GUNS, PARASITES, AND STATES: THREE ESSAYS ON COMPARATIVE DEVELOPMENT AND POLITICAL ECONOMY

BY EMILIO DEPETRIS CHAUVIN

B.A., UNIVERSIDAD DE BUENOS AIRES, 2003 M.A., BROWN UNIVERSITY, 2009

A DISSERTATION SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY IN THE DEPARTMENT OF ECONOMICS AT BROWN UNIVERSITY

PROVIDENCE, RHODE ISLAND MAY 2014 c Copyright 2014 by Emilio Depetris Chauvin To Jennifer with all my love and deepest gratitude This dissertation by Emilio Depetris Chauvin is accepted in its present form by the Department of Economics as satisfying the dissertation requirement for the degree of Doctor of Philosophy.

Date David Weil, Adviser

Recommended to the Graduate Council

Date Pedro Dal B´o, Reader

Date Stylianos Michalopoulos, Reader

Date Brian Knight, Reader

Approved by the Graduate Council

Date Peter Weber, Dean of the Graduate School

iii Vita

Emilio Depetris Chauvin was born on September 3th, 1979 in Buenos Aires, Ar- gentina. He earned his Bachelor’s degree from Universidad de Buenos Aires in 2003. He started his graduated studies in Argentina and completed all the course work for the M.A. in Economics at Universidad de San Andr´es. Before joining Brown Univer- sity, he worked as an economist in several think tanks and as country economist for the World Bank in Argentina. He enrolled in Brown University’s Economics Ph.D. program in 2008 and obtained his M.A. in Economics in 2009. While at Brown, he was a fellow of the Initiative in Spatial Structures in the Social Sciences (S4). In the course of his graduate school career, he was awarded a Brown Graduate School Fellowship, the Abramson Prize for Best Third-year Research Paper, a Merit Disser- tation Fellowship, and the Professor Borts Prize for Oustanding Ph.D. Dissertation in Economics. He received a Ph.D. in 2014 and will continue his research and teaching in Economics as an Assistant Professor at Universidad de los Andes in Colombia.

iv Acknowledgements

This dissertation could not have been finished without the help and support from many professors, colleagues, friends, and my family. I wish to o↵er my most heartfelt thanks to all of them here.

Ioweanimmensedebtofgratitudetomymainadvisor,DavidWeil,andmythesis committee members, Pedro Dal B´o,Stelios Michalopoulos, and Brian Knight. I am particularly grateful to David. His advice, guidance, support, and mentorship during my years at Brown were crucial for my professional development. David is not only one of the smartest scholars I have ever met but also a great human being. I would certainly be remiss in failing to acknowledge the key role played by Pedro in making my journey at Brown possible. He was not only instrumental in bringing me to Brown University but also in making me feel that my lovely Argentina was actually not that far. With his scientific meticulousness, Pedro also taught me how to think outside the box. Brian and Stelios had nurtured my intellectual development by generously providing their time, invaluable comments, and unwavering guidance. I would also like to take this opportunity to thank Oded Galor who was always willing to help. I appreciate the fact that his oce door was always open whenever I needed advice.

Many thanks also go to Peter Howitt, Vernon Henderson, Ken Chay, Louis Put- terman and Blaise Melly for comments, help, and advice in di↵erent stages of my graduate studies. I thank Juan Carlos Hallak, Daniel Heymann, and Walter Sosa Es- cudero for their invaluable support, help, and mentoring in the transition to my Phd studies at Brown University. I wish to thank Boris Gershman, Alejandro Molnar, Ri-

v cardo Perez Truglia, Leandro Gorno, Quamrul Ashraf, Raphael Franck, participants of Macroeconomics Lunch and Macroeconomics Seminar at Brown University, semi- nar participants at Universidad de San Andr´es, NEUDC 2013, Boston-Area Working Group in African Political Economy at the Institute for Quantitative Social Science (Harvard University), UNC at Chapel Hill, Notre Dame University, FGV-EPGV (Rio de Janeiro), FGV-EESP (Sao Paulo), ITAM, and Universidad de los Andes for com- ments and helpful discussions on the main chapter of this dissertation.

I would also like to thank my friend and fairy godmother, Angelica Vargas, who has been supportive not only in practical matters but also emotionally since the first day I arrived to Providence. Her kindness and hilarious sense of humor is something that I will certainly miss. Several friends made my grad school experience a terrific ride and I thank them for their companionship and fun: Omer¨ Ozak,¨ Judith Gallego, Fede Droller, Flor Borreschio, Ruben Durante, Maya Judd, Sarah Overmyer, Angelica Duran, Jack Sweeney, Diego Diaz, Carla Alberti, Paco Jurado, Chaparro-Martinez family, Martin Fiszbein, Seba Di Tella, Meche Politi, and Perez-Truglia family.

I wish to thanks my parents, Juan Manuel and Silvia, not only for their love but also for breeding my passion for learning. I am thankful and grateful to my siblings Nicol´as, Irene, Juli´an, Ana, and Pablo for being always there and for make me laugh. Iamhappyguyandyouareallindeedresponsibleforthat!Thissix-yearsjourney would not have been such a wonderful experience without the arrival of my daughter Violeta. This little person brought immense joy and fulfillment to my life. Violeta, your smile is the most powerful force helping me to push forward!

I dedicate this dissertation to my wife Jennifer. All the sacrifices she made along the way to enable the pursuit of my dream are nothing else but the proof of her unconditional love. Jennifer, I will be always deeply indebted to you. Te amo!

vi Contents

List of Figures x

List of Tables xii

1 State History and Contemporary Conflict: Evidence from Sub-Saharan Africa 1

1.1 Introduction ...... 1

1.2 Relationship with the Existing Literature ...... 7

1.3 A New Index of State History at the Sub-national Level...... 9

1.3.1 Overview of the Construction Procedure ...... 9

1.4 Empirical Relationship between State History and Contemporary Con- flict...... 17

1.4.1 Sources and Description of Conflict Data ...... 17

1.4.2 Cross Sectional Evidence ...... 19

1.4.3 Panel Data Evidence: Weather Induced-Agricultural Produc- tivityShock,StateHistory,andConflict ...... 43

1.5 Identifying Potential Mechanisms at Work: State History and Atti- tudes Towards State Institutions ...... 46

1.6 Conclusion ...... 56

Appendix 1.A: Variable Definitions 75

vii Appendix 1.B: Construction of the Instrument 79

Appendix 1.C: Construction of Weather-Induced Productivity Shock 82

2 Malaria and Early African Development: Evidence from the Sickle Cell Trait 92

2.1 Introduction ...... 92

2.2 Malaria and Sickle Cell Disease ...... 97

2.3 Measuring the Historical Burden of Malaria Using Data on the Sickle CellTrait ...... 101

2.3.1 Measuring the Overall Burden of Malaria ...... 108

2.3.2 Comparison of Malaria Burden to Malaria Ecology ...... 111

2.3.3 Comparison of Malaria Burden to Modern Malaria Mortality Rates ...... 116

2.4 Assessing the Importance of Malaria to Early African Development . 118

2.4.1 EthnicGroupAnalysis ...... 118

2.5 Model-Based Estimates of the Economic Burden of Malaria ...... 127

2.5.1 Direct E↵ect of Malaria Mortality ...... 127

2.5.2 Economic E↵ects of Malaria Morbidity ...... 140

2.6 Conclusion ...... 142

3 Fear of Obama: An Empirical Study of the Demand for Guns and the U.S. 2008 Presidential Election 150

3.1 Introduction ...... 150

3.2 Data and Aggregated Empirical Evidence on Gun Sales ...... 157

3.2.1 A Proxy of the Demand for Guns ...... 157

3.2.2 A Descriptive Analysis of the Aggregated Evolution of Gun Sales159

3.3 Empirical Strategy and Results ...... 163

viii 3.3.1 Quantifying the Obama e↵ect ...... 163

3.3.2 Potential Mechanisms ...... 170

3.3.3 Robustness Check ...... 181

3.4 Conclusions ...... 186

Appendix 3.A: Additional Tables 198

Bibliography 202

ix List of Figures

1.1 Evolution of Historical Map Boundaries (1000 - 1850 CE) ...... 10

1.2 Example of Score Calculation. East Africa (1800 - 1850 CE) . . . . . 12

1.3 SpatialDistributionofStateHistoryIndex ...... 14

1.4 ConflictandStateHistory ...... 25

1.5 Sensitivity of Estimates to Exclusion of Countries ...... 31

1.6 TimeElapsedSinceNeolithicRevolution ...... 36

1.7 AlternativeMeasureUsingHistoricalCities ...... 42

2.1 Sickle Cell Gene Frequency from Piel et al (2010) ...... 101

2.2 E↵ect of a Hypothetical Malaria Eradication on Evolution of Carriers 106

2.3 Implications of Varying Sickle Cell Prevalence for Malaria Burden . . 110

2.4 Worldwide Distribution of Malaria Ecology ...... 112

2.5 MalariaEcologyinAfrica ...... 113

2.6 Population Density by Ethnic Groups from EA (1967) ...... 120

2.7 Consumption and Income Profiles ...... 132

2.8 Change in Survival Probabilities due to Malaria ...... 136

3.1 Presidential Elections and Firearm Background Check Reports . . . . 161

3.2 Growth in Background Checks by State (July08-June09 vs July07- June08) ...... 162

x 3.3 Geographic Distribution Obama Victory E↵ect ...... 170

3.4 Change in Attitudes Toward Gun Control. More important to Protect Gun Rights or Control Gun Ownership? ...... 172

xi List of Tables

1.1 Summary Statistics. Grid Cell Sample ...... 58

1.2 OLS Estimates - Baseline Specification ...... 59

1.3 OLS Estimates - Accounting for Genetic and Ecological Diversity . . 60

1.4 OLSEstimates-AdditionalControls ...... 61

1.5 OLS Estimates. Di↵erentConflictMeasures ...... 62

1.6 OLSEstimates. HeterogeneityAcrossRegions ...... 63

1.7 OLS Estimates. Discount Factors and Importance of Medieval Period 64

1.8 OLS Estimates - Intensive vs Extensive Margin of Political Centralization 65

1.9 First-Stage. NeolithicInstrument ...... 66

1.10 IV Estimates ...... 67

1.11 Alternative Measure. Historical Proximity to Cities ...... 68

1.12 Conflict, State History, and Weather Shocks -Panel Data Evidence (1989-2010)- ...... 69

1.13 Conflict, State History, and Weather Shocks -Panel Data Evidence (1989-2010)- ...... 70

1.14 StateLegitimacyandStateHistory ...... 71

1.15 State Legitimacy and State History. Internal vs External Norms . . . 72

1.16 TrustandStateHistory ...... 73

1.17 Trust in Local Policy Makers, Traditional Leaders, and State History 74

xii 2.1 Components of the Cost of Malaria ...... 144

2.2 Malaria Burden vs. Malaria Ecology: Grid Cell Analysis ...... 145

2.3 Malaria Burden vs. Malaria Ecology: Ethnic Group Analysis . . . . . 146

2.4 Malaria and Population Density ...... 147

2.5 Malaria and Ethnic Prosperity ...... 148

2.6 Years Lost to Disability per capita, WHO AFRO Region ...... 149

3.1 Obama Victory E↵ect ...... 189

3.2 Obama E↵ect...... 190

3.3 Obama E↵ect and Gun-Control Fear ...... 191

3.4 Obama E↵ect and Race Bias ...... 192

3.5 BothHypothesesTogether ...... 193

3.6 OmittingElectionAftermath ...... 194

3.7 UsingPollsData ...... 195

3.8 UsingAlternativeMeasuresforPrejudice ...... 196

3.9 Further Robustness Check ...... 197

3.10 Summary Statistics ...... 199

3.11 StateCharacteristics ...... 200

3.12 StateCharacteristics(Continuation) ...... 201

xiii Preface

This dissertation is composed of three chapters on comparative development and political economy. Chapter I examines empirically the role of historical political cen- tralization on the likelihood of contemporary civil conflict in Sub-Saharan Africa. It combines a wide variety of historical sources to construct an original measure of long- run exposure to statehood at the sub-national level. It then exploits variation in this new measure along with geo-referenced conflict data to document a robust negative relationship between long-run exposure to statehood and contemporary conflict.

Chapter 1 argues that regions with long histories of statehood are better equipped with mechanisms to establish and preserve order. Two pieces of evidence consis- tent with this hypothesis are provided. First, regions with relatively long historical exposure to statehood are less prone to experience conflict when hit by a negative economic shock. Second, exploiting contemporary individual-level survey data for 18 Sub-Saharan countries, Chapter 1 also provides evidence that within-country long historical statehood experience is linked to people’s positive attitudes toward state institutions and traditional leaders

Chapter 2 examines the e↵ect of malaria on economic development in Africa over the very long run. Using data on the prevalence of the mutation that causes sickle cell disease it measures the impact of malaria on mortality in Africa prior to the period in which formal data were collected. The presented estimate suggests that in the more a✏icted regions, malaria lowered the probability of surviving to adulthood by about ten percentage points, which is roughly twice the current burden of the

xiv disease. The reduction in malaria mortality has been roughly equal to the reduction in other causes of mortality. Chapter 2 then asks whether the estimated burden of malaria had an e↵ect on economic development in the period before European contact. Examining both mortality and morbidity, it does not find evidence that the impact of malaria would have been very significant. These model-based findings are corroborated by a more statistically-based approach, which shows little evidence of a negative relationship between malaria ecology and population density or other measures of development, using data measured at the level ethnic groups.

Chapter 3 provides a valuable input for the analysis of future gun policies in the US and their social, economic, and political ramifications. It focuses on the understanding of the economic and non-economic determinants of the decision of acquiring firearms. Particularly, Chapter 3 exploits monthly data constructed from futures markets on presidential election outcomes and a novel proxy for firearm purchases, to analyzes how the demand for guns responded to the likelihood of Barack Obama being elected in 2008. The existence of a large Obama e↵ect on the demand for guns is documented, being this political e↵ect larger than the e↵ect associated to the worsening economic conditions. Furthermore, Chapter 3 presents empirical evidence consistent with the hypotheses that the unprecedented increase in the demand for guns was partially driven by both a fear of a future Obama gun-control policy and racial prejudice.

xv Chapter 1

State History and Contemporary Conflict: Evidence from Sub-Saharan Africa

1.1 Introduction

1 Civil conflict imposes enormous costs on a society. In addition to lives lost as a direct result of violent confrontations, there may be persistent negative consequences to health and social fragmentation. Economic costs extend beyond short-term disrup- tion of markets, as conflict may also shape long-run growth via its e↵ect on human capital accumulation, income inequality, institutions, and culture. Not surprisingly, understanding the determinants of civil conflict has been the aim of a growing body of economic and political science literature.2

The case of Sub-Saharan Africa has received considerable attention for the simple

1I am grateful to Gordon McCord, Stelios Michalopoulos, and Omer Ozak for sharing data, Nickolai Riabov and Lynn Carlson for computational assistance with ArcGIS and R, and Santiago Begueria for helpful discussions and suggestions regarding the Standardized Precipitation Evapo- transpiration Index. 2Blattman and Miguel (2010) provide an extensive review and discussion on the literature of civil conflict, including the theoretical arguments and salient empirical findings on the causes and consequences of civil conflict. 1 reason that civil conflict has been particularly prevalent in this part of the world;2 over two thirds of Sub-Saharan african countries experienced at least one episode of conflict since 1980. Many scholars have pointed to civil conflict as a key factor holding back African economic development (see, for example, Easterly and Levine 1997).

In this paper I explore the relationship between the prevalence of modern civil conflict and historical political centralization. Specifically, I uncover a within-country robust negative relationship between long-run exposure to statehood and the prevalence of contemporary conflict. My approach of studying a historical determinant of modern civil conflict is motivated by the empirical literature showing evidence on the impor- tance of historical persistence for understanding current economic development (see Galor 2011; Nunn 2013; and Spoloare and Wacziarg 2013 for extensive reviews). My paper draws on a strand of this literature which documents that traditional African institutions not only survived the colonial period but that they still play an important role in modern African development (Gennaioli and Rainer, 2007; and Michalopoulos and Papaioannou, 2013, among others).

Why would the long history of statehood matter for contemporary conflict? Similarly to Persson and Tabellini (2009)’s idea of “democratic capital”, I argue that the ac- cumulation of experience with state-like institutions may result in an improved state capacity over time.3 Therefore, regions with long histories of statehood should be bet- ter equipped with mechanisms to establish and preserve order. These institutional capabilities can be manifested, for example, in the ability to negotiate compromises and allocate scarce resources, the existence of traditional collective organizations and legal courts to peacefully settle di↵erences over local disputes, or even a stronger presence of police force. As a result, regions with long history of statehood should experience less conflict.

3State capacity can be broadly defined as the abilities acquired by the state to implement a wide range of di↵erent policies (Besley and Persson 2010). 3 Akeyaspectofmyapproachistoexploitwithin-countrydi↵erences in the preva- lence of modern conflict and its correlates. I take my empirical analysis to a fine sub- national scale for several reasons. First, conflict in Africa is often local and does not extend to a country’s whole territory.4 Second, there is arguably large within-country heterogeneity in historical determinants of conflict, including historical exposure to state institutions. Given that modern borders in Sub-Saharan Africa were artificially drawn during colonial times without consideration of previous historical boundaries (Green, 2012), substantial heterogeneity in location histories and people characteris- tics persists today within those borders. Therefore, the aggregation of these charac- teristics at the country level averages out a rich source of heterogeneity. Third, other determinants of conflict that have previously been highlighted in the literature, such as weather anomalies or topography, are in fact geographical and location-specific. Fourth, exploiting within-country variation in deeply-rooted institutions allows me to abstract from country-level covariates, such as national institutions or the identity of the colonial power that ruled the country.

Pre-colonial Sub-Saharan Africa comprised a large number of polities of di↵erent territorial size and varying degrees of history of political centralization (Murdock, 1967). At one extreme of the spectrum of political centralization were large states, such as Songhai in modern day Mali, which had a king, a professional army, public servants and formal institutions such as courts of law and diplomats. On the other extreme, there were groups of nomadic hunter-gatherers with no formal political head such as the Bushmen of South Africa. Some centralized polities were short-lived (e.g., Kingdom of Butua in Modern day Zimbabwe), some mutated over time (e.g., Songhai), and some still persist today (e.g., Kingdom of Buganda). Historical political centralization varies even within countries. Consider, for example, the case of Nigeria,

4Raleigh et al (2009) argue that civil conflict does not usually expand across more than a quarter of a country’s territory. 4 where the Hausa, the Yoruba, and the Igbo represent almost 70 percent of the national population and have quite di↵erent histories of centralization. Unlike the Hausa and Yoruba, the Igbo had a very short history of state centralization in pre-colonial time despite having settled in southern Nigeria for centuries.

In order to account for this heterogeneity in historical state prevalence, I develope an original measure which I refer to as the State History Index at the sub-national level. For this purpose, I combine a wide variety of historical sources to identify a compre- hensive list of historical states, along with their boundaries and chronologies. In its simplest version, my index measures for a given territory the fraction of years that the territory was under indigenous state-like institutions over the time period 1000 - 1850 CE. I then document a within-country strong negative correlation between my state history index and geo-referenced conflict data. My OLS results are robust to a battery of within-modern countries controls ranging from contemporaneous conflict correlates and geographic factors to historical and deeply-rooted plausible determi- nants of modern conflict.5 Moreover, I show that these results are not driven by historically stateless locations, influential observations, heterogeneity across regions, or the way conflict is coded.

Nonetheless, this uncovered robust statistical association does not neccesarily imply causality. Indeed, history is not a random process in which long-run exposure to statehood has been randomly assigned across regions. The historical formation and evolution of states is a complex phenomenom. Factors underlying the emergence and persistence of states may still operate today. To the extent that some of those factors are unobserved, isolating the causal e↵ect of historical statehood on conflict is a dicult task. I argue, however, that it is unlikely that my OLS results are fully

5For instance, the results are not particularly driven by, inter alia, the confounding e↵ects of genetic diversity (proxied by migratory distance from the cradle of humankind in Ethiopia) and ecological diversity. 5 driven by omitted factors. Following Altonji, Elder, and Taber (2005)’s approach I show that the influence of unobservables would have to be considerable larger than the influence of observables to explain away the uncovered correlation.

To support the case that state history has left its marks on the patterns of contem- poraneous conflict, I present two additional pieces of evidence consistent with my main hypothesis. First, I show that regions with relatively long historical exposure to statehood are remarkably less prone to experience conflict when hit by a negative agricultural productivity shock. Second, I present empirical evidence on potential un- derlying mechanisms by exploiting contemporary individual-level survey data for 18 Sub-Saharan countries and showing that long history of statehood is associated with people’s positive attitudes towards state institutions. In this sense, I show that key state institutions are regarded as more legitimate and trustworthy by people living in districts with long history of statehood. Moreover, I also show that support for local traditional leaders is also significantly larger in those districts. None of these individual-level results is driven by unobservable ethnic characteristics (i.e., estimates are conditional on ethnic indentity fixed e↵ects), which constitutes a striking result and suggests that the institutional history of the location where people currently live matters for people’s opinion about state institutions independently of the history of their ancestors.

Given the obvious limitations in documenting historical boundaries in Sub-Saharan Africa, a significant degree of measurement error is likely to be present in my index which will introduce a bias in my OLS estimates. To tackle this limitation, I follow two instrumental variable approaches. First, I draw on the proposed link between the neolithic revolution and the rise of complex political organizations (Diamond 1997). Using archaeological data containing the date and location of the earliest evidence of crop domestication within Africa I construct a measure of the time elapsed since the 6 neolithic revolution at a fine local level. IV estimates suggest a stronger statistical association between long-run exposure to statehood and my outcome variables; this finding is consistent with the idea that measurement error in my state history index introduces a sizeable downward bias. To address concerns regarding the validity of the exclusion restriction, I examine whether my IV estimates are a↵ected by the inclusion of several biogeographical controls. The addition of a rich set of covariates does not qualitatively a↵ect my results.

My second IV approach to mitigate the bias from measurement error exploits time and cross-sectional variation from a panel of historical African cities. To the extent that kingdoms and empires tended to have a large city as political center, I use time- varying proximity to the closest large city during the time period 1000-1800 CE to construct an alternative measure of the degree of influence from centralized polities. Using this new measure as an instrument for my original state history index, I obtain similar results which provides additional support to my main hypothesis.

The paper is organized as follows. Section 2 discusses the relationship and contribu- tion of this paper to the existing literature. Section 3 introduces an original index of state history at the local level. Section 4 presents the OLS and IV results on the empirical relationship between state history and contemporary conflict both in a cross-section and in a panel data setting in which I exploit additional time variation from weather-induced agricultural shocks. Section 5 reports the empirical results on mechanisms by exploiting individual-level survey data. Section 6 o↵ers concluding remarks. 7 1.2 Relationship with the Existing Literature

This paper belongs to a vibrant body of work within economics tracing the historical roots of contemporary development. Specifically, my work is related to economic re- search on the relationship between institutional history and contemporary outcomes; a line of research which originates in Engerman and Sokolo↵ (1997), La Porta e al. (1999), and Acemoglu, Johnson, and Robinson (2001). In particular, this paper is re- lated to the literature examining the developmental role of state history (Bockstette, Chanda, and Putterman 2002, Hariri 2012, and Bates 2013). It is methodologically related to Bockstette, Chanda, and Putterman (2002) which introduces a State Antiq- uity Index at the country level.6 I contribute to the related literature by constructing an original measure at the local level.

Particularly in the context of Africa, my work is also related to works on the impact of pre-colonial political centralization on contemporary outcomes (Gennaioli and Rainer, 2007; Huillery, 2009, Michalopoulos and Papaioannou, 2013). More importantly, my work contributes to the line of research on how historical factors have shaped the observed pattern of conflict during the African post-colonial era (Michalopoulos and Papaioannou 2011, and Besley and Reynal-Querol 2012).7 Of most relevance to my work is Wig (2013) who finds that ethnic groups with high pre-colonial political cen- tralization and that are not part of the national government are less likely to be involved in ethnic conflicts. While attempting to address a similar question on how historical political centralization may prevent conflict, there are two main di↵erences

6Bockstette, Chanda, and Putterman (2002) introduces the State Antiquity Index and shows that it is correlated with indicators of institutional quality and political stability at the country level. Borcan, Olsson, and Putterman (2013) extends the original index back to 4th millenium BCE. 7Michalopoulos and Papaioannou (2011) exploits a quasi-natural experiment to show that civil conflict is more prevalent in the historical homeland of ethnicities that were partitioned during the scramble for Africa. Besley and Reynal-Querol (2012) provides suggestive evidence of a legacy of historical conflict by documenting a positive empirical relationship between pattern of contemporary conflict and proximity to the location of recorded battles during the time period 1400 - 1700 CE. 8 between Wig (2013) and my work. First, unlike Wig (2013) who only focuses on eth- nic political centralization recorded by ethnographers around the colonization period, Itracethehistoryofstatehoodfurtherbackintimetoaccountfordi↵erences on long-run exposure to statehood. Doing so, I find that not only the extensive but also the intensive margin of prevalence of historical institutions matters crucially to un- derstand contemporary conflict. Second, I provide evidence of potential mechanisms underlying my reduced form findings by documenting a strong relationship between state history and positive attitudes toward state institutions and traditional leaders.8

My work contributes to the literature on the interaction between state capacity (or contemporary institutions in general) and conflict (Fearon and Laitin 2003, Besley and Persson 2008, among others).9 In particular, my paper provides empirical evi- dence that long history of pre-colonial state capacity at the sub-national level may reduce the likelihood of civil conflict in a region of the world where national gov- ernments have limited penetration (Michalopoulos and Papaioannou 2013c). It is worthwhile to note that most of the empirical work on the link between contemporary institutions (in particular state capacity) and conflict is conducted across countries. Methodologically, I depart from this approach. Rather than focusing on contemporary institutional di↵erences at the national level, I investigate the role of deeply-rooted institutional characteristics at the sub-national level in shaping state legitimacy and the propensity to engage in conflict. Finally, my work is also methodologically related to recent literature in economics that takes a local approach to conflict (Besley and Reynal-Querol, 2012; Harari and La Ferrara, 2012).10

8In addition, I do not restrict my analysis to ethnic conflict; rather, I study a more general definition of civil conflict. 9In addition to state capacity, the role of cohesive political institutions (Besley and Persson 2011, Collier, Hoe✏er, and Soderbom 2008) has been also emprically studied. 10In revealing how a deeply-rooted factor relates to contemporary conflict, this paper also connects to recent work by Arbatli, Ashraf, and Galor (2013), which shows that genetic diversity strongly predicts social conflict. 9 1.3 A New Index of State History at the Sub-

national Level

1.3.1 Overview of the Construction Procedure

In this section I present an overview of the construction procedure of my new index of state history at the sub-national level. Two dimensions are relevant for my purpose; the time period to consider for the computation of the index and the definition of a geographical location for which the index is calculated. That is, I have to define the units of analysis that will determine the scope of both the extensive and the intensive margin of state history.

Time period under analysis. I focus on the period 1000-1850 CE for two reasons. First, the aim of my research is to examine the legacy of indigenous state history, thus I consider only pre-colonial times. I am not neglecting, however, the importance of the colonial and post-colonial periods to understand contemporary pattern of conflict. In fact, the persistence of most of the indigenous institutions during and after the colonial indirect rule experience represents an important part of the main argument in this paper. Second, I ignored years before 1000 CE due to the low quality of historical information and to the fact that no much known variation on historical states would have taken place in Sub-Saharan Africa before that period.11 Ithen follow Bockstette, Chanda, and Putterman (2002), and divide the period 1000-1850 CE in 17 half-centuries. For each 50 years period I identify all the polities relevant for that period. I consider a polity to be relevant for a given half-century period if it existed for at least twenty six years during that fifty-years interval. Therefore, I

11There would have been few cases of state formation before 1000 CE in Sub-Saharan Africa: the Aksum and Nubian Kingdoms (Nobadia and Alodia) in the Ethiopian Highland and along the Nile river, the Siwahalli City-States in East Africa, Kanem in Western Chad, and Ghana and Gao in the West African Sahel (Ehret 2002). 10 construct seventeen cross sections of the historical boundaries previously identified in the pre-colonial Sub-Saharan Africa. Figure 1.1 displays the evolution of historical map boundaries over the period 1000-1850 CE.12

Figure 1.1: Evolution of Historical Map Boundaries (1000 - 1850 CE)

Definition of geographic unit. My empirical analysis focuses on two di↵erent defi- nitions for sub-national level (i.e: geographical unit of observation). First, I focus on grid cells; an artificial constructions of 2 by 2 degrees. Second, I also focus on African districts and thus construct a 1-degree radius bu↵er around the centroid of each district.13 Given these di↵erent levels of aggregation to compute my index of

12NOTE on Figure 1.1: The boundaries of the large territory on North Africa appearing during the time period 1000-1200 CE belong to the Fatimid Caliphate which was wrongly included in a previous version of my index. Although this historical map intersects a minor part of North East Sudan it is not considered for the computation of the index used in this paper because the Fatimids are not indigenous to Sudan. 13A district is a second order administrative division with an intermediate level of disagregation between a region or province and a village. 11 state history, I start by constructing the index at a sucient fine level. Therefore, I divide Sub-Saharan Africa in 0.1 by 0.1 degree pixels (0.1 degree is approximately 11 kilometers at the equator). I then dissolve the compiled historical maps into 0.1 by 0.1 degree pixels taking the value 1 when an historical state intersects the pixel, and 0 otherwise.14 For a given level of aggregation i, its state history value would be determined by:

State History = 1850 S with t =1000, 1050, 1100,...,1850 i 1000 t ⇥ i,t P ✓p,t where, Si,t = P is the score of i in period t, with ✓p,t taking the value 1 if the P pixel p is intersected by the map of an historical state in period t, 0 otherwise; and P being the number of pixels in i.15 The variable is the discount factor. Since I do not have any theoretical reason to pick a particular discount factor, I base most of my analysis in a discount factor of 1. Figure 1.2 shows an example of the calculation of the score in East Africa circa 1800 when the level of aggregation is a grid cell of 2 degree by 2 degree.

There are three crucial and challenging pieces in the construction of the index. First, my procedure requires the compilation of a comprehensive list of historical states. Second, the boundaries of those historical states have to be identified, digitized and georeferenced. Third, an even more dicult task is to account for potential expansions and contractions of those boundaries over time.

Identifying historical states. Iuseawidevarietyofsourcestoidentifyhistoricalmaps of states in pre-colonial Sub-Saharan Africa for the time period 1000-1850 CE.16

14Therefore, the pixel will take a value 1 even when an overlap of two historical states exists. That is, a pixel intersected multiple times is considered only once. 15 Therefore, the score Si,t denotes what fraction of the territory of i is under an historical state in the period t. 16I define Sub-Saharan Africa to all the geography contained within the borders of the fol- lowing countries: Angola, Benin, Botswana, Burkina Faso, Burundi, Cameroon, Central African Republic, Chad, Congo DRC, Congo, Cote d’Ivoire, Ethiopia, Eritrea, Gabon, Gambia, Ghana, 12

Figure 1.2: Example of Score Calculation. East Africa (1800 - 1850 CE)

Identifying what constituted a state in the remote past of Africa is not a easy task. Of course, historical records are incomplete and some time the demarcation between tribes and kingdoms was not that clear. Further, heterogeneity in political structures was indeed very large in pre-colonial Africa. Nonetheless, my operative definition of states includes city-states, kingdoms, and empires and it is built upon the conception of a centralized power exercising influence over some periphery. That is, a historical state is the result of the amalgamation of smaller settlement units in a relatively large unit of territory ruled by centralized political head. I consider the existence of an army as a necessary but not sucient condition to constitute a state. For instance, the Galla people (also known as Oromo) in modern Ethiopia developed states only two hundred years after conquering ethiopian soil (Lewis, 1966). Before founding the five Gibe kingdoms, Galla people were governed at the village level. Although

Guinea, Guinea-Bissau, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritania, Mozam- bique, Namibia, Niger, Nigeria, Rwanda, Senegal, Sierra Leone, Somalia, South Africa, Sudan, Swaziland, Tanzania, Togo, Uganda, Zambia, and Zimbabwe. 13 coordinated in the competition against neighboring kingdoms, each local independent group was under its own leader. Thus, I only considered the Galla’s polities once the Gibe kingdoms were established in late eighteenth century. Note that my notion of state is not necessary a proxy for societal complexity.17 Non-political centralized complex societies such as the Igbo in modern Nigeria, which had a complex system of calendars `(Oguafo) and banking (Isusu), are not considered as historical states. In fact, only after conforming the trade confederacy in the year 1690, I consider the Aro, a subgroup of the Igbo, to be taken into account for the computation of my index.

The starting point then was to identify the historical states referenced in the version 3.1 of the State Antiquity Index introduced in Bockstette, Chanda, and Putterman (2002). I complement this initial list with a variety of additional sources (Ajayi and Crowler 1985, Barraclough 1979, Vansina 1969, McEvedy 1995, Murdock 1967, and Ehret 2002). Once I complete the list of all the polities to be taken into account in the computation of my state history index, I document approximate dates of foundation and declination of each polity. Table A.1 in the appendix includes the complete list of polities (with their relevant dates) used in the computation of my index. Note that Ionlyconsiderindigenousstatesinmyanalysis.Therefore,Idonotconsiderforeign states such as the Portuguese colony in the coastal strip of Angola (present for more than four hundred years) or occupations such as Morocco’s in Songhai’s territory at the beginning of the seventeenth century.

Compilation of historical maps. The following task was to identify, digitize and geo- reference the maps of the historical states on the list. Some of the maps were already digitized and some of them were also georeferenced.18 When a map of a given polity

17Note also that stateless does not imply either absence of laws or existence of a small societies. The Nuer of the Souther Sudan and the Tiv of Nigeria serve as good examples. 18For instance, some maps from McEvedy’s (1995) Atlas of African History were already digitized and georeferenced by AfricaMap, a project developed by the Center for Geographic Analysis at Harvard. After checking for inconsistencies with original sources and correcting irregularities in border drawings, I also considered some maps digitized by the ThinkQuest Project of The Oracle 14 was available for more than one period of time, I took into account all of them for my analysis. This helps me to partially account for expansions and contractions of states’ geographic influence over time.19 Some judgment was needed when two sources disagreed in the way the boundaries of a historical state were recorded for a similar historical period. I kept the map I found more reliable.20 Iabstractfornowfromthe diculties (and consequences) of defining historical map boundaries; I discuss this issue below in more detail.

Cross-Sectional Variation. Figure 1.3 displays the cross-sectional variation of my State History Index based on grid-cell aggregation (with a discount factor of 1). Sub-Saharan Africa is divided in 558 grid cells of 2 degree by 2 degree. Following Bockstette, Chanda, and Putterman (2002), I rescale the index by dividing all the values by the maximum possible value; therefore State History [0, 1]. i 2

Figure 1.3: Spatial Distribution of State History Index

Education Foundation. 19For instance, I was able to document how political influence of Songhai’s people evolved over my period of analysis. Figure 1 includes the first Songhai polity (pre-imperial) during the time period c.1000-c.1350CE around the city of Gao, its expansion consistent with the establishment of the Songhai Empire from c.1350 CE to c.1600CE and the late formation of Dendi Kingdom as a result of the Morrocan invasion and declination of the empire in c.1600 CE. 20In some cases I made the decision based on the consistency with natural borders like majors rivers or elevations. 15 Roughly one third of Sub-Saharan Africa has no state history before 1850 CE. State history is more prevalent in the north, particularly in western part of Sahel, the highlands of Ethiopia, and the region along the Nile river. In this sense, proximity to water is a relevant factor to explain the historical presence of states. In particular, proximity to major rivers such as Niger, Benue, Senegal, Volta, Congo, and Zambezi; and great lakes such as Victoria, Tanganyika, Malawi, and Chad correlates with high values of the index. Almost no state history is documented in the African rainforest and South-West Africa.

Major sources of measurement error. Any attempt to rigorously define state bound- aries in pre-colonial Africa is doomed to imperfection for several reasons. Indigenous historical records are scarce in Sub-Saharan Africa; and most of the modern recon- struction of African history relies upon account by travelers, traders and missionaries (particularly during the nineteenth century), the transmission from oral history, or analysis of archaeological sites. Further, this scarcity of historical records exacer- bates the farther south or away from the coast one looks. Most importantly perhaps, almost no indigenous map making existed in pre-colonial Africa (Herbst, 2000). Re- gardless of the problems due to lack of historical records, the extension of authority to the periphery in pre-colonial Africa was itself irregular, contested, and weak. As argued by Herbst (2000), boundaries were, in consequence, a reflection of this di- culty of broadcasting power from the center. Therefore, the lines of demarcation for boundaries of any historical state are, by construction, inevitably imperfect. As a matter of fact, I find di↵erent historical atlases displaying quite dissimilar maps for the same polity under similar period of time. Nevertheless, while bearing in mind the aforementioned caveat, documenting imperfect boundaries provides at least a useful starting point for my empirical analysis.

The aforementioned imperfection in the demarcation of boundaries represents a source 16 of measurement error a↵ecting my econometric analysis. There is little reason to believe that this particular measurement error is correlated with the true measure of state antiquity. Therefore, this would represent a case of classical errors-in-variables that would introduce an attenuation bias in the OLS estimates of the relationship between historical state prevalence and conflict.

An additional source of measurement errors in my state history variable will result from the introduction of an upper bound when computing the index. When consider- ing only historical states starting 1000 CE, I am excluding many years of state history in region with long history of statehood. For instance, I am omitting more than 250 years of the Ghana empire in West Africa. Further, the Kingdom of Aksum, existing during the period 100-950 CE and located in modern day Eritrea and Ethiopia, was not considered in the computation of the state history index. Since locations with some history of state before 1000 CE tend to present high values of my index, the introduction of the bound in the period of analysis for its computation would tend to underestimate the long run exposure to statehood for some regions. Therefore, an additional upward bias in the OLS estimates is introduced. It is precisely for the sake of alleviating the resulting biases due to measurement error in my data what will provide a key motivation for the implementation of an instrumental approach later on. 17 1.4 Empirical Relationship between State History

and Contemporary Conflict

1.4.1 Sources and Description of Conflict Data

In this paper I exploit georeferenced conflict event data to construct di↵erent measures of conflict prevalence at the sub-national level. There are two leading georeferenced conflict datasets for Sub-Saharan Africa, the Uppsala Conflict Data Program Georef- erenced Events Dataset (UCDP GED, from now on) and the Armed Conflict Location Events Dataset (ACLED, from now on). For reasons I detail below, the core of my analysis is based on UCDP GED. However, I show that the main results are not dependent on the choice of the conflict dataset.

The UCDP GED, version 1.5 (November 2012) provides geographically and tempo- rally disaggregated data for conflict events in Africa (for a full description of the dataset, see Sundberg and Melander, 2013). Specifically, UCDP GED provides the date and location (in latitude and longitude) of all conflict events for the period 1989- 2010. A conflict event is defined as “the incidence of the use of armed force by an organized actor against another organized actor, or against civilians, resulting in at least one direct death in either the best, low or high estimate categories at a specific location and for a specific temporal duration” (Sundberg et al, 2010). The dataset comprises of all the actors and conflicts found in the aggregated, annual UCDP data for the same period. UCDP GED traces all the conflict events of “all dyads and actors that have crossed the 25-deaths threshold in any year of the UCDP annual data” (Sundberg et al, 2010). Note that the 25-deaths threshold is the standard coding to define civil conflict and that the definition for dyad does not exclusively need to include the government of a state as a warring actor. Finally, also note 18 that once a dyad crossed the 25-deaths threshold, all the events with at least one death are included in the dataset. That is, these events are included even when they occurred in a year where the 25-deaths threshold was not crossed and regardless of whether they occurred before the year in which the threshold was in fact crossed. The UCDP GED contains 21,858 events related to approximately 400 conflict dyads for the whole African continent. More than 50 percent of those events include the state as one of the warring actors (although only about 10 percent of conflict dyads included the state). For the best estimate category, the total fatality count is approximately 750,000 deaths (Sundberg and Melander, 2013).

I prefer UCDP GED over ACLED for several reasons. First, the definition of conflict event in UCDP GED is restricted to fatal events and it adheres to the general and well established definitions in UCDP–PRIO Armed Conflict Dataset, which has been extensively used in the conflict literature (see for example, Miguel et al, 2004, and Esteban et al, 2012). On the contrary, the definition of event in ACLED includes non- violent events such as troop movements, the establishment of rebel bases, arrests, and political protests. Moreover, the definitions of armed conflict and what constitutes an event in ACLED is not fully specified. This is indeed worrisome because it makes harder to understand the potential scopes of measurement errors in the conflict data. Nonetheless, ACLED data does allow the user to identify battle and other violent events. Second, UCDP GED provides an estimate of number of casualties per event that allows me to calculate an alternative measure of conflict intensity. Third, Eck (2012) argues that ACLED presents higher rates of miscoding. Fourth, the UCDP GED provides a larger temporal coverage (22 years vs 14 years in ACLED).

Despite of my aforementioned reasons to choose UCDP GED over ACLED, the lat- ter has been recently used by economists (see, for instance, Harari and La Ferrara 2013, Besley and Reynal-Querol 2012, and Michalopoulos and Papaioannou 2012). 19 Therefore, I show as a robustness check exercise that using ACLED data does not qualitatively a↵ect the main results of my empirical exercise.

1.4.2 Cross Sectional Evidence

Istartmyempiricalanalysisbylookingatthestatisticalrelationshipbetweenpreva- lence of conflict and state history at the 2 by 2 degree grid cell level. The key motivation to have an arbitrary construction (i.e., grid cell) as unit of observation, as opposed to subnational administrative units, is to mitigate concerns related to the potential endogeneity of the borders of those political units. In particular, politi- cal borders within modern countries may be a direct outcome of either patterns of contemporary conflict or any of its correlates (such as ethnic divisions).21

Table 1 presents summary statistics of the 558 grid cells in my sample. The average area of a grid cell in my sample is 42,400 square kilometer which represents approx- imately one tenth of the average size of a Sub-Saharan African country. A mean conflict prevalence of .189 implies that, during the period 1989-2010, an average grid cell experienced 4 years with at least one conflict event. Approximately one fourth

21The determination of the spatial resolution (i.e., size and position of the unit of observation) may be subject to the modifiable areal unit problem (MAUP), which may a↵ect the results due to the potential existence of an statistical bias resulting from the scaling and zoning methods (see Wrigley et al, 1996). Zoning does not appear to quite relevant in the study of conflict at the grid cell level (Hariri and La Ferrara, 2013). I pragmatically centered the northwesternmost grid cell so it perfectly corresponds with the raster of gridded population data (originally in a resolution of 2.5 arc-minutes -aproximately 5km at the equator-). The election of the size of the unit of observation is a more delicate issue. Choosing a higher resolution facilitates the identification of local factors a↵ecting the prevalence of conflict. However, a higher resolution may not only exacerbate measurement error but also make spatial dependence more relevant for the identification of local e↵ects. On the one hand, higher resolution would make nearby observations more dependent of each other, thus introducing potential underestimation of standard errors of point estimates. This is an issue that can be addressed by implementing spatially robust or clustered robust estimation methods. On the other hand, spatial dependance in the dependent variable is more problematic since neglection of this dependence would bias the point estimates. Therefore, when choosing the size of my unit of observation, I attempt to balance the trade-o↵ between masking subnational heterogeneity and introducing a potential bias due to spatial dependence. I acknowledge that implementing a 2 by 2 degrees approach does not completely overcome spatial dependence issues. In ongoing work, I explore some of these issues. 20 of the grid cells had at least one conflict onset.22 Inowturntotheanalysisofthe empirical relationship between state history and contemporary conflict at the grid cell level. I begin by estimating the following baseline equation:

0 0 0 Conflicti,c = ↵ + State Historyi + Gi + Xi + Ci Z + c + ✏i,c (1.4.1)

where i and c denote grid cells and countries respectively. The variable Conflicti,c is ameasureofconflictprevalenceandrepresentsthefractionofyearswithatleastone conflict event during the period 1989-2010 for the grid cell i in country c. The variable

State Historyi is my new index for state history at the sub-national level i. Therefore,

0 is the main coecient of interest in this exercise. The vector Gi denotes a set of

0 geographic and location specific controls. The vector Xi includes a set of controls for

0 ecological diversity and a proxy for genetic diversity. Ci is also a vector and includes potential confounding variables which may be also arguably outcomes of historical state formation. Thus, including these variables may result in a potential bad control problem (see Angrist and Pischke 2009, for discussion). Finally, c is country c fixed e↵ect included to account for time-invariant and country-specific factors, such as national institutions, that may a↵ect the prevalence of conflict.23

OLS Estimates

Table 2 provides a first statistical test to document a strong negative correlation between state history and contemporary conflict at the sub-national level. Below

22Conflict onset is defined as the first event within a dyad. 23 Each grid cell is assigned to exclusively one country when defining country dummies. When one grid cell crosses country borders it is assigned to the country with the largest share on the grid cell. Given the relevance of proximity to international borders as a correlate of conflict, for the remainder of the paper I will control for a variable indicating the number of countries intersected by each grid cell. 21 each estimation of my coecient of interest I report four di↵erent standard errors. To start with and just for sake of comparison I report robust standard errors which are consistent with arbitrary forms of heterokedasticity. I also report standard errors adjusted for two-dimensional spatial autocorrelation for the cases of 5 degrees and 10 degrees cut-o↵ distances.24 Ifinallyreportstandarderrorsadjustedforclustering at the country level. For all the specifications in Table 2 standard errors clustered at the country level are much larger than under the other alternative methods. This pattern holds for all the specifications presented in this paper. Therefore, clustering at the country level appears to be the most conservative approach to avoid over- rejection of the null hypothesis regarding the statistical significance of the coecient of interest. For the remainder of this paper, I report standard errors and statistics of the hypothesis test that are robust to within-country correlation in the error term.

InowturntotheanalysisoftheestimatesinTable2.ForthefirstcolumnIonlyfocus on the statistical relationship between state history and conflict after controlling for country dummies. The point estimate for suggests a negative (albeit statistically insignificant at standard levels of confidence when clustering standard errors at the cuntry level - p-value = .14) correlation between state history and conflict prevalence.

In column 2 I add a vector of geo-strategic controls that may also correlate with historical prevalence of states.25 Distances to the ocean and the capital of the country are intended to proxy the peripheral location of the grid cell. To further account for the possibility of within-country variation in national state penetration, I also control for terrain’s characteristics (i.e: elevation and ruggedness) that were highlighted in

24I follow Conley (1999)’s methodology in which the asymptotic covariance matrix is estimated as a weighted average of spatial autocovariances where the weights are a product of kernel functions in North-South and East-West dimensions. These weights are zero beyond an specified cuto↵ distance. I consider 3 cuto↵s distances, namely 3, 5, and 10 degrees. 25By geo-strategic dimension I refer to geographical or geo-political characteristic of the grid cell that may a↵ect the likelihood of conflict through its e↵ect on either the capabilities of central government to fight insurgency or the benefits for any of the warring actors (such as seizing the capital or controlling major roads). See appendix to detailed description of all the variables. 22 previous literature (see, for example, Fearon and Laitin 2003, and Cederman 2008). Distance to a major river, total length of major roads, and a capital city dummy are also included to account for their geo-political relevance as main targets for conflict actors.26 Total area of the grid cell is also included among the controls as well as an indicator of the number of countries intersecting each grid cell. The latter accounts for the fact that conflict is more prevalent near international borders (see, for instance, Michalopoulos and Papaioannou, 2012) whereas the former accounts for the smaller size of coastal grid cells. A positive correlation between income from natural resources and conflict has been extensively documented (see, for example, Fearon, 2003, Collier and Hoe✏er, 2004, and Fearon and Latin, 2005). Thus I add a dummy variable taking the value one if at least one natural resource site (i.e: gems, diamond, gas or oil) is located in the grid cell. It is worth noting that most of these controls also help to explain within-country variation in economic development.

All the point estimates (not shown) for the geo-strategic controls present same sign as previously documented in conflict literature (see, in particular, Harari and La Ferrara 2013 for a cross-sectional analysis based on grid cells). More importantly, the point estimate for suggests an statistically significant negative relationship between state history and contemporary conflict. Since the standard deviation for the dependent variable (0.232) is very similar to the standard deviation of my state history index (0.227), the interpretation of the coecient estimates for in terms of standard deviations is straightforward. One standard deviation increase in state history is associated with 0.2 standard deviation reduction in the prevalence of conflict during the period of analysis (roughly one year in the sample period or one fourth of the mean prevalence of conflict).

26One may argue that the location of the modern capital city could be an outcome of state history and thus may constitute a case of “bad control”. Nonetheless, note that most of the location of modern capital cities in Sub-Saharan Africa followed decisions made by colonizers to service their needs and did not necessarily overlap with the preexisting polities (Herbst, 2000). None of the results in this paper are driven by the inclusion of this vector of geo-strategic controls. 23 Inowconsiderthepotentiale↵ects of land endowment and the disease environment. Early state development has been influenced by the geographic, climatic, demographic and disease environment (Diamond 1997, Reid 2012, and Alsan 2013). I first include, in column 3, a measure of soil suitability to grow cereal crops which not only posi- tively correlates with early statehood but also it correlates with modern population density, an important driver of conflict.27 Then, in column 4, I introduce two mea- sures accounting for the ecology of malaria (from Conley, McCord, and Sachs 2010) and the suitability for the tsetse fly. The former weakly correlate with my index of state history whereas the latter is strongly negatively correlated with it. In addition, Cervellati, Sunde, and Valmori (2012) find that persistent exposure to diseases a↵ects the likelihood of conflict by a↵ecting the opportunity cost of engaging in violence. In column 5 I include together the two set of controls. The point estimate for remains unaltered.

Potential confounding e↵ects of genetic and ecological diversity. In Table 3 I explore whether the main correlation of interest documented so far may partially account for the e↵ect of genetic diversity on conflict. Ashraf and Galor (2013a, 2013b) argue that genetic diversity had a long-lasting e↵ect on the pattern of economic development and ethnolinguistic heterogeneity (including fractionalization among other measures). Even more importantly, Arbatli, Ashraf, and Galor (2013) show that genetic diversity strongly correlates with several measures of social conflict. Unfortunately, no data on genetic diversity at the grid cell level exists. To tackle this problem, I use the fact that migratory distance from the location of human origin (i.e: Addis Adaba in Ethiopia) is a strong linear predictor of the degree of genetic diversity in the populations (Ramachandran et al. 2005, Liu et al. 2006, and Ashraf and Galor

27Data on soil suitability for growing cereal comes from the Food and Agriculture Organization (FAO)’s Global Agro-Ecological Zones (GAEZ) database. The suitability of the soil is calculated based on the physical environment (soil moisture conditions, radiation, and temperature) relevant for each crop under rain-fed conditions and low use of inputs. The suitability measure ranges between 0 (not suitable) to 1 (very suitable). 24 2013a).28 Results in column 1 shows that migratory distance to Addis Adaba enters with the expected sign suggesting that genetic diversity has a positive impact on conflict.29 Nevertheless, the point estimate for is a↵ected remarkably little (albeit it slightly decreases in size).

Fenske (2012) shows that ecological diversity is strongly related to the presence of pre-colonial states in Sub-Saharan Africa. Diversity in ecology correlates with poten- tial drivers of conflict such as linguistic or cultural diversity (Michalopoulos 2012, and Moore et al, 2002) and population density (Fenske 2012, Osafo-Kwaako and Robin- son 2013). In addition, herders cope with climate limitations by moving between ecological zones which potentially leads to land-related conflicts with farmers (a well- documented phenomenon in conflict literature, in particular for the Sahel region -see Benjaminsen et al 2012). To account for this potential bias, I follow Fenske (2012) and measure ecological diversity as a Herfindahl index constructed from the shares of each grid’s area that is occupied by each ecological type on White’s (1983) vegetation map of Africa.30 Point estimates in column 2 of Table 3 show that ecological diversity presents indeed a statistically significant and positive correlation with contemporary conflict. The negative association between state history and conflict remains statis- tically strong. Further, I obtain a similar point estimate when controlling for both ecological and genetic diversity in column 3. Figure 1.4 depicts the scatter plot and partial regression line for the statistical relationship between contemporary conflict and state history from the last specification in Table 3 (labels corresponds to the

28Migratory distance from each grid cell’s centroid to Addis Adaba is constructed based on Ozak¨ (2012a, 2012b), who calculated the walking time cost (in weeks) of crossing every square kilometer on land. The algorithm implemented takes into account topographic, climatic, and terrain conditions, as well as human biological abilities (Ozak¨ 2012a). 29Controlling for distance and its square (to account for the fact that genetic diversity has been shown to have a hump-shaped relationship with economics development) does not a↵ect the results. 30They are 18 major ecological types in White’s (1983) map: altimontaine, anthropic, azonal, bushland and thicket, bushland and thicket mosaic, cape shrubland, desert, edaphic grassland mo- saic, forest, forest transition and mosaic, grassland, grassy shrubland, secondary wooded grassland, semi-desert, transitional scrubland, water, woodland, woodland mosaics, and transitions. See ap- pendix. 25 country ISO codes).

Figure 1.4: Conflict and State History

Robustness Checks

Considering potential “bad controls” and potential mediating channels. There are certainly others contemporaneous and historical confounding factors for my analy- sis. I next show how the point estimate for my variable of interest is a↵ected by the inclusion of additional controls which can be arguably considered outcomes of a long-run exposure to centralized polities. While not conclusive, changes in my main point estimate when including these controls may be suggestive of the existence of mediating channels through which state history impacts modern conflict. I focus on pre-colonial economic prosperity, population density, ethnic fractionalization, slave trade prevalence, proximity to historical trade routes and historical conflict sites, and 26 contemporary development (proxied by light density at nights obtained from satellite images). I start with pre-colonial ethnic controls accounting for historical levels of prosperity and economic sophistication.31 Ifocusontwosetsofethnicitylevelvari- ables. First, I consider the subsistence income shares derived from hunting, fishing, animal husbandry, and agriculture (variables v2 to v5 from Ethnographic Atlas).32 Second, I consider a variable describing the pattern of settlement. This variable (v30 from Ethnographic Atlas) is coded in order of increasing settlement sophistication taking values from 1 (nomadic) to 8 (complex settlement). Overall, my point esti- mate for does not change (albeit its precision is improved) with the addition of these controls in column 1.

Next I analyze the confounding e↵ect of population density.33 Unfortunately no de- tailed historical data on population density exists at my level of analysis to the best of knowledge. I only observe population density in 1960 instead and, to the extend that population density may have a persistent e↵ect over time, I use it to proxy for within-country variation of population density in pre-colonial times.34 Further, using population figures from 1960 alleviates concerns of reverse causality from contempo-

31I construct pre-colonial ethnographic measures at the grid cell level based on information from the Ethnographic Atlas (Murdock, 1967) and the spatial distribution of ethnic groups from Mur- dock’s (1959) map. All these measures are 1960 population-weighted averages of traits of ethnic groups whose historical homelands intersect a given grid cell. I basically follow the procedure de- scribed in Alesina, Giuliano, and Nunn (2013). See appendix for details. 32 I omit the category share of income from gathering activities to avoid multicollinearity. 33Population density is positively correlated with the prevalence of conflict (see, among others, Buhaug and Rød, 2006; Raleigh and Hegre, 2009, and Sundberg and Melander, 2013). It has been argued that low population density was one of the main obstacles for state formation in the pre- colonial Sub-Saharan Africa (see, among others, Bates 1983, Diamond 1997, and Herbst 2000). This hypothesis is, however, contested in a recent work by Philip Osafo-Kwaako and James Robinson (2013). On the other hand, high population density in the past may have also negatively a↵ected ethnic diversity by reducing isolation (Ahlerup and Olsson, 2012). 34The use of this proxy can help to illustrate the importance of the bias when including a bad control. Consider for simplicity that conflict (C) is only related to state history (S) and historical population density (P ), then the true model I would like to estimate is: Ci = 0 + 1Si + 2Pi + ui . However, I only have data on population density in 1960 (P 1960 ) which is a function of both 1960 S and P : P = 0 + 1Si + 2Pi + ✏i . When regressing C on S and P1960, I am estimating 0 2 2 1960 ✏i Ci = 0 2 + 1 2 Si + P + ui 2 . Since it is apparent that 2 > 0, 2 > 0, 1 1 1 i 1 and h1 > 0, thei inclusionh of populationi density⇣ in 1960 would⌘ overestimate the negative impact of state history on conflict. 27 rary conflict to population distributions. The point estimates for increases almost 10% and remains strongly statistically significance. I next construct an ethnic frac- tionalization variable based on the index introduced in Alesina et al (2003).35 For similar aforementioned reasons I compute a fractionalization index based on grid pop- ulation in 1960.36 The point estimates for remains unaltered when including ethnic fractionalization as control.37 Inextconsiderslavetrade.38 Iconstructpopulation- weighted averages of slave trade prevalence at the grid cell level using Nathan Nunn’s data. The expected correlation between slave trade prevalence and state history is ex ante ambiguous.39 Results in column 4 show that the introduction of slave trade prevalence as a determinant of contemporary conflict does not a↵ect the estimation of . The inclusion of shortest distance to historical trade routes in column 5 does not a↵ect the results. I next add the distance to the closest historical battle during the period 1400-1700 CE. This variable is constructed upon information recorded and georeferenced by Besley and Reynal-Querol (2012) who find a robust correlation

35Ethnic fractionalization denotes the probability that two individuals randomly selected from a grid cell will be from di↵erent ethnic groups. In order to be consistent throughout this paper my definition of ethnic group is based on Murdock (1959). Therefore, I construct shares of ethnic population using gridded population and the spatial distribution of ethnic groups in Murdock’s map. See appendix for details. 36Ethnic heterogeneity is a commonly stressed determinant of conflict (see, among others, Easterly and Levine 1997 and Collier, 1998) and it is likely to be correlated with state history ( see Bockstette et al, 2002; and Ahlerup and Olsson, 2012). 37I obtain almost identical results (not shown) if I use ethnolinguistic fractionalization (i.e: using ethnologue to compute linguistic distances between pair of ehtnic groups within a grid) instead of ehtnic fractionalization. 38Why would slave trade be important for contemporary conflict? First, Nunn (2008) finds that slave trade resulted in long-run underdevelopment within Africa. More importantly, historical slave trade has been shown to have an e↵ect on ethnic fragmentation (Whatley and Gillezeau, 2011b) and individual’s mistrust (Nunn and Wantchekon, 2011), which are both arguably potential drivers of social conflict. 39On the one hand, Nunn (2008) suggests that slave trade could have been an impediment for pre-colonial state development in Africa. In the same direction, Whatley and Gillezeau (2011a) argues that increasing international demand for slaves might have reduced the incentive to state creation (relative to slave raiding) by driving the marginal value of people as slaves above their marginal value as tax payers. On the other hand, there exist several historical accounts linking the rise of some African kingdoms to the slave trade (see, for example, Law 1977 for the case of the Oyo Empire, and Reid 2012). For instance, while analyzing the role of warfare, slavery and slave-taking in Yoruba state-building, Ejiogu (2011) documents slave-taking campaigns of Oyo against neighboring Nupe (note that Oyo -part of Yoruba - and Nupe share territories within grid cells). 28 between proximity to the location of historical battles and contemporary conflict.40 Results in column 6 are in line with Besley and Reynal-Querol’s (2012) main find- ing. As expected, the point estimate of my variable of interest slightly increases and remains statistically significant. One-standard deviation increase in state history is statistically related to a reduction of the prevalence of conflict of 1/4 of its standard deviation. Neither the inclusion of (ln of) light density, as measured in Michalopoulos and Papaioannou (2013), or the inclusion of the previous variables all together a↵ect the statistical significance of my main finding. Therefore, if anything, the inclusion of these potential confounders makes the negative statistical association between state history and contemporary conflict stronger.

Robustness to the choice of conflict measure (dataset, incidence, onset, and intensity). Inextshowthatthemainresultsarerobusttotheelectionofthegeoreferenced conflict dataset and the way conflict is coded. For column 1 of Table 5, I construct the conflict measure using ACLED. Therefore, the dependent variable accounts for the fraction of years with at least one broadly defined conflict event in ACLED. In column 2 I only consider battle events recorded in ACLED. For column 3 I consider any violent event (i.e: battles and violence against civilians). In column 4 I focus on riots. For all the conflict indicators but riots I find the same pattern: a strong negative statistical relationship between conflict and state history (for the case of conflict the p-value for is slighty above 0.1).

In column 5 I focus on a measure of conflict intensity. The dependent variable is the (log of) number of casualties due to conflict (best estimate in UCDP-GED). The point estimate for rearms the hypothesized negative e↵ect of state history on conflict.

40They also show that proximity to historical conflict site correlates with mistrust, stronger ethnic identity, and weaker sense of national identity. Provided this documented long-lasting e↵ect and considering that violent conflict between and within historical African states was part of the state- building processes in the past (see, among others, Lewis, 1966; Ben-Amos Girshick and Thornton, 2001; Ejiogu 2011; Reid 2012 and Bates 2013), the omission of this control would underestimate the e↵ect of state history on contemporary conflict. 29 My conflict measure under the baseline specification represents the prevalence of conflict violence. It does not make distinction between onset and incidence of violence. That is, this measure does not distinguish a violent event that represents the onset of a new conflict within a dyad from an event that is the continuation of previous confrontations. In column 6 I consider a measure of prevalence of conflict onset (i.e: first confrontation within a dyad). I identify all conflict onsets in the period of analysis and code 1 a grid cell - year observation if at least one onset occurs. As a result, my conflict measure in column 6 represents the fraction of years with at least one conflict onset in the grid cell. Only 149 grid cells experienced at least one conflict onset (out of 417 di↵erent conflicts in the period 1989-2010). The point estimate suggests that the onset of conflict is strongly and negatively related to long history of statehood.

Heterogeneity across regions. Inextexplorewhethertheuncoveredrelationshipbe- tween state history and contemporary conflict holds within Sub-Saharan Africa re- gions. I estimate the specification in column 3 of Table 3 for two regions, namely West-Central Africa and East-South Africa.41 Results in column 1 and 2 of Table 6 are in line with previous results. In column 3 I exclude Western Africa to show that this particular region is not driving the main results.

Excluding countries and influential observations. Isequentiallyestimatethespecifi- cation in column 3 of Table 3 by excluding one country at a time. Figure 1.5 depicts thirty seven di↵erent point estimates with their associated t-statistics; the excluded country is labeled in the x-axis. All the coecients fall in the interval [-0.15, -0.22]. All coecients but the one for the specification excluding Sudan are statistically sig- nificant at the 1 percent level (for the case of Sudan, the p-value is 0.013). There are two reasons for the somehow relative weaker result when excluding Sudan: (1) sample

41I follow UN to classify each country in one of the 5 UN regions (North, South, East, West, and Central). Only one country belongs to North (Sudan) and it is assigned to East. I group the original regions in only 2 regions to balance the number of observations in each sample. 30 size drops by 10 percent increasing the standard errors (The statistical significances and standard errors of other covariates are also a↵ected -results not shown-), and (2) Sudan presents some locations with very high values of the state history index; loca- tions for which my state history may be underestimating their true long-run exposure to statehood.42 Excluding those locations reduce the upward bias in the OLS esti- mate due to the measurement error from bounding the period of analysis above the year 1000 CE.43 The same pattern arises when excluding another country with long history of states before 1000 CE (i.e: Ethiopia). Finally, the strong negative statisti- cal association between state history and conflict persists when excluding influential observations. In this vein, I follow the standard practice of estimating when ex- cluding all the observations for which DFBETA > 2/pN where N is the number | i | of observations and DFBETAi is the di↵erence between the estimate of when the observation i is excluded and included (scaled by standard error calculated when this observation is excluded). The point estimate is -0.16 (statistically significant at the 1percentlevel-resultsnotshown-).

On the discount factor and long-run exposure. InextexplorehowmyOLSestimates are a↵ected by the election of di↵erent discount factors to compute the state history index. I report in columns 1 to 4 of Table 7 results for four di↵erent specifications with discount rates of 5, 10, 25, and 50 percent. For the sake of comparison, I report both the point estimates and the beta standardized coecients. All the specifica- tions include the full set of controls as in Table 3. Only when a discount rate of 50 percent is applied, my coecient of interest is slightly statistically insignificant under the conventional levels of confidence. Two facts are worths to note. First, the

42The Nubian Kingdoms (northern Sudan) were founded several centuries before 1000 CE. 43I estimated additional specifications in which I excluded all the observations with some exposure to states during the period 1000-1100 CE. Consistently with the existence of measurement error from bounding the analysis above 1000 CE introducing an upward bias, the beta standardized coecient slightly decreased about 10 percent (albeit they remained strongly statistically significant -results not shown-) when I excluded those observations. 31

Figure 1.5: Sensitivity of Estimates to Exclusion of Countries higher the discount rate, the lower the statistical significance of the coecient for the corresponding state history measure. Second, the beta standardized coecient is also decreasing on the discount rate suggesting indeed that history has an influence on conflict. For instance, the beta standardized coecient when the discount rate is 0 (i.e., -0.20, not show in Table 7) is more than 50 percent larger that for the case in which the discount rate is 25 percent. For columns 5, 6, and 7 I break my period of analysis in two sub-periods, namely before and after 1500 CE when external influ- ence became more relevant for Africa due to the prevalence of slave trade and early european colonialism. In columns 5 I only consider the accumulation of state expo- sure from 1500 CE to 1850 CE. Albeit statistically and economically weaker, there is still a negative statistical association between state history and modern conflict. In columns 6 I only consider the period 1000 -1500 CE and the coecient of interest is strongly significant and of the similar magnitude when compared with the estimation from the specification using my original measure of state history. When including 32 both measures only the one considering the accumulation from 1000 CE to 1500 CE is strongly statistically and economically significant. This result suggests that the state history that matters the most is the one accumulated before 1500 CE.

Intensive versus extensive margin. To argue that what matter the most is the inten- sive margin of exposure to state institutions (long history) rather than the extensive margin (any state vs. no state at all right before the Scramble for Africa), I estimate anewspecificationincolumn1ofTable8forwhichthestatehistoryvariableisthe state history score the last period considered in the computation of my index (i.e., 1800 - 1850 CE). The coecient estimate, albeit negative, is statistically insignifi- cant. Further, I construct a 1960 population-weighted average of the degree of ethnic centralization in the grid cell using the Ethnographic Atlas’s variable “Juridisctional Hierarchy beyond the Local Community” which ranges from 1 (no juridisction above village level) to 4 (large state). This variable has been used to document the impor- tance of political centralization for current pattern of development (Gennaioli and Rainer 2007a, and Michalopoulos and Papaioannou 2013). Result in column 2 shows that the correlation between late pre-colonial ethnic centralization and the prevalence of modern conflict is not statistically significant. This result is quantitatively very similar to the point estimates in column 1. One can still argue that it is not the long history of state but its complete absence what explains the uncovered statistical association. In this sense, it may be the case that locations with no history of state whatsoever are located in remote and unpopulated regions with little national state penetration where rebel groups can easily operate. In the specification of column 3 I exclude all the observations with no history of state whatsoever (223 grid cells) and show that my main results are not driven by those locations. The point estimate is very similar and strongly statistically significant. If I restrict the sample even more and consider only locations with at least 100 years of state history (Thus, I exclude 329 grid cells) I obtain even stronger results (column 4). 33 Assessing the extent of bias from unobservables. The point estimates reported so far may still be biased due to unobservable factors correlated with both contemporaneous conflict and long-run exposure to states. How large would this selection on unobserv- ables need to be (relative to selection on observables) to attribute the entire OLS estimates previously reported to a unobservable selection e↵ect? I follow the intuitive heuristic in Nunn and Wantchekon (2011) based on Altonji, Elder, and Taber (2005) to assess the degree of omitted variables bias by studying stability of the estimates for . The underlying idea is that, under the assumption that selection on observables is proportional to selection on unobservables, a coecient not changing much as one adds controls would be suggesting that there is little remaining bias. I thus compare the point estimate in the last specification in Table 3 which includes a full set of controls (ˆ = .198) with the point estimate when only a basic set of controls (i.e., 1 country fixed e↵ect and geographical controls) is included (ˆ = .191). The ratio 2 between ˆ and ˆ ˆ (the selection on observables) suggests that selection on unob- 1 1 2 servables would have to be more than 20 times the selection on observables to explain away the entire statistically relationship between state history and contemporaneous conflict.

Instrumental Variable Approach

Ihavealreadydocumentedastrongnegativestatisticalrelationshipbetweenhistory of statehood and contemporary conflict at the sub-national level. This historical link is robust to a battery of within modern countries controls ranging from con- temporaneous conflict correlates, geographic factors, to historical and deeply rooted determinants of social conflict. Unfortunately, history is not a random process. Even in a close to ideal and hypothetical quasi-random historical event determining the geographic assignment of long-run exposure to state capacities within Sub-Saharan 34 Africa, the challenge of isolating the causal e↵ect of state history on contemporary conflict is particularly dicult. Although one could argue that reverse causality from conflict to historical exposure to state-like institutions is not a source of concern for the identification of my parameter of interest, there may still be omitted variables correlated with both state history and contemporary conflict which may be driving the uncovered statistical association. I aim, however, to convince the reader here that hard-to-account-for factors manifested in di↵erences in long-run exposure to central- ized institutions matter crucially to understand contemporary patterns of conflict within Sub-Saharan Africa. In addition to a potential omitted variable bias, mea- surement error may also a↵ect my point estimates in an ex-ante ambiguous direction depending on the true structure of the relevant measurement error. It is precisely for the sake of alleviating the potential bias from measurement error in my state history index what primarily motivates the introduction of an instrumental variable strategy. I follow two strategies. First, I exploit variation across locations on the timing elapsed since the neolithic revolution. Second, I construct an alternative measure to account for the degree of influence from politically centralized states by exploiting variation in the proximity to historical cities for the time period 1000 - 1800 CE.

Time Elapsed since Neolithic Revolution

Several studies have established a link between the neolithic revolution and the rise of complex political organization. For instance, Diamond (1997) argues that the transition from hunter-gathered societies to settled agricultural communities is an essential factor to explain the rise of proto-states, and subsequently the formation of states. Agriculture allowed nomadic societies to settle, generate food surpluses, and shorten birth intervals (Ashraf and Michalopoulos 2010; Diamond 1997), which in turn resulted in denser populations. Further, the storage of those food surpluses 35 allowed the emergence of non-food-producing sectors, hence economic specialization and also social stratification manifested not only in the existence of a labor force involved in the production process (both, foods and non-foods production) but also a labor force designated to provide public services (including armies). By implication, the possibility of taxation and the emergence of political organization, facilitates the rise of states.

A strong positive correlation between the timing elapsed since the neolithic revolution and Bockstette, Chanda, and Putterman (2002)’s state antiquity index has been em- pirically documented at the country level (see, for instance, Hariri 2012, and Petersen and Skaaning, 2010). Further, substantial variation in the timing of the transition to agriculture exists within the Sub-Saharan Africa. I therefore exploit Neolithic arche- ological sites information to construct the time elapsed since neolithic revolution at afinergeographicallevelandusethistoinstrumentformystateantiquityindexat the sub-national level. I discuss the construction of the instrument in the appendix. Figure 1.6 depicts the geographical distribution of the time elapsed since the neolithic revolution.

Table 9 presents point estimates for di↵erent specifications of the first-stage. All the specifications include the set of controls from my prefered specification in Table 3 (column 3). I report 3 di↵erent standard errors (i.e: clustered at the country level and adjusted for spatial autocorrelation for cut-o↵ distances of 5 and 10 degrees). Again, clustering at the country level appears to be the most conservative approach. Point estimate in column 1 suggests a statistically significant association between the time elapased since the neolithic revolution and my state history index. However, this association is only statistically significant at the 3 percent level due to a weaker association within the Central Africa region. Indeed, my constructed measure of the timing since the first adoption of agriculture is statistically insignificant within 36

Figure 1.6: Time Elapsed Since Neolithic Revolution that region (column 2). In particular, observations from four countries explain this weaker statistical performance: Gabon, Congo, Congo DR, and Angola. It is well stablished that a group of Bantu people migrated south from modern-day Cameroon to modern-day Namibia across the rainforest. Although this Bantu migration route crossed the territory of modern-day Gabon, Congo, Congo DR, and Angola, no ar- chaelogical information has been recorded on agricutural adoption (Putterman, 2006). Therefore the interpolated values for most of the observation from these four coun- tries mostly depend on archaelogical evidence from two sites in Cameroon and the Namibia-Botswana border. When I exclude these countries from my sample, the first- stage is much stronger (column 3). An alternative way to statistically improve the performance of the first-stage is to consider the square of the instrument rather than its level. Indeed, the statistical association between the square of my instrument and my index of state history is stronger (column 4). To avoid reducing my sample size by 20 percent due to the exclusion of these countries for which the linear relationship is weaker, I use the square of the time elapsed since the neolithic revolution as an 37 instrument for state history. I provide below evidence that the results are not driven by this particular specification.

In Table 10 I report IV results. The specification in column 1 includes the set of con- trols from specification in column 3 of Table 3. The point estimate is roughly 3 times larger that the previously reported OLS estimates; finding that is consistent with the idea that measurement error in my state history variable was indeed introducing a sizeable bias toward zero (albeit it is also consistent with my instrument picking up the e↵ect of other confounding factors, issue I discuss below). The point estimate for in column 1 suggests that one-standard deviation increase in state history implies a .71-standard deviation reduction in the prevalence of contemporary conflict. This magnitude is equivalent to roughly 4 years of conflict in my period of analysis. My IV estimate may be still biased due to the omission of variables that could plausibly correlate with both contemporary conflict and the timing of the transition to agri- culture. Therefore, I now focus on biogeographical variables which had been shown to correlate with my instrument. In column 2 I add absolute latitude. As Diamond (1997) argues, technologies and institutions have historically spread more easily at similar latitudes where climate and day duration were not drastically di↵erent. This is particularly true for the spread of agriculture within Africa. Regardless of the dis- cussion of the ultimate underlying mechanisms, the high correlation between absolute latitude and development has been widely documented in the economic growth and development literature (see, for example, Spoloare and Wacziarg, 2013). Absolute latitude enters negatively and statistically significant in this specification; reducing 15 percent the size of which remains strongly statistically significant.

Haber (2012) argue that variation in biological (and technological) characteristics of crops had a long-run e↵ect on institution and development. In addition, Sub-Saharan centralized military states were historically more prevalent in areas with soils suitable 38 for the generation of agriculture surpluses to maintain armies (Reid 2012). Given their ease of storage and transport, cereals had a natural advantage over other crops, such as tubers and tree crops, to produce those necessary surpluses.44 For the particular case of Sub-Saharan Africa, the most important indigenous crop for this matter are sorghum and millet. Further, it was precisely the domestication of sorghum and millet what played a crucial role in the transition to the Neolithic in this region. Hence, in column 3 I hold constant biogeographical factors a↵ecting these crops by adding the principal component of the soil suitability to grow millet and sorghum.45 The point estimate for remain statistically significant and economically sizeable (the size of coecient varies little with respect to the baseline specification in column 1).

Inextconsiderthepotentialconfoundinge↵ect of climate variability. Ashraf and Michalopoulos (2013) show that historical climatic volatility has a non-monotonic ef- fect on the timing of the adoption of agriculture. On the other hand, Durante (2009) show that, within Europe, variation in social trust is driven by historical variation in climate. When I include intertemporal temperature volatility, its square, and his- torical mean temperature, the size of my point estimates decreases by 30 percent (albeit it remains statistically significant).46 This fact is consistent with the possi- bility that my hypothesized mitigation e↵ect of a location history on contemporary conflict may partially confound with higher levels of social trust induced by historical climate variability. The addition of the set of pre-colonial ethnic characteristics in column 5 reduces the point estimates by almost 20 percent (albeit it remains strongly statistically significant). This result represents suggestive evidence that pre-colonial

44The tubers and tree crops have relative low levels of storability (compared with cereals) since they typically perish within days or weeks of harvesting. 45Data on soil suitability for growing millet and sorghum comes from the Food and Agriculture Organization (FAO)’s Global Agro-Ecological Zones (GAEZ) database. 46I use variation in modern data to proxy historical climatic variation. Ashraf and Michalopoulos (2013) show that spatial variation in temperature volatility remains largely stable over long periods of time; thus contemporary climate data can be meaningfully employed as informative proxies for prehistoric ones. 39 di↵erences in economic prosperity (proxied by the degree of the sophistication of settlement patterns and the type of economic activities) may represent a mediating channel through which long history of statehood of a given location may result in lower levels of modern conflict. In column 6 I include all the aforementioned controls together. Even under this challenging horse race the point estimates for remains negative and statistically significant at the 5 percent level. The IV point estimates doubles my prefered specification in the OLS case. The Kleibergen-Paap rk Wald F statistic is above the rule of thumb thus weak instruments would not be a concern. In the last column of Table 10 I exclude the four Central African countries for which the linear relationship between the neolithinc instrument and my state history index was statistically weak and show that instrumenting state history with the time elapsed since the neolithic revolution in levels lead to very similar point estimates compared to the case with the squared instrument in the full sample.

A note about hypothesized e↵ects of the neolithic revolution on contemporary out- comes. The onset of the neolithic revolution was certainly one of the most important historical events for humankind. The economic literature has provided mixing evi- dence regarding the existence of a direct association between this historical event and contemporaneous outcomes. Some works have emphasized the long-lasting e↵ect of the neolithic revolution on pre-industrial era’s outcomes (see, among others, Ashraf and Galor 2011). Other works have shown that countries which experienced early transition to agriculture tend to have higher level of per capita income today (par- ticularly after “ancestry adjustment”, see Spoloare and Wacziarg, 2013). This line of argument emphasizes that the neolithic revolution may be responsable for cross- sectional di↵erences in human capital and technologies in the pre-industrial era and, to the extent that those di↵erences may be persistent, may still have an e↵ect on current outcomes. In fact, the reduction on the size of my IV point estimates when including pre-colonial prosperity measures is consistent with this view although it 40 does not explain away the statistical association of interest. In addition, the inclu- sion of light density at nights to proxy for contemporary levels of development does not a↵ect my point estimates (results not shown). Using individual-level data and exploiting variation in my instrument for about 1,600 districts, I do not find evidence that my instrument significantly correlates with education levels (results not shown).

On the other hand, a line of research argues that neolithic still exert a negative e↵ect on contemporary outcomes. In particular, Olsson and Paik (2013) argue that an early transition to agriculture might have directly shaped institutional trajectories by pro- moting autocracy (similar argument is presented in Hariri, 2012) and thus facilitating extractive institutions which turn in lower levels of development. In addition, Paik (2011) shows that, within Europe, early adoption of agriculture positively correlates with strong preference for obedience. This line of argument is based on the idea that precisely the experience with early political organization is the mediating chan- nel through which the neolithic revolution a↵ects contemporary outcomes. Bearing in mind that measuring culture traits is a dicult task, I later show that the time elapsed since the neolithic revolution can be linked to trust in state institutions.

In sum, although I cannot rule out the possibility that other hard-to-account factors may be driven the uncovered statisticaly association, IV estimates exploiting informa- tion on the timing elapsed since the neolithic revolution provides additional evidence consistent with my hypothesized negative e↵ect of long-run exposure to statehood on modern pattern of conflict. This negative statistical association estimated from variation in the timing since the earliest date of domestication of plants is not driven by biogeographical factors such as land quality to grow cereals (cereals in general or sorghum and millet in particular), proximity to water (rivers and oceans), ele- vation, intertemporal climate variability, di↵erences in disease environment (malaria and tse-tse), ecological and genetic diversity or absolute distance to the equator. I 41 find evidence that pre-colonial di↵erences in prosperity may constitute a mediating channel underlying my reduced-form relationship.

Proximity to Historical Cities

I construct an independent imperfect measure of state history by exploiting informa- tion on the location and evolution of above sixty large African cities (of which thirty five were located in Sub-Saharan Africa) during the period 1000 - 1800 CE.47 To the extent that kingdoms and empires tended to have a large city as political center, I consider proximity to a large city as an indicator of the degree of influence from a centralized power. I introduce this new measure for several reasons. First, to show that the negative statistical association uncovered in the OLS case still hold when using an alternative measure. Second, this new measure will overcome a potential caveat in my original measure of state history which assumes an homegeneous e↵ect of centralization within the boundaries of a historical polity. This assumption had two implications: (1) the introduction of a sharp discontinuity at the border of the boundary, and (2) inconsistency with the idea that broadcasting power strenght may depend on the distance from the political center. Third, this new measure can be used to instrument the original state history measure and mitigates the attenuation bias from measurement error.

Construction. This measure exploits time-varying proximity to large cities. There- fore, some cities exert influence to their periphery only for particular time intervals. For instance, Djenne, in modern Mali, only enters in my panel of cities for the period 1300 - 1600 CE (See Table A.3 in appendix for the list of georeferenced cities). For each hundred years period I calculate the shortest distance to closest city from the

47I define a city to be large if it has more than ten thousand inhabitants. The list of cities comes from Chandler (1987) and Eggiman (2000). 42 centroid of each grid cell. I then calculate within-grid average of the distances for the whole period of analysis and map them into a 0 to 1 interval so the grid cell with the minimum average distance takes the value 1. Figure 1.7 displays the cross- sectional variation of this alternative measure.48 As in the case of my original State History Index, this new measure presents higher values in western part of the Sahel, the highlands of Ethiopia, and the region along the Nile river.

Figure 1.7: Alternative Measure Using Historical Cities

In column 1 of Table 11 I present the OLS estimate for the reduced-form conflict and historical proximity to cities. I find the same the pattern as before. Historical proximity to cities for the time period 1000 - 1800 CE is negatively and strongly statistically associated to prevalence of modern conflict. In column 2 I present the IV estimate for my state history index when using historical proximity to cities as ainstrument.IfindresultswhicharesimilartotheIVcaseusingthetimeelapsed

48By construction, due to its dependance on distances, this measure presents a more continuous support. 43 since the neolithic as an instrument. The point estimate is slighlty larger; fact that is consistent with the possibility that historical proximity to cities is picking up the e↵ect of other omitted variables on conflict. Finally, in column 3 I include both instruments and find similar results. With this overidentified model at hands I test the exogeneity of the instruments. The Hansen J statistics suggests no rejection of the null hypothesis that my set of instruments are exogenous. Note that all the specifications where including not only the set of controls from Table 3 but also account for the joint e↵ects of absolute latitude, suitability to grow sorghum and millet, intertemporal climate variability

1.4.3 Panel Data Evidence: Weather Induced-Agricultural

Productivity Shock, State History, and Conflict

In a comprehensive synthesis of the climate-conflict literature, Burke, Hsiang, and Miguel (2013) argue that there is strong causal evidence linking climatic events to conflict. The existence of an income mechanism underlying this causal link has been proposed repeatedly times in the conflict literature but it has not been definitively identified yet. Harari and La Ferrara (2013) present convincing evidence that what drives the observed empirical relationship between weather shocks and conflict in Africa is weather anomalies occurring within the growing season of the main local crops. In addition, Schenkler and Lobell (2010) shows that crop yields are indeed a↵ected by growing season precipitation and temperature. Moreover, Brown et al (2011) argue that persistent drought conditions is the most significant climate influ- ence on GDP per capita growth in Africa. Given the high dependance of Sub-Saharan Africa economies on rainfed agriculture, these results provide strong evidence consis- tent with the existence of an income mechanism. Therefore, I draw upon Harari and La Ferrara (2013) to construct weather-induced agricultural shock by exploiting 44 information on spatial distribution of crops, planting and harvesting calendars, and variability on water balance anomalies across space and time.49 Ihypothesizethat locations with long history of statehood should be better equipped of mechanisms to mitigate the negative e↵ects of weather shocks. To support my hypothesis, I exploit panel data variation (over the time period 1989-2010) in the prevalence of conflict, weather-induced productivity shocks, and the interaction of my state history index with those shocks to estimate the following equation:

Conflict = ↵ + State History + Shock + State History Shock i,c,t i i,t i ⇥ i,t

0 0 0 0 + Gi + Xi + Ci Z + Wi,t⇧ + c + µi + ⌫t + ✏i,c,t (1.4.2)

Where t indexes year. The variable Conflicti,c,t takes the value 1 if at least one conflict event occurs in the grid cell i in year t, and 0 otherwise. The variables

0 0 0 State Historyi, Gi, Xi ,andCi are the same defined for equation (1). The vector

0 Wi,t includes year averages of monthly precipitation and temperature deviation from historical monthly means to account for any independent e↵ect that these variables may have on conflict outside of the growing season. The variable ⌫t denotes a year

fixed e↵ect whereas µi is a collection of grid cell fixed e↵ect which is included only in some specifications (when included I cannot identify , ,, Z,andc). The main coecient of interest in this exercise is . Standard errors are clustered at the grid cell level.

In column 1 of Table 12 I present OLS estimates an specification of equation (2) for which yearly weather variables and grid fixed e↵ect are not included. The point estimates suggest: a) an statistically significant negative correlation between conflict and state history, b) an statistically significant positive impact of negative weather

49I discuss the construction of the weather-induced agricultural shock in the appendix. 45 shocks on conflict, and c) a negative correlation between the interaction term of the two aforementioned variables and conflict, which is consistent with my hypothesized mitigating e↵ect of state history when a location is hit by a shock. In the following columns I present IV estimates. Column 2 shows the results for the same specification as in column 1. One-standard deviation increase in the shock measure statistically relates with 5 percent increase in the likelihood of experiencing at least one conflict event in a grid without history of state (The unconditional probability of having conflict is 0.18). The estimated coecient for suggests that for the grid with the mean value of state history (i.e: 0.16), the negative impact of a weather shock on conflict is quite smaller than for the case of no previous state history. That is, one- standard deviation increase in the weather shock implies only 1.2 percent increase in the likelihood of conflict. In column 3 I add grid cell fixed e↵ect, thus I can- not identify ,E,, ,and⇤. The point estimate for the direct e↵ect of weather shock on conflict remains almost unaltered whereas for the case of the interaction term it is slightly smaller, albeit statistically significant and representing economi- cally meaningful mitigation e↵ect of state history. The addition of yearly measures of precipitation and temperature deviation in column 4 does not substantially a↵ect the previous result. In the last specification in column 5, I include conflict prevalence lagged one period to account for conflict dynamics. Indeed, results in column 5 show that conflict in t 1stronglypredictsconflictint. Albeit slightly smaller in size, the point estimates for the weather shock variable and its interaction with state history remain both statistically significant.

Other interaction e↵ects. Inextconsiderasetofdi↵erent cross-sectional character- istics that when interacted with weather shocks may partially account for the result previously documented, namely that locations with relatively long history of state are less prone to experience conflict when hit by a shock. This set of characteristics includes light density at nights (proxy of regional development), soil suitability for 46 cultivating cereals, pre-colonial agricultural dependence, and historical temperature volatility. All the specifications in Table 13 include both year and grid fixed e↵ects. The first column serves as a comparison. The point estimates from columns 2 to 5 suggest that locations with higher light density at night, better cereal suitability, and higher pre-colonial dependence on agriculture are more prone to experience conflict when hit by a shock. On the contrary, locations with higher temperature volatility are less prone to have conflict. The inclusion of these interaction terms separately or jointly (in column 6) does not wash away the statistical significance of the negative coecient for the interaction term state history and weather shock.

1.5 Identifying Potential Mechanisms at Work: State

History and Attitudes Towards State Institu-

tions

It has been stressed that the lack of state legitimacy represents an underlying cause of the prevalence of civil conflict in Sub-Saharan Africa. It is argued that the lack of legitimacy undermines the institutional capacity and authority of a state to rule by consent rather than by coercion. Not surprisingly, Rotberg (2004) argues that the lack of legitimacy causes, in fact, state fragility; a concept that it is partially defined in terms of a society’s probability to face major conflicts.50 States with low levels of legitimacy tend to devote more resources towards retaining power rather than towards e↵ective governance, which undermines even more its popular support and increases the likelihood of political turnover (Gilley, 2006). On the contrary, citizens that consider a government to be legitimate are less likely to rebel. Does state 50For instance, a legitimacy score accounts for almost 50% of the State Fragility Index computed by the Center for Systemic Peace. Moreover, the operational definition of fragility in the index is associated with state capacity to manage conflict. 47 history at the sub-national level shape individual’s perception of state legitimacy? I argue that attitudes towards state institutions such as tax department, police force, and court of laws provide an informative way to measure views of legitimacy of the state. I thus show a strongly positive and robust correlation between individual’s belief regarding the legitimacy of these key state institutions and state history. I also present additional results suggesting the presence of a legacy of state history on individuals’ trust in state institutions. Moreover, I present suggestive empirical evidence that state history can be linked to modern support for traditional leaders.

Sources and Description of Individual-Level Data

In this section I exploit individual-level survey data to document the aforementioned potential mechanisms at work. My analysis is based on the Round 4 of Afrobarom- eter in 2008 and 2009 (Afrobarometer 4, from now on). The Afrobarometer 4 is a collection of comparative series of nationally representative surveys for twenty coun- tries in Sub-Saharan Africa: Benin, Botswana, Burkina Faso, Cape Verde, Ghana, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali, Mozambique, Namibia, Nigeria, Senegal, South Africa, Tanzania, Uganda, Zambia, and Zimbabwe. These countries have undergone some degree of political and economic liberalization during the last 20 years (Logan, 2013). In addition, the Afrobarometer 4 sample does not include countries under authoritarian regimes or civil wars (Afrobarometer, 2007). Nonethe- less, all the countries in the Afrobarometer 4 sample but Benin, Burkina Faso, Cape Verde, and Malawi experienced violent conflict events during the period 1989-2010.51 They also present high heterogeneity in key variables such as state history, historical

51The list of countries in Afrobarometer 4 with at least one conflict with more than 25 deaths during the period 1989-2010 is (event counts in parenthesis): Botswana (1), Ghana (34), Kenya (307), Lesotho (5), Liberia (510), Madagascar (39), Mali (98), Mozambique (261), Namibia (21), Nigeria (319), Senegal (187), South Africa (2624), Tanzania (9), Uganda (1549), Zambia (10), and Zimbabwe (45). 48 conflict, and other correlates of civil conflict.

The Afrobarometer 4 relies on personal interviews conducted in local languages where the questions are standardized so responses can be compared across countries (Afro- barometer, 2007). These questions asses, among other topics, individuals attitudes toward democracy, markets, and civil society. In particular, I exploit information re- garding individuals attitude toward state institutions and trust in politicians, public servants, and other individuals in general. I also benefit from a module of ques- tions on local traditional authority. As described in detail below, the information in Afrobarometer 4 also allows me to construct controls at the level of village (i.e: enumeration area) and district.

The original sample size in Afrobarometer 4 is over 26,000 respondents. Cape Verde and Lesotho are not included in my analysis.52 In addition, districts that I was not able to georeference, as well as individuals who could not be matched with ethnic names in Murdock’s (1959) map were removed from the sample.53 The final sample consists of 22,527 respondents from 1,625 districts and 221 di↵erent ethnic groups under Murdock’s (1959) classification.54

State History and State Legitimacy

I construct a measure of individual’s attitude toward the legitimacy of state insti- tutions based on the individual’s level of agreement with the following statements: (1) the courts have the right to make decisions that people always have to abide by;

52I exclude Lesotho and Cape Verde from my analysis for several reasons. I exclude Cape Verde because it was not taken into account in the original computation of my state antiquity index, no question on traditional leaders were asked during the round 4 of Afrobarometer, and diculties to match the ethnicities of the respondents with Murdock’s data. I exclude Lesotho due to diculties to match ethnicities. 53My georeferencing work was built upon a previous work by Stelios Michalopoulos. 54320 ethnicities are originally self-reported in my sample. Appendix includes the list of ethnicities and their match with names in Murdock’s (1959) map 49 (2) the police always have the right to make people obey the law; and (3) the tax department always has the right to make people pay taxes. The possible responses, coded from 1 to 5, are: “strongly disagree”, “disagree”, “neither agree nor disagree or don’t know”, “agree” and “strongly agree”. Thus, my measure of state legitimacy is the first principal component of the responses to these questions about the legiti- macy of the court decisions, police enforcement, and the tax department. I examine the statistical relationship between state legitimacy and state history by estimating di↵erent specifications of the following equation:

0 0 0 State Legitimacyi,e,a,d,c = ↵ + State Historyd,c + Ii,e,a,d,c + Aa,d,c + Dd,cE

0 + Xd,cH + ⌘c + ✓e + ✏i,e,a,d,c (1.5.1)

where i, e, a , d,andc index individuals, ethnicity, enumeration area (village), dis- trict, and country, respectively. The variable State Historyd,c represents the state antiquity index for a bu↵er with a 1-degree radius (approximately 100 kilometers)

0 and centroid located at the coordinates of the district. The vector Ii,e,a,d,c denotes a set of the respondent’s characteristics such as age, age squared, education level, living conditions, unemployment status, and gender.55

0 The vector Aa,d,c denotes a set of enumeration area-level covariates including a rural dummy and a subset of variables designed to capture the prevalence of public good provision and proxy for the quality of local government.56 In a study for South Africa,

55 The education variable takes value from 0 (no formal schooling) to 10 (post-graduate). The living condition variable is a self assessment of the respondent and takes values from 1 (very bad) to 5 (very good). Unemployment and gender are dummies variables taking value 1 if the respondent is unemployed and male, respectively. The Afrobarometer 4 does not include information on occupation of the respondent. 56I introduce 6 dummies indicating the presence of police, school, electricity, piped water, sewage system, and health clinic. Note that an enumeration area or village is the lowest order administrative unit available in Afrobarometer 4. 50 Carter (2011) shows that individual who are more satisfied with the quality of public good provision tend to see the state as legitimate. In addition, Gennaioli and Rainer (2007b) argue that history of state centralization had an impact on the quality of local government public provision.57 This hypothesis is contested by Bandyopadhyay and Green (2012) who find no correlation between pre-colonial centralization and local public good provision in Uganda.58 Nonetheless, it worths to note that the introduction of the public good provision dummies has little impact on the estimation of the main coecient of interest.

0 The vector Dd,c is a set of district-level variables accounting for di↵erences in develop- ment, which includes distance to the capital city, infant mortality, and per capita light

59 0 density (in logs). The Xd,c denotes a vectors of district-level covariates, respectively; which are included in di↵erent specifications of equation (3) and are discussed below.

Finally, ⌘c and ✓e are country and ethnicity fixed e↵ect. Since the main variable of interest, i.e: State Historyd,c, varies at the district level, I adjust the standard errors for potential clustering at the district level.60

0 0 0 Basic OLS and IV results. The set of controls Ii,e,a,d,c,Aa,d,c, and Dd,c is included in all the specifications.To capture those ethnic-specific factors that may both a↵ect the state legitimacy and also correlate with my state antiquity index at the district level, I include ethnic fixed e↵ects. It is worths to mention that I am able to identify , even after the introduction of ethnic fixed e↵ect, because almost half of the individuals in my sample are not currently living in the historical homeland of their ancestors.

57Although robust to di↵erent specifications, the evidence in Gennaioli and Rainer (2007b) is arguably far from being conclusive due to pitfalls of aggregation of ethnographic data at the country level (and the number of countries being small). 58Although testing Gennaioli and Rainer’s (2007b) main hypothesis at the local level represents an improvement from the original work, it is unclear that results in Bandyopadhyay and Green (2012) can be regarded as representatives of the whole Sub-Saharan Africa. 59Bandyopadhyay and Green (2012) also show that ethnic pre-colonial centralization positively correlates with level of development at the subnational and individual levels in Uganda. 60See appendix for all the details regarding definitions of the variables included in my analysis. 51 Thus, it is also important to emphasize that the estimated coecient for would be be representing the average statistical relationship between state history of the district and attitude toward legitimacy of state institutions for those individuals living outside the historical homeland of their ethnic groups. The OLS result suggests that there is no statistical relationship between state history at the district level and individual’s opinion about the legitimacy of the state. On the contrary, the IV estimate suggests an statistically strong and positive correlation between these two variables. Therefore, the history of the place where people live, outside of the tradition of the people living in that place, is relevant to shape people’s beliefs regarding the legitimacy of the state institutions. The positive coecient of interest is not only strongly statistically significant, but it is also economically meaningful: one-standard deviation increase in state history (i.e.; 0.26) is associated with more than one-standard deviation increase (i.e; 1.2) in the state legitimacy index. Note that neither Gabon, Congo, Congo DR, nor Angola are included in the Afrobarometer 4. As a result, the first-stage specification using the years elapsed since the neolithic revolution in levels (instead of its square) produces a similar fit. I thus present next the results using the instrument in levels.

I next consider additional district-level controls in a validity exercise which is similar to the one implemented above for the study of the relationship state history and conflict. The introduction of migratory distance, ecological diversity, soil suitability for millet and sorghum, absolute latitude and the set of variables accounting for intertemporal temperature volatility (intertemporal temperature volatility, its square, and historical mean temperature) in column 3 of Table 14 reduces the size of by 15 percent (albeit it remains statistically significant at the 5 percent level).61

Further district-level controls. Inextconsiderasubsetofdistrict-levelcontrolsin-

61Adding these controls separateley lead to similar results. 52 0 cluded in Xd,c. I consider the potential confounding e↵ect of prevalence of historical conflict,62 ethnic fractionzalition,63 and slave trade.64 Imeasureprevalenceofhis- torical conflict with an indicator variable taking the value 1 if at least one historical battle in the period 1400-1700 took place less than 100 kilometers away from the cen- troid of the district. I construct a fractionalization measure at the district level using information of the ethnicities of the respondents. I construct two measures of slave trade prevalence at the district level. First, I follow Nunn and Wantchekon (2011) and calculate the historical slave trade exposure for the ethnic group that historically inhabited the location (district) where the respondent currently lives. Second, I con- struct the weighted average slave trade prevalence of the district based on the slave trade exposure of all the ethnic groups reported in the survey for that district. The addition of these controls slightly increase the size of point estimate for .65

Note that the Kleibergen-Paap rk Wald F statistics are slightly below the “rule of thumb” generally applied to identify a weak instruments problem.66 Nonetheless, under a weak instrument problem my IV estimate would be biased toward OLS which

62Using Afrobarometer data, Besley and Reynal-Querol (2012) show that the prevalence of his- torical conflict at the country level is correlated with less trust, stronger sense of ethnic identity and a weaker sense of national identity. 63For the particular case of rural western Kenya, Miguel and Gugerty (2005) show that ethnic diversity is associated with lower provision of public good at the local level. They argue that this collective action failure follows from the inability to impose social sanctions in highly diverse communities. Although I already control for the degree of public provisions, this inability could also be related to low levels of trust and trustworthiness. In fact, Barr (2003) argues that low levels of trust is related to ethnic heterogeneity (in Zimbabwe). 64Nunn and Wantchekon (2011) show that individuals from ethnic groups that were strongly a↵ected by the slave trade in the past are less trusting today. In particular, those individuals trust less on the local councils. I argued above that the relationship between the history of state formation and slave trade prevalence is ambiguous. Nonetheless, if any relationship exists (regardless of its direction), omitting the impact of slave trade would introduce a bias in the estimation of my coecient of interest. In line with Nunn and Watchekon’s (2011) hypothesis, the prevalence of slave trade at the ethnicity level has indeed a negative impact (and strongly statistically significant) on the legitimacy of the state. 65Adding these controls separately lead to similar results. 66Note that I do not use the Stock and Yogo’s critical values to evaluate the strength of the instrument. Baum, Scha↵er, and Stillman (2007) suggest to apply caution on using Stock and Yogo’s critical values (which were compiled for an i.i.d. case) in cluster robust specifications. For that reason, I still use the Staiger and Stock (1997)’s rule that the F-statistic should be at least 10 for weak identification not to be considered a problem. 53 is close to zero. Further, I also report point estimates based on Fuller 1 estimator; a biased corrected limited information maximum likelihood estimator. It has been argued that this type of k-class estimator have a better finite-sample performance than 2SLS when instruments are potentially weak (Baum, Scha↵er, and Stillman 2007, and Stock, Wright, and Yogo 2002). Point estimates from Fuller 1 estimator are remarkably similar and statistically significant.

Internal vs External Cultural Norms. I attempt to distinguish whether it is the state history of the place where people live versus the state history of the ancestors of the people living in that place what matters for people’s opinion about state legitimacy. For that purpose I also construct the average state history of each respondent’s ethnic groups based on the historical distribution of ethnic homelands (from Murdock 1959). The first specification in column 1 of Table 15 includes ethnic fixed e↵ect and suggests that people living in districts with long history of statehood remarkably regard state institutions as more legitimate. In column 2 I do not include ethnic fixed e↵ect and focus on the average state history of the ethnic group of the respondent. I do not find a statistically significant association between long history of statehood at the ethnic level and views of legitimacy of state institutions. When I introduce district-fixed e↵ect the point estimate for state history of the respondent’s ethnic group increases in size but remains statistically insignificant. These are indeed striking results since ethnicity is arguably one of the most relevant vehicles for cultural norms at the individual level. Therefore, the strong positive impact of state history at the district level on legitimacy (even when holding ethnic characteristic fixed) and the apparent nonexistent statistically association between the ethnic-based state history measure (when holding district characteristics fixed) strongly suggests that it is the long run exposure to statehood of the location, rather of the history of the ancestors of the people living in that location, what determines individual’s belief about state legitimacy. 54 State History, Trust in Institutions and Traditional Leaders

Does history impose a legacy of confidence in state institutions? Lipset (1959) argued that state legitimacy is related to the capacity of a political system to convince its citizens that the prevailing institutions are not only appropriate but also the proper ones for them. Trust in state institutions is therefore a key element on which legit- imacy is built. I next examine the statistical relationship between state history and trust in state institutions by estimating an analogue equation to (3) where the depen- dent variable is Trusti,e,a,d,c instead of State Legitimacy. Trust is each individual’s answer to di↵erent questions on several levels of trust. The question asked “How much do you trust in each of the following” and then it listed specific individuals or institutions. I recoded each original answer to a 5-point scale where 1 is “not at all” and 5 is “a lot”. Following the methodology in Logan (2013), I coded the answers “don’t know” at the mid-point. All the variables on the right-hand side of equation (3) are the same defined above for equation (2).

Trust in State Institutions. Table 16 presents IV estimates of the relationship between state history at the district level and measure of respondent’s trust (OLS results along with Fuller (1) estimates are also reported in Table 16). All the specifications include

0 0 0 the set of controls Ii,e,a,d,c,Aa,d,c,Dd,c, and the set of predetermined district-level controls using the IV validity exercise (migratory distance to Addis Adaba, ecological diversity, absolute latitude, suitability for millet and sorghum, and climate volatility measures ). In column 1 I focus on trust in institutions. This measure is based on the first principal components of each individual’s answer regarding the level of trust in police and courts of law.67Ifindastrongpositivestatisticalassociationbetween trust in institutions and state history at the district level.

67There is no question regarding trust on tax department in Afrobarometer 4. 55 Do individuals living in districts with relative high historical exposure to statehood trust more in general? Results in column 2 and 3 of Table 16 suggest that my previous results was not just picking up a higher level of trust. Respondents living in districts with long history of statehood do not trust more in relatives (column 2) or compatriots (column 3). All the coecients are not statistically di↵erent from zero under usual levels of confidence. In fact, all the point estimates are negative and of the relative small size.

State History and Trust in Politicians. The point estimate in column 4 of Table 16 suggests that individual’s trust in politicians is not strongly statistically related to state history at the district level (a first principal components of each individual’s trust level in the president -or Prime Minister for some countries-, the parliament -or national assembly-, and the opposition political parties). Adding separately each of these components of this measure lead to similar results (not shown).

The role of the Traditional Leaders. Colonization did not eliminate several impor- tant pre-colonial obligations of the African traditional leaders. In fact, local tradi- tional leaders still play an important role on the allocation of land and the resolution of local disputes. The way they still exercise public authority vary between and within countries (Logan, 2013). Michalopoulos and Papaioannou (2013b) document the strong influence of traditional leaders in governing the local community. It is precisely the interaction between local leaders and pre-colonial centralization what provides the foundation for Gennaioli and Rainer (2007b)’s main argument. I next analyze whether state history can explain popular support for local traditional lead- ers. The coecient estimate for in column 1 of Table 17 shows that state history is not statistically associated to a measure of trust in local councilors. On the contraty, results in column 2 suggests that individuals living in district with relative higher his- torical exposure to statehood tend to trust more in local traditional leaders. There 56 is also a strong positive statistical association between state history and individuals’ perception on the influence of traditional leaders governing the local community (col- umn 3).68 Moreover, the relationship between state history and preferred (instead of actual) influence of the traditional leaders is even statistically and economically stronger (column 4).69

1.6 Conclusion

This paper adds to a growing literature in economics that seeks to better understand the role that historical factors play in shaping contemporary development outcomes. In particular, it contributes to the understanding of the developmental role of history of statehood by rigorously looking at the statistical relationship between state history and the prevalence conflict at the sub-national level. For this purpose, I introduce of anovelindexofstatehistoryatthesub-nationallevel.Iuncoverastrongnegative statistical relationship between my state histroy index and the prevalence of modern conflict. This relationship is robust to several confounding factors. Although I cannot rule out the possibility that unobservables are partially accounting for this uncovered statitical association, I argue that the influence of those factors would have to be substantially larger than the documented influence of observed factors to explain away my main result.

Motivated by the possibility that the construction of my index may result in sub- stantial measurement error and thus introduces a bias in OLS estimates, I follow two

68The individuals answered the following question “How much influence do traditional leaders currently have in governing your local community?”. The variable is coded in a 5-point scale from 1 (none) to 5 (great deal of influence). Again, I coded the answer “don’t know” at the mid-point. 69The preferred influence variable is based on the following question “Do you think that the amount of influence traditional leaders have in governing your local community should increase, stay the same, or decrease?”. The variable is coded in a 6-point scale from 1 (decrease a lot) to 5 (increase a lot). Again, I coded the answer “don’t know” at the mid-point whereas and as missing value when people refused to answer. 57 instrumental variable approaches. First, I use the timing elapsed since the neolithic revolution as a source of exogenous variation to document a similar pattern as in the OLS case. Second I use an alternative measure to account for the degreee of historical influence from political centralization based on the proximity to historical cities over the time period 1000 - 1800 CE. The results are again consistent with my hipothesis that locations with long history of statehood should have better abilities to establish and preserve order.

Ialsoexploitpaneldatavariationintheprevalenceofconflict,weather-inducedpro- ductivity shocks, and the interaction of my state history index with those shocks to document that location with relatively high historical exposure to state capacity are remarkably less prone to experience conflict when hit by a negative agricultural productivity shock.

Ithenturntospecificpotentialmechanismsandexamineanexplanationfortheun- covered relationship. By exploiting individual-level survey data, I show that state history can be linked to people’s positive attitudes towards state institutions. In par- ticular, I show that key state institutions, along with traditional leaders, are regarded as more legitimate and trustworthy by people living in district with long history of statehood.

Bearing in mind that identifying a causal e↵ect of historical presence of statehood on contemporary conflict is a dicult task, I present empirical evidence that hard- to-account-for factors manifested in di↵erences in long-run exposure to centralized institutions crucially matters to understand contemporary conflict. 58

Table 1.1: Summary Statistics. Grid Cell Sample Variable Mean Std. Dev. Min Max Conflict Prevalence 0.19 0.23 0.00 1.00 Conflict Onset 0.27 0.44 0.00 1.00 State History 1000 - 1850 CE 0.16 0.23 0.00 1.00 Area (square km) 42367 12239 122 49231 Distance Ocean (’00 km) 6.18 4.75 0.00 16.84 Distance Major River (’00 km) 4.17 3.25 0.00 15.58 Capital Dummy 0.07 0.26 0.00 1.00 Distance Capital (km) 641.7 435.9 24.7 1912.5 Total Road Length (’00 km) 4.36 4.74 0.00 38.58 Mean Elevation (m) 616.5 425.4 -4.6 2221.9 Ruggedness 66583 78892 960 540434 Natural Resources Dummy 0.41 0.49 0.00 1.00 Number of Countries in Grid 1.62 0.72 1.00 4.00 Cereal Suitability 0.28 0.17 0.00 0.71 TseTse Fly Suitability 0.34 0.40 0.00 1.00 Malaria Ecology early 20th Century 5.71 4.90 0.00 18.52 Ethnic Fractionalization in 1960 0.45 0.27 0.00 1.00 ln of Population Density in 1960 0.10 0.14 0.00 0.91 Pre-Colonial Hunting Dependence 0.96 0.91 0.00 4.00 Pre-Colonial Fishing Dependence 0.72 0.72 0.00 5.21 Pre-Colonial Pastoralism Dependence 3.15 2.39 0.00 9.00 Pre-Colonial Agricultural Dependence 4.46 2.12 0.00 8.41 Pre-Colonial Settlement Pattern 4.77 2.23 1.00 8.00 Slave Trade (log of Slave Exports/Area) 4.31 4.19 0.00 14.36 Distance to Historical Conflict (’00 km) 5.74 3.50 0.09 16.79 Ecological Diversity (Herfindhal Index) 0.32 0.23 0.00 0.75 Migratory Distance to Addis Adaba (weeks) 4.70 2.24 0.03 9.45 Note: Sample Size is 558 grid cells. See full details of the variable definitions in Appendix 59

Table 1.2: OLS Estimates - Baseline Specification Dependent Variable: Conflict Prevalence 1989-2010 (fraction of years with at least one conflict event)

(1) (2) (3) (4) (5)

State History 1000 - 1850 CE -0.109** -0.191*** -0.193*** -0.197*** -0.196*** robust std err (0.045) (0.041) (0.041) (0.042) (0.042) spat. adj. std err (5 degrees) (0.054) (0.047) (0.047) (0.048) (0.048) spat. adj. std err (10 degrees) (0.064) (0.054) (0.053) (0.053) (0.056) std err clust country (0.075) (0.061) (0.057) (0.061) (0.060)

Country Dummies Y Y Y Y Y Geo-strategic Controls N Y Y Y Y Cereal Suitability N N Y N Y Disease Environment N N N Y Y

Observations 558 558 558 558 558 R-squared 0.349 0.510 0.517 0.516 0.519

*** p<0.01, ** p<0.05, * p<0.1 (robust case). The unit of observation is a grid cell. The geo-strategic controls are distance to ocean, distance to major river, distance to capital, capital dummy, total road length, mean elevation, ruggedness terrain, total area, dummy for natural resources sites, and number of countries intersected by the grid. Cereal suitability represents the soil suitability for cultivating cereals (FAO’s GAEZ database). Disease environment control include malaria ecology in early 20th century and TseTse fly suitability (predicted distribution from FAO). 60

Table 1.3: OLS Estimates - Accounting for Genetic and Ecological Diversity Dependent Variable: Conflict Prevalence 1989-2010 (fraction of years with at least one conflict event) (1) (2) (3)

-0.185*** -0.209*** -0.198*** State History 1000 - 1850 CE (0.064) (0.060) (0.063) Mig. Distance to Addis Adaba -0.059*** -0.056*** (0.019) (0.018) Ecological Diversity 0.117** 0.111** (0.052) (0.049)

Country Dummies YY Y Geo-strategic Controls YY Y Cereal Suitability YY Y Disease Environment YY Y

Observations 558 558 558 R-squared 0.534 0.529 0.543

Robust standard errors clustered at the country level in parentheses. *** p<0.01, ** p<0.05, * p<0.1. The unit of observation is a grid cell. The basic set of controls is described in Table 2. Migratory Distance to Addis Adaba proxies for genetic diversity. The longer the distance to Addis Adaba, the lower the genetic diversity. Ecological diversity is a Herfindhal index based on Vegetation types from White (1983). 61 0.1. The unit of observation is a grid cell. < 0.05, * p < 0.01, ** p < (1) (2) (3) (4) (5) (6) (7) (8) [0.11] (0.146) (0.037) (0.005) (0.033) (0.007) (0.014) [0.0296] (0.055) (0.060) (0.062) (0.064) (0.064) (0.062) (0.064) (0.064) Table 1.4: OLS Estimates - Additional Controls Prosperity Density Fraction Trade Routes Hist Conflict Density Dependent Variable: Conflict Prevalence 1989-2010 (fraction of years with at least one conflict event) cient Add. Control P-value Joint Sig. 0.367** 0.051 0.001 -0.015 -0.006 0.026* P-value Joint Sig. State History 1000 - 1850 CEAdditional Control -0.199***Coe -0.223***CountryDummies -0.200***Geo-strategic -0.199*** ControlsCereal -0.194*** Suitability PrecolonialDisease Environment -0.212***Migratory Distance Ln to of Addis Pop AdabaEcological -0.213*** Diversity EthnicObservations -0.233*** R-squared Slave Y YRobust Y standard errors Hist clustered Trade atThe the basic country set level of in controls parentheses.*** is p Distance described Y Y in Tables 2 and 3. See appendix Y for Light Y definition Y of Y the additional controls. Y Y Y Y 558 Y All Y 0.549 Y Y Y Y Y 558 Y 0.558 Y Y Y Y Y 558 0.545 Y Y Y 0.543 558 Y Y Y Y 0.543 558 Y Y Y Y Y 0.545 Y 558 Y Y 0.550 Y Y Y 558 Y 0.565 Y Y 558 Y 62 0.1. < 0.05, * p Log of Conflict < 0.01, ** p < Dependent Variable erent Conflict Measures ↵ Battles Violence Riots (1) (2) (3) (4) (5) (6) All (0.061) (0.060) (0.063) (0.030) (0.469) (0.089) Conflicts Casualties Onset Table 1.5: OLS Estimates. Di defined as the first event within a dyad. The set of controls is described in Tables 2 and 3. State History 1000 - 1850 CEDataset -0.093Country DummiesGeo-strategic -0.157** ControlsCereal -0.124* SuitabilityDisease Environment 0.011Migratory Distance to Addis AdabaEcological Diversity -1.408***Robust standard errrors -0.277*** Y clustered atThe Y the unit Y country of level observation in is ACLEDyears parentheses.*** a with p grid at cell. leat ACLED Conflict one measures conflict in event Y Y Y in columns ACLED grid. 1,2,3,4, Y and ACLED Y 6 data represent comprises ACLED the the fraction period of 1997-2010. UCDP-GED Conflict Y onset is UCDP-GED Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 63

Table 1.6: OLS Estimates. Heterogeneity Across Regions Dependent Variable: Conflict Prevalence 1989-2010 (fraction of years with at least one conflict event)

(1) (2) (3)

State History 1000 - 1850 CE -0.133* -0.223* -0.171* (0.068) (0.105) (0.088)

Regions Included in Sample West-Central East-South All but West

Country Dummies Y Y Y Geo-strategic Controls Y Y Y Cereal Suitability Y Y Y Disease Environment Y Y Y Migratory Distance to Addis Adaba Y Y Y Ecological Diversity Y Y Y

Observations 274 284 425 R-squared 0.539 0.632 0.547

Robust standard errors clustered at the country level in parentheses.*** p<0.01, ** p<0.05, * p<0.1. The unit of observation is a grid cell. The basic set of controls is described in Tables 2 and 3. 64 (0.063) (0.063) -0.195*** -0.175*** 0.1. < (0.051) (0.042) 0.05, * p < 0.01, ** p < (1) (2) (3) (4) (5) (6) (7) 5% 10% 25% 50% 0% 0% 0% (0.061) (0.060) (0.057) (0.067) Discount Discount Discount Discount Discount Discount Discount Table 1.7: OLS Estimates. Discount Factors and Importance of Medieval Period Dependent Variable: Conflict Prevalence 1989-2010 (fraction of years with at least one conflict event) cient -0.185 -0.170 -0.130 -0.099 -0.120 -0.201 na Discounted State History 1000 - 1850 CEState History -0.176*** 1500-1850 CE -0.157**State History 1000-1500 -0.114* CE -0.107 Beta coe CountryDummiesGeo-strategic ControlsCereal SuitabilityDisease EnvironmentMigratory Distance to Addis AdabaEcological DiversityObservationsR-squared YRobust standard Y errors Y clustered atThe the unit country of level observation in is parentheses.*** a p grid cell. Y Y The Y basic set Y of Y controls is described in Tables 2 Y and 3. -0.099* Y Y Y Y Y 558 Y Y Y Y Y Y 0.540 558 -0.039 Y 0.537 Y Y Y Y Y 558 0.531 Y Y Y Y Y 0.527 558 Y Y 0.530 Y Y 558 Y Y Y 0.544 Y 558 Y Y 0.545 558 Y 65 100 years of > 0.1. The unit of observation < 0 State History > 0.05, * p < (0.057) (0.101) History 0.01, ** p < (0.015) (1) (2) (3) (4) (0.038) Table 1.8: OLS Estimates - Intensive vs Extensive Margin of Political Centralization Dependent Variable: Conflict Prevalence 1989-2010 (fraction of years with at least one conflict event) State History Score 1800 CEEthnic Centralization (v33 Eth. Atlas)State History 1000 - 1850 CECountry DummiesGeo-strategic Controls -0.048 Cereal SuitabilityDisease EnvironmentMigratory Distance to Addis AdabaEcological -0.019 DiversitySampleObservationsR-squared Y Y YRobust standard errors clustered atis the a country grid level cell. in Y Yperiod State parentheses.*** History p 1800 Score - 1800 1850 CE CE.Ethnic(JurisdictionalHierarchy represents Centralization Beyond -0.207*** Y the is Local fraction 1960-population Community) Y of ranging weighted Ydescribed grid average from Y in of which 1 Tables was Ethnographic to 2 under Atlas’s 5 and a variable (Large 3. v33 centralized States).The state basic during set the of Y controls Y is Y Y -0.217** Y Y Full 558 Y Y 0.524 Full Y 558 Y 0.524 Y Y State 335 Y Y 0.559 Y 0.600 229 66

Table 1.9: First-Stage. Neolithic Instrument Dependent Variable: State History 1000 - 1850 CE

(1) (2) (3) (4)

Time Elapsed Since Neolithic 0.185** 0.054 0.250*** spat. adj. std err (5 degrees) (0.049) (0.078) (0.051) spat. adj. std err (10 degrees) (0.055) (0.087) (0.060) std err clust country (0.085) (0.147) (0.079) Square of Time Elapsed Since Neolithic 0.034*** spat. adj. std err (5 degrees) (0.007) spat. adj. std err (10 degrees) (0.008) std err clust country (0.012)

Country Dummies Y Y Y Y Geo-strategic Controls Y Y Y Y Cereal Suitability Y Y Y Y Disease Environment Y Y Y Y Migratory Distance to Addis Adaba Y Y Y Y Ecological Diversity Y Y Y Y

Sample Full Central Africa Restricted Full

Observations 558 141 465 558 R-squared 0.456 0.533 0.471 0.467

*** p<0.01, ** p<0.05, * p<0.1 (clustered at country level case). The unit of observation is a grid cell. Specification in Column 3 excludes observations from Gabon, Congo, Congo DR, and Angola. The basic set of controls is described in Tables 2 and 3. 67 0.1. The unit of observation is a grid cell. < (0.008)(0.050)(0.005) (0.009) (0.051) (0.005) (0.005) (0.066) (0.006) 0.05, * p < (0.030) (0.027) (0.029) 0.01, ** p < (0.004) (0.006) (0.006) Table 1.10: IV Estimates (1) (2) (3) (4) (5) (6) (7) (0.209) (0.202) (0.204) (0.221) (0.225) (0.186) (0.186) Dependent Variable: Conflict Prevalence 1989-2010 (fraction of years with at least one conflict event) State History 1000 - 1850 CEAbsolute LatitudeSuitability for Sorghum and Millet CultivationHistorical Mean TemperatureTemperature Volatility -0.712*** -0.618*** -0.718***Square of Temperature Volatility -0.527** -0.599***Pre-Colonial Ethnic -0.410** Controls (p-value joint sign.)CountryDummies -0.386** -0.008Geo-strategic ControlsCereal Suitability -0.010**Disease EnvironmentMigratory Distance to Addis AdabaEcological DiversitySpecification for Neolithic InstrumentF (cluster-robust statistics, country level)Observations 0.014R-squared 0.001Robust Y standard Y errrors Y clustered at SquareThe the 7.964 basic -0.026 country set level of in controlsmillet parentheses.*** is is p described the 0.005 Square in principal Y Tables Y component -0.072 -0.0031978-2010 2 [0.1863] of Y and and 7.022 soil proxy 3. Y suitability for Absolute Y for historicaldeviation latitude growingthese figures Square of corresponds two [0.3612] (See monthly crops to Ashraf data. (FAO’s the 0.002 andMichalopoulos, GAEZ grid’s Pre-colonialof 2013). dataset). Y centroid. ethnic settelement Temperature Temperature 9.018 patterns controlsare Suitability volatility data Square 5 complexity for represents is and ethnograhic sorghum Y theSpecification from Y subistenceincome variables and intertemporal 0.002 in the Y representing shares standard Column period within- from Y 7 grid hunting, Y excludes [0.0123] 1960-population fishing, Square observations animal weighted 9.828 from averages husbandry, Gabon, and Congo, agriculture. 0.007 -0.094* Congo 0.009 DR, Y Square and Angola. 13.234 Y Y Y Y Y 558 16.798 -0.112* 0.009 Linear Y Y Y 0.084 Y Y Y 558 22.534 0.170 Y Y Y Y Y Y 558 0.079 Y Y Y 0.233 558 Y Y 0.196 Y Y 558 0.306 Y Y 558 Y 0.369 465 68

Table 1.11: Alternative Measure. Historical Proximity to Cities Dependent Variable: Conflict Prevalence 1989-2010 (fraction of years with at least one conflict event)

(1) OLS (2) IV (3) IV

Historical Proximity Cities -0.542** (0.257) State History 1000 - 1850 CE -0.601*** -0.488*** (0.156) (0.163)

Instrument Proximity Proximity Cities Cities and Neolithic Sq.

F (cluster-robust statistics, country level) 8.674 12.825 Hansen J Statistic Over. Test 0.2075

Country Dummies Y Y Y Geo-strategic Controls Y Y Y Cereal Suitability Y Y Y Disease Environment Y Y Y Migratory Distance to Addis Adaba Y Y Y Ecological Diversity Y Y Y Absolute Latitude Y Y Y Suitability for Sorghum and Millet Y Y Y Intertemporal Temperature Volatility Y Y Y

Observations 558 558 558 R-squared 0.573 0.211 0.274

Robust standard errors clustered at the country level in parentheses. *** p<0.01, ** p<0.05, * p<0.1. The unit of observation is a grid cell. All the controls are described in Tables 2, 3, and 10. 69 (0.017) 0.275*** 0.1. The unit of observation is a grid-year. < 0.05, * p < 0.01, ** p < (0.020)(0.005) (0.071)(0.041) (0.011) (0.070) (0.160) (0.011) (0.069) (0.011) (0.056) (0.009) (1) OLS (2) IV (3) IV-FE (4) IV-FE (5) IV-FE Dependent Variable: Conflict Prevalence (1 if at least one conflict event in grid-year) Table 1.12: Conflict, State History, and Weather Shocks -Panel Data Evidence (1989-2010)- Shock*State History 1000 - 1850 CENegative Weather ShockState History 1000 - 1850 CELagged Conflict -0.035*Country DummiesGrid FEYear -0.245*** FEGeo-strategic Controls 0.017***Cereal Suitability -0.197***Disease -0.229*** EnvironmentMigratory Distance to 0.051*** Addis AdabaEcological Diversity -0.574*** Yearly -0.223*** Precipitation and Temperature VariationF (cluster-robust statistics, 0.052*** country level)Observations (grids) [years]Robust -0.170*** standard N errors clustered Y atSee the appendix 0.050*** grid for level details in regardingYearly parentheses.*** Y temperature the p weather variation Y is shock. the Yearly2, average precipation 3, of variation and monthly is 10. deviation the from standard historical deviation mean of monthly monthly N temperature. Y precipation Y whithin The 0.038*** a set N year. of Y controls Y is described 12,276 (558) in [22] Tables Y Y 12,276 Y (558) [22] 12,276 (558) 24.9 [22] N 12,276 Y Y (558) [22] N Y N 11,718 (558) Y N [21] N 40.1 Y N N Y Y N N N N 40.1 Y N N Y Y N N 39.9 N N Y N N Y N 70 0.1. The unit of observation is a grid-year. See appendix for details < 0.05, * p < Suitability Colonial Dep. Volatility (p-value joint sig.) 0.01, ** p < (0.003) (0.018) (0.002) (0.002) (1) (2) (3) (4) (5) (6) (0.056)(0.009) (0.044) (0.015) (0.047) (0.008) (0.051)(0.017) (0.007) (0.057) (0.017) (0.010) (0.017) (0.052) (0.017) (0.017) (0.017) (0.017) 11,718 (558) [21] 11,718 (558) [21] 11,718 (558) [21] 11,718 (558) [21] 11,718 (558) [21] 11,718 (558) [21] Dependent Variable: Conflict Prevalence (1 if at least one conflict event in year) Additional Interacted Control Light Density Cereal Agric. Pre- Temp. ALL Shock*Additional Interacted Control 0.010*** 0.065*** 0.010*** -0.003* [0.0000] Table 1.13: Conflict, State History, and Weather Shocks -Panel Data Evidence (1989-2010)- Shock* State History 1000 - 1850 CENegative Weather Shock -0.177*** -0.128*** -0.116** 0.039***Lagged Conflict -0.162*** 0.061***Grid FEYear -0.151*** FE 0.011F (cluster-robust statistics, country level)Observations (grids) [years] Robust standard errors -0.006 clustered atregarding the -0.145*** the grid weather level shock in and parentheses.*** additional p interacted controls. 0.276*** Yearly precipation variation is 0.045*** the standard deviation of monthly precipation whithin 0.275*** a year. 24.897 0.274*** Y 40.099 Y 0.019 0.276*** 40.133 0.274*** Y Y 39.93 Y 0.276*** Y 38.568 Y Y Y Y Y Y 71

Table 1.14: State Legitimacy and State History Dependent Variable: Views on State Institutions Legitimacy

(1) OLS (2) IV (3) IV (4) IV

State History 1000 - 1850 CE (District) 0.0224 4.614** 4.051** 4.538** (0.100) (1.964) (1.755) (1.955) Migratory Distance to Addis Adaba -0.0296* -0.0284* (0.015) (0.015) Absolute Latitude District -0.0266** -0.0266** (0.012) (0.013) Ecological Diversity 0.0780 0.0653 (0.138) (0.143) Suitability for Sorghum and Millet Cultivation 0.126** 0.136** (0.053) (0.057) Historical Mean Temperature 0.0117 0.0107 (0.008) (0.009) Temperature Volatility -0.0978 -0.0999 (0.082) (0.087) Square of Temperature Volatility 0.0202 0.0198 (0.015) (0.016)

F (cluster-robust statistics, district level) 8.477 9.547 8.985

Fuller 1 Point Estimate for State History 4.581** 4.025** 4.506** (1.942) (1.738) (1.934)

Old Conflict within 100km radius N N N Y Fractionalization District N N N Y SlaveTradePrevalenceDistrict N N N Y Mean Slave Trade People in District N N N Y

Observations 22,527 22,527 22,527 22,527

Robust standard errors clustered at the district level in parentheses. *** p<0.01, **p<0.05, * p<0.1. The dependent variable is the first principal component of the responses to 3 questions about the legitimacy of the court decisions, police enforcement, and the tax department (see main text for details). The state history variable is calculated for a bu↵er of 100km radius from district’s centroid. All specifications include controls at the individual, village, and district level as well as country and ethnic fixed e↵ects The individual controls are for age, age squared, male indicator, unemployment indicator, education ordered measure (from 0 -no formal education- to 9 -graduate education-), and living condition ordered measure (from 1 -verybad-to5-verygood-).Villagecontrolsare6indicatorsforpublicgoodprovisions:policestation,school, electricity, piped water, sewage, and health clinic. District controls are distance to the capital of the country, infant mortality, per capita light density at nights, and urban indicator. See main text for definition of the additional district controls. 72

Table 1.15: State Legitimacy and State History. Internal vs External Norms Dependent Variable: Views on State Institutions Legitimacy

(1) (2) (3)

State History 1000 - 1850 CE (District) 4.614** (1.964) State History 1000 - 1850 CE (Ethnicity) 0.414 1.561 (1.110) (1.340)

F (cluster-robust statistics, district level) 8.477 8.719 8.859

District Controls Y Y N Country FE Y Y N Ethnic FE Y N N District FE N N Y

Observations 22,527 22,527 22,527

Main results are IV estimates. Robust standard errors clustered at the district level in parentheses. *** p<0.01, **p<0.05, * p<0.1 The dependent variable is the first principal component of the responses to 3 questions about the legitimacy of the court decisions, police enforcement, and the tax department (see main text for details). The state history variable is calculated for a bu↵er of 100km radius from district’s centroid. All specifications include individual and village level controls. The set of controls is described in Table 14. 73 0.1 < 0.05, * p < 0.01, **p < (1.263) (0.859)(1.251) (1.011)(0.087) (0.853) (0.064) (1.169) (1.004) (0.079) (1.161) (0.107) (1) Institutions (2) Relatives (3) Compatriots (4) Politicians er of 100km radius from district’s centroid. The set of controls is described Dependent Variable: Trust in ↵ Table 1.16: Trust and State History State History 1000 - 1850 CE (District)F (cluster-robust statistics, district level)Fuller 1 2.606** Point Estimate for State History 9.597OLS Point Estimate for -1.355 State 2.590** HistoryObservations 9.668Main results -1.348 -0.930 are 0.202** IV estimates.All Robust specifications standard include errors the clustered full at set theThe of district dependent controls level variable in in is column parentheses. the 4 response -0.255*** ***to 9.63 of to p a Table the 5 14. question point -0.924 OLS How scale and much wheredependent do Fuller 1 variable 1.550 1 you is is point trust the “not estimates in first at are (subject)? principal all”The also component I and state reported. of recoded 5 history the is each variable responses original “a is -0.113 answer lot”. to calculatedin the for See Table trust a 14 main questions bu text related for to details.For 12.205 police the case and of court 1.542 trust of in laws. institutions the -0.0107 22,525 22,453 22,155 22,383 74 0.1 < 0.05, * p < 0.01, **p < er of 100km radius from district. ↵ Trust Traditional Leader Influence (1) (2) (3) (4) (1.165)(1.158) (1.386)(0.109) (2.267) (1.374) (0.100) (2.066) (2.242) (0.111) (2.039) (0.089) Local Councilors Traditional Leader Perceived Prefered Table 1.17: Trust in Local Policy Makers, Traditional Leaders, and State History State History 1000 - 1850 CE (District)F (cluster-robust statistics, district level)Fuller 1 Point Estimate -0.480 for State HistoryOLS 9.62 Point Estimate for State History -0.476Observations 2.500*Main results are IV estimates.OLS 9.601 Robust 0.172 and standard Fuller errors 1 clustered point at estimates theHow 2.484* are much district also do level reported. you in 5.335** trust parentheses. For in the ***influence local first p do councilors 2 traditional / columns leaders traditional the currently leaders? dependent have1(none)to5(greatdealofinfluence).Thepreferedinfluencevariableisbasedonthequestion“Doyouthinkthattheamountofinfluence variable in The is governing perceived the your influence local response variable community? to isthe the based The 5.359*** traditional on questions variable 8.619 leaders the is have 0.162 question coded in How 5.300** in governingpoint much a your scale from 5-point local 1 scale community (decrease a from should lot) increase, toAll stay 5 specifications the (increase a include same, lot). the or The full state decrease? history set variable is THe of calculated for variable controls a 5.321*** is in bu coded column 8.657 in 4 a of 5 Table 0.589*** 14. 22,523 0.168* 22,528 22,115 22,137 Appendix A: Variable Definitions (Cross-section of Grid Cells)

Conflict Prevalence: fraction of years with at least one conflict event in the grid cell during the period 1989-2010. Own calculation based on UCDP GED, version 1.5 (November 2012). Conflict Onset: fraction of years with at least one conflict onset in the grid cell. An onset is the first confrontation event within a dyad. Own calculations based on UCDP GED, version 1.5 (November 2012). State History: see main text for definition. Area: Total land area of the grid cell (in square kilometers). Distance Ocean: distance from the centroid of the grid cell to the nearest ocean (in hundred of kilometers). Distance Major River: distance from the centroid of the grid cell to the nearest ma- jor river (in hundred of kilometers). Own calculation based on EMEA rivers dataset from ArcGis Online. Capital Dummy: variable taking value 1 if the capital city of the country to which the grid cell was assigned is in the grid cell. Distance Capital: distance from the centroid of the grid cell to the capital city of the country to which the grid cell was assigned (in kilometers).

75 76 Total Road Length: total length of major roads intersecting the grid cell (in hundred of kilometers). Own calculations based on Global GIS Database compiled by the United States Geological Survey. Mean elevation: within-grid average elevation of the terrain (in meters above the sea level). Own calculation by taking within-grid average across original pixels in source dataset. Data comes from National Oceanic and Atmospheric Administration (NOAA) and U.S. National Geophysical Data Center, TerrainBase, release 1.0, Boul- der, Colorado. Available at http://www.sage.wisc.edu/atlas/data.php?incdataset=Topography Ruggedness: within-grid average ruggedness of the terrain across 30-by-30 arc-second cells. Ruggedness measure comes from Nunn and Puga (2012). Natural Resources Dummy: variable taking value 1 if at least one natural resource site (either gems, diamond, gas or oil) is located in the grid cell. Location of the nat- ural resource sites comes from PRIO’s Diamond Resources and Petroleoum Datasets. Available at www.prio.no/Data/Geographical-and-Resources-Datasets Number of Countries in Grid: total number of countries that are intersected by the grid cell. South Sudan is included in Sudan. Cereal Suitability: within-grid average cereal suitability of the soil from Food and Agriculture Organization (FAO)’s Global Agro-Ecological Zones (GAEZ) database. Tse-tse Fly Suitability: within-grid average predicted suitability for tse-tse flies from FAO/IAEA. Malaria Ecology in early 20th century: within-grid average of average malaria ecology for the time period 1901-1905. Original data from Conley, McCord, and Sachs (2010). Migratory Distance to Addis Adaba: Temporal lenght (in weeks) of the optimal mi- gratory path to Addis Adaba from the centroid of the grid. Constructed based on Ozak¨ (2012a, 2012b). Ecological Diversity: Herfindahl index constructed from the shares of each grid’s area 77 that is occupied by each ecological type on White’s (1983) vegetation map of Africa. Ethnic Fractionalization in 1960: this variable is computed at the grid level i with n 2 the following formula: Fi =1 ↵i,g. Where ↵i,g is the fraction of total population g=1 in grid cell i that live in the portionP of the historical homeland of group g that is intersected by the grid i. Population counts are from 1960 and comes from UNEP GRID Sioux Falls (Nelson 2004). The spatial distribution of ethnic groups is based on Murdock’s (1959) map. Ln of Population Density: log of 1 + population density in 1960 (people per squared kilometer). Population data comes from UNEP GRID Sioux Falls (Nelson 2004). Pre-Colonial Variables: the following variables are 1960 population-weighted averages of traits of ethnic groups whose historical homelands intersect a given grid cell. The weights are the aforementioned ↵i,g (see definition of Ethnic Fractionalization). Pre- colonial dependence variables denote subsistence income shares derived from hunting, fishing, pastoralism, and agricultural (variables v2, v3, v4, and v5 in the Ethno- graphic Atlas (1967) respectively). Pre-Colonial Settlement Pattern denotes the level of settlement complexity (variable v30 from Ethnograhic Atlas). A previous match- ing between ethnic territories (as displayed in Murdock (1959)’s map) and ethnic traits was needed for the computation of the population-weighted averages. Most of the ehtnic traits come from the Ethnographic Atlas and were complemented with information in Atlas Vorkolonialer Gesellschaften (i.e: german for Atlas of Precolo- nial Societies). Matching was based on previous work by Fenske (2012), Nunn and Wantchekon (2011), and the Atlas Vorkolonialer Gesellschaften. Slave Trade Prevalence: Original slave prevalence data comes at the ethnic level (Nunn and Wantchekon, 2011). The total number of slaves taken from a grid cell i, S , is imputed by doing: S = ✓ S where e indexes ethnic group, ✓ = POPi,e , i i i,e e i,e POPe e and POP are 1960 population counts.P Historical Trade Routes: shortest distance (in 100km) from centroid of grid to his- 78 torical trade routes recorded by Brince (1981)’s “An Historical Atlas of Islam”. Distance to Historical Conflict: shortest distance (in 100km) from centroid of grid to historical battle georeferenced in Besley and Reynal-Querol (2012). Light Density: log of 0.01 + within-grid average luminosity. Following Michalopou- los and Papaioannou (2013), average luminosity is calculated for the time period 2007-2008. Appendix B: Construction of the Instrument

Recent works in economics (Olson and Paik, 2012, and Ashraf and Michalopoulos, 2012) have used Pinhasi et al (2005)’s data on location and calibrated C14-dates estimated for archaeological sites to construct the timing of the initial adoption of agriculture at fine geographical level (such as region within countries). Unfortunately, Pinhasi et al (2005)’s data only cover neolithic settlements in Europe and the Middle East. In addition, African archaeology is relative new and has not yet accumulated the density of data as in Europe and America (Shaw et al, 1993). However, we do know that, unlike other regions, plant domestication did not spread from a single point within the African continent: adoptions of indigenous crops independently oc- curred at least in five di↵erent regions of Africa.70 Iuseavarietyofarchaeological sources to compile geographic location and date of earliest plant domestication for 24 representative archaeological sites in Sub-Saharan Africa.71 IthenfollowOlson and Paik (2012) and construct a continuous raster map of the time elapsed since the neolithic by interpolating the earliest date of crop adoption from my compiled data of archaeological sites. The interpolation is based on an inverse distance weighting

70Evidence places those regions of first domestication in the Western Sahel (pearl millet), Middle Niger delta (african wild rice), West Africa (yam), Sudan-Chad (sorghum), and Ethiopia (te↵). 71Table A.2 in appendix lists the archaeological sites. 79 80 method which relies on a underlying assumption that the closest archeological site provides the best information on the approximate earliest date of crops adoption. Formally, the date of earliest domestication of crops for the location i (i.e: a pixel in the raster map) will be given by:

24 T (Li)= s iT (Ls) b P Where T (Ls)isthedateofearliestdomesticationofcropsinthelocationL of archeo-

p 24 p logical site s and i = di,s / s di,s is a weight factor with di,s being distance between location i and location of siteP s. Finally, p is a power parameter determined by min- imizing the root-mean-square prediction error.

Figure 5 (in main text) depicts the geographical distribution of the predicted time elapsed since the neolithic revolution based on archaeological data. The diamond figures represent the location of the archaeological sites (see appendix for list of sites with their source of references), the numbers next to each figure represent the date of earliest domestication of crops.72 Note that the earliest date of domestication (7200 years before present times -BP-) is located in Faiyum region of Egypt and does not represent a case of domestication of an indigenous crop rather a di↵usion of agriculture from the Fertile Crescent, which in fact spread even southward over central Sudan around 5000 BP. Sorghum remains dating to 4000 BP in the Adrar Bous site in the Tnr desert (Niger) represents the earliest evidence of indigenous crop domestication. Archaeological sites in Mali, Ghana, and Mauritania are the earliest evidence of domestication of pearl millet around 3500 BP. By 2600 BP agriculture already spread into northern Senegal, and by 2300 BP in southern Cameroon. The archaeological site with the earliest date of domestication, around 3200 BP, in East

72To avoid an edge e↵ect and fully cover the Sub-Saharan surface in the interpolation process I include 4 edges denoted with the letter X (See Table A.2 in appendix). The edge e↵ect results from the fact that the inverse distance interpolation method cannot estimated a value located beyond the most extreme known value. 81 Africa is located in Kenya. Agriculture adoption happened much later in Malawi (1900 BP), northern South Africa (1700 BP), Zambia (1400 BP), and northwestern Botswana (1100 BP). Appendix C: Construction of Weather-Induced Productivity Shock

Iconstructaweather-inducedproductivityshocktotheagriculturalsectorintwo main steps which I explain in detail below. In the first step I construct five crop- specific weather shocks. In the second step I aggregate these shocks into one indicator. As in Harari and La Ferrara (2013), I construct my weather shocks using the Standard- ized Precipitation-Evapotranspiration Index (SPEI) developed by Vicente-Serrano et al (2010).

Drought index. The SPEI is a multiscalar drought index, which considers the joint e↵ects of temperature and precipitation on droughts (Vicente-Serrano et al 2010). The SPEI is based on the climatic water balance equation which depends on total precipitation and the capacity of the soil to retain water (i.e: evapotranspiration). Formally; the water balance equation for a given month t:

D = Prec PET , t t t

where Prect and PETt are precipitation and potential evapotranspiration (both in

82 83 mm), respectively. The PET need to be estimated using di↵erent climate inputs (such as temperature, cloud cover, and wind speeds) of which temperature is the most relevant. This water balance (deficit or superavit) can be aggregated at di↵erent scales

k k (i.e: number of months). Then, a given Dt is fitted to a Log-logistic distribution to

k obtain the SPEIt for a given month t and scale k over which water deficits/superavits accumulate. Since the SPEI is a standardized variable (with mean value of zero and standard deviation of 1), it can be compared over time and space (Vicente-Serrano et al, 2010) regardless of the election of k and t.73 Low and negative values of the SPEI denote relative high water balance deficits (Droughts).

As discussed in Harari and La Ferrara (2013), the original SPEI series are based on CRU TS3.0 data which relies on gauge data. This poses a problem in the con- text of Sub-Saharan Africa where gauge data (in particular historical data) is scarce, then highly interpolated, and potentially endogenous to the existence of conflict. I therefore recalculate all the necessary SPEI series using more reliable climate data from ECMWF ERA-Interim dataset (Dee et al., 2011) and the NOAA 20th century reanalysis (Earth System Research Laboratory, NOAA, U.S. Department of Com- merce, 2009), and the R package provided by the authors of the original index.

Crop-specific weather shocks. I focus on five staple crops: sorghum, millet, cassava, groundnuts, and maize. According to Schlenker and Lobell (2010), these crops are among the most relevant nutritional sources of calories, protein, and fat in Sub- Saharan Africa. They are also among the most relevant staple crops in terms of production (Depetris-Chauvin et al, 2012). In addition, these crops are highly de- pendent on rain. Although rice and wheat are also very relevant for this region, I excluded them from my analysis because they are highly irrigated (Schenkler and

73In other words, the SPEI is measured in units of standard deviation from the historical average of the water balance (i.e: average over the period for which input climatic variables are available). 84 Lobell, 2010).74 I then follow the main approach in Harari and La Ferrara (2013). For each grid cell and each of the five aforementioned crops I identify the planting and harvesting months.75 Therefore, I identify the length of the growing season (k) and the harvest month (t)foreachcropineachgrid-cell.76 Hence, for a given year,

kc,i SPEItc,i represents a weather shock specific to the crop c in grid i.

Weather-Induced Agricultural Productivity Shock. Icreateanaggregateweather- induced agricultural productivity shock for each grid i and year T by doing:

kc,i Negative W eather Shocki,T = ✓c,i SPEItc,i,T c ⇥ P where kc,i and tc,i are growing season length and harvest month for crop c in grid

77 i, respectively. ✓c,i are the normalized harvest shares for each crop c in grid i. There are two main departures from Harari and La Ferrara (2013) regarding the methodology implemented to create the shock. First, instead of focusing in the main crop (in term of harvested area) within a set of twenty six possible crops, I focus on the five most popular rainfed crops for Sub-Saharan Africa and use their relative importance (in terms of harvested area) to weight them in the aggregation within a grid cell. Second, Harari and La Ferrara (2013) define weather shock as the fraction of consecutive growing season months presenting an SPEI of 4 months of accumulation (scale 4) that is one standard deviation below the historical mean. They do state that their results are robust to di↵erent time scales. I am less agnostic regarding the relevant scale (i.e: the number of months over which water deficits/superavits accumulate) and force it to be determined by the length of each growing season,

74Since spatial variation in irrigation technologies is expected to be highly correlated with weather variation, including highly irrigated-crops would underestimate the statistical relationship between crop-specific weather shocks and conflict. 75All the information on crop calendars comes from Mirca 2000. 76In some regions a crop may have two growing seasons within a year; I focus only in the primary season. 77The shares of areas harvested for each crop are calculated based on M3-Crops. 85 instead.78 My approach allows for a more parsimonious definition of shocks and makes possible the distinction between moderate and extreme drought events.79

78I thank Santiago Bergueria -one of the authors of the SPEI- for this suggestion. 79For instance, between an SPEI value of -1 and -3. 86 Table A.1. List of Historical States

Date of

Establishment (1) Unestablishment (2)

Dongola (Makuria) b 1000 1314

Alwa b 1000 1500

Kanem Empire b 1000 1387

Kingdom of Ghana b 1000 1235

Pre-imperial Mali b 1000 1230

Pre-imperial Songhai (Gao) b 1000 1340

Siwahili city-states3 b 1000 1500

Mossi States 1100 a 1850

Ethiopia (Abyssinia) 1137 a 1850

Akan (Bonoman) 1200 1700

Imperial Mali 1200 1600

Buganda 1300 a 1850

Songhai Empire 1340 1590

Wollof Empire 1350 1549

Bornu-Kanem 1387 a 1850

Kingdom of Congo 1390 a 1850

Kingdom of Bamum 1398 a 1850

Yoruba (Oyo) 1400 a 1850

Nupe Kingdom 1400 a 1850

Darfur (Daju-Tunjur until c1600) 1400 a 1850

Hausa States 1400 1800

Adal Sultanate 1415 1577

Mwenemutapa (Kingdom of Mutapa) 1430 1760

Benin Empire 1440 a 1850

Kingdom of Butua (Butwa) 1450 1683 87 (continuation) Table A.1. List of Historical States

Date of

Establishment (1) Unestablishment (2)

Kingdom of Rwanda 1500 a 1850

Bunyoro-Kitara 1500 a 1850

Kingdom of Merina 1500 a 1850

Maravi Kingdom 1500 1700

Kingdom of Idah (Igala) 1500 a 1850

Kwararafa 1500 1700

Nkore Kingdom (Ankole) 1500 a 1850

Kotoko Kingdom 1500 a 1850

Mandara Kingdom (Wandala) 1500 a 1850

Funj Sultanate 1504 1821

Kingdom of Bagirmi (Baguirmi Sultanate) 1522 a 1850

Kingdom of Ndongo (Angola) 1530 1670

Kingdom of Jolof (Senegal) 1550 a 1850

Kingdom of Menabe 1550 a 1850

Awsa (Aussa Sultanate since c1730) 1577 a 1850

Luba Empire 1585 a 1850

Air Sultanate 1591 a 1850

Dendi Kingdom 1591 a 1850

Teke (Anziku Kigdom) 1600 a 1850

Kingdom of Dahomey 1600 a 1850

Kuba Kingdom (Bushongo) 1625 a 1850

Wadai (Ouaddai Empire) 1635 a 1850

Lunda Empire 1665 a 1850

Kingdom of Burundi 1680 a 1850

Rozwi Empire 1684 1834

Aro trading confederacy 1690 a 1850 88 (continuation) Table A.1. List of Historical States

Date of

Establishment(1) Unestablishment(2)

Kindom of Boina 1690 1808

Ashanti Empire 1700 a 1850

Kingdom of Orungu (Gabon) 1700 a 1850

Kong Empire 1710 a 1850

Bamana Empire (Segu) 1712 a 1850

Imamate of Futa Jallon 1725 a 1850

Lozi Kingdom 1750 a 1850

Mbailundu 1750 a 1850

Calabar (Akwa Akpa) 1750 a 1850

Kaarta (Baambara in Nioro) 1753 a 1850

Imamate of Futa Toro 1776 a 1850

Gibe States 1780 a 1850

Xhosa 1780 a 1850

Azande Kingdom 1800 a 1850

Swaziland (House of Dlamini) 1800 a 1850

Ovimbundu (4) 1800 a 1850

Yaka (4) 1800 a 1850

Borgu States 1800 a 1850

Sokoto Caliphate 1804 a1850

Zulu Kingdom 1816 a1850

Note: (1) b stands for before. (2 ) a stands for after. (3) Mogadishu, Mombasa, Gedi, Pate,

Lamu, Malindi, Zanzibar, Kilwa, and Sofala. (4) approximate date 89 Table A.2. Archaelogical Data Used to Construct Neolithic Instrument

Site Date Lat Lon Reference

Nqoma Site, Botswana 1100 -18.8 21.8 Manning et al (2011) -1-

Shongweni Site, South Africa 1100 -29.9 30.7 Manning et al (2011) -2-

Chundu Site, Zambia 1400 -17.6 25.7 Harlan (1976)

Silver Leaves , South Africa 1700 -24.0 31.0 Manning et al (2011) -3-

Knope Site, Malawi 1900 -15.8 35.0 Harlan (1976)

Bwamb-Sommet Site, Cameroon 2300 2.9 9.9 Manning et al (2011)

Abang Minko’o Site, Cameroon 2300 2.4 11.3 Manning et al (2011)

Walald, Senegal 2600 16.5 -14.2 Manning et al (2011)

Gajiganna, Nigeria -Chad Basin- 2930 11.8 13.2 Harlan (1976)

Jenn Jenno, Mali 3000 13.9 -4.6 Harlan (1976) -4-

Ti-n-Akof, Burkina Fasso 3000 14.5 -0.2 Manning et al (2011)

Deloraine Farm, Kenya 3200 -0.4 36.1 Marshall and Hildebrand (2002)

Ntereso, Ghana 3250 7.5 -2.9 Cowan and Watson (1992)

Tichitt site, Mauritania 3500 18.4 -9.5 Harlan (1976)

Oualata site, Mauritania 3500 17.3 -7.0 Harlan (1976)

Birimi, Ghana 3500 10.5 -0.4 Manning et al (2011)

Lower Tilemsi Site, Mali 3500 16.9 0.2 Manning et al (2011) -5-

Adrar Bous in the Tnr Desert 4000 20.4 9.0 Smith (1995)

El Zakiab, Sudan 5000 15.8 32.6 Marshall and Hildebrand (2002)

Faiyum, Egypt 7200 29.5 30.6 Shaw (1993) 90 (continuation) Table A.2. Archaelogical Data Used to Construct Neolithic Instrument

Auxiliary and Highly Speculative Data

Site Date Lat Lon Reference

South Extreme Edge 1100 -34.8 20.8 Based on Shongweni Site

Lakaton’I Anja Site, Madagascar 1600 -12.3 49.4 Burney et al (2004) -6-

Taolambiby Site, Madagascar 2300 -23.7 44.6 Burney et al (2004) -7-

West Extreme Edge 3000 15.1 -18.1 Somalia (Putterman 2006)

East Extreme Edge 3500 11.4 51.5 Senegal (Putterman 2006)

Notes: 1. Coordinates are for Tsodilo Hills. 2. Coordinates are approximate. 3. Northern Province,

South Africa. Coordinates are approximate. 4. Harlan (1976) mentioned a late domestication

(circa 2000BP) but Putterman (2006) put first date for agriculture at 3000BP based on Harlan’s argument. 5. Coordinates are for Karkarinchinkat Site. 6. Evidence of settlement but probably not long-term occupation. Very speculative. 7. This is the earliest evidence of human presence.

Very speculative. 91 Non-Sub-Saharan Cities Table A.3. Historical Cities Used in Alternative Measure CitySaint-DenisZimbabwe EggimannPort Louis Source ChandlerKilwa ChandlerLoandaSao Salvador 1800Loango Eggimann Chandler, Eggimann Chandler, EggimannCalabar Date 1300-1400 1500Gbara 1600-1800 1800 Chandler, EggimannBenin Agades Zagha EggimannWhydah 1700-1800 1200-1700 CityLagos Chandler QusAllada Chandler, Asyut Dongola Chandler, Eggimann EggimannKumasi 1600-1800 Chandler 1800 1800Abomey Chandler Eggimann ChandlerBonga 1600-1800 Tanta Chandler, Source EggimannIfe Chandler Chandler, Chandler Eggimann Chandler, 1700-1800 EggimannOyo Mahalla Giza el Bulaq 1700-1800 KubraFreetown Chandler 1200-1800 1600-1700 1800 1200 EggimannZaria Damietta 1600 MarrakechMassenya Eggimann Chandler Eggimann Date 1000-1500 Chandler, Eggimann 1000-1400 Kebbi 1400-1800Kano Eggimann Damanhour Chandler Chandler, Eggimann 1700-1800 Eggimann Chandler, EggimannGondar Chandler, 1800 Alexandria Eggimann 1600-1800 1200-1800 Katsina Meknes Chandler, 1200-1800 1800 Eggimann 1800 1800 TripoliSegou Eggimann Chandler, 1800 Eggimann Chandler, Taza EggimannSennar 1600-1800 Chandler, Eggimann Chandler 1200-1800 1800 1000-1800 1700-1800Jenne 1000-1800 Axum Rabat-Sale Azammur Eggimann Chandler, Tlemcen Eggimann Oran Chandler,Soba Chandler Eggimann Tanger Kairwan 1800 1300-1800 Gao 1600-1800 Chandler ChandlerTimbuktu Chandler, 1600-1800 Chandler Eggimann Chandler Eggimann Constantine 1000-1800 1700-1800 Chandler Chandler, Chandler Eggimann Ceuta Eggimann 1500-1800 Chandler Chandler 1000-1800 Bejaia Tagaste 1300-1600 Eggimann 1500 1000-1800 1500 1300-1800 Algiers 1000-1300 1000-1800 1800 Chandler 1500-1800 1000-1500 Chandler Eggimann Tunis 1400-1800 Annaba Eggimann 1200-1400 1500-1600 1200-1800 Chandler, Eggimann Eggimann 1300-1800 1500-1800 1800 Chapter 2

Malaria and Early African Development: Evidence from the Sickle Cell Trait

2.1 Introduction

1 It is impossible to understand the pattern of comparative economic development in the world today without understanding comparative development in the past. Con- sider, for example, a horizon of 500 years. Countries and regions that were highly developed as of the year 1500 are, for the most part, among the most developed today. Exceptions to this regularity, such as China, tend to be growing quickly. Taking into account flows of population over the last half millennium makes this correlation even stronger: Countries populated by people whose ancestors lived in the most developed countries are most likely to rich today. Looking within countries, people who are descended from parts of the world that were highly developed in the year 1500 are

1This chapter is the product of a joint collaboration with David Weil. We are grateful to Ronald D. Lee, Andrew Mason, Gordon McCord, Nathan Nunn, and Fred Piel for sharing data, to Fatima Aqeel, Federico Droller, Daniel Prinz, and Scott Weiner for research assistance, and to Quamrul Ashraf, Adriana Lleras-Muney, Stelios Michalopoulos and seminar participants at Boston University, Harvard University, Harvard Center for Population and Development Studies, Indiana University, New York University, Pennsylvania State University, Princeton University, UCLA, University of Connecticut, and University of Pennsylvania for helpful comments 92 93 on average higher up in the income distribution than people descended from regions that were not developed.2

Going back further back in time, there is still strong predictive power of past develop- ment for present outcomes. Comin, Easterly, and Gong (2010) show that not only is a countrys level of technology from 500 years ago predictive of income today, but so is the level of technology 2,000 or 3,000 years ago. Hibbs and Olsson (2004) show that the date at which the transition from hunting and gathering to settled agriculture took place is predictive of a countrys income today.

The fact that development in the past is so predictive of development today suggests two possible theories. First, it may be that the same factors that influenced develop- ment in previous historical eras are still operative in the present. Examples of such factors are genetic attributes of populations, slowly changing aspects of culture or institutions, and characteristics of geography or climate. 3 Alternatively, it may be that the specific factors that caused past underdevelopment are no longer relevant today, but that the fact of past underdevelopment itself is causal of current under- development. For example, it could be that the early development advantage of the Eurasian land mass arose from the historical presence of plentiful species of large seeded grasses and domesticable animals, as argued by Diamond (1997), but that the continuation of the development gap between Eurasia and other regions results from the e↵ect of colonial institutions that Europeans were able to impose on much of the rest of the world as a result of this initial advantage.4

2Chanda and Putterman (2007), Putterman and Weil (2010). 3See, for example, Ashraf and Galor (2012) and Sachs, Malaney, and Spielman (2004). Spolaore and Wacziarg (2013) discuss the di↵erent possible channels by which characteristics that impact economic outcomes might be transmitted intergenerationally. 4See, for example, Acemoglu, Johnson, and Robinson (2001) and Nunn (2008). A related argu- ment, stressed by Spolaore and Wacziarg (2013) is that past di↵erences in development are causal for current outcomes because of barriers to transmission of productivity enhancing innovations among populations with di↵erent historical roots. 94 Whichever of these theories is correct (and obviously it is possible for both of them to have some validity), there is clearly much to be learned by looking at the roots of development di↵erences in the past. In this paper, we examine the historical impact on development of malaria. Malaria is one of the most significant diseases in the world today in terms of its humanitarian burden. Malarias control is widely studied by biologists and social scientists. Economists actively debate its role in a↵ecting growth in the modern world.5 However, as the above discussion makes clear, it would be possible that even if malaria were not important in a↵ecting economic development today directly, it could nonetheless have been an important determinant of development historically and via that channel indirectly a↵ect development today.

In studying the role of malaria in long run development, we are also inevitably study- ing the long run development of Africa. Both historically and today, Africa has been the major focus of the disease. Indeed, malaria was not present in the tropical re- gions of the new world until it was accidentally brought there by Europeans (McNeill, 1977). Historians of Africa attribute a large role to diseases in general, and malaria in particular, in shaping development (Akyeampong, 2006). For example, Webb (2006) describes malaria, along with trypanosomiasis (transmitted by the tsetse fly) as hav- ing profoundly influenced African patterns of settlement as well as culture. Alsan (2013) also finds a large role for trypanosomiasis in shaping population density in Africa. The data we analyze below gives us a unique opportunity to assess the role of malaria in shaping geographic heterogeneity in Africa.6

5The widely quoted estimate of Gallup and Sachs (2001) is that malaria reduces growth of GDP per capita by 1.3 percent per year in the African countries most a✏icted. See Weil (2010) for an extensive critique of this literature. 6Weil (2011) paints a picture of African development in 1500, both relative to the rest of the world and heterogeneity within the continent itself, using as his indicators population density, urbanization, technological advancement, and political development. Ignoring North Africa, which was generally part of the Mediterranean world, the highest levels of development by many indicators are found in Ethiopia and in the broad swathe of West African countries running from Cameroon and Nigeria eastward along the coast and the Niger river. In this latter region, the available measures show a level of development just below or sometimes equal to that in the belt of Eurasia running from Japan and China, through South Asia and the Middle East, into Europe. Depending on the index used, 95 Analysis of the role played by malaria in shaping historical development is severely hampered by a lack of data. Biologists only came to understand the nature of the disease in the late nineteenth century. Accounts from travelers and other historical records provide some evidence of the impact of the disease going back millennia, but these are hardly sucient to draw firm conclusions.7 Even today, trained medical per- sonnel have trouble distinguishing between malaria and other diseases without the use of microscopy or diagnostic tests. As discussed below, there do exist data (malaria ecology) which measure the extent to which the environment in di↵erent geographical regions is supportive of malaria. One can look at the empirical relationship between this malaria ecology measure and economic development, either currently or histori- cally. We discuss such a statistical approach below. However, such an approach faces severe limitations. One problem is that the malaria ecology variable is not scaled in way that allows for easy economic interpretation. Further, it is dicult to know whether one has controlled for correlates of malaria ecology that might independently influence the process of development. These correlates could be other factors that directly influence output today (either other diseases, or the tropical climate, which a↵ects agriculture) or they could be the result of historical processes, for example institutional quality, which Acemoglu, Johnson, and Robinson (2001) argue was in- fluenced by the disease environment. In this paper we address the lack of information on malarias impact historically by using genetic data. In the worst a✏icted areas, malaria left an imprint on the human genome that can be read today. Specifically, we look at the prevalence of the gene that causes sickle cell disease. Carrying one copy of this gene provided individuals with a significant level of protection against malaria, but people who carried two copies of the gene died before reaching reproductive age. Thus the degree of selective pressure exerted by malaria determined the equilibrium

West Africa was above or below the level of development in the Northern Andes and Mexico. Much of the rest of Africa was at a significantly lower level of development, although still more advanced than the bulk of the Americas or Australia. 7Akyeampong (2006), Mabogunje and Richards (1985). 96 prevalence of the gene in the population. By measuring the prevalence of the gene in modern populations, we can thus back out an estimate of the severity of malaria historically. We compare these estimates to the burden of malaria today and discuss how the change in malaria mortality has compared to the change in mortality from other sources.

With estimates of the extent of malaria mortality in hand, we then turn to look at the impact of the disease on economic development. We take two approaches. The first is statistical: we present regressions of a number of measures of development within Africa on a malaria burden measure we create based on sickle cell prevalence. Of particular note, we apply our analysis to a data set measured at the level of ethnic groups as an alternative to more common country-level analyses. The sec- ond approach eschews regressions, instead using the machinery of standard economic modeling, along the lines of Ashraf, Lester, and Weil (2007). In the current paper, we focus in particular on quantifying the economic e↵ects of malaria mortality of the magnitude estimated from our genetic data.

The rest of this paper is organized as follows. In Section 1, we discuss the biology of the link between malaria and sickle cell disease. Section 2 presents and applies our model for using the current level of sickle cell prevalence to estimate the historical burden of malaria, including comparisons of malarias historical burden to its current level and to the historical burden of other diseases. We then turn to the question of how malaria a↵ected development. Section 3 presents a statistical analysis of the relationship between the malaria burden measure we create and a number of measures of development within Africa. In Section 4 we take a model-based approach to evaluating cost of the malaria mortality, and also discuss the e↵ect of malarial morbidity. Section 5 concludes. 97 2.2 Malaria and Sickle Cell Disease

Background

Malaria is caused by the plasmodium parasite, which is transmitted to humans through the bite of a female anopheles mosquito. Early symptoms of malaria in- clude fever, chills, severe headache, and vomiting. In severe cases these are followed by respiratory distress, severe anemia, or neurological symptoms (cerebral malaria). Infants are protected from the disease in the first few months of life by a combination of maternal antibodies and characteristics of the structure of fetal hemoglobin. In malaria endemic areas, most children have developed substantial immunity by the age of five.

Africa currently accounts for 85 percent of world malaria cases and 90 percent of world malaria deaths. The geographical pattern of malaria’s severity is largely determined by the climactic conditions that support mosquito breeding as well as by the mix of mosquito species present. There are significant di↵erences in the vectorial capacity among the approximately 20 species of anopheles that transmit malaria, based on factors such as the mosquito’s preferred targets, biting frequency, and lifespan. The most e↵ective vector, Anopheles Gambiae, is the principal vector in Africa.

Several mutations have arisen in human populations that provide resistance to malaria. These include the mutation causing thalassemia, which is present in Mediterranean, Arab, and Asian populations; the absence of the Du↵y blood group in west Africa; hemoglobin E in Southeast Asia; and hemoglobin C in West Africa (Allison, 2002; Nelson and Williams, 2006). The most important such mutation is the one that causes sickle cell disease.

The sickle cell trait is a mutation in the gene the produces hemoglobin, the oxygen- 98 carrying component in red blood cells. Individuals carry two copies of this gene, one received from each parent. Individuals who carry one normal copy of the gene (referred to as A type) and one copy with the sickle cell mutation (S type) are carriers of the disease. In individuals of the AS genotype, a fraction of the hemoglobin in their red blood cells have an abnormal structure. In individuals who have two copies of the sickle cell gene (SS genotype), almost all hemoglobin molecules are of the abnormal type.

In conditions of inadequate oxygen supply (hypoxia), hemoglobin produced by the S gene becomes rigid, leading to a characteristic sickle shape of red blood cells. Carriers of sickle cell trait generally do not su↵er many adverse e↵ects.8 However, there can be negative consequences from sickling in conditions of low oxygen such as unpressurized airplane flights and extremely rigorous exercise (Motulsky, 1964). In individuals of the SS genotype, such sickling of red blood cells is far more common, leading to acute episodes of disease in which abnormally shaped cells restrict blood flow to organs. Such individuals also su↵er from anemia and reduced resistance to infection. In 1994, life expectancy for SS children in the United States was 42 years for males and 48 years for females. In the absence of modern medical care, individuals of the SS genotype are not able to survive to adulthood.

The sickle cell mutation is relevant to malaria because infection of a red blood cell with the malaria parasite leads to hypoxia. In individuals of the AS genotype such blood cells sickle and are then eliminated by the body’s immune system, lessening the burden of infection. Carriers of the sickle cell trait are particularly resistant to severe malarial episodes; they are less resistant to mild cases. The mechanism by which AS carriers are protected from malaria is di↵erent than the acquired immunity that both AA and AS individuals achieve following repeated exposure to the disease.

8Williams et al. (2005) show the absence of any significant e↵ect of carrier status on a wide range of childhood diseases. 99 The benefit that possessing a single copy of the sickle cell gene conveys counterbal- ances the biological cost incurred when homozygous SS children are stricken with sickle cell disease. An individual of the AS genotype is more likely to reach adult- hood than is an individual of the AA genotype, but the former is also more likely to see his/her child die of sickle cell disease. This is known as a heterozygote advan- tage or balanced polymorphism. As shown more formally below, the stronger is the pressure of malaria on survival, the more advantaged are individuals who carry the S gene, and in equilibrium, the higher the percentage of the population who will be carriers. Indeed, it was the correlation of high prevalence of the sickle cell gene and the presence of malaria that first led scientists to understand the protective role of the sickle cell mutation. As will be seen in the next section, the underlying genetic mechanism by which the sickle cell trait is transmitted provides a means of mapping sickle cell prevalence into an estimate of the mortality burden of malaria.

Piel et al. (2010) present a global geo-database of S gene frequency based on compre- hensive electronic search of academic publications presenting S gene frequency figures. Each reference included in the dataset meets the criterion that the surveyed popu- lation was representative of the indigenous population of a particular location. Piel et al. assign a geographic coordinate to all samples with the distribution of AS and AA genotypes that meet their strict inclusion criteria. Using a Bayesian model-based geostatistical framework they then create a continuous map of the sickle cell gene frequency resulting in 10 km by 10 km resolution global raster grid. It is important to note that throughout our paper we use the terminology sickle cell trait prevalence (the fraction of people who are carriers of the allele S) instead of sickle cell gene (or S allele) frequency as in Piel et al (2010). Since very few S homozygotes survive to adulthood, the sickle cell trait prevalence is very close to 1/2 of the sickle cell gene frequency. 100 Figure 2.1 presents the geographic distribution of S gene frequency from Piel et al. The maximum levels of the mutation are located in West-Central Africa (Gabon and Congo). The west part of Africa, from Cameroon to Senegal, shows a large hetero- geneity in the prevalence of the mutation, ranging from almost complete absence in some places and very high values (above 20 percent) in others. Medium level fre- quencies are also located in the proximity of Lake Victoria. Finally, no mutation is documented in the southern part of the continent.9

9Several theories have been proposed regarding the origin of the sickle cell mutation. Most of them deal with its location while some attempts have been made to estimate its age. A single mutation theory was initially postulated. Two di↵erent places of origin were proposed, Singer (1958) proposed the Rwenzori Mountains in Central Africa whereas Lehmann (1964) postulated the Arabian Peninsula (which was a fertile plain during Neolithic times). However, substantial genetic evidence supports the multicentric origin hypothesis today. According to this hypothesis the sickle cell mutation was the result of independent events happening in at least five di↵erent places (four in Africa and one in Asia). Studies in molecular genetics found that the HbS gene is associated with at least four di↵erent beta-globin haplotypes in Africa, which di↵er in at least three di↵erent genetic markers (in other words, the mutation presents four or more chromosomes from separate locations) providing evidence of the multicentric origin of the mutation. The four di↵erent haplotypes are referred to as Benin, Senegal, Cameroon, and Bantu (Central Africa). Regarding the estimation of the date of the origin for the mutation some scholars have argued that the mutations probably arose less than 6000 years ago in response to the spread of agriculture. In particular, they argued that the time of origin coincided with the time malaria became endemic due to the development of slash-and-burn agriculture (see, for example Wiesenfeld, 1967 and Livingstone, 1958). On the contrary, in the first attempts applying statistical calculations it was suggested that the mutation developed between 3,000 and 6,000 generations or approximately between 70,000 and 150,000 years ago (see, for example, Kurnit, 1979 and Soloman, 1979). A more recent genetic study based on an ethnically well-dened population (lacking of recent admixture and amalgamation) suggests a much younger age for at least one the original mutations. According to Currat et al (2002), mutation related to the Senegal haplotype arose less than 3,000 years ago 101

Figure 2.1: Sickle Cell Gene Frequency from Piel et al (2010)

2.3 Measuring the Historical Burden of Malaria

Using Data on the Sickle Cell Trait

Model

Our goal is to examine what the prevalence of the sickle cell trait among African pop- ulations tells us about the impact of malaria historically. As described above, every adult carries two alleles. These can be either sickle cell (S) or normal (A). A person with the SS genotype will develop sickle cell disease and not survive to adulthood. A person who carries the sickle cell trait (AS) will have a survival advantage against 102 malaria in comparison to someone who doesnt (AA).10

We consider a simple model in which deaths occur due to either malaria or to other causes. Let M be the probability of dying of malaria, conditional on not dying of something else. Similarly, let P be the probability of dying of something else, conditional on not dying from malaria.11 The deaths that we are concerned with are those between birth and adulthood, which is taken to be the age at which children are produced. The number of adults from a cohort of newborns will be given by:

Surviving Adults =(1 M)(1 P )Newborns (2.3.1)

Throughout the analysis, we will assume that the probability of dying from non- malaria causes, P, is the same for individuals with the AS and AA genotypes. The probabilities of dying from malaria, di↵er between AA and AS genotypes. We des- ignate these probabilities M AA and M AS. It is also useful to designate the relative survival rates of these two genotypes, which we call :

(1 M AA)(1 P ) (1 M AA) = = (2.3.2) (1 M AS)(1 P ) (1 M AS)

is the probability of a non-carrier living into adulthood relative to the probability of a carrier living into adulthood. The smaller is , the larger is the advantage of the AS genotype. A value of =1wouldindicatethatthereisnoadvantagetocarrying the sickle cell gene. The values for M AA and M AS, and thus for , will depend on both the disease environment and the state of medical technology. For example, in

10We ignore other mutations. In the presence of other mutations that also control malaria, the genetic benefit of carrying the S trait is reduced, but its cost remains the same. Thus our analysis, based only on the S trait, will understate the full burden of malaria. 11For the purposes of this part of the analysis, it does not matter whether deaths due to sickle cell disease are counted as being due to malaria or due to a non-malaria cause. In our analysis below, we include deaths due to sickle cell disease as part of our measure of malaria burden, because in the absence of the disease, such deaths would not occur. 103 aplacewheretherearenomalariamosquitoes,M AA and M AS will both be equal to zero, and will be equal to one. Clearly, the availability of modern medical care should mean that both M AA and M AS are lower today than they were in the past. However, it is not clear a priori which mortality rate would be reduced by more.

Relative Survival in Modern Populations. Although our main objective is asking what role malaria played historically, it is of interest to see what information is available about relative survival today. One way to measure relative survival is to compare prevalence among adults to that among newborns. The relevant equation is:

AA Adults (1 M AA)(1 P )AA newborns = (2.3.3) AS Adults (1 M AS)(1 P )AS newborns

Morrow and Moss (2006) report on data from the Garki study, conducted in a region of high malaria transmission in Nigeria in the 1970s. Among adults, 28.96 percent of the population was AS, while 70.2 percent were AA. Among newborns, 23.6 percent were AS and 73.78 percent AA. Entering these figures in the equation above gives avalueof =77.5percent.Moregenerally,MorrowandMossreportthatthe prevalence of the sickle cell trait in West Africa trait rises from 20-24 percent among newborns to 26-29 percent among adults.12

Another approach is to look at survival directly. Motulsky (1964, table 2) examined the relative survival of over 15,000 children in the Congo in the 1950s, a time and place where modern treatments for malaria would have been relatively scarce. The study compared families where one parent was AS and one AA, on the one hand, to

12Examining the Baamba of Uganda, who live in extremely high malaria environment, Lehmann and Raper (1956) report that the fraction of sicklers (AS or SS) rises from 30 percent of those under five years age to 37 percent among adults. Using these figures in equation (2.3.3), this would imply a value of = 73 percent. This calculation understates the size of the mortality di↵erential, however, both because those in the under five age group will have already experienced di↵erential mortality from malaria and because the group of young sicklers presumably includes a larger fraction of SS individuals than does the adult group. 104 families in which both parents were AA, on the other. Since half of the children in the former group were carriers, compared to none in the latter, one can back out the relative survival of AS vs. AA children. The study found mortality from all causes of 24 percent among AS children vs. 27.4 percent among AA. The implied value of is .955. Part of the explanation for the di↵erent estimates in the Congo vs. West Africa may be a di↵erence in the severity of malaria. In the Congo, the malaria ecology index is 12.1; in West Africa it is generally in the neighborhood of 20.

Measuring Relative Survival in Historical Populations

We now turn to our main line of inquiry, which is using observed frequency of the sickle cell trait to back out the severity of malaria. Let be the fraction of adults in generation t who are carriers (AS). We assume that no one born with SS lives into adulthood. Thus the fraction of the adult population who are not carriers is . The fraction of alleles in the adult generation that are S is simply . Assuming that mating between carriers and non-carriers is random, the fractions of children born who are of each type are:

2 AA : 1 ⇡t 2 AS : ⇡ 1 ⇡t t 2 ⇡t 2 SS : 2 The di↵erence equation for ⇡, which relates prevalence among adults in successive generations, is:

⇡t ⇡t 1 2 ⇡t ⇡t+1 = = (2.3.4) ⇡ ⇡ 2 ⇡ 1 t + 1 t + ⇡t 1 t 2 2 2 105

We solve for the steady state by setting ⇡t = ⇡t+1:

1 ⇡ss = (2.3.5) 1 2

This has the properties we would expect: the smaller is the , that is the greater is the survival advantage of being a carrier, the larger is the equilibrium fraction of the adult population that will be carriers.

We can turn this equation around to infer the burden of malaria on survival based on the prevalence of the sickle cell trait among adults:

1 ⇡ = ss (2.3.6) 1 ⇡ss 2

This equation says that if 20 percent of the adult population are carriers in a steady state, then = .89 , in other words that people with the AA were only 89 percent as likely to live to adulthood as those with AS.

Current vs. Historical Prevalence. The analysis above considers a population that is in equilibrium in terms of the selective impact of malaria and the prevalence of the sickle cell trait. Such a steady state presumably existed in Africa in the period before European contact. However, the only data on sickle cell prevalence available comes from observations over the last 60 years.13 One might worry that modern prevalence rates are not the same as those that held historically, because the health environment has been changing over time. To address this question, it is straightforward to use equation (2.3.4) to look at how prevalence ⇡ changes in response to a change in relative survival ().

13Testing predates modern technology for genetic analysis. Carriers of the sickle cell gene can be reliably diagnosed by taking a drop of blood and mixing in a reducing agent to induce hypoxia, then examining with a microscope whether blood cells have sickled. 106 As an example, we consider the case where there is initially a steady state of ⇡ = .20 , and correspondingly =0.89. In generation 1, the value of is set to one, corre- sponding to the complete eradication of malaria, or the removal of the population to a place where the disease is not present. Figure 2 shows the fraction of the population that will be carriers of the sickle cell trait after a set number of generations. The figure shows that the initial decline is very rapid, but that there is long tailing o↵ once the prevalence gets suciently low.

Figure 2.2: E↵ect of a Hypothetical Malaria Eradication on Evolution of Carriers

In principle, one could test this model by looking at how the prevalence of the sickle cell trait changes in a population that was removed from exposure to malaria at a known time. Although the data are in practice not sucient for a formal test, the case of African Americans provides a rough consistency check. We start by calculating the fraction of adults who are carriers in the countries that were the source of slaves that came to the United States. Specifically, we combined two data sources. We used data from Piel et al. (2010) on the prevalence of the sickle cell trait among adults in modern African countries. We combined these with estimates from Putterman and Weil (2010, main appendix, part II.3) on the fractions of the slaves who came to the United States originating in each African country. Multiplying and summing these 107 two series, we estimate that 16.2 percent of slaves who came to the United States were carriers of the sickle cell trait (calculation available on request).

The other piece of information needed is the date on which the ancestors of todays African Americans left Africa. Obviously, this took place over several centuries, and we do not know of an estimate of the average date of departure. As a rough estimate that the average ancestor of todays African Americans left Africa around 1750 – a span of roughly 10 generations.

Starting with 16.2 percent prevalence and applying equation (2.3.4) for ten genera- tions would lead to a prevalence of 9.0 percent today. However, one has to deal with the issue of admixture with other populations. Putterman and Weil (2010, Appendix B) summarize literature on the fraction of African American heritage that is due to non-Africans, reporting 20 percent as a rough consensus figure. That admixture took place at unknown points in time over the last three centuries. The earlier that admixture took place, the higher would be the prevalence of the trait among African Americans today. If the entire admixture took place in the last generation, the preva- lence today would be 7.2 percent. By contrast, if the entire admixture took place ten generations ago, our calculation would yield a prevalence of 7.9 percent today

In fact, the prevalence of the trait among African Americans today is 8 percent.14 Thus the model slightly under-predicts the prevalence of the sickle cell trait among African Americans. One possible source of this error could be that that African slaves brought to the Americas did not find themselves in a completely malaria-free environment. For example, McGuire and Coelho (2011) stress slaves immunity to malaria as one of the reasons that southern planters favored them over indentured servants.

14National Heart, Lung, and Blood Institute (1996). 108 In terms of our use of current prevalence of the sickle cell trait to measure the historical burden of malaria, it is not clear that this analysis of the dynamics of prevalence matters much, since there is little reason to believe that contact with Europeans did anything to reduce the impact of malaria in Africa until the second half of the twentieth century, which is when most of the measures of sickle cell come from.

2.3.1 Measuring the Overall Burden of Malaria

Knowing the relative survival of carriers and non-carriers does not tell us the overall e↵ect of malaria, for two reasons. First, in addition to deaths from malaria, we must take into account the cost of sickle-cell disease itself. This issue is easily addressed by looking at the fraction of adults who are carriers, from which we can derive the fraction of children who will su↵er from sickle cell disease. The underlying data on the number of carriers is the same data used to estimate .

The second reason that knowing (relative survival) does not tell us the overall burden of malaria is because a given value of could be consistent with di↵erent levels of absolute survival. For example, = .80 could be consistent with M AA =0.20 and M AS =0,butitcouldalsobeconsistentwith M AA =0.60 and M AS =0.50. Dealing with this issue requires bringing to bear additional data. Specifically, we need an additional piece of information on M AA, M AS, or their ratio. One can look at modern populations for some information, with the caveat that modern data on survival is not necessarily informative about survival in Africa prior to European contact, where both the disease environment and the level of medical care di↵ered from today.

Allison (2002, Table 2) reports results from an examination of 104 child malaria deaths from di↵erent countries in Africa, in which the weighted average prevalence of the 109 sickle cell trait was 21 percent. Only one child examined had the sickle cell trait, which would suggest that the trait is almost completely protective against malaria death. However, a di↵erent set of investigations (Allison 2002, Table 1) that looked at severe P. falciparum infections rather than deaths found a relative incidence of infections in AS that was 46 percent as high that for AA. Both sets of studies just described were conducted in the 1950s or very early 1960s. A larger and more recent study (Hill et al., 1991) examined children in The Gambia. Children who were severely ill with malaria were compared to a control group. The severely ill children had cerebral malaria or severe malarial anemia. Without treatment, most of the children in this group would have died.

Among the severe malaria group, the frequency of the AS genotype was 1.2 percent, while among the control group it was 12.9 percent. This implies that the relative risk of developing severe symptoms (and presumably dying without medical care) in AS as compared to AA is 0.08. 15 A third study (Williams et al, 2005) concluded that HbAS was 90 percent protective against severe or complicated malaria. These studies suggest that reasonable bounds on M AS are zero on the low end, and to assume that

M AS 16 M AA =0.08 on the upper end.

With estimates of M AS and M AA, we are in a position to look at the overall costs of malaria. There are three components to this cost: deaths from malaria among children who are carriers of the sickle cell trait, death from malaria among children who are not carriers, and deaths from sickle cell disease. Table 1 shows the fractions of

15Another study (Greenwood, Marsh, and Snow, 1991) examined children in Kenya, finding that the sickle cell trait was present in only 1.8 percent of children with severe malaria anemia but 3.9 percent of children with uncomplicated malaria. This finding confirms that the trait is more protective against severe malaria than against mild cases. However, because no data are given on the prevalence of the trait in the overall population, one cannot back out the relative risk of AA vs. AS. 16Given a ratio of malaria mortality in the two groups, along with the equation for , we can AA 1 AS X(1 ) solve the for the group rates of malaria mortality. These are M = 1 X and M = 1 X , where X is the ratio of M AS to M AA 110 births that fall into each category, the death rate for each group, and the total fraction of child deaths (from malaria or sickle cell disease) that are due to each category. The overall fraction of children who die due to malaria and sickle cell disease is simply the sum of the three terms in the right hand column.

Figure 2.3 does a more extensive analysis, considering di↵erent values of ⇡, the preva- lence of the sickle cell trait among the adult population. We consider values ranging from zero to 40 percent, which is the highest level observed among specific popula- tions. For each value of ⇡, we calculate the implied value of , assuming that the prevalence represents a steady state (equation 2.3.6). We also show the fraction of newborn children who will die of sickle cell disease and the malaria death rates for non-carriers under the two di↵erent assumptions about the death rate for carriers dis-

AS M AS cussed above, specifically that M =0andthat M AA =0.08. Finally, we show the overall burden of malaria and sickle cell disease, once again for the two assumptions about the death rate for carriers discussed above.

Figure 2.3: Implications of Varying Sickle Cell Prevalence for Malaria Burden 111 The figure shows that, at least within the range of the two estimates we have available, the assumption regarding the degree of protection a↵orded to carriers of the sickle cell mutation is not very important.

The fraction of the overall burden that takes the form of sickle cell disease rises with prevalence. For example, when ⇡ =0.20, roughly one-tenth of the overall burden is in the form of deaths from sickle cell disease, with the other nine tenths due to malaria cases. When ⇡ =0.20, sickle cell deaths account for roughly 20 percent of the burden. It is also of interest to calculate the net benefit of the sickle cell mutation, that is, the level of mortality in the presence of the mutation relative to the case where it is absent. The level of mortality absent the mutation can simply be read from the line representing M AA, since in this case everyone would have the mortality rate of non-carriers. For example, in the case of 20 percent prevalence of the sickle cell trait, overall mortality due to malaria and sickle cell disease is 10

2.3.2 Comparison of Malaria Burden to Malaria Ecology

As mentioned above, our malaria burden index is not the first attempt to construct a systematic measure of the impact of malaria. The malaria ecology index of Kisezewski et al. (2004) has been extensively used in the literature. The index takes into ac- count both climactic factors and the dominant vector species to give an overall mea- sure of how congenial the environment is to the spread of malaria. It does not take into account public health interventions such as swampdraining or di↵erences among countries in economic development or health care infrastructure. In other words, it represents the component of malaria variation that is exogenous to human interven- tion. The index is calculated for grid squares of 0.5 degree longitude by 0.5 degree latitude. Figure 2.4 shows data for the whole world, and Figure 2.5 shows a close-up of 112 Africa. With the exception of New Guinea and some areas of southeast Asian, Africa is the only part of the world in which the index reaches its highest levels. Regions in which malaria played a significant role historically but has now been eradicated, such as Greece, Southern Italy, and the American South, are all seen to have relatively low values for the malaria ecology index. Within Africa, there is substantial variation in the index.

Figure 2.4: Worldwide Distribution of Malaria Ecology

We can similarly construct our measure of malaria burden, starting with data on the prevalence of the sickle cell mutation among indigenous populations. Specifically, we use the 10 km. by 10 km. resolution global raster grid of S gene prevalence constructed by Piel et al (2010). We assume that the malaria mortality of AS genotype relative to the AA type is 0.08. None of the results that follow are qualitatively a↵ected by this assumption.17 The malaria burden index is a transformation of the data in Figure 2.1 and looks very similar to that figure.

In this section we compare the performance of these two indices in explaining malaria prevalence both in the past and in the present. We focus our analysis in two di↵erent

17Results are available upon request. 113

Figure 2.5: Malaria Ecology in Africa levels of aggregation: first, grid squares of 0.5 degree longitude by 0.5 degree latitude, and second, ethnic groups mapped by Murdock (1959). We show that regardless of the level of aggregation our measure explains a larger fraction of the variation of two di↵erent measures of malaria prevalence.

We use two di↵erent dependent variables: (1) a dummy variable for having a highly malarious environment c.1900, and (2) malaria endemicity in the year 2007. The first measure is constructed based on Lysenkos (1968) map categorizing the world at the beginning of the twentieth century into six classes of malaria transmission intensity in order of severity: no malaria, epidemic, hypoendemic, mesoendemic, hyperendemic, and holoendemic. We focus in the two worst malaria environments as highly malarious, and construct a measure which is the fraction of the territory of our unit of analysis (grid cell or ethnic group) in which malaria fell in this range. 114 The second measure accounts for the average plasmodium falciparum transmission intensity (parasite rate) in 2007 and comes from Hay et al. (2009).

Table 2 presents the main statistical results at the grid cell level. We adjust standard errors for two-way spatial autocorrelation with cut-o↵ distance of 5 degrees (approx- imately 550 km at the equator). In addition to the point estimates and standard errors we report standardized coecients in brackets (i.e, the fraction of standard de- viation changes in the dependent variable due to a one-standard deviation change in the relevant malaria measure). For columns 1 to 3 the dependent variable is the share of grid cell under highly malarious environment. For columns 4 to 6, the dependent variable is malaria endemicity in 2007. For the two sets of regressions our measure of malaria burden explains a larger variation of the dependent variable. According to the results in columns 1 and 2, our measure explains 15 percent more variation of circa 1900 malaria severity. When running a horserace both independent variables remain highly statistically significant, although the t-statistic for our measure de- creases far less. The same pattern holds when we look at modern malaria endemicity as dependent variable (columns 4 to 6). Across all the specifications in Table 2 the standardized coecients for our measure are consistently larger than for the case of malaria ecology.

We repeat the exercise focusing our analysis on ethnic groups mapped by Murdock (1959) at the time of colonization in Africa. We restrict our analysis to the 525 ethnic groups that we include later on in our study of the relationship between malaria burden and early development. Table 3 presents the main statistical results. We cluster the standard errors at the ethnolinguistic family level.18 We find the same pattern documented in Table 2: our measure explain more variation of both highly

18The statistical significance of these results is not a↵ected if we adjust the standard error for spatial autocorrelation in the error term. Moreover, the standard errors when clustering at the ethnolinguistic family level tend to be larger than when adjusting by spatial autocorrelation. 115 malarious environment c.1900 and malaria endemicity in the year 2007. In fact, the di↵erence in the R-squared is much larger than in the grid cell case, with our measure explaining 80 percent more of the variation of malaria severity in early 20th century and almost 50 percent more of current levels of malaria endemicity.19 Again, the standardized coecients for our measure are consistently larger across all the specifications.

We also experimented other robustness checks.20 Motivated by the possibility that climate change occurred during late 20th century may be responsible of the relatively weaker performance of the malaria ecology index in explaining highly malarious envi- ronment circa 1900, we repeat the same exercise using a malaria ecology index based on early 20th climate data.21 We find the same pattern in the data. We also cre- ate 1960 population-weighted measures of both the dependent and the independent variables.22 Again, the same pattern documented in Table 2 and 3 holds. We obtain similar results when we create an additional measure of malaria endemicity c.1900 by averaging the six classes of malaria transmission intensity documented in Lysenko map (recall that each of the six classes listed in order of severity and assigned to an integer value in a 0 to 5 scale).

Overall, our finding is that the new malaria burden measure is superior to malaria

19We additionally investigated ethnic groups in which the malaria ecology and malaria burden measure give very di↵erent values. The bulk of these have low levels of malaria burden relative to their level of malaria ecology. They are all highly clustered in three particular places: Burkina Faso and Southern Mali; Southern Chad; and Eastern Sudan. In addition, these ethnic groups tend to rely more on cereals as their main crop (as opposed to roots and tree fruits, or no agriculture activity whatsoever). Modern genetic work has documented the low frequency of sickle cell gene (and other known protective genes) for ethnic groups living in Burkina Faso, where malaria transmission is very high. These groups have an unexpectedly low susceptibility to falciparum malaria, which would point out to the possibility that other unknown genetic factors of resistance to malaria might be involved 20Results are available upon request. 21We thank Gordon McCord for providing this data. This version of the malaria ecology is the average ecology index for the period 1900 -1905 CE. 22We create population-weighted averages using estimates of population in 1960 at approximately 0.1 decimal degree resolutions (approximately 1km at the equator) from the African Population Database Documentation, UNEP GRID Sioux Falls. 116 ecology for predicting the severity of malaria in Africa, although clearly there is more information in the two indices taken together than in either one separately.23 An additional advantage of the malaria burden measure that is worth mentioning is that its quantitative interpretation is very simple: it represents the fraction of children expected to die from malaria or sickle cell disease, conditional on not dying of some- thing else. By contrast, malaria ecology is an index of the stability of transmission of the malaria parasite and does not have a simple interpretation in terms of the human e↵ect of the disease. Using it to assess the importance of malaria for health or eco- nomic outcomes requires scaling the malaria ecology index in a regression framework, as in Sachs (2003).

2.3.3 Comparison of Malaria Burden to Modern Malaria Mor-

tality Rates

For the WHO AFRO region, the under-five death rate from malaria is 0.59 percent per year. Multiplying this number by five gives an approximation to the probability of dying from malaria in the first five years of life, which is very close to the probability of dying from malaria before reproductive age.

The fraction of children who die of malaria is not exactly comparable to what we estimate in the historical data, because our measure M is the probability of dying of malaria conditional on not dying of something else. Such a measure is not usually examined in modern data, but it can be constructed relatively easily. Consider the case of Nigeria, which is a very heavily a✏icted country. The life table for Nigeria for 2006 shows that the probability of woman surviving to age 25 is approximately 0.75.

23Outside of Africa we do not view our measure as a viable alternative to the malaria ecology index because the sickle cell mutation is not present among many indigenous populations. 117 Taking this age to be the equivalent of adulthood in our historical data, we have:

0.75 = (1 M)(1 P )(2.3.7)

Annual malaria deaths for children under five are estimated to be 8.8 per thousand, or 0.88 percent. This implies that roughly 4.4 percent of children will die of malaria before their fifth birthday. We assume that there are no further malaria deaths beyond this age. To incorporate this data into an estimate, we need to deal with the ambiguity of timing in the simple demographic model with which we started. Specifically, the model implies that a fraction (1 M)(1 P )ofchildrenwillsurvive both malaria and other conditions, but it is less clear about what those who don’t survive die of.24 If malaria mortality comes before that from other conditions, then afractionMwilldieofmalariaandafraction(1 M)P will die of other causes; if other conditions come first, then a fraction P will die of other causes and (1 P )M will die of malaria. The truth is obviously somewhere in the middle both malaria and non-malaria mortality is highly concentrated among the very young. For lack of any firm data, we simply assume that deaths due to malaria and ”other” had equal time profiles, which implies:

PM2 0.044 = M(1 P )+ (2.3.8) M + P

Solving for equations (2.3.7) and (2.3.8) yields M =0.054 and P =0.207. To compare the malaria burden, we can look at the fraction of the Nigerian population who are carriers of the sickle cell trait today. In the data of Piel et al., 9.8 percent of adult

24The fraction who die is M + P MP. This can be rewritten as M(1 P )+P (1 M)+MP, where the first term is children who died of malaria but would not have died otherwise, the second term is children who died of something else but would not have died of malaria, and the third term is children who died of one but would have died of the other. 118 alleles are of the S type (this is the average over the area Nigeria, weighted by current population density), implying that roughly 19.6 percent of adults are carriers. This in turn implies that slightly less than 10 percent of children would have died of malaria before reproductive age, conditional on not dying of something else. Thus the burden of malaria has fallen by a little less than half. Below we discuss the comparison of the level of decline in malaria mortality to the decline in mortality from other causes.

2.4 Assessing the Importance of Malaria to Early

African Development

We turn now to an statistical analysis on the importance of malaria to early African development. We view this analysis as exploratory. In the absence of a source of exogenous variation on historical malaria burden, it is dicult to identify the precise impact of malaria on development in the past with any reasonable degree of certainty. Although we attempt to account for several confounding factors in our analysis, bias due to measurement error in our malaria burden variable and some degree of reverse causality is likely to be present in the point estimates we report next. Therefore, our strategy is to simply investigate whether the conditional correlations between our historical malaria burden variable and several measures of early development are consistent with a sizeable and statistically significant negative impact of malaria on early African development.

2.4.1 Ethnic Group Analysis

We start our analysis by looking at the statistical association between population den- sity and our measure of historical burden of malaria. We focus on population density, 119 rather than income, for the usual Malthusian reasons. Using population figures from Murdock (1967)s Ethnographic Atlas (EA) we construct population density figures at the ethnic group level for the colonial period. The EA is a database on 1,167 eth- nic groups of six di↵erent regions of the world, including Sub-Saharan Africa. This database includes a variety of information from the level of subsistence economy to the degree of political integration of each group. For some of the African ethnicities, the EA also includes information on population size and year of its estimation. We identified figures and date for over 250 African ethnic groups. In order to compute the population density we use total land area for each ethnic group as implied from shape-file from the digitalized version of the ethnicities map in Murdock (1959). Since there is no perfect match between ethnicity names in Murdock (1967) and Murdock (1959)s map we were initially able to locate only 219 ethnicities with population fig- ures. We then follow Fenske (2012) and match 23 additional ethnicities ending with atotalof242populationdensityobservationsinoursample.Thedateofestimation of population figures varies in the range period 1880-1960. More than 60 percent of the sample belongs to the first half of the 20th century.

Figure 2.6 displays the population density for the ethnicities in our sample. We divide the sample in quintiles of the number of people per square kilometer at the ethnicity level. Ethnic groups from Central, East and West Africa are more prevalent in our sample whereas only 5 groups belong to North Africa Except for the cluster of very densely populated groups near Lake Victoria, the quintiles seem to be evenly distributed across in the Sub-Saharan Africa. A closer look to West Africa suggests that there is variation in population density even within that region. 120

Figure 2.6: Population Density by Ethnic Groups from EA (1967)

The e↵ect of Malaria on Population Density during Colonial

Time

In order to assess the e↵ect of malaria environment in our measure of ethnicity pros- perity during the colonial time we estimate several di↵erent specifications of the following equation 25:

ln(P ) = ↵ + M + L + M L + ✓0T + ⇢0G + ⇡0I + µ + ✏ (2.4.1) i i i i ⇥ i i i i R i

Where the subscript i denotes ethnic group, P is population density, M is our measure of historical malaria burden, L is a land quality measure, T is a vector of 8 decade

25We use our malaria burden measure for the case in which AS relative malaria mortality of 0.08 in all the empirical analysis in this paper. Using a di↵erent assumptions regarding the relative protection of AS genotypes does not a↵ect our statistical results. 121 dummies for the period 1880 -1950, G and I are vectors of geography/climate and ex- ternal influence controls, respectively, and µR is a collection of 13 ethnographic region dummies from EA (1967). We describe these controls in detail below. Finally, ✏ is the error term that is allowed to be heteroskedastic. Since several ethnic groups share common ancestors and belong to the same ethnolinguistic family, the error term is likely to be correlated within an ethnolinguistic family.26 We thus follow Michalopou- los and Papaioannou (2011) and cluster the standard errors at the ethnolininguistic family level.27

Table 4 presents the first statistical results. In column 1 we only control for the decade dummies and the set of geographic controls. The statistical relationship between geo- graphic factors and development, either through their direct or indirect causal mech- anisms, is well documented in the empirical literature. Those factors may correlate also with the disease environment. To account for other diseases environment, we include the share of each ethnic groups territory with tropical climate following the Koeppen-Geiger classification.28 To account to the fact that the presence of water bodies may positively correlate with both population density and malaria prevalence (such as around Lake Victoria), we also control for the log total area of water bodies accessible for the ethnic group (note also that humidity tends to be high near rivers facilitating the availability of breeding sites) and geodesic distance of the centroid of the historical homeland of each ethnic group from the nearest coastline (the greater the distance to the coast, the lower the population density). To account to the fact that malaria is more prevalent in less ecologically diverse areas which also tend to

26There is an alternative approach of adjusting the standard errors for spatial autocorrelation. Although this method will account for some of the autocorrelation in the error term, it will not account for situations in which migratory phenomena (such as the bantu expansion) placed apart two ethnic groups belonging to the same linguistic family. 27There are 94 ethnolinguistic families in the EA for African societies (the number of clusters in our sample is 80). 28For instance, malaria environment considerably overlap with the distribution of the Tse-Tse fly which transmits the parasite causing sleeping sickness. Dengue and yellow fever are also common diseases in tropical Africa. 122 be less densely populated, we include a measure of ecological diversity from Fenske (2013).29 Finally, we also include mean elevation of terrain to account for the fact that elevation has a strong negative e↵ect on malaria prevalence and may also have an independent e↵ect on development.30 Interestingly, the coecient estimate for suggests a positive (albeit statistically insignificant) correlation between our measure of malaria burden and population density. This striking positive association is also documented in other works based on modern data at the country level (such as in Gallup, Sachs, and Mellinger 1999). Note, however, that this correlation turns slightly negative (albeit statistically insignificant) when we add ethnographic region dummies in column 2.31

Several empirical and theoretical studies have identified soil suitability for agriculture as one of the main drivers of population density. We then pay special attention to di↵erence in land quality (L) and introduce the mean value of the index of land suitability for agriculture as a control in column 3. This index was constructed by Ramankutty et al. (2002) and represents the probability that a particular grid cell (of the size of about 50 x 50 km) may be cultivated.32 The introduction of this control does not a↵ect the previous results. Another potential bias in the estimation of results from the omission of ethnic-group agricultural practices. Using data for 60 communities in East and West Africa, Wiesenfeld (1967) shows a positive correlation between the prevalence of the sickle cell trait and the cultivation of crops associated

29This measure of ecological diversity consists in a Herfindhal index based on the territorial shares of di↵erent ecological types within the boundaries of each ethnic group’s homeland 30For instance, Gallup, Sachs, and Mellinger (1999) shows that population density is greater (lower) at high altitudes in the tropics (temperate zones). 31The ethnographic region dummies are African Hunters, South African Bantu, Central Bantu, Northeast Bantu, Equatorial Bantu, Guinea Coast, Western Sudan, Nigeria Plateau, Eastern Sudan, Upper Nile, Ethiopia/Horn, Moslem Sudan, Sahara, and North Africa. 32The authors assume that suitability for cultivation is solely a function of climate (temperature, precipitation, and potential sunshine hours) and soil properties (total organic components measured by carbon density and nutrient availability based on soil pH). It is important to note that the index does not account for the productivity of a particular piece of land, but simply whether the characteristics of the land are favorable for crop cultivation. 123 with swidden agriculture. Swidden agriculture is an extensive method consisting on clearing small areas of forest with slash and burn practices which multiplies the number of breeding places for the Anopheles Gambiae. Roots and tree fruits are the main crops associated with this agricultural method and the most important ones for the African case are yams and bananas. To account for this potential omitted variable, we add the mean suitability for cultivation of yams and bananas in column 4.33 There is little e↵ect on our estimate of .

Our population data belongs to the African colonial time. If some ethnic groups located in places where the disease environment and other geographic factors handi- capped development were also the most a↵ected by the colonial rule, then not taking into account cross-ethnicity variation in colonial power might lead to an important bias in the estimates of the e↵ect of malaria on population density. In column 5 we add dummy variables indicating the colonial power ruling the land of each ethnic group and three additional variables accounting for external influence in the devel- opment of the ethnic group.34 In particular, we account for the importance of slave trades for the period 1400-1900 (i.e: log of 1+ total slaves exports normalized by area of homeland), and two dummy variables indicating if European explorers traveled (between 1768 and 1894), or a historical trade route passed through, the homeland of the ethnic group.35 The addition of this new set of controls does not a↵ect the significance of the statistical association between our malaria burden measure and population density, although the size of the point estimate increases and remains negative.

33We use suitability for cultivation of yams and bananas rather than data on actual cultiva- tion of these crops (which is available in the Ethnographic Atlas) because the latter is likely to be endogenous. Specifically, Wiesenfeld (1967) argues that the sickle-cell trait could reduced the environmental limitation of malaria, allowing populations to embrace slash-and-burn practices that have high yields per unit labor. 34We classify colonial rule into British, French, Other Colonial Power, and Never Colonized. 35We acknowledge a potential endogeneity of slave trades. The use of an alternative measure to proxy slave trade, such as ruggedness of ethnic territory, may help to alleviate these concerns. 124 Finally, we explore the possibility of the existence of heterogeneity in the relationship between malaria burden and population density. In the last column of Table 4 we add the interaction of land quality and malaria burden. Although the signs of the relevant coecients are consistent with the idea of an heterogeneous e↵ect of malaria burden through land quality, their statistical significance is null.36 As a further robustness check, we omit ethnic group observations from North Africa (results not shown). The inclusion of these ethnic groups was not driving the previous findings.

In sum, we find little evidence of a statistical negative relationship between our mea- sure of malaria burden and population density.37 In the next section, we exploit additional cross-ethnic variation in a rich set of variables to asses if malaria did hold back economic development in other dimensions.

Other Measures of Development

We turn now to the relationship between malaria burden on other measures of ethnic development. Following Nunn and Wantchekon (2011), we exploit a rich source of ethnic information provided by the EA (1967) and estimate a new version of equation (2.4.1) where our dependent variable is now a given measure of ethnic development. Table 5 presents the main results. All the specifications include the full set of con- trols as in column 5 of Table 4.38 Since we do not longer rely on the availability of population figures, the sample size for each specification will depend only on the availability of the ethnographic variable for ethnic groups that can be located in the

36Note that land quality and malaria burden are not demeaned, thus their point estimates from column 6 are not directly comparable with the ones from column 5. 37We also experimented with other specifications including region dummies (i.e: West, East, Central, North, and South) and country dummies. The results are not substantially a↵ected. 38We use the same controls as in the analysis of population density with the addition of total land area of the homeland of the ethnic group as a geographic control (the results does not depend on the inclusion of this control though). 125 Murdocks map (our largest sample consists of 523 ethnic groups).39 The assignment of the decade dummy is based on the year to which the ethnographic data pertain (Murdock, 1967). Note, however, that adding a time control in our regressions is probably not crucial for most of these measures of ethnic traits since the political and economic structure of a given ethnic group was likely to be determined in earlier periods (i.e: pre-colonial times) and transmitted inter-generationally.

All the dependent variables in Table 5 are binary variables for which 1 indicates a higher prosperity level (see note in table) and come from EA (except for the variable in column 2 which is constructed using Bairoch, 1988).40 We first study the statistical relationship between malaria burden and the mean size of local communities for each ethnic group. The coecient estimate in column 1 suggests that the probability for the mean size of the local community having 1,000 people or more is positively and statistically associated to our historical measure of the burden of malaria. Similar results are obtained in column 2 when considering the probability of at least one city with population above 20 thousand people located in the homeland of the ethnic group by 1850. Further, the point estimate in column 3 suggests an statistically significant and positive association between our measure of malaria burden and the complexity of settlement pattern of the ethnic group (i.e: whether the ethnic group settlement pattern is either compact and relatively permanent or complex, as opposed of being fully nomadic or semi sedentary). These results suggest that malaria did not impose important impediments for the presence of complex settlement and urbanization.

We next look at the statistical association between our malaria burden and precolo- nial political institutions. Following Gennaioli and Rainer (2007), we construct a

39We follow Fenske (2012) to match ethnicity names in EA (from Murdock 1967) and Murdock (1959)s map 40All the results in Table 4 are OLS coecients. We also estimated probit and ordered probit (for the original categorical variables) and reached qualitatively similar results. Thats, we do not find evidence of historical malaria holding back development. 126 measure of centralization of political power that indicates 1 if the ethnic group has any jurisdictional level transcending the local community (i.e; 0 indicates either no political authority beyond the community or just the existence of petty chiefdoms). In line with previous statistical associations, point estimates from column 4 suggest that ethnic groups located in areas with worst malaria burden tend to have more sophisticated political institutions beyond the local communities (i.e; large states in the highest possible category in the spectrum of sophistication).

We next investigate if malaria burden negatively impact the ability of an ethnic groups to generate agricultural surpluses. From column 5 in Table 5, malaria burden is not statistically associated to intensive agriculture being the main contributor of the subsistence economy (as opposed to gathering, fishing, hunting, pastoralism or casual and extensive agriculture). As expected, land quality is positively impact the probability of intensive agriculture being the main economic activity of the ethnic group.

To summarize, we do not find evidence that the impact of malaria would have been very significant on early African development. We do not find any negative statisti- cally significant correlation between our measure of malaria burden and population density when we focus on ethnic groups. We do not find consistent evidence of any negative e↵ect on other measures of ethnic development and prosperity such as mean size of local communities, settlement patterns, the degree of subsistence economy, and one measure of sophistication of the political institutions. On the contrary, we find that high historical malaria burden positively correlates with most of the aforemen- tioned indicators. 127 2.5 Model-Based Estimates of the Economic Bur-

den of Malaria

2.5.1 Direct E↵ect of Malaria Mortality

Current analyses of the burden of disease focus on measures such as years of life lost or disability free life years lost. From this perspective, the death of a young child is particularly costly because he or she had so many potential life years. The ethical considerations regarding the allocation of scarce lifesaving resources, and implicitly the cost of death and disease experienced at di↵erent ages, are quite complex (see Persad, Werthheimer, and Emanuael, 2009). In assessing the role that disease played in a↵ecting development historically, however, it seems reasonable to take a purely instrumental view of life and health, in which the primary considerations are how much society has invested in an individual and that individual’s potential to produce services for society in the future.

To formalize this idea, consider a simple model of production and consumption with individuals of di↵erent ages. Let ci be the consumption of an individual of age i.We assume that there is no storage of output between periods. Let Ni by the number of people in age group i. The social budget constraint is

T

Y = Nici (2.5.1) 0 X

Where Y is total output and T is the maximum lifespan. We assume that consumption at each age is determined by two things: a consumption level of individuals at some benchmark age (for example, prime age adults), which we callc ¯ , and some age-varying relative consumption coecientc ˜. 128 ci =˜cic¯

The values ofc ˜ presumably reflect changing biological needs for consumption over the course of the life cycle as well as the arrangements by which consumption is divided up among di↵erent groups in society. One would not necessarily expect the pattern of consumption to the same in all societies at all times. However, as discussed below, available data do not vary all that much.

We assume that output is produced by a fixed quantity of land, X, and labor. Let

◆˜i be the quantity of labor supplied by an individual of age i, again, relative to some benchmark level. As with consumption, the pattern of relative labor input across age groups reflects both biological di↵erences and di↵erences among societies in the economic value of di↵erent characterisics (for example, strength vs. wisdom vs. manual dexterity). Taking the production function to be Cobb-Douglas, we have

1 ↵ T ↵ Y = AX Ni◆˜i (2.5.2) i=0 ! X

Letting N be the total population and N˜i the fraction of the population in age group i, we can solve for the level of benchmark consumption as a function of population size and demographic structure:

↵ T N˜i◆˜i X i=0 c¯ = AX↵ 0 1 0 1 (2.5.3) T PT ˜ ˜ BN Ni◆˜i C B Nic˜i C B i=0 C B i=0 C @ P A @ P A The first term in parenthesis is the ratio of land to eciency-adjusted labor. It a↵ects the level of benchmark consumption for the usual Malthusian reasons. The second term in parentheses can be thought of as the demographic eciency of the economy. 129 Consumption will be higher in a society where the population is distributed relatively more toward ages in which people supply labor relative to ages in which they consume.

We now turn to the determination of population density. Our assumption is that in the historical period we are examining, Africa was in Malthusian population equi- librium. In such an equilibrium, as long as the quantity of land is constant and the production technology is fixed, the growth rate of population is zero and the popula- tion size is constant (or, more realistically, the population size and standard of living are mean reverting in response to stochastic shocks). The near constancy of human populations over long historical eras suggests that such a mechanism must have been operating in most places for most of human history (Galor and Weil,2000). Although there are clearly times when Malthusian constraints were lifted (such as the peopling of the Americas in pre-historic times), a simple analysis of the e↵ect of compounding shows that most of the time, average population growth rates must have been quite close to zero. Without a homeostatic model of the Malthusian sort, it is vanishingly unlikely that such near constancy of population would obtain.

Herbst (2000) argues that the abundant land was a persistent characteristic of African economies, as a result of which it was the control of people, rather than territory, that was of primary interest to rulers. In our view, however, historians are too quick to dismiss the Malthusian model without having another explanation for the near constancy of population over time. It is conceivable that, rather than land being the binding resource, the role of equilibrating population size was played by density- dependent disease. This is the view taken by McGuire and Coelho (2011). One can work through a model with density dependent disease producing results very similar to those we show here.41

41Several recent studies have argued that higher disease, through its e↵ect on the ratio of popu- lation to land, could raise income in some circumstances and conversely that health improvements could lower income. Voigtlander and Voth (2013) argue that in Europe before the industrial revo- lution, income rose as a result of increased mortality due to plague, urbanization, and warfare, and 130 Following Kremer (1993) and many subsequent papers, we model the Malthusian equilibrium by assuming that population size adjusts such that the level of consump- tion (for the benchmark age) is held constant. In the empirical sections above, the measure of development on which we focus is population density. Here, rather than looking at the simple population density (that is N/X)wedoademographicadjust- ment. The idea is that, just as a society populated by a high number of working age adults relative to children can produce more output per capita, a society populated by a high number of working age adults relative to children is also more dense for agivenlevelofpopulationsize.Wethusscalethenumberofpeoplebytheirlabor input weights in defining e↵ective density. That is

T T 1/↵ N N˜i◆˜i ↵ N˜i◆˜i i=0 A i=0 density = = 0 1 (2.5.4) PX c¯ PT ✓ ◆ ˜ B Nic˜i C B i=0 C @ P A For our primary exercise, we consider changes in age structure and density that hold the benchmark level of consumptionc ¯ constant

To use this equation to assess the e↵ect of disease mortality on the consumption benchmark, we need estimates of the consumption and labor input profiles and the exponent on land in the production function, as well as an estimate of the e↵ect of that this rise in income was instrumental in knocking the continent onto the path of industrializa- tion. Acemoglu and Johnson (2009), in their analysis of the worldwide epidemiological transition of the mid 20th century, find that countries that experienced greater increases in life expectancy saw slower growth in income per capita. And Young (2005) claims that higher mortality and lower fertility due to HIV in South Africa will more than compensate for declines in worker productivity due to the disease, so that income per capita will actually rise as a result of the epidemic. Ashraf, Lester, and Weil (2008) examine the Malthusian channel as part of a broader analysis of how health improvements a↵ect growth. They find that higher survival indeed pushes income lower through the Malthusian channel (as well as through capital dilution), although this e↵ect is more than compen- sated for in the long run by higher labor productivity due to better health. Such an e↵ect could be induced in our model as well, if increases in the benchmark level of fertility,c ¯, are required to increase fertility in order to make up for higher mortality. This would be an additional channel (beyond the demographic eciency that we examine) by which higher mortality would lower population density. 131 disease on the age structure of the population.

Consumption and Income Profiles

A number of sources provide data on the life cycle profiles of consumption and labor input. Mueller (1976) synthesizes data from nine societies practicing what she calls peasant agriculture, by which she means agricultural systems which use primarily traditional methods of cultivation, small landholdings, and low capital inputs. South and Southeast Asia are primarily what she has in mind. The profiles are shown in Figure 2.7. Note that Muellers data on labor input apply to production of output as it would appear in measured GDP but exclude home production. Much of the latter is done by women, so in her data, productivity by prime age women is only 30of output, we focus on the male profiles. For consumption, Mueller provides two profiles in which consumption of people at each age (and of each gender) is compared to males aged 20-54. For the medium consumption profile, children age 0-4 have a value of 0.32; for the low consumption profile, the value is 0.12 (prime aged women get a value of 0.80). We use the medium profile.42

Figure 2.7, along with the life table examined below, can be used to think about the cost to society of deaths at di↵erent ages. The most costly deaths are those that take place in young adulthood, at the age where the consumption and income curves cross. These are individuals in whom society has invested appreciable resources (food, childcare, education), and who have ahead of them many expected years producing

42We also examined data from two other sources, which paint a similar picture. First, Lee and Mason (2009) look at data from four contemporary hunter-gatherer societies originally studied by Kaplan (1994) and Howell (in press). The underlying data are in terms of calories collected and consumed. Their data are quite similar to the profiles in Mueller. Second, Lee and Mason also look at data for the four poorest countries that are part of the National Transfer Account project: Kenya, Philippines, Indonesia, and India. Income in this case is labor income, including unpaid family labor, and pertains to both men and women. These data di↵er from the other two sources primarily in showing a decline in income of the elderly that is not present in the peasant agriculture or hunter-gatherer data. 132

Figure 2.7: Consumption and Income Profiles asurplus.Deathsofpeopleatolderagesarenotascostly,sincesuchindividuals have fewer expected years of surplus production; and similarly deaths of very young children are less costly because society would still have to invest in them for many years before they started producing a surplus. Under this interpretation, malaria deaths are relatively low cost, with the exception of deaths of women in their first pregnancies, who are near the peak of their value as assets to society in terms of the balance between resources invested in them and services they can deliver.

Land in the Production Function

A number of studies have modeled pre-modern Malthusian economies and/or the agri- cultural sectors of more modern economies, in which the only inputs into production are land and labor Kremer (1993) uses 1/3 as an upper-end estimate of lands share, based on evidence from sharecropping contracts. In the model of Hansen and Prescott (2002), the Malthus sector, which is the only part of the economy producing output prior to the industrial revolution, has a land share of 0.3. Stokey (2001) applies a 133 Cobb-Douglas production function to the agricultural sector for Britain in 1850, with an exponent on land of 0.45. In the calculation reported below we use a value of one-third as the land share in agriculture.43

Population Age Structure

The other piece of data used in the equation (2.5.4) is N˜i, the relative number of people in each age group. In general, this will be a function of both the probability of survival to each age and the history of births or population growth. However, for the long historical periods that we are considering, population growth must have been very close to zero, which implies a constant number of births per year. We can thus approximate the age structure of the population N˜i with the fraction of survivors at each age from the life table. Thus our approach to assessing the role of disease in a↵ecting consumption possibilities is to start with a baseline life table and then consider alternations that would result from eliminating or adding particular sources of mortality, using information on the age of death from di↵erent diseases.

This exercise requires choosing a baseline life table that represents Africa in the pre-colonial period. Systematic data on life expectancy in Africa is widely available only starting in the 1950s. For the period 1950-55, the United Nations estimate of life expectancy at birth in sub-Saharan Africa is 37.8 years (United Nations, 2009). Acemoglu and Johnson (2007) date the beginning of the international epidemiological transition, driven by more e↵ective public health measures, the discovery of new chemicals and drugs, and international interventions to 1940. Although the transition

43The other issue relevant here is the elasticity of substitution between land and other inputs. The studies discussed here all use a Cobb Douglas production function, in which the elasticity of substitution is one. Weil and Wilde (2009) use modern data on the share of natural resources paid to fixed factors of production to estimate this elasticity of substitution. Their estimate is close to 2, although very imprecise. The higher is the elasticity of substitution, the larger will be the e↵ect of change in demographic structure on population density. 134 came late to Africa, it is very likely that the 1950-55 figure represents an improvement relative to previous decades. Clearly after 1955 the pace of change was rapid. The UN estimates that life expectancy in sub-Saharan Africa rose by two years in each of the subsequent five year periods. Further evidence that health improvements were already underway by 1950 comes from data on total population size. Africa’s population grew at a rate of 1.0 percent per year between 1900 and 1950, compared to a growth rate of 0.2 percent per year over the previous century (United Nations, 1999).

Data for the period prior to 1950 are very sparse. Acemoglu and Johnson (2007, Appendix C) pull together disparate sources to present a few estimates for the period before 1950. These are, Angola in 1940: 35 (both sexes); Mozambique in 1940: 45 (both sexes); Ghana in 1948: 38 (both sexes); and Mauritius, 1942-46: 32.3 (male) and 33.8 (female). Riley (2005) estimates that prior to the health transition (he uses adi↵erent definition than Acemoglu and Johnson) that began in Africa in the 1920s, life expectancy at birth averaged 26.4 years (this is the mean of 12 estimates, which range from 22.5 to 31.0.).44 In Asia, life expectancy prior to the health transition, which started there between 1870 and 1890, was 27.4. In Europe the transition started in the 1770s, and prior to it life expectancy was 34.3.

Riley comments that available estimates of African mortality prior to the health transition all come from European colonies in Africa. There is a reasonable basis for thinking that life expectancy may have been higher prior to colonization, the arrival of Arabic speaking merchants, and the dislocations produced by the slave trade. Unfortunately, almost no information for this period is available. Steyn (2003) examines mortality in the pre-colonial period in northern South Africa thorugh an examination of skeletal remains. She estimates life expectancy in the period 1000-1300

44Some represntative values are Angola: 27 years in 1940; Egypt: 30-33 years in the 1930s; Ghana: 28 years in 1921;Kenya: 23.9 years in the 1930s; South African black population: 38.1-40 years in 1935-40; Tunisia: 28.8 in the 1920s; Uganda: 23.9 in the 1930s; Zimbabwe: 26.4 in mid 1930s. 135 AD at 23.2, with the probability of surviving to age 20 being 48 percent. Remains for the post-1830 period show a slight decline in life expectancy after the expansion of European influence.

Based on the above discussion, we use as our baseline the United Nations (1982) model life table for a population with life expectancy at birth of 35 years (male and female combined).45 This is the lowest life expectancy for which the model life table is available. 46

To apply this framework to di↵erent diseases, one needs a profile of the mortality e↵ect of the disease at di↵erent ages. For malaria, we use age-group death rates from Murray and Lopez (1996).47 To model the e↵ect of malaria on the historical life table we proceed as follows. We start with the UN life table (life expectancy of 35) discussed above. We take this as a benchmark. To this we add additional mortality at each age proportional to the current age profile of malaria deaths. Specifically, we take the current death rates from malaria and multiply them by a scaling factor, and add these death rates to the death rates in the UN life table. The scaling factor is chosen to match the magnitude of malaria deaths we want to model. Above, for the case where sickle cell prevalence is moderately high (⇡ =0.20), we showed that M, the implied probability of dying from malaria conditional on not dying of something

45Unfortunately, the model life tables that are available, to the extent that they reflect African data at all, certainly do not reflect the pattern of age-dependent mortality that existed in the period before the modern health environment of both disease and treatment was in place. It is likely that the pattern of mortality, and in particular the ratio of deaths at di↵erent ages, di↵ered from what is observed today, but a priori there is no basis for guess the nature of this di↵erence. 46Once we have a life table, we can also address the issue of how the decline in the non-malaria death probability P compares to the decline in the malaria death probability M. The UN Model life table with life expectancy of 35 (general model, for females) implies 57.2 percent of girls will survive to age 25, which we can take as an average for childbearing. Thus, 0.572 = (1 M)(1 P ). Using a value of M =0.10 (consistent with the prevalence of the S allele in Nigeria, as discussed above) implies a value of P =0.364. As discussed above, our analysis of modern data in Nigeria imply P =0.207 and M =0.054. Thus the proportional decline in malaria mortality has been almost exactly the same as the decline in mortality from other causes. However, this calculation is very sensitive to the choice of life expectancy in the historical period. 47These are 0.00559 for ages 0-4; 0.00042 for ages 5-14; 0.00033 for ages 15-44; and 0.00036 for ages 45-59. 136 else prior to childbearing was 0.10. We set the scaling factor to match this probability, taking 25 to be the age of childbearing.48 The implied scaling factor is 2.95. Figure 2.8 shows the survival curves for the baseline case and the case with high malaria.49 Life expectancy falls by 5.1 years from this additional malaria mortality.

Figure 2.8: Change in Survival Probabilities due to Malaria

E↵ects of Malaria on Density

Applying equation (2.5.4), we can calculate consumption per capita using both the original life table and the life table modified to reflect malaria mortality. Our finding is that adding malaria mortality reduces density by only 2.8 percent. This is obviously an extremely small e↵ect for such a large change in mortality. We can put further this number in context two di↵erent ways. First, Alsan (2013) also examining African data, finds that a one standard deviation change in an index of tsetse fly impact

48Specifically, we choose the scaling factor x such that: 0.90 = (1 0.00559x)5(1 0.00042x)10(1 0.00033x)10 49Of course the UN table already includes deaths due to malaria. Another approach would be to take as the baseline a current life table stripped of malaria mortality, and then to add malaria mortality back in. This procedure would yield very similar results. 137 leads to a 45 percent change in population density in the period prior to European settlement. This e↵ect is much larger than ours. Second, we can compare the e↵ect of malaria to variation in land quality. Ashraf and Galor (2011), using data for 1500, estimate the coecient in a regression of log population density on the log of a land quality index as 0.587.50 A reduction in density of the same magnitude as the one we attribute to malaria could thus be induced by a reduction of 0.048 in the log of land quality, which is less than one twenty-fifth of the standard deviation of that measure, once again showing that the e↵ect of malaria on density that we estimate is small.

The reason that our estimate of the e↵ect of malaria is so small is two-fold. First, malaria deaths are concentrated at young ages, and second, consumption of young children is low relative to consumption of adults. Putting these together, most deaths from malaria do not, in this model, represent a significant loss of resources to society. In our calculation, deaths beyond age five account for only 1/3 of the reduction in life expectancy due to malaria, but for 2/3 of the economic cost of the disease.

We can easily compare the implied e↵ect of malaria on density in this calculation with the regression coecients estimated in the previous section. The calculation here says that going from a malaria burden of zero to a malaria burden of 0.1 (which is consistent with equilibrium prevalence of the sickle cell trait ⇡ =0.2) would reduce density by 2.8 percent. In a regression of log population density on the level of malaria burden, this variation would produce a regression coecient of -0.28. In comparison to the regression coecients in Table 4, this number is quite close to zero, and in specifications where the null of zero is not rejected, this value is not rejected either.

We assess the robustness of this result with respect to three variations on the calcula- tion. First, we consider the cost of gestation. In the model presented above, the cost of death of a newborn is zero, which is clearly unrealistic. Having said this, we do not

50Table 2, column 2; the t-statistic is 8.3. 138 have a good estimate for the consumption price of producing a newborn child. For lack of better data, we assume that the price is equivalent to one years consumption for a child aged 0-4, which in Muellers data is 0.32 of the consumption of a prime age adult. Implementing this change increases the reduction in density from 2.8 percent to 3.4 percent.

The second variation that we consider is to allow for costs from maternal mortality. The death of a child necessitates, in equilibrium, an additional birth, which in turn exposes a prime age woman to mortality risk. Maternal mortality is far more costly to society than the death of a child, because a prime aged woman has already passed through the years in which her consumption was higher than her labor input: using our model, the cost to society of a 1 percent chance of death of a one-year-old is only 0.011 percent of average consumption, while the cost of a 1 percent chance of death of a 25 year old is 0.14 percent of average consumption. To see how this e↵ect raises the economic burden of malaria, we would ideally want an estimate of the maternal mortality rate (MMR) in Africa during the pre-colonial period, which is not available. As an alternative approach, we ask how large the MMR would have to be so that the burden of malaria we calculate was doubled. The answer is that this would require an MMR of 7 percent. To benchmark this number, MMRs in the worst-a✏icted developing countries today are roughly 1 percent, while in historical data from Sweden, the MMR in 1750 was 0.9 percent (Hgberg and Wall, 1986). It thus seems to us unlikely that this channel would have a major quantitative impact on our estimate of the economic burden of malaria.

The third variation that we consider is lowering the baseline life expectancy in our calculation. Intuitively, the death of a child is costly to society because of the resources that have to be used to replace him or her. The higher is baseline mortality, the more expensive it is to produce a child of given age, because there is a lower chance of a 139 newborn reaching that age. As mentioned above, the lowest life expectancy in the UN model life tables is 35 years. However, using the Brass life table methodology, we can generate a family of life tables with the same general shape as the UN table, but with lower life expectancies.51 We do this to create life tables with life expectancies at birth of 30, 25, and 20 years. We then adjusted these life tables with the addition of age-specific malaria mortality, as described in our baseline analysis. The result is that in the presence of malaria, population density is reduced by 3.5, 4.6, and 6.4 percent, respectively. Thus it is true that in the presence of lower life expectancy, the impact of malaria on population density is amplified, but for reasonable values of life expectancy the e↵ect remains small. (Note that when life expectancy in the absence of malaria is 20 years, life expectancy inclusive of malaria is 16.0 years, which is significantly lower than any historical estimate that we know of.)

The above analysis shows that, at least in terms of the extra costs associated with raising children who were subsequently going to die of malaria, the e↵ect of the disease on population density in Africa should have been relatively low. However, it is possible that there are other channels through which high malaria mortality may have mattered. Death from disease may impose an obstacle to economic development beyond the instrumental cost discussed here. Stone (1977) argues that in the face of high child mortality, pre-modern Europeans avoided forming emotional bonds with their young children, even to the point of giving the same name to two living children, expecting only one to survive. In such an environment, one might also expect to see less investment of tangible resources in the human capital (both health and education) of their children. Thus even if high child mortality from malaria were not important in the strict sense of draining resources from the economy, it might have contributed

51The Brass transformation is a follows. Let l(x) be the probability of living to age x in a given life table (we use the UN life table with life expectancy of 35). We can generate an alternative table 1 1 l(x) via the transformation l⇤ =(1+exp(2↵ +2(l(x)))) ,where(l(x)) = ln l(x) /2. Holding the value of constant at one, we vary the value of ↵ in order to change lifeh expectancyi while maintaining the general shape of the survival curve. See United Nations (1983), chapter 1. 140 to reduced investment in children. Several recent theories of economic growth have stressed the importance of such investments to long-term development.

2.5.2 Economic E↵ects of Malaria Morbidity

To pursue the question of how much malaria a↵ects labor input of adults, we use data from the World Health Organization on Years Lost to Disability (YLDs) from particular diseases. A countrys YLD for a given disease is constructed as:

YLD= IxDWxL (2.5.5) where I is the number of incident (newly-arising) cases in a period, DW is the dis- ability weight attached to the disease, and L is the average duration of the disease until remission or death. The crucial parameter here is the disability weight, which is intended to be a cardinal measure of the severity of di↵erent diseases or impairments, on a scale from 0, indicating perfect health, to 1, indicating death. Disability weights are constructed by panels of healthcare providers and medical experts using a “person trade-o↵” protocol which establishes utility equivalences between years of life lived in di↵erent states of health. One year lived with a disability provides the same utility as (1 DW)yearsliveddisability-free(Murray,1996)).Disabilityweightsaretherefore not primarily intended as a measure of labor supply. Nevertheless, these estimates provide at least some basis for comparing the e↵ects of di↵erent diseases.52

To give an example of the interpretation of YLD: in the data below, the average YLD from all causes for Africa is 0.12. This means that in the average year, the

52Some examples of disability weights are blindness (0.600), deafness (0.216), HIV (0.136), AIDS (0.505), tuberculosis sero-negative for HIV (0.264), severe iron-deficiency anemia (0.093), malaria episodes (0.172) and neurological sequelae of malaria (0.473). 141 average man su↵ers disease episodes that deprive him of the equivalent of 12 percent of a year’s disability free life. This could mean being fully disabled for 12 percent of the year, 50 percent disabled for 24 percent of the year, and so on. It could also mean su↵ering an incident that leaves him, say, 1 percent disabled for the next 12 years.

Table 6 shows the YLDs by disease type and age (for males) for the WHO’s AFRO region. YLDsarecountedattheageinwhichadiseaseincidentoccurs.Thus,for example, the neurological sequelae of malaria are counted as years lost to disability in the age 0-4 age group, even thought the actual years lost are spread through the individual’s whole life. (Note that the ”total” column is based on the distribution of the current population among age groups. Thus disabilities a↵ecting the elderly, who are a very small percentage of the current African population, play a very minor role in determining the total figure.)

The table shows that overall, malaria accounts for only 5 percent of total years lost to disability. Further, almost all of these lost years were due to incidents in the first five years of life. Some of the years lost to disability in this case a↵ected adults, through the neurological sequelae of the disease, but much of the disability burden fell directly on children. Although disability (which in this case really just measures su↵ering) among children does not directly a↵ect production, one could argue that it might have a↵ected their accumulation of skills or human capital.53 In any case, however, the malaria burden is relatively small in comparison to that of other diseases.

As mentioned above, another potential channel through which malaria might have a↵ected economic economic development is through shifting population away from

53Bleakley (2010), Cutler et al. (2010), and Lucas (2010) all use national anti-malaria campaigns in the middle of the 20th century as quasi-experiments in order to study the e↵ect of childhood exposure to the disease on human capital accumulation and adult economic outcomes. Their findings are highly variable, with Cutler et al. estimating a small e↵ect and Bleakley a very large one. Lucas, whose findings fall between the other two, estimates that a 10 percent reduction in malaria incidence raises completed schooling by 0.1 years. 142 potentially productive areas. Gallup and Sachs give examples of this occurring in Europe. In the context of Africa, we do not know of evidence that particular regions were not settled because of malaria, and there is certainly evidence of people settling in areas of tremendously high malaria transmission. Given that malaria deaths were concentrated among the very young, and that mortality in this group was very high from other causes in any case, it is hard to see how malaria would have had a great influence on settlement patterns.

2.6 Conclusion

Malaney, Spielman, and Sachs (2004) write, how better to evince the power of the parasite than with a potentially lethal modification of the genetic code as a desperate Darwinian defense against the even more deadly ravages of malaria? Accordingly, it may be expected that a force strong enough to rewrite our DNA will rewrite many of the lives and economies that it touches. In this paper we have tried to address this issue directly. That is, we have used the extent which malaria left its mark on the human genome to back out the severity of the diseases impact, and then in turn we have tried to assess how large the economic impact of that disease would have been.

In areas of high malaria transmission, 20 percent of the population carry the sickle cell trait. Our estimate is that this implies that historically between 10 and 11 percent of children died from malaria or sickle cell disease before reaching adulthood. Such a death rate is roughly twice the current burden of malaria in such regions. Comparing the most a↵ected to least a↵ected areas, malaria may have been responsible for a ten percentage point di↵erence in the proability of surviving to adulthood. In areas of high malaria transmission, our estimate is that life expectancy at birth was reduced by approximately five years. In terms of its burden relative to other causes of mortality, 143 malaria appears to have been perhaps slightly less important historically than it is today, although we certainly can’t rule out the decline in malaria mortality has been proportional to the decline in mortality from other diseases.

Thus, malaria imposed a heavy mortality burden. Did it hold back economic develop- ment? We find little reason to believe that it did. Examining the economic burden of malaria mortality in a simple life cycle model suggests that the disease was not very important, primarily because the vast majority of deaths that it caused were among the very young, in whom society had invested few resources. Our analysis of malaria morbidity, which is necessarily more speculative, also suggests a relatively minor ef- fect of the disease on labor input, and thus on economic activity. These model-based findings corroborate the findings of our statistical examination. Within Africa, areas with higher malaria burden, as evidenced by the prevalence of the sickle-cell trait, do not show lower levels of economic development or population density in the colonial era data that we examine. 144 Table 2.1: Components of the Cost of Malaria Group Fraction of Death Rate from Fraction of all Births Malaria or Sickle Children who Cell Disease Die in Category ⇡ 2 AA ⇡ 2 AA Non-Carriers (AA) 1 2 M 1 2 M ⇡ AS ⇡ AS Carriers (AS) ⇡ 1 2 M ⇡ 1 2 M 2 2 Sickle Cell Disease (SS) ⇡ 1 ⇡ 2 2 145 distance). ↵ cients are reported in Brackets. Highly Malarious represents the (0.003) (0.003) (0.001) (0.001) [0.5880] [0.3894] [0.6558] [0.4292] HighlyMalariousc.1900 AverageEndemicity2007 (1) (2) (3) (4) (5) (6) (0.458) (0.492) (0.228) (0.200) [0.6282] [0.4601] [0.7102] [0.5249] 0.1. Standardized coe < Table 2.2: Malaria Burden vs. Malaria Ecology: Grid Cell Analysis 0.05, * p < 0.01, ** p < Malaria BurdenMalaria Ecology 7.571***ObservationsR-squaredStandard errors adjusted for 2-dimensional*** spatial p autocorrelation in parenthesesshare (5-degrees of 5.546*** cut-o ethnic group’s territoryrepresents under plasmodium 3.693*** either falciparum 0.0286*** a transmission hyper intensityGrid or (basic 10,220 cell holoendemic reproductive 0.0190*** size malaria number). is evironment. Observations 0.5 represent Average degree grid Endemicity by in cell’s 0.5 2007 averages. degree. 10,220 0.395 10,220 0.346 0.0138*** 10,220 2.730*** 0.518 10,220 0.00902*** 0.504 0.430 10,220 0.654 Table 2.3: Malaria Burden vs. Malaria Ecology: Ethnic Group Analysis HighlyMalariousc.1900 AverageEndemicity2007 (1) (2) (3) (4) (5) (6)

Malaria Burden 5.544*** 4.673*** 3.149*** 2.546*** (0.663) (0.653) (0.315) (0.322) [0.5621] [0.4738] [0.6618] [0.5350] 146 Malaria Ecology 0.0177*** 0.0107*** 0.0112*** 0.00739*** (0.004) (0.004) (0.002) (0.002) [0.4176] [0.2509] [0.5488] [0.3605] Observations 525 525 525 525 525 525 R-squared 0.316 0.174 0.371 0.438 0.301 0.552 Standard errors clustered at ethnolinguistic family level in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Standardized coecients are reported in Brackets. Highly Malarious represents the share of ethnic group’s territory under either a hyper or holoendemic malaria evironment. Average Endemicity in 2007 represents plasmodium falciparum transmission intensity (basic reproductive number). Observations represent ethnic groups’s averages. 147 (11.36) 0.1. < 0.05, * p < (0.437) (0.443) (0.462) (0.651) 0.01, ** p < (1) (2) (3) (4) (5) (6) (3.427) (4.569) (4.667) (4.618) (4.469) (6.675) Table 2.4: Malaria and Population Density Dependent Variable: Log of Population Density in Colonial Times (Ethnographic Atlas, 1967) Malaria BurdenLand QualityLand Quality * Malaria BurdenDecade DummiesGeography and Climate ControlsEthno-Region DummiesSuitability for Slash-and-Burn CropsExternal Infuence ControlsObservationsR-squared 4.612Standard errors clustered at -0.148 ethnolinguisticThe family decade level dummies in cover -0.0113 Y parentheses. theecological period *** diversity p 1840-1950 (Fenske, N 2012), (omitted -0.143 share decadethe of 1960).Climate centroid tropical and of climate Geography the under controls historical Kppen include -2.489log homeland climate total of classification, squared Y each geodesic kilometers ethnic of distanceHunters, group water of South N from 4.001 bodies African the N to Bantu, nearest which Central Y coastline,Nigeria ethnic Bantu, mean Plateau, group Northeast elevation Eastern has Bantu, of Sudan, Equatorial N access. terrain, Upper Bantu,Slash-and-Burn The and Nile, Guinea Crops Y ethnographic Ethiopia/Horn, Coast, refers regions Moslem Western to are Sudan, Sudan, mean Ninclude African Sahara, soil dummies Y and suitability for North for Y colonial 0.0773 Africa. Yam powerroutes and Suitability ruling or Banana/Plantain for the cultivation. historical N largest trade External share routes influence Y 0.107of of intercept controls homeland ethnic homeland (Nunn, group of 2008) Y territory, group, two Y and dummy log Y variables 0.215 of if 1+ explorer total N slaves 242 exports normalized Y 0.696 by area Y Y 0.249 Y 242 N -12.73 Y 0.490 Y Y 242 Y 0.490 Y 0.490 242 Y Y 0.510 Y 242 0.513 242 148 0.1. < 0.05, * p < 0.01, ** p < in 1850 Complexity Political Power Agriculture (1) (2) (3) (4) (5) (1.352)(0.202) (0.309) (0.0492) (0.999) (0.119) (0.723) (0.0931) (0.852) (0.121) Urbanization 20K City Settlement Centralization of Intensive Table 2.5: Malaria and Ethnic Prosperity Malaria BurdenLandQualityDecade DummiesGeography and Climate ControlsEthno-Region DummiesSuitability for Slash-and-Burn CropsExternal Infuence Controls 2.899**Observations YR-squared Y 0.529*Standard errors 0.0212 clustered at ethnolinguisticThe family decade level 2.735*** dummies in cover Y parentheses. theecological period *** Y Y diversity p 1840-1950 (Fenske, 2012), (omitted share 0.0690 decade Y Ythe of 1960).Climate centroid tropical and of climate Geography the under controls historical Kppen include log homeland climate total of classification, squared 1.394* each geodesic kilometers ethnic of distanceHunters, group water of South 0.108 from bodies African the to Bantu, nearest which Central coastline, YNigeria ethnic Y Bantu, mean Plateau, N group Northeast elevation Eastern has Bantu, of Sudan, Equatorial access. Y terrain, Upper Bantu,Slash-and-Burn The and Y Nile, Guinea Crops ethnographic Ethiopia/Horn, Coast, refers regions Moslem Western to are Sudan, Sudan, meaninclude African Sahara, soil -0.731 dummies and suitability for 167 North for colonial Africa. Yam -0.0731 powerroutes and Suitability ruling or Banana/Plantain for the cultivation. historical largest trade External share Y routes influenceof of intercept Y controls homeland 0.458 ethnic homeland (Nunn, group of 2008). territory, group, two Dependent Y and dummy Variable log Y variables Definitions of if in 1+ explorer main total 524 Y text 0.283** slaves exports normalized by area 0.151 Y 486 Y 0.304 Y Y Y 0.153 523 Y Y Y 0.255 523 149 Age Group 0-4 5-14 15-29 30-44 45-59 60-69 70-79 80+ total 0.112 0.0280.047 0.04 0.019 0.037 0.037 0.021 0.035 0.015 0.021 0.013 0.014 0.01 0.0120.027 0.046 0.01 0.001 0.002 0.031 0.001 0.001 0.001 0.001 0.001 0.006 Table 2.6: Years Lost to Disability per capita, WHO AFRO Region Source: Global Burden of Disease, 2002 revision malaria AllCausesCommunicable,maternal, perinatal,and nutritional conditions infectious and parasitic diseases tuberculosisHIV/AIDSdiarrhoeal diseaseschildhoodclusterdiseasestropicalclusterdiseasesNoncommunicableDiseasesInjuries 0.174 0.068 0.133 0.134 0.141 0.007 0.004 0.126 0 0.002 0.047 0 0.115 0.001 0.001 0.019 0.103 0 0.009 0.001 0.001 0.066 0.122 0.011 0.002 0 0.074 0.004 0 0.01 0.108 0 0.002 0.105 0.009 0 0.002 0.017 0.098 0.004 0 0.001 0.015 0.089 0.003 0.001 0.004 0.056 0.003 0 0.002 0.001 0.015 0.008 0 0.021 0 0 0.027 0 0.023 0.011 0 0.001 0 0.006 0.004 0.007 0.003 0 0.021 0.001 Chapter 3

Fear of Obama: An Empirical Study of the Demand for Guns and the U.S. 2008 Presidential Election

3.1 Introduction

During 2008 and early 2009, the United States media reported on skyrocketing sales of firearms as well as shortages in common types of handgun ammunition (see, for example, Johnson (2008); Bohn (2008); and NPR (2009)). The increase in gun sales was measurably large: federal tax receipts from sales of pistols and revolvers increased by almost 90 percent during the fourth quarter of 2008 compared to the same quarter ayearearlier.1 Some states experienced substantial increases in the number of appli- cations for permits to carry concealed weapons.2 Even though this nationwide gun

1 This figure doubles the highest growth rates during 1993-1994 when both the Brady Handgun Violence Prevention Act and the Federal Assault Weapons Ban were introduced (arguably two of the most important gun control laws since the Gun Control Act of 1968 signed by President Lyndon Johnson), and more than doubles the panic buying in the aftermath of the 9/11 terrorist attacks. 2 For instance, Florida distributed 42,895 concealed weapon / firearm applications in November 2008 (an increase of 172 percent vs November 2007). Minnesota received 21,646 permit to carry applications in 2008, up 132 percent from 9,327 a year earlier. For some states this abrupt change was not only sizeable in terms of the flow of permits and licensees but also in their stock: the 2009- 2008 flow in Arkansas’s new carry concealed weapon licensees (i.e: 38,442 new licensees) represented 80 percent of the total stock of licensees as of December 2007. 150 151 phenomenon was particularly evident in the weeks following the presidential election of Barack Obama, gun sales began to spike before Election Day. In some parts of the United States gun purchases reached unprecedented peaks in July and September 2008, the months following Hillary Clinton’s concession speech and the Democratic Convention, respectively. As firearm sales soared, President-elect Obama urged gun owners “do not rush out and stock up on guns” in December 2008 (Pallasch (2008)).

Although the concurrent timing of growing gun sales and permit applications with the 2008 US presidential election is suggestive of an e↵ect of Obama’s election on the demand for guns, these correlations could also arise from other confounding fac- tors, such as worsening economic conditions or a more general election e↵ect. In this paper I quantify how the anticipation and realization of Obama’s success in the pres- idential election a↵ected the demand for guns. Using data constructed from futures markets on presidential election outcomes and a novel proxy for firearm purchases (i.e: the FBI’s firearm background check reports), I show how the demand for guns responded to monthly information regarding the likelihood of Obama being elected. After controlling for state fixed e↵ects, di↵erent time fixed e↵ect specifications, and state level-time varying covariates accounting for the economic climate, my point estimate provides strong evidence for the existence of a large “Obama e↵ect:” ac- cording to my most conservative specification, a 10-point increase in the probability of Obama being elected is associated with a 4.5 percent increase in the demand for guns nationwide. Moreover, this political e↵ect is larger than the e↵ect associated to worsening economic conditions.

Why would the election of Barack Obama have a↵ected the demand for guns? I study two potential underlying mechanisms by exploiting monthly variation in the odds of an Obama victory interacted with cross-sectional variation in a set of relevant state characteristics. First, a common explanation for this gun sales surge was the 152 perception and fear that the election of Obama would lead to stronger legal restric- tions on gun ownership and use in the near future (see, for example, Johnson (2008), and Neary (2009)). I refer to this potential mechanism as the “fear of gun control” hypothesis and exploit heterogeneity in gun laws to evaluate its validity. While there is an important federal component to gun regulations, most gun control policy in the United States is decentralized. As a result, there exist substantial cross-state di↵erences in the degree of gun law strictness regulating the sale, possession, and use of firearms. My identification strategy assumes forward looking agents and presumes that the potential enactment of a more restrictive federal gun control legislation was expected to be binding in states with weak gun control and could trigger the surge in gun sales.3 Consistent with the “fear of gun control” hypothesis, I find that the e↵ect of the Obama election on the demand for guns was much larger in states with weaker gun laws.

I also evaluate racial prejudice as another potential mechanism, which I refer to as the “race bias” hypothesis. Although some social scientists have argued that the election of Obama is the prima facie evidence that race does not matter anymore in American politics, empirical work analysing Obama’s performance during the 2008 election has shown mixed results (e.g., Mas and Moretti (2009); Lewis-Beck, Nadeau, and Tien (2009); Hutchings (2009); Enos (2010); Piston (2010); and Stephens-Davidowitz (2011)). Moreover, Sears and Tesler (2010) argue that the 2008 presidential campaign was “the most sharply racialized campaign” in the last two decades since public opinion was “largely polarized by racial attitudes.” Thus, it is conceivable that for some individuals the election of the United States’ first African-American president was seen as a personal threat (see, for example, Huppke (2009), and U.S. Department of Homeland Security (2009)). In fact, previous research has suggested that the

3 There is empirical and anecdotal evidence supporting the hypothesis that consumers of durable goods are forward-looking (e.g., Chevalier and Goolsbee (2009), and Mullin (2001) for the particular case of guns). 153 perceived threat of racial violence and white antipathy toward blacks fostered the decision of acquiring firearms during the 1970s (Northwood, Westgard, and Barb 1978). Consistent with the “race bias” hypothesis, I find that the Obama e↵ect was also larger in states with higher levels of prejudice against blacks.

Why study the demand for guns? Gun prevalence, particularly of handguns, is very high in the United States when compared to other developed countries.4 Moreover, gun violence has enormous implications for mortality and morbidity.5 Firearm-related homicide, suicide, and fatal accident rates are far higher than those in other high- income countries.6 Futhermore, guns represent “one of the most intensely divisive cultural issues” in United States (Rostron 2009). The gun debate has taken over not only the political arena but also academia.7 Irrespective of their stance, participants from both sides of the debate agree that guns matter. In fact, the private decision to acquire a firearm may represent an important externality for the rest of society. It is still under scrutiny whether the net externality is positive (by deterring criminals and increasing society’s overall safety levels) or a negative (by increasing crime, gun accidents and suicide rates). Nonetheless, Cook and Ludwig (2000) estimated the annual social cost of gun violence at $ 100 billion. Therefore, understanding the economic and non-economic determinants of this private decision provides a valuable input for the analysis of future gun policies and their social, economic, and political ramifications. I exploit a unique historical event to shed light on the determinants of

4 Di↵erent studies suggest that gun ownership is as high as 35 to 40 percent and as many as 300 million firearms are privately owned in the United States (see, for example, Duggan, Hjalmarsson, and Jacob 2010 for a background on gun ownership). 5 According to the Center for Disease Control and Prevention, in 2009 about 30,000 people died from gun-related homicides, suicides, and accidents, and about 70,000 su↵ered non-fatal injuries from firearm shots. By comparison, car accidents were the leading cause of injury deaths in 2009 with 35,000 fatalities. Statistics available here: http://www.cdc.gov/injury/wisqars/LeadingCauses.html 6 When compared to 23 high-income countries, firearm-related homicide, suicide, and uninten- tional fatality rates in the United States are 19.5, 5.8, and 6.9 times higher, respectively (Hemenway and Richardson, 2011). 7 Researchers in economics, political sciences, criminology, and public health have mainly focused on the study of the relationships between gun prevalence and (a) crime rates, and (b) gun-related suicide and accident rates. 154 the demand for guns.

My work contributes to the empirical literature on the e↵ects of both the realization and anticipation of political events, such as the passage of a new law or the election of a candidate, on di↵erent economic outcomes.8 In particular, it advances the empirical and theoretical work on consumers’ behavior in anticipation of future gun policies in two respects.9 Firstly, it provides empirical evidence consistent with stockpiling behavior as a reaction to expected future increases in the cost of acquiring firearms. Secondly, it sidesteps previous empirical impediments due to limitations of the data. Specifically, my panel data setting has a considerably improved statistical power compared to previous work and allows me to incorporate di↵erent fixed e↵ects in both atimeandacrosssectionaldimensiontoaddresspotentialomittedvariableproblems. Moreover, my analysis of (relative) high-frequency data for my gun purchases proxy mitigates the concern regarding the reverse-causality from the gun market to the likelihood of a political event.10 Finally, by interacting the time variation in the prospect of an Obama presidency and cross-state di↵erences in the stringency of the gun control laws, my setting also helps to identify heterogeneous reaction across states that may be otherwise masked in the aggregation of the data at the national level.11

8 Economic outcomes such as the stock market performance of private firms (Gyourko and Sinai (2004) and Knight (2006)), the spread between taxable and municipal securities (Greimel and Slem- rod 1999); investment decisions (Durnev 2011), nominal interest rates (Fowler, 2006), and a variety of financial indices (Snowberg, Wolfers, and Zitzewitz 2007). 9 Bice and Hemley (2002) estimate a supply and demand model using aggregate U.S. annual data for the period 1961-1994 and show that the demand for new handguns increased during the years of the discussion and passage of the 1968 Gun Control Act (GCA). Mullin (2001) presents a conceptual model of consumer demand for guns, in which the e↵ect of a buyback program on gun ownership depends on whether the program is permanent or unanticipated and never-to-be-repeated. 10 For instance, the assassinations of Robert Kennedy and Martin Luther King occurred after the GCA was introduced as a bill, but before it had been passed by the congress and enacted by President Johnson in October 1968. Are the discussion and passage of GCA independent of the evolution of the demand for guns? By using annual data is not possible to accurately test the timing and direction of the causality between the GCA and the demand for guns. 11 My paper is also related to the empirical literature studying the demand for guns and its relationship with the existing gun regulation environment. In particular, Glaeser and Glendon (1998), and Kleck and Kovandzic (2009) argue that gun-control laws in general do not appear to a↵ect gun ownership. By studying how gun tracking (a key element for the secondary market) across states responds to cross-state di↵erences in gun policies, Knight (2011) finds that the necessary 155 My paper also contributes to the empirical literature on how racial attitudes relate to firearms prevalence (Northwood, Westgard, and Barb (1978); Lizzote, Bordua, and White (1981); Young (1985); Ellison (1991); and Kleck and Kovandzic (2009)).12 To the best of my knowledge, my work is the first empirical study linking high- frequency gun purchases data (a flow) to racial attitudes at the state-level. Whether this partially racially-motivated change in the flow of guns a↵ected their stocks (or gun ownership rates) by race or led to any change in racially motivated incidents involving firearms is an interesting topic for future research.13

In addition, my paper contributes to the literature in economics and political science on the e↵ect of black candidates on behavior (see, for example, Citrin, Green, and Sears (1990); and Washington (2006)). In particular, it is related to the growing lit- erature on the relationship between race attitudes and the candidacy and subsequent election of Obama (e.g., Brader and Valentino (2011), DellaVigna (2010), Mas and Moretti (2009), and Stephens-Davidowitz (2011)).14 Most of previous empirical work shows little evidence of the existence of a link between negative racial attitudes and condition for the existence of cross-state externalities is empirically satisfied. 12 In which is perhaps the most convincing empirical work in this area, Kleck and Kovandzic (2009) show that handgun ownership among non-blacks (in particular Whites) is associated with racially prejudiced views toward Blacks.This strong positive correlation persists even after controlling for an extensive set of individual-level characteristics (and city-level covariates as well), including political and religious views, and subjective traits such as attitudes, opinions, and perceptions regarding prior victimization, fear of crime, proviolent attitudes, and punitiveness. Nonetheless, it is worth to mention that the main focus of Kleck and Kovandzic (2009)’s analysis is not the link prejudice- handgun ownership but the relationship between handgun ownership and city-level characteristics (i.e: crime and security). 13 More generally, my paper also contributes to a broader literature considering the relationship between negative racial attitudes and a variety of di↵erent outcomes such as racial preferences in dating (as in Fisman et al (2008)) and attitudes towards immigration (Dustmann and Prestion 2007) to racial wage gaps (Charles and Guryan 2008) and residential segregation (Card, Mas, and Rothstein 2008). 14 For instance, Brader and Valentino (2011) documents a decline in perceptions of racial dis- crimination after the election of Obama. DellaVigna (2010) finds no evidence that key events in Obama’s election a↵ected either discriminatory behavior in whites or di↵erent economic outcomes for blacks. Mas and Moretti (2009) argue that the available evidence does not suggest that racial attitudes negatively a↵ected Obama’s vote share in the 2008 presidential election whereas Stephens- Davidowitz (2011) finds opposite conclusions -as some political science research (see for instance, Lewis-Beck, Nadeau, and Tien (2009); Hutchings (2009); and Piston (2010))-. 156 the election of Obama and appears to contradict my findings. My analysis, however, is based on a di↵erent margin. My paper focuses on a specific sub-population who owns or is willing to acquire a firearm and may substantially di↵er, for instance, from the median American voter.

Finally, the evidence presented in my paper tangentially relates to the literature on the relationship between the characteristics of policy makers and policy outcomes (e.g., Pande (2003); Butler, Lee, and Moretti (2004); Chattopadhyay and Duflo (2004); and Washington (2008)).15 My findings suggest that di↵erent dimensions of Obama personality, besides his Democratic aliation, might had helped to trigger the surge in gun sales. Perhaps, as Washington (2006) suggests, a black Democrat may be perceived “as far more liberal than their non-black counterparts” and, thus, more willing to push a gun-control agenda.

My work provides empirical evidence on the existence of an Obama e↵ect on the demand for guns and the simultaneous feasibility of the two aforementioned hypothe- ses regarding the mechanisms underlying this Obama e↵ect. Moreover, the main results of my work are robust to a variety of checks and extensions. Neither current or expected worsening economic conditions at the state level nor heterogeneous re- actions to Obama’s election due to cross-state di↵erences in previous levels of gun ownership and Republican prevalence (as a proxy for Conservatism) explain away the two hypothesized underlying mechanisms. Controlling for the probability of another Democrat candidate winning the election and its interaction with the relevant state characteristics does not substantially a↵ect the results. The results are not driven by southern states or the inclusion of the aftermath of November 2008. Further, the results are also robust to the use of poll data, instead of futures market data,

15 According to these empirical works, not only party aliation would matter for policy decisions (as in Butler, Lee, and Moretti (2004)) but also characteristics such as gender (Chattopadhyay and Duflo 2004), family composition (Washington 2008) and ethnic group membership (Pande 2003). 157 and alternative measures of prejudice. Heterogeneity due to cross-state di↵erences in consumer confidence, income, previous levels of crime and other demographic charac- teristics such as share of rural population and relative share of black population does not qualitatively a↵ect the main results.

The remainder of the paper is structured as follows. In section 2 I describe my measure of the demand for guns and provide a background on the evolution of the market for legal guns, with a focus on the 2008 presidential election. In section 3 I describe the empirical framework and how it identifies and quantifies the Obama e↵ect, as well as discuss potential underlying mechanisms. I also conduct the empirical analysis and robustness checks. Section 4 concludes.

3.2 Data and Aggregated Empirical Evidence on

Gun Sales

3.2.1 A Proxy of the Demand for Guns

Measuring the demand for guns is not straightforward.16 Neither national nor state level data on gun purchases is available. Moreover, most states do not require registra- tion of firearms and only a few states require permits to buy them (Azrael, Cook, and Miller 2004). Even among the states where some requirements exist, the information is incomplete, outdated and dicult to obtain (Jacob 2002). Quarterly data on tax

16 The gun-related empirical works have mainly relied on the use of proxies for gun prevalence across geographic regions. Di↵erent alternatives has been proposed such as gun magazine subscrip- tions, suicides with guns, percentage of di↵erent type of crimes with guns, fatal gun accident rates, NRA members per 100,000 resident population, hunting license rate, carry permits rate, Federal firearms license rate, stock of guns manufactured in United States, and weapon arrests as percent- age of total arrests. None of these proxies is pertinent for my work because they are not available on monthly basis to the best of knowledge. Therefore, I cannot exploit variation within a year to study the moments before and after 2008 presidential election. 158 receipts from gun sales is available only at the national level what prevents me from examining time variation across states. However, there is a federal data source that provides some indication of the level of the demand for guns by state and month: the National Instant Criminal Background Check System (NICS henceforth), launched by the FBI in 1998 and mandated by the Brady Handgun Violence Prevention Act of 1993 (BHVPA). Before selling any firearms, federal licensee dealers must call the FBI or other designated agencies to ensure that a potential buyer does not have a criminal record or is not ineligible to acquire a firearm (Adams et al 2010).

Nevertheless, it is pertinent to note that the number of background check reports does not necessarily indicate an exact number of firearms sold in a given state for a given month (FBI 2011). First, after a report is emitted a customer might buy several guns, or decide not to buy any after all. Second, if the prospective buyer fails the criminal background check no sale is consummated. Third, some states do not exclusively use the NICS to conduct background checks on a potential gun sale at federal licensee dealers (Mayors Against Illegal Guns 2008).17 Finally, private-party gun sales are exempt of background check unless required by state law (see Wintermute (2009)). Specifically, gun shows represent an emblematic case in the gun control debate since federal statutes do not regulate them. Unlicensed vendors or “occasional” sellers, who are not required to initiate a background check make up to 50 percent of all gun sales at gun shows (Bureau of Alcohol, Tobacco and Firearms 1999).

Despite the aforementioned drawbacks, the number of background check reports pro- vides a good proxy for the overall demand for guns for several reasons. NICS’s figures are the most common firearm sales indicator used in the firearm industry’s reports.

17 Some states use the NICS system for the application and renewal of gun permits or for purchases in secondary markets such as gun shows. Meanwhile, NICS figures for Kentucky are exaggeratedly inflated since they appear to have been performing monthly NICS checks on concealed carry per- mit holders (Mayors Against Illegal Guns 2008). For that reason Kentucky is not included in my empirical analysis. 159 The number of background checks is highly correlated with tax receipts from firearm sales at the national level.18 Moreover, the number of background check reports in per capita terms is also highly correlated with measures of gun ownership at state level.19 It can be argued then that the higher the gun prevalence (stock) in a given state, the higher the level of desired transactions (flow) per number of inhabitants in that state. Furthermore, according to business publications in the firearm indus- try, about 98 percent of all inquiries to the NICS turn into a firearm sale.20 Finally, although there are thousands of gun shows annually in the United States (Duggan, Hjalmarsson, and Jacob 2010) they represent a minor percentage of all gun sales in the country accounting for between 4 and 9 percent of total sales, according to the best estimates available (Wintemute 2009).21 Therefore, gun sales at gun shows that are not captured by NICS’s statistics would represent no more than 4.5 percent of total gun sales in the United States.

3.2.2 A Descriptive Analysis of the Aggregated Evolution of

Gun Sales

In this section I present preliminary evidence on the existence of an unusual change in the demand for guns in the proximity of the 2008 presidential election. Its primary purpose is solely to provide a descriptive account of the evolution of gun sales by the inspection of aggregated figures. The discussion on the application of stricter

18 I find a correlation of 0.70 when comparing quarterly data on tax receipts from firearm sales and number of background check reports at the national level for the period 1999-2010. 19 The correlation with the state-level gun ownership estimated from the Behavioral Risk Factor Surveillance System from North Carolina (BRFSS) in 2001 is 0.78. This correlation is high not only in a cross section but also in a time-series analysis. The correlation of the proxy with the 9-census region level (there is no panel data of gun ownership at the state level) gun ownership from GSS for 5di↵erent waves is 0.84. 20 See, for instance, the American Firearms Industry Magazine (June 2010). 21 According to the U.S. Department of Justice [2007], estimates on the number of gun shows per year range between 2000 and 5200. 160 firearm regulations has been a hot topic in almost every presidential election. Thus, I provide a first piece of evidence by comparing the evolution of my proxy of gun sales in the proximity of the 2008 presidential election with its evolution in the proximity of the 2000 and 2004 presidential elections in Figure 3.1. For a two-year symmetric window covering the period before and after the election week in each of the last three presidential elections, I plot the aggregate evolution of the weekly number of firearms background check reports relative to the same week a year earlier. The vertical black line denotes the week of the election (week 0). The solid black line, representing the relative weekly numbers for the weeks surrounding the 2000 presidential election, does not suggest any increasing demand for guns in that period, with the exception of the aftermath of the 9-11 terrorist attacks (weeks 45 to 48).22 It is important to remark that the gun control debate was arguably a very important issue during that election race, in part a↵ected by the Columbine High School massacre, which occurred one year prior. In fact, many believe that Al Gore lost his home state Tennessee, and also Arkansas, New Hampshire and West Virginia, due to his vocal pro-gun control stance during the Democratic primaries (Cowan and Kessler (2001)).

No abnormal evolution in the demand for guns for the two-year period surrounding the 2004 presidential election can be inferred from the inspection of the solid gray line.23 The implied year-on-year weekly growth rate of the number of background check reports for that period ranges between -10 percent and 10 percent. The solid black line with diamond markers shows a sharp increase in the demand for guns in the week of the 2008 presidential election and in the weeks after. The average implied year-on-year weekly growth rate of the number of background check reports for the

22 There was almost 470,000 more firearm background checks during the six months following the terrorist attacks. The same figures ascend to 1.6 million during the six months following the 2008 presidential election day. 23 Due to an internal software problem, the NICS temporarily stopped for three days on May 11, 2000; halting all gun sales nationwide. For that reason, the relative numbers of background checks were artificially underestimated and overestimated for the weeks 26/27 and -26/-27, respectively. I omitted those four weeks, which explains the two discontinuities in the solid gray line. 161

Figure 3.1: Presidential Elections and Firearm Background Check Reports

24-week period following that election is above 30 percent with the highest figure of 60 percent occurring immediately after the election. In addition, a 30 percent increase in the firearm background check reports takes place in mid-July after Obama wins the Democrat primaries.

Figure 3.2 presents a geographical distribution of the annual increase in the demand for guns for the period July 2008 - June 2009. I aggregate the data to annual cumu- lative values to facilitate the exposition. I will later exploit monthly variation in the number of firearm background check reports. I divide the map into quintiles in terms of the growth rate of my proxy for the demand for guns. It is worthwhile to note that although southern states have on average higher growth rates, there seems to be some variation even within that region.24 The states with the highest growth rates, from 31 percent in Texas to 78 percent in Utah, are roughly evenly distributed across

24 In the robustness checks of the econometric analysis I show that none of the main results are driven by southern states. There is no evidence of spatial autocorrelation in the growth rate of background check reports (Moran’s I statistic of 0.0362). 162 the U.S. from the East Coast (Connecticut, Rhode Island, and New Jersey) and the South (Georgia and Tennessee), to the central part of the Mountain Region (Nevada, Utah, and Colorado) and Hawaii. Although the group with the second highest figures is mostly represented by southern states (South Carolina, Delaware, Florida, North Carolina, Oklahoma, and Maryland) it also includes central states such as Illinois and Kansas. The group of states with the lowest growth rate (growth rates from -21 percent in Massachusetts to 12 percent in Vermont) is very heterogeneous not only in terms of geographic location but also in terms of both the level of ruralism and average income. New York, California, and Massachusetts as well as Idaho, Montana, Mississippi, and North Dakota belong to this group.

Figure 3.2: Growth in Background Checks by State (July08-June09 vs July07-June08) 163 3.3 Empirical Strategy and Results

3.3.1 Quantifying the Obama e↵ect

Obama Victory E↵ect

Table 1 provides a first statistical test for the existence of an Obama e↵ect in the demand for guns. Using monthly data at the state level for the number of background check reports in per capita terms for the period March 2007-December 2009, I estimate the following equation:25

log (y )=↵ + Obama + Z + ⌧ + + + " (3.3.1) i,t ⇤ t ⇤ i,t ⇤ t i t i,t

26 where the subscripts i and t denote state and month respectively. The variable yi,t is the number of firearm background check reports per 1,000 inhabitants. Obamat is a dummy variable that takes the value 1 starting in November 2008. Zi,t is a monthly indicator of the economic situation at the state level. ⌧t is a simple time trend, and i is the state i fixed-e↵ect. The term t denotes a collection of time related fixed-e↵ects consisting of season, year, month of year, and month of year ⇥ state fixed-e↵ects. Table I presents the main results for the di↵erent specifications of equation (1). Below each coecient two standard errors are reported. In parenthesis, Ipresentstandarderrorsadjustedforclusteringwithinstate.Inbrackets,Ipresent standard errors adjusted for clustering within month. Since I am especially interested on the e↵ect of information regarding Obama election on the demand for guns at the

25 I start the analysis in March 2007 in order to be consistent with the rest of the paper. In addition, I use state monthly data instead of weekly data because the latter is not publicly available through the FBI’s NICS section. 26 Due to the lack of data for one of the main explanatory variables I focus my analysis on 43 states. The omitted states are Hawaii, Idaho, Kentucky, Maine, Nebraska, Nevada, and North Dakota. 164 state level, whereas that information is the same for all the states in a given month, clustering at the month level appears to be the most conservative approach. In fact, standard errors clustered at the month level are much larger than at the state level. Therefore, for the rest of the paper I report only the standard errors clustered at the month level.27 All the regressions are weighted by state population in 2008 and include state and year fixed e↵ects to capture time-invariant state characteristics and year specific conditions that may be related to the demand for guns. In order to capture within year specific time variation I also include season fixed-e↵ects in columns 1 and 2 whereas in columns 3 I include month of year fixed-e↵ects.28 In specifications from column 4 to 6 I include month of year state fixed-e↵ects to capture monthly ⇥ seasonal pattern in the demand for guns that may be state specific.

Column 1, where I consider = =0,suggeststhatdemandforgunsissignificantly positively related to the election of Obama. The point estimate from my semi-log specification indicates that the election victory of Obama would have been related to a39.1percentincreaseinthedemandforguns(i.e.,100 (e0.33 1) = 39.1). The ⇥ next specification in column 2 includes a time trend which does not a↵ect previous finding. The introduction of month of year, and month of year state fixed-e↵ects in ⇥ columns 3 and 4 slightly reduces the size of the main Obama e↵ect although does not a↵ect its statistical significance. In column 5 I control for the state of the economy. There is no substantial empirical evidence on the strong causal link from economic conditions to crime rates (see Cook and Wilson (1985); and Freeman (2001) among others). However, if people perceive that this causal mechanism does exist then it is possible that the demand for guns presents a counter-cyclical behavior. That is, fear of future crime due to a deteriorated economic condition would make people more

27 None of the main results is a↵ected when clustering at the state level. 28 Gun sales peak during the Fall, when hunting season begins, and the holidays; whereas they reach their lower levels during the Summer. 165 willing to buy a gun during a recession.29 Since the election of Obama coincided with the worsening economic condition in 2008, omitting variables related to the business cycle that potentially a↵ect also the demand for guns would introduce a bias on the estimation of the Obama e↵ect. In order to account for the business cycle shocks in each individual state, I add the growth rate of the state coincident index.30 As expected, the introduction of this economic indicator reduces the size of the main Obama e↵ect. However, this e↵ect associated with the election of Obama remains statistically large. The point estimates in this specification suggest that the election of Obama is associated with almost 25 percent increase in the demand for guns and that the current worsening economic conditions played a role as a determinant of the increasing demand for guns in my period of analysis: a one point decrease in the monthly growth rate of activity (more than 10-point decrease in annual terms) implies an increase in the demand for guns of 13.4 percent.31 Nonetheless, an indicator of the future, rather than current, condition of the economy is arguably a better proxy to account for a fear of future crime. In fact, adding a monthly leading indicator accounting for the six-month forecast of the growth rate of the coincident index kills the statistical significance of the contemparaneous growth rate (result not shown). Thus, in the last specification (column 6) I substitute the growth rate of the coinci- dent index by its six-month forecast (i.e: leading indicator).32 A one-point expected decrease in the six-month growth rate is statistically associated to 3 percent increase in the demand for guns whereas the election of Obama is associated with a 22 percent increase. Thus, according to the point estimates, the suggested Obama e↵ect on the

29 Glaeser and Glendon (1998) suggests that fear of crime is associated with ownership of pistols only and no other form of firearms. On the other hand, Kleck and Kovandzic (2009) argue that handgun ownership is a response to homicide, but not necessarily to fear of crime. 30 The coincident index combines four state-level indicators (i.e: nonfarm payroll employment, average hours worked in manufacturing, unemployment rate, and real wage and salary disbursement) to summarize current economic conditions in a single statistic (Clayton-Matthews and Crone 2005). 31 I also estimate additional specifications taking into account state-level growth of unemployment and state-level growth of quarterly income per capita. The results (not shown) are consistent with the specification using the coincident index. 32This leading indicator is also produced by the Federal Reserve Bank of Philadelphia. 166 demand for guns would be equivalent to the induced e↵ect of a enormous decrease of approximately 15 points in the expected growth rate of activity in one year.

Incorporating Information from Future Market on Election Outcomes

Now I focus on the 2008 presidential election race and the month following Novem- ber 2008 by studying how the change in the perception of Barack Obama being the most likely winner of the election a↵ects the demand for guns. I continue basing my empirical analysis on the period March 2007-December 2009.33 As mentioned before with the analysis of Figure 1, the aggregated evolution of the number of background check reports shows a significant increase not only in November 2008 and afterward but also in July 2008; coinciding with Obama winning the Democrat primaries. Using data from the Iowa Electronic Market (IEM) I construct a proxy for the probability of Obama winning the presidential election. The IEM is an on-line futures market where participants trade future contracts with payo↵sbasedonelectionoutcomes (Each participant can invest between $ 5 and $ 500).34 That is, the liquidation value of the contract that represents the actual outcome of an election, will be $ 1 whereas all other contracts will have no value.35The IEM provides information on two future markets that are relevant for my purposes: one where payo↵s were determined by the outcomes of the 2008 U.S. National Convention of the Democratic Party and another

33 March 2007 is the first month in my analysis because Barack Obama announced his candidacy for president on February 10th, 2007. 34 Intrade is an alternative and similar futures market for the 2008 election. I chose IEM data over Intrade’s just because the former is available online at no cost. Nonetheless, one should not expect sizable and permanent price di↵erences across markets over time since this would imply the existence risk-free profit opportunities from cross-market arbitrage. 35 I use market data instead of polls data for several reasons. First, market data eases the calculation of the probability of Obama winning the election for the whole period of analysis because all the information needed is available in a higher frequency basis and in a homogenous way. Second, under non-arbitrage opportunities the market prices should reflect a consensus regarding the forecast of votes share for each candidate (Berg, Forrest, and Rietzet 2008). Third, recent research provides evidence not only on the relative accuracy of prediction markets (Berg, Forrest, and Rietzet 2008) but also on the impediments to accurate prediction for polls data due to the existence of di↵erent bias (Malter 2010). Nonetheless, using polls data does not alter the main results of my empirical work (see robustness check). 167 one where the outcome depended on the results of the 2008 U.S. Presidential elec- tion. The price of the asset in the first market accounts for the probability of Obama winning the primaries while the price of the asset in the second market accounts for the probability of a Democrat candidate winning the presidential election.36 By mul- tiplying these two probabilities I obtain a proxy for the probability of Barack Obama being elected president. Given that in my new specification I attempt to disentangle the Obama e↵ect from an overall Democrat e↵ect, I also construct another control variable denoted probability of other Democrat candidate winning the presidential election using the same aforementioned approach.37 38 My new identification strat- egy is summarized in the following equation:

log (yi,t)=↵+ P (Obama)t + P (Democ Obama)t +⇢ Zi,t + ⌧t +i + t +"i,t (3.3.2) ⇤ ⇤ ⇤ ⇤

where P (Obama)t and P (Democ Obama)t are the 2008 election victory probabilities for Barack Obama and any other Democrat candidate receptively, as implied from future contracts from the IEM.39 The remaining right-hand-side variables are the same defined above for equation (1). Table 2 presents the main results for the di↵erent specifications of equation (2). In column 1, where I consider = ⇢ = =0,thepoint estimate suggests that 10 points increase in the probability of Obama winning the election is statistically associated to 5.6 percent increase in the demand for guns. The

36 In order to build a monthly probability value I use all the daily available information on prices and transactions for a given month. The final monthly value is the result of a weighted average of the daily available prices, where the daily price is the average price of the day and the weights are the shares of total daily transactions on total amount of monthly transactions. 37 Basically, the probability of other Democrat candidate winning the presidential election is the result of the probability of Obama losing the primary multiplied by the probability of a Democrat candidate winning the presidential election. 38 Note that these probabilities do not vary across states. Therefore, no cross-state di↵erence in perceptions regarding the likelihood of Obama’s victory, as implied from IEM data, is an implicit identification assumption in my econometric strategy. Again, without arbitrage opportunities, the implied probability of Obama winning the election (i.e: a market price) should reflect the consensus of market’s participants, independently of their locations. 39 Note that the variable P (Obama) takes the value 1 from November 2008 onwards while the variable P (Democ Obama) takes the value 0 from September 2008 on. See robustness check for results omitting November 2008 aftermaths. 168 Democrat Party is long identified with gun control. Therefore, if a fear to future gun control policies is operating during the election race then it could be expected that no matter the identity of the candidate, as long as that candidate would be Democrat, advocates of gun rights would buy more guns when a Democrat is more likely to be elected president. In order to di↵erentiate an Obama e↵ect from a Democrat e↵ect, I control for the probability of any other Democrat candidate being elected in column 2. As expected, due to the fact that the two probabilities are (strongly) negatively correlated, the Obama e↵ect is much larger than in the first specification while the precision of the estimate is not a↵ected. Note also that the probability of other Democrat being elected is significantly and positively related to the demand for guns but the size of the coecient estimate is less than half of the estimate for Obama. Specifically, the election of Obama would imply a 63.4 percentage increase in the demand for guns while the election of another Democrat would imply a 25.4 percentage increase.

The estimated Obama e↵ect decreases to 51.8 percent when I control for the leading indicator of the economic activity at the state level in column 3. Under this spec- ification the coecient estimate for the e↵ect of the election of another Democrat increases considerably to 0.42. Additionally, 1 point increase in the expected six- month growth rate of economic activity, as measured by the leading indicator, would imply a 2.6 percent reduction in the demand for guns. The addition of a time trend in column 4 again decreases the size of the Obama e↵ect and increases coecient for the e↵ect of another Democrat. However, the introduction of month of year fixed e↵ect in column 5 substantially reduces the size of the latter e↵ect to the point of making it statistically insignificant when clustering standard errors at the month level. Finally, the introduction of month of year state fixed-e↵ects in columns 6 -arguably the most ⇥ conservative specification possible- reduces the size of the main Obama e↵ect to 0.42 although does not a↵ect its statistical significance whereas it eliminates the statisti- 169 cal significance of the coecient for the probability of other Democrat winning the election when I cluster the standard errors at the month level. The point estimate for the leading indicator variable remains fairly constant across specifications.40

Geographical Pattern

In this section, I explore the geographic distribution of the main Obama e↵ect. In Figure 3 I represent a map of United States with the implied Obama victory e↵ect on the demand for guns by state. That is, I map the coecient estimates from the following regression.41

log (y )=↵ + Obama Statei + Z + + ⌧ + + " (3.3.3) i,t i ⇤ t ⇥ i ⇤ i,t i t t i,t X

State ii is a dummy variable that takes the value 1 for the state i, and 0 otherwise.

The variables i, ⌧t, t are the state, season, and year fixed e↵ects. The darker the color of the state in Figure 3.3, the larger the Obama victory e↵ect (white colored states with lines have no estimated coecients). A close inspection of the map reveals some noteworthy patterns. The states in the highest quintile of the distribution of the estimated Obama e↵ect are not clustered in any specific region of US.42 Mid- western and western states are more prevalent in the lowest quintiles while southern states are more common in the two highest quintiles of the distribution of the esti- mated Obama e↵ect.43 Interestingly, states with high levels of gun ownership such 40Adding a time-varying control for the evolution of consumer expectations at the national level (using either the Reuters/University of Michigan Index of Consumer Sentiment or the Conference Board’s Consumer Confidence Index) does not explain away the estimated Obama e↵ect (results not shown). 41 Using these coecients one can calculate state-specific semi-elasticities that can be interpreted as the percentage increase in the demand for guns in a given state due to the election of Obama (by doing 100 (ei 1)). 42 The highest⇥ quintile consists of Tennessee, Utah, Connecticut, New Jersey, South Carolina, Indiana, Rhode Island, and Delaware. 43 The lowest quintile consists of Massachusetts, California, Wisconsin, Minnesota, Montana, New 170 as Wyoming, Montana, South Dakota, West Virginia, Mississippi and Alaska present point estimates for the Obama e↵ect below the median.44

Figure 3.3: Geographic Distribution Obama Victory E↵ect

3.3.2 Potential Mechanisms

In this section I examine two potential mechanisms that may have accounted for the Obama e↵ect on the demand for guns: fears of future federal gun-control policies (i.e., “fear of gun control” hypothesis) and negative racial attitudes toward blacks (i.e., “race bias” hypothesis). The empirical strategy in equations (1) and (2) does

Mexico, Vermont, and Arizona 44 In addition, I also run a similar regression to equation 3 but instead of specifying interaction terms with state dummies, I introduce nine census division dummies (result available upon request). The positive statistical relationship between the demand for guns and the election of Obama is larger in southern states, especially in the East South Central division (Kentucky, Tennessee, Mississippi, and Alabama) and lower in New England and the Pacific. Overall, it appears that the distribution of the largest reactions of the demand for guns to the election of Obama is mostly concentrated in the Appalachians and the Bible Belt. Notably, the increase in the Democrat vote share in the 2008 presidential election (relative to the 2004 election) was significantly smaller in that part of U.S. (Mas and Moretti 2009) what would point to the possibility that negative attitude towards Obama were presented. 171 not allow me to identify heterogeneous reactions to the election of Obama. In fact, it may be possible that the aggregate e↵ect documented in Table 1 and 2 is the net result of o↵setting e↵ects from heterogeneous groups. Thus, a more detailed analysis of the monthly evolution of the demand for guns seems to be pertinent to identify the potential existence of di↵erent marginal reactions that may vary not only over time but also across states. The heterogeneity in State characteristics, such as strength of gun laws and average prejudice, interacted with time variation in the likelihood of Obama being elected would help me to shed some light on the potential mechanisms underlying the Obama e↵ect.

Fear of Gun Control

The stockpiling of firearms as a reaction to potential restrictions on gun purchases seems a reasonable and plausible explanation for the surge in sales in a context with forward looking agents who recognize Democrats as more prone to support gun control laws, Obama leading the polls, and a constant negative advertising about his position on gun issues.45

Consistently with this line of argument, trend in attitudes toward gun control started to show a substantial change in the proximity of the presidential election of 2008. According to Smith (2007), support to stricter regulation on sales and possession of firearms was widespread in 2006.46 However, there has been an increasing support

45 The National Rifle Association (NRA) spent 7 million dollars on a campaign advocating the position that Obama posed a serious threat to the “Second Amendment rights” (Brady Campaign to Prevent Gun Violence, 2008). This figure represents more than thirty times the amount devoted against Al Gore in 2000 (Brady Campaign to Prevent Gun Violence 2008). 46 According to the National Opinion Research Center, around 90 percent of surveyed supported making it illegal to use guns while under the influence of alcohol, more than 80 percent wanted limitations to the sale of 50 caliber rifles, semiautomatic, and assault weapons. Above 80 percent favored criminal background checks for all sales of guns, including private sales between individuals, and requiring a police permit to buy guns. 172 to gun right since 2008 (Pew Research Center 2010).47 As documented in Figure 4, the fraction of people saying it was more important to control gun ownership almost doubled the fraction of people saying it was more important to protect gun rights in April 2007. Note, however, that theses figures are likely to be influenced by the tragic event occurred at Virginia Tech. Nonetheless, the gap starts to shrink before the 2008 election and it is completely closed in March 2010. Interestingly, the Tucson Shooting of January, 2011 did not appear to a↵ect gun control preferences.48

Figure 3.4: Change in Attitudes Toward Gun Control. More important to Protect Gun Rights or Control Gun Ownership?

The existence of statistically significant coecients for either the dummy Obama or the Probability of Obama winning the election, as documented in previous section, is no evidence of the existence of a stockpiling decision originated from fears of future anti-gun laws. It is possible that some people could have been making the decision

47 Taking into account the period 1993-2008, the vast majorities surveyed by the Pew Research Center had consistently said it was more important to control gun ownership than to protect the right to own guns. According to Smith (2007), not even the fear after 9/11 terrorist attacks could undermine the support to gun regulations. 48 All Regions experience an increasing support to gun rights during this period although this support is significantly lower in the East. 173 of buying guns based on impressions related not only to his stance on gun control but also to other di↵erent perceived characteristics of Obama’s identity (even inde- pendently of his race). For instance, people could perceive his Muslim sounded like name as an indication of a potential threat to their security. Other people could perceive Obama as a potential socialist and being against American values. In order to attempt to identify a gun-control fear mechanism underlying the Obama e↵ect I estimate di↵erent specifications of the following equation:

log (y )=↵ + Obama + Obama SGC + Obama X + ⇢ Z i,t ⇤ t ⇤ t ⇥ i s ⇤ t ⇥ s,i ⇤ i,t P

+ i + t + "i,t (3.3.4)

Equation (4) focuses only on the event of the election victory of Obama. However, the fears of stricter gun controls were already widely spread before the election week, in part fueled by the NRA’s negative advertising showing Barack Obama as a real threat to the “second amendment” (See Martin (2008), and Lapierre (2008)). Then, I also estimate below an analogous specification to equation (4), in which I also exploit information regarding the market consensus on the likelihood of Obama being elected president.

d log (yi,t)=↵ + P (Obama)t + P (Democ Obama)t + P (Obama)t SGCi ⇤ ⇤ ⇤ ⇥

+ P (Obama) X + ⇢ Z + + + " (3.3.5) s t ⇥ s,i ⇤ i,t i t i,t X

Where SGCi is a measure of stringency of gun laws based on the 2008 Brady Score Card for state i. The Brady Center to Prevent Gun Violence computes a state scorecard that grades, from 0 to 100, each state according to the strength of its 174 49 gun laws (the higher the score, the stronger the state gun laws). Xs,i is a vector of characteristics s for state i that might correlate with both the demand for guns and the strength of state gun laws. I pay special attention to the characteristics Republican prevalence and gun ownership levels. I include a Republican prevalence interaction term for two reasons. First, I would like to account for a proxy of conservative concern that also correlates with lower levels of gun control since the values associated with the gun culture are mostly politically related to the Republican party, which has historically been against gun control measures (Spitzer (1988); and Gimpel (1998)). Note, however, that the empirical research linking ideology and attitudes toward gun control has shown mixed results (Curry and Jiobu 2001). Second, given the two party system of the United States, a person who is “afraid of Obama” is more likely to be Republican. The inclusion of the last interaction terms try to control for the fact that gun ownership is more common among Republicans and Conservatives (Curry and Jiobu (2001); and Azrael et al (2007)) and also higher in states with weaker state gun control. Republican prevalence is measured as the average margin of votes for the Republican candidates in 1992, 1996, 2000, 2004, and 2008 presidential elections whereas gun ownership levels are 2001 household estimates from the Behavioral Risk Factor Surveillance System in North Carolina (BRFSS).50 51

Panels A and B in Table 3 present regression outcomes for equations (4) and (5), respectively. For both Panels SGC =100 2008 Brady Score Card thus the higher i i the value of SGCi, the weaker the state gun law. The remainder variables Zi,t, i, and t are the same as defined for equations (1) and (2). All the regressions are

49 See Table A.2 for each state score card. 50 In 2001 the BRFSS in North Carolina surveyed 201,881 respondents nationwide, asking them, ”Are any firearms now kept in or around your home? Include those kept in a garage, outdoor storage area, car, truck, or other motor vehicle.” 51I also allow flexibility in the functional form for the interaction terms.Table S2 in supplementary appendix includes the specifications for which the interaction terms are discrete variables. In those additional regressions SGC (Xs,i ) is a dummy variable for whether the state i presents a value of SGC (Xs,i ) below (above) the median. 175 weighted by state population in 2008. The error term is allowed to be heteroskedastic and correlated at the month-level.

Since the interaction terms are not demeaned, an statistically insignificant coecient for the main Obama term (i.e: )shouldnottobeinterpretedasevidenceofthe absence of the Obama e↵ect. That is, the coecient estimates for in equations (4) and (5) are not directly comparable with the ones in Tables 1 and 2. Nonetheless, for each specification I report the implied Obama victory e↵ect (i.e., P (Obama)= Obama = 1) at the mean value of interacted variables along with the F-statistics for the null hypothesis = = s =0(i.e.,noexistenceoftheObamae↵ect). Finally, the coecient captures the di↵erential Obama e↵ect in the demand for guns for state with weaker gun laws. Therefore, > 0 would be consistent with the hypothesis of the existence of a firearm stockpiling reaction induced by fears of stronger legal restrictions on guns.52

Results in column 1 in Panel A of Table 3 are consistent with the existence of a large e↵ect of the election of Obama in the demand for guns (F-Statistics 36.8 for the null = =0).Moreimportantly,thepointestimatesfor suggests that there was an di↵erential increase in sales in states with lower levels of state gun control (significant at the 1 percent level). The magnitude of the e↵ect is large. The point estimates suggest, for instance, that given the election of Obama, states with no compulsory universal background checks and gun purchase permit requirements (25 points in Brady Score) hypothetically experienced a demand for guns 9 percent larger than states where those requirements existed in 2008. The addition of the other interaction

52 Although there is no accurate statistics about the exact number and location of gun shows, Lott (2003) collected state level figures for the period 1989-2001 from the Gun Show Calendar. Using that data I find that the prevalence of gun shows is negatively associated to some di↵erent measures of state gun control laws. Then, if the “gun control fear” hypothesis is correct one may expect to see an increase of the demand for guns in the secondary market (which includes gun shows) as well. If that is the case, the negative correlation between the weakness of state gun laws and the prevalence of gun shows would suggest that my point estimates for should be interpreted as lower bounds. 176 terms in column 2 does not substantially a↵ect the size of but improves its statistical precision (t-statistic increases more than 10 percent). Interestingly, after the election of Obama the demand for guns was also higher in states with higher Republican prevalence but smaller in states with high levels of gun ownership. The addition of month of year state fixed-e↵ects in column 3 or month fixed-e↵ect in column 4 does ⇥ not substantially a↵ect the results for . In sum, the election of Obama implied a di↵erential increase the demand for guns with low levels of gun control and a mean e↵ect between 24 and 32 percent (mean Obama e↵ect= exp(ˆ+ˆ SGC+ˆ X ) 1). ⇥ s⇥ s Finally, the coecient estimates capturing the e↵ect of future shocks to the economic activity on the demand for guns are consistent with the results for the specifications presented in Table 1. Nonetheless, the statistically significance of this economic e↵ect vanishes when I include month fixed-e↵ect.53

In Panel B in Table 3 I present the regression outcomes for di↵erent specifications of equation (5) where I exploit variation in the probability of Barack Obama being elected president as implied from future market prices. After controlling for state, season, and year fixed-e↵ects, the probability of other Democrat being elected and the growth rate of economic activity, the point estimate for in column 1 in Panel Bsuggeststhat1-pointincreaseinSGCisstatisticallyassociatedwithanadditional 0.6 percent increase in the demand for guns. Accounting for Republican prevalence and gun ownership interacted both with the probability of Obama victory in column 2doesnota↵ect the size of the di↵erential e↵ect (significant at 1 percent level). The addition of month of year state fixed-e↵ects or month fixed-e↵ect in columns 3 ⇥ and 4, respectively, does not substantially a↵ect the results, namely: (i) the implied

53 Most of the variation (95 percent) of this economic variable is within variation (i.e: over time). Therefore, the month fixed-e↵ect is absorbing most of the variation needed to identify the economic shock e↵ect. See Table A.1 in appendix. 177 partial e↵ect of an Obama Victory at the mean value of the interacted controls is con- sistent with 44 to 57 percent increments in the demand for guns; and (ii) in line with a pattern consistent with a fear of future gun control measures, the Obama e↵ect was statistically larger in states with weaker gun control laws. The point estimate in the most conservative case (column 4, in which I include state and month fixed-e↵ects), suggests that the fear of the hypothetical implementation of the same gun measures currently existing in California (the state with the strongest gun control measures ac- cording to the Brady Campaign) would make the Obama e↵ect more than 30 percent larger in Arizona. The estimate coecient for the Republican prevalence interaction term is also statistically significant and represents a meaningful magnitude: increasing the average Republican margin by 10 points would make the Obama e↵ect 7 percent higher (a 10-point increase in the average Republican margin in Georgia, for instance, would lead to the level of Republican prevalence in Kansas). In addition, for all the specifications I obtain large F-statistics for the null hypothesis = = s =0.

Race Bias

It could be argued that, despite of the NRA’s negative advertising; there was no clear rationale for believing ex-ante that Obama’s gun policies would be extraordinarily more restrictive than those of Hillary Clinton or John McCain. Although Obama had consistently supported gun control measures in the past (Brady Campaign to Prevent Gun Violence 2008), he also made repeated claims to advocate the upholding and respect of the “Second Amendment”.54 On the other hand, John McCain sponsored the McCain-Lieberman gun-show bill which would have given the federal government the legal tools to close the gun show “loophole” on a national basis. Prior to the 2008

54 For example Obama said at a campaign rally in Lebanon, Va., on September 9, 2008: “I believe in the second amendment. I believe in people’s lawful right to bear arms. I will not take your shotguns away. I will not take your rifle away. I won’t take your handgun away...there are some common-sense gun safety laws that I believe in. But I am not going to take your guns away”. 178 election, the NRA had referred to McCain as “one of the premier flag-carriers for the enemies of the Second Amendment,” rating him with a “C” when he was seeking reelection as senator from Arizona in 2004 (Rostron 2009). Regarding the other Democratic Party presidential frontrunner, NRA executive Vice President Wayne LaPierre stated that there would be “nightmare years ahead for gun owners if Hillary Clinton is elected president of the United States” (See Lapierre (2008) ). The fact that Barack Obama did not have a particularly relative stronger anti-gun record or campaign positions begs the question as to why the likelihood of his election would induce a surge in gun sales.

Focusing on local biracial elections, Rich (1989) argues that Black candidates may result in ”intense apprehensions and fear” among Whites. Northwood, Westgard, and Barb (1978) suggest that white working class amtiphaty towards blacks partially explained the rise of gun ownership in the urban non-South during the ’70s. In this vein, the election of the first Black president might have represented a unique driver for racial tension, specially among prejudiced people. There is some anecdotal evidence pointing toward this possibility. The major white nationalist web site temporarily shut down in the days following the presidential election due to the large amount of internet trac (see Wright (2011) and Huppke (2009)). The Southern Poverty Poverty Law Center (2009) reported an increasing activities of racialized right-wing groups.55 Homeland Security (2009) stated that “the high volume of purchases and stockpiling of weapons and ammunition by right-wind extremist (...) [is a] primary concern to law enforcement”. Can prejudice partially account for the unusual surge in gun sales documented in the previous section? In order to study this question I consider regressions of a similar form to equations (4) and (5). Nonetheless, instead of focusing on the interaction term including SGCi Iexaminehowameasureof

55 Although Bill Clinton’s administration also faced virulent right-wing group, which were weak- ened by the end of the 1990s; they were not primarily motivated by race hate (Southern Poverty Law Center 2009). 179 racial attitude towards blacks at the state level interacted with the time variation of information regarding Obama’s likelihood of becoming president is statistically associated with the demand for guns. I thus include a variable P rejudicei proxying for racial prejudice against black at the state level i. The prejudice index comes from Charles and Guryan (2008) and represents an average over a 30 years period for an aggregate measure of racially prejudiced sentiments at the the state level.56

A measure of prejudice is not required to be perfect for the purpose of my estimations; rather it solely needs to provide some indication on racial prejudice di↵erences across states. Nevertheless, the geographic distribution of prejudice might have changed over time introducing measurement error in my analysis. Under classical errors-in-variables my OLS estimation of prejudice interacted term would tend to be underestimated and biased the results against finding statistically significant results. Notwithstanding, it is not clear what is the magnitude and nature of the measurement error in my prejudice variable, I show that the main results are robust to the use of di↵erent prejudice measures that capture most current trends on racial sentiments (see Table 8inrobustnesschecksection).

The two panels in Table 4 emulate Table 3. I continue controlling for the interaction terms including Republican prevalence and gun ownership in order to partially ac- count for di↵erences in Conservatism. My measure of prejudice against blacks may have a component of symbolic racism which has been argued to confound with con- servative ideology (Feldman and Huddy 2005). Note that the coecient estimates capturing the di↵erential Obama e↵ect for higher levels of prejudice are statistically significant at the 1 percent level in all the specifications. Results from column 1

56 Using multiple waves (1972-2004) of the GSS and responses from white people aged 18 and older to more than 20 di↵erent racial prejudice questions, Charles and Guryan (2008) build a proxy for prejudice at the state level. They focus only on questions that are exclusively related to racial prejudice, omitting in their analysis the ones touching on government policies and race. After normalizing responses, they construct a individual-level prejudice index for each respondent and then aggregate these figures at the state level (see Table A.2 for each state value of the index). 180 in Panel A suggest that the election of Obama is statistically associated with a 26- percent increase in the demand for guns for a state with mean values of prejudice. The point estimate (ˆ =0.252)fromcolumn4inPanelAsuggeststhatatwostandard deviation increase in the level of average prejudice (for instance, Alabama’s prejudice level is 2 standard deviation above Tennessee’s) would imply an additional 10 percent increase in the demand for guns when Obama was elected. Note that the coecient estimates for all the other covariates are consistent with the ones obtained in Table 3. In addition, the F-statistics for the hypothesis testing of the existence of the Obama e↵ect are very large. The implied Obama e↵ects evaluated at the mean are slightly smaller for all the specifications but remains very similar to the ones obtained in Table 3.

Ifindalargere↵ect of prejudice when I use market prices from IEM in Panel B. All the coecient estimates for the prejudice interaction terms are positive and strongly significant, indicating a pattern consistent with racial sentiments playing a role in the unusual increase of the demand for guns during my period of analysis. The magni- tude of this race related e↵ect is very large. For instance, for the most conservative estimation it implies that a hypothetical 2 standard deviation increase in the level of average prejudice is statistically associated with a 13-percent increment in the de- mand for guns when Obama is elected. The estimate coecient for the Republican prevalence interaction term is also statistically significant and represents a meaning- ful magnitude: increasing the average Republican margin by 10 points would make the Obama e↵ect 11 percent larger. Nonetheless, this coecient estimate is upward- biased due to the omission of the interaction term capturing the weakness of State Gun Laws which are positive correlated with Republican prevalence over the last 20 years (see point estimates in Table 5). 181 3.3.3 Robustness Check

In this section, I consider a variety of robustness checks. I first provide evidence that the two aforementioned mechanisms are not competing or mutually excludable. Results in Table 5 show that the joint inclusion of both interaction terms in all my specifications does not substantially a↵ect the main results obtained in Tables 3and4.57 That is, coecient estimates for both interaction terms are statistically significant, with the expected signs, and in similar magnitudes.58

All the specifications estimated so far are based on the period March 2007-December 2009. In my second robustness check, I exclusively focus on the election race, omitting all state-month observations after November 2008. Column 1 in Table 6 shows that the Obama e↵ects remain strongly statistically significant suggesting that a 10-point increase in the probability of Obama victory is associated with an increase in the de- mand of guns of 5.8 percent. Remarkably, the coecient estimate for the probability of other Democrat winning the election is now significant at the 10 percent level sug- gesting that a 10-point increase in P (Democ Obama)t would increase the demand for guns in 4.6 percent. The coecients for the main Obama term and its interactions in column 2 are jointly statistically significantly di↵erent from zero (F-Statistic is 23.32) whereas the implied Obama e↵ect evaluated at the mean values of interacted variables is 0.64. Note that the point estimates for the interaction between the probability of Obama winning the election and the weakness of state gun control measure substan- tially increases in size and gains more precision. The results for both the average

57 To save space Table 5 only reproduces specifications with Probability of Obama Victory and Continuous interaction terms (to be compared with Panels B in Tables 3 and 4). The same qualitative results remain for Panels A (results available upon request). 58 Although estimates weighted by state population seem to be the most appropriate and pre- ferred estimates for general inference and discussion of policy implications, I also estimated the 4 specifications in Table V without using population weights (results not shown). The point estimates are larger for the main Obama e↵ect and smaller for both weak state gun and prejudice interaction terms. The former interaction term is always statistically significant at 1 percent level whereas the latter presents p-values slightly above 0.1 for 2 specifications. 182 prejudice and average Republican margin interaction terms are statistically weaker. The introduction of month fixed e↵ects in column 3 does not alter the key findings. Note, however, that the sharp drop in sample size in these specifications is likely to partially account for this reduction in the precision of the estimation of some of the coecients. In sum, these results would suggest that the fear of gun control was the most important underlying mechanism driving the Obama e↵ect during the election race.

My identification strategy somehow assumes that individuals are informed about the evolution of the each probability implied from IEM data. Ordinary individuals may have diculties understanding the concepts involved in the calculation of those probabilities and their implications.59 In addition, general knowledge of the existence of IEM data may not be widely spread. Therefore, it is conceivable that individuals end up using poll data as their primary source of information (or proxy) to update the likelihoods of each of the election outcomes. In Table 7 I use normalized poll data for both the 2008 presidential and Democrat primary elections instead of the IEM data to account for the likelihood of Obama and other Democrat winning the election.60 The results are similar to and consistent with the ones obtained in my preferred specifications in Tables 3 and 4. However, the results in columns 4 through

59 Another potential objection is, in line with Tversky and Kahneman (1974), that individuals may make decisions based of subjective assessments of probabilities which, in some cases, may be quite di↵erent from the true (objective) probabilities. 60 The source of the poll data is pollster.com. In order to have some measure of comparability between my proxy and the probability data from IEM I proceed as follows. I construct the normalized (i.e: I transform the shares of all candidates so they add to one) poll data for Obama winning the primary by using the monthly average of the polls asking for the favorite Democrat candidate. Then I follow the same procedure using the polls on Obama vs McCain (assuming that McCain was the Republican candidate with probability equal to 1) and obtaining a proxy for the unconditional probability of Obama winning the election. By taking the product of the two normalized poll figures (i.e: Obama winning the primary and Obama vs McCain) I obtain the poll data on the likelihood of Obama winning the election. In order to construct the poll proxy for other Democrat candidate winning election I use the same procedure using the probability of Obama losing the primary and the data on polls “Clinton vs McCain” (assuming that Clinton was the only Democrat alternative to Obama). Note that, as in previous specifications, P (Obama) takes the value 1 in November 2008 and afterward while P (Democ Obama) takes the value 0 from September 2008 on. 183 6arestatisticallystrongerwhencomparedtoTable6(whenIreducetheperiodof analysis to March 2007-November 2008). In particular, the prejudice interaction term is now statistically significant at the 5 percent level.

In my fourth robustness check exercise I focus on alternative measures for prejudice. All the specifications include state and month fixed-e↵ects. In column 1 of Table 8 I account for the number of racially motivated hate crimes reported to the FBI in 2008 (per million of people covered by the agencies reporting to the FBI). The coecient estimate for its interaction term with the probability of Obama winning the election is positive and statistically di↵erent from zero (at the 10 percent level) suggesting that 2 standard deviations increase in the number of racially motivated hate crimes per million of inhabitants would be statistically related to a 2 percent increase in the demand for guns when Obama is elected. This magnitude is relative small when comparing with my previous findings. The accuracy of FBI’s hate crime data is subject of debate among experts (see, for example, Perry (2001); and Rubenstein (2004)) since measurement error is likely to be present.61 For the second specification in column 2 I use a racial attitude measure from Fisman et al (2008).62 The coecient for this interaction term is also positive and strongly statistically significant. A two standard deviation increase in the level of prejudice is statistically associated with almost 16 percent increase in the demand for guns when Obama is elected. In column

61 Even the FBI cautions of making cross-sectional comparison based on hate crime data. Further- more, according to the Bureau of Justice Assistance (1997) “...many police jurisdictions, especially those in rural areas, simply do not have the manpower, inclination, or technical expertise to record hate crimes, and other jurisdictions fear that admitting the existence of hate crimes will cause their communities cultural, political, and economic repercussions”. Law enforcement and citizen attitude toward reporting hate crimes might be correlated with the true level of prejudice of a given location. Since hate crime under-reporting would be more likely in states where race prejudice is in some degree more institutionalized, this measurement error might be introducing a downward bias in the estimation of the relevant coecient. 62This measure accounts for the fraction of respondents that answered yes to the GSS’s question “Do you think there should be laws against marriages between (Negroes/Blacks/African-Americans) and whites?” They compute this variable at the state level using all the responses for the period 1988- 1991. Note that this variable take into consideration all the respondents no matter their race so it will not be picking up the e↵ect of the specific negative attitude towards black from white people (as in Charles and Guryan (2008)) but an overall prejudice e↵ect. 184 3IuseMasandMoretti(2009)’smeasureofprejudice.63 Ifindsimilarresults.64 In the last specification in column 4 I use a proxy for racial animus developed in Stephens- Davidowitz (2011) which is based in the volume of (normalized) Google search for racially charged language by State.65 Again, I find similar results: a two standard deviation increase in state’s racially charged search index is statistically associated with more than 14 percent increase in the demand for guns when Obama is been elected.

Table 9 includes further robustness checks. All specifications include state and month fixed e↵ects. Therefore, they are intended to be compared with column 4 in Table 5. In column 1 I add the four interactions between the probability of other Democrat winning the election and the relevant state characteristics (i.e: gun control weakness, prejudice, Republican prevalence, and gun ownsership rates) to assure that previous results were not just picking up a more general Democrat e↵ect. None of the four co- ecients for the P (Democ Obama)t interaction terms are statistically significant at the standard levels of confidence (results not reported in table). The point estimates for the interaction terms are broadly consistent with my previous findings. Nonetheless, the coecient estimates for the interaction term for average prejudice become larger and more statistically significant whereas the weak state gun control interaction term is somewhat weaker although still significant at the 10 percent level. I acknowledge that applying my regression analysis to election of 1992 may be arguably a better robustness check. In fact, federal tax receipt from firearms sales rose by an average

63 Mas and Moretti (2009) use a similar measure as in Fisman et al (2008) since it is based on the response to same GSS’s question. The most important di↵erence is that Mas and Moretti (2009) take into consideration only the fraction of whites who support anti-interracial-marriage laws.They also expand the sample size including all GSS’s waves between 1990 and 2006. 64 Both prejudice measures from Fisman et al (2008) and Mas and Moretti (2009) are not available for New Mexico thus regressions in columns 2 and 3 are based in 42 states. 65 Stephens-Davidowits (2011)’s measure is based on the percentage of an state’s searches that included the word “nigger” or its plural for the period 2004-2007. Each percentage is divided by West Virginia percentage (i.e: state with the larger percentage) and multiplied by 100. Therefore, West Virginia’s racial animus measure is 100. 185 25 percent during the 4 quarters following the election of Bill Clinton and concur- ring with the discussion in Congress of the Brady Handgun Violence Prevention Act and the Federal Assault Weapons Ban. Unfortunately, there is no monthly data on cross-state variation in gun purchases -or any proxy- for that period (NIC’s data is available since 1999).

In my next robustness check I explore whether any of my main results is driven by the South. According to the average prejudice index computed from GSS data by Charles and Guryan (2008), southern states tend to have levels of prejudice above the national average. In addition, southern states also had larger increases in the demand for guns after the election of Obama. Thus, in column 2, I omit from the sample all the southern states. Results remain unaltered.66

From column 3 to 5 I consider state heterogeneity in other confounding variables. First, in column 3 I add an interaction term exploiting cross-state variation in public perception about US economy for 2008.67 Although states in which consumers had lower confidence in both the current and future state of the economy experienced higher increases in the demand for guns when the odds of the election of Obama increased, results obtained in Table 5 are not a↵ected. In column 4 I use informa- tion from Google Trends about the relative intensity of searches including the terms “stimulus” and “economic stimulus” for the period January-September 2008 as an- other proxy for public concern’s about the state of the economy.68 Consistently with

66The results are unaltered when I omit each southern census region at a time. None of the three southern sub-regions appear to be driving the main results. Results available upon request 67I use Gallup’s economic confidence index which is based in 2 questions regarding public per- ception of the current and future economic conditions. Residents of states mostly a↵ected by unem- ployment in 2008 (such as Michigan and Rhode Island) had the most negative perceptions in 2008. Oil-producing states (such as Texas, Utah and North Dakota) that were somehow benefited from the surge in oil prices were the least negative in 2008 (Saad, 2009). 68Each state’s score (i.e: Search Volume Index) is relative to the state with the highest score (wich is always 100) of relative searches for a particular term. In my specification I use the average of the states’ score for the terms “stimulus” and “economic stimulus”. Using exclusively either the former or the latter does not a↵ect the results (results available upon request) 186 the previous finding, states with relative more search volume for the two terms also experienced a higher demand for guns when Obama election probability was higher. The inclusion of this new interaction term does not a↵fect the statistical significance of the prejudice and weak gun control interaction terms although it reduces the size of the estimated coecient for the latter.

In the last column of Table 9 I add four additional interaction terms to account for state characteristics that may confound with my prejudice and weak gun control meassures. I include black population (relative to white population) which is strongly and positively correlated with my measure of prejudice, average crime rate in the period 2006-2007 (positively correlated with weak gun control), income per capita in 2007 (negatively correlated with the two measures), and rural population share (positively correlated weak gun control). The coecients for weak gun control and prejudice interactions are statiscally significant at 1 the percent level after controlling for this new set of interactions (adding one control at a time does not a↵ect the conclusions. Results available upon request).

3.4 Conclusions

My reading of the overall body of evidence is that the election of Barack Obama had asignificante↵ect on the demand for guns. Using monthly data constructed from futures markets on presidential election outcomes and monthly-state information on a novel proxy for demand for guns, I showed how the demand for guns responded to information regarding the final outcome of the election. After controlling for state fixed e↵ects, di↵erent time fixed e↵ect specifications, and state level-time varying covariates accounting for the economic climate, my point estimates suggest a large Obama e↵ect on the demand for guns. 187 However, the reaction to Obama was not homogeneous across the U.S. I presented empirical evidence consistent with the hypothesis that the unusual increase in the demand for guns was partially driven by fears of Obama’s future gun-control policy. The response of the demand for guns to increases in the likelihood of the election of Obama was significantly larger in states with weaker state gun control. I also presented evidence consistent with the idea that racial attitudes also played a dif- ferentiating role since the Obama e↵ect was larger in states with higher levels of prejudice against blacks (result robust to alternative measures of prejudice). I also showed that these two hypotheses are complementary rather that competing. The evidence presented would also suggest that not only the party aliation but also the identity of the candidate would matter to explain the demand for guns in the months surrounding the 2008 presidential election.

My conclusions must be understood in terms of a macro level e↵ect since my analysis attempts to infer individuals’ behavior by analyzing patterns from aggregate data. Thus, the evidence I present does not prove that individuals reacted in such a way but rather provides evidence for the validity of these hypotheses. On the other hand, my empirical setting cannot shed light on the race of the people buying the firearms. It is equally possible that the existence of racial tension in a given state -measured by the prejudice among whites-, led both blacks and whites to buy firearms. Yet further macro and micro-empirical analysis would be necessary to address the question of whether the racial composition of gun ownership (a stock) was a↵ected by the unusual change in flow.69 Moreover, I do not have evidence that the firearms sold during the period of analysis actually implied an increase in overall gun ownership. The available data from NICS allows no distinction between people buying guns for the first time

69 The GSS provides micro level data on gun ownership, race, political ideology and race attitude among others. The gun ownership figures from the GSS are the most accepted at the macro level. However, they are only representative at the national and nine census regional levels (Davis and Smith 1998). 188 and previous gun owners increasing their stocks. The lack of clear consensus on the best way to measure both racial attitudes and their e↵ect on political and social attitudes (Feldman and Huddy 2009) represents another drawback to identifying the racial attitudes di↵erentiate shift in the demand for guns.

Unfortunately, I cannot fully control for unobserved heterogeneity across states. There- fore, there are other plausible explanations consistent with the heterogeneous reac- tions to Obama, aside from the fear of future gun control and race bias motivation. Nonetheless, these explanations are more likely to be complementary rather than competing. For instance, since gun prevalence appears to be a social norm when there is mistrust of public justice or where there is a tradition of private retribution (Glaeser and Glendon 1998), it could be argued that the reaction to Obama is a mere consequence of general mistrust of the Federal government, or opposition to governmental intrusion into the private lives of citizens that may correlate with fear of losing gun rights or animosity against blacks. Although I include two interaction terms (i.e: Republican prevalence, as a proxy for conservatism, and gun ownership) trying to mitigate these problems and partially rule out other explanations, careful micro-analysis could further support my findings. Nevertheless, there is anecdotal evidence that provides some additional support to the gun control fear explanation and its scope. First, some states have been pushing to expand new pro-gun laws, even regulations that have been previously rejected, in apparent anticipation of expected tighter future federal gun legislation (Urbina 2010). Second, according to a Gallup’s survey of October 2009, 41 percent of those surveyed believed that Obama would try to ban gun sales while this figures rose to 55 percent when taking into account only gun owner respondents.70

70 http://www.gallup.com/poll/123602/Many-Gun-Owners-Think-Obama-Will-Try-Ban-Gun- Sales.aspx 189 -0.0297 [0.00564]*** (0.00687)*** [0.0253]*** (0.0310)*** ect ↵ Table 3.1: Obama Victory E (1) (2) (3) (4) (5) (6) [0.0375]*** [0.0431]*** [0.0475]*** [0.0575]*** [0.0578]*** [0.0536]*** (0.0264)*** (0.0243)*** (0.0167)*** (0.0203)*** (0.0238)*** (0.0259)*** Dependent variable: Log of Criminal Firearm Background Checks per 1,000 inhabitants Obama% Growth State Coincident IndexLeading Indicator State FESeason FEYear 0.333 FETrendMonth of Year FEMonthofYear-StateFE 0.323R-squared*** significant at 0.309 the 1in percent.** parentheses significant (in at brackets). the AllSample 5 models size percent.* are is significant weighted 1,462 at by state-month 10of state observations percent. the population (43 coincident Standard in states). 0.309 index. errors 2008. The clustered All Y leading N at specifications indicator state Y for included level each constant (month state (not level) N predicts reported). the six-month growth rate Y 0.221 N Y N -0.134 Y 0.507 N Y 0.2 0.508 Y Y N N Y Y 0.563 Y Y Y N N 0.780 Y Y Y Y 0.798 N N Y 0.801 Y Y Y N N Y Y 190 ect ↵ [0.00776]*** [0.00691]*** [0.00497]*** [0.00635]*** (0.00572)*** (0.00637)*** (0.00636)*** (0.00816)*** Table 3.2: Obama E [0.267] [0.246]* [0.221]** [0.212] [0.257] (0.0579)*** (0.0740)*** (0.0897)*** (0.0789)*** (0.0912)*** (1) (2) (3) (4) (5) (6) [0.0597]*** [0.0851]*** [0.0724]*** [0.0766]*** [0.0946]*** [0.113]*** (0.0442)*** (0.0507)*** (0.0537)*** (0.0525)*** (0.0425)*** (0.0514)*** Dependent variable: Log of Criminal Firearm Background Checks per 1,000 inhabitants P(Obama)P(Democ-Obama) 0.563Leading Indicator 0.639 0.254 0.518State FESeason FE 0.42 0.485Year FETrend 0.416Month 0.494 of Year FEMonthofYear-StateFE 0.275R-squared 0.416 *** significant at the 1 Yin N percent.** parentheses significant Y (in at brackets). the AllSample 0.274 5 N models size percent.* are is significant weighted 1,462 at by state-month 10of state Y observations percent. the population (43 coincident Standard in -0.0264 states). index. errors 2008. The clustered All leading at specifications indicator state for included level each constant (month state (not level) N predicts reported). Y N the six-month Y growth 0.511 rate -0.0298 N Y -0.0271 0.513 N Y N Y N Y 0.527 -0.027 N Y N Y N 0.531 Y Y Y N 0.585 N Y Y Y 0.801 Y Y N N Y Y 191 0.413 0.407 0.258 (0.247) (0.249) (0.264) ect and Gun-Control Fear ↵ (0.00171) (0.000643)(0.00129) (0.00170) (0.000423) (0.00130) (0.00228) (0.000951) (0.00181) (0.00220) (0.000583) (0.00179) PanelA:F(Obama)=Obama PanelB:F(Obama)=P(Obama) (1) (2) (3) (4) (1) (2) (3) (4) (0.0626) (0.0823) (0.0613) (0.0996) (0.126) (0.117) (0.00638) (0.00661) (0.00538) (0.00367) (0.00757) (0.00787) (0.00617) (0.00363) (0.000882) (0.000764) (0.000556) (0.000788) (0.00130) (0.00112) (0.000740) (0.00112) Table 3.3: Obama E Dependent variable: Log of Criminal Firearm Background Checks per 1,000 inhabitants ect 36.80 41.86 55.72 28.42 31.17 68.76 ↵ ect 0.322 0.304 0.232 0.569 0.553 0.443 ↵ F (Obama)F (Obama) * Weakness State Gun ControlF 0.00360*** (Obama) * Average Republican 0.00353*** MarginF 0.00292*** (Obama) * Gun Ownership 0.00324***Leading 0.00519*** Indicator 0.00502***P 0.00443*** (Democ-Obama) 0.00508*** -0.0144 0.00470*** 0.00447***R-squared 0.00568*** 0.182**Mean Obama E F-Statistic Obama E 0.196****** -0.00543*** significant at the -0.00600*** -0.0276*** 1All percent.** models significant are at -0.00506*** weighted the by -0.0271*** 0.00693***Columns 5 state (1) percent.* population and significant in (2) at 2008. also 10The 0.00620*** -0.0292*** Sample include percent. leading size Season indicator Standard is and for errors 1,462 Year each clustered FEs. state-month 0.00778*** state at observations Columns predicts month (with (3) the level -0.00184 43 include six-month in states). Month agrowth parentheses. of All rate Year specifications of X the include 0.146 State coincident State -0.0253*** and index FEs. Year FEs. -0.00717*** Columns (4) -0.0243*** include -0.00757*** Month FE. 0.414*** 0.565 -0.0252*** -0.00690*** -0.000395 0.366*** 0.573 0.827 0.657 0.571 0.579 0.837 0.664 192 0.435* 0.410 0.258 (0.245) (0.249) (0.263) ect and Race Bias ↵ (0.00156) (0.000734)(0.00158) (0.00155) (0.000487) (0.00158) (0.00209) (0.000833) (0.00226) (0.00204) (0.000753) (0.00223) Panel A: F(Obama) = Obama Panel B: F(Obama) = P (Obama) Table 3.4: Obama E (1) (2) (3) (4) (1) (2) (3) (4) (0.0291) (0.0534)(0.0874) (0.0552) (0.0673) (0.0288) (0.0715) (0.132) (0.0706) (0.0924) (0.102) (0.114) (0.0401) (0.105) (0.00651) (0.00666) (0.00563) (0.00393) (0.00782) (0.00795) (0.00654) (0.00394) Dependent variable: Log of Criminal Firearm Background Checks per 1,000 inhabitants ect 33.94 31.35 73.92 25.75 21.50 82.46 ↵ ect 0.264 0.284 0.219 0.499 0.533 0.427 ↵ F (Obama)F (Obama) * PrejudiceF (Obama) * Average Republican MarginF (Obama) * Gun OwnershipLeading Indicator 0.328*** 0.00817***P (Democ-Obama) 0.216*** 0.282*** 0.00702*** 0.00851*** 0.361***R-squared 0.197***Mean Obama 0.342*** E 0.252***F-Statistic Obama E -0.00333** 0.441****** significant at -0.0301*** -0.00409*** the 1 0.0113***All percent.** 0.367*** models significant -0.0275*** are at -0.00311* weighted the byColumns 5 state 0.0101*** (1) percent.* population and -0.0292*** significant in (2) at 0.283*** 2008. also 10The Sample include percent. leading size Season indicator Standard 0.475*** is and for errors 1,462 0.0119*** Year -0.00251 each clustered FEs. state-month state at observations Columns predicts month (with (3) the level 43 0.331*** include six-month in states). 0.666*** Month agrowth parentheses. -0.0287*** of All rate Year specifications of X the include State -0.0247*** coincident State and index FEs. Year FEs. 0.586*** Columns -0.00403* -0.0253*** (4) include Month FE. 0.546 -0.00460*** -0.000948 -0.00389* 0.571 0.824 0.655 0.546 0.575 0.831 0.660 193 (0.00232) (0.000925)(0.00172) (0.000576) (0.00224) (0.00170) (1) (2) (3) (4) (0.247) (0.248) (0.262) (0.0962) (0.120)(0.0919) (0.117) (0.0921) (0.0329) (0.0961) (0.00112) (0.000988) (0.000702) (0.000991) (0.00762) (0.00790) (0.00630) (0.00384) Table 3.5: Both Hypotheses Together ect 20.02 25.16 71.27 ↵ ect 0.552 0.534 0.426 ↵ Dependent variable: Log of Criminal Firearm Background Checks per 1,000 inhabitants (bm)WansSaeuCnrl0047* .02**0035* 0.00402*** 0.00385*** 0.00424*** 0.00457*** P(Obama)P(Obama)*WeaknessStateGunControl P(Obama)*AveragePrejudiceP(Obama)*AverageRepublicanMarginP(Obama)*GunOwnershipLeading Indicator 0.248** -0.00745*** -0.00800*** P(Democ-Obama) 0.284*** 0.166* 0.00767*** -0.00776*** 0.208***R-squared 0.00674*** 0.465***Mean Obama E F-Statistic Obama E 0.00839*** 0.401*** 0.254** *** significant at the 0.422* 1level percent.** in significant parentheses. at All the models(with 0.417 5 are 43 percent.* -0.0267*** weighted states). significant by at All state 10Columns specifications population percent. (3) include in -0.0259*** include State Standard 2008. Month FEs. errors 0.270 Sample of clusteredstate Columns size Year at predicts (1) X is month the and State 1,462 -0.0265*** six-month (2) and state-month agrowth also Year observations rate FEs. include of Season Columns the and (4) coincident Year includes index. FEs. Month FE. The leading indicator for each -0.00341 0.576 0.586 0.840 0.670 194 (0.180) (0.181) (0.00157) (0.00157) (0.00352) (0.00364) (0.00288) (0.00292) (1) (2) (3) (0.253) (0.253) (0.0634) (0.224) (0.0113) (0.0110) (0.00749) ect from results in column 2 is 23.32. The implied Obama ↵ Table 3.6: Omitting Election Aftermath Dependent variable: Log of Criminal Firearm Background Checks per 1,000 inhabitants ect evaluated at the mean values of interacted variables is 0.64. The leading indicator for each state predicts the ↵ P(Obama)P(Obama)*WeaknessStateGunControlP(Obama)*AveragePrejudiceP(Obama)*AverageRepublicanMargin 0.00618*** 0.00619*** P(Obama)*GunOwnershipLeading IndicatorP(Democ-Obama)*** significant at the 1at percent.** month significant level at in the parentheses.observations 5 All (with percent.* models 43 significant states). are at weighted 10 AllColumns by percent. specifications (3) state include includes Standard population State Month errors 0.292 in FE. FEs. clustered e 2008. 0.586*** F-Statistic Columns Sample for (1) size the and is Obama (2)six-month 903 E also growth state-month includes rate Season of and the Year coincident FE. the index. 0.00652* -0.00447 -0.00442 0.246 0.00667* 0.243 0.462* 0.452* -0.00517 -0.00182 0.00238 195 (0.0821) (0.0872) (0.129) (0.132) (0.00219) (0.00214)(0.00159) (0.00158) (0.00326) (0.00332) (0.00224) (0.00230) (0.000898) (0.000915) (0.00134) (0.00136) (1) (2) (3) (4) (5) (6) Table 3.7: Using Polls Data (0.157) (0.157) (0.182) (0.182) (0.0727) (0.114) (0.0773) (0.170) (0.00768) (0.00765) (0.00392) (0.00839) (0.00820) (0.00764) ect 21.29 12.98 ↵ Dependent variable: Log of Criminal Firearm Background Checks per 1,000 inhabitants ect 0.484 0.581 ↵ (bm)WansSaeuCnrl0033* 0.00366*** 0.00608*** P(Obama) 0.00383*** 0.00606*** P(Obama)*WeaknessStateGunControl P(Obama)*AveragePrejudiceP(Obama)*AverageRepublicanMarginP(Obama)*GunOwnershipLeading 0.416*** Indicator 0.468*** -0.00665***P(Democ-Obama) 0.00727*** -0.00689*** -0.00212 -0.00211 0.149 0.00778*** 0.520*** Mean Obama 0.277*** E F-Statistic Obama E Period 0.256****** significant at the 1All percent.** models significant are at weighted the by 0.00625*All 5 state -0.0213*** specifications percent.* population include significant in State at 2008. FEs. 10The Sample percent. leading -0.0210*** Columns size indicator (1), Standard in for (2), errors first each (4), clustered (last) 0.00616* state and at 3 predicts (5) month columns the level also 0.244 is six-month in include -0.00411 1462 0.346** growth parentheses. Season (903) rate and state-month of Year observations the FEs. (with coincident Columns 43 index. (3) states). 0.000896 and (6) include Month FE. 0.347** 0.243 0.00242 0.000258 0.266 Mar-2007/Dec-2009 0.263 Mar-2007/Nov-2008 196 (1) (2) (3) (4) (0.00201) (0.00205)(0.00259) (0.00198) (0.00275)(0.00389) (0.00234) (0.00408) (0.00213) (0.00397) (0.00232) (0.00382) (0.000531) (0.449) (0.210) (0.00169) Hate Crimes Fisman et al Mas and Moretti Stephens-Davidowitz in 2008 (FBI) (2008) (2008) (2011) Table 3.8: Using Alternative Measures for Prejudice Dependent variable: Log of Criminal Firearm Background Checks per 1,000 inhabitants P (Obama) * Alternative Prejudice MeasureP(Obama)*AverageRepublicanMargin 0.000982*P(Obama)*GunOwnership 2.625*** -0.00579** 0.0122*** -0.00356 -2.54e-05 Leading -0.00251 0.788*** Indicator 0.0121***Prejudice 0.0113*** Measure 0.00518*** 0.0126*** *** significant at the 1level percent.** in significant parentheses. at All the modelsand 5 are (3)).in percent.* weighted colum significant by (1) at state and 10represents population (4) percent. the in (columns number Standard 2008. (2). of errors Sample racially All clusteredPrejudice size motivated specifications at measure hate is include month in crimes 1462 State colum per (1438) and (4)(2011). million state-month Month is observations of The FEs. normalized 0.00334 people leading Google Prejudice covered indicator measure search by for volume in agencies each for column reporting state racially (1) to predicts charged the the language FBI. six-month from growth Stephens-Davidowitz rate 0.00495 of the coincident index. 0.00163 -0.000943 197 (0.128) 0.000784 (0.00220) (0.00252) (0.00195) (0.00120) (1) (2) (3) (4) (5) (0.145) (0.126) (0.0962) (0.0965) (0.0634) (0.00163) (0.00118) (0.000988) (0.000802)(0.00525) (0.00116) (0.00219)(0.00337) (0.00231) (0.00246) (0.00203) (0.00173) (0.00167) (0.00180) (0.00270) (0.00381) (0.00734) (0.00428) (0.00384) (0.00423) Table 3.9: Further Robustness Check Dependent variable: Log of Criminal Firearm Background Checks per 1,000 inhabitants (bm)WansSaeuCnrl0039 .09**0037* .03**0.00427*** 0.00239*** 0.00397*** 0.00492*** 0.00329* P(Obama)*WeaknessStateGunControl -0.00892*** P(Obama)*AveragePrejudice -0.0130*** P (Obama) * -0.00777*** Average Republican MarginP(Obama)*GunOwnership -0.0133*** P(Obama)*EconomicConfidenceIndex2008 0.00986* -0.00884** 0.426*** 0.0145***P(Obama)*Google’sVolumeSearchfor -0.00345*** ”Stimulus” 0.0112*** and 0.334** ”Economic Stimulus”P(Obama)*RelativeBlack-WhitePopulation 0.0109*** 0.255** 0.00981*** P(Obama)*IncomePerCapita 0.251**P(Obama)*CrimeRate 0.186*** P(Obama)*ShareofRuralPopulation 0.00628*** Leading IndicatorR-squaredState-Month ObservationsNumberofStates*** significant at the 1in percent.** parentheses. significant All at models theindicator are 5 for weighted percent.* by each significant state state at population predicts 10personal in the percent. income 2008. six-month in Standard growth All 2007. errors rate specifications clustered Crime of include at rate the State month is coincident and (0.00140) index. the Month average Income Fes. crime Per The rate Capita leading in is 2006-2007. average -0.00379 quarterly 1,462 -0.00480 0.00405** 0.00970*** 0.0752 -0.00645 952 0.670 -0.00441 43 1,462 -0.00239 0.598 28 1,462 0.671 1,462 43 0.675 0.674 43 43 Appendix A: Additional Tables

198 199 betweenwithinbetweenwithin 0.59 0.25 -0.87 2.13 0.29 1.99 n = 43 0.68 T = 34 1.66 -2.57 0.20 -6.86 3.49 n = 43 T = 34 Table 3.10: Summary Statistics VariableLn of # BCR/1k inhab.Prob (Obama), from IEMProb (Obama), from pollsProb(Democrat-Obama),fromIEM dataProb (Democrat -Obama ),Leading from Indicator polls (Prediction data of Six-Month Growth Rate) overallBrady State Score CardAveragePrejudice 2008 (SGC)AverageRepublicanMarginGun Ownership overall overall (within) -0.87 overall(within)BCR: Background check reports. N, overall n, (within) and 0.16 T Level overall 1.79 denote of (within) state-month, 0.14 Variation state, and month Mean observations respectively. 0.55 0.17 Std. -8.03 0.58 Dev. 0.17 1.17 3.64 Min 0.39 overall (between) 0.00 0.37 0.00 Max 0.63 N 18.49 = 0.40 1462 0.13 Observations 0.44 0.09 18.64 -1.23 1.00 T = 1.00 overall(between) 34 T=34 2.52 2.00 T -0.26 = 34 T N 79.00 = = overall(between) 34 1462 13.68 0.05 n = 43 -26.03 overall (between) 30.77 0.20 37.38 n=43 -0.22 12.93 0.66 12.30 59.70 n=43 n = 43 200 Table 3.11: State Characteristics /1k inhab. 08 Prejudice Margin Ownership Ln of # BCR Brady Score Average Average Rep. Gun StateAlabamaAlaskaArizonaArkansas 1.61 MeanCalifornia Std. Dev 0.31Colorado 1.94 Value 0.96Connecticut Group 1.69 0.21 15Delaware Value 0.61 0.17 Group 0.27Florida 1.31 1.53 Low 0.12 Value 4Georgia 6 0.66 Group 0.26 0.23Illinois 6 0.53 High 79 High ValueIndiana High -0.11 Group High 15.16 54 16 0.27Iowa 0.80 -0.07 Low Low 0.52 High 1.04Kansas Low -0.13 Low Low 0.21 20.95 High 22 51.7Louisiana Low 1.40 -0.13 -0.15 0.30 4.99 High 1.05 High Maryland 0.08 -14.41 Low Low Low 6 High 57.8 0.21Massachusetts Low High -14.95 0.11 0.31 7 -0.24 31.1 High 1.06Michigan 1.30 1.44 High 21.3 55.3 Low 0.45 High 28 Low Low Minnesota High 0.24 0.17 0.34 -13.81 High Low 8 16.7 0.29 0.34Mississippi 0.25 34.7 0.37 Low High Low 0.24 Low Missouri High Low High 1.03 0.05 16 -0.32 25.5 7 2 1.45 54Montana 0.15 High 6.81 53 1.54 Low Low Low 0.19NewHampshire High High -15.84 High Low 0.21 High -0.06 24.5 Low 1.47 0.36 0.00 Low -0.15 0.24 1.49 9.40 22 40.3 Low -0.03 Low 2.13 11 High Low 20.2 High 0.15 High High 0.25 Low Low 5.11 5 16.89 -26.03 Low Low 4.82 39.1 0.16 -17.00 0.00 High Low High 11 -0.06 High Low High 4 Low Low 42.8 42.1 12.6 Low 8 0.37 Low 44.1 21.3 High High High -9.12 Low High -8.86 -0.17 High Low High 0.16 Low 12.77 Low Low -0.06 High 38.4 High -4.18 41.7 Low -1.16 55.3 Low High Low 9.67 Low High 30.0 High 41.7 Low 57.7 High High 201 Table 3.12: State Characteristics (Continuation) /1k inhab. 08 Prejudice Margin Ownership Ln of # BCR Brady Score Average Average Rep. Gun StateNewJerseyNewMexicoNewYork -0.87North Mean 1.45 Carolina 0.23Ohio Std. 1.08 Dev 0.18 -0.04Oklahoma Value 63 0.27Oregon Group 0.20 Value 6Pennsylvania Low Group 1.58 20Rhode 51 Island -0.06 High 0.93 1.38 ValueSouthCarolina Low 0.25 Low -0.03 Group 1.35 Low -0.04SouthDakota 0.26 0.18 1.21 0.02 -11.66 Value Low 0.46Tennessee Group 0.23 0.20 2 High Low 1.81 -6.06 0.29Texas High 13 26 12.3 6.09 -22.97Utah High Low 47 18 0.31 Low Low 1.38 Low 9Vermont Low High 0.07 34.8 Low Low 0.10 0.07Virginia 41.3 18.0 High 0.36 Low 6 High -0.15 -0.19 High HighWashington High 1.13 20.15 Low 0.25 Low LowWest High -1.43 -7.04 Virginia 1.05 High 7 1.71 High -25.71 0.25Wisconsin 1.37 0.01 -7.80 Low Low 42.9 1.05 1.87 11.92 0.21 LowWyoming High High 0.43 Low 32.4 34.7 High 0.18 High 9 12.8 0.26 0.28 11.23 0.26 39.8 Low Low 0.99 9 42.3 Low High 4 High High 18 Low 2.02 18 High 0.29 4 56.6 High 5.23 0.07 High Low -0.06 High Low 0.20 High High -0.16 High -0.22 12 Low 0.06 12.87 Low 43.9 0.31 Low -21.01 9 High Low High High 30.77 High -10.77 Low -0.07 35.9 3.25 High High Low 0.90 42.0 Low Low -0.08 43.9 High 33.1 High High -5.84 Low High 35.1 Low 55.4 Low 26.13 Low High High 44.4 59.7 High High Bibliography

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