) Birim Mun. akyem, ndc (0, .493) fante, ndc (0, .924) Suhum-Kraboa-Coaltar akuapem, ndc (0, .925) Akuapem South Mun. akuapem, ndc (0, .623) Akwapem North akuapem, ndc (0, .706) guan4, ndc (0, .784) New Juaben Mun. asante, ndc (0, .752) Yilo Krobo dangme, ndc (.119, .343) dangme, npp (.012, .656) Asuogyaman dangme, ndc (.089, .459) dangme, npp (.037, .813) Lower Manya ewe, ndc (0, .643) Upper Manya dangme, ndc (.196, .371) dangme, npp (0, .336) Fanteakwa dangme, ndc (0, .770) akyem, ndc (0, .877) East Akim Mun. akyem, ndc (0, .473) Atiwa akyem, ndc (0, .461) Kwaebibirem akyem, ndc (0, .527) Birim North akyem, ndc (0, .678) Kwahu West Mun. kwahu, ndc (0, .272) kwahu, npp (.182, 1) Kwahu South kwahu, ndc (0, .396)

384 Table B-4. Continued tribe (lower bound, upper bound) District NDC NPP Kwahu East kwahu, ndc (0, .304) Kwahu North ewe, ndc (0, .719) Atwima Mponua asante, ndc (0, .473) Amansie West asante, ndc (0, .121) asante, npp (.771, 1) Amansie Central asante, ndc (0, .123) asante, npp (.799, 1) Adansi South asante, ndc (0, .932) Obuasi Mun. asante, ndc (0, .310) Adansi North asante, ndc (0, .271) asante, npp (.342, 1) Bekwai Mun. asante, ndc (0, .094) asante, npp (.841, 1) Bosome Freho asante, ndc (0, .261) asante, npp (.429, 1) Asante Akim South asante, ndc (0, .605) Asante Akim North Mun. asante, ndc (0, .253) asante, npp (.521, 1) Ejisu Juaben Mun. asante, ndc (0, .145) asante, npp (.688, 1) Bosumtwi asante, ndc (0, .244) asante, npp (.476, 1) Atwima Kwanwoma asante, ndc (0, .096) asante, npp (.811, 1) Kumasi Metro. asante, ndc (0, .168) asante, npp (.402, 1) Atwima Nwabiagya asante, ndc (0, .165) asante, npp (.706, 1) Ahafo Ano South asante, ndc (0, .579) Ahafo Ano North asante, ndc (0, .720) Offinso Mun. asante, ndc (0, .323) asante, npp (.239, 1) Afigya Kwabre asante, ndc (0, .256) asante, npp (.558, 1) Afigya Sekyere asante, ndc (0, .247) asante, npp (.574, 1) Kwabre East asante, ndc (0, .132) asante, npp (.765, 1) Mampong Mun. asante, ndc (0, .218) asante, npp (.566, 1) Sekyere East asante, ndc (0, .199) asante, npp (.663, 1) Sekyere Afram Plains asante, ndc (0, .243) asante, npp (.078, 1) Sekyere Central asante, ndc (0, .318) Ejura-Sekyedumase asante, ndc (0, .898) Offinso North asante, ndc (0, .595) Asunafo South asante, ndc (0, .624) Asunafo North Mun. asante, ndc (0, .644) Asutifi asante, ndc (0, .816) Dormaa Mun. boron, ndc (.046, .359) boron, npp (.219, .901) Dormaa East boron, ndc (.052, .235) boron, npp (.492, .887) Tano South asante, ndc (0, .816) Sunyani Mun. boron, ndc (0, .362) Sunyani West boron, ndc (0, .388) Berekum Mun. boron, ndc (.033, .245) boron, npp (.503, .933) Jaman South boron, ndc (.079, .226) boron, npp (.286, .749) Tain boron, ndc (0, .305) Wenchi Mun. boron, ndc (0, .514) dagarte, ndc (0, .770)

385 Table B-4. Continued tribe (lower bound, upper bound) District NDC NPP Techiman Mun. boron, ndc (0, .565) Nkoranza South boron, ndc (0, .518) Kintampo South boron, ndc (0, .783) Sawla-Tuna-Kalba dagarte, ndc (0, .573) othergrusi1, ndc (0, .994) West Gonja guan5, ndc (0, .683) Gonja Central guan5, ndc (0, .511) East Gonja guan5, ndc (0, .410) Kpandi kokomba, ndc (0, .559) Nanumba South kokomba, ndc (0, .549) Nanumba North kokomba, ndc (.004, .611) kokomba, npp (0, .987) Zabzugu Tatali kokomba, ndc (0, .387) dagomba, ndc (0, .797) Yendi Mun. kokomba, ndc (0, .626) dagomba, ndc (0, .362) Tamale Metro. dagomba, ndc (.032, .287) dagomba, npp (.142, .904) Tolon Kumbugu dagomba, ndc (.327, .365) dagomba, npp (.289, .364) Savelugu Nanton dagomba, ndc (.338, .431) dagomba, npp (.251, .412) Karaga dagomba, ndc (0, .478) Saboba kokomba, ndc (.300, .402) kokomba, npp (.112, .338) Chereponi chokosi, ndc (0, .382) Bunkpurugu Yonyo bimoba, ndc (0, .280) kokomba, ndc (0, .513) Mamprusi East mamprusi, ndc (0, .576) Mamprusi West mamprusi, ndc (.023, .300) mamprusi, npp (0, .612) Builsa builsa, ndc (.190, .317) builsa, npp (0, .284) Kasena Nankana West kasena, ndc (0, .597) Kasena Nankana East nankansi, ndc (0, .624) kasena, ndc (0, .842) Bolgatanga Mun. nankansi, ndc (0, .245) Talensi Nabdam nankansi, ndc (0, .471) namnam, ndc (0, .929) Bongo nankansi, ndc (.349, .376) nankansi, npp (.075, .141) Bawku West kusasi, ndc (.122, .422) kusasi, npp (0, .268) Garu Tempane kusasi, ndc (0, .486) bimoba, ndc (0, .911) Bawku Mun. kusasi, ndc (0, .600) Wa West dagarte, ndc (0, .864) Wa East dagarte, ndc (0, .807) Sissala East sisala, ndc (.099, .352) sisala, npp (0, .157) Nadowli dagarte, ndc (.188, .473) dagarte, npp (0, .224) Jirapa dagarte, ndc (.456, .506) dagarte, npp (0, .057) Lambussie Karni dagarte, ndc (0, .688) Lawra dagarte, ndc (.366, .430) dagarte, npp (.005, .155)

386 Table B-5. District-Level Bounds of Votes by Tribe - 2000 Pres. Runoﬀ tribe (lower bound, upper bound) District NDC NPP Ellembelle nzema, ndc (.028, .452) nzema, npp (.548, .972) Ahanta West ahanta, ndc (0, .728) ahanta, npp (.272, 1) Shama fante, ndc (.046, .495) fante, npp (.505, .954) Sefwi-Bibiani-Ahwiaso sefwi, ndc (.098, .831) sefwi, npp (.169, .902) KEEA fante, ndc (.360, .576) fante, npp (.424, .640) Cape Coast Metro. fante, ndc (0, .758) fante, npp (.242, 1) Abura-Asebu-Kwamankese fante, ndc (.355, .507) fante, npp (.493, .645) Mfantsiman Mun. fante, ndc (.409, .612) fante, npp (.388, .591) Ajumako-Enyan-Essiam fante, ndc (.305, .374) fante, npp (.626, .695) Gomoa West fante, ndc (.378, .478) fante, npp (.522, .622) Eﬀutu Mun. fante, ndc (0, .973) fante, npp (.027, 1) Asikuma-Odoben-Brakwa fante, ndc (.195, .481) fante, npp (.519, .805) Dangbe West dangme, ndc (.384, 1) dangme, npp (0, .616) Dangbe East dangme, ndc (.553, 1) dangme, npp (0, .447) South Tongu ewe, ndc (.937, .990) ewe, npp (.010, .063) Keta Mun. ewe, ndc (.963, .979) ewe, npp (.021, .037) Ketu South ewe, ndc (.931, .980) ewe, npp (.020, .069) Ketu North ewe, ndc (.891, .920) ewe, npp (.080, .109) Akatsi ewe, ndc (.919, .941) ewe, npp (.059, .081) North Tongu ewe, ndc (.911, .963) ewe, npp (.037, .089) Adaklu Anyigbe ewe, ndc (.927, 1) ewe, npp (0, .073) Ho Mun. ewe, ndc (.899, 1) ewe, npp (0, .101) South Dayi ewe, ndc (.843, .978) ewe, npp (.022, .157) North Dayi ewe, ndc (.880, 1) ewe, npp (0, .120) Hohoe Mun. ewe, ndc (.782, 1) ewe, npp (0, .218) Birim South akyem, ndc (.654, 1) akyem, npp (.346, 1) Yilo Krobo dangme, ndc (.285, .851) dangme, npp (.149, .715) Asuogyaman dangme, ndc (.149, .962) dangme, npp (.038, .851) Upper Manya dangme, ndc (.519, 1) dangme, npp (0, .481) Kwahu West Mun. kwahu, ndc (0, .547) kwahu, npp (.453, 1) Kwahu South kwahu, ndc (0, .733) kwahu, npp (.267, 1) Kwahu East kwahu, ndc (0, .804) kwahu, npp (.196, 1) Kwahu North ewe, ndc (.060, 1) ewe, npp (0, .940) Amansie West asante, ndc (0, .159) asante, npp (.841, 1) Amansie Central asante, ndc (0, .145) asante, npp (.855, 1) Obuasi Mun. asante, ndc (0, .855) asante, npp (.145, 1) Adansi North asante, ndc (0, .440) asante, npp (.560, 1) Bekwai Mun. asante, ndc (0, .119) asante, npp (.881, 1) Bosome Freho asante, ndc (0, .356) asante, npp (.644, 1) Asante Akim North Mun. asante, ndc (0, .398) asante, npp (.602, 1) Ejisu Juaben Mun. asante, ndc (0, .213) asante, npp (.787, 1)

387 Table B-5. Continued tribe (lower bound, upper bound) District NDC NPP Bosumtwi asante, ndc (0, .226) asante, npp (.774, 1) Atwima Kwanwoma asante, ndc (0, .120) asante, npp (.880, 1) Kumasi Metro. asante, ndc (0, .458) asante, npp (.542, 1) Atwima Nwabiagya asante, ndc (0, .228) asante, npp (.772, 1) Offinso Mun. asante, ndc (0, .591) asante, npp (.409, 1) Afigya Kwabre asante, ndc (0, .309) asante, npp (.691, 1) Afigya Sekyere asante, ndc (0, .350) asante, npp (.650, 1) Kwabre East asante, ndc (0, .207) asante, npp (.793, 1) Mampong Mun. asante, ndc (0, .312) asante, npp (.688, 1) Sekyere East asante, ndc (0, .254) asante, npp (.746, 1) Sekyere Afram Plains asante, ndc (0, .523) asante, npp (.477, 1) Sekyere Central asante, ndc (0, .810) asante, npp (.190, 1) Dormaa Mun. boron, ndc (0, .642) boron, npp (.358, 1) Dormaa East boron, ndc (0, .374) boron, npp (.626, 1) Sunyani West boron, ndc (0, .721) boron, npp (.279, 1) Berekum Mun. boron, ndc (0, .418) boron, npp (.582, 1) Jaman South boron, ndc (.161, .628) boron, npp (.372, .839) Tain boron, ndc (0, .926) boron, npp (.074, 1) Kpandai kokomba, ndc (.085, 1) kokomba, npp (0, .915) Nanumba North kokomba, ndc (.038, 1) kokomba, npp (0, .962) Tamale Metro. dagomba, ndc (.064, .580) dagomba, npp (.420, .936) Tolon Kumbugu dagomba, ndc (.512, .575) dagomba, npp (.425, .488) Savelugu Nanton dagomba, ndc (.525, .700) dagomba, npp (.330, .475) Saboba kokomba, ndc (.457, .633) kokomba, npp (.367, .543) Mamprusi West mamprusi, ndc (.086, .702) mamprusi, npp (.298, .914) Builsa builsa, ndc (.386, .641) builsa, npp (.359, .614) Bolgatanga Mun. nankansi, ndc (0, .649) nankansi, npp (.351, 1) Bongo nankansi, ndc (.647, .697) nankansi, npp (.303, .353) Bawku West kusasi, ndc (.377, 1) kusasi, npp (0, .623) Sissala East sisala, ndc (.068, .547) sisala, npp (.453, .932) Nadowli dagarte, ndc (.416, 1) dagarte, npp (0, .584) Jirapa dagarte, ndc (.808, .910) dagarte, npp (.090, .192) Lawra dagarte, ndc (.747, .893) dagarte, npp (.107, .253)

388 Table B-6. District-Level Bounds of Votes by Tribe - 2004 Presidential tribe (lower bound, upper bound) District NDC NPP Jomoro nzema, ndc (0, .422) nzema, npp (.002, .613) Ellembelle nzema, ndc (.127, .299) nzema, npp (.370, .543) Nzema East evalue, ndc (0, .786) nzema, ndc (0, .910) Ahanta West ahanta, ndc (0, .275) ahanta, npp (.349, .980) Sekondi Takoradi Metro. fante, ndc (0, .433) fante, npp (.158, 1) Shama fante, ndc (.124, .349) fante, npp (.377, .601) Mpohor-Wassa East fante, ndc (0, .932) wasa, ndc (0, .820) Tarkwa Nsuaem Mun. wasa, ndc (0, .675) Prestea/Huni Valley wasa, ndc (0, .917) Wassa Amenfi East wasa, ndc (0, .603) wasa, npp (.028, 1) Wassa Amenfi West wasa, npp (0, .990) Sefwi Wiawso sefwi, ndc (.251, .924) sefwi, npp (0, .555) Sefwi Bibiani-Ahwiaso B. sefwi, ndc (.185, .596) sefwi, npp (.226, .636) Juabeso sefwi, ndc (.035, 1) sefwi, npp (0, .563) KEEA fante, ndc (.266, .376) fante, npp (.371, .481) Cape Coast Metro. fante, ndc (.052, .513) fante, npp (.238, .699) Abura-Asebu-Kwamnkese fante, ndc (.330, .408) fante, npp (.403, .481) Mfantsiman Mun. fante, ndc (.324, .425) fante, npp (.343, .444) Ajumako-Enyan-Essiam fante, ndc (.254, .290) fante, npp (.494, .530) Gomoa West fante, ndc (.250, .295) fante, npp (.465, .509) Effutu Mun. guan3, ndc (0, .623) guan3, npp (.054, .806) Gomoa East fante, ndc (0, .488) fante, npp (.179, .685) Agona East agona, ndc (0, .996) fante, ndc (0, .660) Agona West Mun. agona, ndc (0, .835) fante, ndc (0, .656) Asikuma-Odoben-Brakwa fante, ndc (.174, .337) fante, npp (.461, .624) Assin South asen, ndc (0, .577) asen, npp (.068, 1) fante, ndc (0, .853) Twifo-Heman-Lower fante, ndc (0, .942) Denkyira Upper Denkyira East Mun. denkyira, ndc (0, .721) Upper Denkyira West denkyira, ndc (0, .545) denkyira, npp (.112, .1) Ashaiman Mun. ewe, npp (0, .978) Dangbe West dangme, ndc (.465, .939) dangme, npp (0, .254) Dangbe East dangme, ndc (.560, .774) dangme, npp (0, .183) South Tongu ewe, ndc (.809, .849) ewe, npp (.030, .070) Keta Mun. ewe, ndc (.783, .796) ewe, npp (.023, .036) Ketu South ewe, ndc (.795, .827) ewe, npp (.027, .060)

389 Table B-6. Continued tribe (lower bound, upper bound) District NDC NPP Ketu North ewe, ndc (.679, .697) ewe, npp (.166, .184) Akatsi ewe, ndc (.767, .781) ewe, npp (.068, .083) North Tongu ewe, ndc (.756, .797) ewe, npp (.029, .070) Adaklu Anyigbe ewe, ndc (.774, .903) ewe, npp (0, .075) Ho Mun. ewe, ndc (.756, .854) ewe, npp (0, .095) South Dayi ewe, ndc (.715, .779) ewe, npp (.057, .122) North Dayi ewe, ndc (.760, .834) ewe, npp (.018, .092) Hohoe Mun. ewe, ndc (.669, 1) ewe, npp (0, .141) guan1, npp (0, .982) Biakoye ewe, ndc (.159, 1) ewe, npp (0, .507) Jasikan ewe, ndc (.279, 1) ewe, npp (0, .387) guan1, ndc (.052, 1) guan1, npp (0, .508) Kadjebi ewe, ndc (.019, .1) ewe, npp (0, .609) Krachi East guan7, npp (0, .800) kokomba, npp (0, .647) Krachi West kokomba, npp (0, .622) Nkwanta South kokomba, ndc (.277, .596) kokomba, npp (.133, .452) Nkwanta North akyem, ndc (0, .347) akyem, npp (.413, 1) Birim South akyem, ndc (0, .496) akyem, npp (.178, 1) fante, ndc (0, .930) Suhum-Kraboa-Coaltar akuapem, ndc (0, .788) Akwapem South Mun. akuapem, ndc (0, .781) guan4, ndc (0, .868) Akwapem North asante, ndc (0, .985) New Juaben Mun. dangme, ndc (.386, .609) dangme, npp (.152, .375) Yilo Krobo dangme, ndc (.339, .709) dangme, npp (.055, .424) Lower Manya ewe, ndc (0, .964) ewe, npp (0, .857) Asuogyaman dangme, ndc (.439, .615) dangme, npp (.154, .329) Upper Manya dangme, ndc (0, .891) Fanteakwa akyem, ndc (0, .561) akyem, npp (.107, 1) East Akim Mun. akyem, ndc (0, .721) Kwaebibirem akyem, ndc (0, .502) akyem, npp (.149, 1) Akyem Mansa akyem, ndc (0, .894) Birim North akyem, ndc (0, .319) akyem, npp (.413, 1) Atiwa kwahu, ndc (0, .266) kwahu, npp (.368, .958) Kwahu West Mun. kwahu, ndc (0, .182) kwahu, npp (0, .459) Kwahu South kwahu, ndc (0, .210) kwahu, npp (0, .553) ewe, ndc (0, .764) Kwahu East ewe, ndc (.280, 1) ewe, npp (0, .343) Kwahu North asante, ndc (0, .521) asante, npp (.228, 1) Atwima Mponua asante, ndc (0, .127) asante, npp (.711, .910)

390 Table B-6. Continued tribe (lower bound, upper bound) District NDC NPP Amansie West asante, ndc (0, .108) asante, npp (.752, .894) Adansi South asante, ndc (0, .466) asante, npp (.251, 1) Obuasi Mun. asante, ndc (0, .293) asante, npp (.503, 1) Adansi North asante, ndc (0, .104) asante, npp (.797, .980) Bekwai Mun. asante, ndc (0, .209) asante, npp (.641, 1) Bosome Freho asante, ndc (0, .570) asante, npp (.013, 1) Asante Akim South asante, ndc (0, .274) asante, npp (.539, .955) Asante Akim North Mun. asante, ndc (0, .195) asante, npp (.635, .977) Ejisu Juaben Mun. asante, ndc (0, .151) asante, npp (.678, .959) Bosumtwi asante, ndc (0, .125) asante, npp (.705, .999) Atwima Kwanwoma asante, ndc (0, .354) asante, npp (.406, 1) Kumasi Metro. asante, ndc (0, .220) asante, npp (.599, 1) Atwima Nwabiagya asante, ndc (0, .585) asnate, npp (.152, 1) Ahafo Ano South asante, ndc (0, .816) Ahafo Ano North asante, ndc (0, .445) asante, npp (.319, 1) Offinso Mun. asante, ndc (0, .270) asante, npp (.589, .980) Afigya Kwabre asante, ndc (0, .200) asante, npp (.618, 1) Kwabre East asante, ndc (0, .236) asante, npp (.637, .945) Afigya Sekyere asante, ndc (0, .261) asante, npp (.564, 1) Mampong Mun. asante, ndc (0, .187) asante, npp (.656, .895) Sekyere East asante, ndc (0, .302) asante, npp (.443, .967) Sekyere Afram Plains asante, ndc (0, .413) asante, npp (.303, .939) Ejura Sekyere Dumasi asante, ndc (0, .957) Asunafo South asante, ndc (0, .850) Asutifi boron, ndc (.161, .475) boron, npp (.283, .597) Dormaa Mun. boron, ndc (.135, .317) boron, npp (.447, .630) Tano North boron, ndc (0, .630) Sunyani Mun. boron, ndc (0, .484) boron, npp (.268, .815) Sunyani West boron, ndc (.156, .368) boron, npp (.406, .618) Berekum Mun. boron, ndc (.171, .320) boron, npp (.401, .550) Jaman South boron, ndc (.306, .450) boron, npp (.223, .367) Jaman North boron, ndc (.149, .462) boron, npp (.199, .512) Tain boron, ndc (0, .698) boron, npp (0, .915) Wenchi Mun. boron, ndc (0, .869) boron, npp (0, .757) Techiman Mun. boron, ndc (0, .787) boron, npp (0, .741) Nkoranza South boron, ndc (0, .551) boron, npp (.008, .652) Atebubu Amantin guan8, npp (0, .866) Sene kokomba, npp (0, .894) Kintampo North Mun. guan5, npp (0, .914) dagarte, npp (0, .739) Bole dagarte, ndc (0, .905) dagarte, npp (0, .287) othergrusi1, npp (0, .499)

391 Table B-6. Continued tribe (lower bound, upper bound) District NDC NPP Sawla-Tuna-Kalba guan5, ndc (0, .934) guan5, npp (0, .952) West Gonja guan5, ndc (0, .570) guan5, npp (0, .401) Gonja Central guan5, ndc (0, .893) guan5, npp (0, .874) East Gonja kokomba, ndc (.196, .842) kokomba, npp (0, .512) Kpandai kokomba, ndc (.087, .668) kokomba, npp (.075, .657) Nanumba South kokomba, ndc (.037, .645) kokomba, npp (.031, .639) Nanumba North kokomba, ndc (0, .878) kokomba, npp (0, .717) Zabzugu Tatali dagomba, ndc (.019, .696) dagomba, npp (.052, .729) Yendi Mun. dagomba, ndc (.546, .801) dagomba, npp (.031, .286) Tamale Metro. dagomba, ndc (.580, .618) dagomba, npp (.225, .263) Tolon Kumbugu dagomba, ndc (.545, .637) dagomba, npp (.247, .340) Savelugu Nanton dagomba, ndc (.422, .703) dagomba, npp (.100, .381) Karaga dagomba, ndc (.043, .719) dagomba, npp (.078, .754) Gushiegu kokomba, ndc (.306, .408) kokomba, npp (.403, .505) Saboba chokosi, ndc (.011, .501) chokosi, npp (.100, .589) Chereponi bimoba, ndc (0, .738) bimoba, npp (0, .447) kokomba, npp (0, .818) Bunkpurugu Yonyo mamprusi, ndc (0, .648) mamprusi, npp (0, .473) Mamprusi East mamprusi, ndc (.139, .416) mamprusi, npp (.034, .311) Mamprusi West builsa, ndc (.288, .415) builsa, npp (.150, .277) Builsa kasena, ndc (.202, .810) nankansi, npp (0, .880) kasena, npp (0, .362) Kasena Nankana West nankansi, ndc (0, .711) nankansi, npp (0, .866) kasena, ndc (0, .960) Kasena Nankana East nankansi, ndc (.137, .446) nankansi, npp (0, .299) Bolgatanga Mun. nankansi, ndc (.083, .628) nankansi, npp (0, .338) namnam, npp (0, .667) Talensi Nabdam nankansi, ndc (.481, .508) nankansi, npp (.253, .280) Bongo kusasi, ndc (.220, .520) kusasi, npp (0, .286) Bawku West kusasi, ndc (0, .954) kusasi, npp (0, .519) bimoba, npp (0, .973) Garu Tempane kusasi, ndc (.031, .932) kusasi, npp (0, .454) Bawku Mun. dagarte, ndc (0, .875) dagarte, npp (0, .371) Wa West wali, npp (0, .906) Wa Mun. dagarte, ndc (0, .824) dagarte, npp (0, .863) Wa East sisala, ndc (.099, .252) sisala, npp (.223, .375) Sissala East dagarte, ndc (.353, .638) dagarte, npp (0, .273) Nadowli dagarte, ndc (.521, .572) dagarte, npp (.102, .153) Jirapa sisala, ndc (.041, .429) sisala, npp (0, .344) Sissala West dagarte, ndc (0, .806) dagarte, npp (0, .403) sisala, npp (0, .699) Lambussie Karni dagarte, ndc (.467, .532) dagarte, npp (.185, .250)

392 Table B-7. District-Level Bounds of Votes by Tribe - 2004 Parliamentary tribe (lower bound, upper bound) District NDC NPP Jomoro nzema, ndc (0, .484) nzema, npp (0, .436) Ellembelle nzema, ndc (.161, .334) Nzema East evalue, ndc (0, .760) nzema, ndc (0, .880) Ahanta West ahanta, ndc (0, .258) ahanta, npp (.165, .795) fante, ndc (0, .939) Sekondi Takoradi Metro. fante, ndc (0, .418) fante, npp (.042, 1) Shama fante, ndc (0 .177) fante, npp (.269, .493) Mpohor-Wassa East fante, ndc (0, .667) wasa, ndc (0, .587) Tarkwa Nsuaem Mun. wasa, ndc (0, .644) Prestea/Huni Valley wasa, ndc (0, .604) fante, ndc (0, .992) Wassa Amenfi East wasa, ndc (0, .604) wasa, npp (.054, 1) Wassa Amenfi West wasa, npp (0, .990) Sefwi Wiawso sefwi, ndc (.236, .908) sefwi, npp (0, .587) Sefwi Bibiani-Ahwiaso B. sefwi, ndc (.189, .599) sefwi, npp (.241, .652) Juabeso sefwi, ndc (.043, 1) sefwi, npp (0, .562) KEEA fante, ndc (.202, .312) fante, npp (.371, .481) Cape Coast Metro. fante, ndc (.093, .554) fante, npp (.176, .637) Abura-Asebu-Kwamankese fante, ndc (.297, .375) fante, npp (.390, .468) Mfantsiman Mun. fante, ndc (.292, .393) fante, npp (.372, .473) Ajumako-Enyan-Essiam fante, ndc (.297, .333) fante, npp (.459, .495) Gomoa West fante, ndc (.228, .272) fante, npp (.485, .530) Effutu Mun. guan3, ndc (0, .686) guan3, npp (.000, .752) Gomoa East fante, ndc (0, .464) fante, npp (.124, .631) Ewutu Senya fante, ndc (0, .971) Agona East agona, ndc (0, .493) fante, ndc (0, .327) Agona West Mun. fante, ndc (0, .789) Asikuma-Odoben-Brakwa fante, ndc (.234, .397) fante, npp (.414, .577) Assin South asen, ndc (0, .589) asen, npp (.042, 1) fante, ndc (0, .871) Assin North Mun. asen, ndc (0, .970) fante, ndc (0, .953) Upper Denkyira East Mun. denkyira, ndc (0, .545) Upper Denkyira West denkyira, ndc (0, .556) denkyira, npp (.122, 1) Ledzokuku/Krowor Mun. ga, ndc (0, .937) ga, npp (0, .969) Ashaiman Mun. ewe, npp (0, .931) Dangbe West dangme, ndc (.426, .900) dangme, npp (0, .355)

393 Table B-7. Continued tribe (lower bound, upper bound) District NDC NPP Dangbe East dangme, ndc (.466, .680) dangme, npp (0, .185) South Tongu ewe, ndc (.773, .814) ewe, npp (.062, .102) Keta Mun. ewe, ndc (.612, .624) ewe, npp (.041, .054) Ketu South ewe, ndc (.582, 614) ewe, npp (.029, .061) Ketu North ewe, ndc (.649, .667) ewe, npp (.225, .243) Akatsi ewe, ndc (.712, .726) ewe, npp (.103, .117) North Tongu ewe, ndc (.597, .638) ewe, npp (.023, .064) Adaklu Anyigbe ewe, ndc (.323, .453) ewe, npp (0, .072) Ho Mun. ewe, ndc (.704, .802) ewe, npp (.008, .106) South Dayi ewe, ndc (.114, .178) ewe, npp (.064, .128) North Dayi ewe, ndc (.678, .753) ewe, npp (.027, .101) Hohoe Mun. ewe, ndc (.603, .939) ewe, npp (0, .203) Biakoye ewe, npp (0, .493) Jasikan ewe, ndc (.111, 1) ewe, npp (0, .413) guan1, ndc (.052, 1) guan1, npp (0, .544) Kadjebi ewe, npp (0, .769) Krachi East guan7, npp (0, .800) ewe, npp (0, .693) Krachi West kokomba, npp (0, .628) guan7, npp (0, .777) Nkwanta South kokomba, npp (0, .631) Nkwanta North kokomba, ndc (0, .312) kokomba, npp (.036, .356) akyem, npp (.413, 1) Birim South akyem, ndc (0, .430) akyem, npp (.345, .936) Birim Mun. akyem, ndc (0, .480) akyem, npp (.198, 1) fante, ndc (0, .901) Akwapem South Mun. akuapem, ndc (0, .740) akuapem, npp (0, .991) guan4, ndc (0, .868) Akwapem North akuapem, ndc (0, .796) guan4, ndc (0, .884) New Juaben Mun. asante, ndc (0, .911) Yilo Krobo dangme, ndc (.354, .577) Lower Manya dangme, ndc (.237, .607) dangme, npp (.128, .498) Asuogyaman ewe, ndc (0, .849) ewe, npp (0, .952) Upper Manya dangme, ndc (.426, .601) dangme, npp (.202, .378) Fanteakwa dangme, ndc (0, .932) East Akim Mun. akyem, ndc (0, .559) akyem, npp (0.046, 1) Kwaebibirem akyem, ndc (0, .759) Akyem Mansa akyem, ndc (0, .482) akyem, npp (.033, 1) fante, ndc (0, .993) Birim North akyem, ndc (0, .778) akyem, npp (.413, 1) Atiwa akyem, ndc (0, .346) akyem, npp (.353, 1)

394 Table B-7. Continued tribe (lower bound, upper bound) District NDC NPP Kwahu West Mun. akyem, ndc (0, .956) kwahu, npp (.136, .726) asante, ndc (0, .563) kwahu, ndc (0, .050) ewe, ndc (0, .512) Kwahu South kwahu, ndc (0, .211) kwahu, npp (0, .463) ewe, ndc (0, .764) Kwahu East kwahu, ndc (0, .195) kwahu, npp (0, .474) ewe, ndc (0, .708) Kwahu North ewe, ndc (.239, 1) ewe, npp (0, .478) Atwima Mponua asante, ndc (0, .525) asante, npp (.242, 1) Amansie West asante, ndc (0, .120) asante, npp (.672, .872) Amansie Central asante, ndc (0, .131) asante, npp (.695, .837) Obuasi Mun. asante, ndc (0, .163) asante, npp (.109, .982) fante, ndc (0, .638) Adansi North asante, ndc (0, .209) asante, npp (.083, .855) fante, ndc (0, .848) Bekwai Mun. asante, ndc (0, .091) asante, npp (.662, .845) Bosome Freho asante, ndc (0, .215) asante, npp (.641, 1) Asante Akim South asante, ndc (0, .593) Asante Akim North Mun. asante, ndc (0, .269) asante, npp (.535, .950) Ejisu Juaben Mun. asante, ndc (0, .203) asante, npp (.584, .926) Bosumtwi asante, ndc (0, .162) asante, npp (.648, .929) Atwima Kwanwoma asante, ndc (0, .134) asante, npp (.644, .939) Kumasi Metro. asante, ndc (0, .294) asante, npp (.368, 1) Atwima Nwabiagya asante, ndc (0, .213) asnate, npp (.583, 1) Ahafo Ano South asante, ndc (0, .638) asante, npp (.140, 1) Ahafo Ano North asante, ndc (0, .905) Offinso Mun. asante, ndc (0, .478) asante, npp (.062, .809) Afigya Kwabre asante, ndc (0, .254) asante, npp (.594, .985) Kwabre East asante, ndc (0, .204) asante, npp (.614, 1) Afigya Sekyere asante, ndc (0, .243) asante, npp (.617, .926) Mampong Mun. asante, ndc (0, .257) asante, npp (.550, 1) Sekyere East asante, ndc (0, .133) asante, npp (.389, .628) Sekyere Afram Plains asante, ndc (0, .268) asante, npp (.383, .907) Sekyere Central asante, ndc (0, .442) asante, npp (.258, .894) Asunafo South asante, npp (0, .963) Asunafo North Mun. asante, ndc (0, .927) Dormaa Mun. boron, ndc (.214, .527) boron, npp (.241, .554) Dormaa East boron, ndc (.188, .371) boron, npp (.385, .567) Sunyani Mun. boron, ndc (0, .618) Sunyani West boron, ndc (0, .445) boron, npp (.158, .705)

395 Table B-7. Continued tribe (lower bound, upper bound) District NDC NPP Berekum Mun. boron, ndc (.190, .402) boron, npp (.390, .601) Jaman South boron, ndc (.192, .341) boron, npp (.355, .503) Jaman North boron, ndc (.298, .442) boron, npp (.239, .383) Tain boron, ndc (.168, .481) boron, npp (.197, .510) Wenchi Mun. boron, npp (0, .929)

Techiman Mun. boron, ndc (0, .820) boron, npp (0, .722) Nkoranza South boron, ndc (0, .727) boron, npp (.003, .827) Nkoranza North boron, npp (0, .367) Sene guan8, npp (0, .982) Kintampo South boron, npp (0, .941) Kintampo North Mun. guan5, npp (0, .914) Bole dagarte, npp (0, .824) Sawla-Tuna-Kalba dagarte, ndc (0, .822) dagarte, npp (0, .271) other grusi1, npp (0, .470) West Gonja guan5, ndc (0, .931) guan5, npp (0, .954) Gonja Central guan5, ndc (.036, .632) guan5, npp (0, .555) East Gonja guan5, ndc (0, .649) guan5, npp (0 .728) Kpandai kokomba, ndc (0, .582) kokomba, npp (0, .364) Nanumba South kokomba, ndc (.003, .584) kokomba, npp (.039, .621) Nanumba North kokomba, ndc (.104, .711) kokomba, npp (0, .574) Zabzugu Tatali kokomba, ndc (0, .868) kokomba, npp (0, .816) Yendi Mun. kokomba, ndc (0, .942) dagomba, npp (0, .654) dagomba, ndc (0, .546) Tamale Metro. dagomba, ndc (.505, .760) dagomba, npp (.063, .318) Tolon Kumbugu dagomba, ndc (.588, .626) dagomba, npp (.239, 277) Savelugu Nanton dagomba, ndc (.547, .640) dagomba, npp (.253, .346) Karaga dagomba, ndc (.441, .722) dagomba, npp (.117, .398) Gushiegu dagomba, ndc (.031, .707) kokomba, npp (.114, .790) Saboba kokomba, ndc (.290, .393) kokomba, npp (.417, .520) chokosi, ndc (.011, .501) Chereponi chokosi, ndc (0, .431) chokosi, npp (0, .455) bimoba, ndc (0, .738) Bunkpurugu Yonyo bimoba, ndc (0, .326) bimoba, npp (0, .348) kokomba, ndc (0, .596) kokomba, npp (0, .637) Mamprusi East mamprusi, ndc (0, .579) mamprusi, npp (0, .582) Mamprusi West mamprusi, ndc (.124, .401) mamprusi, npp (.068, .345) Builsa builsa, ndc (.219, .346) builsa, npp (.175, .302) kasena, npp (0, .362) Kasena Nankana West kasena, ndc (0, .589) nankansi, npp (0, .755) kasena, npp (0 .311)

396 Table B-7. Continued tribe (lower bound, upper bound) District NDC NPP Kasena Nankana East nankansi, ndc (0, .384) nankansi, npp (0, .771) kasena, ndc (0, .519) Bolgatanga Mun. nankansi, ndc (.044, .353) nankansi, npp (0, .244) Talensi Nabdam nankansi, ndc (.006, .552) nankansi, npp (0, .320) namnam, npp (0, .632)

Bongo nankansi, ndc (.475, .502) nankansi, npp (.270, .297) Bawku West kusasi, ndc (.081, .381) kusasi, npp (.011, .311) Garu Tempane kusasi, ndc (0, .868) kusasi, npp (0, .606) Bawku Mun. kusasi, ndc (0, .777) kusasi, npp (0, .442) Wa West dagarte, ndc (0, .890) dagarte, npp (0, .408) Wa Mun. dagarte, ndc (0, .798) dagarte, npp (0, .600) wali, npp (0, .459) mosi, npp (0, .830) Wa East sisala, ndc (.099, .252) dagarte, npp (0, .942) Sissala East sisala, ndc (.096, .248) dagarte, npp (0, .264) Nadowli dagarte, ndc (.253, .538) dagarte, npp (.104, .155) Jirapa dagarte, ndc (.513, .564) sisala, npp (0, .344) Sissala West sisala, ndc (0, .342) sisala, npp (0, .318) Lambussie Karni dagarte, ndc (0, .740) dagarte, npp (0, .514) sisala, npp (0, .891) Lawra dagarte, npp (.444, .509)

397 Table B-8. District-Level Bounds of Votes by Tribe - 2008 Presidential tribe (lower bound, upper bound) District NDC NPP Jomoro nzema, ndc (0, .380) nzema, npp (0, .304) Ellembelle nzema, ndc (.138, .311) nzema, npp (.137, .310) Nzema East evalue, ndc (0, .769) nzema, ndc (0, .890) Ahanta West ahanta, ndc (0, .319) ahanta, npp (0, .609) Sekondi Takoradi Metro. fante, ndc (0, .470) fante, npp (0, .813) Shama fante, ndc (.185, .409) fante, npp (.184, .408) Mpohor-Wassa East wasa, ndc (0, .864) wasa, npp (0, .893) fante, ndc (0, .983) Tarkwa Nsuaem Mun. wasa, ndc (0, .693) Wassa Amenfi East wasa, ndc (0, .640) wasa, npp (0, .781) Wassa Amenfi West wasa, ndc (0, .974) wasa, npp (0, .702) Aowin/Suaman aowin, npp (0, .723) Sefwi Akontombra sefwi, npp (0, .875) Sefwi Wiawso sefwi, ndc (.042, .714) sefwi, npp (0, .520) Sefwi Bibiani-Ahwiaso B. sefwi, ndc (.124, .534) sefwi, npp (.144, .555) Juabeso sefwi, ndc (0, .918) sefwi, npp (0, .394) Bia sefwi, npp (0, .747) KEEA fante, ndc (.302, .412) fante, npp (.171, .281) Cape Coast Metro. fante, ndc (.074, .535) fante, npp (0, .451) Abura-Asebu-Kwamankese fante, ndc (.350, .428) fante, npp (.190, .268) Mfantsiman Mun. fante, ndc (.352, .453) fante, npp (.182, .283) Ajumako-Enyan-Essiam fante, ndc (.330, .366) fante, npp (.300, .336) Gomoa West fante, ndc (.312, .357) fante, npp (.208, .253) Effutu Mun. guan3, ndc (0, .656) guan3, npp (0, .588) Gomoa East fante, ndc (.033, .539) fante, npp (0, .378) Ewutu Senya guan3, ndc (0, .966) guan3, npp (0, .923) Agona East fante, ndc (0, .773) fante, npp (0, .704) Agona West Mun. fante, ndc (0, .677) fante, npp (0, .794) agona, ndc (0, .862) Asikuma-Odoben-Brakwa fante, ndc (.218, .380) fante, npp (.232, .394) Assin South asen, ndc (0, .619) asen, npp (0, .820) fante, ndc (0, .916) Assin North Mun. asen, ndc (0, .976) fante, ndc (0, .959) Twifo-Heman-Lower fante, ndc (0, .901) fante, npp (0, .838) Denkyira Upper Denkyira East Mun. denkyira, ndc (0, .687) Upper Denkyira West denkyira, ndc (0, .556) denkyira, npp (0, .943) Adenta Mun. ewe, npp (0, .892) Ledzokuku/Krowor ga, ndc (0, .967) ga, npp (0, .779)

398 Table B-8. Continued tribe (lower bound, upper bound) District NDC NPP Ashaiman ewe, npp (0, .664) Dangbe West dangme, ndc (.317, .792) dangme, npp (0, .176) ewe, npp (0, .760) Dangbe East dangme, ndc (.450, .665) dangme, npp (0, .146) South Tongu ewe, ndc (.653, .694) ewe, npp (.010, .050) Keta Mun. ewe, ndc (.670, .683) ewe, npp (.008, .021) Ketu South ewe, ndc (.561, .593) ewe, npp (0, .030) Ketu North ewe, ndc (.509, .527) ewe, npp (.128, .146) Akatsi ewe, ndc (.550, .564) ewe, npp (.032, .046) North Tongu ewe, ndc (.597, .638) ewe, npp (.028, .070) Adaklu Anyigbe ewe, ndc (.549, .679) ewe, npp (0, .044) kotokoli, npp (0, .764) Ho Mun. ewe, ndc (.561, .659) ewe, npp (0, .054) South Dayi ewe, ndc (.525, .590) ewe, npp (0, .058) North Dayi ewe, ndc (.533, .608) ewe, npp (0, .069) Hohoe Mun. ewe, ndc (.394, .731) ewe, npp (0, .093) guan1, npp (0, .649) Biakoye ewe, npp (0, .391) guan6, npp (0, .932) Jasikan ewe, npp (0, .321) guan1, npp (0, .422) Kadjebi ewe, ndc (0, .946) ewe, npp (0, .439) kotokoli, npp (0, .928) Krachi East ewe, npp (0, .688) Krachi West guan7, npp (0, .719) kokomba, npp (0, .582) Nkwanta South kokomba, npp (0, .680) Nkwanta North kokomba, ndc (.147, .466) kokomba, npp (.123, .443) Birim South akyem, ndc (0, .362) akyem, npp (.179, .770) Birim Mun. akyem, ndc (0, .490) akyem, npp (0, .974) fante, ndc (0, .919) Akwapem South Mun. akuapem, ndc (0, .724) akuapem, npp (0, .952) Akwapem North akuapem, ndc (0, .707) guan4, ndc (0, .785) New Juaben Mun. asante, ndc (0, .921) Yilo Krobo dangme, ndc (.287, .510) dangme, npp (.038, .261) Lower Manya dangme, ndc (.232, .601) dangme, npp (0, .309) Asuogyaman ewe, ndc (0, .779) ewe, npp (0, .673) Upper Manya dangme, ndc (.329, .504) dangme, npp (.031, .206) Fanteakwa akyem, ndc (0, .823) dangme, ndc (0, .723) East Akim Mun. akyem, ndc (0, .465)

399 Table B-8. Continued tribe (lower bound, upper bound) District NDC NPP Akyem Mansa akyem, ndc (0, .488) akyem, npp (0, .894) Birim South akyem, ndc (0, .883) Atiwa akyem, ndc (0, .293) akyem, npp (.196, 1) Kwahu West Mun. kwahu, ndc (0, .294) kwahu, npp (.177, .767) Kwahu South kwahu, ndc (0, .318) kwahu, npp (.146, .670) Kwahu East kwahu, ndc (0, .282) kwahu, npp (.121, .679) Kwahu North ewe, ndc (0, .856) ewe, npp (0, .256) Atwima Mponua asante, ndc (0, .511) asante, npp (.015, .944) Amansie West asante, ndc (0, .129) asante, npp (.545, .744) Amansie Central asante, ndc (0, .097) asante, npp (.594, .736) Obuasi Mun. asante, ndc (0, .438) asante, npp (0, .870) Adansi North asante, ndc (0, .284) asante, npp (.210, .982) Bekwai Mun. asante, ndc (0, .106) asante, npp (.606, .789) Bosome Freho asante, ndc (0, .205) asante, npp (.452, .831) Asante Akim South asante, ndc (0, .516) Asante Akim North Mun. asante, ndc (0, .251) asante, npp (.354, .769) Ejisu Juaben Mun. asante, ndc (0, .182) asante, npp (.495, .837) Bosumtwi asante, ndc (0, .179) asante, npp (.507, .787) Atwima Kwanwoma asante, ndc (0, .150) asante, npp (.535, .830) Kumasi Metro. asante, ndc (0, .304) asante, npp (.179, .844) Atwima Nwabiagya asante, ndc (0, .215) asante, npp (.394, .813) Ahafo Ano South asante, ndc (0, .556) asante, npp (0, .908) Ahafo Ano North asante, ndc (0, .769) asante, npp (0, .914) Offinso Mun. asante, ndc (0, .378) asante, npp (.132, .879) Afigya Kwabre asante, ndc (0, .259) asante, npp (.400, .791) Kwabre East asante, ndc (0, .185) asante, npp (.436, .827) Afigya Sekyere asante, ndc (0, .212) asante, npp (.468, .776) Mampong Mun. asante, ndc (0, .241) asante, npp (.364, .889) Sekyere East asante, ndc (0, .175) asante, npp (.511, .750) Sekyere Afram Plains asante, ndc (0, .256) asante, npp (.292, .816) Sekyere Central asante, ndc (0, .329) asante, npp (.193, .829) Offinso North asante, ndc (0, .677) asante, npp (0, .901) Asunafo South asante, ndc (0, .891) asante, npp (0, .914) Asunafo North Mun. asante, ndc (0, .782) asante, npp (0, .975) Asutifi asante, ndc (0, .951) Dormaa Mun. boron, ndc (.107, .420) boron, npp (.145, .458) Dormaa East boron, ndc (.098, .281) boron, npp (.305, .487) Tano South asante, ndc (0, .961) Sunyani Mun. boron, ndc (0, .568) boron, npp (0, .930) Sunyani West boron, ndc (0, .442) boron, npp (.092, .639) Berekum Mun. boron, ndc (.137, .349) boron, npp (.226, .438) Jaman South boron, ndc (.143, .291) boron, npp (.266, .415)

400 Table B-8. Continued tribe (lower bound, upper bound) District NDC NPP Jaman North boron, ndc (.254, .398) boron, npp (.130, .274) Tain boron, ndc (.082, .395) boron, npp (.051, .364) Wenchi Mun. boron, ndc (0, .586) boron, npp (0, .732) dagarte, ndc (0, .878) Techiman Mun. boron, ndc (0, .683) boron, npp (0, .648) Nkoranza South boron, ndc (0, .665) boron, npp (0, .624) Nkoranza North boron, ndc (0, .472) boron, npp (0, .538) Sene ewe, npp (0, .961) guan8, npp (0, .732) Pru kokomba, npp (0, .859) Kintampo South boron, npp (0, .880) Bole guan5, npp (0, .656) dagarte, npp (0, .530) Sawla-Tuna-Kalba dagarte, ndc (0, .721) dagarte, npp (0, .358) othergrusi1, npp (0, .622) West Gonja guan5, ndc (0, .843) guan5, npp (0, .683) Gonja Central guan5, ndc (0, .543) guan5, npp (0, .460) East Gonja guan5, ndc (0, .857) guan5, npp (0, .697) Kpandai kokomba, ndc (0, .627) kokomba, npp (0, .454) Nanumba South kokomba, ndc (0, .536) kokomba, npp (0, .571) Nanumba North kokomba, ndc (0, .428) kokomba, npp (.165, .773) nanumba, ndc (0, .836) Zabzugu Tatali kokomba, ndc (0, .598) kokomba, npp (0, .798) Yendi Mun. dagomba, ndc (0, .582) dagomba, npp (0, .583) Tamale Metro. dagomba, ndc (.409, .664) dagomba, npp (0, .229) Tolon Kumbugu dagomba, ndc (.422, .460) dagomba, npp (.227, .265) Savelugu Nanton dagomba, ndc (.397, .490) dagomba, npp (.224, .317) Karaga dagomba, ndc (.260, .541) dagomba, npp (.071, .352) Gushiegu dagomba, ndc (0, .669) dagomba, npp (0, .593) Saboba kokomba, ndc (.330, .432) kokomba, npp (.270, .372) Chereponi chokosi, ndc (0, .485) chokosi, npp (.067, .556) Bunkpurugu Yonyo bimoba, ndc (.050, .831) bimoba, npp (0, .295) kokomba, npp (0, .540) Mamprusi East mamprusi, ndc (0, .791) mamprusi, npp (0, .455) Mamprusi West mamprusi, ndc (.162, .439) mamprusi, npp (.090, .367) Builsa builsa, ndc (.301, .428) builsa, npp (.116, .243) Kasena Nankana West kasena, ndc (.024, .632) kasena, npp (0, .351) nankansi, npp (0, .853) Kasena Nankana East nankansi, ndc (0, .780) nankansi, npp (0, .761) Bolgatanga Mun. nankansi, ndc (.244, .552) nankansi, npp (0, .242)

401 Table B-8. Continued tribe (lower bound, upper bound) District NDC NPP Talensi Nabdam nankansi, ndc (.004, .549) nankansi, npp (0, .370) namnam, npp (0, .731) Bongo nankansi, ndc (.393, .420) nankansi, npp (.208, .235) Bawku West kusasi, ndc (.249, .549) kusasi, npp (0, .274) Garu Tempane kusasi, ndc (0, .764) kusasi, npp (0, .493) bimoba, npp (0, .924) Bawku Mun. kusasi, ndc (0, .673) kusasi, npp (0, .517) Wa West dagarte, ndc (0, .791) dagarte, npp (0, .310) othergrusi1, npp (0, .906) Wa Mun. wali, npp (0, .882) Wa East dagarte, ndc (0, .728) dagarte, npp (0, .767) Sissala East sisala, ndc (.144, .296) sisala, npp (.198, .351) Nadowli dagarte, ndc (.233, .518) dagarte, npp (0, .230) Jirapa dagarte, ndc (.345, .396) dagarte, npp (.151, .202) Sissala West sisala, ndc (.044, .433) sisala, npp (0, .351) Lambussie Karni dagarte, ndc (0, .535) dagarte, npp (0, .552) sisala, ndc (0, .927) sisala, npp (0, .956) Lawra dagarte, ndc (.286, .351) dagarte, npp (.222, .287)

402 Table B-9. District-Level Bounds of Votes by Tribe - 2008 Parliamentary tribe (lower bound, upper bound) District NDC NPP Jomoro nzema, ndc (0, .324) nzema, npp (0, .154) fante, npp (0, .597) Ellembelle nzema, ndc (.168, .341) Nzema East evalue, ndc (0, .629) nzema, ndc (0, .728) Ahanta West ahanta, ndc (0, .274) ahanta, npp (0, .609) fante, ndc (0, .996) Sekondi Takoradi Metro. fante, ndc (0, .415) fante, npp (0, .746) Shama fante, ndc (.191, .416) fante, npp (.168, .392) Mpohor-Wassa East fante, ndc (0, .828) fante, npp (0, .914) wasa, ndc (0, .728) wasa, npp (0, .804) Tarkwa Nsuaem Mun. wasa, ndc (0, .708) Prestea/Huni Valley wasa, npp (0, .784) Wassa Amenfi East wasa, ndc (0, .949) wasa, npp (0, .772) Wassa Amenfi West wasa, ndc (0, .869) wasa, npp (0, .663) Aowin/Suaman aowin, ndc (0, .954) aowin, npp (0, .772) Sefwi Akontombra sefwi, npp (0, .932) Sefwi Wiawso sefwi, ndc (0, .647) sefwi, npp (0, .635) Sefwi Bibiani-Ahwiaso B. sefwi, ndc (.096, .507) sefwi, npp (.148, .558) Juabeso sefwi, ndc (0, .914) sefwi, npp (0, .390) Bia sefwi, npp (0, .795) KEEA fante, ndc (.268, .378) fante, npp (.210, .320) Cape Coast Metro. fante, ndc (.084, .545) fante, npp (0, .443) Abura-Asebu-Kwamankese fante, ndc (.327, .405) fante, npp (.193, .271) Mfantsiman Mun. fante, ndc (.290, .392) fante, npp (.238, .339) Ajumako-Enyan-Essiam fante, ndc (.336, .372) fante, npp (.277, .314) Gomoa West fante, ndc (.264, .308) fante, npp (.200, .241) Effutu Mun. guan3, ndc (0, .692) guan3, npp (0, .551) Gomoa East fante, ndc (.007, .513) fante, npp (0, .361) Ewutu Senya guan3, ndc (0, .964) guan3, npp (0, .876) fante, npp (0, .990) Agona East fante, ndc (0, .759) fante, npp (0, .761) Agona West Mun. fante, ndc (0, .677) fante, npp (0, .650) agona, ndc (0, .551) Asikuma-Odoben-Brakwa fante, ndc (.227, .389) fante, npp (.235, .398) Assin South asen, ndc (0, .663) asen, npp (0, .784) fante, ndc (0, .980) Assin North Mun. asen, ndc (0, .909) fante, ndc (0, .893) Twifo-Heman-Lower fante, ndc (0, .901) fante, npp (0, .782) Denkyira

403 Table B-9. Continued tribe (lower bound, upper bound) District NDC NPP Upper Denkyira East Mun. asante, ndc (0, .873) denkyira, ndc (0, .415) fante, ndc (0, .947) Upper Denkyira West denkyira, ndc (0, .618) denkyira, npp (0, .825) Adenta Mun. ewe, npp (0, .878) Ledzokuku/Krowor ga, ndc (0, .976) ga, npp (0, .723) Ashaiman ewe, npp (0, .701) Dangbe West dangme, ndc (.235, .709) dangme, npp (0, .240) Dangbe East dangme, ndc (.400, .614) dangme, npp (0, .214) South Tongu ewe, ndc (.612, .653) ewe, npp (.022, .062) Keta Mun. ewe, ndc (.630, .643) ewe, npp (.019, .032) Ketu South ewe, ndc (.543, .575) ewe, npp (.004, .036) Ketu North ewe, ndc (.444, .462) ewe, npp (.191, .209) Akatsi ewe, ndc (.422, .436) ewe, npp (.022, .036) North Tongu ewe, ndc (.513, .555) ewe, npp (.074, .115) Adaklu Anyigbe ewe, ndc (.291, .421) ewe, npp (0, .024) kotokoli, npp (0, .410) Ho Mun. ewe, ndc (.526, .624) ewe, npp (0, .082) South Dayi ewe, ndc (.512, .577) ewe, npp (0, .058) North Dayi ewe, ndc (.457, .532) ewe, npp (.050, .124) Hohoe Mun. ewe, ndc (.321, .658) ewe, npp (0, .137) guan1, npp (0, .955) Biakoye ewe, npp (0, .450) Jasikan ewe, ndc (0, .936) ewe, npp (0, .391) guan1, npp (0, .515) Kadjebi ewe, ndc (0, .814) ewe, npp (0, .474) Krachi East ewe, ndc (0, .999) ewe, npp (0, .669) Krachi West guan7, ndc (0, .975) guan7, npp (0, .715) kokomba, ndc (0, .789) kokomba, npp (0, .578)

Nkwanta South kokomba, npp (0, .702) Nkwanta North kokomba, ndc (0, .146) kokomba, npp (.106, .425) Birim South akyem, ndc (0, .417) akyem, npp (.152, .743) Birim Mun. akyem, ndc (0, .539) akyem, npp (0, .932) Suhum-Kraboa-Coaltar akuapem, ndc (0, .993) Akwapem South Mun. akuapem, ndc (0, .623) Akwapem North akuapem, ndc (0, .658) guan4, ndc (0, .731) New Juaben Mun. asante, ndc (0, .824) Yilo Krobo dangme, ndc (.200, .422) dangme, npp (.091, .314) Lower Manya dangme, ndc (.154, .524) dangme, npp (.031, .401) Asuogyaman ewe, ndc (0, .745) ewe, npp (0, .694)

404 Table B-9. Continued tribe (lower bound, upper bound) District NDC NPP Upper Manya dangme, ndc (.308, .483) dangme, npp (.047, .223) Fanteakwa dangme, ndc (0, .737) dangme, npp (0, .999) akyem, ndc (0, .839) East Akim Mun. akyem, ndc (0, .470) Akyem Mansa akyem, ndc (0, .459) akyem, npp (0, .788) fante, ndc (0, .945) Birim North akyem, ndc (0, .859) Atiwa akyem, ndc (0, .309) akyem, npp (.187, 1) Kwahu West Mun. kwahu, ndc (0, .059) kwahu, npp (0, .434) asante, ndc (0, .667) ewe, ndc (0, .606) Kwahu South kwahu, ndc (0, .302) kwahu, npp (.172, .696) Kwahu East kwahu, ndc (0, .279) kwahu, npp (.121, .679) Kwahu North ewe, ndc (0, .801) ewe, npp (0, .336) Atwima Mponua asante, ndc (0, .470) asante, npp (0, .827) Amansie West asante, ndc (0, .125) asante, npp (.450, .649) Amansie Central asante, ndc (0, .097) asante, npp (.566, .708) Obuasi Mun. asante, ndc (0, .463) asante, npp (0, .836) Adansi North asante, ndc (0, .269) asante, npp (.153, .925) Bekwai Mun. asante, ndc (0, .036) asante, npp (0, .176) Bosome Freho asante, ndc (0, .121) asante, npp (.065, .444) Asante Akim South asante, ndc (0, .558) asante, npp (0, .961) Asante Akim North asante, ndc (0, .185) asante, npp (.280, .695) Municipal Ejisu Juaben Mun. asante, ndc (0, .157) asante, npp (.505, .847) Bosumtwi asante, ndc (0, .273) asante, npp (.410, .690) Atwima Kwanwoma asante, ndc (0, .147) asante, npp (.511, .806) Kumasi Metro. asante, ndc (0, .266) asante, npp (.146, .811) Atwima Nwabiagya asante, ndc (0, .178) asante, npp (.248, .667) Ahafo Ano South asante, ndc (0, .598) asante, npp (0, .861) Ahafo Ano North asante, ndc (0, .804) asante, npp (0, .881) Offinso Mun. asante, ndc (0, .430) asante, npp (.083, .830) Afigya Kwabre asante, ndc (0, .247) asante, npp (.400, .789) Kwabre East asante, ndc (0, .126) asante, npp (.412, .802) Afigya Sekyere asante, ndc (0, .216) asante, npp (.450, .759) Mampong Mun. asante, ndc (0, .224) asante, npp (.375, .900) Sekyere East asante, ndc (0, .179) asante, npp (.518, .757) Sekyere Afram Plains asante, ndc (0, .172) asante, npp (.089, .613) Sekyere Central asante, ndc (0, .119) asante, npp (0, .599) kokomba, ndc (0, .756) Offinso North asante, ndc (0, .657) asante, npp (0, .846)

405 Table B-9. Continued tribe (lower bound, upper bound) District NDC NPP Asunafo South asante, ndc (0, .898) asante, npp (0, .935) Asunafo North Mun. asante, ndc (0, .829) asante, npp (0, .951) Dormaa Mun. boron, ndc (.123, .436) boron, npp (.141, .454) Dormaa East boron, ndc (.083, .266) boron, npp (.283, .466) Tano South asante, ndc (0, .990) Sunyani Mun. boron, ndc (0, .533) boron, npp (0, .956) Sunyani West boron, ndc (0, .399) boron, npp (.127, .674) Berekum Mun. boron, ndc (.154, .365) boron, npp (.200, .412) Jaman South boron, ndc (.010, .248) boron, npp (.255, .404) Jaman North boron, ndc (.247, .391) boron, npp (.160, .304) Tain boron, ndc (.052, .365) boron, npp (0, .294) Wenchi Mun. boron, ndc (0, .560) boron, npp (0, .768) dagarte, ndc (0, .838) Techiman Mun. boron, ndc (0, .665) boron, npp (0, .671) Nkoranza South boron, ndc (0, .626) boron, npp (0, .688) Nkoranza North boron, ndc (0, .475) boron, npp (0, .540) Atebubu Amantin boron, npp (0, .955) Sene ewe, npp (0, .961) guan8, npp (0, .966) Pru kokomba, ndc (0, .998) kokomba, npp (0, .686) Kintampo South boron, npp (0, .887) Bole guan5, npp (0, .742) dagarte, npp (0, .600) Sawla-Tuna-Kalba dagarte, ndc (0, .646) dagarte, npp (0, .424) othergrusi1, npp (0, .737) West Gonja guan5, ndc (0, .813) guan5, npp (0, .653) Gonja Central guan5, ndc (0, .503) guan5, npp (0, .497) East Gonja guan5, ndc (0, .857) guan5, npp (0, .732) Kpandai kokomba, ndc (0, .431) kokomba, npp (0, .454) Nanumba South kokomba, ndc (0, .362) kokomba, npp (0, .371) nanumba, ndc (0, .833) nanumba, npp (0, .854) Nanumba North kokomba, ndc (0, .457) kokomba, npp (.169, .777) nanumba, ndc (0, .894) Zabzugu Tatali kokomba, ndc (0, .500) kokomba, npp (0, .836) Yendi Mun. dagomba, ndc (0, .494) dagomba, npp (0, .473) kokomba, ndc (0, .854) kokomba, npp (0, .818) Tamale Metro. dagomba, ndc (.358, .613) Tolon Kumbugu dagomba, ndc (.403, .441) dagomba, npp (.245, .284) Savelugu Nanton dagomba, ndc (.350, .443) dagomba, npp (.241, .333) Karaga dagomba, ndc (.253, .534) dagomba, npp (.092, .373) Gushiegu dagomba, ndc (0, .650) dagomba, npp (0, .620)

406 Table B-9. Continued tribe (lower bound, upper bound) District NDC NPP Saboba kokomba, ndc (.329, .431) kokomba, npp (.271, .373) Chereponi chokosi, ndc (0, .490) chokosi, npp (.099, .588) Bunkpurugu Yonyo bimoba, ndc (0, .373) bimoba, npp (0, .275) kokomba, ndc (0, .682) kokomba, npp (0, .504) Mamprusi East mamprusi, ndc (0, .644) mamprusi, npp (0, .477) Mamprusi West mamprusi, ndc (.141, .418) mamprusi, npp (.093, .370) Builsa builsa, ndc (.202, .329) builsa, npp (.140, .267) Kasena Nankana West kasena, ndc (0, .313) kasena, npp (0, .353) nankansi, ndc (0, .761) nankansi, npp (0, .858) Kasena Nankana East nankansi, ndc (0, .726) nankansi, npp (0, .788) kasena, ndc (0, .980) Bolgatanga Mun. nankansi, ndc (.203, .512) nankansi, npp (0, .180) Talensi Nabdam nankansi, ndc (0, .484) nankansi, npp (0, .409) namnam, npp (0, .807) Bongo nankansi, ndc (.379, .406) nankansi, npp (.206, .233) Bawku West kusasi, ndc (.084, .384) kusasi, npp (.007, .308) Garu Tempane kusasi, ndc (0, .640) kusasi, npp (0, .614) bimoba, npp (0, .924) Bawku Mun. kusasi, ndc (0, .585) kusasi, npp (0, .581) Wa West dagarte, ndc (0, .595) dagarte, npp (0, .253) wali, npp (0, .823) othergrusi1, npp (0, .739) Wa Mun. wali, npp (0, .880) Wa East dagarte, ndc (0, .664) dagarte, npp (0, .739) Sissala East sisala, ndc (.147, .300) sisala, npp (.066, .218) Nadowli dagarte, ndc (.185, .470) dagarte, npp (0, .221) Jirapa dagarte, ndc (.342, .392) dagarte, npp (.162, .213) Sissala West sisala, ndc (0, .331) sisala, npp (0, .273) dagarte, npp (0, .884) Lambussie Karni dagarte, ndc (0, .468) dagarte, npp (0, .646) sisala, ndc (0, .811) Lawra dagarte, ndc (.241, .306) dagarte, npp (.262, .327)

407 Table B-10. District-Level Bounds of Votes by Tribe - 2008 Presidential Runoff tribe (lower bound, upper bound) District NDC NPP Jomoro nzema, ndc (0, .481) nzema, npp (0, .405) Ellembelle nzema, ndc (.184, .357) nzema, npp (.160, .333) Nzema East evalue, ndc (0, .919) Ahanta West ahanta, ndc (0, .372) ahanta, npp (.024, .654) Sekondi Takoradi Metro. fante, ndc (0, .552) fante, npp (0, .865) Shama fante, ndc (.221, .445) fante, npp (.188, .412) Mpohor-Wassa East wasa, ndc (0, .986) wasa, npp (0, .927) Tarkwa Nsuaem Mun. wasa, ndc (0, .775) Wassa Amenfi East wasa, ndc (0, .678) wasa, npp (0, .815) Wassa Amenfi West wasa, npp (0, .668) Aowin/Suaman aowin, npp (0, .661) Sefwi Akontombra sefwi, npp (0, .782) Sefwi Wiawso sefwi, ndc (.100, .772) sefwi, npp (0, .450) Sefwi Bibiani-Ahwiaso B. sefwi, ndc (.153, .564) sefwi, npp (.152, .563) Juabeso sefwi, npp (0, .358) Bia sefwi, npp (0, .697) KEEA fante, ndc (.366, .476) fante, npp (.191, .301) Cape Coast Metro. fante, ndc (.101, .562) fante, npp (0, .444) Abura-Asebu-Kwamankese fante, ndc (.379, .457) fante, npp (.215, .293) Mfantsiman fante, ndc (.373, .474) fante, npp (.191, .292) Ajumako-Enyan-Essiam fante, ndc (.348, .384) fante, npp (.301, .338) Gomoa West fante, ndc (.347, .391) fante, npp (.219, .264) Effutu Mun. guan3, ndc (0, .669) guan3, npp (0, .553) Gomoa East fante, ndc (.069, .576) fante, npp (0, .392) Agona East fante, ndc (0, .798) fante, npp (0, .740) Agona West Mun. fante, ndc (0, .758) fante, npp (0, .794) agona, ndc (0, .965) Asikuma-Odoben-Brakwa fante, ndc (.237, .399) fante, npp (.247, .409) Assin South asen, ndc (0, .657) asen, npp (0, .866) fante, ndc (0, .971) Twifo-Heman-Lower fante, ndc (0, .996) fante, npp (0, .823) Denkyira Upper Denkyira East Mun. denkyira, ndc (0, .809) Upper Denkyira West denkyira, ndc (0, .630) denkyira, npp (0, .962) Adenta Mun. ewe, npp (0, .932) Ledzokuku/Krowor Mun. ga, npp (0, .785) Ashaiman Mun. ewe, npp (0, .683) Dangbe West dangme, ndc (.376, .850) dangme, npp (0, .168) ewe, npp (0, .725) Dangbe East dangme, ndc (.482, .696) dangme, npp (0, .149) South Tongu ewe, ndc (.703, .743) ewe, npp (.026, .066)

408 Table B-10. Continued tribe (lower bound, upper bound) District NDC NPP Keta Mun. ewe, ndc (.783, .796) ewe, npp (.014, .027) Ketu South ewe, ndc (.688, .720) ewe, npp (.004, .036) Ketu North ewe, ndc (.578, .596) ewe, npp (.104, .122) Akatsi ewe, ndc (.679, .693) ewe, npp (.035, .049) North Tongu ewe, ndc (.708, .749) ewe, npp (.042, .084) Adaklu Anyigbe ewe, ndc (.723, .853) ewe, npp (0, .059) Ho Mun. ewe, ndc (.656, .754) ewe, npp (0, .058) South Dayi ewe, ndc (.604, .669) ewe, npp (0, .060) North Dayi ewe, ndc (.591, .665) ewe, npp (0, .072) Hohoe Mun. ewe, ndc (.506, .843) ewe, npp (0, .099) guan1, npp (0, .690) Biakoye ewe, npp (0, .398) guan6, npp (0, .948) Jasikan ewe, npp (0, .294) guan1, npp (0, .387) Kadjebi ewe, ndc (0, .996) ewe, npp (0, .401) kotokoli, npp (0, .848) Krachi East ewe, npp (0, .679) Krachi West guan7, npp (0, .799) kokomba, npp (0, .646) Nkwanta South kokomba, npp (0, .653) Nkwanta North kokomba, ndc (.176, .495) kokomba, npp (.109, .428) Birim South akyem, ndc (0, .395) akyem, npp (.228, .819) Birim Mun. akyem, ndc (0, .527) fante, ndc (0, .989) Akwapem South Mun. akuapem, ndc (0, .764) akuapem, npp (0, .956) Akwapem North akuapem, ndc (0, .748) guan4, ndc (0, .831) New Juaben Mun. asante, ndc (0, .981) Yilo Krobo dangme, ndc (.349, .572) dangme, npp (.015, .238) Lower Manya dangme, ndc (.253, .623) dangme, npp (0, .277) Asuogyaman ewe, ndc (0, .836) ewe, npp (0, .686) Upper Manya dangme, ndc (.367, .542) dangme, npp (.005, .181) Fanteakwa akyem, ndc (0, .852) dangme, ndc (0, .748) East Akim Mun. akyem, ndc (0, .473) akyem, npp (.046, 1) Kwaebibirem akyem, ndc (0, .648) Akyem Mansa akyem, ndc (0, .549) akyem, npp (0, .952) Birim North akyem, ndc (0, .933) Atiwa akyem, ndc (0, .308) akyem, npp (.317, 1) Kwahu West Mun. kwahu, ndc (0, .316) kwahu, npp (.225, .815) Kwahu South kwahu, ndc (0, .343) kwahu, npp (.194, .718) Kwahu East kwahu, ndc (0, .326) kwahu, npp (.166, .724)

409 Table B-10. Continued tribe (lower bound, upper bound) District NDC NPP Kwahu North ewe, ndc (.098, .981) ewe, npp (0, .280) Atwima Mponua asante, ndc (0, .558) asante, npp (.081, 1) Amansie West asante, ndc (0, .143) asante, npp (.661, .860) Amansie Central asante, ndc (0, .112) asante, npp (.689, .831) Obuasi Mun. asante, ndc (0, .474) asante, npp (.051, .924) Adansi North asante, ndc (0, .314) asante, npp (.294, 1) Bekwai Mun. asante, ndc (0, .121) asante, npp (.715, .897) Bosome Freho asante, ndc (0, .223) asante, npp (.602, .982) Asante Akim South asante, ndc (0, .561) Asante Akim North Mun. asante, ndc (0, .280) asante, npp (.427, .842) Ejisu Juaben Mun. asante, ndc (0, .200) asante, npp (.622, .963) Bosumtwi asante, ndc (0, .191) asante, npp (.545, .826) Atwima Kwanwoma asante, ndc (0, .163) asante, npp (.680, .975) Kumasi Metro. asante, ndc (0, .332) asante, npp (.433, 1) Atwima Nwabiagya asante, ndc (0, .236) asante, npp (.487, .906) Ahafo Ano South asante, ndc (0, .614) Ahafo Ano North asante, ndc (0, .813) asante, npp (0, . 957) Offinso Mun. asante, ndc (0, .409) asante, npp (.238, .985) Afigya Kwabre asante, ndc (0, .286) asante, npp (.510, .902) Kwabre East asante, ndc (0, .207) asante, npp (.560, .951) Afigya Sekyere asante, ndc (0, .221) asante, npp (.627, .935) Mampong Mun. asante, ndc (0, .266) asante, npp (.457, .982) Sekyere East asante, ndc (0, .205) asante, npp (.579, .818) Sekyere Afram Plains asante, ndc (0, .303) asante, npp (.418, .942) Sekyere Central asante, ndc (0, .360) asante, npp (.384, 1) Offinso North asante, ndc (0, .741) Asunafo South asante, ndc (0, .916) asante, npp (0, .924) Asunafo North Mun. asante, ndc (0, .833) asante, npp (0, .993) Dormaa Mun. boron, ndc (.128, .441) boron, npp (.165, .479) Dormaa East boron, ndc (.128, .311) boron, npp (.321, .503) Sunyani Mun. boron, ndc (0, .630) boron, npp (0, .958) Sunyani West boron, ndc (0, .476) boron, npp (.080, .627) Berekum Mun. boron, ndc (.159, .371) boron, npp (.231, .443) Jaman South boron, ndc (.165, .313) boron, npp (.285, .434) Jaman North boron, ndc (.269, .413) boron, npp (.108, .252) Tain boron, ndc (.164, .477) boron, npp (0, .050) dagarte, npp (0, .232) Wenchi Mun. boron, ndc (0, .679) boron, npp (0, .766) Techiman Mun. boron, ndc (0, .740) boron, npp (0, .642) Nkoranza South boron, ndc (0, .718) boron, npp (0, .640) Nkoranza North boron, ndc (0, .515) boron, npp (0, .554) Atebubu Amantin boron, npp (0, .969)

410 Table B-10. Continued tribe (lower bound, upper bound) District NDC NPP Sene ewe, npp (0, .961) guan8, npp (0, .732) Pru kokomba, npp (0, .835) Kintampo South boron, npp (0, .882) Bole guan5, npp (0, .639) dagarte, npp (0, .517) Sawla-Tuna-Kalba dagarte, ndc (0, .780) dagarte, npp (0, .324) othergrusi1, npp (0, .563) West Gonja guan5, ndc (0, .981) guan5, npp (0, .695) Gonja Central guan5, ndc (0, .595) guan5, npp (0, .447) East Gonja guan5, ndc (0, .902) guan5, npp (0, .675) Kpandai kokomba, ndc (.058, .704) kokomba, npp (0, .452) Nanumba South kokomba, ndc (0, .513) kokomba, npp (.023, .605) Nanumba North kokomba, ndc (0, .466) kokomba, npp (.081, .688) nanumba, ndc (0, .912) Zabzugu Tatali kokomba, ndc (0, .625) kokomba, npp (0, .793) Yendi Mun. dagomba, ndc (0, .631) dagomba, npp (0, .624) Tamale Metro. dagomba, ndc (.493, .748) dagomba, npp (0, .243) Tolon Kumbugu dagomba, ndc (.494, .532) dagomba, npp (.249, .287) Savelugu Nanton dagomba, ndc (.447, .540) dagomba, npp (.245, .338) Karaga dagomba, ndc (.294, .575) dagomba, npp (.077, .358) Gushiegu dagomba, ndc (.013, .689) dagomba, npp (0, .612) Saboba kokomba, ndc (.374, .477) kokomba, npp (.271, .373) Chereponi chokosi, ndc (0, .466) chokosi, npp (.128, .618) Bunkpurugu Yonyo bimoba, ndc (.209, .990) bimoba, npp (0, .313) kokomba, npp (0, .572) Mamprusi East mamprusi, ndc (0, .994) mamprusi, npp (0, .479) Mamprusi West mamprusi, ndc (.246, .523) mamprusi, npp (.132, .409) Builsa builsa, ndc (.417, .544) builsa, npp (.101, .228) Kasena Nankana West kasena, ndc (.186, .794) kasena, npp (0, .355) nankansi, npp (0, .861) Kasena Nankana East nankansi, ndc (0, .926) nankansi, npp (0, .774) Bolgatanga Mun. nankansi, ndc (.330, .638) nankansi, npp (0, .277) Talensi Nabdam nankansi, ndc (.137, .682) nankansi, npp (0, .350) namnam, npp (0, .690) Bongo nankansi, ndc (.431, .458) nankansi, npp (.212, .239) Bawku West kusasi, ndc (.306, .607) kusasi, npp (0, .293) Garu Tempane kusasi, ndc (0, .846) kusasi, npp (0, .466) bimoba, npp (0, .874) Bawku Mun. kusasi, ndc (0, .846) kusasi, npp (0, .481) Wa West dagarte, ndc (0, .824) dagarte, npp (0, .309) Wa Mun. wali, npp (0, .897)

411 Table B-10. Continued tribe (lower bound, upper bound) District NDC NPP Wa East dagarte, ndc (0, .839) dagarte, npp (0, .777) Sissala East sisala, ndc (.195, .347) sisala, npp (.288, .440) Nadowli dagarte, ndc (.325, .610) dagarte, npp (0, .220) Jirapa dagarte, ndc (.418, .468) dagarte, npp (.116, .167) Sissala West sisala, ndc (.115, .503) sisala, npp (.061, .449) Lambussie Karni dagarte, ndc (0, .685) dagarte, npp (0, .438) sisala, npp (0, .759) Lawra dagarte, ndc (.351, .416) dagarte, npp (.199, .264)

412 Table B-11. District-Level Bounds of Votes by Tribe - 2012 Presidential tribe (lower bound, upper bound) District NDC NPP Ellembelle nzema, ndc (.490, .731) nzema, npp (.269, .510) Shama fante, ndc (.386, .716) fante, npp (.284, .614) Sefwi Wiawso sefwi, ndc (.227, 1) sefwi, npp (0, .773) Sefwi Bibiani-Ahwiaso B. sefwi, ndc (.224, .751) sefwi, npp (.249, .776) KEEA fante, ndc (.550, .726) fante, npp (.274, .450) Cape Coast Metro. fante, ndc (.241, .909) fante, npp (.091, .759) Abura-Asebu-Kwamankese fante, ndc (.576, .686) fante, npp (.314, .424) Mfantsiman Mun. fante, ndc (.549, .687) fante, npp (.313, .451) Ajumako-Enyan-Essiam fante, ndc (.511, .560) fante, npp (.440, .489) Gomoa West fante, ndc (.580, .645) fante, npp (.355, .420) Gomoa East fante, ndc (.199, 1) fante, npp (0, .801) Asikuma-Odoben-Brakwa fante, ndc (.394, .621) fante, npp (.379, .606) Dangbe West dangme, ndc (.551, 1) dangme, npp (0, .449) Dangbe East dangme, ndc (.769, 1) dangme, npp (0, .231) South Tongu ewe, ndc (.936, .990) ewe, npp (.010, .064) Keta Mun. ewe, ndc (.962, .979) ewe, npp (.021, .038) Ketu South ewe, ndc (.938, .986) ewe, npp (.014, .062) Ketu North ewe, ndc (.875, .900) ewe, npp (.100, .125) Akatsi ewe, ndc (.924, .944) ewe, npp (.056, .076) North Tongu ewe, ndc (.916, .972) ewe, npp (.028, .084) Adaklu Anyigbe ewe, ndc (.917, .1) ewe, npp (0, .083) Ho Mun. ewe, ndc (.914, 1) ewe, npp (0, .086) South Dayi ewe, ndc (.913, 1) ewe, npp (0, .087) North Dayi ewe, ndc (.900, 1) ewe, npp (0, .100) Hohoe Mun. ewe, ndc (.848, 1) ewe, npp (0, .152) Biakoye ewe, npp (0, .354) Jasikan ewe, ndc (.056, 1) ewe, npp (0, .944) Kadjebi ewe, ndc (.014, 1) ewe, npp (0, .986) Nkwanta North kokomba, ndc (.500, .973) kokomba, npp (.027, .500) Birim South akyem, ndc (0, .621) akyem, npp (.379, 1) Akwapem North guan4, ndc (0, .944) Yilo Krobo dangme, ndc (.589, .903) dangme, npp (.097, .411) Lower Manya dangme, ndc (.495, 1) dangme, npp (0, .505) Upper Manya dangme, ndc (.589, .903) dangme, npp (.136, .398) Fanteakwa dangme, ndc (0, .813) East Akim Mun. akyem, ndc (0, .935) akyem, npp (.065, 1) Atiwa akyem, ndc (0, .588) akyem, npp (.412, 1) Kwahu West Mun. kwahu, ndc (0, .542) kwahu, npp (.458, 1) Kwahu South kwahu, ndc (0, .548) kwahu, npp (.452, 1) Kwahu East kwahu, ndc (0, .626) kwahu, npp (.374, 1) Kwahu North ewe, ndc (.314, 1) ewe, npp (0, .686) Amansie West asante, ndc (0, .242) asante, npp (.758, 1)

413 Table B-11. Continued tribe (lower bound, upper bound) District NDC NPP Amansie Central asante, ndc (.008, .184) asante, npp (.816, .992) Obuasi Mun. asante, ndc (0, .859) asante, npp (.141, 1) Adansi North asante, ndc (0, .574) asante, npp (.426, 1) Bekwai Mun. asante, ndc (0, .171) asante, npp (.829, 1) Bosome Freho asante, ndc (0, .384) asante, npp (.616, 1) Asante Akim North Mun. asante, ndc (0, .448) asante, npp (.552, 1) Ejisu Juaben Mun. asante, ndc (0, .293) asante, npp (.707, 1) Bosumtwi asante, ndc (0, .291) asante, npp (.709, 1) Atwima Kwanwoma asante, ndc (0, .244) asante, npp (.756, 1) Kumasi Metro. asante, ndc (0, .513) asante, npp (.487, 1) Atwima Nwabiagya asante, ndc (0, .349) asante, npp (.651, 1) Offinso Mun. asante, ndc (0, .731) asante, npp (.269, 1) Afigya Kwabre asante, ndc (0, .320) asante, npp (.680, 1) Kwabre East asante, ndc (0, .305) asante, npp (.695, 1) Afigya Sekyere asante, ndc (0, .327) asante, npp (.673, 1) Mampong Mun. asante, ndc (0, .430) asante, npp (.570, 1) Sekyere East asante, ndc (0, .279) asante, npp (.721, 1) Sekyere Afram Plains asante, ndc (0, .568) asante, npp (.432, 1) Sekyere Central asante, ndc (0, .646) asante, npp (.354, 1) Dormaa Mun. boron, ndc (.286, .719) boron, npp (.281, .714) Dormaa East boron, ndc (.252, .490) boron, npp (.510, .748) Sunyani West boron, ndc (.032, .798) boron, npp (.202, .968) Berekum Mun. boron, ndc (.258, .549) boron, npp (.451, .742) Jaman South boron, ndc (.311, .521) boron, npp (.479, .689) Jaman North boron, ndc (.494, .712) boron, npp (.288, .506) Tain boron, ndc (.337, .822) boron, npp (.178, .663) Gonja Central guan5, ndc (.173, 1) guan5, npp (0, .827) Kpandai kokomba, ndc (.200, 1) kokomba, npp (0, .780) Nanumba South kokomba, ndc (.151, 1) kokomba, npp (0, .849) Nanumba North kokomba, ndc (0, .856) kokomba, npp (.144, 1) Tamale Metro. dagomba, ndc (.643, 1) dagomba, npp (0, .357) Tolon Kumbugu dagomba, ndc (.605, .658) dagomba, npp (.342, .395) Savelugu Nanton dagomba, ndc (.547, .666) dagomba, npp (.334, .453) Karaga dagomba, ndc (.412, .816) dagomba, npp (.184, .588) Gushiegu dagomba, ndc (.014, 1) dagomba, ndc (0, .986) Saboba kokomba, ndc (.460, .583) kokomba, npp (.417, .540) Chereponi chokosi, ndc (.123, .813) chokosi, npp (.187, .877) Bunkpurugu Yonyo bimoba, ndc (.187, 1) bimoba, npp (0, .813) Mamprusi West mamprusi, ndc (.236, .648) mamprusi, npp (.352, .764) Builsa builsa, ndc (.698, .904) builsa, npp (.096, .302)

414 Table B-11. Continued tribe (lower bound, upper bound) District NDC NPP Kasena Nankana West kasena, ndc (.553, 1) kasena, npp (0, .447) Bolgatanga Mun. nankansi, ndc (.654, 1) nankansi, npp (0, .346) Talensi Nabdam nankansi, ndc (.262, 1) nankansi, npp (0, .738) Bongo nankansi, ndc (.699, .741) nankansi, npp (.259, .301) Bawku West kusasi, ndc (.497, .959) kusasi, npp (.041, .503) Garu Tempane kusasi, ndc (0, .918) Sissala East sisala, ndc (.556, .791) sisala, npp (.209, .444) Nadowli dagarte, ndc (.600, 1) dagarte, npp (0, .400) Jirapa dagarte, ndc (.848, .926) dagarte, npp (.074, .152) Sissala West sisala, ndc (.413, 1) sisala, npp (0, .587) Lambussie Karni dagarte, ndc (.474, 1) dagarte, npp (0, .526) Lawra dagarte, ndc (.689, .773) dagarte, npp (.227, .311)

415 Table B-12. District-Level Bounds of Votes by Tribe - 2012 Parliamentary tribe (lower bound, upper bound) District NDC NPP Jomoro nzema, ndc (0, .951) nzema, npp (0, .423) Ellembelle nzema, ndc (.475, .707) nzema, npp (.253, .485) Ahanta West ahanta, ndc (0, .896) Shama fante, ndc (.318, .630) fante, npp (.275, .587) Sefwi Wiawso sefwi, ndc (.091, 1) sefwi, npp (0, .861) Sefwi Bibiani-Ahwiaso B. sefwi, ndc (.193, .698) sefwi, npp (.241, .746) Juabeso sefwi, npp (0, .740) KEEA fante, ndc (.120, .267) fante, npp (.276, .424) Cape Coast Metro. fante, ndc (.187, .824) fante, npp (.100, .737) Abura-Asebu-Kwamankese fante, ndc (.500, .603) fante, npp (.303, .406) Mfantsiman Mun. fante, ndc (.474, .604) fante, npp (.354, .485) Ajumako-Enyan-Essiam fante, ndc (.494, .540) fante, npp (.422, .468) Gomoa West fante, ndc (.529, .590) fante, npp (.351, .412) Gomoa East fante, ndc (.155, .963) fante, npp (0, .755) Asikuma-Odoben-Brakwa fante, ndc (.402, .618) fante, npp (.329, .545) Dangbe West dangme, ndc (.226, .956) dangme, npp (0, .587) Dangbe East dangme, ndc (.530, .813) dangme, npp (.020, .303) South Tongu ewe, ndc (.803, .856) ewe, npp (0, .051) Keta Mun. ewe, ndc (.856, .872) ewe, npp (.048, .064) Ketu South ewe, ndc (.867, .914) ewe, npp (.002, .048) Ketu North ewe, ndc (.746, .770) ewe, npp (.161, .186) Akatsi ewe, ndc (.680, .699) ewe, npp (.054, .072) North Tongu ewe, ndc (.858, .912) ewe, npp (.043, .096) Adaklu Anyigbe ewe, ndc (.778, .966) ewe, npp (0, .096) Ho Mun. ewe, ndc (.863, .995) ewe, npp (0, .105) South Dayi ewe, ndc (.582, .670) ewe, npp (0, .059) North Dayi ewe, ndc (.744, .846) ewe, npp (.034, .136) Hohoe Mun. ewe, ndc (.723, 1) ewe, npp (0, .200) Jasikan ewe, npp (0, .950) Kadjebi ewe, ndc (0, .912) ewe, npp (0, .684) Nkwanta North kokomba, ndc (.433, .870) kokomba, npp (.076, .512) Birim South akyem, ndc (0, .554) akyem, npp (.253, 1) Birim Mun. akyem, ndc (0, .840) Yilo Krobo dangme, ndc (.485, .786) dangme, npp (.128, .429) Lower Manya dangme, ndc (.252, .780) dangme, npp (0, .518) Upper Manya dangme, ndc (.482, .726) dangme, npp (.162, .406) East Akim Mun. akyem, ndc (0, .892) akyem, npp (.046, 1) Atiwa akyem, ndc (0, .589) akyem, npp (.371, 1) Kwahu West Mun. kwahu, ndc (0, .569) kwahu, npp (.394, 1) Kwahu South kwahu, ndc (0, .465) kwahu, npp (.421, 1) Kwahu East kwahu, ndc (0, .655) kwahu, npp (.320, 1)

416 Table B-12. Continued tribe (lower bound, upper bound) District NDC NPP Kwahu North ewe, ndc (.034, 1) ewe, npp (0, .742) Atwima Mponua asante, ndc (0, .978) Amansie West asante, ndc (.007, .258) asante, npp (.689, .940) Amansie Central asante, ndc (.033, .203) asante, npp (.729, .899) Obuasi Mun. asante, ndc (0, .835) asante, npp (.089, 1) Adansi North asante, ndc (0, .644) asante, npp (.318, 1) Bekwai Mun. asante, ndc (0, .157) asante, npp (.810, 1) Bosome Freho asante, ndc (0, .323) asante, npp (.357, .845) Asante Akim North Mun. asante, ndc (0, .397) asante, npp (.325, .842) Ejisu Juaben Mun. asante, ndc (0, .281) asante, npp (.690, 1) Bosumtwi asante, ndc (.102, .438) asante, npp (.548, .883) Atwima Kwanwoma asante, ndc (0, .228) asante, npp (.688, 1) Kumasi Metro. asante, ndc (0, .468) asante, npp (.468, 1) Atwima Nwabiagya asante, ndc (0, .293) asante, npp (.502, 1) Ahafo Ano South asante, ndc (0, .989) Offinso Mun. asante, ndc (0, .637) asante, npp (.305, 1) Afigya Kwabre asante, ndc (0, .287) asante, npp (.647, 1) Kwabre East asante, ndc (0, .378) asante, npp (.541, 1) Afigya Sekyere asante, ndc (0, .332) asante, npp (.602, .970) Mampong Mun. asante, ndc (0, .415) asante, npp (.526, 1) Sekyere East asante, ndc (0, .275) asante, npp (.685, .967) Sekyere Afram Plains asante, ndc (0, .443) asante, npp (.260, .934) Sekyere Central asante, ndc (0, .659) asante, npp (.305, 1) Dormaa Mun. boron, ndc (.260, .671) boron, npp (.285, .696) Dormaa East boron, ndc (.269, .496) boron, npp (.460, .687) Sunyani West boron, ndc (.055, .788) boron, npp (.178, .919) Berekum Mun. boron, ndc (.236, .518) boron, npp (.439, .720) Jaman South boron, ndc (.286, .486) boron, npp (.488, .688) Jaman North boron, ndc (.381, .585) boron, npp (.389, .593) Tain boron, ndc (.328, .790) boron, npp (.156, .618) Nkoranza South boron, ndc (.010, 1) boron, npp (0, .943) Nkoranza North boron, ndc (0, .883) boron, npp (.006, .949) Gonja Central guan5, ndc (0, .909) guan5, npp (0, .873) Kpandai kokomba, ndc (0, .659) kokomba, npp (0, .775) Nanumba South kokomba, ndc (0, .705) kokomba, npp (0, .648) Nanumba North kokomba, ndc (0, .602) kokomba, npp (.178, 1) Zabzugu Tatali kokomba, ndc (0, .926) kokomba, npp (0, .989) Yendi Mun. dagomba, ndc (0, .620) dagomba, npp (0, .830) Tamale Metro. dagomba, ndc (.517, .862) dagomba, npp (0, .341) Tolon Kumbugu dagomba, ndc (.448, .496) dagomba, npp (.341, .389) Savelugu Nanton dagomba, ndc (.467, .578) dagomba, npp (.357, .469)

417 Table B-12. Continued tribe (lower bound, upper bound) District NDC NPP Karaga dagomba, ndc (.307, .667) dagomba, npp (.199, .559) Gushiegu dagomba, ndc (.032, .915) dagomba, ndc (.015, .899) Saboba kokomba, ndc (.440, .558) kokomba, npp (.412, .530) Chereponi chokosi, ndc (.112, .728) chokosi, npp (.189, .805) Bunkpurugu Yonyo bimoba, ndc (0, .998) bimoba, npp (0, .766) Mamprusi West mamprusi, ndc (.124, .493) mamprusi, npp (.237, .605) Builsa builsa, ndc (.339, .505) builsa, npp (.089, .256) Kasena Nankana West kasena, ndc (.227, 1) kasena, npp (0, .414) Bolgatanga Mun. nankansi, ndc (.407, .833) nankansi, npp (0, .382) Talensi Nabdam nankansi, ndc (0, .698) nankansi, npp (0, .824) Bongo nankansi, ndc (.500, .537) nankansi, npp (.283, .321) Bawku West kusasi, ndc (.321, .729) kusasi, npp (.160, .569) Garu Tempane kusasi, ndc (0, .918) Wa West dagarte, ndc (0, .926) Sissala East sisala, ndc (.228, .414) sisala, npp (.118, .304) Nadowli dagarte, ndc (.368, .746) dagarte, npp (.165, .543) Jirapa dagarte, ndc (.363, .430) dagarte, npp (.016, .083) Sissala West sisala, ndc (.235, .727) sisala, npp (.033, .524) Lambussie Karni dagarte, npp (0, .362) Lawra dagarte, ndc (.589, .668) dagarte, npp (.280, .359)

418 APPENDIX C ECOLOGICAL INFERENCE RESULTS

419 Table C-1. 2012 Presidential Vote Estimates by Tribe (urban covariate, ﬂat priors

NDC NPP Third Party No Vote tribe Mean 2.5% 97.5% Mean 2.5% 97.5% Mean 2.5% 97.5% Mean 2.5% 97.5% agona 0.406 0.268 0.461 0.534 0.479 0.601 0.013 0.001 0.039 0.025 0.001 0.134 ahafo 0.540 0.453 0.667 0.425 0.288 0.506 0.004 0.001 0.011 0.018 0.003 0.052 ahanta 0.177 0.102 0.347 0.634 0.461 0.721 0.023 0.006 0.047 0.154 0.106 0.247 akuapem 0.194 0.132 0.261 0.716 0.665 0.767 0.005 0.002 0.009 0.073 0.025 0.125 akwamu 0.515 0.498 0.540 0.473 0.448 0.489 0.003 0.001 0.007 0.002 0.001 0.006 akyem(d) 0.140 0.084 0.211 0.729 0.654 0.788 0.005 0.002 0.009 0.114 0.060 0.184 aowin 0.389 0.293 0.469 0.316 0.241 0.386 0.016 0.007 0.029 0.258 0.170 0.331 asante(d) 0.105 0.081 0.130 0.762 0.742 0.784 0.002 0.001 0.003 0.126 0.110 0.144 asen 0.420 0.315 0.517 0.469 0.379 0.566 0.017 0.004 0.039 0.081 0.018 0.170 boron(d) 0.347 0.317 0.372 0.448 0.427 0.466 0.006 0.004 0.008 0.182 0.159 0.207 chokosi(d) 0.457 0.366 0.516 0.445 0.389 0.513 0.027 0.014 0.042 0.054 0.014 0.095 denkyira 0.257 0.159 0.365 0.548 0.439 0.637 0.013 0.004 0.028 0.167 0.099 0.256 evalue 0.499 0.299 0.728 0.233 0.042 0.436 0.025 0.005 0.060 0.212 0.034 0.432

420 fante(d) 0.407 0.367 0.443 0.313 0.273 0.349 0.013 0.010 0.016 0.250 0.218 0.289 kwahu(d) 0.174 0.061 0.306 0.666 0.534 0.793 0.006 0.002 0.011 0.145 0.080 0.282 nzema(d) 0.336 0.259 0.421 0.353 0.227 0.446 0.024 0.016 0.036 0.268 0.214 0.354 sefwi(d) 0.534 0.497 0.572 0.278 0.247 0.307 0.005 0.001 0.011 0.165 0.131 0.205 wasa 0.347 0.269 0.447 0.317 0.263 0.381 0.010 0.003 0.018 0.314 0.228 0.388 bawle 0.403 0.258 0.474 0.044 0.002 0.185 0.531 0.342 0.571 0.015 0.002 0.083 other akan 0.095 0.046 0.114 0.867 0.847 0.906 0.027 0.004 0.039 0.005 0.001 0.026 dangme(d) 0.523 0.452 0.582 0.239 0.192 0.287 0.007 0.004 0.010 0.212 0.166 0.262 ga 0.493 0.343 0.665 0.280 0.192 0.375 0.006 0.001 0.011 0.217 0.059 0.427 other ga 0.326 0.133 0.529 0.566 0.368 0.668 0.067 0.001 0.222 0.027 0.002 0.112 ewe(d) 0.584 0.523 0.652 0.045 0.030 0.066 0.006 0.005 0.008 0.351 0.289 0.405 guan1 0.940 0.927 0.947 0.013 0.007 0.019 0.004 0.0002 0.012 0.002 0.001 0.007 guan2 0.320 0.286 0.368 0.565 0.480 0.622 0.067 0.025 0.098 0.004 0.001 0.011 guan3 0.342 0.191 0.450 0.455 0.358 0.542 0.014 0.003 0.034 0.169 0.067 0.309 guan4(d) 0.132 0.118 0.146 0.852 0.828 0.869 0.004 0.001 0.011 0.009 0.003 0.026 guan5(d) 0.490 0.314 0.642 0.258 0.143 0.457 0.012 0.004 0.025 0.225 0.148 0.343 guan6 0.712 0.448 0.911 0.086 0.016 0.217 0.016 0.003 0.036 0.179 0.028 0.439 guan7 0.745 0.668 0.844 0.112 0.079 0.156 0.023 0.006 0.046 0.102 0.021 0.170 guan8 0.818 0.487 0.925 0.069 0.002 0.309 0.013 0.001 0.027 0.031 0.002 0.211 other guan 0.621 0.599 0.635 0.283 0.262 0.303 0.086 0.059 0.113 0.003 0.001 0.005 Table C-1. Continued

NDC NPP Third Party No Vote tribe Mean 2.5% 97.5% Mean 2.5% 97.5% Mean 2.5% 97.5% Mean 2.5% 97.5% bimoba(d) 0.749 0.669 0.840 0.032 0.002 0.128 0.022 0.004 0.042 0.169 0.092 0.237 kokomba(d) 0.323 0.262 0.407 0.383 0.323 0.432 0.017 0.009 0.027 0.229 0.179 0.273 basare 0.737 0.589 0.872 0.179 0.112 0.257 0.032 0.003 0.079 0.043 0.004 0.120 pilapila 0.350 0.324 0.380 0.206 0.184 0.224 0.386 0.317 0.441 0.010 0.003 0.034 salfalba 0.200 0.148 0.295 0.651 0.529 0.756 0.110 0.008 0.198 0.029 0.007 0.102 kotokoli 0.294 0.123 0.521 0.239 0.050 0.465 0.042 0.015 0.076 0.378 0.128 0.624 chamba 0.842 0.834 0.851 0.011 0.007 0.014 0.102 0.092 0.112 0.002 0.001 0.004 other gruma 0.942 0.898 0.969 0.042 0.016 0.074 0.006 0.001 0.022 0.005 0.001 0.016 builsa(d) 0.671 0.624 0.711 0.124 0.092 0.163 0.059 0.044 0.081 0.116 0.101 0.135 dagarte(d) 0.504 0.435 0.567 0.108 0.081 0.143 0.017 0.011 0.023 0.326 0.272 0.381 wali 0.588 0.542 0.609 0.368 0.337 0.392 0.028 0.006 0.053 0.007 0.002 0.032 dagomba(d) 0.533 0.495 0.567 0.253 0.227 0.282 0.016 0.010 0.022 0.181 0.158 0.215 kusasi(d) 0.663 0.589 0.723 0.085 0.049 0.127 0.012 0.005 0.020 0.204 0.142 0.270

421 mamprusi(d) 0.368 0.279 0.465 0.359 0.299 0.420 0.019 0.008 0.034 0.216 0.153 0.328 namnam 0.301 0.265 0.339 0.559 0.530 0.594 0.081 0.046 0.145 0.005 0.001 0.016 nankansi(d) 0.535 0.396 0.637 0.140 0.070 0.242 0.024 0.015 0.035 0.273 0.217 0.375 nanumba 0.473 0.262 0.759 0.251 0.037 0.524 0.095 0.041 0.167 0.146 0.017 0.401 mosi 0.489 0.410 0.528 0.480 0.459 0.532 0.008 0.001 0.030 0.018 0.005 0.039 other mole 0.825 0.813 0.833 0.054 0.050 0.058 0.021 0.009 0.025 0.002 0.001 0.005 kasena(d) 0.350 0.251 0.470 0.518 0.395 0.575 0.015 0.002 0.036 0.098 0.041 0.183 mo 0.790 0.773 0.837 0.194 0.142 0.210 0.005 0.0004 0.013 0.003 0.0004 0.012 sisala(d) 0.535 0.322 0.687 0.269 0.151 0.362 0.056 0.042 0.077 0.111 0.066 0.269 vagala 0.327 0.005 0.545 0.161 0.004 0.529 0.440 0.281 0.552 0.046 0.006 0.177 othergrusi1 0.324 0.184 0.557 0.240 0.127 0.342 0.031 0.009 0.057 0.353 0.203 0.492 othergrusi2 0.750 0.722 0.760 0.237 0.190 0.253 0.008 0.0004 0.032 0.002 0.0004 0.007 busanga 0.135 0.106 0.161 0.838 0.805 0.871 0.011 0.001 0.041 0.008 0.002 0.024 wangara 0.436 0.424 0.443 0.560 0.554 0.571 0.001 0.0003 0.004 0.002 0.001 0.002 othermande 0.235 0.163 0.357 0.571 0.307 0.701 0.105 0.004 0.370 0.017 0.004 0.043 other inside 0.338 0.262 0.440 0.559 0.440 0.666 0.037 0.003 0.101 0.058 0.011 0.156 other outside 0.318 0.074 0.667 0.154 0.040 0.433 0.053 0.019 0.098 0.408 0.129 0.669 Note: bold = spread of at least 10 points between party CIs; italics = Party CIs don’t cross over; (d) = deterministic bounds info. contributed to this group’s vote estimates Table C-2. 2012 Parliamentary Results by Tribe (urban covariate, ﬂat priors)

NDC NPP Third Party No Vote tribe Mean 2.5% 97.5% Mean 2.5% 97.5% Mean 2.5% 97.5% Mean 2.5% 97.5% agona 0.654 0.532 0.752 0.307 0.200 0.418 0.006 0.001 0.023 0.014 0.001 0.066 ahafo 0.596 0.387 0.739 0.386 0.242 0.610 0.004 0.001 0.013 0.009 0.003 0.023 ahanta(d) 0.221 0.134 0.362 0.557 0.389 0.658 0.069 0.029 0.124 0.142 0.098 0.191 akuapem 0.484 0.457 0.497 0.511 0.499 0.535 0.002 0.0003 0.004 0.002 0.001 0.005 akwamu 0.419 0.383 0.460 0.566 0.522 0.605 0.006 0.0002 0.028 0.006 0.002 0.018 akyem(d) 0.128 0.083 0.177 0.723 0.636 0.784 0.043 0.014 0.088 0.100 0.058 0.160 aowin 0.385 0.279 0.496 0.351 0.268 0.433 0.020 0.002 0.041 0.230 0.129 0.325 asante(d) 0.111 0.092 0.128 0.713 0.688 0.732 0.028 0.024 0.038 0.166 0.142 0.190 asen 0.374 0.232 0.523 0.521 0.367 0.644 0.031 0.008 0.053 0.060 0.011 0.132 boron(d) 0.358 0.324 0.392 0.441 0.395 0.471 0.011 0.005 0.017 0.185 0.155 0.216 chokosi(d) 0.594 0.568 0.623 0.380 0.345 0.405 0.006 0.001 0.018 0.006 0.002 0.025 denkyira 0.285 0.178 0.381 0.490 0.393 0.588 0.052 0.023 0.079 0.161 0.071 0.271 evalue 0.312 0.075 0.459 0.590 0.399 0.693 0.010 0.001 0.038 0.075 0.005 0.325

422 fante(d) 0.352 0.318 0.401 0.288 0.242 0.342 0.043 0.032 0.055 0.309 0.266 0.360 kwahu(d) 0.177 0.072 0.358 0.434 0.316 0.620 0.019 0.006 0.047 0.362 0.138 0.557 nzema(d) 0.368 0.253 0.488 0.147 0.105 0.242 0.133 0.112 0.189 0.334 0.247 0.439 sefwi(d) 0.468 0.421 0.513 0.309 0.282 0.336 0.069 0.057 0.080 0.145 0.110 0.185 wasa 0.410 0.301 0.519 0.339 0.267 0.443 0.018 0.002 0.045 0.226 0.150 0.328 bawle 0.113 0.094 0.128 0.427 0.412 0.442 0.009 0.003 0.020 0.002 0.002 0.004 other akan 0.876 0.847 0.898 0.093 0.077 0.109 0.017 0.004 0.038 0.004 0.001 0.013 dangme(d) 0.495 0.429 0.568 0.259 0.182 0.334 0.048 0.030 0.073 0.187 0.133 0.249 ga 0.477 0.266 0.636 0.181 0.027 0.363 0.015 0.005 0.028 0.322 0.108 0.537 other ga 0.008 0.002 0.013 0.764 0.741 0.783 0.003 0.0003 0.008 0.002 0.001 0.005 ewe(d) 0.401 0.358 0.444 0.148 0.081 0.201 0.066 0.046 0.082 0.377 0.318 0.443 guan1 0.850 0.796 0.877 0.118 0.092 0.159 0.018 0.002 0.037 0.005 0.001 0.013 guan2 0.777 0.749 0.797 0.163 0.146 0.182 0.053 0.039 0.069 0.003 0.001 0.007 guan3 0.488 0.477 0.498 0.473 0.462 0.483 0.019 0.013 0.027 0.003 0.002 0.005 guan4 0.357 0.333 0.392 0.615 0.580 0.643 0.007 0.001 0.022 0.006 0.002 0.013 guan5(d) 0.399 0.240 0.590 0.238 0.189 0.316 0.087 0.020 0.218 0.265 0.145 0.451 guan6 0.423 0.387 0.464 0.562 0.522 0.596 0.010 0.002 0.018 0.004 0.001 0.010 guan7 0.907 0.749 0.956 0.050 0.005 0.192 0.008 0.001 0.024 0.011 0.001 0.048 guan8 0.715 0.620 0.870 0.245 0.029 0.352 0.004 0.0002 0.015 0.026 0.002 0.164 other guan 0.509 0.394 0.593 0.115 0.043 0.199 0.195 0.152 0.243 0.019 0.008 0.048 Table C-2. Continued

NDC NPP Third Party No Vote tribe Mean 2.5% 97.5% Mean 2.5% 97.5% Mean 2.5% 97.5% Mean 2.5% 97.5% bimoba(d) 0.634 0.502 0.720 0.182 0.029 0.300 0.061 0.004 0.126 0.108 0.022 0.239 kokomba(d) 0.169 0.122 0.236 0.349 0.295 0.408 0.185 0.162 0.205 0.280 0.234 0.326 basare 0.634 0.577 0.697 0.333 0.252 0.400 0.009 0.002 0.023 0.020 0.009 0.049 pilapila 0.933 0.819 0.968 0.023 0.005 0.098 0.036 0.006 0.087 0.009 0.001 0.047 salfalba 0.369 0.353 0.399 0.448 0.430 0.465 0.100 0.058 0.112 0.003 0.001 0.007 kotokoli 0.522 0.140 0.721 0.111 0.020 0.416 0.068 0.013 0.201 0.266 0.141 0.464 chamba 0.153 0.013 0.274 0.043 0.004 0.109 0.603 0.517 0.688 0.035 0.008 0.149 other gruma 0.729 0.682 0.774 0.239 0.174 0.289 0.005 0.001 0.019 0.008 0.003 0.031 builsa(d) 0.190 0.158 0.230 0.512 0.454 0.560 0.137 0.121 0.160 0.130 0.106 0.175 dagarte(d) 0.407 0.308 0.523 0.161 0.080 0.276 0.059 0.047 0.077 0.356 0.282 0.426 wali 0.210 0.093 0.282 0.517 0.452 0.621 0.232 0.167 0.276 0.027 0.003 0.137 dagomba(d) 0.474 0.386 0.522 0.274 0.230 0.320 0.060 0.045 0.076 0.179 0.148 0.224 kusasi(d) 0.553 0.464 0.620 0.140 0.085 0.195 0.022 0.009 0.035 0.279 0.217 0.350

423 mamprusi(d) 0.344 0.223 0.460 0.355 0.270 0.490 0.071 0.063 0.084 0.215 0.149 0.353 namnam 0.279 0.116 0.375 0.597 0.484 0.679 0.038 0.003 0.229 0.025 0.001 0.115 nankansi(d) 0.450 0.325 0.521 0.202 0.156 0.278 0.071 0.052 0.089 0.265 0.206 0.345 nanumba 0.623 0.576 0.659 0.252 0.197 0.299 0.049 0.017 0.100 0.036 0.011 0.076 mosi 0.492 0.461 0.510 0.424 0.403 0.457 0.008 0.001 0.022 0.003 0.002 0.007 other mole 0.430 0.387 0.508 0.476 0.430 0.552 0.077 0.040 0.107 0.005 0.002 0.007 kasena(d) 0.507 0.412 0.569 0.335 0.253 0.388 0.045 0.005 0.067 0.097 0.020 0.190 mo 0.503 0.486 0.517 0.491 0.480 0.505 0.001 0.0001 0.003 0.003 0.001 0.005 sisala(d) 0.378 0.198 0.457 0.354 0.297 0.456 0.134 0.105 0.183 0.120 0.072 0.274 vagala 0.104 0.023 0.205 0.697 0.583 0.829 0.042 0.003 0.142 0.044 0.007 0.139 othergrusi1 0.383 0.201 0.602 0.440 0.263 0.595 0.028 0.004 0.063 0.111 0.008 0.269 othergrusi2 0.618 0.485 0.829 0.277 0.137 0.347 0.009 0.001 0.030 0.031 0.003 0.100 busanga 0.293 0.286 0.302 0.710 0.700 0.717 0.002 0.0004 0.005 0.003 0.001 0.005 wangara 0.346 0.268 0.393 0.635 0.597 0.697 0.002 0.001 0.006 0.006 0.003 0.015 othermande 0.286 0.018 0.516 0.192 0.071 0.317 0.156 0.105 0.274 0.054 0.005 0.188 other inside 0.370 0.234 0.470 0.529 0.451 0.649 0.008 0.001 0.031 0.037 0.004 0.122 other outside 0.714 0.671 0.735 0.173 0.157 0.200 0.002 0.0004 0.006 0.003 0.001 0.007 Note: bold = spread of at least 10 points between party CIs; italics = Party CIs don’t cross over; (d) = deterministic bounds info. contributed to this group’s vote estimates Table C-3. 2008 Presidential Runoﬀ Votes by Tribe (urban covariate, ﬂat priors)

NDC NPP Third Party No Vote tribe Mean 2.5% 97.5% Mean 2.5% 97.5% Mean 2.5% 97.5% Mean 2.5% 97.5% agona(d) 0.328 0.181 0.437 0.587 0.501 0.662 0.014 0.002 0.029 0.072 0.010 0.210 ahafo 0.491 0.382 0.585 0.411 0.259 0.558 0.007 0.001 0.018 0.090 0.015 0.251 ahanta(d) 0.087 0.047 0.136 0.705 0.631 0.757 0.007 0.002 0.016 0.201 0.145 0.283 akuapem(d) 0.118 0.043 0.251 0.619 0.451 0.709 0.006 0.003 0.010 0.256 0.163 0.361 akwamu 0.404 0.227 0.589 0.447 0.196 0.607 0.022 0.005 0.063 0.127 0.014 0.371 akyem(d) 0.142 0.048 0.289 0.675 0.545 0.784 0.005 0.002 0.009 0.178 0.090 0.318 aowin(d) 0.297 0.160 0.490 0.214 0.068 0.347 0.017 0.003 0.046 0.472 0.338 0.629 asante(d) 0.052 0.036 0.070 0.793 0.761 0.821 0.002 0.001 0.003 0.153 0.123 0.187 asen(d) 0.280 0.239 0.321 0.684 0.618 0.725 0.005 0.001 0.011 0.031 0.005 0.108 boron(d) 0.273 0.239 0.312 0.345 0.316 0.371 0.004 0.003 0.006 0.378 0.340 0.414 chokosi 0.268 0.143 0.385 0.557 0.421 0.696 0.025 0.009 0.045 0.150 0.082 0.250 denkyira(d) 0.211 0.092 0.328 0.583 0.445 0.723 0.013 0.004 0.035 0.194 0.099 0.308 evalue(d) 0.261 0.182 0.349 0.674 0.544 0.774 0.038 0.003 0.107 0.027 0.004 0.085

424 fante(d) 0.351 0.297 0.394 0.266 0.214 0.317 0.006 0.005 0.007 0.378 0.314 0.427 kwahu(d) 0.113 0.037 0.291 0.531 0.297 0.760 0.005 0.002 0.010 0.351 0.161 0.634 nzema(d) 0.256 0.172 0.391 0.254 0.174 0.382 0.009 0.004 0.016 0.481 0.387 0.602 sefwi(d) 0.473 0.427 0.517 0.259 0.233 0.284 0.004 0.002 0.008 0.264 0.218 0.309 wasa(d) 0.315 0.211 0.426 0.391 0.324 0.457 0.006 0.002 0.013 0.288 0.182 0.413 bawle 0.174 0.120 0.337 0.295 0.026 0.524 0.503 0.242 0.816 0.027 0.002 0.257 other akan 0.905 0.874 0.926 0.069 0.051 0.104 0.020 0.003 0.035 0.006 0.0004 0.036 dangme(d) 0.465 0.341 0.573 0.189 0.123 0.267 0.006 0.003 0.009 0.340 0.259 0.434 ga(d) 0.534 0.327 0.723 0.269 0.098 0.410 0.002 0.0004 0.005 0.195 0.074 0.402 other ga 0.330 0.102 0.501 0.612 0.445 0.856 0.028 0.005 0.078 0.030 0.004 0.117 ewe(d) 0.584 0.497 0.655 0.043 0.027 0.072 0.004 0.003 0.005 0.369 0.299 0.455 guan1(d) 0.316 0.079 0.674 0.353 0.146 0.460 0.010 0.003 0.023 0.320 0.092 0.516 guan2 0.869 0.681 0.930 0.093 0.032 0.286 0.032 0.009 0.050 0.006 0.0003 0.035 guan3(d) 0.361 0.243 0.471 0.328 0.205 0.444 0.016 0.003 0.037 0.294 0.133 0.448 guan4(d) 0.126 0.090 0.175 0.843 0.779 0.881 0.009 0.002 0.021 0.021 0.001 0.080 guan5(d) 0.219 0.146 0.349 0.450 0.246 0.589 0.011 0.004 0.023 0.320 0.213 0.510 guan6(d) 0.952 0.858 0.980 0.034 0.014 0.114 0.007 0.001 0.018 0.008 0.0005 0.042 guan7(d) 0.743 0.502 0.972 0.090 0.004 0.232 0.010 0.003 0.020 0.157 0.004 0.369 guan8(d) 0.513 0.300 0.821 0.071 0.004 0.169 0.047 0.005 0.095 0.369 0.115 0.564 other guan 0.840 0.756 0.967 0.108 0.015 0.170 0.038 0.007 0.059 0.014 0.001 0.074 Table C-3. Continued

NDC NPP Third Party No Vote tribe Mean 2.5% 97.5% Mean 2.5% 97.5% Mean 2.5% 97.5% Mean 2.5% 97.5% bimoba(d) 0.674 0.467 0.823 0.134 0.066 0.223 0.014 0.003 0.026 0.178 0.023 0.387 kokomba(d) 0.317 0.255 0.373 0.245 0.204 0.298 0.013 0.008 0.017 0.426 0.375 0.470 basare 0.101 0.069 0.141 0.885 0.838 0.917 0.005 0.0004 0.016 0.009 0.003 0.020 pilapila 0.152 0.004 0.355 0.221 0.083 0.346 0.575 0.456 0.691 0.052 0.015 0.156 salfalba 0.692 0.444 0.772 0.033 0.002 0.146 0.249 0.114 0.426 0.026 0.005 0.094 kotokoli(d) 0.547 0.110 0.748 0.242 0.032 0.395 0.016 0.005 0.042 0.196 0.002 0.781 chamba 0.727 0.691 0.778 0.040 0.013 0.076 0.212 0.151 0.254 0.022 0.006 0.064 other gurma 0.983 0.969 0.988 0.006 0.003 0.009 0.009 0.005 0.019 0.002 0.0002 0.013 builsa(d) 0.720 0.680 0.757 0.106 0.079 0.136 0.012 0.004 0.022 0.162 0.140 0.189 dagarte(d) 0.456 0.355 0.560 0.100 0.070 0.140 0.010 0.006 0.014 0.434 0.317 0.535 wali(d) 0.317 0.110 0.464 0.445 0.367 0.534 0.023 0.007 0.045 0.215 0.073 0.424 dagomba(d) 0.566 0.526 0.603 0.239 0.207 0.267 0.008 0.006 0.011 0.187 0.165 0.220 kusasi(d) 0.496 0.369 0.603 0.113 0.069 0.182 0.009 0.005 0.016 0.381 0.287 0.494

425 mamprusi(d) 0.518 0.427 0.581 0.248 0.187 0.315 0.012 0.007 0.020 0.222 0.161 0.318 namnam 0.764 0.331 0.969 0.107 0.003 0.444 0.024 0.002 0.050 0.105 0.003 0.504 nankansi(d) 0.470 0.298 0.573 0.227 0.157 0.365 0.013 0.008 0.020 0.290 0.219 0.398 nanumba(d) 0.229 0.189 0.252 0.758 0.724 0.789 0.007 0.002 0.021 0.005 0.001 0.018 mosi 0.768 0.658 0.872 0.193 0.080 0.265 0.006 0.0004 0.024 0.032 0.004 0.173 other mole 0.927 0.865 0.967 0.056 0.008 0.115 0.008 0.0004 0.022 0.009 0.001 0.045 kasena(d) 0.492 0.286 0.636 0.276 0.117 0.500 0.022 0.008 0.042 0.209 0.087 0.448 mo 0.906 0.850 0.938 0.078 0.048 0.117 0.008 0.0004 0.028 0.008 0.0004 0.026 sisala(d) 0.425 0.252 0.559 0.302 0.215 0.463 0.013 0.006 0.027 0.260 0.151 0.440 vagala 0.225 0.057 0.409 0.515 0.262 0.663 0.110 0.021 0.193 0.150 0.003 0.481 othergrusi1(d) 0.733 0.567 0.823 0.223 0.153 0.330 0.018 0.003 0.044 0.027 0.003 0.108 othergrusi2 0.011 0.002 0.019 0.984 0.972 0.993 0.003 0.0005 0.009 0.002 0.0004 0.011 busanga 0.753 0.714 0.782 0.234 0.208 0.270 0.006 0.001 0.014 0.008 0.002 0.018 wangara 0.359 0.316 0.405 0.630 0.565 0.671 0.005 0.001 0.033 0.005 0.002 0.017 othermande 0.564 0.286 0.780 0.149 0.0004 0.479 0.240 0.089 0.402 0.047 0.003 0.201 other inside 0.496 0.396 0.579 0.468 0.382 0.545 0.018 0.004 0.040 0.017 0.003 0.055 other outside 0.473 0.366 0.639 0.439 0.213 0.525 0.060 0.042 0.086 0.027 0.003 0.139 Note: bold = spread of at least 10 points between party CIs; italics = Party CIs don’t cross over; (d) = deterministic bounds info. contributed to this group’s vote estimates Table C-4. 2008 Presidential Votes by Tribe (urban covariate, ﬂat priors)

NDC NPP Third Party No Vote tribe Mean 2.5% 97.5% Mean 2.5% 97.5% Mean 2.5% 97.5% Mean 2.5% 97.5% agona(d) 0.377 0.349 0.410 0.564 0.529 0.588 0.015 0.004 0.027 0.007 0.003 0.013 ahafo 0.522 0.486 0.553 0.439 0.399 0.475 0.004 0.0003 0.015 0.016 0.005 0.039 ahanta(d) 0.113 0.033 0.242 0.593 0.460 0.688 0.051 0.022 0.095 0.221 0.174 0.293 akuapem(d) 0.108 0.019 0.196 0.673 0.616 0.728 0.009 0.004 0.016 0.197 0.111 0.269 akwamu 0.233 0.203 0.270 0.735 0.683 0.767 0.008 0.001 0.022 0.016 0.005 0.039 akyem(d) 0.134 0.052 0.253 0.582 0.498 0.688 0.007 0.003 0.014 0.266 0.169 0.381 aowin 0.226 0.125 0.343 0.233 0.146 0.329 0.027 0.009 0.055 0.393 0.273 0.511 asante(d) 0.053 0.036 0.067 0.664 0.640 0.685 0.003 0.002 0.005 0.275 0.257 0.295 asen(d) 0.228 0.068 0.375 0.470 0.355 0.617 0.022 0.006 0.057 0.251 0.154 0.391 boron(d) 0.292 0.264 0.323 0.348 0.319 0.373 0.005 0.003 0.008 0.345 0.314 0.378 chokosi(d) 0.402 0.197 0.569 0.336 0.221 0.458 0.048 0.017 0.096 0.152 0.051 0.293 denkyira(d) 0.109 0.043 0.212 0.620 0.521 0.719 0.047 0.018 0.086 0.198 0.113 0.295 evalue(d) 0.231 0.204 0.257 0.675 0.642 0.703 0.076 0.050 0.112 0.003 0.001 0.009

426 fante(d) 0.319 0.291 0.352 0.273 0.238 0.312 0.018 0.013 0.023 0.374 0.330 0.426 kwahu(d) 0.171 0.037 0.377 0.393 0.295 0.515 0.013 0.006 0.028 0.413 0.209 0.619 nzema(d) 0.182 0.125 0.263 0.193 0.126 0.303 0.151 0.102 0.219 0.441 0.371 0.527 sefwi(d) 0.442 0.398 0.482 0.279 0.252 0.307 0.006 0.002 0.012 0.253 0.212 0.299 wasa(d) 0.207 0.151 0.267 0.416 0.345 0.497 0.050 0.020 0.087 0.313 0.242 0.397 bawle 0.396 0.288 0.435 0.463 0.395 0.585 0.057 0.024 0.091 0.012 0.003 0.042 other akan 0.850 0.700 0.969 0.029 0.004 0.059 0.082 0.001 0.234 0.014 0.003 0.058 dangme(d) 0.473 0.405 0.544 0.203 0.120 0.261 0.013 0.009 0.019 0.296 0.239 0.388 ga(d) 0.678 0.548 0.780 0.251 0.150 0.315 0.007 0.0005 0.015 0.062 0.001 0.196 other ga 0.807 0.713 0.883 0.071 0.053 0.095 0.100 0.007 0.178 0.006 0.001 0.015 ewe(d) 0.445 0.410 0.489 0.045 0.027 0.073 0.008 0.006 0.011 0.492 0.434 0.532 guan1(d) 0.878 0.807 0.916 0.085 0.056 0.127 0.012 0.003 0.025 0.012 0.001 0.041 guan2 0.059 0.018 0.108 0.573 0.523 0.625 0.189 0.126 0.218 0.015 0.003 0.124 guan3(d) 0.318 0.197 0.450 0.243 0.140 0.345 0.030 0.007 0.063 0.376 0.226 0.504 guan4(d) 0.246 0.237 0.258 0.692 0.682 0.705 0.030 0.024 0.036 0.001 0.0004 0.003 guan5(d) 0.352 0.287 0.423 0.343 0.256 0.413 0.019 0.007 0.039 0.272 0.207 0.366 guan6(d) 0.741 0.585 0.862 0.170 0.088 0.268 0.010 0.001 0.029 0.067 0.008 0.177 guan7(d) 0.420 0.284 0.710 0.127 0.019 0.287 0.026 0.008 0.051 0.389 0.177 0.556 guan8(d) 0.649 0.204 0.821 0.146 0.028 0.215 0.029 0.007 0.053 0.147 0.004 0.610 other guan 0.319 0.269 0.432 0.483 0.389 0.527 0.176 0.008 0.233 0.012 0.002 0.052 Table C-4. Continued

NDC NPP Third Party No Vote tribe Mean 2.5% 97.5% Mean 2.5% 97.5% Mean 2.5% 97.5% Mean 2.5% 97.5% bimoba(d) 0.586 0.426 0.744 0.080 0.014 0.173 0.070 0.040 0.097 0.214 0.090 0.320 kokomba(d) 0.337 0.285 0.383 0.299 0.239 0.354 0.027 0.017 0.039 0.303 0.256 0.360 basare 0.056 0.032 0.068 0.892 0.864 0.911 0.027 0.008 0.037 0.006 0.002 0.035 pilapila 0.024 0.009 0.044 0.188 0.125 0.262 0.362 0.225 0.451 0.011 0.002 0.021 salfalba 0.412 0.111 0.610 0.035 0.003 0.175 0.234 0.117 0.404 0.036 0.003 0.143 kotokoli(d) 0.806 0.743 0.868 0.172 0.109 0.232 0.003 0.0004 0.017 0.008 0.003 0.023 chamba 0.134 0.125 0.145 0.028 0.001 0.052 0.014 0.007 0.021 0.004 0.001 0.018 other gurma 0.942 0.931 0.949 0.012 0.006 0.021 0.003 0.0004 0.010 0.003 0.001 0.005 builsa(d) 0.477 0.384 0.541 0.165 0.126 0.250 0.114 0.066 0.175 0.200 0.156 0.258 dagarte(d) 0.250 0.203 0.304 0.147 0.111 0.195 0.029 0.022 0.039 0.545 0.482 0.598 wali(d) 0.845 0.708 0.939 0.035 0.008 0.066 0.012 0.002 0.031 0.094 0.005 0.223 dagomba(d) 0.489 0.457 0.521 0.240 0.207 0.278 0.020 0.013 0.029 0.236 0.207 0.268 kusasi(d) 0.601 0.536 0.659 0.129 0.081 0.185 0.012 0.005 0.021 0.235 0.180 0.295

427 mamprusi(d) 0.303 0.232 0.378 0.312 0.220 0.393 0.095 0.067 0.131 0.259 0.193 0.351 namnam 0.201 0.077 0.355 0.409 0.169 0.566 0.224 0.061 0.281 0.095 0.005 0.415 nankansi(d) 0.402 0.312 0.489 0.194 0.135 0.269 0.047 0.031 0.079 0.327 0.268 0.401 nanumba(d) 0.282 0.153 0.454 0.569 0.359 0.771 0.047 0.008 0.099 0.081 0.005 0.238 mosi 0.241 0.140 0.313 0.659 0.587 0.746 0.031 0.003 0.109 0.051 0.011 0.141 other mole 0.072 0.002 0.308 0.208 0.051 0.354 0.350 0.307 0.448 0.019 0.004 0.071 kasena(d) 0.570 0.459 0.677 0.233 0.126 0.319 0.062 0.007 0.126 0.110 0.019 0.216 mo 0.876 0.768 0.964 0.068 0.001 0.183 0.005 0.001 0.016 0.031 0.005 0.097 sisala 0.202 0.145 0.270 0.556 0.485 0.615 0.067 0.041 0.086 0.144 0.105 0.191 vagala 0.073 0.015 0.133 0.652 0.606 0.739 0.160 0.061 0.235 0.017 0.003 0.055 othergrusi1(d) 0.676 0.648 0.697 0.217 0.184 0.243 0.018 0.001 0.045 0.008 0.002 0.029 othergrusi2 0.193 0.170 0.246 0.747 0.701 0.795 0.042 0.003 0.103 0.006 0.002 0.018 busanga 0.616 0.535 0.702 0.343 0.261 0.422 0.008 0.002 0.020 0.014 0.003 0.039 wangara 0.226 0.213 0.244 0.762 0.741 0.776 0.004 0.001 0.012 0.004 0.002 0.007 othermande 0.273 0.055 0.578 0.543 0.224 0.693 0.049 0.001 0.133 0.042 0.006 0.267 other inside 0.623 0.501 0.757 0.105 0.024 0.221 0.199 0.034 0.315 0.038 0.001 0.165 other outside 0.494 0.366 0.661 0.223 0.078 0.375 0.033 0.009 0.079 0.216 0.049 0.391 Note: bold = spread of at least 10 points between party CIs; italics = Party CIs don’t cross over; (d) = deterministic bounds info. contributed to this group’s vote estimates Table C-5. 2008 Parliamentary Votes by Tribe (urban covariate, ﬂat priors)

NDC NPP Third Party No Vote tribe Mean 2.5% 97.5% Mean 2.5% 97.5% Mean 2.5% 97.5% Mean 2.5% 97.5% agona(d) 0.191 0.031 0.360 0.258 0.079 0.507 0.231 0.035 0.349 0.279 0.068 0.503 ahafo 0.484 0.417 0.556 0.465 0.339 0.523 0.014 0.002 0.040 0.027 0.008 0.103 ahanta(d) 0.161 0.035 0.301 0.554 0.386 0.694 0.097 0.034 0.226 0.172 0.103 0.300 akuapem(d) 0.091 0.026 0.193 0.713 0.672 0.748 0.025 0.007 0.043 0.164 0.086 0.236 akwamu 0.320 0.207 0.384 0.595 0.364 0.674 0.026 0.002 0.121 0.049 0.003 0.312 akyem(d) 0.161 0.063 0.297 0.634 0.500 0.734 0.020 0.010 0.032 0.177 0.120 0.248 aowin(d) 0.433 0.272 0.580 0.228 0.045 0.384 0.024 0.005 0.060 0.303 0.126 0.510 asante(d) 0.098 0.059 0.133 0.556 0.517 0.600 0.072 0.057 0.085 0.270 0.234 0.303 asen(d) 0.210 0.100 0.350 0.631 0.481 0.738 0.010 0.004 0.021 0.139 0.053 0.236 boron(d) 0.292 0.241 0.332 0.356 0.317 0.401 0.019 0.011 0.029 0.327 0.284 0.374 chokosi(d) 0.532 0.368 0.648 0.221 0.130 0.350 0.035 0.010 0.074 0.163 0.053 0.305 denkyira(d) 0.165 0.066 0.296 0.469 0.272 0.616 0.219 0.130 0.333 0.136 0.051 0.256 evalue(d) 0.103 0.014 0.274 0.360 0.153 0.535 0.204 0.083 0.319 0.297 0.087 0.566

428 fante(d) 0.314 0.256 0.392 0.265 0.202 0.318 0.044 0.029 0.068 0.368 0.313 0.449 kwahu(d) 0.107 0.031 0.278 0.362 0.184 0.621 0.110 0.090 0.151 0.406 0.216 0.629 nzema(d) 0.276 0.133 0.386 0.094 0.013 0.251 0.288 0.248 0.365 0.295 0.226 0.371 sefwi(d) 0.464 0.412 0.514 0.325 0.282 0.365 0.013 0.003 0.025 0.193 0.134 0.245 wasa(d) 0.256 0.174 0.352 0.341 0.208 0.476 0.162 0.106 0.224 0.232 0.113 0.345 bawle 0.237 0.153 0.311 0.558 0.448 0.640 0.024 0.003 0.078 0.017 0.004 0.047 other akan 0.836 0.784 0.872 0.108 0.081 0.129 0.013 0.001 0.048 0.005 0.001 0.014 dangme(d) 0.383 0.275 0.483 0.253 0.181 0.345 0.023 0.013 0.039 0.331 0.242 0.447 ga(d) 0.685 0.542 0.823 0.067 0.020 0.137 0.027 0.002 0.065 0.215 0.072 0.363 other ga 0.022 0.009 0.045 0.848 0.831 0.867 0.049 0.025 0.067 0.003 0.001 0.005 ewe(d) 0.421 0.352 0.490 0.054 0.039 0.082 0.036 0.027 0.051 0.481 0.412 0.552 guan1(d) 0.803 0.767 0.850 0.166 0.115 0.200 0.016 0.005 0.032 0.005 0.001 0.017 guan2 0.746 0.716 0.772 0.090 0.070 0.137 0.095 0.065 0.121 0.005 0.001 0.014 guan3(d) 0.483 0.351 0.621 0.173 0.086 0.264 0.020 0.004 0.043 0.307 0.136 0.456 guan4(d) 0.138 0.018 0.247 0.736 0.581 0.835 0.028 0.002 0.093 0.084 0.003 0.183 guan5(d) 0.264 0.145 0.508 0.371 0.206 0.580 0.028 0.008 0.076 0.317 0.193 0.518 guan6 0.624 0.563 0.679 0.292 0.241 0.343 0.062 0.028 0.089 0.012 0.003 0.032 guan7(d) 0.240 0.052 0.457 0.226 0.063 0.497 0.126 0.022 0.332 0.380 0.166 0.620 guan8(d) 0.393 0.136 0.598 0.392 0.119 0.653 0.021 0.002 0.062 0.157 0.006 0.506 other guan 0.548 0.519 0.574 0.428 0.390 0.465 0.013 0.005 0.023 0.004 0.001 0.013 Table C-5. Continued

NDC NPP Third Party No Vote tribe Mean 2.5% 97.5% Mean 2.5% 97.5% Mean 2.5% 97.5% Mean 2.5% 97.5% bimoba(d) 0.478 0.372 0.572 0.149 0.108 0.209 0.186 0.110 0.234 0.166 0.067 0.291 kokomba(d) 0.129 0.067 0.205 0.377 0.299 0.440 0.165 0.112 0.212 0.310 0.232 0.395 basare 0.408 0.333 0.523 0.554 0.405 0.639 0.009 0.001 0.036 0.021 0.003 0.084 pilapila 0.047 0.009 0.115 0.870 0.798 0.916 0.019 0.001 0.054 0.009 0.002 0.023 salfalba 0.085 0.002 0.270 0.649 0.184 0.920 0.128 0.017 0.302 0.058 0.005 0.235 kotokoli(d) 0.131 0.082 0.193 0.683 0.616 0.743 0.033 0.009 0.066 0.134 0.070 0.207 chamba 0.004 0.0002 0.014 0.841 0.649 0.902 0.057 0.011 0.084 0.021 0.001 0.084 other gruma 0.895 0.879 0.913 0.085 0.050 0.101 0.003 0.0002 0.012 0.006 0.001 0.027 builsa(d) 0.176 0.138 0.232 0.495 0.376 0.556 0.125 0.099 0.160 0.169 0.125 0.268 dagarte 0.227 0.152 0.341 0.211 0.164 0.276 0.052 0.039 0.069 0.492 0.378 0.583 wali(d) 0.595 0.510 0.663 0.322 0.249 0.372 0.048 0.007 0.089 0.019 0.002 0.062 dagomba(d) 0.472 0.414 0.523 0.204 0.162 0.248 0.061 0.041 0.094 0.251 0.202 0.306 kusasi(d) 0.234 0.143 0.364 0.313 0.205 0.439 0.057 0.043 0.076 0.376 0.271 0.479

429 mamprusi(d) 0.310 0.220 0.377 0.312 0.219 0.388 0.125 0.088 0.159 0.234 0.172 0.321 namnam 0.180 0.013 0.300 0.635 0.555 0.696 0.092 0.015 0.269 0.054 0.013 0.098 nankansi(d) 0.325 0.252 0.457 0.208 0.086 0.346 0.097 0.071 0.144 0.344 0.239 0.472 nanumba(d) 0.522 0.354 0.628 0.257 0.167 0.363 0.057 0.010 0.124 0.120 0.035 0.224 mosi 0.537 0.159 0.818 0.093 0.023 0.220 0.044 0.010 0.101 0.290 0.047 0.693 other mole 0.347 0.341 0.353 0.110 0.104 0.116 0.539 0.529 0.547 0.001 0.0004 0.002 kasena(d) 0.290 0.178 0.410 0.350 0.177 0.519 0.195 0.151 0.239 0.145 0.061 0.251 mo 0.624 0.435 0.768 0.299 0.173 0.445 0.029 0.002 0.120 0.034 0.007 0.110 sisala(d) 0.234 0.114 0.416 0.351 0.162 0.542 0.137 0.116 0.164 0.248 0.158 0.376 vagala 0.733 0.688 0.759 0.005 0.0004 0.015 0.205 0.188 0.247 0.006 0.002 0.017 othergrusi1(d) 0.631 0.548 0.696 0.241 0.197 0.317 0.030 0.008 0.063 0.055 0.019 0.109 othergrusi2 0.513 0.372 0.571 0.398 0.166 0.478 0.049 0.001 0.225 0.036 0.002 0.166 busganga 0.505 0.341 0.611 0.430 0.356 0.520 0.017 0.001 0.044 0.035 0.006 0.193 wangara 0.602 0.580 0.653 0.381 0.302 0.410 0.009 0.001 0.042 0.006 0.001 0.026 othermande 0.139 0.092 0.254 0.025 0.003 0.086 0.693 0.572 0.735 0.009 0.001 0.069 other inside 0.411 0.129 0.786 0.334 0.007 0.616 0.197 0.147 0.286 0.032 0.004 0.141 other outside 0.344 0.287 0.364 0.635 0.606 0.660 0.005 0.001 0.013 0.006 0.001 0.029 Note: bold = spread of at least 10 points between party CIs; italics = Party CIs don’t cross over; (d) = deterministic bounds info. contributed to this group’s vote estimates Table C-6. 2004 Presidential Votes by Tribe (urban covariate, ﬂat priors)

NDC NPP Third Party No Vote tribe Mean 2.5% 97.5% Mean 2.5% 97.5% Mean 2.5% 97.5% Mean 2.5% 97.5% agona 0.349 0.310 0.376 0.640 0.608 0.674 0.006 0.0004 0.019 0.004 0.001 0.011 ahafo 0.555 0.417 0.627 0.356 0.262 0.465 0.011 0.002 0.028 0.062 0.012 0.137 ahanta(d) 0.094 0.012 0.196 0.714 0.568 0.845 0.018 0.004 0.037 0.147 0.056 0.298 akuapem(d) 0.071 0.012 0.104 0.903 0.876 0.932 0.006 0.003 0.012 0.017 0.003 0.077 akwamu 0.349 0.319 0.365 0.637 0.624 0.648 0.007 0.001 0.035 0.004 0.001 0.009 akyem(d) 0.236 0.121 0.315 0.703 0.638 0.797 0.005 0.002 0.014 0.048 0.015 0.114 aowin 0.364 0.272 0.441 0.481 0.390 0.567 0.011 0.001 0.030 0.128 0.024 0.235 asante(d) 0.056 0.041 0.068 0.837 0.817 0.855 0.003 0.002 0.007 0.094 0.079 0.110 asen(d) 0.151 0.108 0.188 0.738 0.706 0.781 0.098 0.073 0.116 0.007 0.001 0.023 boron(d) 0.321 0.288 0.356 0.477 0.449 0.505 0.009 0.005 0.014 0.182 0.155 0.213 chokosi(d) 0.458 0.426 0.489 0.467 0.424 0.488 0.045 0.013 0.088 0.004 0.001 0.012 denkyira(d) 0.121 0.102 0.146 0.793 0.760 0.844 0.064 0.001 0.094 0.005 0.001 0.019 evalue(d) 0.200 0.120 0.267 0.739 0.674 0.781 0.043 0.002 0.110 0.010 0.002 0.041

430 fante(d) 0.263 0.235 0.299 0.514 0.477 0.543 0.015 0.011 0.019 0.190 0.165 0.218 kwahu(d) 0.044 0.012 0.109 0.437 0.267 0.585 0.016 0.006 0.034 0.487 0.346 0.654 nzema(d) 0.232 0.150 0.332 0.378 0.324 0.464 0.049 0.032 0.071 0.311 0.231 0.395 sefwi(d) 0.593 0.560 0.623 0.306 0.281 0.330 0.006 0.002 0.011 0.069 0.045 0.093 wasa(d) 0.190 0.124 0.251 0.665 0.575 0.734 0.017 0.005 0.032 0.105 0.052 0.184 bawle 0.726 0.697 0.756 0.236 0.172 0.267 0.019 0.005 0.041 0.012 0.001 0.063 other akan 0.867 0.805 0.893 0.108 0.098 0.125 0.008 0.0004 0.073 0.003 0.001 0.012 dangme(d) 0.498 0.420 0.561 0.286 0.205 0.343 0.015 0.010 0.023 0.175 0.122 0.302 ga 0.640 0.555 0.714 0.343 0.272 0.426 0.010 0.007 0.014 0.005 0.001 0.017 other ga 0.751 0.717 0.779 0.213 0.168 0.247 0.023 0.002 0.041 0.005 0.001 0.014 ewe(d) 0.635 0.604 0.674 0.090 0.054 0.149 0.009 0.007 0.011 0.243 0.183 0.293 guan1(d) 0.888 0.797 0.943 0.071 0.018 0.131 0.022 0.005 0.040 0.013 0.001 0.057 guan2 0.411 0.333 0.478 0.523 0.450 0.583 0.056 0.021 0.089 0.008 0.002 0.024 guan3(d) 0.420 0.323 0.535 0.422 0.290 0.523 0.017 0.004 0.035 0.119 0.057 0.210 guan4 0.400 0.132 0.493 0.530 0.481 0.646 0.018 0.007 0.030 0.038 0.002 0.199 guan5(d) 0.311 0.235 0.400 0.421 0.333 0.484 0.022 0.008 0.042 0.223 0.164 0.297 guan6 0.669 0.599 0.732 0.245 0.210 0.277 0.029 0.001 0.113 0.036 0.006 0.101 guan7(d) 0.582 0.467 0.694 0.121 0.057 0.204 0.038 0.010 0.074 0.176 0.081 0.273 guan8(d) 0.931 0.888 0.960 0.027 0.003 0.064 0.026 0.009 0.049 0.006 0.001 0.017 other guan 0.345 0.334 0.359 0.639 0.627 0.649 0.012 0.001 0.027 0.003 0.001 0.005 Table C-6. Continued

NDC NPP Third Party No Vote tribe Mean 2.5% 97.5% Mean 2.5% 97.5% Mean 2.5% 97.5% Mean 2.5% 97.5% bimoba(d) 0.568 0.555 0.587 0.420 0.388 0.436 0.007 0.001 0.027 0.003 0.002 0.006 kokomba(d) 0.341 0.295 0.399 0.368 0.323 0.400 0.066 0.044 0.089 0.145 0.120 0.169 basare 0.560 0.500 0.595 0.366 0.305 0.432 0.026 0.006 0.072 0.033 0.006 0.120 pilapila 0.459 0.403 0.525 0.437 0.316 0.518 0.068 0.009 0.144 0.014 0.003 0.038 salfalba 0.969 0.911 0.984 0.009 0.004 0.022 0.011 0.002 0.051 0.005 0.001 0.021 kotokoli 0.584 0.551 0.615 0.371 0.339 0.404 0.021 0.002 0.052 0.014 0.006 0.029 chamba 0.516 0.269 0.604 0.145 0.021 0.420 0.264 0.010 0.361 0.049 0.005 0.239 other gurma 0.789 0.754 0.838 0.101 0.083 0.117 0.087 0.032 0.132 0.008 0.001 0.024 builsa(d) 0.422 0.319 0.512 0.252 0.165 0.348 0.144 0.108 0.197 0.142 0.093 0.217 dagarte(d) 0.423 0.367 0.483 0.164 0.116 0.208 0.034 0.025 0.046 0.309 0.256 0.357 wali(d) 0.129 0.018 0.251 0.640 0.556 0.703 0.137 0.052 0.188 0.078 0.008 0.228 dagomba(d) 0.620 0.572 0.648 0.262 0.233 0.303 0.016 0.008 0.027 0.085 0.070 0.109 kusasi(d) 0.647 0.612 0.678 0.101 0.088 0.128 0.050 0.039 0.061 0.183 0.154 0.218

431 mamprusi(d) 0.261 0.153 0.350 0.248 0.169 0.329 0.231 0.188 0.278 0.199 0.138 0.278 namnam(d) 0.505 0.417 0.595 0.455 0.384 0.526 0.019 0.0002 0.048 0.008 0.002 0.019 nankansi(d) 0.339 0.235 0.419 0.249 0.184 0.340 0.124 0.100 0.153 0.251 0.163 0.356 nanumba 0.423 0.355 0.476 0.519 0.472 0.582 0.040 0.005 0.075 0.011 0.003 0.030 mosi 0.604 0.586 0.619 0.373 0.353 0.387 0.018 0.001 0.032 0.003 0.001 0.004 other mole 0.147 0.031 0.222 0.105 0.032 0.215 0.672 0.424 0.779 0.013 0.003 0.051 kasena(d) 0.681 0.550 0.783 0.144 0.057 0.242 0.059 0.011 0.138 0.095 0.028 0.184 mo 0.522 0.454 0.552 0.464 0.441 0.509 0.007 0.001 0.022 0.005 0.001 0.024 sisala(d) 0.521 0.430 0.572 0.208 0.170 0.255 0.166 0.143 0.187 0.083 0.051 0.130 vagala 0.192 0.064 0.415 0.541 0.403 0.680 0.238 0.072 0.371 0.018 0.003 0.051 othergrusi1(d) 0.941 0.813 0.969 0.037 0.019 0.108 0.015 0.002 0.071 0.004 0.001 0.015 othergrusi2 0.741 0.713 0.757 0.137 0.121 0.149 0.112 0.096 0.136 0.004 0.001 0.011 busganga 0.177 0.152 0.224 0.814 0.765 0.838 0.005 0.001 0.011 0.003 0.001 0.010 wangara 0.552 0.538 0.585 0.436 0.401 0.450 0.003 0.001 0.009 0.003 0.001 0.008 othermande 0.553 0.505 0.632 0.206 0.161 0.259 0.182 0.023 0.282 0.036 0.006 0.089 other inside 0.130 0.080 0.168 0.746 0.687 0.816 0.097 0.005 0.181 0.013 0.004 0.031 other outside 0.660 0.648 0.671 0.327 0.316 0.337 0.002 0.0004 0.009 0.003 0.002 0.005 Note: bold = spread of at least 10 points between party CIs; italics = Party CIs don’t cross over; (d) = deterministic bounds info. contributed to this group’s vote estimates Table C-7. 2004 Presidential Votes by Tribe (urban covariate, ﬂat priors)

NDC NPP Third Party No Vote tribe Mean 2.5% 97.5% Mean 2.5% 97.5% Mean 2.5% 97.5% Mean 2.5% 97.5% agona(d) 0.156 0.108 0.218 0.832 0.760 0.859 0.006 0.0003 0.029 0.004 0.001 0.015 ahafo 0.487 0.390 0.627 0.404 0.202 0.527 0.011 0.001 0.029 0.075 0.024 0.221 ahanta(d) 0.054 0.013 0.093 0.831 0.784 0.874 0.019 0.006 0.032 0.078 0.042 0.112 akuapem(d) 0.069 0.014 0.145 0.819 0.696 0.879 0.018 0.006 0.034 0.084 0.008 0.244 akwamu 0.411 0.375 0.442 0.574 0.534 0.605 0.005 0.001 0.012 0.005 0.001 0.017 akyem(d) 0.219 0.145 0.285 0.734 0.662 0.787 0.006 0.002 0.015 0.034 0.009 0.078 aowin 0.525 0.434 0.613 0.336 0.255 0.416 0.016 0.003 0.034 0.110 0.029 0.196 asante(d) 0.050 0.038 0.063 0.845 0.827 0.858 0.004 0.002 0.006 0.093 0.076 0.112 asen(d) 0.092 0.041 0.110 0.878 0.821 0.906 0.017 0.007 0.039 0.010 0.0005 0.072 boron(d) 0.312 0.276 0.351 0.482 0.454 0.506 0.010 0.006 0.014 0.189 0.158 0.224 chokosi(d) 0.362 0.316 0.392 0.545 0.497 0.599 0.059 0.015 0.092 0.008 0.003 0.020 denkyira(d) 0.089 0.024 0.183 0.834 0.710 0.926 0.029 0.005 0.077 0.034 0.007 0.089 evalue(d) 0.054 0.021 0.086 0.919 0.880 0.965 0.018 0.002 0.034 0.005 0.001 0.017

432 fante(d) 0.294 0.263 0.335 0.461 0.431 0.490 0.015 0.011 0.020 0.209 0.179 0.246 kwahu(d) 0.052 0.008 0.202 0.363 0.260 0.569 0.011 0.004 0.025 0.557 0.362 0.689 nzema(d) 0.199 0.123 0.287 0.436 0.338 0.530 0.054 0.031 0.089 0.283 0.216 0.374 sefwi(d) 0.597 0.563 0.631 0.311 0.280 0.343 0.005 0.001 0.010 0.062 0.037 0.089 wasa(d) 0.171 0.111 0.252 0.703 0.642 0.765 0.021 0.004 0.044 0.087 0.047 0.140 bawle 0.433 0.352 0.498 0.493 0.360 0.592 0.045 0.003 0.107 0.018 0.006 0.068 other akan 0.104 0.001 0.161 0.821 0.787 0.952 0.069 0.022 0.088 0.002 0.0004 0.008 dangme(d) 0.476 0.384 0.573 0.295 0.217 0.378 0.016 0.010 0.026 0.180 0.132 0.234 ga(d) 0.646 0.633 0.664 0.351 0.330 0.364 0.0005 0.00003 0.002 0.001 0.0004 0.004 other ga 0.040 0.027 0.063 0.233 0.016 0.491 0.659 0.441 0.872 0.016 0.004 0.050 ewe(d) 0.656 0.612 0.704 0.101 0.061 0.148 0.009 0.007 0.012 0.214 0.184 0.249 guan1(d) 0.832 0.799 0.857 0.137 0.114 0.166 0.014 0.002 0.031 0.011 0.003 0.030 guan2 0.674 0.646 0.701 0.291 0.251 0.329 0.028 0.001 0.065 0.004 0.001 0.011 guan3(d) 0.263 0.196 0.343 0.620 0.516 0.694 0.011 0.002 0.029 0.086 0.014 0.182 guan4 0.230 0.220 0.239 0.760 0.751 0.770 0.001 0.0001 0.001 0.002 0.0005 0.003 guan5(d) 0.338 0.244 0.424 0.355 0.284 0.430 0.024 0.011 0.046 0.257 0.190 0.340 guan6 0.527 0.321 0.653 0.424 0.312 0.549 0.013 0.001 0.035 0.031 0.002 0.161 guan7(d) 0.636 0.316 0.766 0.168 0.051 0.287 0.034 0.001 0.081 0.088 0.004 0.344 guan8(d) 0.881 0.833 0.924 0.089 0.042 0.126 0.008 0.0001 0.033 0.010 0.001 0.040 other guan 0.423 0.335 0.470 0.514 0.471 0.583 0.049 0.044 0.055 0.006 0.001 0.018 Table C-7. Continued

NDC NPP Third Party No Vote tribe Mean 2.5% 97.5% Mean 2.5% 97.5% Mean 2.5% 97.5% Mean 2.5% 97.5% bimoba(d) 0.624 0.606 0.640 0.363 0.347 0.382 0.006 0.001 0.017 0.005 0.002 0.016 kokomba(d) 0.353 0.317 0.408 0.317 0.286 0.350 0.076 0.062 0.090 0.166 0.131 0.192 basare 0.619 0.544 0.664 0.331 0.308 0.353 0.004 0.001 0.015 0.027 0.003 0.109 pilapila 0.390 0.211 0.552 0.139 0.024 0.289 0.336 0.185 0.493 0.049 0.007 0.140 salfalba 0.283 0.230 0.326 0.379 0.331 0.428 0.308 0.274 0.356 0.007 0.002 0.018 kotokoli 0.768 0.729 0.794 0.211 0.184 0.245 0.007 0.002 0.017 0.007 0.004 0.017 chamba 0.089 0.081 0.105 0.244 0.233 0.254 0.659 0.635 0.672 0.002 0.001 0.004 other gurma 0.405 0.386 0.444 0.577 0.518 0.594 0.007 0.001 0.023 0.003 0.001 0.010 builsa(d) 0.249 0.196 0.317 0.406 0.352 0.457 0.138 0.107 0.230 0.159 0.106 0.244 dagarte(d) 0.486 0.423 0.554 0.098 0.070 0.136 0.031 0.022 0.043 0.307 0.260 0.357 wali(d) 0.566 0.505 0.637 0.337 0.279 0.385 0.075 0.018 0.105 0.010 0.003 0.027 dagomba(d) 0.623 0.594 0.652 0.265 0.241 0.292 0.014 0.008 0.022 0.082 0.067 0.103 kusasi(d) 0.675 0.597 0.723 0.082 0.046 0.132 0.051 0.034 0.065 0.174 0.125 0.225

433 mamprusi(d) 0.330 0.267 0.389 0.223 0.183 0.269 0.237 0.204 0.283 0.167 0.112 0.228 namnam(d) 0.030 0.002 0.099 0.500 0.246 0.620 0.428 0.297 0.511 0.027 0.004 0.156 nankansi(d) 0.395 0.278 0.484 0.182 0.129 0.277 0.112 0.090 0.140 0.272 0.208 0.367 nanumba 0.409 0.373 0.440 0.540 0.474 0.592 0.031 0.002 0.084 0.012 0.003 0.036 mosi(d) 0.476 0.445 0.497 0.490 0.470 0.509 0.026 0.002 0.049 0.004 0.002 0.008 other mole 0.322 0.098 0.469 0.605 0.463 0.857 0.034 0.008 0.132 0.026 0.002 0.187 kasena(d) 0.590 0.475 0.670 0.349 0.303 0.420 0.041 0.003 0.090 0.014 0.001 0.041 mo 0.891 0.822 0.920 0.068 0.036 0.111 0.025 0.003 0.055 0.011 0.001 0.065 sisala(d) 0.122 0.085 0.160 0.603 0.562 0.639 0.175 0.142 0.196 0.077 0.049 0.113 vagala 0.426 0.380 0.471 0.200 0.180 0.235 0.356 0.324 0.388 0.005 0.002 0.016 othergrusi1(d) 0.522 0.479 0.558 0.431 0.387 0.463 0.035 0.018 0.061 0.007 0.002 0.030 othergrusi2 0.610 0.471 0.704 0.291 0.213 0.359 0.029 0.004 0.121 0.063 0.021 0.152 busanga 0.508 0.372 0.584 0.429 0.314 0.525 0.019 0.001 0.067 0.037 0.003 0.123 wangara 0.191 0.169 0.209 0.779 0.755 0.802 0.025 0.009 0.044 0.003 0.001 0.005 othermande 0.112 0.014 0.194 0.547 0.468 0.640 0.304 0.243 0.347 0.016 0.003 0.034 other inside 0.636 0.625 0.650 0.295 0.274 0.317 0.054 0.031 0.076 0.003 0.002 0.007 other outside 0.528 0.492 0.543 0.452 0.436 0.464 0.010 0.0003 0.053 0.003 0.002 0.005 Note: bold = spread of at least 10 points between party CIs; italics = Party CIs don’t cross over; (d) = deterministic bounds info. contributed to this group’s vote estimates Table C-8. 2000 Presidential Runoﬀ Votes by Tribe (urban covariate, ﬂat priors)

NDC NPP Third Party No Vote tribe Mean 2.5% 97.5% Mean 2.5% 97.5% Mean 2.5% 97.5% Mean 2.5% 97.5% agona 0.153 0.114 0.195 0.832 0.774 0.866 0.007 0.0003 0.021 0.008 0.002 0.035 ahafo 0.458 0.405 0.506 0.523 0.471 0.573 0.005 0.001 0.011 0.014 0.002 0.036 ahanta(d) 0.073 0.019 0.150 0.430 0.233 0.754 0.013 0.004 0.027 0.484 0.173 0.680 akuapem 0.271 0.059 0.444 0.453 0.252 0.656 0.006 0.002 0.010 0.271 0.044 0.462 akwamu 0.961 0.931 0.982 0.027 0.003 0.062 0.007 0.002 0.015 0.005 0.0004 0.021 akyem(d) 0.118 0.048 0.266 0.590 0.457 0.703 0.003 0.002 0.006 0.289 0.195 0.440 aowin 0.063 0.006 0.159 0.340 0.238 0.423 0.005 0.002 0.014 0.592 0.482 0.701 asante(d) 0.028 0.019 0.040 0.613 0.583 0.640 0.001 0.001 0.002 0.358 0.331 0.386 asen 0.202 0.090 0.410 0.719 0.489 0.804 0.009 0.003 0.020 0.070 0.004 0.187 boron(d) 0.174 0.142 0.205 0.340 0.308 0.366 0.004 0.002 0.005 0.482 0.444 0.518 chokosi 0.301 0.163 0.424 0.372 0.256 0.475 0.025 0.008 0.051 0.302 0.211 0.424 denkyira 0.105 0.015 0.203 0.703 0.564 0.810 0.006 0.002 0.012 0.187 0.048 0.332 evalue 0.180 0.038 0.290 0.707 0.484 0.827 0.012 0.002 0.034 0.101 0.002 0.332

434 fante(d) 0.218 0.183 0.264 0.304 0.250 0.351 0.005 0.003 0.006 0.473 0.421 0.542 kwahu(d) 0.102 0.044 0.225 0.513 0.294 0.703 0.005 0.002 0.010 0.380 0.223 0.599 nzema(d) 0.145 0.073 0.244 0.219 0.163 0.293 0.008 0.004 0.014 0.627 0.527 0.709 sefwi(d) 0.414 0.367 0.464 0.197 0.157 0.241 0.002 0.001 0.004 0.387 0.334 0.438 wasa 0.106 0.054 0.153 0.533 0.446 0.604 0.004 0.001 0.008 0.357 0.276 0.443 bawle 0.714 0.615 0.759 0.164 0.116 0.339 0.117 0.033 0.159 0.005 0.002 0.015 other akan 0.933 0.859 0.969 0.030 0.005 0.075 0.027 0.002 0.052 0.010 0.001 0.045 dangme(d) 0.267 0.185 0.368 0.222 0.108 0.335 0.005 0.003 0.008 0.505 0.343 0.662 ga 0.266 0.129 0.413 0.156 0.026 0.431 0.003 0.0004 0.007 0.576 0.262 0.817 other ga 0.580 0.311 0.735 0.208 0.067 0.446 0.140 0.066 0.269 0.072 0.007 0.368 ewe(d) 0.440 0.409 0.470 0.127 0.056 0.193 0.007 0.006 0.008 0.426 0.348 0.514 guan1 0.606 0.311 0.808 0.251 0.117 0.348 0.016 0.003 0.035 0.127 0.004 0.341 guan2 0.729 0.673 0.769 0.246 0.195 0.291 0.016 0.001 0.065 0.009 0.001 0.025 guan3 0.172 0.080 0.271 0.295 0.163 0.423 0.017 0.007 0.036 0.515 0.380 0.647 guan4 0.077 0.009 0.224 0.801 0.370 0.977 0.006 0.001 0.018 0.116 0.006 0.421 guan5 0.237 0.118 0.356 0.424 0.192 0.557 0.012 0.005 0.021 0.327 0.225 0.611 guan6 0.813 0.732 0.846 0.170 0.138 0.225 0.010 0.001 0.021 0.006 0.001 0.030 guan7 0.711 0.457 0.857 0.170 0.037 0.303 0.018 0.001 0.045 0.102 0.009 0.326 guan8 0.938 0.685 0.990 0.019 0.001 0.117 0.013 0.002 0.036 0.029 0.001 0.246 other guan 0.177 0.064 0.348 0.744 0.524 0.882 0.037 0.009 0.116 0.042 0.007 0.119 Table C-8. Continued

NDC NPP Third Party No Vote tribe Mean 2.5% 97.5% Mean 2.5% 97.5% Mean 2.5% 97.5% Mean 2.5% 97.5% bimoba 0.535 0.406 0.640 0.205 0.118 0.304 0.013 0.004 0.025 0.247 0.145 0.361 kokomba(d) 0.292 0.212 0.390 0.124 0.081 0.188 0.014 0.007 0.021 0.570 0.485 0.660 basare 0.808 0.546 0.886 0.139 0.066 0.196 0.009 0.001 0.031 0.044 0.001 0.349 pilapila 0.205 0.009 0.356 0.748 0.627 0.983 0.021 0.001 0.083 0.027 0.002 0.141 salfalba 0.416 0.212 0.560 0.407 0.120 0.601 0.116 0.005 0.366 0.061 0.011 0.272 kotokoli 0.971 0.890 0.993 0.014 0.003 0.062 0.005 0.001 0.022 0.009 0.001 0.071 chamba 0.606 0.274 0.769 0.067 0.012 0.144 0.295 0.132 0.593 0.032 0.005 0.158 other gruma 0.866 0.840 0.882 0.122 0.106 0.144 0.006 0.001 0.018 0.006 0.002 0.015 builsa(d) 0.451 0.202 0.571 0.208 0.155 0.272 0.023 0.004 0.052 0.319 0.210 0.530 dagarte(d) 0.265 0.201 0.334 0.080 0.046 0.144 0.012 0.008 0.016 0.643 0.557 0.709 wali 0.497 0.410 0.613 0.375 0.184 0.488 0.017 0.002 0.045 0.111 0.038 0.219 dagomba(d) 0.259 0.213 0.306 0.337 0.285 0.393 0.009 0.006 0.012 0.395 0.336 0.459 kusasi(d) 0.368 0.280 0.450 0.165 0.058 0.285 0.009 0.003 0.016 0.458 0.330 0.613 mamprusi(d) 0.281 0.154 0.414 0.277 0.191 0.382 0.022 0.011 0.035 0.420 0.297 0.559 435 namnam 0.439 0.277 0.635 0.451 0.212 0.611 0.030 0.006 0.043 0.081 0.006 0.285 nankansi(d) 0.193 0.150 0.241 0.349 0.244 0.418 0.013 0.007 0.019 0.446 0.377 0.559 nanumba 0.232 0.140 0.326 0.603 0.478 0.691 0.038 0.010 0.057 0.127 0.040 0.243 mosi 0.391 0.182 0.490 0.585 0.499 0.802 0.007 0.001 0.027 0.017 0.004 0.051 other mole 0.317 0.286 0.411 0.652 0.452 0.699 0.016 0.001 0.085 0.014 0.001 0.093 kasena 0.216 0.136 0.296 0.549 0.440 0.630 0.020 0.006 0.037 0.216 0.128 0.348 mo 0.014 0.0003 0.045 0.978 0.932 0.998 0.004 0.0004 0.012 0.004 0.0003 0.018 sisala(d) 0.571 0.529 0.627 0.236 0.178 0.285 0.012 0.002 0.024 0.181 0.129 0.241 vagala 0.218 0.008 0.591 0.601 0.332 0.862 0.085 0.005 0.185 0.096 0.008 0.352 othergrusi1 0.354 0.133 0.689 0.135 0.036 0.267 0.035 0.016 0.062 0.476 0.179 0.744 othergrusi2 0.195 0.176 0.216 0.797 0.772 0.817 0.005 0.0004 0.015 0.003 0.001 0.012 busanga 0.220 0.069 0.497 0.328 0.097 0.587 0.020 0.005 0.037 0.433 0.066 0.758 wangara 0.157 0.151 0.164 0.838 0.832 0.844 0.003 0.0004 0.008 0.002 0.001 0.003 othermande 0.648 0.507 0.827 0.193 0.026 0.290 0.119 0.015 0.227 0.040 0.005 0.156 other inside 0.395 0.360 0.406 0.591 0.581 0.613 0.013 0.010 0.018 0.002 0.001 0.008 other outside 0.511 0.491 0.526 0.477 0.429 0.492 0.005 0.0005 0.027 0.007 0.002 0.042

Note: bold = spread of at least 10 points between party CIs; italics = Party CIs don’t cross over; (d) = deterministic bounds info. contributed to this group’s vote estimates Table C-9. 2000 Presidential Votes by Tribe (urban covariate, ﬂat priors)

NDC NPP Third Party No Vote tribe Mean 2.5% 97.5% Mean 2.5% 97.5% Mean 2.5% 97.5% Mean 2.5% 97.5% agona 0.118 0.108 0.137 0.814 0.788 0.826 0.067 0.060 0.076 0.001 0.0004 0.003 ahafo 0.447 0.430 0.469 0.548 0.522 0.564 0.003 0.0004 0.008 0.002 0.001 0.005 ahanta 0.146 0.120 0.168 0.662 0.625 0.695 0.028 0.010 0.046 0.157 0.120 0.203 akuapem 0.370 0.355 0.386 0.615 0.593 0.629 0.010 0.002 0.019 0.005 0.002 0.011 akwamu 0.751 0.741 0.758 0.148 0.140 0.161 0.099 0.090 0.106 0.001 0.0003 0.002 akyem(d) 0.189 0.100 0.308 0.523 0.426 0.644 0.014 0.004 0.037 0.269 0.185 0.401 aowin 0.309 0.211 0.421 0.137 0.060 0.199 0.105 0.078 0.130 0.434 0.347 0.534 asante(d) 0.033 0.022 0.046 0.590 0.554 0.617 0.003 0.002 0.007 0.368 0.341 0.409 asen 0.340 0.327 0.354 0.535 0.526 0.544 0.123 0.107 0.130 0.002 0.001 0.003 boron(d) 0.210 0.182 0.239 0.319 0.284 0.346 0.011 0.006 0.019 0.458 0.426 0.492 chokosi 0.408 0.286 0.490 0.106 0.051 0.194 0.229 0.164 0.290 0.235 0.157 0.314 denkyira 0.190 0.078 0.268 0.673 0.577 0.731 0.019 0.004 0.038 0.107 0.028 0.215 evalue 0.370 0.321 0.416 0.477 0.434 0.524 0.138 0.104 0.171 0.010 0.002 0.032

436 fante(d) 0.256 0.215 0.292 0.245 0.206 0.288 0.031 0.024 0.042 0.454 0.415 0.498 kwahu(d) 0.183 0.052 0.433 0.526 0.284 0.692 0.023 0.006 0.076 0.260 0.200 0.427 nzema(d) 0.118 0.081 0.162 0.284 0.156 0.355 0.069 0.038 0.118 0.511 0.449 0.627 sefwi(d) 0.470 0.421 0.511 0.215 0.186 0.241 0.005 0.001 0.009 0.303 0.257 0.358 wasa 0.270 0.210 0.328 0.454 0.395 0.503 0.018 0.005 0.034 0.251 0.178 0.327 bawle 0.468 0.458 0.478 0.056 0.051 0.065 0.470 0.458 0.481 0.002 0.001 0.002 other akan 0.814 0.725 0.856 0.108 0.087 0.156 0.068 0.046 0.132 0.008 0.001 0.037 dangme(d) 0.306 0.230 0.388 0.195 0.100 0.293 0.034 0.024 0.050 0.455 0.337 0.557 ga 0.366 0.269 0.478 0.232 0.135 0.353 0.012 0.007 0.020 0.389 0.260 0.538 other ga 0.006 0.001 0.014 0.925 0.908 0.935 0.063 0.056 0.079 0.005 0.001 0.011 ewe(d) 0.369 0.337 0.397 0.038 0.021 0.065 0.019 0.016 0.025 0.567 0.530 0.607 guan1 0.660 0.641 0.706 0.137 0.084 0.159 0.200 0.186 0.212 0.003 0.001 0.007 guan2 0.859 0.836 0.871 0.130 0.120 0.148 0.007 0.001 0.023 0.003 0.0002 0.015 guan3 0.288 0.205 0.376 0.294 0.194 0.379 0.074 0.027 0.126 0.319 0.234 0.420 guan4 0.255 0.157 0.329 0.454 0.347 0.547 0.043 0.013 0.082 0.227 0.146 0.327 guan5 0.125 0.077 0.172 0.520 0.459 0.573 0.043 0.014 0.090 0.292 0.240 0.336 guan6 0.775 0.728 0.837 0.182 0.149 0.209 0.039 0.001 0.072 0.004 0.001 0.013 guan7 0.810 0.784 0.835 0.134 0.095 0.163 0.051 0.037 0.073 0.004 0.001 0.008 guan8 0.518 0.287 0.769 0.160 0.049 0.257 0.046 0.015 0.089 0.274 0.041 0.495 other guan 0.379 0.288 0.425 0.292 0.256 0.359 0.321 0.284 0.348 0.007 0.002 0.016 Table C-9. Continued

NDC NPP Third Party No Vote tribe Mean 2.5% 97.5% Mean 2.5% 97.5% Mean 2.5% 97.5% Mean 2.5% 97.5% bimoba 0.571 0.494 0.655 0.085 0.031 0.156 0.184 0.118 0.236 0.149 0.075 0.237 kokomba(d) 0.262 0.216 0.311 0.098 0.062 0.141 0.055 0.033 0.075 0.534 0.481 0.584 basare 0.717 0.713 0.721 0.277 0.270 0.284 0.005 0.0003 0.013 0.001 0.001 0.002 pilapila 0.643 0.603 0.691 0.319 0.294 0.341 0.030 0.002 0.074 0.006 0.002 0.018 salfalba 0.100 0.004 0.182 0.325 0.169 0.596 0.556 0.367 0.697 0.014 0.003 0.050 kotokoli 0.596 0.451 0.814 0.042 0.014 0.082 0.053 0.019 0.098 0.303 0.108 0.440 chamba 0.682 0.651 0.709 0.071 0.050 0.101 0.242 0.220 0.265 0.003 0.002 0.006 other gruma 0.727 0.715 0.736 0.188 0.176 0.208 0.081 0.057 0.093 0.004 0.001 0.014 builsa(d) 0.337 0.292 0.392 0.170 0.151 0.193 0.224 0.184 0.267 0.267 0.237 0.305 dagarte(d) 0.216 0.181 0.254 0.060 0.030 0.125 0.047 0.035 0.061 0.658 0.601 0.706 wali 0.409 0.364 0.456 0.341 0.298 0.373 0.227 0.188 0.258 0.023 0.006 0.054 dagomba(d) 0.270 0.241 0.304 0.232 0.188 0.282 0.110 0.092 0.139 0.364 0.312 0.417 kusasi(d) 0.160 0.105 0.217 0.229 0.112 0.318 0.074 0.057 0.103 0.525 0.436 0.626

437 mamprusi(d) 0.174 0.079 0.324 0.043 0.011 0.166 0.300 0.246 0.381 0.450 0.324 0.554 namnam 0.637 0.613 0.662 0.008 0.0001 0.022 0.351 0.324 0.376 0.003 0.001 0.007 nankansi(d) 0.157 0.104 0.270 0.114 0.075 0.250 0.152 0.124 0.197 0.568 0.431 0.656 nanumba 0.309 0.246 0.388 0.594 0.539 0.635 0.071 0.008 0.114 0.023 0.006 0.051 mosi 0.696 0.686 0.704 0.299 0.292 0.308 0.003 0.001 0.010 0.002 0.001 0.002 other mole 0.283 0.263 0.316 0.508 0.440 0.545 0.199 0.179 0.226 0.009 0.001 0.036 kasena(d) 0.448 0.380 0.484 0.362 0.318 0.395 0.052 0.014 0.096 0.137 0.091 0.220 mo 0.294 0.237 0.359 0.562 0.500 0.609 0.133 0.097 0.176 0.011 0.002 0.032 sisala(d) 0.371 0.289 0.437 0.236 0.208 0.272 0.220 0.190 0.252 0.164 0.110 0.220 vagala 0.547 0.507 0.565 0.314 0.289 0.343 0.133 0.111 0.151 0.004 0.001 0.010 othergrusi1 0.683 0.648 0.722 0.193 0.166 0.231 0.113 0.051 0.160 0.010 0.004 0.023 othergrusi2 0.947 0.941 0.950 0.002 0.0002 0.005 0.050 0.047 0.055 0.001 0.0003 0.005 busanga 0.750 0.727 0.766 0.236 0.226 0.251 0.006 0.001 0.018 0.007 0.002 0.020 wangara 0.772 0.688 0.805 0.212 0.175 0.299 0.010 0.001 0.064 0.006 0.002 0.018 othermande 0.148 0.065 0.200 0.646 0.571 0.786 0.186 0.089 0.254 0.015 0.002 0.065 other inside 0.470 0.426 0.498 0.137 0.120 0.161 0.387 0.353 0.439 0.005 0.002 0.013 other outside 0.569 0.560 0.578 0.383 0.284 0.432 0.045 0.002 0.142 0.003 0.001 0.008 Note: bold = spread of at least 10 points between party CIs; italics = Party CIs don’t cross over; (d) = deterministic bounds info. contributed to this group’s vote estimates Table C-10. 2000 Parliamentary Votes by Tribe (urban covariate, ﬂat priors)

NDC NPP Third Party No Vote tribe Mean 2.5% 97.5% Mean 2.5% 97.5% Mean 2.5% 97.5% Mean 2.5% 97.5% agona(d) 0.464 0.375 0.570 0.514 0.404 0.610 0.007 0.001 0.027 0.012 0.003 0.032 ahafo 0.405 0.365 0.438 0.566 0.520 0.620 0.022 0.002 0.037 0.004 0.002 0.010 ahanta(d) 0.107 0.021 0.239 0.644 0.515 0.736 0.029 0.005 0.071 0.210 0.152 0.272 akuapem(d) 0.130 0.035 0.260 0.585 0.444 0.696 0.052 0.034 0.071 0.222 0.137 0.324 akwamu 0.713 0.685 0.771 0.277 0.213 0.298 0.002 0.0004 0.006 0.004 0.001 0.010 akyem(d) 0.221 0.123 0.338 0.569 0.448 0.651 0.015 0.005 0.026 0.192 0.128 0.269 aowin(d) 0.288 0.145 0.435 0.181 0.037 0.293 0.076 0.005 0.136 0.439 0.266 0.611 asante(d) 0.053 0.034 0.084 0.577 0.542 0.611 0.016 0.010 0.023 0.351 0.316 0.388 asen(d) 0.223 0.083 0.317 0.632 0.525 0.760 0.014 0.003 0.028 0.112 0.019 0.251 boron(d) 0.183 0.148 0.212 0.317 0.289 0.341 0.039 0.019 0.057 0.455 0.412 0.510 chokosi(d) 0.410 0.238 0.549 0.046 0.017 0.082 0.194 0.106 0.288 0.309 0.186 0.474 denkyira(d) 0.432 0.321 0.545 0.300 0.203 0.407 0.023 0.005 0.050 0.231 0.107 0.358 evalue(d) 0.418 0.224 0.556 0.054 0.003 0.140 0.368 0.192 0.531 0.140 0.023 0.371

438 fante(d) 0.212 0.166 0.257 0.289 0.251 0.341 0.073 0.058 0.090 0.415 0.366 0.471 kwahu(d) 0.110 0.073 0.157 0.610 0.492 0.663 0.041 0.022 0.076 0.233 0.180 0.352 nzema(d) 0.113 0.067 0.246 0.021 0.004 0.059 0.397 0.262 0.465 0.459 0.399 0.534 sefwi(d) 0.460 0.398 0.519 0.227 0.173 0.283 0.011 0.001 0.036 0.292 0.234 0.347 wasa(d) 0.164 0.102 0.226 0.454 0.366 0.559 0.095 0.024 0.146 0.282 0.192 0.374 bawle 0.399 0.278 0.644 0.402 0.112 0.644 0.093 0.005 0.409 0.044 0.007 0.217 other akan 0.581 0.473 0.708 0.061 0.004 0.128 0.340 0.204 0.481 0.009 0.001 0.041 dangme(d) 0.235 0.180 0.295 0.173 0.082 0.272 0.113 0.067 0.167 0.460 0.373 0.561 ga(d) 0.475 0.357 0.560 0.013 0.001 0.051 0.027 0.004 0.073 0.483 0.370 0.615 other ga 0.399 0.384 0.418 0.393 0.366 0.416 0.200 0.177 0.214 0.002 0.001 0.005 ewe(d) 0.320 0.288 0.368 0.049 0.030 0.073 0.099 0.085 0.112 0.525 0.459 0.565 guan1(d) 0.224 0.032 0.485 0.324 0.197 0.467 0.206 0.050 0.359 0.208 0.036 0.412 guan2 0.779 0.763 0.790 0.152 0.144 0.165 0.065 0.054 0.074 0.002 0.0003 0.006 guan3(d) 0.334 0.202 0.439 0.236 0.114 0.329 0.047 0.010 0.121 0.354 0.269 0.456 guan4(d) 0.497 0.431 0.530 0.476 0.446 0.545 0.006 0.001 0.013 0.004 0.001 0.012 guan5(d) 0.102 0.058 0.157 0.528 0.474 0.577 0.109 0.073 0.156 0.234 0.175 0.294 guan6 0.626 0.594 0.655 0.060 0.037 0.085 0.303 0.275 0.333 0.005 0.0004 0.019 guan7 0.981 0.919 0.994 0.009 0.002 0.052 0.003 0.0002 0.021 0.004 0.0002 0.025 guan8 0.469 0.138 0.663 0.289 0.071 0.411 0.047 0.005 0.120 0.181 0.005 0.506 other guan 0.577 0.573 0.582 0.371 0.362 0.379 0.019 0.014 0.024 0.001 0.001 0.002 Table C-10. Continued

NDC NPP Third Party No Vote tribe Mean 2.5% 97.5% Mean 2.5% 97.5% Mean 2.5% 97.5% Mean 2.5% 97.5% bimoba(d) 0.184 0.076 0.322 0.102 0.046 0.195 0.200 0.101 0.331 0.479 0.274 0.631 kokomba(d) 0.096 0.056 0.141 0.184 0.140 0.228 0.225 0.176 0.284 0.459 0.391 0.529 basare 0.835 0.674 0.928 0.052 0.011 0.119 0.021 0.004 0.056 0.085 0.022 0.222 pilapila 0.145 0.124 0.166 0.664 0.517 0.779 0.108 0.003 0.272 0.016 0.003 0.047 salfalba 0.487 0.362 0.575 0.451 0.384 0.522 0.019 0.007 0.051 0.012 0.002 0.049 kotokoli 0.147 0.050 0.331 0.165 0.030 0.456 0.324 0.041 0.583 0.342 0.152 0.659 chamba 0.642 0.403 0.767 0.207 0.079 0.322 0.051 0.001 0.230 0.075 0.002 0.349 other gruma 0.874 0.798 0.913 0.005 0.001 0.014 0.113 0.082 0.166 0.004 0.001 0.018 builsa(d) 0.502 0.406 0.606 0.113 0.041 0.169 0.147 0.130 0.178 0.207 0.167 0.253 dagarte(d) 0.262 0.196 0.338 0.052 0.027 0.092 0.103 0.070 0.135 0.552 0.462 0.626 wali 0.532 0.474 0.573 0.398 0.340 0.448 0.028 0.004 0.069 0.016 0.003 0.041 dagomba(d) 0.242 0.211 0.279 0.200 0.158 0.256 0.151 0.106 0.199 0.381 0.318 0.456 kusasi(d) 0.134 0.065 0.246 0.267 0.138 0.365 0.138 0.107 0.163 0.436 0.349 0.580

439 mamprusi(d) 0.204 0.101 0.329 0.085 0.023 0.207 0.288 0.232 0.378 0.385 0.292 0.498 namnam(d) 0.031 0.008 0.066 0.612 0.573 0.635 0.349 0.315 0.369 0.006 0.001 0.019 nankansi(d) 0.245 0.178 0.341 0.054 0.025 0.109 0.141 0.124 0.171 0.529 0.442 0.601 nanumba 0.928 0.892 0.974 0.008 0.0004 0.029 0.048 0.005 0.092 0.011 0.001 0.057 mosi 0.368 0.104 0.498 0.482 0.364 0.546 0.029 0.006 0.161 0.111 0.003 0.388 other mole 0.797 0.782 0.818 0.012 0.004 0.028 0.012 0.001 0.037 0.004 0.0004 0.021 kasena(d) 0.257 0.137 0.363 0.489 0.393 0.561 0.052 0.018 0.083 0.173 0.100 0.267 mo 0.158 0.020 0.400 0.245 0.058 0.458 0.244 0.045 0.492 0.297 0.089 0.572 sisala(d) 0.513 0.378 0.599 0.040 0.008 0.093 0.225 0.160 0.287 0.189 0.121 0.298 vagala 0.655 0.601 0.673 0.326 0.311 0.358 0.008 0.004 0.015 0.003 0.001 0.012 othergrusi1(d) 0.531 0.318 0.736 0.047 0.011 0.107 0.065 0.010 0.176 0.299 0.137 0.489 othergrusi2 0.326 0.319 0.342 0.670 0.654 0.680 0.001 0.0003 0.004 0.001 0.001 0.005 busanga 0.751 0.679 0.942 0.071 0.011 0.185 0.030 0.002 0.106 0.141 0.016 0.273 wangara 0.757 0.710 0.778 0.005 0.0003 0.021 0.236 0.210 0.265 0.002 0.001 0.008 othermande 0.400 0.343 0.449 0.344 0.176 0.442 0.060 0.044 0.110 0.011 0.002 0.034 other inside 0.350 0.083 0.521 0.064 0.019 0.165 0.083 0.022 0.293 0.429 0.251 0.602 other outside 0.996 0.996 0.997 0.003 0.002 0.003 0.0001 0.0001 0.0003 0.0002 0.0001 0.0004 Note: bold = spread of at least 10 points between party CIs; italics = Party CIs don’t cross over; (d) = deterministic bounds info. contributed to this group’s vote estimates Table C-11. 1996 Presidential Votes by Tribe (urban covariate, ﬂat priors)

NDC NPP Third Party No Vote tribe Mean 2.5% 97.5% Mean 2.5% 97.5% Mean 2.5% 97.5% Mean 2.5% 97.5% agona 0.582 0.533 0.630 0.398 0.350 0.441 0.014 0.001 0.031 0.005 0.002 0.014 ahafo 0.492 0.472 0.526 0.501 0.464 0.523 0.001 0.0004 0.004 0.005 0.002 0.008 ahanta(d) 0.341 0.332 0.357 0.596 0.583 0.609 0.060 0.047 0.070 0.002 0.001 0.005 akuapem 0.357 0.246 0.482 0.544 0.478 0.611 0.014 0.003 0.040 0.083 0.008 0.184 akwamu 0.858 0.711 0.880 0.125 0.111 0.186 0.007 0.001 0.023 0.008 0.0005 0.084 akyem(d) 0.264 0.204 0.354 0.642 0.492 0.718 0.006 0.001 0.015 0.086 0.037 0.188 aowin 0.492 0.396 0.606 0.151 0.100 0.221 0.012 0.006 0.022 0.344 0.244 0.438 asante(d) 0.116 0.094 0.142 0.638 0.597 0.668 0.006 0.002 0.015 0.232 0.202 0.259 asen(d) 0.591 0.566 0.618 0.401 0.370 0.425 0.003 0.0004 0.008 0.004 0.001 0.009 boron(d) 0.367 0.326 0.404 0.264 0.237 0.285 0.008 0.004 0.013 0.351 0.318 0.382 chokosi(d) 0.706 0.656 0.761 0.139 0.094 0.172 0.083 0.021 0.123 0.071 0.032 0.129 denkyira(d) 0.555 0.494 0.606 0.415 0.325 0.454 0.005 0.001 0.014 0.023 0.004 0.103 evalue 0.348 0.240 0.429 0.557 0.365 0.667 0.030 0.0003 0.075 0.065 0.003 0.250

440 fante(d) 0.346 0.305 0.391 0.362 0.327 0.407 0.009 0.006 0.014 0.267 0.229 0.316 kwahu(d) 0.145 0.097 0.292 0.610 0.449 0.754 0.017 0.003 0.068 0.225 0.116 0.378 nzema(d) 0.202 0.157 0.267 0.338 0.273 0.382 0.019 0.013 0.026 0.438 0.395 0.482 sefwi(d) 0.709 0.665 0.754 0.088 0.065 0.113 0.002 0.001 0.004 0.180 0.134 0.222 wasa(d) 0.312 0.216 0.396 0.494 0.432 0.563 0.008 0.003 0.015 0.165 0.082 0.251 bawle 0.695 0.645 0.740 0.150 0.125 0.168 0.146 0.109 0.184 0.004 0.002 0.008 other akan 0.658 0.492 0.728 0.285 0.245 0.366 0.047 0.019 0.159 0.007 0.002 0.029 dangme(d) 0.499 0.434 0.611 0.142 0.071 0.220 0.010 0.005 0.024 0.340 0.224 0.418 ga 0.517 0.400 0.683 0.312 0.237 0.397 0.018 0.002 0.043 0.152 0.039 0.307 other ga 0.251 0.222 0.272 0.286 0.267 0.314 0.460 0.431 0.480 0.002 0.001 0.005 ewe(d) 0.731 0.659 0.776 0.025 0.013 0.043 0.007 0.003 0.014 0.233 0.195 0.308 guan1(d) 0.853 0.838 0.863 0.140 0.131 0.154 0.006 0.004 0.008 0.001 0.0003 0.003 guan2 0.997 0.988 0.999 0.0004 0.00003 0.002 0.002 0.0002 0.006 0.001 0.0001 0.006 guan3(d) 0.420 0.322 0.531 0.275 0.192 0.336 0.016 0.004 0.039 0.278 0.176 0.364 guan4 0.341 0.319 0.370 0.642 0.590 0.668 0.006 0.001 0.018 0.010 0.004 0.060 guan5(d) 0.279 0.162 0.450 0.309 0.165 0.507 0.050 0.017 0.113 0.348 0.239 0.548 guan6 0.872 0.867 0.876 0.124 0.120 0.129 0.002 0.0001 0.005 0.001 0.0003 0.002 guan7 0.941 0.934 0.951 0.056 0.041 0.064 0.002 0.0002 0.010 0.001 0.0003 0.005 guan8 0.851 0.770 0.902 0.069 0.013 0.116 0.061 0.041 0.079 0.014 0.001 0.082 other guan 0.614 0.585 0.639 0.327 0.299 0.363 0.054 0.033 0.073 0.005 0.002 0.009 Table C-11. Continued

NDC NPP Third Party No Vote tribe Mean 2.5% 97.5% Mean 2.5% 97.5% Mean 2.5% 97.5% Mean 2.5% 97.5% bimoba(d) 0.968 0.966 0.969 0.001 0.0002 0.002 0.031 0.030 0.032 0.0002 0.0001 0.0004 kokomba(d) 0.382 0.330 0.429 0.157 0.130 0.194 0.063 0.048 0.080 0.363 0.321 0.420 basare 0.810 0.801 0.818 0.187 0.178 0.194 0.002 0.0003 0.005 0.002 0.001 0.003 pilapila 0.238 0.218 0.257 0.750 0.733 0.770 0.007 0.002 0.017 0.003 0.002 0.005 salfalba 0.758 0.684 0.798 0.211 0.190 0.233 0.020 0.001 0.088 0.010 0.001 0.036 kotokoli 0.719 0.691 0.723 0.274 0.263 0.292 0.007 0.001 0.017 0.001 0.0002 0.005 chamba 0.181 0.177 0.185 0.591 0.579 0.599 0.225 0.217 0.235 0.002 0.001 0.004 other gruma 0.520 0.514 0.526 0.475 0.463 0.483 0.003 0.0002 0.018 0.002 0.001 0.003 builsa(d) 0.679 0.653 0.698 0.080 0.066 0.093 0.094 0.083 0.109 0.121 0.108 0.136 dagarte(d) 0.511 0.430 0.586 0.054 0.028 0.136 0.032 0.025 0.043 0.366 0.308 0.424 wali 0.662 0.597 0.718 0.089 0.058 0.125 0.232 0.152 0.268 0.014 0.003 0.038 dagomba(d) 0.397 0.360 0.440 0.363 0.326 0.399 0.020 0.012 0.034 0.211 0.180 0.267 kusasi(d) 0.638 0.569 0.695 0.156 0.102 0.204 0.059 0.038 0.068 0.122 0.078 0.175

441 mamprusi(d) 0.486 0.402 0.557 0.189 0.082 0.245 0.085 0.051 0.106 0.208 0.145 0.299 namnam(d) 0.578 0.516 0.621 0.194 0.160 0.247 0.221 0.188 0.247 0.005 0.001 0.016 nankansi(d) 0.522 0.428 0.586 0.084 0.059 0.131 0.081 0.071 0.101 0.247 0.198 0.339 nanumba 0.693 0.676 0.707 0.299 0.286 0.315 0.005 0.002 0.008 0.003 0.001 0.007 mosi 0.543 0.515 0.554 0.444 0.434 0.475 0.011 0.002 0.016 0.002 0.001 0.006 other mole 0.827 0.819 0.833 0.0005 0.0002 0.001 0.171 0.165 0.179 0.001 0.001 0.002 kasena(d) 0.412 0.390 0.433 0.432 0.412 0.475 0.152 0.125 0.171 0.003 0.001 0.008 mo 0.579 0.565 0.593 0.300 0.286 0.316 0.118 0.112 0.125 0.002 0.001 0.004 sisala(d) 0.470 0.422 0.519 0.228 0.207 0.257 0.177 0.145 0.202 0.098 0.059 0.143 vagala 0.545 0.505 0.572 0.358 0.339 0.384 0.089 0.071 0.117 0.004 0.001 0.012 othergrusi1 0.465 0.447 0.503 0.529 0.488 0.548 0.002 0.0002 0.006 0.003 0.001 0.008 othergrusi2 0.450 0.443 0.458 0.543 0.531 0.553 0.005 0.0004 0.014 0.002 0.001 0.003 busanga 0.175 0.043 0.516 0.111 0.027 0.299 0.031 0.007 0.085 0.663 0.371 0.866 wangara 0.374 0.367 0.379 0.624 0.619 0.630 0.001 0.0002 0.003 0.001 0.001 0.002 othermande 0.198 0.187 0.214 0.559 0.523 0.579 0.237 0.223 0.268 0.003 0.001 0.005 other inside 0.386 0.313 0.448 0.451 0.392 0.517 0.155 0.149 0.162 0.007 0.003 0.012 other outside 0.860 0.853 0.871 0.093 0.071 0.102 0.044 0.038 0.057 0.002 0.001 0.005 Note: bold = spread of at least 10 points between party CIs; italics = Party CIs don’t cross over; (d) = deterministic bounds info. contributed to this group’s vote estimates Table C-12. 1996 Parliamentary Votes by Tribe (urban covariate, ﬂat priors)

NDC NPP Third Party No Vote tribe Mean 2.5% 97.5% Mean 2.5% 97.5% Mean 2.5% 97.5% Mean 2.5% 97.5% agona 0.635 0.537 0.691 0.353 0.299 0.424 0.001 0.0001 0.007 0.008 0.0002 0.041 ahafo 0.980 0.951 0.998 0.015 0.0002 0.043 0.001 0.0001 0.004 0.003 0.0001 0.019 ahanta(d) 0.375 0.198 0.547 0.140 0.089 0.236 0.107 0.014 0.170 0.374 0.192 0.537 akuapem(d) 0.519 0.512 0.525 0.477 0.468 0.484 0.001 0.0002 0.002 0.004 0.001 0.008 akwamu 0.999 0.997 0.999 0.001 0.0003 0.001 0.0002 0.0001 0.0004 0.0002 0.00003 0.001 akyem(d) 0.263 0.204 0.331 0.546 0.358 0.634 0.050 0.030 0.082 0.137 0.060 0.280 aowin 0.695 0.562 0.820 0.080 0.001 0.265 0.026 0.001 0.097 0.199 0.049 0.361 asante(d) 0.081 0.064 0.102 0.672 0.614 0.707 0.065 0.046 0.108 0.176 0.142 0.219 asen(d) 0.881 0.857 0.897 0.032 0.003 0.082 0.004 0.0002 0.017 0.076 0.026 0.104 boron(d) 0.345 0.309 0.382 0.303 0.256 0.337 0.033 0.014 0.053 0.309 0.259 0.353 chokosi(d) 0.709 0.637 0.758 0.025 0.002 0.061 0.103 0.030 0.187 0.161 0.051 0.242 denkyira(d) 0.716 0.641 0.787 0.166 0.119 0.233 0.002 0.0004 0.011 0.105 0.032 0.174 evalue(d) 0.310 0.089 0.522 0.371 0.067 0.530 0.013 0.002 0.056 0.304 0.102 0.500

442 fante(d) 0.264 0.230 0.301 0.381 0.337 0.432 0.155 0.126 0.187 0.193 0.156 0.240 kwahu(d) 0.207 0.113 0.323 0.261 0.209 0.383 0.113 0.030 0.324 0.414 0.209 0.548 nzema(d) 0.440 0.368 0.486 0.010 0.001 0.040 0.138 0.101 0.207 0.409 0.336 0.458 sefwi(d) 0.710 0.625 0.795 0.149 0.082 0.212 0.001 0.0002 0.002 0.137 0.061 0.214 wasa(d) 0.484 0.392 0.565 0.116 0.064 0.186 0.182 0.125 0.262 0.206 0.123 0.291 bawle 0.983 0.969 0.990 0.004 0.001 0.009 0.006 0.003 0.012 0.005 0.001 0.017 other akan 0.914 0.912 0.917 0.083 0.081 0.085 0.001 0.0003 0.003 0.001 0.0002 0.003 dangme(d) 0.345 0.283 0.436 0.129 0.072 0.205 0.108 0.067 0.169 0.413 0.318 0.490 ga 0.808 0.759 0.877 0.170 0.065 0.233 0.003 0.0001 0.017 0.019 0.0004 0.152 other ga 0.997 0.994 0.999 0.0002 0.0001 0.001 0.002 0.0005 0.003 0.001 0.0001 0.003 ewe(d) 0.479 0.451 0.510 0.099 0.074 0.142 0.168 0.131 0.199 0.246 0.208 0.281 guan1 0.923 0.920 0.926 0.076 0.073 0.079 0.0004 0.00004 0.002 0.0005 0.0002 0.001 guan2 0.885 0.849 0.893 0.110 0.103 0.133 0.003 0.001 0.009 0.001 0.0002 0.009 guan3(d) 0.467 0.399 0.569 0.232 0.015 0.411 0.030 0.001 0.108 0.265 0.110 0.415 guan4 0.296 0.130 0.414 0.155 0.070 0.304 0.091 0.012 0.169 0.456 0.348 0.588 guan5(d) 0.608 0.509 0.692 0.141 0.092 0.183 0.066 0.048 0.093 0.176 0.120 0.238 guan6 0.983 0.874 1.000 0.002 0.00002 0.013 0.003 0.00003 0.018 0.012 0.00003 0.098 guan7 0.998 0.995 0.999 0.001 0.0002 0.001 0.0004 0.0002 0.001 0.0005 0.00004 0.003 guan8 0.999 0.998 1.000 0.0001 0.00001 0.0005 0.0003 0.00001 0.001 0.0002 0.00003 0.001 other guan 0.454 0.452 0.461 0.544 0.535 0.546 0.001 0.0002 0.001 0.001 0.0003 0.003 Table C-12. Continued

NDC NPP Third Party No Vote tribe Mean 2.5% 97.5% Mean 2.5% 97.5% Mean 2.5% 97.5% Mean 2.5% 97.5% bimoba(d) 0.998 0.995 0.999 0.001 0.00003 0.004 0.001 0.0001 0.001 0.0002 0.00004 0.001 kokomba(d) 0.189 0.145 0.250 0.171 0.119 0.230 0.350 0.265 0.432 0.276 0.179 0.364 basare 0.926 0.901 0.949 0.072 0.050 0.096 0.001 0.0001 0.003 0.001 0.0001 0.004 pilapila 0.987 0.985 0.990 0.008 0.007 0.010 0.003 0.0003 0.005 0.001 0.0002 0.002 salfalba 0.997 0.996 0.998 0.0003 0.00005 0.001 0.002 0.001 0.002 0.0003 0.00002 0.001 kotokoli 0.809 0.801 0.814 0.190 0.184 0.197 0.0004 0.00005 0.002 0.001 0.0002 0.003 chamba 0.749 0.694 0.763 0.244 0.223 0.291 0.004 0.0002 0.030 0.002 0.0003 0.010 other gruma 0.997 0.991 0.999 0.0001 0.00003 0.0003 0.002 0.001 0.008 0.001 0.0001 0.003 builsa(d) 0.763 0.748 0.778 0.015 0.002 0.020 0.113 0.107 0.117 0.093 0.079 0.107 dagarte(d) 0.334 0.282 0.400 0.165 0.112 0.231 0.106 0.075 0.135 0.366 0.272 0.457 wali 0.994 0.990 0.997 0.003 0.002 0.005 0.001 0.0003 0.001 0.001 0.0002 0.005 dagomba(d) 0.319 0.267 0.373 0.198 0.155 0.259 0.155 0.127 0.188 0.322 0.264 0.385 kusasi(d) 0.209 0.124 0.302 0.051 0.028 0.086 0.249 0.232 0.267 0.471 0.376 0.554

443 mamprusi(d) 0.405 0.305 0.514 0.034 0.008 0.097 0.163 0.128 0.213 0.379 0.284 0.474 namnam(d) 0.695 0.246 0.998 0.038 0.001 0.161 0.055 0.00002 0.205 0.086 0.0001 0.244 nankansi(d) 0.319 0.246 0.456 0.162 0.113 0.268 0.156 0.129 0.201 0.321 0.204 0.425 nanumba 0.503 0.437 0.556 0.015 0.001 0.100 0.022 0.002 0.082 0.459 0.378 0.527 mosi 0.995 0.987 0.999 0.004 0.001 0.012 0.0003 0.0001 0.001 0.001 0.0001 0.002 other mole 0.359 0.152 0.588 0.281 0.065 0.559 0.031 0.006 0.102 0.319 0.127 0.636 kasena(d) 0.814 0.756 0.844 0.002 0.001 0.009 0.066 0.001 0.119 0.101 0.033 0.179 mo 0.956 0.739 0.999 0.002 0.0001 0.012 0.002 0.00004 0.023 0.035 0.0001 0.209 sisala(d) 0.739 0.682 0.788 0.009 0.001 0.028 0.145 0.124 0.156 0.088 0.031 0.146 vagala 0.998 0.998 0.999 0.0005 0.0001 0.001 0.001 0.001 0.001 0.0001 0.00004 0.0003 othergrusi1 0.284 0.113 0.448 0.019 0.006 0.051 0.126 0.023 0.222 0.544 0.392 0.694 othergrusi2 0.844 0.798 0.920 0.152 0.061 0.196 0.001 0.0001 0.006 0.003 0.0002 0.020 busanga 0.999 0.999 1.000 0.0002 0.0002 0.0004 0.00004 0.00001 0.0001 0.0001 0.00004 0.0002 wangara 0.823 0.781 0.836 0.174 0.162 0.212 0.001 0.0001 0.005 0.001 0.0002 0.005 othermande 0.983 0.979 0.987 0.013 0.010 0.015 0.002 0.0002 0.003 0.001 0.0001 0.002 other inside 0.999 0.999 1.000 0.0004 0.0002 0.001 0.0001 0.00003 0.0001 0.0001 0.00003 0.001 other outside 0.999 0.999 1.000 0.0005 0.0003 0.001 0.0001 0.0001 0.0001 0.0001 0.00001 0.0002 Note: bold = spread of at least 10 points between party CIs; italics = Party CIs don’t cross over; (d) = deterministic bounds info. contributed to this group’s vote estimates APPENDIX D SURVEY: MISSING RESPONSE BIAS CHECK

Table D-1. Logit Models Missing Response Bias Check

miss- swingvoter miss- miss- family miss- 2012 NDC partymember votes the same Dev. (1) (2) (3) (4) female -0.239 -0.065 -0.013 0.287 (0.201) (0.191) (0.190) (0.214) age -0.255∗∗∗ -0.018∗∗ -0.001 -0.005 (0.020) (0.007) (0.007) (0.007) rally -0.197 0.338∗ -0.866∗∗∗ -0.397∗ (0.198) (0.195) (0.198) (0.214) living 0.119 0.028 -0.061 0.055 condition1 (0.104) (0.101) (0.102) (0.112) cell own -0.166 0.135 0.253 -0.100 (0.281) (0.262) (0.263) (0.257) water inside 0.638∗∗ 0.219 0.623∗∗ 0.181 (0.312) (0.287) (0.248) (0.338) Bosome Freho 0.371 0.229 0.802∗∗ (0.441) (0.370) (0.364) Birim South 0.134 -0.144 -1.140∗∗ -0.316 (0.428) (0.368) (0.466) (0.402) Adaklu 0.911∗∗ 0.601 -0.234 -0.833∗ (0.385) (0.314) (0.372) (0.455) Ketu South 0.767∗∗ (0.337) Mfantsiman 0.276 -0.255 0.211 -0.359 (0.408) (0.381) (0.332) (0.417) AOB 0.256 0.105 -0.012 0.230 (0.439) (0.360) (0.345) (0.380) Constant 5.257∗∗∗ -2.223∗∗∗ -2.461∗∗∗ -2.440∗∗∗ (0.672) (0.502) (0.457) (0.542)

Obs. 1,669 1,669 1,669 1,669 Log Likelihood -335.659 -432.415 -422.732 -364.937 AIC 695.318 888.830 869.464 753.875

Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

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461 BIOGRAPHICAL SKETCH Jennifer C. Boylan started her education as a student in the racially-diverse Maryland suburbs of Washington, DC. Formally trained in Political Science and Leadership Studies at the

University of Richmond, Jennifer found her interest in domestic racial politics in the U.S. akin to the study of ethnic divides within sub-Saharan Africa after a study abroad trip to Ghana in 2007. After college, she began graduate school within the Department of Political Science at the University of Florida. Her research interests expanded beyond ethnic politics to include the eﬀects of constitutions, institutions and political parties on identity-driven divides, leading to a dissertation on the eﬀects of local government institutions on vote decisions in Ghana. Her other research interests include development initiatives and local-service delivery in sub-Saharan Africa.

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