Evidence from the Solomon Islands

The Peace–Economy Nexus: Evidence from the Solomon Islands

Luke Forau

A thesis in fulfilment of the requirements for the degree of

Doctor of Philosophy

School of Business

The University of New South Wales

December 2015

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Abstract An increasing body of research has recently focused on economic strategies for sustaining peace in post-conflict societies. One strand of this literature investigates the nexus between peace and economic growth. However, economic contributions of peace to the economic recovery in a post-conflict society have not been explicitly emphasised in the current debate. Furthermore, the empirical measurement of peace remains contentious due to its theoretical underpinnings, which stem from the definition of peace which refers to the ‘absence of personal and structural violence’. This engenders two continua in the definition of peace – negative peace (i.e. ‘absence of personal violence’) and positive peace (i.e. ‘absence of structural violence’). A definition of this kind partially measures peace if one evaluates only one continuum. There is scope for an alternative. The legitimate peace measured by a peace perception index or PPI offers such an alternative. This thesis investigates the economic contributions of peace to the recovery in a post-conflict economy based on the PPI, with particular reference to the Solomon Islands. It explores the role of peace on the recovery in the economy and examines the extent to which this recovery helps to sustain peace in the long-term.

Two theoretical frameworks were employed to empirically evaluate peace. Firstly, at a microeconomic level, a partial equilibrium framework, using household level data, was employed to analyse the impact of peace on (household) income. The findings showed that a one percent improvement in the level of peace associates with a 1.4 percent increase in (household) income. The result also finds that peace transmits to income both directly and indirectly, with the latter coming through foreign and domestic investments. Secondly, at the macroeconomic level, a computable general equilibrium (CGE) framework was applied to analyse the impact of peace on the economy. Simulation results reveal that in the short-run, peace contributed 1.7 percent to GDP and the trade balance, and 2.6 percent to employment. The majority of other sectors of the economy also experienced increases. The CGE simulation also shows that the boost to the economy through the recovery in the private sector improves peace by 1.3 percent. Therefore, this thesis argues that peace is necessary for the growth of enterprises; at the same time improvement in the economy is important for the long-term sustainability of peace – thus the thesis is that there is a strong nexus between peace and economic growth. iv

Journal article under peer review Forau, L. and Chand, S. (2015). ‘Measuring peace using household-level data from post-conflict Solomon Islands’. This article will be published in the 2016 September issue by the Journal of Conflict, Security, and Development, and has already been peer reviewed.

Acknowledgements I would like to acknowledge the invaluable assistance rendered to me during the course of my studies. First, I am profoundly grateful to my mentor and supervisor, Professor Satish Chand, for the support, guidance and constructive feedbacks all throughout the course of this thesis. Thank you so much Satish. God bless you. Of course, any errors and shortfalls in this thesis are solely mine. I also extend my gratitude to the School of Business’ administration for providing me with the necessary support to successfully complete this study. Thank you too to the Australian Government for providing me with a scholarship.

I am also indebted to the Central Bank of Solomon Islands (CBSI) for supporting my study; the staffers of the Economics, Research, and Statistics Department, thank you for providing the necessary data.

To the landowners of the Guadalcanal Plains, in particular the Guadalcanal Plains Resources Development Association (GPRDA) and the Guadalcanal Plains Resources Development Company Ltd (GPRDCL), thank you very much for allowing me to conduct my fieldwork in your communities. Thank you especially, to Brian, Clerrie, and Emily for helping out in my fieldwork. I also extend my sincere gratitude to Mr and Mrs Reuben Tovutovu, for facilitating and supporting my fieldwork.

I am also grateful to Roger Benzi, former GPPOL General Manager, and other GPPOL officers for granting permission and their time to interview them. Also to the staffers of the Prime Minister’s Office; Ministry of National Unity, Reconciliation and Peace; and Director Foreign Investment Division, thank you all for your time.

I also wish to extend my appreciation to the St Philips O’Connor Parish members for the fellowship and support you have rendered to my family. God bless you all. To my officemate, colleague and friend, Tarek Rana, thank you so much for being around to share our experiences.

It would be remiss of me not to acknowledge my extended family. They were (and still are) part of my upbringing, and I am grateful for they are also part of my life. Thus, I extend my utmost sincere thanks to my one and only dear brother and sister-in-law (Sae Rangitisa) and the Children, Sae Nukumotiti, and my dear sisters Agnes and the late Patricia (aka Nau Nukuomai). Fakapere lasi atu kia kotou katoa pereuamaa. Also, I

extend my gratitude to koko’o Rosina (aka Tole) and Robert, and aunty Bronnie for their continuous support. More importantly, a very humongous thank you to my loving and caring parents, Pama Nukutauia, for raising me and be the kind of person I am today.

Finally, but not the least, this journey would not have been completed without the continuous and unswerving support of my dear family. They are part of me, and have stood by my side, both in good times and bad times. My heartfelt and sincere gratitude to my loving dear wife, May O. Forau, and our dear children Hellen, Luke Jr, Roseanne, Emily, Nester, and Peter. Indeed, there is nothing more rewarding than having you guys by my side. I love you all.

This thesis is dedicated to my loving wife and children, my dear parents, my brothers and sisters, and to the rest of my extended family. Glory Be to God. Amen.

Luke Forau (Pae Teafangamao)

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Table of Contents Copyright and Authenticity Statements ...... ii Original Statement ...... iii Abstract ...... iv Journal article under peer review ...... v Acknowledgements ...... vi Table of Contents ...... viii List of Figures ...... xv List of Tables ...... xvii List of Abbreviations ...... xix

CHAPTER 1 ...... 1 INTRODUCTION ...... 1 1.1 Introduction ...... 1 1.2 Motivation – The Research Problem ...... 1 1.3 Objective and Methodology ...... 5 1.4 Main contributions ...... 6 1.5 Outline of the Thesis ...... 6

CHAPTER 2 ...... 10 LITERATURE REVIEW: ECONOMICS OF POST-CONFLICT PEACE...... 10 2.1 Introduction ...... 10 2.2 Post-Cold War era ...... 11 2.2.1 Most conflicts have economic bearings ...... 11 2.3 Theory of peace: defining peace ...... 13 2.3.1 Counter arguments regarding the theory and definition of peace ...... 14 2.4 Measuring peace ...... 17 2.4.1 Negative peace: measured by conflict-related deaths – a minimalist view ...... 17 2.4.2 Positive peace: measured by polity – a maximalist view ...... 20 2.4.2.1 Political instability ...... 21 2.4.2.1 Democracy ...... 24 2.5 Economic benefits of peace – peace dividend ...... 26 2.5.1 Rates of economic growth in post-conflict economies ...... 27 2.5.2 Post-conflict aid ...... 29 2.5.3 Peace industry ...... 30 2.6 Sustaining peace through (post-conflict) economic recovery ...... 31

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2.6.1 Sequencing and synchronizing policies to sustain peace: short-term strategy ...... 32 2.6.2 Abnormal times require abnormal solutions: short- to medium-term strategies ..... 32 2.6.3 Role of the private sector: long-term strategy ...... 34 2.7 Towards a practical definition of peace ...... 36 2.8 Conclusion ...... 38

CHAPTER 3 ...... 40 THE SOLOMON ISLANDS ECONOMY ...... 40 3.1 Introduction ...... 40 3.2 Patterns of economic development in Solomon Islands ...... 41 3.2.1 The characteristics and structure of Solomon Islands economy ...... 41 3.2.2 Concentration of economic development on Guadalcanal triggered the civil conflict 42 3.3 The eruption of the civil conflict, 1998 – 2003 June ...... 43 3.3.1 Peace talk attempts ...... 44 3.4 The Regional Assistance Mission to Solomon Islands (RAMSI) – an external intervention force ...... 44 3.5 Macroeconomic Performance ...... 47 3.5.1 Patterns of Economic Growth ...... 47 3.5.2 Misguided policies ...... 49 3.6 The Major Industries ...... 50 3.6.1 Agriculture Sector ...... 50 3.6.1.1 Copra and Cocoa ...... 50 3.6.2 Fishing ...... 52 3.6.3 Forestry ...... 54 3.6.3.1 Log exports as a share of total exports ...... 56 3.6.4 Minerals ...... 57 3.7 The impact of the civil conflict on the economy...... 59 3.8 The impact of peace: The presence of RAMSI ...... 62 3.9 Government Peace Policies ...... 64 3.10 Conclusion ...... 64

CHAPTER 4 ...... 66 PEACE OIL PALM: GUADALCANAL PLAIN PALM OIL LTD (GPPOL) ...... 66 4.1 Introduction ...... 66 4.2 Peace Oil Palm: Guadalcanal Plains Palm Oil Limited (GPPOL) ...... 67 4.3 Background of Oil Palm in Solomon Islands ...... 67 ix

4.4 Impact of the Civil Conflict on the Oil palm ...... 69 4.5 Conception of GPPOL ...... 70 4.5.1 Inception of GPPOL and the benefits to the landowners ...... 70 4.6 Benefits of Peace ...... 71 4.7 GPPOL in the peace onset period ...... 73 4.8 Guadalcanal Plains Resources Development Association (GPRDA) ...... 74 4.9 Guadalcanal Plains Resources Development Company Ltd (GPRDCL) ...... 75 4.10 Government Peace Reconciliation Programs ...... 75 4.11 Contribution of oil palm to the local economy ...... 76 4.12 Major tree crops compared ...... 79 4.13 Fiscal impact of oil palm on the economy ...... 79 4.14 Conclusion ...... 80

CHAPTER 5 ...... 81 RESEARCH DESIGN AND METHODOLOGY ...... 81 5.1 Introduction ...... 81 5.2 Mixed Methods Research...... 82 5.2.1 Advantages of using mixed methods research ...... 84 5.2.2 Approaches to mixed methods ...... 87 5.3 Rationale for using mixed methods in this study ...... 90 5.4 Study Design ...... 90 5.4.1 Desk-top Study ...... 93 5.4.2 Qualitative approach involved in the sampling selection ...... 94 5.4.3 Quantitative (secondary) data ...... 95 5.5 Human Research Ethics Committee (HREC) approval ...... 95 5.6 Research permit from the Solomon Islands Government, GPPOL, and the Landowners 96 5.7 The field work ...... 96 5.7.1 The unit of analysis ...... 97 5.8 Design of questionnaire ...... 97 5.9 Pre-testing of (and amendments to) the questionnaires ...... 98 5.10 Sample size and sample selection - unit of analysis ...... 100 5.11 Interview Process ...... 106 5.11.1 Location Details ...... 107 5.11.2 Demographic characteristics ...... 108

5.11.3 Land Tenure and Oil palm Characteristics ...... 110 5.11.4 Livelihood Characteristics ...... 112 5.11.5 Peace variables ...... 115 5.12 Data input process ...... 117 5.13 Reliability ...... 117 5.13.1 Internal reliability ...... 118 5.14 Validity ...... 119 5.14.1 Face validity ...... 120 5.14.2 Internal validity ...... 120 5.14.3 External Validity ...... 120 5.14.4 Concurrent validity ...... 120 5.14.5 Construct validity ...... 121 5.15 Conclusion ...... 121

CHAPTER 6 ...... 123 RESULTS AND ANALYSES OF THE IMPACT OF PEACE: A PARTIAL EQUILIBRIUM ANALYSIS ...... 123 6.1 Introduction ...... 123 PART I ...... 124 6.2 Results from the household survey ...... 124 6.2.1 Geographic, demographic and household characteristics ...... 124 6.2.2 Land arrangement and oil palm characteristics ...... 128 6.2.3 Peace perception characteristics ...... 130 6.2.3.1 Other variables related to peace perception characteristics ...... 134 6.3.1 Livelihood Characteristics ...... 138 6.3.1.1 Financial livelihood characteristics ...... 138 6.3.1.2 Non-financial livelihood characteristics ...... 139 6.3.1.3 Landowners that leased land to GPPOL ...... 141 6.3.1.4 Smallholders (out-growers) ...... 143 PART II ...... 145 6.3 Derivation of a peace perception index (PPI) ...... 145 6.4 Conceptual Analysis of the Impact of Peace ...... 150 6.4.1 Model Specification for Models 1 and 2 ...... 151 6.4.2 Data for Models 1 and 2 ...... 152 6.4.3 Results for Model 1 and 2 ...... 154 6.4.4 Robustness tests of the probit model ...... 158

6.4.5 Model 3 specification: impact of peace on income ...... 158 6.4.6 Data for Model 3 ...... 160 6.4.7 Tests for endogeneity ...... 161 6.4.8 Results for Model 3 ...... 162 6.4.9 Diagnostic Tests ...... 165 6.5 Conclusion ...... 166

CHAPTER 7 ...... 168 THE COMPUTABLE GENERAL EQUILIBRIUM FRAMEWORK ...... 168 7.1 Introduction ...... 168 7.2 The modern computable general equilibrium (CGE) ...... 170 7.2.1 Criticisms of the general equilibrium theory ...... 172 7.2.2 Use of CGE models and the different approaches ...... 173 7.2.3 Different schools of CGE ...... 175 7.3 Methodology for quantifying peace and a conceptual framework for peace in the CGE analysis ...... 175 7.3.1 Quantifying the output value for peace ...... 176 7.3.2 Conceptual framework for investigating the impact of peace ...... 178 7.3.2.1 Consumption of the commodity peace ...... 179 7.3.2.2 Effect of peace innovation ...... 180 7.3.2.3 Impact of increase in the oil palm industry ...... 185 7.4 The theoretical framework of SIORANIG ...... 186 7.5 Data calibrations and Input-Output (IO) Tables ...... 191 7.5.1 The IO Table ...... 194 7.6 Equations of the Model ...... 196 7.6.1 Input-Output multi production ...... 198 7.6.2 Investment demand equation ...... 201 7.6.3 Household demand equation ...... 202 7.6.4 Export demand equation ...... 203 7.6.5 Government demand equation ...... 204 7.7 Variables, coefficients, and parameters ...... 204 7.8 Model Closure and Modelling Process ...... 205 7.8.1 Model Closure ...... 205 7.8.2 Modelling Process ...... 209 7.9 Model Solution...... 210 7.10 Conclusion ...... 212 xii

CHAPTER 8 ...... 214 ANALYSIS OF THE CGE SIMULATION RESULTS ...... 214 8.1 Introduction ...... 214 8.2 Testing for homogeneity assumption ...... 215 8.3 Modelling the improvement in peace innovation in the short-run ...... 217 8.3.1 Macroeconomic Impact...... 218 8.3.2 Sectoral Impact ...... 219 8.3.2.1 Impact of peace on the oil palm industry ...... 220 8.3.2.2 Impact of peace on the ‘Other sectors’ ...... 223 8.4 Modelling the increase of peace innovation in the long-run ...... 227 8.4.1 Macroeconomic Impact...... 228 8.4.2 Sectoral Impact ...... 229 8.4.2.1 Impact of peace on the oil palm industry ...... 230 8.4.2.2 Impact of peace on the ‘Other sectors’ ...... 231 8.5 Modelling the short-run impacts of the expansion in the oil palm industry ...... 236 8.5.1 Macroeconomic Impact...... 237 8.5.2 Sectoral Impact ...... 239 8.6 Modelling the long-run impact of the expansion in the oil palm industry ...... 244 8.6.1 Macroeconomic Impact...... 244 8.6.2 Sectoral Impact ...... 247 8.7 Conclusion ...... 254

CHAPTER 9 ...... 256 CONCLUSION AND POLICY IMPLICATIONS ...... 256 9.1 Overview of the Study ...... 256 9.2 Main empirical findings ...... 257 9.2.1 Main findings from the analysis of the household survey (econometric analysis) ...... 257 9.2.2 Main findings from the CGE Analysis ...... 258 9.3 Theoretical and policy implications ...... 261 9.3.1 Theoretical implications ...... 261 9.3.2 Policy Implications ...... 262 9.4 Study Limitations and Future Research Areas ...... 264 9.5 Concluding Statement ...... 266 REFERENCES ...... 267 Appendix A6.0a Table A6.0a Monthly royalty from household survey ...... 289

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Appendix A6.0b Table A6.0b Quarterly land rentals from household survey ...... 290 Appendix A6.0c Annual dividend from household survey ...... 291 Appendix A6.1a: ...... 292 Framework for the binary dependent variable ...... 292 Maximum Likelihood Estimation ...... 293 Appendix A6.1b Tests for Heteroskedasticity for Model 1 and Model 2 ...... 295 Appendix A6.2a Haussmann test for endogeneity ...... 297 Appendix A6.2b Haussmann Test for endogeneity: Model 3 ...... 298 Appendix A6.3: ...... 300 Eviews Output Results for Model 3.1 ...... 300 Eviews Output Results for Model 3.2 ...... 300 Eviews Output Results for Model 3.3 ...... 301 Appendix A6.4: ...... 302 CUSUM Test ...... 302 CUSUM of Squares test ...... 302 Appendix A6.5 Eviews Output: Ramsey RESET Test ...... 305 Appendix A6.6 Breusch-Pagan-Godfrey Test for Heteroskedasticity ...... 306 Appendix A7.0: GEMPACK names of the original exogenous variables ...... 307

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List of Figures Figure 2.1 Behaviour of growth rates in post-conflict countries ...... 28 Figure 2.2 Annual aid flows as a share of GDP (average values in percentages) 29 Figure 3.1 Estimated cost of RAMSI operations in Solomon Islands from July 2003 – July 2013 ...... 47 Figure 3.2 RGDP ($ million) and RGDP Growth (%) ...... 48 Figure 3.3 Copra and Cocoa production (tons) ...... 51 Figure 3.4 Export value for Copra and Cocoa (SBD$ millions) ...... 52 Figure 3.5 Fish Catch (tons) ...... 53 Figure 3.6 Value of fish exports and as a share of total exports ...... 54 Figure 3.7 Log Production (cubic meters) ...... 55 Figure 3.8 Log exports and as a share of total exports ...... 56 Figure 3.9 Log export duties ($millions) ...... 57 Figure 3.10 International Reserves ($ millions) ...... 60 Figure 3.11 Government Revenues from tax (1992 – 2012) ...... 61 Figure 3.12 Government Debts ($ millions) ...... 61 Figure 3.13 Government recurrent operations ($’000’) ...... 62 Figure 4.1 map of Guadalcanal showing the oil palm region ...... 68 Figure 4.2 Pre-conflict Palm Oil Exports, 1993 – 1999 ...... 70 Figure 4.3: Palm Oil and Palm Kernel Production (mt) ...... 77 Figure 4.4 Value of the major export tree crops ($ millions) ...... 78 Figure 4.5 Major tree crops exports compared ...... 79 Figure 5.1 Classifying mixed methods research in terms of priority and sequence ...... 88 Figure 5.2 Schematic of the research process ...... 92 Figure 5.3 Schematic of developing the survey...... 98 Figure 6.1 Transmission Mechanism of Peace to income ...... 151 Figure 7.1a Schematic diagram of the impact of peace innovation on the economy ...... 180 Figure 7.1b The Short-run causation ...... 182 Figure 7.2: The ORANI-G Flows Database ...... 188 Figure 7.3 Structure of Production ...... 199 Figure 7.4 Structure of Investment Demand...... 201 Figure 7.5 Structure of Household Demand ...... 203 Figure 7.6 Comparative static interpretations of results ...... 209 Figure 7.7a Building a model-specific EXE file ...... 211 xv

Figure 7.7b Using the model-specific EXE to run a simulation ...... 211 Figure 8.1 Sectoral impact of a short-run peace simulation ...... 222 Figure 8.2 Gross Enrolment Rate (GER) ...... 225 Figure 8.3 Sectoral impact of the long-run peace simulation ...... 235 Figure 8.4 Graphical presentations of the short-run simulation results ...... 243 Figure 8.5 Losers and Winners of a long-run oil palm simulation ...... 253

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List of Tables Table 2.1: Direct and indirect effects of democracy on growth ...... 25 Table 3.1 Export Volume of Gold and Silver ...... 58 Table 5.1 PPS technique to sample 312 households ...... 104 Table 5.2: 1.0 Location details ...... 108 Table 5.3: 2.0 Personal and household characteristics ...... 110 Table 5.4: 3.0 Land and oil palm characteristics ...... 111 Table 5.5 Livelihood opportunities characteristics ...... 114 Table 5.6: Current peace environment ...... 116 Table 6.1 Tabulation of AGE and the Households characteristics ...... 125 Table 6.2 Tabulation of AGE, Marital Status, and Educational level by sex of household heads ...... 126 Table 6.3 Tabulation of tribe and land arrangements (EVIEWS OUTPUT) ...... 129 Table 6.4 Tabulation of the Peace characteristics ...... 130 Table 6.5 Tabulation of the safety in the community and Honiara ...... 132 Table 6.6 Tabulation of day and night safety ...... 133 Table 6.7 Tabulation of safety in the household and safety during the day/night ...... 134 Table 6.8 Tabulation of RAMSI_LEAVE and SEX ...... 135 Table 6.9 Tabulation of RAMSI_REMAIN and RAMSI_SCALEDOWN ...... 136 Table 6.10 Descriptive Statistics for Police Confidence ...... 137 Table 6.11 Tabulation of POLICE_CONFIDENCE ...... 138 Table 6.12 Tabulation of descriptive statistics for the financial livelihood characteristics ...... 139 Table 6.13 Tabulation of the non-financial livelihood characteristics ...... 140 Table 6.14 House structure ...... 141 Table 6.15 Direct financial benefits to the Landowners ...... 142 Table 6.16 Tabulation of the responses on smallholder inputs ...... 143 Table 6.17 Eviews Output: Principal Components Analysis ...... 148 Table 6.18 Descriptive statistics for peace perception ...... 150 Table 6.19 Summary results of the probit regression models 1 and 2 ...... 155 Table 6.20 Results of the OLS estimations ...... 162 Table 7.1 Commodities and Industries for the SIORANIG model ...... 192 Table 7.2 Equations of the SIORANIG model...... 197 Table 7.2 Tally of variables and Equations ...... 206 Table 7.3 Tally of Exogenous variables ...... 208 Table 8.1a Homogeneity simulation results of selected nominal macroeconomic variables: shocked by one percent ...... 216 Table 8.1b Homogeneity simulation results of selected real macroeconomic variables: shocked by one percent ...... 216 Table 8.2 Short-run actions for new exogenous variables ...... 218 Table 8.3 Short Run Simulation of Peace on selected Macroeconomic Variables ...... 219 Table 8.4a Main winners of a short run peace simulation ...... 221 Table87.4b Losers of a short run peace simulation...... 221 Table 8.7a Long-run simulation results on selected macroeconomic variables ...... 229 Table 8.7b Main winners of a long-run peace simulation ...... 230 Table 8.7c Main losers of a long-run peace simulation ...... 234 xvii

Table 8.9a Short-run simulation results of selected macroeconomic variables ...... 238 Table 8.9b Main winners of a short run oil palm simulation ...... 239 Table 8.9c Main Losers of a short-run oil palm simulation ...... 242 Table 8.10a Long-run simulation results of selected macroeconomic variables ...... 245 Table 8.10b Winners of long-run oil palm simulation ...... 249 Table 8.10c Losers of a long-run oil palm simulation ...... 252

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List of Abbreviations

ACD Armed Conflict Data AusAID Australian Aid CAIN Conflict Archive on the Internet CBSI Central Bank of Solomon Islands CDC Commonwealth Development Corporation CDP Civilian Development Program CGE Computable General Equilibrium Centre for International Development CIDCM Conflict Management COW Correlate Of War CPO Crude Palm Oil CSR Corporate Social Responsibility CTF Combined Task Force Disarmament, Demobilization, and DDR Reintegration FDI Foreign Direct Investment FFB Fresh Fruit Bunches GDP Gross Domestic Product GEMPACK General Equilibrium Modelling Package GPPOL Guadalcanal Plains Palm Oil Limited Guadalcanal Plain Resources Development GPRDA Association Guadalcanal Plain Resources Development GPRDCL Company Limited GPS Global Position System GRA Guadalcanal Revolution Army GRML Gold Ridge Mining Limited HREC Human Research Ethics Committee IEP Institute for Economics and Peace IFM Isatabu Freedom Movement IMF International Monetary Fund MEF Malaita Eagle Force Ministry of Education and Human MEHRD Resources Development Ministry of National Unity, Reconciliation MNURP and Peace MOU Memorandum of Understanding NBPOL New Britain Palm Oil Limited NFD National Fisheries Development PIF Pacific Islands Forum PKO Palm Kernel Oil PPF Participating Police Force PPI Peace Perception Index PPS Probability Proportional to Size

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RAMSI Regional Assistance to Solomon Islands RS Random Start RSIPF Royal Solomon Islands Police Force SFPL Solomon Fish Processing Limited SICA Solomon Islands Christian Association SINSO Solomon Islands National Statistics Office SIPL Solomon Islands Plantation Limited STL Solomon Taiyo Limited TPA Townsville Peace Agreement UN United Nations UNSW University of New South Wales WWII World War Two

CHAPTER 1

INTRODUCTION

1.1 Introduction Peace is necessary for economic prosperity. However, the economics of peace did not become a research field until the end of the Cold War. An increasing body of research has recently begun to explore economic strategies for sustaining peace in post-conflict societies. The global economic benefit of peace has increased by 1.9 percentage points in 2008 to 86.6 percent of the global GDP in 2015 (Institute for Economics and Peace, 2015a).1 This thesis examines the economic contributions (benefits) of peace to the recovery in the economy, with particular reference to post-conflict Solomon Islands. It employs two theoretical frameworks: (i) a partial equilibrium analysis, and (ii) a computable general equilibrium (CGE) model. This chapter underlines the relevance of this study by highlighting key components. Section 1.2 discusses the motivation for this study by identifying the research problem/questions through the identification of the knowledge gaps in the peace literature. Section 1.3 outlines the objectives of this study and the methodology applied to achieve these objectives. Section 1.4 presents the main contributions of this study. Finally, section 1.5 concludes the chapter by outlining the structure of the rest of the thesis.

1.2 Motivation – The Research Problem The debate on the nexus between (post-conflict) peace and economic growth, or more broadly economic development, is gaining momentum in empirical research on post- conflict countries.2 However, the theoretical underpinning of the term ‘peace’ remains a subject for empirical debate. One of the key issues stemming from this is the lack of uniformity of the methodological measurement of peace. As such, the economic benefits and contribution of peace to economic development are often, by default, understated.

1 See section 2.5 of Chapter 2 for details. 2 Chapter 2 presents the full Literature Review. 1

Given its socially constructed nature, peace has been deductively analysed using various proxies, drawing mainly from the definition grounded in contradistinction to violence, which refers to the ‘absence of personal and structural violence’ (Galtung, 1969: 183). This definition has engendered two continua. On the one hand, is the definition of peace as the ‘absence of personal violence’, commonly referred to as ‘negative peace’ and the other, is the ‘absence of structural violence’, commonly referred to as ‘positive peace’ or social justice (ibid).

‘Negative peace’ is a minimalist view (Anders and Ohlson, 2014: 62) which draws from the very notion that the fewer the number of people killed in a battle, the more peace is achieved. Such a view measures peace by the number of battle-related deaths. This has been employed by numerous scholars (see for example; Collier et al., 2004, Collier et al., 2008, Collier, 1999, Fearon and Laitini, 2003, Blomberg and Hess, 2002, Besley and Mueller, 2012, Hegre et al., 2010, Edward Miguel et al., 2004, Chen et al., 2008, de Soysa, 2002, Lujala et al., 2005, Rustad and Binningsboe, 2012) to analyse the presence of peace (or the reduction in conflict). In this definition, two commonly used minimum thresholds of death to designate a conflict have been employed, each from different datasets compiled by two different institutions. One dataset, by Small and Singer (1982), under the Correlate of Wars (COW) project, nominates a minimum of 1,000 battle-related deaths. The other dataset is the Armed Conflict Data (ACD) by the University of Uppsala, where 25 conflict-related deaths is the minimum threshold.

‘Positive peace’, on the other hand, is a maximalist view (Anders and Ohlson, 2014: 62) where better and quality institutions and structures are the central focus. It views peace as the improvement and strengthening of institutions and structures to cater for people’s needs. Thus, two common proxies that have been used to measure and empirically analyse the presence (or lack thereof) of peace are: political instability; (see for instance; Asteriou and Price, 2001, Polachek and Sevastianova, 2012, Butkiewicz and Yanikkaya, 2005, Santhirasegaram, 2008, Alesina and Perotti, 1996, Venieris and Gupta, 1986, Alesina et al., 1996, Barro, 1991, Easterly and Rebelo, 1993, Chen and Feng, 1996, Levine and Zervos, 1996, Hibbs, 1973, Gupta, 1990) and democracy; (see; Doucouliagos and Ulubasoglu, 2008, Comeau, 2003, Narayan et al., 2011, Fida and

Zakaria, 2011, Gerring, 2005, Alesina et al., 1996, Barro, 1996, Tavares and Wacziarg, 2001, Fidrmuc, 2003, Jacob and Osang, 2015, Rachdi and Saidi, 2015).

Thus far, the empirical results from the ‘negative peace’ measurement have generally been consistent with a positive relationship between peace and growth, although with different measures, (see for example; Collier et al., 2004, Collier et al., 2008, Collier, 1999, Fearon and Laitini, 2003, Blomberg and Hess, 2002, Besley and Mueller, 2012, Hegre et al., 2010, Edward Miguel et al., 2004, Chen et al., 2008, de Soysa, 2002, Lujala et al., 2005, Rustad and Binningsboe, 2012). However, the empirical evidence of ‘positive peace’ has been mixed, with the political instability-growth nexus showing a negative association (see for example; Asteriou and Price, 2001, Polachek and Sevastianova, 2012, Butkiewicz and Yanikkaya, 2005, Santhirasegaram, 2008, Alesina and Perotti, 1996, Venieris and Gupta, 1986, Alesina et al., 1996, Barro, 1991, Easterly and Rebelo, 1993, Chen and Feng, 1996, Levine and Zervos, 1996, Hibbs, 1973, Gupta, 1990) while the nexus between democracy and growth remains inconclusive (see for example; Doucouliagos and Ulubasoglu, 2008, Comeau, 2003, Narayan et al., 2011, Rachdi and Saidi, 2015, Jacob and Osang, 2015).

Despite the methodological appeal of the above measurements of peace, there is scope for alternative definitions and measurements of peace. Consequently, three knowledge gaps were identified from the literature review that rationalises this research. First, a consistent measure for the variable peace is lacking. The minimalist approach employs only the number of conflict-related deaths to measure peace. It does not account for the psychological and emotional harm suffered by both the perpetrators and victims. The maximalist view, on the other hand, measures peace by employing proxies that are totally grounded on different constructions, such as political instability and democracy polities. The maximalist view is conceptually far-fetched and subject to various [mis]interpretations, which could lead to more than one measurement.3 It also rarely accommodates the views of the minority groups. Both the negative and positive peace metrics measure peace indirectly, because they defined peace based on it being in contradistinction to violence. Hence, there is a paucity of direct measurements as to

3 See Chapter 2, section 2.3.1 for explanations. 3

‘what peace is’. The second knowledge gap is that much of the past empirical work on peace has been analysed using econometrics (a partial equilibrium framework). A framework of this type mainly analyses partial effects of peace, assuming ceteris paribus, and thus ignores the multiple feedback mechanisms of the economy as a whole. Finally, empirical analyses of peace in post-conflict Solomon Islands economy are limited in the literature. Consequent to the identification of these knowledge gaps, the following central and sub-research questions are formulated to address them:

 What is the contribution of peace to economic recovery in a post-conflict country? 1. How can peace be quantified? 2. What impact does peace have on the likelihood that foreign investors will remain in a post-conflict society? 3. What impact does peace have on the likelihood that locals will start small businesses? 4. What is the impact of peace on household incomes? 5. What is the impact of peace on GDP, palm oil output and other key macroeconomic indicators? 6. What is the impact of increasing the investment in palm oil on peace, GDP and other key macroeconomic indicators?

The context for this research is the Solomon Islands. The rationale for conducting this research on the Solomon Islands is motivated by the following two reasons. First, Solomon Islands experienced a civil conflict, which saw a collapse in GDP (elaborated in Chapter 3). However, following an Australian led intervention in the form of a Regional Assistance Mission to Solomon Islands (RAMSI),4 peace was restored, with the economy rebounding to an average growth rate of 7.3 percent per year between 2003 and 2013.5 Therefore, this study seeks to understand the role of peace in underpinning the (post-conflict) growth of the economy. Second, empirical analysis of the peace phenomenon is limited (or even absent) for post-conflict Solomon Islands, despite the conflict lasting for five and a half years. Much of the peace literature about

4 RAMSI is a regional peacekeeping force set up under the auspices of the Pacific Islands Forum Secretariat PIFS, which comprised twelve member countries including; Australia, Cook Islands, Fiji, Kiribati, Nauru, New Zealand, Niue, Papua New Guinea, Samoa, Solomon Islands, Tonga, Tuvalu, and Vanuatu. 5 Sourced from the Central Bank of Solomon Islands’ Database 4

the Solomon Islands has been inductively analysed, (see for example; Braithwaite, 2010, Liloqula, 2000, Paina, 2000).

1.3 Objective and Methodology The main objectives of this thesis are three-fold: (i) to investigate the economic benefits of (post-conflict) peace in the post-conflict Solomon Islands; (ii) to examine the transmission mechanism of the improvement in peace on the economy, and (iii) to investigate the extent to which the private sector promotes peace. To address these objectives, two methodologies are employed; namely an econometric analysis and a computable general equilibrium (CGE) model. For the econometrics model, a household survey was conducted to collect the requisite data for the analyses. The data for the CGE, on the other hand, were obtained from secondary sources. For each of the above, the transmission mechanism of the impact of peace on the economy has been articulated. This is depicted in Chapter 6 (for the partial equilibrium analysis - econometrics) and Chapter 7 (for the CGE model).

The application of the partial equilibrium analysis was carried out mainly to investigate the impact of peace on (household) income. A household level survey was conducted in the conflict-affected communities. A sample of 312 households was selected through the Probability Proportional to Size (PPS) sampling technique, and the household heads interviewed. Ethics approval from the University of New South Wales was secured for this survey. The collected data were then used to quantify peace using observations on perceptions. Applying econometric techniques, I investigated the impact of peace on household income. I also evaluated the indirect role of peace in inducing income growth. In doing so, I investigated the contribution of the private sector (in this case the palm oil industry) to income growth and consolidating peace. Furthermore, I also investigate the role of peace in facilitating households to engage in other support activities. The rationale for the micro perspective is three-fold. First, is to investigate whether or not income is sensitive to peace. Second, to compute the income elasticity of peace, as it is then fed into the CGE model to compute the economic contribution of peace. Finally, the micro perspective is useful for purposes of formulating specifically- tailored policies to sustain peace and avoid a relapse of conflict.

The CGE6 analysis, on the other hand, investigates the impact of peace on the key macroeconomic variables including national income (GDP), employment, balance of trade, exports, consumption, government expenditure, and investment. It also investigates the effect of changes in the level of peace on the output of the various sectors of the economy. Furthermore, the analysis also extends to investigating the impact of the private sector, in particular, the oil palm sector, in consolidating peace and in the growth of household income. The simulations are carried out in a static comparative CGE framework using the GEMPACK software developed by the Centre of Policy Studies at Victoria University, Melbourne.

1.4 Main contributions This study provides three empirical contributions to the body of knowledge in this area. The first is that of the computation of the peace perception index or PPI. As already mentioned above, the current distinction between negative peace and positive peace indirectly measures peace. In contrast, the computation of the PPI directly measures peace because it is derived from the community’s perception of peace.7 The second contribution is the application of the CGE model to analyse the impact of peace on the economy. In section 1.2 above, it was noted that previous empirical studies of peace focus only on partial equilibrium analysis, and therefore provide a partial view on the impact of peace. Such an analysis assumes that peace does not have any feedback on other sectors of the economy. An economy-wide analysis of such variables as peace is important for understanding the role and sustainability of peace in the economy. Finally, the application of the CGE framework for analysing the Solomon Islands economy is not only new as far as the literature is concerned, but the construction of the CGE model itself for the Solomon Islands is also nascent.

1.5 Outline of the Thesis The rest of the thesis is organised into eight chapters. Chapter 2 presents a review of the literature on post-conflict peace. It explores the significance of this research within the

6 The CGE model constructed for the Solomon Islands is derived from the ORANIG framework built by the Centre of Policy Studies, Victoria University. 7 Details can be seen in Chapter 6. 6

broad context of the literature on the economic benefits of (post-conflict) peace. The review attempts to understand the subjective definition of peace, the empirical measurement of peace, and the role of peace in promoting and sustaining economic growth in nations that have suffered conflict. It begins by underlining key manifestations in post-conflict countries before exploring the theoretical underpinnings of the construct peace. It then evaluates the various measurements and proxies of peace, and subsequently considers how post-conflict peace has been sustained. Arising out of the review, the knowledge gaps are identified and formulated respectively, which form the basis for the investigation of the construct peace.

Chapter 3 provides the context for this study by evaluating the Solomon Islands economy. The chapter identifies key attributes of the economy. It presents the country’s development patterns by evaluating growth performances and its dynamics, highlighting the characteristics and structures of the economy, and emphasising the crucial elements that had contributed to triggering the eruption of the civil conflict which prompted the arrival of RAMSI.

Chapter 4 focuses on the micro level perspective by evaluating the operations of the business model of the private sector, in this case the Guadalcanal Plain Palm Oil Limited or GPPOL, in communities where conflicts occurred. It examines how GPPOL accommodates landowners’ interests to ensure peace is maintained. The aim of the chapter is to elucidate that such a business model has merits and is capable of promoting long-term peace.

Chapter 5 outlines the research design and methodology under which this thesis was conducted. The chapter sets out the research methodology adopted in this study, the ethical considerations, the collection of secondary data, and the sampling methods used to collect the primary data. It also presents the process involved in the designing of the questionnaire, and how each of the variables is measured, along with key reliability and validity issues. Given that peace is a social construct, this thesis adopted a mixed method research technique, with greater emphasis on a deductive approach.

Chapter 6 is presented in two parts. Part I provides the descriptive statistics of the household survey. Part II presents the quantification of peace, which resulted in the derivation of a peace perception index (PPI). This PPI forms the basis from which peace was empirically evaluated. The second part also presented the construction of a conceptual framework for the transmission of peace. This conceptual framework is then applied to econometric analysis to investigate the impact of peace on household income.

In Chapter 7, a theoretical framework for the application of the CGE model is developed for the macroeconomic analysis of the impact of a change in peace. The CGE analysis draws closely from that of the ORANIG developed by the Centre of Policy Studies, Victoria University. It models the impact of peace (when it is shocked) on the macro economy.

Chapter 8 presents the simulation results of the impact of peace. Results from two separate simulations are presented here; namely: (i) investigating the role of peace on the economy, and (ii) investigating the role of the private sector on peace and the economy. The first simulation investigates a 12 percent improvement in peace, simulating both the short-run and long-run impacts. In both simulations, the results show that a 12 percent shock in the level of peace induces a net positive impact on the overall economy, measured by the GDP, of 1.7 percent. The second simulation computes the impact of the expansion in the oil palm sector on the economy, again both in the short-run and the long-run. As in the first simulation, the results also show a net positive impact with a 0.21 percent rise in GDP.

The final chapter concludes the thesis, and discusses some policy implications. The main conclusion stemming from this study is that peace and economic progress are intertwined. Therefore, all stakeholders including the government, development partners, civil society, non-governmental organisations and the churches can promote peace, and use the forces of resilience in the economy to consolidate peace, so as to create a virtuous feedback loop between the two. This is the nexus between the two and this is the thesis. In terms of the policy implications, the case study of the growth in oil

palm and its contribution to consolidating peace may have lessons for similar situations elsewhere.

CHAPTER 2

LITERATURE REVIEW: ECONOMICS OF POST-CONFLICT PEACE

2.1 Introduction This chapter places the thesis within the broader context of the literature on the economic benefits of (post-conflict) peace. From a macroeconomic lens, peace enables a country to realize its full potential and provides impetus for economic prosperity. Resources are freed up that would otherwise have been wasted in a conflict. That most businesses thrive only during peace times attests to the fact that peace is necessary for the establishment and growth of enterprises. Thus, this chapter examines the role of peace in an economy for seeding and sustaining economic progress in post-conflict countries. It began by exploring problems with the measurement of peace. This study argues that the existing metrics have only partially measured the construct peace due to the current way it is defined, thereby presenting an incomplete analysis of what constitutes peace.

The rest of this chapter is organised as follows. Section 2.2 highlights the patterns of conflict in the post-Cold War period, as well as underlining the current debate on post- conflict peace. Section 2.3 examines the theoretical framework upon which the concept of peace is grounded. It evaluates the subjective definition of peace, and highlights its deficiencies with a view to developing an operational definition for empirical analysis.8 Section 2.4 analyses the proxies, based on the theoretical framework examined in section 2.3, that have been employed to measure the impact of peace on growth, with their limitations identified as a justification for the need to develop an alternative measure of peace. Section 2.5 evaluates the economic benefits of peace on post-conflict economies, investigating the impact of peace on growth and other key economic indicators. Section 2.6 assesses some of the strategies for consolidating and sustaining peace in post-conflict economies, and emphasises the need for distinct or “abnormal” solutions in the short to medium term. For long-term strategies, it examines the role of the private sector in promoting peace. Section 2.8 concludes this chapter, and identifies knowledge gaps.

8 Details of quantifying peace as a variable are explained in Chapter 5. 10

2.2 Post-Cold War era Since the end of the Cold War in the early 1990s, the number of conflicts (both inter- and intra-country) has declined significantly, with a total of 372 (out of 403) ending between 1946 and 2005 (Kreutz, 2010: 245). Of those conflicts that ended, about half of them restart within a decade (Elbadawi et al., 2008a, Kreutz, 2010). For example, the 124 wars that occurred between 1944 and 1997 took place in just 69 states (Doyle and Sambanis, 2000), and the 108 civil wars reported by the Correlate of Wars (COW) dataset, in that same period, occurred in only 54 countries (Quinn et al., 2007: 168). This suggests that the risk of conflict recurrence in post-conflict countries is significant.

The global average of the probability of a country reverting to conflict within its first decade of peace has been estimated to be 31 percent (Bigombe et al., 2000: 2). For a typical post-conflict country, it is estimated at 40 percent (Collier, 2009: 101) but only 9 percent for a typically similar country that has not experienced any violent conflict (Collier et al., 2007). The fact that many post-conflict countries are also developing economies suggests that the normal developmental challenges of a typical developing country are exacerbated by conflict.

2.2.1 Most conflicts have economic bearings The causes of most conflicts in the past had economic connotations (see for example; Collier et al., 2004, Fearon and Laitini, 2003, Collier, 2000, Collier, 1999, Edward Miguel et al., 2004, Taydas and Peksen, 2012). Yet, the extant literature on post-conflict countries has been dominated by sociology (Coser, 1956, Oberschall, 1978), political economy (Mason and Krane, 1989, Ransom, 1989, Lichbach and Gurr, 1981, Duvall and Welfling, 1973), psychology (Hosin and Cairns, 1984, Curran, 1988, Christie and Hanley, 1994), demography (Curlin et al., 1976) and international relations (Rasler, 1983) perspectives.

It was not until the end of the Cold War that scholars realised the importance of economics to post-conflict reconstruction. Consequently, new waves of peace economics scholars emerged, and provided a range of different kinds of economic analyses on peace. These scholars include (see for instance; Bruck and Schindler, 2008, Blattman and Annan, 2010, Aliyu et al., 2011, Bundervoet et al., 2009, Ghobarah et al.,

2003, Narayan and Prasad, 2007, Omba Kalonda, 2011, Voors et al., 2012, Chen et al., 2008, Collier, 1999, Collier and Duponchel, 2013, Collier, 1994, Bassil, 2013, Blattman and Miguel, 2010, Blomberg and Hess, 2002, Miguel et al., 2004) who evaluate the economic costs and/or impacts of conflict to justify the need for peace. Other scholars, such as (see for example; Castillo, 2001, Castillo, 2008, Collier, 2009, Adam et al., 2008, Addison, 2005, Aron, 2003, Boyce, 1995a, Boyce, 1995b, Boyce, 2007, Boyce, 2008, Bigombe et al., 2000, Chand, 2003, Chand, 2005, Dumas, 2006, Elbadawi, 2008, Elbadawi et al., 2008a, Carbonnier, 2002), focus on the economic reconstruction of post-conflict countries to emphasise their rapid return to economic growth.

Another strand of research, such as studies by Collier and Hoeffler (2004b) and Collier et al. (2009), explore the motivation for, and causes of, conflicts, while scholars like (see for example; Elbadawi et al., 2008b, Fearon et al., 2009, Hacioglu et al., 2012, Besley and Mueller, 2012) investigate the presence of peace in the aftermath of conflict to understand the benefits of peace. Other scholars, such as Collier and Hoeffler (2002), and Chand and Coffman (2008) estimate the durations to exit, for external interventions, in post-conflict countries to understand the optimal timing to withdraw by external peacekeepers. Finally, scholars such as (see for example; Collier and Hoeffler, 2002, Collier and Hoeffler, 2004a, Collier et al., 2004, Khan and Ahmed, 2014, Wennmann, 2012, Gurses and Rost, 2013), examine economic strategies as means of avoiding the recurrence of conflict. All these studies essentially edify the dogmas of peace.

However, there is still a lacuna in the literature in terms of directly investigating the nexus between peace and growth. This is because, according to the above studies, peace has been measured indirectly, which has created two continua: (i) the negative peace continuum (for example see; Collier et al., 2008, Collier and Hoeffler, 2004a, Dahl and Høyland, 2012, Small and Singer, 1982, Lacina and Gleidditsch, 2005, Collier, 1999, Blomberg and Hess, 2002); and (ii) the positive peace continuum (see for example; Comeau, 2003, Doucouliagos and Ulubasoglu, 2008, Narayan et al., 2011, Polachek and Sevastianova, 2012, Butkiewicz and Yanikkaya, 2005, Alesina et al., 1996, Barro, 1991). These two continua are examined in the next section to underscore the theoretical frameworks upon which they are constructed.

2.3 Theory of peace: defining peace The theory of peace can be traced back to the pioneering work of Galtung (1964), who established the Journal of Peace Research to focus solely on peace and peace research. In his most cited article, entitled Violence, Peace, and Peace Research, Galtung (1969) defines peace based on its contradistinction to violence, with his elaboration of violence being the difference between the potential and the actual, between what could have been and what is (ibid., p.168). In rationalizing this definition, the author underlines what he believes to be the most important dimensions of violence that need to be differentiated.

The author categorises six main dimensions of violence. The first distinguishes between physical and psychological violence (ibid., p.169), with the former being that which affects the physical body and soul, and the latter including attributes such as “lies, brainwashing, indoctrination of any kinds, threats, etc…that serve to decrease the mental potentialities” (ibid., p.169). In the second, violence should be differentiated between ‘negative and positive approach to influence’ (ibid., p.170). The author argues that the influencer can coerce a person/victim, either negatively or positively, the net result of which is under-realisation of the potential of the person/victim. Such an ideology rests on the reward-oriented system that promises blissfulness but limits options for potential actions. The third is the need to recognise whether there is an object that has been hurt (ibid), as a threat of violence is violence regardless of whether the victim (object) was actually physically hit or hurt (ibid).

The fourth dimension of violence is the recognition of who commits the act of violence, that is, ‘whether or not there is a subject who acts’ (ibid) which is separated into personal and structural violence. As the former represents ‘an actor committing violence’, the subject- object is clear and direct with a known number of deaths and/or injuries.9 On the other hand, as the latter is built into structures and systems, which the author also calls social justice, the subject– object relationship is not clear (ibid., p.171). In both cases, a person can die or be “mutilated, hit or hurt, and manipulated by means of stick or carrot strategies” (ibid., p.170).

9 This includes a tangible action such as war, civil conflict, or domestic violence that causes injury to people. 13

The fifth differentiates between intended and unintended violence. The former embraces the notion of guilt than consequence, and the latter is the norm when one talks about violence (ibid). Also the unintended cause, the presence of which is embedded in structural violence, should be included (ibid., p.172). Finally, the author distinguishes between manifest and latent violence. The former, which is personal and structural, can be observed indirectly through the notion of “potential realisation” while the latter is ‘something that was not there, but can easily come about’ (ibid).

Accordingly, Galtung (1969) refers to peace as being like a coin with two sides. On one side is the ‘absence of personal violence’, and on the other, the ‘absence of structural violence’ (or social justice) (ibid., p.183), which he refers to as ‘negative peace’ and ‘positive peace’ respectively. Therefore, peace is defined as ‘the absence of personal and structural violence’ (Galtung, 1969: 183) which in short, is the ‘absence of violence’ (ibid., p.167).

2.3.1 Counter arguments regarding the theory and definition of peace Galtung’s (1969) definition is clearly organised around characterising the ‘absence of violence’, and is an obvious manifestation of constructing a phenomenon from another distinct construct. This creates a mismatch when one views peace as a phenomenon in its own right, because the above definition plainly articulates what violence is not, as opposed to what peace is. Thus, the author defines the theory of violence instead of the theory of peace (Barnett, 2008: 77). However, the theory of peace should be constructed around a statement of ‘what peace is’ instead of being grounded in ‘what violence is not’ (ibid). Just like if one has to study the properties of light, it does not require studying the properties of darkness (Institute for Economics and Peace, 2015b). Such contradistinction discounts the invariably increasing social tensions between the peace– violence relationships (ibid).

Secondly, the ‘absence of personal violence’ or negative peace is a minimalist notion (Anders and Ohlson, 2014: 62). Quantitative peace researchers have measured peace in

this way because of its methodological appeal. However, such a minimalist view undermines the imperatives of, and impetus for peace, because ‘it gives a[n] [incomplete and] distorted picture of local socio-political… dynamics’ (ibid., p.67). Societies’ and communities’ senses of insecurity and vulnerability are not factored in within this minimalist deductive methodology (ibid). In particular, a minimalist does not account for ‘criminal … violence’ (ibid); for example, in post-conflict Solomon Islands, greater damage to the country was caused by criminal violence (Howard, 2003) than by those physically killed or maimed.

Thirdly, structural violence or the absence of positive peace involves an environment of unequal power that deepens the unrealised potential of a person without causing any direct somatic harm (Galtung, 1969). This construction implies that characteristics such as geographic isolation from markets and important infrastructure, inherited cultures that clash with modern civilisation and other social challenges are also manifestations of structural violence. However, as most of these structures are inherently “historical and geographically manifest themselves on different people” (Barnett, 2008: 78), it is incorrect and unfair to categorise them as violence. In fact, structural violence can perhaps best be considered a ‘metaphor’ rather than a theory (Boulding, 1977).

Fourthly, empirical studies of positive peace (i.e., the ‘absence of structural violence’) are non-uniform regarding its measurement, as demonstrated in section 2.4.2. This is due to the many attributes attached to the definition of positive peace, which render it open-ended and difficult to render with a single measurement. This maximalist view of peace (Anders and Ohlson, 2014: 62) was popularised by the then Secretary General of the UN Boutros-Gali (1992) when he promoted his agenda for peace. Among other things, he proposed interventions for promoting good governance and institutional strengthening in attempts to avoid the proliferation of violent conflict which include, but are not limited to, market and economic reforms, judicial reforms, anti-corruption interventions, designs of policies for poverty reduction and electoral reforms (Anders and Ohlson, 2014: 69).

The difficulty of measuring such a maximalist view of peace stems from the following two issues. Firstly, the support provided by the international communities to post- conflict countries has always included agendas on reforming institutional structures for good governance (Anders and Ohlson, 2014: 70) that have sometimes failed to acknowledge existing intricacies. Such a top-down agenda often ends up reinforcing oppressive structures that create social tensions (Chandler, 2010, Duffield, 2007) or create room for new structures for new conflicts. This view is premised on the notion that international institutions are inclined to impose “free” market-oriented structures in post-conflict societies, and ignore the voice of the minority, and in the end “reproduce the conditions and possibilities for conflict” (Chandler, 2010: 140). Secondly, conflict- afflicted communities are often imposed upon, although not literally, to accept so called top-down peace resolutions, without fully understanding their content. When locals do not fully understand what is in the ‘peace package’, a relapse into conflict is likely to occur (Mac Ginty, 2006, Richards, 2005 , Richmond, 2007, Richmond, 2008).

Fifthly, the term ‘positive peace’ may be conceptually far-fetched (Satori, 1970: 1035) and its meaning obscured, thereby allowing people to develop their own subjective interpretations (Schmid, 1968: 223). Such a conceptual stretch becomes clearly problematic in terms of implementing peace (Anders and Ohlson, 2014: 71). For instance, censoring local media as a way of averting ethnic violence and xenophobia in post-conflict communities could, in fact, amount to violating the right to freedom of speech (ibid). Such a contradiction raises questions as to whether positive peace is fully achieved.

Sixthly, the maximalist view of peace tends to ‘unintendedly’ reduce the local ownership of any ‘peace package’, and local communities for which the ‘peace package’ is meant often become ‘passive receivers of economic aid’ (Anders and Ohlson, 2014: 71). This has to be the case because donors tend to assume that they have the necessary ‘expertise to craft efficient state institutions’ (ibid). However, such an approach is often beyond the capacities of local communities, and results in projects failing and conflict recurring. Tellingly, such a presumption is inevitably vested in improving structures. However, “it is not … good to gain an understanding of whether a society is ‘at risk’ by merely referring to structures; it is people that either award or 16

withhold legitimacy to a peace process, not structures, just as people – not structures – make war” (Anders and Ohlson, 2014: 71). Similarly, the roles of individuals and society in creating peace have become less valued (Richmond, 2008: 13) as the maximalist view tends to focus more on structures that are usually beyond the capacities of local individuals to change. Creating such a dichotomy between the owning of peace and ‘imposing’ peace is clearly a recipe for conflict recurrence.

Finally, defining peace as the ‘absence of violence’ can also misguide the exit strategy of external intervention peacekeepers in a conflict-prevalent state (Anders and Ohlson, 2014: 68). This can occur when the external intervention force withdraws once a peaceful resolution is reached between warring parties. Despite the current trend towards a ten-year exit timeframe, as argued for by Collier and Hoeffler (2004a), evidence on the ground suggests that states with weak and dysfunctional structures continue to face criminal violence. For instance, when delivering his speech marking the end of ten years of the regional intervention force, the Regional Assistance Mission to the Solomon Islands (RAMSI),10 the then Prime Minister Gordon Darcy Lilo, conceded that while law and order had been restored, ‘our ability to curb petty crime … to ensure sustainability [in peace] remains to be seen’ (Lilo, 2013: 3).11 Such sentiments suggest that ending a third-party intervention prematurely is not helpful for the sustainability of peace in a post-conflict country.

2.4 Measuring peace This section evaluates some of the different empirical measurements of peace based on the definition proposed by Galtung (1969) as demonstrated in section 2.3, which distinguishes between ‘negative peace’ and ‘positive peace’.

2.4.1 Negative peace: measured by conflict-related deaths – a minimalist view A large strand of the peace literature measures peace by employing the negative peace definition. The commonly used proxy to measure the magnitude of violence in order to

10 Details on RAMSI are explained in chapter 3. 11 RAMSI, an Australian-led regional intervention force, was established to contain the conflict in the Solomon Islands. On its 10th anniversary of the intervention RAMSI has now been shifted to become a bilateral arrangement between Australian and the Solomon Islands. More details in chapter 3. 17

imply the level of peacefulness is the numerical death threshold. Two popularly employed minimum thresholds are: (i) the 1,000 conflict-related deaths a year by Small and Singer (1982: 213) under the Correlate of War (COW) project dataset or its variants; and (ii) the 25 conflict-related deaths by the Armed Conflict Data (ACD) PRIO/Uppsala or its variants.12 Some studies that have employed such metrics are those of (see for example; Collier et al., 2004, Collier et al., 2008, Collier, 1999, Fearon and Laitini, 2003, Blomberg and Hess, 2002, Besley and Mueller, 2012, Hegre et al., 2010, Edward Miguel et al., 2004, Chen et al., 2008, de Soysa, 2002, Lujala et al., 2005, Rustad and Binningsboe, 2012). These studies apply various methodologies to analyse peace.

The work by Collier et al. (2004) is one of the many studies that apply the minimum threshold of 1,000 battle-related deaths per year. Their study evaluates the duration of the ‘presence of conflict’ (or absence of peace) using a panel of 77 civil wars, and apply a hazard function based on a maximum likelihood estimation of ‘monthly transition rates from war to peace’ (ibid., p.258). Their model entails:

퐵 ℎ(푡; 푥휏, 휇, 휃) = exp(푥휏훽 + 휇) ℎ (푡) … … … … … … … … … … … … … … … … . (2.1)

where, 푥휏is a vector of observed exogenous variables; 휃 a vector of unknown parameters, where 훽 is a sub-vector; 휇 a country-specific unobserved random effect 퐵 assumed orthogonal to 푥휏; and ℎ the baseline hazard. For the base line hazard, the authors develop a piecewise exponential function and divide the time axis into W intervals by points c1, c2, c3,…, cw. The constant baseline hazard rates within each interval are assumed to be: 푊 퐵 ℎ (푡) = exp⁡(훼 + ∑ 휆푤푑푤(푡)) … … … … … … … … … … … … … … … . . (2.2), 푤=2 where 푑푤(푡) is a duration dummy variable equal to 1 if 푐푤−1 < 푡 ≤ 푐푤 for 푐0 = 0 and⁡푐푤 = ∞, and 0 otherwise; ∝ an intercept; and 휆2, … . , 휆푊 the baseline hazard parameters to be estimated. The Collier et al. (2004) model hinges on investigating the variables that may have caused a conflict to be prolonged.

12 This is a dataset set up and maintained by Peace Research in Oslo (PRIO) at the University of Uppsala. 18

In their empirical results, the authors find that prolonged conflicts are associated with the pre-conflict structural conditions of the economy, such as low income per capita and high inequality. They also find that a sudden surge in export commodity prices partially contributes to prolonging conflict, especially when a commodity is under rebel control.

A cross-sectional study such as the one above can point out the inefficiencies of a particular policy variable at the macro level, such as low income per capita or high inequality. However, it cannot address the underlying cause(s) of the low per capita income in each country because of country-specific characteristics, which can only be understood at a country level. An example of a study that captures country-specific characteristics is that of Besley and Mueller (2012), although with a different measurement of peace (or rather, the absence of violence). The authors investigate the impact of the peace process in Northern Ireland to estimate the size of the peace dividend13, which is measured by changes in house prices. They measure the level of violence to proxy for the number of people killed during the conflict. They use the Conflict Archive on the Internet (CAIN) website, which records all killings that occurred in Ireland during the conflict, and map them on a quarterly basis. They establish a baseline scenario, by estimating the correlation between house prices and killings using the semi-log model:

ln(퐻푟푡) = 훼푟 + 훼푡 + 훽푦푟푡−1 + 휀푟푡 … … … … … … … … … … … … … … . . (2.3),

where ln(Hrt) is the natural log of the house price index for region r at time t; yt-1 the number of killings in region r lagged one quarter, i.e., at time t-1; αr region dummies

(region fixed effects) and αt quarterly time dummies (time fixed effects). Equation 2.3 is estimated with the errors εrt clustered by region, with β interpreted as the average effect of a ‘killing’ on the house price index and expected to be negative. In this benchmark equation, their key assumption is that, conditional on (αr, αt), the pattern of violence does not depend on economic factors. In their result, Besley and Mueller (2012) find that there is, indeed, a negative correlation between killings and house prices, with or without considering region effects, time effects, region-time effects, lagged two-quarter killings, and unemployment variables.

13 Peace dividend will be clarified in section 2.5. 19

The authors then establish a model of the peace process by developing a theoretical framework that links house prices and violence. They apply a Markov chain analysis, with a core model established that estimates the impact of killings on house prices. Thus their core model is:

ln(퐻푟푡) = 훼푟 + 훼푡 + 훽푃퐷푉̂푟푡−1 + 휀푟푡 … … … … … … … … … … … … … … … … … . . (2.4),

where, 훼푟⁡are regional dummies, 훼푡 quarterly time dummies, and 푃퐷푉̂푟푡−1 the expected discounted number of future killings. Their findings show that there is a peace dividend, measured by soaring house prices, when killings are reduced.

Despite its methodological appeal, the Collier et al. (2004) and Besley and Mueller (2012) studies above have something in common. First, they define peace in contradistinction to violence-related deaths. Thus, peace was proxied with the number of deaths, although using different data sources; for instance, Collier et al. (2004) use 1,000 battle-related deaths in the COW dataset, while Besley and Mueller (2012) apply the number of deaths on the CAIN websites. However, measurements of this type clearly do not account for people with physical and emotional injuries caused by the conflict. Furthermore, criminal activities, which are the main cause of violence, are not included in these measurements. Finally, both studies apply econometrics, which is a partial equilibrium framework. Consequently, these metrics only partially measure peace because the above studies only focus on negative peace.

2.4.2 Positive peace: measured by polity – a maximalist view Another strand of the peace literature focuses on ‘positive peace’ (i.e. the ‘absence of structural violence’ or ‘social justice’) as defined by Galtung (1969: 183). It considers peace through an institutional lens whereby institutions and structures must be improved and strengthened to cater for people’s needs. The two most commonly used polity measures are political instability and democracy.

2.4.2.1 Political instability Some of the scholars who employ indices of political instability to measure for the presence of structural violence include (see; Asteriou and Price, 2001, Polachek and Sevastianova, 2012, Butkiewicz and Yanikkaya, 2005, Santhirasegaram, 2008, Alesina and Perotti, 1996, Venieris and Gupta, 1986, Alesina et al., 1996, Barro, 1991, Easterly and Rebelo, 1993, Chen and Feng, 1996, Levine and Zervos, 1996, Hibbs, 1973, Gupta, 1990). This polity measure has varying methodologies for gauging the impact of structural violence, or absence of positive peace, on economic growth, with the findings from all the above studies generally pointing to a negative association between political instability and economic growth.

According to Alesina et al. (1996: 191), who studied a sample of 113 countries between 1952 and 1982, political instability can be defined as ‘the propensity of a change in the executive power, either by constitutional or unconstitutional means’, which refers to the frequency of changes in political government. In their model, they estimate two simultaneous equations on growth and political instability. As their objectives are to investigate: (i) the impact of government change on growth, and (ii) the impact of growth on government change, they specify two models as:

푃푟표푏(퐺퐶퐻퐴푁퐺퐸) = 휑(훾푐퐺푅푂푊푇퐻 + 훼푐1퐸푋퐴퐷퐽−1 + 훼푐2퐺퐶퐻퐴푁퐺퐸−1 + 훽푐0 + 훽푐1퐺푅푂푊푇퐻−1 +

훽푐2푊퐺푅푂푊푇퐻−1 + 훽푐3퐿퐴푇퐼푁 + 훽푐4퐴퐹푅퐼퐶퐴) … . … … … . … … … … … … … … … … … … … … … … … . (2.5), and

퐺푅푂푊푇퐻 = 훾푦퐺퐶퐻퐴푁퐺퐸 + 훼푦1퐸퐷푈퐶 + 훽푦0 + 훽푦1퐺푅푂푊푇퐻−1 +⁡훽푦2푊퐺푅푂푊푇퐻−1 + 훽푦3퐿퐴푇퐼푁

+ 훽푦4퐴퐹푅퐼퐶퐴 + 푢푦 … … … … … … … … … … … … … … … … … … … … … … … … … … . . (2.6). where GCHANGE is the government change proxy for political instability; GROWTH the economic growth rate; GROWTH-1 the lagged growth; EXADJ-1 the lagged executive adjustments, an indicator of emerging political unrest; GCHANGE-1 the lagged government change; WGROWTH-1 the lagged world growth; LATIN and AFRICA continent dummies to account for institutional features such as electoral laws, history of democracy, and authoritarianism; and EDUC the primary education enrolment rate. Their findings reveal an average frequency of government change in all the sampled countries to be approximately every three years (0.28 to be exact). Their results also

showed that growth dropped from 2.8 percent without government change to 1.5 percent with government change. Thus, the more politically unstable a government the lower the growth rate of the economy. This is regardless of whether or not the frequent changes are by constitutional process, coup, or other violent means. They also find that the reverse causality does not hold, that is, low growth does not increase the propensity of a government change.

On the other hand, Santhirasegaram (2008) measures peace by scores of peace building capacity based on socio-political causes, including self-determination, discrimination, regime types, durability of regime, social capacity for peace, and neighbourhoods in conflict. These socio-political causes are categorised by the Centre for International Development Conflict Management (CIDCM). The author estimates from a pool data of 70 developing countries and runs an OLS model to investigate the impact of peace on growth. He finds that peace has a positive effect on growth, both directly and indirectly. Applying the new classical economic theory,14 Santhirasegaram (2008: 811-812) finds that peace increases capital accumulation which, in turn, triggers economic growth. Under this new classical theory, the destruction, or the lack of accumulation, of physical capital due to conflict have negative impacts on growth. Similarly, the incapacitation of human capital from the conflict also causes a reduction in output.

Both the above studies employ cross-sectional data from several countries, with only one value of political instability assigned to each country, to indicate their political riskiness. These measurement, even though they provide a consistent estimator, do ‘not offer detail(ed) information about particular events, and their influences on economic growth’ (Asteriou and Price, 2001: 384).

Consequently, Asteriou and Price (2001) construct indices to measure the political instability of a single country, the UK from 1960 to 1997, by applying quarterly time series data. Such a single country time series analysis of this phenomenon provides in- depth information about the institutions and historical features of the particular country. In addition, an examination of the dynamic evolution of the economy, which is lacking

14 This refers to where physical and human capitals are understood to be the main determinants of growth. 22

in cross-sectional analysis, can be realised. Unlike Alesina et al. (1996), Asteriou and Price (2001) provide a proxy of political instability, by including the number of terrorist incidents (TERROR); the number of strikes (STRIKES); an election dummy (ELECT); a dummy variable for government changes from one party to another (REGIME); a dummy variable for the Falkland war in 1982 (FALK); and a dummy variable for the Gulf War in 1994 (GULF). According to the authors, these variables, in one way, capture some aspects of political instability, and therefore, a reduction in the number of terrorist incidents and/or the number of strikes, or the absence of war, implies improvement in peace, hence an increase in growth. An election dummy (ELECT), however, directly measures political instability. Employing a reduced form approach to account for effects via government spending and private investment decisions, they specify the following three models; (i)OLS model; 4 6

∆ log(푦푡) = 훼0 + ∑ ∑ 푏푖푗푋푖푡−푗 + 푢푡 … … … … … … … … … … … … … … … … … . . (2.7), 푗=1 푖=1 where yt is GDP per capita and Xit denotes a set of political instability proxies, and ut is the error term.

(ii) GARCH model;

∆ log(푦푡) = 훼0 ⁡ 4 4 6

+ 훼1푖 ∑ ∆ log(푦푡−1) + 훼2푖 ∑ ∆ log(푖푛푣푡−푖) + ∑ 푑푗푋푗푡 + 푒푡 … … (2.8) 푖=0 푖=0 푗=1 2 Where 푒푡~푁(0, ℎ푡),⁡⁡⁡⁡⁡ℎ푡 = 푏1푒푡−1 + 푏2ℎ푡−1.⁡

In model two, GDP is specified as an ARDL(4) process taking into account contemporaneous and lagged investment growth along with the lagged political instability proxies Xjt. The above model utilizes the instrumental variable due to the perceived endogeneity problem.

(iii) GARCH_M model;

∆ log(푦푡) = 훼0 4

+ ∑ 훼1푖∆ log(푦푡−1) 푖=0 4

+ ∑ 훼2푖∆ log(푖푛푣푡−푖) + 훾ℎ푡 + 푒푡 … … … … … … . (2.9) 푖=0 2 6 Where 푒푡~푁(0, ℎ푡),⁡⁡⁡⁡푎푛푑⁡⁡⁡ℎ푡 = 푏1푒푡−1 + 푏2ℎ푡−1 + ∑푖=1 푏3푖푋푖푡

The third model extends from model two to include uncertainty which may affect growth directly.

All the three models’ specifications highlight the nexus between institutions and growth. The findings from the three specifications all point to a strong negative relationship between political instability and growth. This implies that increasing political instability reduces growth. Despite the methodological appeal of these three studies, the characterization of peace is constructed in contradistinction to political instability (structural violence), rather than using a positive metric of peace. Again, the estimation technique applies the partial equilibrium framework.

2.4.2.1 Democracy The literature on the relationship between democracy and growth is increasing, though the debate remains inconclusive. For example, Alesina et al. (1996: 204) reveal that democracy neither increases nor decreases growth, which implies that regime type has an ambiguous effect, if any, on growth. However, in a meta-analysis of 84 studies combining 483 published studies, Doucouliagos and Ulubasoglu (2008) claim that democracy is beneficial for economic growth, as shown in Table 2.1.

Table 2.1: Direct and indirect effects of democracy on growth

Association Result Direct effect of democracy on growth Zero Robust indirect effects of democracy on growth: Economic freedom Positive Human capital Positive Inflation Positive Political stability Positive Other indirect effects of democracy on growth: Size of government Negative Convergence Positive Inequality Unclear International trade Negative Source: adapted from Doucouliagos and Ulubasoglu (2008: 74)

The combined data of the 483 published studies show that there is no direct impact of democracy on growth (ibid). Despite the claim that there is a positive relationship, there is a lack of uniformity in measuring democracy in all these studies, as shown by the variables such as economic freedom, human capital formation, lower inflation and high degree of political stability.

In another study that uses cross sectional data from 82 countries from 1979 to 1989, Comeau (2003) investigates the relationship between democracy and growth. The author evaluates five political variables, two political regime types, two representing political heritage (initial political endowment and initial democratic capital) and one proxy for socio-political instability constructed from the political rights index of The Annual Survey of Political Rights and Civil Liberties (Gastil, 1972). The political instability variable is proxied by the standard deviation of the Gastil political rights series, derived from the Freedom House’s 7-class political rights series. The results reveal both direct and indirect positive associations between democracy and growth, and suggest that political instability is negatively correlated with economic growth.

Therefore, Comeau’s (2003) analysis of the democratic types of political regimes tends to favour economic progress, although the relationship between growth and regime type is non-linear. The initial democratic capital is assessed as the level of democracy in the first year of the sample. In addition, the results show that the presence of political stability strengthens the effects of democratic regime type on growth.

Narayan et al. (2011) further examined the causation between democracy and economic growth in 30 sub-Saharan countries. In their study, democracy was proxied by the democracy index constructed by Freedom House, and their results tested for sensitivity using the Legislative Index of Electoral Competitiveness (LIEC). One of the authors’ results reveals that an improvement in democracy has a positive effect on growth for Botswana, Madagascar, Rwanda, South Africa and Swaziland, but that it has negative effect on real income for Gabon and Sierra Leone. This study also reveals that democracy neither induces nor reduces growth in the majority of the sampled countries. This finding suggests that the nexus between democracy and growth remains inconclusive.

The above, and other empirical studies which have proxied peace for democracy, simply imply that peace is equal to democracy, or that peace is democracy. These assumptions undermine the importance of peace, because these two phenomena (i.e. peace and democracy) are grounded on two distinctive constructs. Importantly, as highlighted elsewhere in section 2.3.1, measuring peace using democracy polity as described above is problematic, because it fails to account for the grievances and/or views of minority groups.

2.5 Economic benefits of peace – peace dividend Despite measures of peace not being uniform, peace (however one defines it) generates what is called the peace dividend which, strictly speaking, refers to the savings obtained from the reduction in defence or military spending (see for example; Mintz and Huang, 1990, Ward and Davis, 1992, Chan, 1995, Barker et al., 1991, Bergstrand, 1992, Aslam, 2007). In spite of the voluminous literature on the peace dividend, the empirics on the nexus between defence spending and economic growth remains inconclusive. Studies such as those of Benoit (1973) and Kennedy (1983) lend support to a positive

relationship, while those of (see for example; Smith, 1980, Smith, 1992, Deger, 1986, Rasler and Thomson, 1988, Ward and Davis, 1992, Mintz and Huang, 1990, Mintz and Huang, 1991) find a negative association. Even single studies, such as that of Mintz and Stevenson (1995: 294), who conducted a time series analysis of 103 countries, find that 10 percent of countries lend support to the positive military spending–growth nexus while 90 percent do not.

Following the end of the Cold War, the peace dividend phenomenon had broadened to refer to all the positive economic benefits stemming from the restoration and sustainability of peace. Since the end of some major conflicts scholars such as the following (see for example; Gyimah-Brempong and Traynor, 1999, Abu-Bader, 2003, Comeau, 2003, Miguel et al., 2004, Collier and Hoeffler, 2004a, Gerring, 2005, Jing, 2006, Santhirasegaram, 2008, Besley and Mueller, 2012, Elbadawi et al., 2008b) have been prominent in the study of the economic benefits of peace. To put it into perspective, the global economic benefit of violence has declined in 2008 from 15.3 percent to 13.4 percent of global GDP in 2015 (Institute for Economics and Peace, 2015a). This means that the global economic benefit of peace has improved from 84.7 percent in 2008 to 86.6 percent of global GDP in 2015. Evaluated below are some of the economic benefits of peace that also implicitly embed the peace dividend phenomenon.

2.5.1 Rates of economic growth in post-conflict economies A higher growth rate (measured by GDP growth) is one of the key benefits of peace (Cohen and Ben-Porat, 2008: 428). Collier and Hoeffler (2004a) apply the Correlate of War dataset to study 17 post-conflict societies in their first decade of peace, and find that one of the key results stemming from post-conflict recovery is the unprecedented higher rate of economic growth. Employing an event-study framework to analyse the prospects for recovery in post-conflict countries, Chen et al. (2008: 71) find growth to be 2.4 percent higher than its pre-conflict level. Interestingly, the study by Collier and Hoeffler (2004a) reveals that growth rates within the first decade after attaining peace are supra-normal, with the pattern of growth displaying an inverted U shape (ibid., p.1130). This means that, within the first decade of peace, the growth rate increases up

to a certain level, and then starts declining, as illustrated in Figure 2.1, which suggests a non-persistent or temporary boost to growth arising from the achievement of peace.

Figure 2.1 Behaviour of growth rates in post-conflict countries

Growth rates (%)

Source: Author’s computations based on above text. Time

The driving factors behind higher growth rates are high foreign aid and sound macroeconomic policies (Collier and Dollar, 2002, Collier and Hoeffler, 2004a). The former is found to induce growth by two percentage points per year above the normal growth rate (Collier and Hoeffler, 2004a: 8) and exhibit diminishing returns (Collier and Dollar, 2002, Elbadawi et al., 2008b). However, Chen et al. (2008: 71) argue that the higher growth rates in the first decade of peace were due mainly to the potential for a rapid catch-up in growth.

However, this proposition is refuted by Dahl and Høyland (2012), who replicate the Collier and Hoeffler (2004a) study using a different dataset, the UCDP/PRIO Armed Conflict Database. In their analysis, they find that the effect of economic growth in reducing the recurrence of conflict is mixed, with some models actually showing positive relationship between growth and conflict risk. Regardless of which factors drive higher growth rates, the fact remains that with the support of the presence of an external intervention force (Collier et al., 2008), higher growth rates help mitigate the risk of conflict recurrence (Edward Flores and Nooruddin, 2009, Bigombe et al., 2000).

2.5.2 Post-conflict aid Foreign aid sometimes leads the installation of peace. However, in most cases aid follows peace, and post-conflict countries tend to experience unprecedented inflows. For example, the 39 post-conflict countries studied by Elbadawi et al. (2008b: 117) experienced high inflows in aid during the first stages of peace (see Figure 2.2). Thus, aid is an important determinant of growth in post-conflict economies (Elbadawi et al., 2008b: 130, Collier and Hoeffler, 2004a). Moreover, it is more effective in a post- conflict transition than the normal peace environment (Collier and Hoeffler, 2002), and it reduces over time (as shown in Figure 2.2), displaying diminishing returns as alluded to by Collier and Dollar (2002) and Elbadawi et al. (2008b).

Figure 2.2 Annual aid flows as a share of GDP (average values in percentages)

Aid/GDP 4

2 PeaceOnset PrePeace PostConf1 PostConf2 0 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 Time

Source: Adopted from Elbadawi et al. (2008b: 117)

Due to the importance of aid to post-conflict recovery, the early withdrawal of donor assistance15 does not reduce the risk of conflict recurrence (Collier and Hoeffler, 2004a), as the fragility of peace is very obvious once conflicts have ceased. For that reason, the authors propose that, once conflicts have ended, aid assistance (including peacekeeping) should remain for up to a decade, before petering out. This particular study, carried out under the auspices of the World Bank, has led donor communities to

15 Traditionally, donors withdraw their assistance to post-conflict countries within three to five years; See Collier, P. & Hoeffler, A. 2002. Aid, policy and Peace: Reducing the Risks of Civil Conflict. Defence and Peace Economics, 13, 435-450. 29

adjust the fungibility and target duration of their aid. For example, RAMSI (an Australian-led intervention force) has adopted this strategy in the recent post-conflict Solomon Islands. By July 2013, exactly ten years after its inception in July 2003, RAMSI withdrew its major components, including its military and regional contingents’, leaving only skeletal support.16

However, Chand and Coffman (2008), who studied four different post-conflict episodes in Timor Leste, Solomon Islands, Mozambique, and Liberia, find that the external intervention exit duration is, in fact, longer than ten years. In their study they show that donors can, on average, successfully exit from a post-conflict country between 15 and 27 years after their initial involvement.17 The authors find that, for the Solomon Islands, the intervention should remain for up to 23 years, which suggests that the decade-long exit strategy of Collier and Hoeffler (2004a) may be an under-estimation. Importantly, donors need to ask the question whether their assistance should be time or task bound before allocating their resources. Addressing this question provides donors with a clear framework that ensures that their assistance is maximised and more likely to be successful in the recipient post-conflict country.

2.5.3 Peace industry The revitalisation of the ‘peace industries’ is also one of the beneficiaries of peace. Coined by the Institute for Economics and Peace (IEP), this term refers to “industries or businesses that thrive during peace times and are adversely affected in times of violent conflict or when there is increasing threat” (Institute for Economics and Peace, 2008: 8). Once flourishing, such industries can contribute to raising the rate of economic growth and creating employment opportunities. More importantly, they can stabilise the economy to foster long-term peace and growth.

Accordingly, the main industries that normally fall victims during times of conflict include the agriculture, manufacturing (excluding arms and weapons manufacturing),

16 RAMSI is now a bilateral arrangement between Australia and the Solomon Islands; see Chapter 3 for more details on RAMSI. 17 This is a necessary but not a sufficient condition. It requires certain preconditions, such as successful funding of the budget coming from domestic revenue rather than depending on donors. 30

tourism, and general services (excluding security and military) sectors. In Mozambique, for instance, the civil conflict, which ended in 1992, caused the number of cattle to decline from 1.3 million in 1982 to 0.25 million (Ministerio da Agricultura, 1994). Per capita food productivity reached 90 percent of the pre-conflict level by 1996 (Bozzoli and Bruck, 2009), and the mean farm productivity level for the maize crop dropped to 396kg/ha in 1994 (ibid., p.380). This is compared with mean yields of 1,500kg/ha in southern and eastern African countries, and a mean yield of 2,700 kg/ha in developing countries in the period from 1995 to 1997 (Heisey and Edmeades, 1999: 44, 62). In addition, the protracted violent conflict in Sierra Leone, which saw the services sector decline from 50 percent of GDP in 1980 to 15 percent by 2002, rebounded to 30 percent in 2007 as soon as peace was restored (Collier and Duponchel, 2013: 72).

Beside the peace industries, there are certain industries that thrive during conflict periods, including those related to/and associated with the manufacture and supply of arms and weapons, security firms and other related businesses that provide logistics to combatants. However, such industries are fewer in number and magnitude, but their contributions to the destruction of the economy, and the disunity they promote in the society, especially when the conflict is internal, are huge (Institute for Economics and Peace, 2008).

2.6 Sustaining peace through (post-conflict) economic recovery As the economic benefits of peace become obvious, the next course of action is to sustain the (post-conflict) peace. Thus, implementing economic reform programs aimed at achieving growth rates higher than those in historical records, accompanied by a sustained employment creation policy, contributes to sustaining peace (Ohiorhenuan, 2011: 3). Therefore, economic reform policies should be implemented immediately, in parallel with the peace onset consolidation phase (Collier et al., 2008), although it would be better if they were implemented before complete termination of the conflict (Brauer and Dunne, 2010: 5). A well-articulated economic reform program, targeted at a post-conflict economy, aiming to stimulate its growth by an average rate of 10 percent per annum, reduces the risk of conflict recurrence from 40 percent to 26.9 percent within the first decade (Collier et al., 2008). In contrast, a post-conflict country whose economy remains stagnant throughout the first decade will experience a 42 percent

probability of the relapse of conflict (ibid., p.469). For the robust allocation of resources, post-conflict strategies need sequencing in time horizons, as shown below.

2.6.1 Sequencing and synchronizing policies to sustain peace: short-term strategy The distinction between time horizons is important in prioritizing policies for implementation (Castillo, 2001, Collier, 2009). The first on the list is the disarmament, demobilisation and reintegration (DDR) of former combatants, the sequencing of which should be consistent with the overall economic reform policies (Spear, 2006: 178). Importantly, former combatants must agree to these reforms. For instance, in Liberia in 2003, former combatants perceived the DDR package to be too small and rejected it (ibid. 179)18, which led to the prolongation of violence. Conversely, in Mozambique in the early 1990s, the DDR package, which included food, tools, seeds, clothes, transport home, and small value vouchers, acted as an incentive for former combatants to cease fighting (Wurst, 1994: 38).

In addition, post-conflict countries that do not have a military force and/or suffer from dysfunctional law enforcement institutions, the DDR process could require the presence of an external force to maintain the fragile peace achieved to date (Collier, 2009: 103). In his analysis of the United Nation (UN) peacekeeping forces Correlates of War (COW) dataset, Collier et al. (2008) find that spending on external peacekeeping greatly reduces the risk of reverting to conflict. Also, the presence of an external force on the ground, in itself, acts as a deterrent to regrouping (Jones and Kane, 2012: 227).

2.6.2 Abnormal times require abnormal solutions: short- to medium-term strategies Parallel with the implementation of the DDR program, and in light of the fragile peace environment, implementing business as usual policies would be counter-productive to peace. Such periods are “abnormal times”.19 Thus, implementing “distinct” or

18 This package includes food rations, and a $300 stipend and psychological counselling (Spear, 26: 1178]. 19 ‘Abnormal times’ are those in which it is possible to implement ‘distinctive’ policies that could not have been considered during peace times. 32

“abnormal” solutions is necessary.20 Accordingly, Boyce (2004) proposes two distinctive solution scenarios for such times: one he calls “doing some things differently” (ibid., p.7) and the other, “doing different things” (ibid., p.11). The former implies that, in order to address post-conflict challenges, existing policies and/or procedures should be reviewed to suit the special context on the ground (ibid., p.7). On the other hand, the latter refers to addressing issues that can be avoided during normal peace times but become inevitable during conflict (ibid., p.11).

Additionally, due to the post-conflict peculiarities, Castillo (2001: 1970) suggests three overarching guiding principles when making policies: (i) setting economic priorities, guided by political considerations; (ii) providing preferential treatment for the perpetrators and/or victims by addressing their grievances; and (iii) allowing space for sub-optimal policies. The first guiding principle is an old political rhetoric that need to be changed. Economic priorities targeted for peace consolidation must take precedence over any political considerations. Such a political rhetoric is a submissive policy, with Collier et al (2006: 471) arguing that political election (which has political connotations) and other political aspirations should be secondary to economic recovery policies in order to reduce the risk of conflict reversion. Similarly, Flores and Nooruddin (2009: 5) use duration analysis techniques to produce a post-conflict economic recovery dataset, and find that post-conflict countries that immediately undertake massive ‘democratisation’ do not perform better economically than those that do not. This means that post-conflict elections and other institutional governance policies may be necessary for peace but are not mechanisms for long-term peace (Collier et al, 2006: 471).

Meanwhile, the second principle, ‘preferential treatment for the perpetrators or victims by addressing their grievances’, entails violating the equity principle for the sake of peace. In post-conflict El Salvador, for example, 300,000 landless peasants were excluded from the land program, which benefited only former combatants and their supporters.21 The third principle, ‘allowing for sub-optimal policies’, stemmed from the

20 ‘Distinct’ and ‘abnormal’ solutions are used interchangeably in this context but have the same meaning. 21 See footnote 6 in Castillo, D.G. 2001. Post-conflict Reconstruction and the Challenges to international organizations: The case of El Salvador. World Development, 29, 1967 – 1985. 33

fact that, given the peculiarities inherent in post-conflict transition societies, “it would be unreasonable to expect … optimal allocation of resources” (ibid). For example, although the arms-for-land program implemented in post-conflict El Salvador was not an economically viable land reform program, it contributed to national reconciliation and benefited former combatants without resorting to arms.22

Accordingly, harnessing Castillo’s (2001) guiding principles with Boyce’s (2004) two distinctive solution scenarios, guiding principle (i) above can be classified under the “doing some things differently” and (ii) and (iii) with “doing different things”. By implication, both Castillo (2001) and Boyce (2004) advocate adopting ‘abnormal policies’ during ‘abnormal times’, with the correct priorities for the consolidation of peace, which supplants the principles of equity and optimisation.

2.6.3 Role of the private sector: long-term strategy The literature is inundated with strategic policy measures a typical post-conflict country can employ to pursue long-term strategies to sustain peace. Some of these include, increase foreign aid (see for example; Collier and Hoeffler, 2002, Collier and Hoeffler, 2004a, Castillo, 2001), rehabilitate physical capital and the accumulation of technology (Jones and Kane, 2012), improvement in macroeconomic conditions and increase in domestic value addition (Barrientos et al., 2014), all of which are indeed vital for post- conflict recovery. However, their impacts are highly aggregated because, as these measures are usually implemented at an institutional (macro) level, the rural mass wherein former combatants usually reside rarely felt the impact due to bureaucratic red tape and inefficiencies. Moreover, there is a perception by the rural population that there is an uneven distribution of gains from such policies. This perception can, in fact, derail the short-term peace achieved to date, and may result in a relapse into conflict.

Nevertheless, a post-conflict economy with an improved macroeconomic environment can complement the private sector, playing an active role in promoting peace. The connection between peace and economic growth allows for greater participation and

22 Castillo, D.G. 2001. Post-conflict Reconstruction and the Challenge to International Organizations: The Case of El Salvador. World Development, 29, 1967-1985. 34

influence of the private sector in the peace process (Cohen and Ben-Porat, 2008: 428). Thus, the role of the private sector in a post-conflict society in terms of company– community relationships is crucial for long-term peace. This is because the outcome of any peace dividend is basically the result of the investment strategies of private businesses that contribute to growth (ibid).

For that reason, Fort and Schipani (2007: 364-367) have determined that there are four possible scenarios in which businesses can play a role in promoting a peaceful society: (i) fostering economic development, (ii) promoting a sense of community, (iii) embracing external evaluation principles, and (iv) adopting track-two diplomacy.23 Fostering economic development entails providing such things as employment opportunities and other positive externalities that improve the communities’ standard of living (ibid). For example, in post-conflict Uruguay a company revived a bankrupt meat processing plant, which saw 800 jobs created in an impoverished community with no other major employer (ibid., p.364).

Promoting a sense of community requires being accommodating and allowing community voices to be heard. Philanthropic investment is such an instrument, which is associated with a company’s corporate social responsibility (CSR) commitment (Wennmann, 2012: 932). In post-conflict Latin American countries, for example, it was found that cash donations were the usual form of engaging with communities (ibid). However, such cash benefits sometimes attract opportunists in a community, who can be difficult to find and remove. In South Africa and Brazil, companies share their managerial and technical expertise by including communities in designing public safety plans, providing office space for community meetings, and sponsoring their social activities (Vogelman, 1990, Capobianco, 2005). Indeed, these are the kinds of policies Boyce (2004) means when he refers to ‘doing things differently’ for the sake of peace.

23 We focus on the first two possibilities, as they are relevant to this study. Regarding the other two options, embracing external evaluation refers to transparency with regard to corruption and support for the rule of law, while track-two diplomacy necessitates a company providing employment opportunities for different people, such as two warring parties in a conflict, to work towards a common goal. Fort, T.L & Schipani, C.A. 2007. An Action Plan for the Role of Business in Fostering Peace, American Business Law Journal, 44, 359-377. 35

Moreover, companies24 have come to embrace the ideology of ‘safe communities’ (Wennmann, 2012: 933). Safe communities refer to investing in community capacities to respond to conflict, thereby protecting both company and community interests in broader peace and stability (Ganson and Wennmann, 2012). For example, in New Caledonia, a Brazilian Vale mining company provides education and training, environment, social and cultural programs to local landowners under a program known as the ‘Sustainable Development Pact’ (Benke, 2010).

2.7 Towards a practical definition of peace The analyses and syntheses in the above sections have positioned this review in the broad context of the peace literature based on the peace definition constructed by Galtung (1969). The distinction between negative peace and positive peace has created two continua, as previously mentioned, which undoubtedly have set the minimum and maximum thresholds for successful peace consolidation. However, ‘for practical and research purposes, they are not satisfactory’ (Anders and Ohlson, 2014: 72) as, if only one continuum is studied, only a partial view of peace is provided.

Consequently, there is a lacuna in both the definition and measurement of peace. More adequate examples, that embed elements of the minimalist and maximalist views of peace, can fill these gaps. Accordingly, Anders and Ohlson (2014: 77) offer for consideration what they called legitimate peace, which refers to “the relative improvement of the attitudes that post-conflict groups have towards the state and other communities”.25 ‘Relative improvement’ is the key phrase in this definition, as opposed to the absolute policy goals of the maximalist and minimalist views of peace, which embraces both a vertical and horizontal legitimacy. The former refers to ‘consent and loyalty to the idea(s) of the state and its institutions’, and the notion that society believes in the rule of law imposed by the state (Holsti, 1996: 84). This means that the state is the authority, and the society complies with its rules, that is, a relationship between the responsible authority and the voluntary compliance of the society (Anders and Ohlson, 2014: 74). On the other hand, as horizontal legitimacy refers to the ‘perception among

24 The terms companies, businesses or investor are used interchangeably in this context. 25 For full discussion on legitimate peace, see Anders, T. & Ohlson, T. 2014. Legitimate Peace in Post-civil War States: Towards Attaining the Unattainable. Conflict, Security & Development, 14, 61-81. 36

society and the state or elites about who is to be included in the sample’ (ibid., p.74), the society reinforces its beliefs and trust in the state whether or not the latter protects the society (ibid).

Essentially, legitimate peace is concerned with how a society perceives the state of its surroundings and capturing its sentiments indicates whether a conflict-afflicted community is at risk (ibid., p.76). Perceptions and sentiments provide nuances that can offer better insights as to the likely risk of the recurrence of conflict. Especially in post- conflict societies, unpleasant rumours cannot be taken lightly (ibid). However, the formation of a positive perception about society generates social capital that can be utilised to build good governance and enhance economic development (ibid).

Furthermore, vertical and horizontal legitimacy can re-enforce the foundation for peace builders. Legitimate peace focuses on agency, as opposed to structures, which are emphasised in the positive peace or the maximalist view (ibid). The society pledges loyalty to the state (vertical legitimacy) or community (horizontal legitimacy) rather than to the structures of the state or community. To ensure that a society has attained peace, one can ask questions of people. For vertical legitimacy, for example, if you witness a crime, would you report it to the police; and how do you rank the state’s involvement in reducing crime; and for horizontal legitimacy, should members of group B (e.g., another ethnic group) be allowed to own land in your area; and do you feel safe living next to a former militant? (ibid). Importantly, the kinds of questions asked depend on the circumstances of the post-conflict society under investigation, in other words, they are context-specific.

To obtain the indicators for peace, Anders and Ohlson (2014) outline the methodological tools for collecting empirical data, including large-N surveys, focus group interviews, experiments, and social and mainstream media. However, what they do not show is how to compute an indicator of peace or a single peace score or index to gauge the peacefulness of the society under investigation.

2.8 Conclusion This chapter reviewed the literature on post-conflict peace, to position this study in the literature on the economics of peace. It found that half of post-conflict societies have suffered repeated reoccurrences of conflict (Elbadawi et al., 2008a, Kreutz, 2010). Therefore, it is important that the first attempt to achieve peace is consolidated and sustained. Since the first phase of the onset of peace is considered fragile, a space for implementing ‘abnormal’ or ‘distinct’ solutions is necessary, which renders support for applying ‘doing some things differently’ and ‘doing different things’ strategies (Boyce, 2004).

This review also explored the subjective definition of peace as ‘the absence of personal and structural violence’ (Galtung, 1969), which entails two extremes. One focuses on peace as the absence of the physical infliction of violence or conflict, with its most common measure the number of conflict related-deaths. This definition is the minimalist view of peace, commonly referred to as ‘negative peace’. The other extreme emphasises peace as being the improvement and strengthening of structures and institutions (both formal and informal). The most common institutions employed are polity measures such as political instability and democracy. This view is the maximalist view of peace, also known as ‘positive peace’.

Current empirical analyses of the effect of peace on an economy are based on either of the above extreme definitions. Empirical work on the minimalist view or negative peace, for example, have determined that there is a negative relationship between the number of killings and economic growth, while those who take the maximalist or positive peace view have mixed results. While the nexus between political instability and growth was found to have a negative association, research on the democracy – growth nexus remain inconclusive and contestable. More importantly, the direct link between peace and growth is still unresolved.

Therefore, the review identified three knowledge gaps this thesis considers it necessary to fill to advance the intellectual debate on the concept of peace. First, this study attempts to address the two extremes used to measure peace by introducing a middle

ground. In doing so, it draws on the definition of legitimate peace by Anders and Ohlson (2014). We extend the authors’ measurement by quantifying peace, using data from a household survey conducted by the author of the current study for this specific purpose. Chapter 4 presents the research design for conducting the household survey, and Chapter five explains the theoretical framework and method undertaken to quantify peace, while also presenting the results for the level of peacefulness and an analysis of the impact of peace on household income.

The second knowledge gap is analysing the direct link of peace and growth. Past studies employed indirect measures as a proxy for peace. For example, under negative peace the number of conflict-related deaths is used to measure the level of peacefulness. These measurements suffer from excluding somatically and emotionally injured victims. Moreover, the measurement is based on their contradistinction to violence. In terms of positive peace, the measurement suffers from utilizing such polity oriented measures as political instability and democracy to imply the level of peacefulness. Again, these are indirect measures of peace. This study attempts to measure the peace–growth nexus directly, utilizing the peace perception index (PPI) obtained from the household survey (see Chapter 5).

The third knowledge gap is that past studies have empirically analysed the impact of peace by applying a partial equilibrium framework, which rests on the ceteris paribus assumption. However, as it does not explain the other feedback mechanisms of peace that could be at work in the rest of the economy, this study addresses this gap by employing a computable general equilibrium (CGE) framework. To the best of the author’s knowledge, there is no such analysis of peace in the literature. Chapter 6 explains the theoretical CGE framework and Chapter 7 provides the empirical results for the impact of peace on the economy.

CHAPTER 3

THE SOLOMON ISLANDS ECONOMY

3.1 Introduction Having examined the significance of this study within the broad literature on the economics of peace and having identified the knowledge gaps in the previous chapter, this chapter presents the key aspects of the Solomon Islands economy to contextualise this research. It argues that economic factors have triggered the conflict, and it is therefore appropriate to employ economic policies to maintain peace for economic prosperity.

Solomon Islands is an archipelago made up of more than 900 scattered isolated islands, ranging from rugged mountainous islands to coral atolls. It stretches for around 1,600 kilometres in the Southwest Pacific, with a total land area of 28,896 square kilometres (WorldAtlas., 2015), and lays claim to 1.38 million square kilometres in its exclusive economic zone (F.A.O, 2015). The archipelago is characterised by six main islands, namely Guadalcanal, Malaita, Isabel, New Georgia, Choiseul, and Makira. It attained political independence on 7th July, 1978, following more than eight decades under a British Protectorate (Allan, 1957).

The remainder of the chapter is organised as follows. Section 3.2 examines the patterns of economic development in the Solomon Islands. It presents the characteristics and structure of the economy, and discusses the underlying (economic) factors that had contributed to the uprise in the civil conflict. Section 3.3 provides an overview of the civil conflict while section 3.4 details the establishment of an external intervention force to contain the conflict. Section 3.5 evaluates the main indices of macroeconomic performance during the pre-conflict, conflict and post-conflict periods. It assesses the patterns of growth, as well as emphasising some of the misguided policies that had influenced growth. Section 3.6 describes the major industries that have contributed to growth, in order to rationalise their importance to the CGE theoretical framework, which is discussed in Chapter 6. Sections 3.7 and 3.8 respectively analyses the impact

of the conflict and the impact of peace, on the economy. Section 3.9 highlights some of the government policies that have been implemented to sustain peace. Section 3.10 concludes this chapter.

3.2 Patterns of economic development in Solomon Islands

3.2.1 The characteristics and structure of Solomon Islands economy A key economic characteristic of the Solomon Islands economy in the pre-independence era was the presence of large foreign owned and operated plantations that contributed significantly to exports. The Levers Pacific Plantations Ltd (LPPL) began the commercialization of large copra and cocoa plantations in the 1920s, particularly on the Russell Islands and Guadalcanal. This came about as a result of the colonial administration’s introduction of land alienation and imperial investment policies (Scarr, 1967). These policies were seen as a vehicle for foreigners to acquire land and operate commercial estates. For instance, LPPL expanded extensively by acquiring more land instead of improving efficiency (Fraser, 1997: 42). Consequently, 90 per cent of the coastal fertile plains on Guadalcanal, which accounts for around 6 percent of the total land area of the Solomon Islands, came under foreign control (Allan, 1957: 60). Important for the discussion that follows; foreign investment took place on the alluvial plains and as such accounted for a large proportion of the productive land of the nation.

By the 1970s, a few large foreign investors had owned and operated estates on Guadalcanal and the Central Islands, some of which included: Solomon Rice Limited; Solomon Islands Plantation Limited (SIPL); Solomon Taiyo Limited (STL); and the National Fisheries Development (NFD) Company. In addition, after the Second World War (WWII), the colonial administration’s Headquarter was relocated from Tulagi in the Central Islands Province to Honiara on Guadalcanal.26 Honiara became the capital city of the Solomon Islands when the country gained independence. Subsequently, commercialisation gradually gravitated towards Guadalcanal, as did government administration.

26 The move was basically to take advantage of the infrastructures that were was left behind by the Allied Forces after WWII. 41

Furthermore, these investments created the demand for and/or attracted potential workers from across the country. This led to the recruitment of workers from other neighbouring islands, which changed the population dynamics of the Solomon Islands. In subsequent years, the foci for the movement of people became Guadalcanal, the Central Islands, and then later the Western Province. The migration, especially to Guadalcanal, that followed the commercialisation had major impact on the genesis of the conflict, as will be explained in the next section below.

3.2.2 Concentration of economic development on Guadalcanal triggered the civil conflict Honiara and its periphery became the centre for government business and commercialisation, as infrastructure development was concentrated around it (Kabutaulaka, 2001). Meanwhile, the influx of people from the other islands in search of economic opportunities continued. Neighbouring Malaita Province, being the largest province in terms of population,27 had the highest number of settlers. For example, from 1914 to 1939 about 10 percent of the entire population of Malaita were engaged as plantation workers away from home (Bennett, 1987: 189). In addition, Guadalcanal comprised the largest fertile plain in the archipelago, with the best agricultural prospects.

A significant difference between Guadalcanal and the majority of the other neighbouring islands, including Malaita, was the system of the transmission of rights to land held by the clans. In the case of Guadalcanal, land is transmitted through the female progeny – this being referred to as the matrilineal system of land inheritance. In contrast, on Malaita, for example, the system was patrilineal, in that rights to the land are inherited via the males in the clan. With the matrilineal system practiced on Guadalcanal, male settlers married to a Guadalcanal female meant that they could access to customary land on Guadalcanal through their children. This important difference in the system of land inheritance also played a part in the conflict.

27 The population of Malaita accounts for one-third of the total population of the Solomon Islands. 42

Despite the fact that settlers legally or traditionally acquired land, the traditional landowners hold the view that they still own the land, simply because of the ‘absence of a freehold land concept on Guadalcanal’ (Wairiu, 2007). This caused differences between the traditional landowners and the settlers (ibid). As well, the settlers tended to settle closer to the main infrastructures, allowing them easier access and opportunities to pursue commercial activities. This is especially true in almost all of the British colonies, as in the case of Kenya and other sub-Saharan African countries (Feder and Noronha, 1987: 153). As such, they tended to attain relative economic advantage over their indigenous counterparts.28 This created envy and resentments from fellow indigenous Guadalcanal people. Consequently, these built-up frustrations ended up erupting into a civil conflict, as is explained in the next section below.

3.3 The eruption of the civil conflict, 1998 – 2003 June In late 1998, some Guadalcanal men, who called themselves the ‘Guadalcanal Revolutionary Army’ (GRA), and later changed their name to the ‘Isatabu Freedom Movement’ (IFM), led by Harold Keke, began terrorizing and harassing settlers from Malaita. These harassments began on the plains of Guadalcanal29 and spread across the settlements throughout Guadalcanal. Many women were raped, people were wounded and some killed (Solomon Islands Christian Association, 2002).30 The victims who lost their properties on Guadalcanal pressured the Government to contain the IFM militant activities, but with limited success. Beginning in 2000, the displaced Malaitans retaliated by establishing their own militant group, the ‘Malaita Eagle Force’ (MEF), located in Honiara and supported by some prominent Malaitans (Kabutaulaka, 2004: 5). MEF started attacking villages and IFM strongholds around the periphery of Honiara.

On 5th June, 2000, MEF, along with rogue elements of the Royal Solomon Islands Police Force (RSIPF), raided the police armoury in Honiara.31 The split in the Police Force paralysed the institutions of law enforcement, making it difficult to contain the crisis. Honiara, which hosts all core functions of government, international

28 Author’s interviews with concerned indigenous landowners. 29 This is where the oil palm company, SIPL is located. 30 The number of Malaitans killed could not be verified. 31 The Solomon Islands does not have a military force, except a Field Force, a division of the RSIPF, which was been likened to a paramilitary force. 43

communities, and the commercial business centre, became insecure as thugs and criminals took control of the city. Given the ill-discipline of militants on both sides, the exchanges between the two spiralled into serious human rights atrocities being committed by both groups (Solomon Islands Christian Association, 2002). This resulted in a serious breakdown in the law and order situation, forcing investors, development partners, and other foreigners to leave the country. The civil conflict, which became known as the ethnic tension, intensified with more people from both sides wounded and/or killed.

3.3.1 Peace talk attempts On 15th October, 2000 a major peace agreement, which came to be known as the Townsville Peace Agreement (TPA), was facilitated by Australia, and was reached following several failed attempts.32 Despite the TPA, the conflict took a new twist in the form of a spike in criminal violence. The IFM militants began terrorising their own people. For example, on 20th August, 2002 a Cabinet Minister who was an indigenous inhabitant of Guadalcanal and a Catholic Priest was shot dead by Harold Keke, the warlord, with support of his followers (The Sydney Morning Herald, 2005). In Honiara, on the other hand, criminal elements masquerading as MEF militants continued harassing and looting properties in and around the capital, making it even harder to contain their activities (Howard, 2003: 1). The so-called militants extorted money from the national Treasury under the cloak of compensation payments.

3.4 The Regional Assistance Mission to Solomon Islands (RAMSI) – an external intervention force In April 2003, the then Prime Minister of the Solomon Islands, Sir Allan Kemakeza, requested Australia for assistance to contain the lawlessness (Howard, 2003: 2). The Australian Government spearheaded a regional approach with the mandate of the Pacific Islands Forum (PIF).33 At the PIF’s foreign ministers’ meeting, held in Sydney on 30th June, 2003 the Biketawa Declaration initiated by the PIF foreign ministers in 2000 was enforced. The Biketawa Declaration outlines the guiding principles for good

32 For the various peace talks, see Kabutaulaka, T. T. 2001. Beyond Ethnicity: The Political Economy of the Guadalcanal Crisis in Solomon Islands. State, Society and Governance in Melanesia Project. 33 The members of PIF which form RAMSI are Australia, Cook Islands, Federated States of Micronesia (FSM), Fiji, Kiribati, Marshall Islands, Nauru, New Zealand, Niue, Palau, Papua New Guinea, Samoa, Tonga, Tuvalu, Vanuatu. 44

governance and sets out actions for regional response to crises in the region. Specifically,

Forum leaders recognise the need in time of crisis or in response to members’ request for assistance, for action to be taken on the basis of all members of the Forum being part of the Pacific Islands extended family… Any regional response to a crisis … actions are discussed with the authorities in the country concerned (Pacific Islands Forum, 2000: 1).

Based on this, a Regional Assistance Mission to the Solomon Islands (RAMSI) was agreed upon to restore peace and revive the economy. In Australia, the then Prime Minister John Howard, told the Australian House of Representatives on 12th August, 2003 that the presence of RAMSI is in the interest of Australia (Howard, 2003: 2). Crean (2003), who was then the Leader of the Opposition in Australia, echoed similar sentiments in support of the parliamentary motion to initiate and deploy RAMSI.

Consequently, on 24th July 2003 2,200 RAMSI military and police personnel (resembling a UN like peacekeeping operation) landed at the Henderson International Airport. Their immediate task was to restore law and order. DDR programs began immediately. Within a span of just four months RAMSI, which was code named Helpem Fren34, collected more than 3,700 guns, including 700 high powered military- style weapons (Warner, 2004), resulting in the containment of criminal lawlessness and the return to peace, albeit an enforced one.

RAMSI’s initial framework covered two broad areas; civil order and the economy (Solomon Islands Government, 2003: 1). Under the civil order, RAMSI aimed to: (i) restore security in Honiara and the rest of the country; while for the economy, the aim was to (ii) stabilize Government finances, (iii) promote longer term economic recovery, and (iv) rebuild essential machinery of Government.

Recognising the need to address the above areas, RAMSI was structured accordingly into: (i) Combined Task Force (CTF) or the military component; (ii) Participating

34 Helpem Fren is a Pidgin English term, which in English literally means helping a friend. 45

Police Force (PPF); and (iii) Civilian Development Programs (CDP). The CTF provides logistic support to PPF. Thus, the first mandate (restore security in Honiara and the rest of the country) came under the purview of the CTF and PPF. The last three mandates were to be addressed by the CDP.

The significant improvement in law and order allowed the second phase – reconstruction – to commence, under the CDP component. The CDP had three broad focus areas. They were: (i) machinery of Government; (ii) Economic Governance; and (iii) Law and Justice. These three areas were the pillars for moving the Solomon Islands forward, and they were claimed then to be ‘unique and complex’ (Warner, 2004). The CDP RAMSI officers were recruited as advisors, and placed in line positions in government ministries. In 2009, the Solomon Islands Government and RAMSI re- enforced these ‘pillars’, and a new Partnership Framework was drawn up. Under the new Partnership Framework, RAMSI continued to focus on these three broad issues; that is, (i) Law and Justice; (ii) Economic Governance and Growth; and (iii) Machinery of Government (Regional Assistance Mission to Solomon Islands., 2009).

Within the first decade of intervention (i.e. from July 2003 to July 2013), Australia spent AUD$2.6 billion on RAMSI operations in the Solomon Islands (Hayward-Jones, 2014: 3). Of this, the law and justice pillar accounted for 83 percent (AUD$2.2 billion), economic governance received 8 percent ($223 million), machinery of government consumed 4 percent (AUD$103 million), and cross-mission activities accounted for 5 percent (AUD$133 million).35 New Zealand, on the other hand, spent about AUD$173 million in that same period (ibid). From August 2013 RAMSI technically ceased to exist as a regional intervention force, and support has shifted, and now comes under Australia’s bilateral assistance to the Solomon Islands. Despite the A$2.4 billion expended on RAMSI, and the restoration of peace, the actual fiscal impact on the local economy of this investment remains to be gauged.

35 See Figure 3.1 below 46

Figure 3.1 Estimated cost of RAMSI operations in Solomon Islands from July 2003 – July 2013

Machinery of Cross- Economic Governance mission Governance 4% activities 8% 5%

Law and Justice 83%

Source: Adapted from Hayward-Jones (2014).

3.5 Macroeconomic Performance This section discusses the macroeconomic outcomes for the Solomon Islands economy since the pre-conflict period. It evaluates its growth pattern and the main determinants of growth.

3.5.1 Patterns of Economic Growth Since the 1970s, the Solomon Islands economy has seen an expansion in output, with economic growth averaging 5.8 percent per year (See Figure 3.2 below). Attributing to this outcome was a strong surplus in the trade balance, accounting for an average 67 percent of GDP, thereby strengthened the current account position (World Bank, 2012). The positive performance stemmed from exports from the agricultural sector, which accounted for 90 percent of total export income and 70 percent of GDP (Easterly, 2003: 18). The fiscal budget was also kept balanced with support from the British Treasury in the form of subsidies to operational expenditure (Premdas and Steeves, 1985: 10). Inflation was kept at an average of eight percent (World Bank, 2012). Despite the strong growth investments of the colonial administration merely sought to find raw materials for their companies back in Britain (Premdas and Steeves, 1985: 10). For example, the Levers Pacific Plantations invested in coconut plantations to provide copra for their

soap factory in Britain (Scarr, 1967). This is the reason for the concentration of commercial operations on Guadalcanal to the neglect of the other islands.

In the 1980s, despite the continued expansion of output, growth slowed down to an annual average of 3.8 percent. Several factors were seen to be behind this. First, in the early 1980s the global economy slowed down, which had an unfavourable effect on the local economy’s terms of trade (International Monetary Fund, 2004: 3). This led to a significant drop in export growth to an average of 6 per cent per annum (World Development Indicators, 2013). Inflation also shot up to an unprecedented 12 percent (ibid). Second, in 1986 cyclone Namu destroyed the major agricultural export commodities (including oil palm, cocoa and copra), while damaging domestic infrastructure and local production. As a result, in 1985, 1986 and 1987 the economy barely grew at 1.8 percent, 1.9 percent and 1.4 percent respectively (See Figure 3.2 below). Finally, the country’s ill preparedness for independence (Waena, 2013), combined with bad governance and economic mismanagement, caused the economy to enter a downward spiral. This led to a new climate of business corruption, which had its roots in the 1980s, as early as the first post-independence Mamaloni Government in 1981 and 1984 (Lamour, 1983: 270 - 273). As a result, the vicious cycle of mismanagement and corruption (Department of Foreign Affairs, 2004: 3) became ubiquitous.

Figure 3.2 RGDP ($ million) and RGDP Growth (%)

600 15.0

500 10.0 5.0 400 0.0 300 -5.0

SBD$millions 200 -10.0

100 -15.0

0 -20.0

1980 2008 1972 1974 1976 1978 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2010 2012

GDP (LHS) Growth (RHS) Linear (GDP (LHS))

Source: Computations by the author using data provided by the Central Bank of Solomon Islands (CBSI).

In the 1990s, real output (GDP) level jumped well above the trend line (See Figure 3.2 above), tracking a boom trajectory. This however did not translate into steady growth rates (see Figure 3.2 above), as the mid-90s showed a declining trend in the rate of growth of GDP. Between 1992 and 1995, the economy grew by an average rate of 7.8 percent, with 1992 recording the highest growth rate of 11.4 percent. Between 1995 and 1999, the economy was barely growing, recording an annual average of 0.3 percent. A boom in exports of logs underpinned the expansion of GDP.36 The boom in log exports, however, failed to generate sustained investment while undermining external viability (International Monetary Fund., 1998). As a result, the ‘country remained poor’ (Chand, 2002: 154) and exposed to external vulnerability. The Asian economic crisis in 1997 revealed the vulnerability of the logging sector to demand for hardwood in East Asia, resulting in a widening balance of payments deficit (International Monetary Fund., 1998).

3.5.2 Misguided policies Absent natural disasters and other externally driven shocks, the post-independence era demonstrates the leaders’ inability to institute policies to diversify sources of GDP and raise domestic investment. The amendment to the logging ordinances to allow for trees on customary land to be logged (Wairiu, 2007: 238) is but one of many imprudent policies.37 By 1990, logging companies moved from Government owned land to customary land. This shift brought about at least, two major changes: (i) an increase of Asian logging companies; and (ii) increased disputes among landowners about rents from logging (ibid). The influx of Asian logging companies was induced by the increased demand for logs in the international market, mainly from China.

From a macroeconomic perspective, economic instability became evident in the 1990s due to fiscal mismanagement. For example, import duty remissions granted by the Minister of Finance, coupled with poor valuation and monitoring procedures, resulted in the decline in the effective rate of taxation on exports of logs (International Monetary Fund., 1998). The problems were exacerbated by weak administration, which saw an

36 More details are shown in section 3.2.6.3 37 This occurred during the Prime Ministership of Solomon Mamaloni between 1981 – 84 and 1989 – 93. 49

erosion in the receipts from import duties and domestic taxes.38 Up to August 1995, poor budget controls led the government to borrow heavily from the banking system to finance its deficits (ibid). Consequently, the government began accumulating debt arrears, which resulted in a default on its debt obligations, leading to a collapse in the domestic securities market (Central Bank of Solomon Islands., 1995). By 1997, outstanding government debt arrears rose to 13 percent of GDP, eroding public confidence, discouraging capital inflows and undermining investment incentives (International Monetary Fund., 1998).39

3.6 The Major Industries This section presents the major industries that characterises and supports the local economy, and which are considered to have had significant impacts on the economy. These industries are important for the CGE framework that will be discussed in Chapter 6. The rest of the industries are discussed in Chapter 6.

3.6.1 Agriculture Sector40

3.6.1.1 Copra and Cocoa Copra and cocoa are the mainstay of the economy, since they are produced mainly by villagers, and have provided strong backward linkages into the rural economy. Approximately 60 percent and 40 percent of the rural village produce copra and cocoa respectively (Ministry of Finance, 1997). During the civil conflict period, when large and foreign owned investors withdrew, copra and cocoa from village producers kept a trickle of export income flowing into the Solomon Islands (Hou, 2002: 2).

Production data for copra and cocoa are only available back to 1980 (see Figure 3.3 below). In the 1980s, copra production averaged at 32,000 tons, with the highest tonnage of 42, 586 tons being produced in 1984. In the 1990s, production dropped to an

38 International Monetary Fund. 1998. IMF Concludes Article IV Consultation with Solomon Islands [Online]. Washington, D.C. Available: http://www.imf.org/external/np/sec/pn/1998/pn9850.htm [Accessed 13/02 2015]. 39 See Figure 3.11 in section 3.2.7. 40 Oil palm also comes under the agriculture sector, but we analyse oil palm separately in part II, as it is our sector of interest. 50

average of 25,000 tons, an average decline of 22 percent from the previous decade. During the conflict, copra production plunged to a record low of 1,701 tons in 2001, but rebounded rapidly on the restoration of peace. The rapid turnaround came about as a result of the resilience of smallholders (Central Bank of Solomon Islands., 2003: 21).

Figure 3.3 Copra and Cocoa production (tons)

45,000 40,000 35,000 30,000 25,000 20,000 15,000 10,000 5,000

2008 2009 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2010 2011 2012

Copra Cocoa

Source: Compilations by the author using data provided by CBSI.

Similarly, for cocoa, after registering a record low in 2001, production returned to pre- conflict levels. As Figure 3.3 above shows, cocoa production fell from 3,454 tons in 1998 to 2,038 tons in 2001. Following the civil conflict, production jumped to well above the pre-conflict levels of 4,615 tons in 1991, and has remained high since. Again, the rural economy was the key contributor to the production of cocoa since the restoration of peace (Central Bank of Solomon Islands., 2003: 21). Since 2003 the combined export values of these two commodities has increased to an average 15 percent of total exports.41 Figure 3.3 below shows export values of both commodities.

41 Sourced from various CBSI reports 51

Figure 3.4 Export value for Copra and Cocoa (SBD$ millions) 42

400.0 350.0 300.0 250.0 200.0 150.0 100.0 50.0

0.0

2000 2003 1993 1994 1995 1996 1997 1998 1999 2001 2002 2004 2005 2006 2007 2008 2009 2010 2011 2012

copra cocoa

Source: Computations by the author using data provided by CBSI.

3.6.2 Fishing Data on the gross fish catch are sourced from Soltuna Ltd and NFD Ltd through the Central Bank of Solomon Islands. The level of fish caught in the 1980s was around 30,000 tons on average, with a peak catch in 1986 of 44,207 tons (See Figure 3.5 below). The average catch increased in the 1990s to around 40,000 tons, peaking at 56,133 tons in 1995. During the conflict, the fish catch dropped to below 20,000 tons. Apart from fishing, Soltuna43 also operates a fish cannery.

42 Unless specified, the dollar “$” sign means Solomon Islands Dollars (SBD$). As of 21/07/2015, the exchange rate for SBD$1 is equivalent to AUD$0.1697 and US$0.1250. 43 Soltuna was formerly known as Solomon Taiyo Ltd, a joint venture between a Japanese company and the Solomon Islands Government. It closed down because of the civil conflict. 52

Figure 3.5 Fish Catch (tons)

60,000

50,000

40,000

30,000

20,000

10,000

- 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012

Source: Computations by the author using data provided by Soltuna, NFD, and CBSI.

Following the closure of Solomon Taiyo Limited, the government took the initiative to revive the company by taking it over through a joint venture with the Western Province, under a new name, Solomon Fish Processing Limited (SFPL). The company began operations on an undercapitalized footing, causing subsequent financial problems (Central Bank of Solomon Islands., 2001: 20). This led to a restructuring in the company; which saw fishing activities done away with, and an agreement with NFD for the latter to provide fish for its cannery.44 Following Tri Marine’s taking a stake in SFPL, the company has again undergone a name change, to Soltuna Ltd. Despite the new arrangement, the industry is yet to reach the pre-conflict catch - the post-conflict average production is around 24,888 tons.

In the 1990s, the fishing industry brought in foreign exchange equivalent to an average of 30 percent of total exports, with average exports valued at $149 million.45 This fell to 15 percent of total exports during the conflict, and remained below that level, despite high export value since 2010. The decline in the fishing catch in the post conflict period was attributed to several factors; the main one was the huge financial constraints faced by the new company after taking over from Solomon Taiyo Ltd. This was exacerbated

44 Tri Marine Ltd now owns a stake in Soltuna Ltd. 45 Sourced from CBSI various reports 53

after the main shareholders, Solomon Islands Government and the Western Province Government, could not inject funds into the company for recapitalization.

Figure 3.6 Value of fish exports and as a share of total exports

450.0 35% 400.0 30% 350.0 25% 300.0 250.0 20%

200.0 15% 150.0 10% 100.0 5% 50.0 0.0 0%

Fish exports (SBD$millions) As a share of total exports (%)

Source: Computations by the author using data provided by CBSI.

3.6.3 Forestry Prior to the 1980s, logging activities were only carried out on Government owned land or Government leased land (Bennett, 1995), mainly in Isabel, Western, and Temotu Provinces. The industry then, was dominated by only three logging companies: Levers Pacific Timber, Allardyce Lumber Company, and Kalona Timber Company Ltd (Wairiu, 2007). During the 1970s log production was around 200,000 cubic meters, with 75 percent coming from Levers Pacific Timbers Ltd (Fraser, 1997: 46). In the 1980s, average production volumes increased by 50 percent to an average of around 300,000 cubic meters of round logs (see Figure 3.7 below).

A study conducted by the Ministry of Natural Resources via the Australian Development Agency in the 1990s concluded that the level of production in the 1980s was at a sustainable level of harvest (Ministry of Natural Resources, 1994). The same study also revealed that at the current rate of logging, natural forest would be depleted by 2015. However, it appeared that the study underestimated the rate of harvesting, as 54

log production continued to increase rapidly. For instance, log production rose by 196 percent in 2012 from the levels at 1994, when the study was carried out.

Figure 3.7 Log Production (cubic meters)

2,500

2,000

1,500

1,000

500

- 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012

Source: Computations by the author using data provided by the Ministry of Forestry and CBSI.

By the 1990s, following the liberalisation of the forestry sector, as well as the favourable market for logs in the international market, more foreign investors, mainly from Southeast Asia, flooded the industry. This resulted in the doubling of average volume in logs harvested to around 600,000 cubic meters, with 791,000 cubic meters recorded as the highest volume ever produced, in 1996 (See Figure 3.7). This level of production doubles the sustainable rate of volume. During the conflict, the level of the log harvest remained well above the recommended sustainable volume of 300,000 cubic meters. The reason was that most of the logging activities were carried out in provinces other than Guadalcanal, where the conflict took place.

In 2003, production jumped 34 percent, from 550,000 cubic meters in 2002 to 736,000 cubic meters. By 2004, production surpassed the one million cubic meters mark, three times the sustainable rate of harvest. Since then, production continued to increase without any sign of declining. In 2012, log production reached 1,948 million cubic

meters, up 165 percent from the levels in 2003. This is close to seven times the recommended sustainable harvest. Congruent with the increase in production, the number of investments in the forestry sector also increased over the years, with 25 new licenses issued in 2011, amounting to 92 licenses in total in 2011(Central Bank of Solomon Islands., 2011: 16 - 17), as compared to 26 licenses in 2000 (Central Bank of Solomon Islands., 2000 18). Due to the economy’s heavy reliance on this sector, its demise would cause a great deal of uncertainty in the economy.

3.6.3.1 Log exports as a share of total exports In the 1990s log exports accounted for around 50 percent of total exports (or foreign exchange), up from 34 percent in the 1980s. This increased to more than 70 percent during the height of the conflict.46 The jump stemmed from the closures, and reduction in exports, of the other major commodities. The contribution of logs to foreign exchange remained at above 60 percent until 2008 (See Figure 3.8 below), making it the largest foreign exchange earner for the country. The proportion, in terms of total exports, has fallen to just below 50 percent recently, and indicates a declining trend. The drop was due to the revival and increased exports in oil palm, fish, and recently mineral exports. Despite the decline in the total share of exports, it still remained the single most exported commodity.

Figure 3.8 Log exports and as a share of total exports

1800.0 80% 1600.0 70% 1400.0 60% 1200.0 50% 1000.0 40% 800.0 30% 600.0 400.0 20% 200.0 10% 0.0 0%

Log exports (SBD$millions, LHS) As a share of total exports (%, RHS)

Source: Computations by the author using data provided by CBSI.

46 Sourced from the CBSI Database 56

In terms of contributions to government revenue, log export duties have accounted for around 50 percent in the 1980s, and increased to more than 60 percent during the boom era in the 1990s (See Figure 3.9). In recent years, however, there has been a declining trend in the share of government revenue, as shown in Figure 3.9 below. By 2012, log exports as a share of government revenue was 29 percent. The decline correlates with the increase exports of oil palm, fish, and minerals. The decline can also be attributed to poor governance, as the government had also lost revenues to exemptions and remissions, for example in 1997 the government lost 20.2 million in remissions.47 Nonetheless, logging remains the major contributor to government revenue. Concerns are, however, mounting due to the terminal nature of the industry. There is a need to transition to other sectors of the economy to avoid a collapse that could trigger internal conflict as a result of social disruptions and lack of economic opportunities.

Figure 3.9 Log export duties ($millions)

900.0 70 800.0 60 700.0 50 600.0 500.0 40 400.0 30 300.0 20 200.0 100.0 10 0.0 0

Log export duties (LHS) Log duties as % of Govt Rev (RHS)

Source: Computations by the author using data provided by the Ministry of Finance and CBSI.

3.6.4 Minerals Prior to 1997, the mining sector was predominantly involved in gold panning activities, on a small (household) scale. There were no large-scale operations of any mining

47 Sourced from the CBSI database 57

companies. However, in 1997, Guadalcanal saw the establishment of a large-scale mining operation, Gold Ridge Mining Ltd48. By the following year, 1998, the first gold pours from this large operation, totalling 45,229 ounces, along with 47,415 ounces of silver, were exported (See Table 3.1 below). In 1999, gold and silver production increased sharply by 147 percent and 42 percent to 111,897 ounces and 67,312 ounces respectively. However, the eruption of the civil conflict49 forced the company to close down operations in 1999 due to insecurity, and severe law and order problems.

Table 3.1 Export Volume of Gold and Silver

Years Gold (oz.) Silver (oz.)

1993 990 0

1994 280 0

1995 529 0

1996 141 0

1997 71 0

1998 45,229 47,415

1999 111,897 67,312

2000 49,954 20,744

2001 0 0

. . .

2010 0 0

2011 51,054 48,036

2012 67,819 28,993

Source: Data sourced from CBSI various Annual Reports.

48 Gold Ridge Mining Ltd was owned by Ross Mining and Delta Gold Ltd. 49 The Gold Ridge Mining area saw a lot of violent conflict. 58

In 2010, Gold Ridge Mining Ltd (GRML) reopened, with a new owner, Allied Gold Ltd. In 2011, it began exporting, with a total of 51,054 ounces in gold and 48,036 ounces in silver (see Table 3.1 above) during the year. This increased in 2012 to 67,819 ounces of gold while silver dropped to 28,993 ounces. In terms of export value, mineral exports comprised around 7 percent of total exports in 1999, and increased in 2012 to 24 percent of total exports. The increase stemmed from high international prices for gold compared to the prices in 1999. In 2012, mineral exports became the country’s second largest (after log) foreign exchange earner. In general, the mining sector is still in its nascent stage, with the potential to increase and improve the country’s terms of trade.

3.7 The impact of the civil conflict on the economy The civil conflict left a shattered economic landscape. GDP contracted by an annual average of 6.6 percent during the conflict period, with the worst contraction reaching minus 14.2 percent in 2000 (See Figure 3.2 in section 3.2.5.1). The contraction stemmed from the significant declines in the mineral sector by 51.4 percent, fisheries by 42.4 percent, agriculture by 25.2 percent, construction (by 31.5 percent); electricity and water (by 32 percent); transport and communication (by 20 percent); and manufacturing by 19.8 percent (Central Bank of Solomon Islands., 2000). The shrinkage in these sectors had adverse impact on private income as a consequence of increased unemployment. The Central Bank of Solomon Islands (CBSI) estimated the number of workers that became unemployed during the civil conflict to be around 10,000 or close to 30 percent of the total formal sector work force of 35,000.50

Furthermore, the dampening activities in the real sector led to a weakening export sector, resulting in the deterioration of the trade balance. This led to the widening in the current account deficit from $173.3 million in 2000 to $223.8 million in 2001. Consequently, the overall balance of payment remained in deficits for two consecutive years, $96 million in 2000 and $57.2 million in 2001 (Central Bank of Solomon Islands., 2001). This seriously impacted on the country’s level of international reserves,

50 Sourced from the CBSI database 59

which were depleted at a rate of 2.6 percent per month. By end of 2001, the level of international reserves stood at 1.3 months (or $102 million) of import cover (See Figure 3.10 below).

Figure 3.10 International Reserves ($ millions)

4500.0 12.0 4000.0 10.0 3500.0 3000.0 8.0 2500.0 6.0 2000.0

SBD$ SBD$ millions 1500.0 4.0 months months imports of 1000.0 2.0 500.0 0.0 0.0

Reserves (LHS) Import cover (RHS)

Source: Computations by the author using data provided by CBSI

Meanwhile, government finances also deteriorated, constraining government operations. Entrenched by the embedded weak budget controls and fiscal mismanagement, strings of budget deficits persisted as a result of ‘continued harassment of Treasury staff by certain elements of the rogue police’ (Central Bank of Solomon Islands., 2002: 9). Figure 3.11 below shows the government’s revenue from tax (i.e. domestically sourced revenue). As shown in the figure below, tax revenue dropped to as low as $220 million in 2001 (or 15 percent of GDP), owing to 84 percent and 71 percent declines in the Inland Revenue tax and the Customs and Excise tax respectively.

Figure 3.11 Government Revenues from tax (1992 – 2012)

3000

2500

2000

1500

1000

500

Source: Computations by the author using data provided by CBSI.

The precarious state of government finances inhibited the government from delivering essential services to its citizens. Debt servicing and other government obligations were also halted, and debt arrears were accumulating. Figure 3.12 below depicts the stock of government debts, inclusive of arrears. The built-up in debts after the conflict was due mainly to an accumulation of arrears, as the government was not borrowing. Figure 3.13 below shows the government’s recurrent operations. As can be seen, during the period 2000 to 2004 recurrent expenditures exceeded the recurrent revenues, resulting in no recurrent savings.

Figure 3.12 Government Debts ($ millions)

4000 3500 3000 2500 2000 1500 1000 500 0 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

External Debt Domestic Debt Total Debts

Source: Data provided by CBSI, but computations by the author.

Figure 3.13 Government recurrent operations ($’000’)

3000

2500

2000

1500

1000

500

-500

Tax Revenue Recurrent Expenditure Savings/(Deficit)

Source: Data provided by CBSI, but the computations by the author

3.8 The impact of peace: The presence of RAMSI The arrival of RAMSI in July 2003 significantly raised public confidence, and reinvigorated commercial activities. The economy rebounded, with growth reversing from a negative 2.8 percent in 2002 to 6.5 percent in 2003 (See Figure 3.2 in section 3.2.5.1 above). From 2003 to 2008 the economy was growing at an annual average of 7.3 percent, reaching 10.8 percent in 2007 (Central Bank of Solomon Islands., 2009). This saw improvement in the tree crop agriculture, forestry, fishery, and the construction sectors. As a result, the trade account improved due to exports, reversing the strings of deficits, leading to a surplus in the overall balance of payments (Central Bank of Solomon Islands., 2003: 25). By end of 2003, combined with donor inflows, the level of international reserves jumped to 4.9 months of import cover. These high growth rate episodes corroborate the assertion by Collier and Hoeffler (2004a) of a typical post-conflict country as seen elsewhere in Chapter 2.

Also, one of the major benefits of (the restoration of) peace was the stabilization of government finances. RAMSI advisors seconded to the Ministry of Finance and Treasury effectively instituted stringent budgetary measures. This led to the budget deficit decreasing to 5.8 percent of GDP (or $103.9 million) from 19.6 percent of GDP

(or $300 million) in 2002 (ibid). Investors and donors were re-engaged. By 2007, coupled with favourable conditions in the international market, the pace of economic recovery picked up rapidly as explained above.

Absent the improvement in peace brought about by RAMSI, the presence of an external intervention can also generate unintended consequences that could either enhance or detract from the consolidation of peace and economic recovery (Carnahan et al., 2006 11). It can also raise the level of economic activities of some sectors of the economy (Luif, 2010 13 - 14), triggering not only inflationary pressures, but also crowding out (human) resources from other sectors. Evidently RAMSI’s presence in the Solomon Islands benefited the security services, construction, increased demand for hardware and construction materials, and the accommodation sectors (Holt et al., 2010 21). One of the unintended consequences was the surge in rental prices in the accommodation sector, pricing locals out so that they had to reside in the periphery, because they could not afford rents closer to the city.51 This resulted in inflation surging to as high as 17.3 percent in 2008.52

Meanwhile, the actual fiscal impact of RAMSI on the local economy remains unclear. Despite the AUD$2.6 billion investment on RAMSI (Hayward-Jones, 2014), as mentioned elsewhere above, Holt et al. (2010) estimate that the local fiscal impact53 of the presence of RAMSI and other donors was minimal. Of the total AUD$260 million donor funds54 expended in 2007/08, the actual money spent in the local economy accounted for only 16 percent of this (Holt et al., 2010: 15). One of the key reasons for the minimal impact was (and still is) due to low local absorptive capacity, thereby prompting the external sourcing of logistics and procurements (ibid., p.16).

51 Prior to RAMSI’s arrival an executive accommodation would be letting at an average of $5,000 per month. The demand for accommodation by RAMSI pushed rental prices to $18,000 per month on average. 52 Sourced from the 2008 Central Bank of Solomon Islands’ Annual Report. 53 The authors defined ‘local fiscal impact’ as the value of money that remains within the economy from a donor’s procurement of domestically sourced goods, services, and labour (p.5). 54 This total amount was comprised of only RAMSI (AUD$208.1 million), AusAID (AUD$38.8 million), and NZAID (AUD$13.4 million). 63

3.9 Government Peace Policies55 In 2002, the government established a division for National Reconciliation and Peace within the Ministry of Provincial Government. However, the on-going deterioration of law and order saw the need to establish a separate ministry. Hence, in 2005 it established the Ministry of National Unity, Reconciliation and Peace (MNURP). Among its five initial key objectives, one of which is ‘to promote, facilitate and monitor the participation of all sectors of society in working towards sustainable peace and development’(Ministry of National Unity Reconciliation and Peace, 2010: 8).56 Revisions to the key objectives in subsequent years made them broader (as stipulated the Ministry’s 2010 Annual Report). The current broad policy objectives are silent on the economic implications of peace. Many of the revised key policies focus on the socio-political environment of communities, with the emphasis being more on unity and peaceful co-existence. There is no evidence concerning the socio-economic environment of a post-conflict society where it is important to sustain peace. 57

3.10 Conclusion This chapter presented the context of the thesis under which the benefits of peace are to be analysed. It examines the macroeconomic developments in the Solomon Islands. It was shown that average economic growth in the 1970s was higher than in the 1980s. The 1990s growth rates were, although high, mainly driven by the unsustainable harvesting of logs through felling of indigenous hardwood forests. The chapter had also explored some of the likely factors that have contributed to the eruption of the civil conflict. A contributory factor was the movement of people in search of economic opportunities, especially from Malaita to Guadalcanal that then transformed the population dynamics in the country. This transformation was perceived to have displaced the indigenous Guadalcanal people, disadvantaging them from gaining economic status. This caused resentment and envy towards settlers, leading to the eruption of the civil conflict. Peace was only established through an external

55 A National Peace Policy paper was submitted for Cabinet approval. It had not yet made available to the public when the author conducted the interview. 56 The initial key objectives can be accessed from Ministry of National Unity Reconciliation and Peace 2010. 2010 Annual Report. Honiara: Solomon Islands Government. 57 See ibid. 64

intervention force in the form of the Regional Assistance Mission to the Solomon Islands (RAMSI). Post-conflict economic recovery is driven by plantation agriculture, of which oil palm is a key sector, and logging, with the latter being unsustainable.

CHAPTER 4

PEACE OIL PALM: GUADALCANAL PLAIN PALM OIL LTD (GPPOL)

4.1 Introduction This chapter discusses the micro level framework for the analysis of peace. The case of the Guadalcanal Plains Palm Oil Limited (GPPOL) Company is used to demonstrate the value of economic growth in sustaining peace in a post-conflict environment such as the Solomon Islands. It established the context of: (i) how peace had a positive impact on the revival of the private sector and the wider economy; and (ii) how the private sector in turn has undergirded peace. This chapter argues that the private sector has a role to maintaining and sustaining peace.

The rest of this chapter is organised as follows. Section 4.2 discusses the oil palm industry and rationalising why this study chooses the said industry. Section 4.3 provides a brief historical background on the establishment of the industry. In section 4.4, it evaluates the economic costs of the conflict on the oil palm industry. Section 4.5 presents the planning for and establishment of GPPOL, and highlights some of the resultant benefits accruing to the landowners. In section 4.6, this chapter shows how the industry benefited from the return of peace. It examines how peace supported the revival of the oil palm industry, as well as presenting how the revival of the oil palm industry has supported the maintenance of peace. Section 4.7 discusses GPPOL’s operations during the peace onset phase. Sections 4.8 and 4.9 describe the establishment of the landowners’ association and company, respectively. Section 4.10 discusses some of the government’s peace reconciliation programs targeting the victims of the conflict. Section 4.11 evaluates the contribution of the oil palm industry in the economy. Section 4.12 compares the contributions of the major tree export crops to highlight the significance of the oil palm industry. In section 4.13, it analyses the fiscal impact of the oil palm industry on the economy. Section 4.14 concludes the chapter.

4.2 Peace Oil Palm: Guadalcanal Plains Palm Oil Limited (GPPOL) The second part of the chapter explores the microeconomic peace environment in the Solomon Islands economy, focusing specifically on the oil palm sector. This offers a rationale for the conceptual framework used in the subsequent chapters. The case study is that of Guadalcanal Plains Palm Oil Limited (GPPOL). There are three underlying reasons for studying GPPOL. First, GPPOL’s main operations and headquarters are located in the conflict affected local communities of Guadalcanal plains. Second, the conflict began in these communities and resulted in the closure of the former oil palm company, Solomon Islands Plantation Limited (SIPL). Finally, the livelihoods of these communities were severely affected. Thus, tracking the volume of oil palm production, and understanding the role of GPPOL in the above, helps us understand the role of peace in these communities.

4.3 Background of Oil Palm in Solomon Islands The oil palm industry started in the early 1970s. In 1971, the initial acquisition of 1,478 ha of land was made by SIPL for their nursery around the Ngalimbiu and Metapona Rivers (Moore, 2013). Following successful tests on the preliminary nursery, SIPL expanded and acquired more land further northeast in the Tetere and Mberande areas (ibid). Figure 4.1 displays the location of the oil palm plantations on the Guadalcanal plains.

Figure 4.1 map of Guadalcanal showing the oil palm region

Oil Palm Area

Source: Google Map

The SIPL was a partnership venture between a British registered Commonwealth Development Corporation (CDC), holding 68 percent in equity, the Solomon Islands Government with 30 percent of shares, and the local Landowners with two percent equity (Moore, 2013). Under the arrangement SIPL was supposed to have a scheme that would allow smallholder out-growers (Scheffler and Larmour, 1987: 314) similar to those in Malaysia, Indonesia, and Papua New Guinea. However, the smallholders did not partake in production, leaving SIPL as an estate operation (Allen, 2012). Apart from their two percent equity, the Landowners also received annual land rentals of $65 per ha58, but no royalties were paid on production (ibid., p.304). The total workforce reached 1,800 prior to closing down in 1999, with the majority (approximately 65 percent) coming from neighbouring island of Malaita (ibid).

4.4 Impact of the Civil Conflict on the Oil palm SIPL Company was severely affected by the eruption of the civil conflict on the plains of Guadalcanal. The IFM militants terrorised the SIPL properties, company vehicles and non-Guadalcanal employees. Furthermore, the neighbouring Malaitans who settled in and around the SIPL proximity became the targets of harassment and killings (Kabutaulaka, 2001: 3). More than 30,000 people from the rural plains and SIPL workforce were displaced (Central Bank of Solomon Islands., 1999: 7). By June 1999, SIPL evacuated all its employees to Honiara and closed down all its operations. Consequently, CDC withdrew from the country, and the company headquarter at Tetere 2 became the IFM’s eastern troop’s base (Kabutaulaka, 2001).

On the macroeconomic front, the closure of SIPL had significant adverse effects on the economy. Prior to the conflict, palm oil generated an annual average of $61.1 million in foreign exchange (See Figure 4.2 below) or 11 percent of total export income. Given that the oil palm industry was the third largest foreign exchange earner for the country, and an important revenue source for the government (Central Bank of Solomon Islands.,

58 Land rentals were increased to $100 per ha in the 1990s. 69

1999: 7), SIPL’s closure had a severe detrimental impact on the economy. Between 2000 and 2005, the country earned nothing from the industry as is shown in Figure 4.3.

Figure 4.2 Pre-conflict Palm Oil Exports, 1993 – 1999

35,000 120000

30,000 100000

25,000 80000 20,000 60000

15,000 SBD$millions metric tons metric 40000 10,000

5,000 20000

0 0 1993 1994 1995 1996 1997 1998 1999

Crude Palm Oil (CPO) Palm Kernel Oil (PKO) CPO & PKO Export Value

Source: Data provided by CBSI, but computations by the author.

4.5 Conception of GPPOL The restoration of peace boosted investor confidence. This paved the way for an initial discussion between the Solomon Islands Government (SIG), the Guadalcanal Plains landowners, and a potential investor, New Britain Palm Oil Limited (NBPOL) that was operating in neighbouring Papua New Guinea. The government’s serious intent to revive the oil palm industry gave extra confidence for NBPOL to invest in the Solomon Islands.

4.5.1 Inception of GPPOL and the benefits to the landowners In 2004, NBPOL bought out the shares of CDC and SIG in the former SIPL. NBPOL is a listed company on the London Stock Exchange and Port Moresby Stock Exchange.59 The speedy incorporation of GPPOL was attributed to the government’s commitment to

59 At the time of incorporation, NBPOL was 51 percent owned by the Malaysian State owned company Kulim (Malaysia) Berhard Fraenkel, J., Allen, M. & Brock, H. 2010. The Resumption of Palm Oil Production on Guadalcanal's Northern Plains. Pacific Economic Bulletin, 25, 64 - 75. 70

re-open the oil palm industry as soon as possible, given its importance to the economy. As lamented by one of the landowners;

We [landowners] were requested particularly by the Prime Minister (during our meeting with him) to re- open our land for a foreign investor, so that oil palm [production] can start again. Our answer to the PM was ‘yes we can engage a foreign investor, but on two conditions, (i) the Government will not have any share in the new Company, and (ii) our share must increase to 20 percent.’60

The request for 20 percent equity was a bargain reached in the name of peace.61 Following the successful negotiation between NBPOL, the National Government, Guadalcanal Provincial Government, and the Landowners, the new company, Guadalcanal Plains Palm Oil Limited (GPPOL) was subsequently incorporated, 62 with NBPOL owning 80 percent of the shares. The landowners, through their local company, Guadalcanal Plains Resources Development Company Ltd (GPRDCL), on the other hand, held the other 20 percent. This was commensurate with what Fort and Schipani (2004: 364 - 367) meant when they argued for a mechanism to ‘promote a sense of community’. That is, if an investor wants to embrace the communities to be part of the investment, the communities [landowners] must be recognised and empowered economically. In the case of NBPOL, the landowners were granted 20 percent equity in GPPOL. This means that whatever investment decision GPPOL is making, it is for the good of the company as well as the landowners. This is also what Boyce (2004: 11) meant when he refers to ‘doings different things’ in post-conflict societies for the sake of peace.

4.6 Benefits of Peace There were three key improvements in this arrangement compared to the former SIPL’s. They were: (i) landowners’ shares in the company rose from two percent to 20 percent while the Solomon Islands Government does not have any stake in the company; (ii) landowners receive royalties from the volume of production compared to none in the former SIPL; and (iii) a smallholders out-grower scheme was introduced. In accordance

60 Interview with Landowners. 61 Response from one of the local pioneers of the negotiation. 62 For a detailed survey of the re-opening of the oil palm industry and the kind of arrangements made with the landowners, see Fraenkel, J., Allen, M. & Brock, H. 2010. The Resumption of Palm Oil Production on Guadalcanal's Northern Plains. Pacific Economic Bulletin, 25, 64 - 75. 71

with the MOU between NBPOL and the landowners (through GPRDCL), the first dividend payout was made in 2012, totalling $15 million.63

The out-grower (or smallholder) scheme was introduced as an alternative for landowners who did not want to lease their land to GPPOL. Emulating a similar ‘nucleus enterprise model’ adopted by NBPOL in the Sepik region of PNG, a total of 817 ha came under the out-grower scheme, mainly in the Binu and Okea region (Fraenkel et al., 2010: 68). By 2013, the total land area included in the out-grower scheme increased to more than a thousand hectares. Many landowners that I interviewed expressed their satisfaction with the scheme, as it provides alternative income for families. Interestingly, some of the landowners who leased their land to GPPOL have now also become out-growers themselves.

Furthermore, the landowners have also expressed their willingness to release more land for oil palm production, were peace to improve further.64 Interest in venturing into the out-grower scheme was induced by the fact that all income accrues to the household, as they receive 100 percent of the farm gate price when the fresh fruit bunches (FFB) are sold. This is one account from an out-grower;

My family is willing to increase land for oil palm because income flows in every month, but the problem is the company [GPPOL] does not allow us to increase land beyond three hectares.

GPPOL is also conscious of ensuring that the company does not put undue pressure on small-holders that might cause them to produce poor quality fruits.

In terms of the royalty payment, clause 14.1 of the MOU states that “GPPOL shall pay a royalty ... in respect of production and harvesting of Fresh Fruit Bunches ....” (p.17). Furthermore, “the royalty shall be paid at the rate of 10% of the Farm Gate Price ...”

63The Memorandum of Understanding (MOU) between NBPOL and GPRDA (representing Landowners), clause 6.6(a) states that “.... dividend will be paid before the eighth year of the project ... depending on the profitability of the project.” 64 Author’s interview with landowners. 72

(p.17). That is, the royalty is 10 percent of the farm gate price of the FFB, compared to the out-growers who receive 100 percent of the farm gate price.

Moreover, under the three separate MOUs between the investor, NBPOL, and the three stakeholders, namely SIG; Guadalcanal Province; and the landowners, the recruitment policy of GPPOL stipulates that;

“in respect of other positions, GPPOL will give first opportunity of employment to the owners of the plantation estates and their families and thereafter to other owners of land on the Guadalcanal plains and their families, persons who in custom originate from the Province and their families and those who in custom originate from other provinces, in that order of opportunity”.

Again, the above policy clearly is in line with the ‘doing different things’ solutions argued for by (Boyce, 2008: 11) if peace were to be sustained.65 The above clause referenced positions other than senior and middle level management and technical positions; recruitment for these positions is on merit according to GPPOL, based on qualifications and suitability for the position.

One other significant development that took place during the GPPOL take-over was the review in the land rental rate. At $100 per ha per year, a review was supposed to be followed as soon as all land titles in the MOU were in place. The MOU between NBPOL and GPRDA was signed in October 2004 and all land titles were in place at the end of 2005. Thus, the review was scheduled to be carried out in 2012, however for reasons yet to be reported this was delayed to 2013. Consequently, land rents were reviewed upwards to $176 per ha on September 2013, with payments backdated to the beginning of 2012. The next review of land rents is due in 2016.

4.7 GPPOL in the peace onset period In the initial years of establishment, GPPOL faced many problems because landowners’ expectations were beyond the company’s operational capability. During the first year of operations GPPOL found it hard to work with the landowners due to a variety of claims

65 This policy has since been relaxed to accommodate recruitment on merit, regardless of origin. 73

and demands from them. The landowners’ high expectations stemmed from the fact that most of the landowners were not fully aware of the MOUs and/or did not fully understand their contents. The consequence of these high expectations saw, in 2006, the disgruntled landowners burning down GPPOL’s main administration building. This particular incident meant that NBPOL gave serious consideration to withdrawing, but swift actions from the government and the landowners’ association saved GPPOL.

Having learnt from this incident, GPPOL commenced its community awareness programs. The leaders of communities and the Landowners’ Association executives were included in the awareness program. Thereafter GPPOL employed an accommodative and strategic approach to its investments. As a result, GPPOL holds regular meetings with the Landowners Association, does a lot of awareness raising in the communities,66 exactly emulating what Fort and Schipani (2004) are suggesting. This include conducting awareness raising events about the contents of the MOU: (i) that specifies landowners and their families are given priority recruitment for employment in GPPOL; (ii) landowners shares in GPPOL have increased from 2 percent to 20 percent; (iii) payment schedule of the land rentals, royalties, and dividends; (iv) initiation and establishment of out-grower scheme; (v) outsourcing contract jobs to landowners; (vi) allowing landowners and their families to use company facilities such as schools and clinics, and (vii) the formation of a landowners’ company.

4.8 Guadalcanal Plains Resources Development Association (GPRDA) GPPOL lease the land they use from five tribes, the Ghaubata, Thogo, Thimbo, Lathi, and Nekama. These five tribes formed an association, called the Guadalcanal Plains Resources Development Association (GDPRDA), which negotiates with GPOL on behalf of the landowners. Hence, a MOU was agreed and signed between the NBPOL and GDPRDA. The Board of Directors of the GDPRDA comprised ten people – two persons (one female and one male) from each tribe. Issues for the attention of GPPOL management are routed through the GPRDA.

66 Personal interviews with the GPPOL management. 74

GDPRDA, on the other hand, has a duty to promote awareness of its members (the landowners) about the content of the MOU, matters of importance to GPPOL, and what it means for their livelihood opportunities. By 2011 the landowners had come to realize the importance of GPPOL67, and that they (through their investment company) own 20 percent shares in GPPOL, as will be shown below. Since then criminal activities and harassment in and around the GPPOL vicinity have reduced significantly. GPPOL has since outsourced security services to some locals (landowners). According to the GPPOL General Manager, the relationship between the landowners and GPPOL has improved significantly compared to the first four years of operation. 68

4.9 Guadalcanal Plains Resources Development Company Ltd (GPRDCL) Under the MOU between NBPOL and GPRDA (the Association), the latter undertook to establish a company to be called the Guadalcanal Plains Resources Development Company Ltd (GPRDCL). This establishment is stipulated in clause 6 subsection 6.1 of the MOU which states that;

“...the Association shall... incorporate the Association Company by the name of Guadalcanal Plains Resources Development Company Limited ... “(p.8).

GPRDCL ‘shall act solely as trustee for the Association’s interest’ (p.8). The GPRDCL has since expanded its activities to investments in other asset holdings, including hospitality and retailing. It is also exploring potential investments in property and the equity market. This has instilled confidence among the landowners and their communities that it is now prudent to engage in economic activities, so that the community now has a stake in the performance of the broader economy.

4.10 Government Peace Reconciliation Programs The Solomon Islands Government has peace reconciliation programs, which focus especially on people affected, directly or indirectly, by the civil conflict. The Government had already started reconciliation ceremonies with the communities of the

67 This came about when the Landowners’ investment company hired a financial advisor to help them with their investment plans. 68 Interviews by the author with the then General Manager, Roger Benzi. 75

Weather Coast of Guadalcanal.69 However, accounts by the Guadalcanal plains landowners revealed that the government has yet to reconcile with the Guadalcanal plains communities for the alleged shootings by the Police Force at Tetere in late 1999 and early 2000.70 These communities (that is, Guadalcanal Plains) were where the most intense and violent conflict took place at the height of the ‘tension’. That the government seems to have forgotten them was the view angrily echoed by one of the interviewees;

We were the most affected ones during the height of the tension, and the government seemed to have forgotten to reconcile with us. Here, in our village we hate to see any Police Officer coming around. They were the ones shooting at us during the tension.

Meanwhile, the landowners noted the good work RAMSI was doing in restoring peace, but felt that the RAMSI personnel had not actually been in their village to arrest criminals. This led to the perception that the police force is ineffective, as will be explained in Chapter 5.

4.11 Contribution of oil palm to the local economy This section explores the contribution of oil palm to the economy. Full operation of oil palm production commenced in 1973, with the completion of the construction of a mill (Fraenkel et al., 2010: 66), and cultivation of a total land area of more than 7,000 hectares. Exports began in 1976, with 4,535 metric tonnes in crude palm oil (CPO) and 358 metric tonnes in palm kernel oil (PKO). In the second year, crude palm oil production grew by 55 percent to 7,044 metric tonnes. Within the first decade of operation palm oil production (available for exports) was growing at an annual average rate of 20 percent, with the peak volume of 20,000 metric tonnes produced in 1985 (See Figure 4.3 below). However, in 1986 following the destruction by cyclone Namu, production plunged to the level of 1980. Two years later, production rebounded, and continued to increase thereafter. Prior to the eruption of the conflict in 1998, production appeared to have been stabilised, with the highest volume of 30,986 metric tonnes produced in 1993. By comparison, annual production in the 1980s averaged at just

69 Chart 3.1 shows the location of the Weather Coast. 70 Author’s interviews with landowners. 76

above 17,000 metric tonnes. This increased to more than 26,000 metric tonnes in the 1990s before SIPL closed down operations.

Figure 4.3: Palm Oil and Palm Kernel Production (mt)

40000 35000 C O 30000 25000 N

20000 SIPL F L GPPOL 15000 I 10000 C 5000 T 0

1986 1990 1994 1978 1980 1982 1984 1988 1992 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 1976 Crude Palm Oil Palm kernel Oil

Source: Data sourced from the Central Bank of Solomon Islands but computations by the author.

The reopening of the oil palm operation in 2005 saw a total of 5,427 metric tonnes of crude palm oil and 1,236 metric tonnes of palm kernel oil exported in 2006. These exports were derived from the mature Fresh Fruit Bunche (FFB) of the old palm trees, which had been left derelict since SIPL’s closure. In the second year, production shot up to 17,151 metric tonnes and 4,828 metric tonnes of CPO and PKO respectively. By 2011, productions of CPO and PKO had exceeded the pre-conflict peak level to 31,591 metric tonnes and 7,532 metric tonnes respectively. Thereafter, production continued to surge. With more than 2000 employees,71 GPPOL now accounts for more than half of the formal agricultural sector employees in the country.

In terms of its contribution to the total value of all agricultural commodities, oil palm’s value dropped from an average of 37 percent in the 1990s to around 27 percent from

71 In 2006, GPPOL employed around 2408 employees. This has dropped to 1400 employees in 2013, though it is still one of the major employers in the Solomon Islands. 77

2003 to 2012.72 As a share of GDP, this has dropped from an annual average of 6 percent in the 1990s to 4 percent since 2006 to 2012. There is optimism within the company that oil palm’s share of GDP will increase to around 10 percent within the next decade.73

Oil palm is also the highest foreign exchange earner when compared to the other major tree export commodities, namely cocoa and copra. For example, in the 1990s, oil palm accounted for an average of 11 percent of total exports, whereas cocoa and copra accounted for 4 percent and 5 percent respectively. In the post-conflict years (for instance in 2010), oil palm remained significant in foreign exchange terms compared to cocoa and copra, soaring to 14 percent of total exports. Figure 4.4 below shows exports of the major tree crops. Exports resumed in 2006 with the fruits from the old palm plantations. New plantings were also made in 2005 after the felling of the old palm trees. With an 18 month wait to harvest, the first harvest of the new palm trees began in the second half of 2007. This resulted in a significant (580 percent) jump in oil palm exports in 2007. In 2008, there was a moderate 18 percent dip in exports, due mainly to the adverse effects of the global financial crisis on commodity prices, including that of oil palm. This was in contrast to production, which continued to increase, as shown in Figure 4.3 above.

Figure 4.4 Value of the major export tree crops ($ millions)

350.0 300.0 250.0 200.0 150.0 100.0 50.0 0.0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

copra & coconut oil palm oil & palm kernel cocoa

Source: Data sourced from CBSI, but computations by the author.

72 Data provided by CBSI, but computations by the author. 73 Personal interview with GPPOL’s General Manager 78

4.12 Major tree crops compared Although copra and cocoa remain the mainstay of the rural economy, as alluded to in section 3.2.6.1 above, oil palm has been able to earn the country more foreign exchange than copra and cocoa. With combined earnings for copra and cocoa, oil palm accounts for almost 60 percent of the total tree crops’ export value (See Figure 4.5 below).

Figure 4.5 Major tree crops exports compared74

1995 2012 Cocoa, Cocoa, 65.9, 15.7, 14% 15% Copra, Copra, 29.2, 128.4, 27% 27% Palm oil Palm oil & & kernels, 63.3, kernels, 277.0, 58% 59%

Source: Data provided by CBSI but computations by the author.

4.13 Fiscal impact of oil palm on the economy Despite the oil palm sector’s limited coverage in the country, the backward linkages are strong in areas where the industry operates. In terms of the fiscal impact on the economy, GPPOL has contributed a total of around $429.4 million in the period 2009 to 2012, with $147.7 million injected in 2012 alone into the economy.75 These have come in the form of wages to the workers, taxes to national and provincial governments, rents to landowners, smallholder payments, local suppliers, and local contractors. The total fiscal injection into the economy in 2012 alone accounted for around 30 percent of GDP.76 In terms of livelihood opportunities, the presence of GPPOL is important. Landowners who receive rents from GPPOL in the form of royalties, land rent, dividends, proceeds from sale of FFB, and payments for local suppliers and contractors

74 The 1995 Graph indicates the period with large-scale copra and cocoa plantations, whereas the 2012 Graph shows copra and cocoa produced mainly by smallholders. 75 Figures provided by GPPOL. The author could not verify these figures with the Ministry of Finance and Treasury. 76 Computations by the author based on the raw data provided by GPPOL. 79

are able to improve their standard of living. The landowners’ direct stake in the profitability of the industry also lends support to peace as is explained in Chapters 5 and 7.

4.14 Conclusion This chapter focused on how peace plays an important role at the microeconomic level. It examines the establishment of GPPOL on communities once affected by conflict. It was demonstrated that accommodating the economic desires of the landowners is fundamental for long-term peace. Landowners were given, in recognition of this, a 20 percent equity in the GPPOL Company. In addition, landowners also receive royalties on production and rentals on the use of their land. Furthermore, GPPOL also supports landowners who wish to become out-growers. These arrangements are seen as the most significant step towards maintaining and sustaining peace.

CHAPTER 5

RESEARCH DESIGN AND METHODOLOGY

5.1 Introduction This chapter outlines the research design and methodology of this study. As in all types of research, the choice of method should not only be appropriate, but also relevant for achieving the research aims and objectives. The method selected should also be able to adequately address the research questions. Needless to say, the method selection phase is a ‘highly complex and continuously evolving process’ (Bryman and Bell, 2011: 29). Thus, the researcher needs to identify factors that may influence the choice of method. Accordingly, Buchanan and Bryman (2007) nominate the following factors as being important: organizational (i.e. size and pace of change), historical (i.e. previous studies of the topic), political (i.e. obtain permission to access respondents), ethical (i.e. role of ethical review), evidential (i.e. expectations from different audiences on the research outcomes), and personal (i.e. interviewer preference to various methods). These factors form a central part of the research process, and therefore should not be seen as obstacles (ibid).

Above all, understanding the ontology and epistemology of a social entity to be investigated is imperative to operationalize the chosen method. The research design in this study, therefore, is informed by the author’s understanding of the reality (ontology) on the social entity to be investigated and how knowledge adds value to that entity. In deciding the optimal research design and methodology for this study, the author has assessed other available research methods along with their benefits and limitations. Among them are the ontological and epistemological underpinnings of each method, as these feed in to how research questions are formulated and research is carried out (Bryman and Bell, 2011: 23). Considerations were also given to which method was best suited for collecting quantitative data as a primary (chief) data source, while at the same time accommodating the qualitative data (subsidiary) stemming from the structured interview instrument.

Having done the assessment, this study adopts a mixed methods research, with an orientation towards a deductive approach. Naturally, the ontological and epistemological assumptions and commitments of the deductive and positivist approach orientates toward objectivism and positivism. However, in this study, given the socially constructed nature of peace (i.e. the phenomenon under investigation) there are also fundamental elements of interpretivism and constructionism applied, thus the use of the mixed methods approach. As will be shown elsewhere below some of the variables have been constructed in line with the qualitative ontology and epistemology.

The rest of the chapter is organised as follows. Section 5.2 discusses what mixed methods research is, the current stance on mixed methods research in the literature, the advantages of using mixed methods, and where in the spectrum of mixed methods approaches this study fits. Section 5.3 provides the rationale for adopting such approach in this study. Section 5.4 outlines the research design adopted in this research. In section 5.5, the processes of ethics approval from the University’s Human Research Ethics Committee are discussed. In section 5.6, we provide evidence of the research permit from the Solomon Islands, the country in which this research was carried out. Section 5.7 presents details of the field study and discusses the unit of analysis. In sections 5.8 and 5.9, it outlines respectively the design and pre-testing of the questionnaires. Section 5.10 provides the sample size and the unit of analysis. Section 5.11 discusses the interview process, as well as specifying the variables employed in the analysis. Section 5.12 outlines the process for inputting the collected data. Sections 5.13 and 5.14 outline the reliability and validity issues of the survey respectively. Section 5.15 concludes the chapter.

5.2 Mixed Methods Research Until the 1980s, the use of mixed methods research was not that popular. This paucity stemmed from the fact that scholars and researchers tended to apply quantitative and qualitative research techniques exclusively to their investigations; rarely were both combined. This, however, has changed in the last three decades. Scholars, especially in the social science disciplines (see for example; Wajcman and Martin, 2002, O'Cathain et al., 2007, Denscombe, 2008, Molina-Azorίn, 2011, Ananthram et al., 2012, Rinne and

Fairweather, 2012, Harrison, 2013) have increasingly combined quantitative and qualitative (mixed methods) techniques in their investigations.

Mixed methods research essentially combines quantitative and qualitative research techniques within a single project (Bryman and Bell, 2011). In the business discipline, however, the use of mixed methods research is still relatively sparse. In the field of economics, the use of mixed methods is virtually non-existent. Generally, the business disciplines, including economics, have employed quantitative research techniques in their investigations. One plausible explanation for the lack of use of mixed methods research in the business disciplines is that the research questions are usually tailored around the measurement of a phenomenon that normally demands quantification (Bryman and Bell, 2011). The other reason is that there are difficulties in blending quantitative and qualitative research due to epistemological and ontological concerns (Bryman and Bell, 2011, Harrison, 2013). Another reason was the lack of attention given to interpretive methods in what is offered and endorsed in graduate education training (Harrison, 2013).

Fundamentally, there are also concerns against the use of mixed methods research. These concerns stem from two main arguments; the ‘embedded methods argument’ and the ‘paradigm argument’ (Bryman and Bell, 2011: 629). The embedded methods argument infers that the two research strategies (i.e. quantitative and qualitative) are inevitably situated in separate and distinct sets of epistemological and ontological commitments (ibid). As such, they cannot be easily mixed or combined because, for instance, participatory observation research is not about collecting data only, but is also a commitment to understand an epistemological position that is consistent with interpretivism but contradictory to positivism (ibid). Given that these two methods are distinctively different in their epistemological position, their procedures for undertaking research is also different, and so researchers like Smith (1983) reject for any possible complementarities.

The paradigm argument, on the other hand, considers quantitative and qualitative research as paradigms (Kuhn, 1970). This argument holds the view that these paradigms

are inconsistent with each other due to the fact that they are grounded on divergent assumptions, values, and method, and so are incommensurable (Guba, 1985, Morgan, 1998). Thus, any integration of the quantitative and qualitative methods would only be on a ‘superficial level and within a single paradigm’ (Bryman and Bell, 2011: 629).

However, a counter argument to the above two arguments is that the epistemological and ontological line between the two research methods are not clearly demarcated (Bryman and Bell, 2011: 629). Instead, they can be put to task as will be shown in the discussion of the advantages of mixed methods research below. Furthermore, it is not clear whether the quantitative and qualitative research methods are in fact paradigms77 - as will be shown below that there are some common grounds between them.

Despite these concerns, the rise and use of mixed methods research is increasing and becoming popular. For example, Bryman (2009) has found that the use of mixed methods research have tripled in the period 1994 – 2003. Furthermore, in 2007 a specialist journal called the Journal of Mixed Methods Research was been established to publish material on mixed methods research techniques only. Since the inception of this specialist journal the published mixed methods related studies have seen an exponential growth (Biddle and Schafft, 2014: 2). This dedicated journal has indeed grounded the existence of mixed methods research. More importantly, it has transformed the landscape of research methodologies. In the field of business alone a specialist mixed methods journal, International Journal of Mixed Methods for Applied Business and Policy Research has recently been established. At its inception in 2011, the journal published three articles (Lopez-Gamero et al., 2011, Keske et al., 2011, Takavarasha Jr et al., 2011). The establishment of these two journals hence, is a manifestation of the emergence of mixed methods research as central to research in business, and that this trend is irreversible.

5.2.1 Advantages of using mixed methods research The rise of mixed methods research came about from the ongoing polemic that exists between quantitative and qualitative research approaches that engender the debate. This

77 Ibid. 84

polarization emanated from the fact that these two main research techniques are premised on distinct epistemology and ontological commitments (as already mentioned above). For example, quantitative research is premised on positivism epistemology and objectivism ontology, and is therefore inconsistent to, for example, participatory observation. Conversely, qualitative research conforms to the interpretivism epistemology and constructionism ontology; hence it is incommensurate with structured interview techniques. Due to these differences, mixed methods research techniques emerged as a compromise, not only to offset the weaknesses in the traditional approaches (quantitative and qualitative), but also as a distinctive research approach (Bryman and Bell, 2011).

Accordingly, Sieber (1973) and Madey (1982) argue that using mixed methods research can be effective at the research design, data collection, and data analysis stages. The authors argue that at the research design stage, qualitative data support the quantitative part of the study to develop concepts and instruments. Similarly, ‘qualitative data can provide to better come up with hypotheses due to the fact that the questions are unstructured and open-ended while the quantitative data can support the qualitative component to identify representative samples and outliers’ (Bryman and Bell, 2011: 634). In terms of the data collection stage, the qualitative data can assist in facilitating the data collection process, while quantitative data can establish benchmark data for the qualitative research (Sieber, 1973, Madey, 1982). At the data analysis stage, qualitative data can assist in interpreting, clarifying, describing, and validating quantitative results (ibid). Similarly, the quantitative data can assist in the assessment of the subjectivity of the qualitative data, which can provide meaningful interpretations of the qualitative results (ibid). Such a combination indeed complements and enhances the richness of the data/information collected.

Moreover, in the data analysis stage Rossman and Wilson (1985) provide three perspectives as to why quantitative and qualitative research methods should be combined to better understand the phenomenon in question. First, the combination of quantitative and qualitative data ensures corroboration and confirmation of each method through triangulation (ibid). Triangulation refers to the use of quantitative research to corroborate qualitative research findings or vice versa (Hammersley, 2002, Denzine, 85

1978). More specifically, triangulation entails the following: (i) assures the researcher of the results; (ii) creates new ways of collecting data; (iii) enhances richness in data; (iv) promotes integration of theories; (v) discovers contradictions; and (vi) can act as a litmus test for competing theories (Jick, 1979).

The second perspective Rossman and Wilson (1985) noted was that the combination enhances understanding of the phenomenon in question by elaborating the findings of the other approach, thus providing richness in the data. That is to say, quantitative approach facilitates qualitative approach, and vice versa (Tripp et al., 2002, Deery et al., 2002, Scase and Goffee, 1989, Storey et al., 2002). One plausible way this might occur is when qualitative data provides explanation to a phenomenon that was carried out quantitatively but failed to yield the anticipated results (Weinholtz and Rocklin, 1995).

The final perspective Rossman and Wilson (1985) espoused was that the combinations provide researchers with access to new dimensions of the phenomenon in question. Sometimes new meanings also emerge, thus creating new interpretations and conclusions when confronted by the convergence in the two approaches. Therefore, the use of mixed methods research in recent years has proved that such methods are viable, and they continue to gain momentum in the social sciences.

In reference to the discipline of economics, researchers such as (Carson and Mitchell, 1993, Kaplowitz et al., 2004, Boyle, 2003, Kontogianni et al., 2001) note that economists have also employed mixed methods. This is done to reduce omission errors in their economic models, as well as to identify appropriate wordings for their survey questions, which enriches and adds to the validity of the survey instrument. The main qualitative technique used by economists is the focus group interview to guide the survey instrument construction and design. This study embraces the qualitative technique (as a supporting approach) to the research design, data collection, and data analysis stages.

5.2.2 Approaches to mixed methods With the rise of mixed methods research various nomenclatures have been used to describe its existence by different scholars, such as blended research (Thomas, 2003), integrative research (Johnson and Onwuegbuzie, 2004), multi-method research (Morse, 2003), triangulated studies (Sandelowski, 2003), mixed research (Johnson et al., 2007), and mixed methods research (Bryman, 2009)78. There are also various ways to combine the quantitative and qualitative techniques, as discussed by Johnson et al. (2007) and Bryman and Bell (2011). The combinations advocated by Bryman and Bell (2011) are, however, quite disaggregated and easier to follow. With two additional features drawn from Morgan (1998), the combination of integrated mixed methods, in terms of priority and sequence, are depicted in Figure 5.1. Priority refers to what extent a quantitative and qualitative approach will be followed in terms of the principle data gathering, or whether they will be given equal weight. By sequence, it refers to which method (quantitative or qualitative) precedes which, or is the data collection conducted concurrently, with each given equal weight.

78 For consistency, this study favours mixed methods research as a term because it is easy to understand. 87

Figure 5.1 Classifying mixed methods research in terms of priority and sequence

MIXED METHODS RESEARCH

QUANTITATIVE EQUAL WEIGHT QUALITATIVE Priority

3. Concurrent 7. Quantitative 8. Qualitative 9. Concurrent 1. Quantitative 2. Qualitative 4. Quantitative 5. Qualitative 6. Concurrent Sequence qual QUAN QUAL quan QUAL + quan QUAN qual QUAL quan QUAN + qual QUAN QUAL QUAL QUAN QUAN + QUAL

Adopted from Bryman, A. and Bell, E., 2011. Business Research Methods, (3rd ed.), Oxford University Press Inc., New York

As Figure 5.1 above shows, mixed methods research has two continua, with one end of the spectrum leaning towards quantitative research while the other end orienting itself towards qualitative research. In the middle is a research design that gives equal weight to both the approaches. With this range of combinations, it yields nine different possibilities (see Figure 5.1). In terms of priority and sequence, upper case indicates priority – for instance, “QUAN” indicates that quantitative research is the core data collection technique. Lower case, such as “qual”, on the other hand, shows that qualitative approach played a subsidiary role. This means that the research is quantitatively oriented with a supporting role from the qualitative approach. The arrows show the sequence, that is, which method precedes which. For example, 푄푈퐴푁⁡ → 푞푢푎푙⁡indicates that the collection of quantitative data come first because they are the chief data collection approach, as opposed to qualitative data which plays a subsidiary role. The plus (+) sign on the other hand shows that quantitative and qualitative data are collected concurrently, and given equal weight in the research. Sometimes, however, it might be difficult to pick out which component is the priority and which one is the sequence in a study. Nonetheless, the procedures underscore the core principles of a mixed method research design.

Similarly, Morse and Niehaus (2009) also note that there should be a core data collecting technique and a supplementary one, which coincides with one of Bryman and Bell’s (2011) options. The choice of which depends on the theoretical underpinning of the research. On that same note, researchers should not think of mixed methods as two separate components; instead the researcher should work out how qualitative supports quantitative (or vice versa) in a single research project (Bryman and Bell, 2011: 644). For example, a mixed methods approach can be incorrectly applied (in a single investigation) when researchers analyse their findings from quantitative and qualitative data separately, instead of integrating them together.79

79 Ibid. 89

5.3 Rationale for using mixed methods in this study In line with Morse and Niehaus (2009) and Bryman and Bell (2011) narratives, this study adopts a deductive approach as the core data collecting technique, with the qualitative data being the supplementary or supporting component. In particular, this study follows combination one (1) in Bryman and Bell’s (2011: 632) possible combinations shown in Figure 5.1 above – where Quantitative is the priority, and 푸푼푨푵⁡ → 풒풖풂풍 the sequence. This study called this combination, quantitative mixed methods.

The underlying central research question of this study lends itself to the use of (quantitative) mixed methods. As noted in Chapter 1, the central research question in this study is: “What is the contribution of peace to the economic recovery in a post- conflict country?” This research question entails three things. First, the recovery in the economy has to be measured (i.e. quantified), and hence requires a deductive approach. Second, the main explanatory variable, peace (the focus of this study), that induces the recovery in the economy, has to be measured (i.e. quantified) as well – this again requires a deductive approach. Finally, peace is a socially constructed variable (or notion) that is grounded on interpretive epistemology, thus it requires qualitative judgment. In other words, this study attempts to quantify peace as a variable of interest, knowing very well that peace is a subjective variable that can only be measured by ordinal means. Hence, this requires the complement of an interpretive epistemology to support the quantitative data.

The next section describes the study design and how the field work was carried out. The section begins by describing the qualitative (subsidiary) data collecting approaches, followed by a detailed description on the fieldwork, up to the operationalization of the questionnaires.

5.4 Study Design The process this research was carried out is outlined in the flow chart shown in Figure 5.2 below. The sub-research questions address the broad peace – income/growth nexus.

A desk-top analysis was conducted to explore and position this study in the broad literature on the economics of peace, as discussed in Chapter 2.

Having been informed by the desk-top analysis, a field study was carried out to investigate, at the micro level, the peace – income nexus. The fieldwork processes are outlined in the sections below. The results obtained from this micro level data (i.e. fieldwork) are used in two ways. Firstly, to investigate the impact of peace on household income, and secondly, it will be further calibrated and applied to investigate the peace - income relationship at the macro level. For the macro level analyses, secondary data will also be collected (as explained below). The manner in which I have approached to this study was to elucidate that mixed methods technique is a research method in its own right, with non-dichotomous characterization of qualitative and quantitative techniques.

Figure 5.2 Schematic of the research process

Desk-top study analysis

1. What is the key risk in post-conflict economy? 2. How has the definition of peace been constructed? 3. How has peace been measured? 4. What are the economic benefits of peace? 5. What strategies have been used to sustain peace?

Computable General Equilibrium analysis Partial Equilibrium analysis (PEA) – (CGE) – Secondary data Household Survey data

10. What is the impact of peace on GDP, oil 6. How can peace be quantified? palm production and other key 7. What impact does peace have on the macroeconomic indicators? WHAT IS THE CONTRIBUTION OF PEACE TO THE ECONOMIC likelihood that GPPOL remains in the post- 11. What is the impact of Oil palm on peace, RECOVERY IN A POST-CONFLICT COUNTRY? conflict Solomon Islands? GDP and other key macroeconomic 8. What impact does peace have on the indicators? likelihood that landowners will own businesses? 9. What is the impact of peace on (household) incomes?

Towards sustaining peace

Source: Author’s Conceptualization

As Figure 5.2 illustrates the process begins with the desk-top analysis, which attempts to identify possible gap(s) in the existing body of knowledge. Following the identification of the knowledge gaps, two frameworks – the partial equilibrium analysis and the computable general equilibrium analysis – are then employed to address these gaps. Each framework is assigned specific sub-research questions as shown in Figure 5.2. These additional sub-research questions are formulated to enhance and deepen understanding and thereby contribute to intellectual advancement.

The sub-research questions under the partial equilibrium framework (See Figure 5.2) are addressed through the primary data collected from the household survey. The data will be calibrated and analysed by employing the Eviews Student Version 8 econometric software package. Such a partial equilibrium analysis is appropriate to address the microeconomic perspectives. Complementing these quantitative analyses are qualitative statements obtained from the structured interview to understand better the phenomenon of peace.

The sub-research questions under the CGE framework (see Figure 5.2) will be addressed by using the GEMPACK software. Data sources for this are explained in section 5.4.3 below. As well, some of the output from the household survey results analysed in the partial equilibrium framework were employed in this CGE framework.

5.4.1 Desk-top Study The desktop study used an existing body of knowledge to focus on pertinent issues for this study through reviewing, analysing, and synthesizing the literature. This process involves identifying possible gap(s) in the literature to situate this study meaningfully, in order to contribute original insights to the area. The desktop analysis includes a review of the existing literature on the economic benefits of (post-conflict) peace. It also explores the definitions and various empirical measurements of peace, as well as evaluating the sustainability of peace (after conflict) in relation to some key economic indicators. Moreover, the review assesses the current debate on the relationship between peace and income, and provides an operational definition for measuring peace.

5.4.2 Qualitative approach involved in the sampling selection Having researched the relationship between peace and economics in the literature, and identified the knowledge gaps, the author proceeded to preparing for the fieldwork. With very scant information available, coupled with the author’s limited prior knowledge of the target population, a visit to the interview site was the only plausible option to gather relevant information required for the sample selection. This is in line with Sieber (1973) and Madey (1982), who point out that at the design stage of the research, qualitative forms of information are useful to facilitate the survey when limited information about the unit of analysis are available. Such an approach in fact provides ‘in-depth knowledge on the social context to inform the design of questionnaires’ (Bryman and Bell, 2011: 634). Researchers such as Deery et al. (2002) had employed this strategy before designing their questionnaire. Consequently, the author visited the interview sites and manually counted the number of villages and households. The visitation actually created an opportunity to establish rapport with the village people – which was an unintended and felicitous benefit. During the visit, the author informed the village people of the purpose and the intention of this study.

Prior to executing the above, the author met with the executives and Board of Directors of both the Guadalcanal Plains Resources Development Association (GPRDA) and the Guadalcanal Plains Resources Development Company Limited (GPRDCL).80 The meeting was to seek permission to undertake this study in their communities. Their support for this study and the proposed survey was overwhelming, which also contributed to the cooperation and willingness of the respondents to participate. This kind of approach stems from the obligation to appreciate the epistemological position consistent with an interpretive approach (Bryman and Bell, 2011), which then facilitated the structured interview. Put differently, my approach to talk to the village people and the landowners’ association underscores my commitment to understand the local context (i.e. interpretive epistemology). This helped when the (structured) interviews were conducted.

80 See Chapter 4 for details on GPRDA and GPRDCL. 94

5.4.3 Quantitative (secondary) data As well as including qualitative methods in the research design, collection, and data analysis stages, I also collected and collated secondary (quantitative) data. This data will be employed in the computable general analysis. The main data collected were the key macroeconomic indicators relating to the Real Sector, such as GDP, employment, investment, and production; the Balance of Payments data such as trade (exports and imports), foreign direct investments (FDI), international reserves, and the exchange rate; Government Finance data such as revenues, expenditures, and government debts; and Monetary and Financial data such as money supply and inflation. The main data sources for this data were the Central Bank of Solomon Islands (CBSI), the Solomon Islands National Statistics Office (SINSO), the IMF, and the World Bank. The analyses drawing on this data are discussed in Chapters 7 and 8. The methodology for this particular study design is explained in detail in Chapter 7, which deals with the computable general equilibrium (CGE) model.

5.5 Human Research Ethics Committee (HREC) approval The University of New South Wales (UNSW) has strict human research ethic protocols. All research that involves the participation of humans has to go through the UNSW’s Human Research Ethics Committee (HREC) for approval. The details of this study and the data collection procedures were included in a submission to the full panel of the Human Research Ethics Committee of UNSW. Approval was granted following the provision of a ‘Security Protocol’ and responses to queries on how the data would be recorded and stored to protect confidentiality of the respondents. HREC was also provided with details on how the research findings would be disseminated. Further information on the university’s ethical code can be accessed at https://research.unsw.edu.au/human-research-ethics-home.

The ‘Safety Protocol’ required that the research is conducted in a fair and just manner, and outlines in detail what the researcher is going to do, for example at what time of the day will the interviews be conducted, and, what measures are in place should anything untoward occur. The safety protocol is necessary for research undertaken in post- conflict contexts such as the Solomon Islands.

Participation in this study was voluntary and there were no inducements provided to individuals to participate. At the beginning of the interview, it was made clear to respondents that participation in the interview was voluntary and that no compensation would be provided. The respondents were also told that should they agree to be interviewed, they were free to terminate the interview at any time without having to provide a reason for doing so.

5.6 Research permit from the Solomon Islands Government, GPPOL, and the Landowners The research permit is a legal requirement under The Research Act 1982 (N0.9 of 1982) of Solomon Islands Government in order to conduct a research in the Solomon Islands. The Ministry of Education and Human Resources Development (MEHRD) oversees this process. Thus, prior to submitting an application to the HREC, an application was submitted to MEHRD requesting a research permit. Consequently, a research permit for this study was granted, and a copy is attached as Appendix A5.0.

In addition, the UNSW HREC requires that the target population for the survey approve of the survey being conducted. As a result, permission was sought and verbal approval granted, from both the GPRDCL and GPRDA executives, on behalf of their communities, so was approval by the GPPOL General Manager to study the company.

5.7 The field work Having obtained the research permit from the Government and ethics approval from UNSW-HREC, I then conducted the fieldwork. As mentioned elsewhere above, this study conducted a household survey as a means of collecting the primary data by applying a structured interview schedule as a research instrument. The rationale for using such a research instrument is that it provides standardization for both the asking of questions and the recording of answers (Bryman and Bell, 2011: 202). In addition, from the quantitative research perspective, such an instrument (i.e. structured interview) embeds two salient features; (i) it reduces error due to interviewer variability, and (ii) improves accuracy and ease of data processing (ibid).

5.7.1 The unit of analysis The unit of analysis, which is the primary target for investigation, influences the choice of a sample size, methodology and data collection (Nardi, 2006). It forms a major aspect for this study as it contributes to defining the research process. With that in mind, a representative sample of rural villages on the Guadalcanal Plains was selected to investigate the relationship between peace and income (economic output), or in a wider context the impact of peace on the economy. Thus, the unit of analysis for this study is the household heads of the rural villages on Guadalcanal Plains, most of whom are owners of the land where GPPOL (the oil palm company) operates. This unit of analysis was chosen for the following reasons: (i) the region was once the major “hotspot” of violent conflict during the height of the civil unrest; (ii) this study focuses on the contribution of peace to the rebound in (household) income; (iii) the sustainability of peace in a post-conflict environment is also considered in this study; and (iv) this particular region hosts one of the nations’ major foreign investments (oil palm) which makes a significant contribution to the overall economy.

5.8 Design of questionnaire The design phase of the questionnaire is the most important part of the research process, and requires a lot of thought as to what sort of questions need to be asked, how they should be asked, in what sequence they should be asked, and how they should be worded. It is acknowledged by Bryman and Bell (2011: 255) that the questionnaire design phase ‘is one of the easiest areas to make mistakes’. Therefore, the formulation of questions should be geared towards answering the research questions (ibid). Figure 5.3 outlines the process involved in developing the survey up to the data analysis stage.

Figure 5.3 Schematic of developing the survey

Design of Questionnaires Questionnaire formulation based on both qualitative constructs and the need to measure the variables (quantitative)

Pre-testing of Questionnaires Questionnaires amended 8 Samples selected accordingly

Selection of Sample Two-staged stratified Probability Stage 1 : Villages Proportional to Size (PPS) Stage 2 : Households

Interview Process Data Collection Data Input

Data Analysis Reliability Validity

Source: Author’s conceptualization

5.9 Pre-testing of (and amendments to) the questionnaires Pre-testing the questionnaires is desirable for the kind of structured interviews carried out in this study. This test is to ‘ensure that the survey questions operate well and the whole operation function well’ (Bryman and Bell, 2011: 262). The questionnaires were piloted with a sample of eight individuals, after which the questions were refined; some were omitted and replaced. I know the eight people initially selected professionally. Bryman and Bell (2011: 263) note that the pre-testing of questionnaires should not be carried out on potential participants because it affects the representativeness of the sample. This is particularly true when the sample will be randomly selected. Instead, the testing should be conducted on a small set of respondents comparable to the population (ibid). In this regard, the selection of this pre-tested sample was based mainly on the following: their local knowledge on the Guadalcanal Plains; their knowledge on the general peace environment; and most of all their knowledge of the local economy. Questionnaires were given to this pre-selected eight individuals and each of them was

then asked to provide feedback on the relevance and clarity of meaning of the questions posed.

Having piloted the questionnaires, I made the following amendments to the questions below:

Question 3.1, the initial options of answers to the question, “which tribe do you belong?” were; 1 = Ghaubata, 2 = Thimbo, 3 = Lathi, 4 = Thogo, 5 = Nekama, After pre-testing, an additional option was included, so the options were;

1 = Ghaubata, 2 = Thimbo, 3 = Lathi, 4 = Thogo, 5 = Nekama 6 = None

This addition was necessary after the author learnt that settlers (non-landowners) of Guadalcanal origins also reside within the Guadalcanal Plains communities.

Question 3.2, the initial options of answers to the question “What type of land arrangements you have with GPPOL?” were; 1 = Lease to GPPOL, 2 = Smallholder (Out-grower), 3 = None.” This was revised to;

1= lease to GPPOL, 2 = Smallholder (Out-grower), 3 = both 1 and 2, 4 = None. This revision was suggested after I was told that some of the landowners both lease their land to GPPOL and grow oil palm as well, which I did not know. This is one of the advantages to piloting questions.

The other question revised following the pilot was on income. The initial question asked about how much income they receive in one month. The author learned that the intended target participants are very secretive about disclosing their income. As a result, the question was amended, and instead asked about their expenditures and savings as a proxy for income (See Appendix D, Q5.12 to Q5.15).

The questionnaires are pre-coded with a total of five broad questions, which are characterised into location details, demographic details, land and oil palm characteristics, peace indicators, and livelihood opportunity characteristics.

5.10 Sample size and sample selection - unit of analysis Following the piloting of questionnaires, the sample size and the unit of analysis were selected. The choice of the sample size influences the functional form of the model needed to run an econometric analysis. Taking into consideration the limited time, difficulties in the logistics of fieldwork and constraints on financial resources, this study selected a sample of 312 household heads.81 To select the sample size, I applied a two- staged probability proportional to size (PPS) sampling technique. This sampling technique is commonly used in surveys where the probability of selecting the unit of analysis (sampling unit) is proportional to the population size. The method ensures that the larger the size of the sampling unit the higher the probability of being selected (and vice versa), thereby reducing the standard error and bias. Typically, the number of sample units in a cluster is constraint by financial resources (Higgins, 2014).

The rationale behind using the PPS technique in this survey was due to the fact that the villages vary in their size in terms of the number of households. As expected, villages with a higher number of households had a higher probability of being selected than the smaller villages (See Table 5.1 below - Column E). However, to ensure that all household heads in the population have the same probability of being sampled, regardless of the size of villages, each of the hierarchical levels has to be sampled according to the size of the final sampling frame (household heads). Thus, it is essential to sample the same number of household heads from each village at the last level. As a

81 The main determining factor in deciding upon this sample size was time and cost. 100

result, the second phase saw smaller sampled villages having a higher probability (See Table 5.1 below – Column G). In the overall weighting (final level) all the sampled villages have an equal probability of being selected (See Table 5.1 below – Column H). Such an approach ensures that there is an equal probability of a household head being selected in both the large and small villages. Note that it is possible for larger villages to be selected more than once. For this study, three villages82 were selected twice due to their large size. In such case the village counts as two sites. The advantage of this technique was that it provides a pre-determined number of participants to be interviewed, which is useful when costs and time were binding constraints.

Enumerated next are the steps taken in applying the PPS sampling technique, which was applied in two stages.

Let’s suppose we have: T = total population (number of households) at GPPOL Plains

Xi = The number of households in the selected village i (where i = 1, 2, 3,....n) xj = final (ultimate) size of households to be selected from the selected villages, Xi, (where j = 1,2,3,...,n; but i ≠ j) n = total number of villages selected Stage 1: – Larger clusters (of villages) have a higher probability of being selected. At this stage it is possible for large villages to be selected more than once.

Therefore, the probability (π1) of selecting the villages is given by this formula;

Xi × 푛 휋1 = … … … … … … … … … … … … … … … … … … … … … … … … . . (5.1) 푇

Stage 2: - Sample the same number of households per cluster (village). Individual households in large clusters have a lower probability of being selected. This second stage offsets the first stage. Called π2 the formula is as follows;

푥j 휋2 = … … … … … … … … … … … … … … … … … … … … … … … … … … … . (5.2) 푋i

Overall Weight: - The overall weight will show that each household in the population has the same probability of being selected. This is calculated by taking the inverse of the two probabilities above. That is:

82 Marked with “*” in Column 1 of Table 4.1. 101

1 휑 = ⁡ … … … … … … … … … … … … … … … … … … … … … … … … … … … (5.3) 휋1⁡×⁡⁡휋2

The following steps detail the application of PPS in this study and Table 5.1 below presents the completed steps. 1. First, list the primary sampling units (villages) along with their population sizes. In an excel spreadsheet, the names of all the villages are listed in Column A and in Column B (Table 5.1 below) their respective population (i.e. total number of households in each village). For this study, the author visited all the sites and physically counted the number of households in each village, prior to the actual interview.

2. In Column C, calculate the running cumulative balance of the population sizes, with the last figure at the bottom of Column C being the total (population) number of households. Hence, the total number of households was 1263.

3. Determine the number of clusters (villages) that will be sampled from the total primary sampling units. For this study, due to cost and time constraints I chose 24 villages (out of the total 31 villages) to be sampled.

4. Randomly select the 24 villages83 from the total (30) number of villages, using PPS – Stage 1. To do this, we need to get a sampling interval (SI). This is derived from dividing the total population of households (1263) by the number of villages (clusters) to be sampled (i.e. 24 in this case).

5. Next we calculate the random start (RS), which is derived by choosing a random number from between 1 and the sampling interval inclusive. The excel command is [=rand()*S]. The RS number in fact identifies the first cluster (village) to be selected which is contained in the cumulative population (Column C).

6. Calculate the following series: RS; RS+SI; RS+2SI; .....; RS+23SI. These serial numbers are contained in the cumulative population (Column C) and corresponds to the clusters (villages) to be selected. It is possible to have selected larger villages (clusters) more than once. List the serial numbers in Column D, corresponding to the selected villages.

7. Calculate for each of the selected clusters (villages) the probability of each village being selected (π1) in Column E. The formula is as in equation 5.1 above, or in words;

83 Twenty-one villages were actually selected, but because three villages larger in size were selected twice, this made it up to 24. 102

휋1 푠푒푙푒푐푡푒푑⁡푐푙푢푠푡푒푟⁡(푣푖푙푙푎푔푒)푠푖푧푒)⁡× 푛푢푚푏푒푟⁡표푓⁡푣푖푙푙푎푔푒푠⁡푠푒푙푒푐푡푒푑 = … … 5.4) 푡표푡푎푙⁡푝표푝푢푙푎푡푖표푛

8. Determine the number of households to be sampled in each selected village. To ensure that all households in the population have an equal probability of being selected regardless of the size of villages, the same number has to be sampled from each village, keeping in mind that larger villages may be selected more than once. Consequently, for this study three villages (marked with “*”) with larger sizes were selected twice. Column F in Table 5.1 below list the same number of households for each selected village. For this study, 13 households (312/24) were chosen from each selected village.

9. Calculate for each of the sampled villages the probability of each household head being sampled in each village (π2) in Column G. This uses formula in equation 5.2 above, and expressing it in words;

푛푢푚푏푒푟⁡표푓⁡ℎ표푢푠푒ℎ표푙푑푠⁡푠푎푚푝푙푒푑⁡푖푛⁡푒푎푐ℎ⁡푣푖푙푙푎푔푒 휋2 = … … … … … … … (5.5) 푠푎푚푝푙푒⁡푠푖푧푒

10. Calculate the overall weight (φ) (Column H) of a household being sampled in the population by applying equation 5.3 above.

11. Finally, the selection of respondents was randomised. To derive this (Column I), divide the total number of households in each sampled village by the number of households to be sampled in each village (i.e. identified in 8 above). Due to time and difficulties in accessing most of the villages, the first household that we approached becomes the RS household for that village. Table 5.1 below calculates the sampling process for the household survey. To avoid potential identification of villages, I have instead replaced the names of villages with numbers.

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Table 5.1 PPS technique to sample 312 households

Number Cumul of ative Proba Househo Proba Househ House Cluster bility lds bility Overall Respon Villages olds holds sampled π1 Sampled π2 Weight dents A B C D E F G H I 1 39 39 38 0.74 13 0.33 4.0 3 2 48 87 3 35 122 91 0.67 13 0.37 4.0 3 4 22 144 143 0.42 13 0.59 4.0 2 5 18 162 6 30 192 7 25 217 196 0.48 13 0.52 4.0 2 8* 85 302 249 1.62 13 0.15 4.0 7 9* 85 302 301 1.62 13 0.15 4.0 7 10 50 352 11 28 380 354 0.53 13 0.46 4.0 2 12 30 410 406 0.57 13 0.43 4.0 2 13 95 505 459 1.81 13 0.14 4.0 7 14* 60 565 512 1.14 13 0.22 4.0 5 15* 60 565 564 1.14 13 0.22 4.0 5 16 46 611 17 35 646 617 0.67 13 0.37 4.0 3 18 35 681 670 0.67 13 0.37 4.0 3 19 44 725 722 0.84 13 0.30 4.0 3 20 50 775 775 0.95 13 0.26 4.0 4 21 45 820 22 59 879 827 1.12 13 0.22 4.0 5 23 50 929 880 0.95 13 0.26 4.0 4 24* 64 993 933 1.22 13 0.20 4.0 5 25* 64 993 985 1.22 13 0.20 4.0 5 26 66 1059 1038 1.25 13 0.20 4.0 5 27 26 1085 28 42 1127 1091 0.80 13 0.31 4.0 3 29 30 1157 1143 0.57 13 0.43 4.0 2 30 46 1203 1196 0.87 13 0.28 4.0 4 31 60 1263 1248 1.14 13 0.22 4.0 5 312

Basic Information: Total number of households (population) = 1263 Number of villages selected (sampling frame) = 24 Sample size selected = 312 Number of households to be sampled in each village = 13

Sampling Interval (SIS) = 1263/24 = 53

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Random Start (RS) = = 푟푎푛푑( ) ∗ 푆 = = 푟푎푛푑( ) ∗ 53 = 38 Calculate the following series in Step 6 above:

RS 38 RS+8SI 459 RS+16SI 880 RS+SI 91 RS+9SI 512 RS+17SI 933 RS+2SI 143 RS+10SI 564 RS+18SI 985 RS+3SI 196 RS+11SI 617 RS+19SI 1038 RS+4SI 249 RS+12SI 670 RS+20SI 1091 RS+5SI 301 RS+13SI 722 RS+21SI 1143 RS+6SI 354 RS+14SI 775 RS+22SI 1196 RS+7SI 406 RS+15SI 827 RS+23SI 1248

As shown in Table 5.1 above, these series of numbers are entered in Column D. Each number is contained in the cumulative sum (Column C). For instance, the first number, 38 is contained in the first village, hence village 1 is selected for the sample. The second series, 91 is contained in village 3, hence it is selected for the sample. The third series, 143 falls in village 4 and so it is also included in the sample. This continues until 24 villages are reached. As can be seen, the 24th village is village 31, which contains 1248. Also notice that villages 8, 14, 24 appear twice; that is because they have a large population and therefore the probability of them being chosen is higher than the other villages.

Then we calculate the following probabilities:

Probability 1 (π1): probability of each village being selected.

Probability 2 (π2): probability of each household head being selected in each village.

Following the formulas in steps 7 and 9 above respectively, the respective Columns E and G provide the probabilities. As mentioned in step 8 above, Column F lists the number of households to be sampled in each village, and they are the same for all selected villages. Column H provides the overall basic weight of the PPS technique to smooth out any discrepancies that may have caused biased selections. This weight is calculated by taking the inverse of the two probabilities. The last column (Column I) identifies the selection of respondents and is calculated by taking the population of

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households in each selected village divided by the selected number of households (in this case 13).

Thus, k = Ti/n ; where k is the selected respondent (household), Ti is the total (household) population in each selected village (village i), and n is the number of households sampled in each village. This means that commencing from the random start the next household to be interviewed is the kth household. For instance, in this study village 1, starting from a random start, the next kth household head to be interviewed will be the third one, as for village 3. For village 4, the kth household will be every second household. For village 30, the kth household to be interviewed is every 4th households, and so on.

A decision was also reached that in cases where the household is either not willing to participate or absent when the team arrives we will move to the next immediate subsequent household. This decision was made based on the time factor and costs, as well as logistical difficulties in arranging for revisits.

5.11 Interview Process Interviews commenced on 21st November, 2013 and were completed on 11th March, 2014. The author recruited three research assistants (RA) from within the communities of the Guadalcanal Plains. The author provided training for the RAs on how to conduct face-to-face interview, as well as the meaning of each question. The interview time was initially planned to last 30 minutes for each household, but invariably took longer as time was invested establishing rapport with the respondents. As part of the appropriate cultural etiquette we approached each household with utmost respect. The RAs were accorded the privilege of greeting the household head (or whoever came first to meet us) before the researcher exchanged greetings. Such is in line with what Bryman and Bell (2011: 211) suggest, that ‘it is important for the interviewer to achieve rapport with the respondents’.

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As suggested by Bryman and Bell (2011: 210), at the beginning of every interview the author explained the purpose of the visit, the rationale for the research, and presented the consent form. The discussions took place in Solomon Islands Pidgin language – a common language understood by everyone. Where the interviewee could not fully understand Pidgin the RAs translated it into the local dialect. We did not seek a signature on the consent form until after the interviewee agreed to participate. This process took between 20 to 30 minutes. The actual interview took approximately another 30 minutes. After the end of each interview, another 10 to 20 minutes was spent talking with the interviewee (and members of his/her households) for further clarification. During these informal talks I found that useful qualitative information emerged. Also, respondents found themselves expressing their views and opinions freely on issues relating to peace and related concerns, without confining themselves to the options provided in the questionnaire. Such an approach emphasises an interpretative epistemology that truly complements and facilitates the structured interview in this study.

5.11.1 Location Details The first question records the location details of the household – the GPS coordinates and the household identification number, along with household photos. The main purpose for these details is to allow for a possible follow-up research in the future to further investigate if there have been any changes in these households. Future studies would be able to compare (with this study) the extent of peace in the environment, as well as the livelihood opportunities open to these households. In addition, the household photos would indicate whether a house built with traditional materials has been improved over the years to a permanent building, which would indicate an improvement in the standard of living. Table 5.2 below depicts the location details as they appeared in the questionnaire.

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Table 5.2: 1.0 Location details

Questions Response

1.1 Household Id: 001, 002, 003, …, etc

1.2 GPS coordinates: longitude (deg, dec)

1.3 GPS coordinates : latitude (deg, dec)

1.5 GPS coordinates: elevation (meters)

In the analysis of this data, only aggregates are presented, without the GPS coordinates and house photos. However, to continue with the conversation and the advancement of knowledge on this subject, the author has permission to use some of these materials in venues such as conferences, workshop and seminars. Condensed GPS coordinates will be used that show only dots without the exact coordinates to maintain the anonymity of respondents. In addition, household identification numbers are removed from the presentations.

The rest of the remaining main characteristics are important to addressing the research questions. These main characteristics will be utilized, in the next chapter, to derive new variables such as distance, labour force, dependency, and peace.

5.11.2 Demographic characteristics The demographic indicators are important features in any survey to identify the characteristics of respondents. They are designed to establish what factors may influence a respondent’s answers. Tabulating the answers provides the researcher with information to see how subgroups vary in their response. Accordingly, question 2 in the questionnaire asks about personal and household details. Table 5.3 shows the demographic information.

The first question (2.1) asks about the age of the respondent. The age variable is important in the analysis because it determines how knowledgeable and experienced a respondent is likely to be, about issues such as peace during the pre and post conflict periods. For example, a 65 years respondent may have better knowledge (than a 30

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years old respondent) of what may have triggered the conflict, and thus his/her perception on peace may have been informed by prior knowledge.

Question 2.2 asks about the respondent’s sex. Inclusion of this variable is important to determine whether or not their gender have any influence on their answer.. As shown by Bruck and Schindler (2008) females in post-conflict Mozambique became more strategic (than their male counterparts after the conflict) in order to cope with the post- conflict environment. They find that these women became household heads and bread winners.

Marital status is the other variable which appears in question 2.3. Married people might respond differently (compared to people who are single, divorced, or separated) to questions on their perception on the current peace and livelihood opportunities. For example, married female respondents may be more concerned about peace and livelihood opportunities than an unmarried male.

The highest level of education completed (question 2.4) is another variable included in this category. The level of education is vital to the general understanding of how the investment (GPPOL) on their land relates to their daily livelihood, and other related fundamentals necessary for the peaceful progress of any society. Individuals with higher education levels tend to participate productively in societies. For this study, well educated people tend to have a better understanding of the land arrangements they have with GPPOL, thereby minimizing the likelihood of conflict.

Questions 2.5 to 2.10 are concerned with the productive nature of the household, and investigate the total number of individuals in the household and the number of dependents. This information allows me to calculate the labour force available to the household and the dependency ratio (i.e. number of dependents per adult in the household).

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Table 5.3: 2.0 Personal and household characteristics

Questions Response 2.1 Age = Number of years 2.2 Sex: Male = 1 Female = 0 2.3 Marital status: Married = 1 Single = 2 Divorced = 3 Widowed = 4 De facto = 5 2.4 Highest Education level completed: 1 = University 2 = College (technical or vocational) 3 = Secondary 4 = Primary 5 = Did not complete primary school 6 = Never attended school 2.5 Total number of people in the house: 2.6 Total number of males in the house: 2.7 Number of people less than 15 years 2.8 Number of people above 65 years: 2.9 Number of people in school: 2.10 Number of disabled people aged 15 – 65 in the house:

5.11.3 Land Tenure and Oil palm Characteristics Concerning land tenure characteristics, this study explores whether or not the type of land tenure arrangement (relating to oil palm) the respondents have has any influence on the current peace. The current land arrangements come in two forms. The first one involves landowners leasing their land to the GPPOL Company to plant oil palm, and in return, these landowners receive land rentals, royalty payments, and dividends. The second option is that landowners grow oil palm (known as out-growers or smallholders) on their own land and sell the fresh fruit bunches to GPPOL Company. The smallholders are further disaggregated into two groups, depending on whether their land is registered or is customarily owned.

Furthermore, the respondents are identified by whether or not they belong to any of the five tribes. This data will be analysed, together with the land tenure type, to determine which tribes make their land most available for oil palm investment.

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Questions were also asked about how much the respondents receive from the economic rents if they lease land to GPPOL. This variable is then compared to smallholders, and investigates which land arrangement type earns better returns for landowners.

The questions then focus on smallholders, by asking about the inputs they use in growing oil palm, to try and identify whether these out-growers have the know-how and capacity to operate as smallholders, or whether they should lease to GPPOL and receive the same benefits as the former group. This variable also will indicate whether or not such an undertaking promotes peace in the communities. Table 5.4 below shows the questions relevant for this section.

Table 5.4: 3.0 Land and oil palm characteristics

Questions Response 3.1 Which tribe do you belong to? 1 = Ghaubata 2 = Thimbo 3 = Lathi 4 = Thogo 5 = Nekama 6 = None 3.2 What type of land arrangements you have with GPPOL? 1 = Lease to GPPOL 0 = Smallholder (Out-grower) 3.3 Number of hectares leased to GPPOL (if lease to GPPOL) 3.4 If you are a smallholder (Out-grower), which of the following land arrangement do you operate under? 1 = Registered Land 0 = Customary Land 3.4.1 How many blocks do you have for outgrowing? Note: 1 ha = 1 block 3.4.2 How many oil palm trees do you have in one block? 3.4.3 How many times do you harvest in one year?

3.5 Landowners that lease to GPPOL – Financial characteristics 3.5.1 Monthly royalty payments: 3.5.2 Land rentals received: 3.5.3 Dividends received in one year:

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4.0 For Smallholders - Input Variables Questions Response 4.1 Use of fertilizer: 1 = Yes 0 = No 4.2 How many bags per year/season? 4.3 Use of Herbicide: 1 = Yes 0 = No 4.4 How many litres per year/season? 4.5 Do you employ labourers? 1 = Yes 0 = No 4.6 If yes, where do your labourers come from? 1 = Family members (including extended family) 2 = Relatives 3 = People from the same village 4 = People from close-by villages 5 = Others, please specify 4.7 Wheel barrow: 1 = Yes 0 = No 4.8 Tractor: 1 = Yes 0 = No 4.9 Hand tools: 1 = Yes 0 = No 4.10 Knapsack sprayer: 1 = Yes 0 = No 4.11 Technical advice from GPPOL: 1 = Yes 0 = No 4.12 Training attended: 1 = Yes 0 = No 4.13 Share proceeds of harvest: 1 = Yes 0 = No

5.11.4 Livelihood Characteristics In terms of the livelihood variables, this study investigates the current livelihood status of the communities and explores the possible opportunities for the future production of the oil palm and the economy as whole (See Table 5.5 below).

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Question 5.1 asks respondents whether they have other incomes apart from being an Out-grower (who receives monthly income from the sale of fresh fruit bunch) or landowner (who receives monthly royalty, quarterly land rentals, and annual dividends). This variable is vital for the identification of alternative sources of income. Then they are asked if they own small businesses (question 5.2). Again, this variable provides an indication as to whether or not the respondent can support his/her household members’ livelihood without the rents from the oil palm.

In question 5.3, we ask about the type of house they have, with three options; permanent, semi-permanent, or thatched roof. This variable is important, in that it implies that respondents with permanent houses have a better living standard than those whose home has a thatched roof. Along with this, question 5.4 asks about the year the house was built, to indicate two time periods ex ante and ex post conflict.

Questions 5.5 to 5.10 compared the pre-conflict company, SIPL and the post-conflict, GPPOL. It asks the respondents to compare the two companies based on their experiences, knowledge, and opinions. We then ask the respondents to indicate which of the companies promotes peace. For this, we want to find out whether the respondents’ perception on peace will have any implications for the likelihood for GPPOL’s withdrawal (from the respondent’s perspective). We utilize the interactions84 of question 5.7 to 5.10 as a proxy for GPPOL’s likelihood to remain in operation.

Finally, we ask about the respondents’ spending behaviour, specifically, weekly spending on basic (necessary) goods and luxury items. We also asked about how much they spend on annual school fees, and how much they save in a month, if any. These variables were used to measure the level of income, especially the interaction of spending on basic and luxury goods, and savings.

84 Details in Chapter 6. 113

Table 5.5 Livelihood opportunities characteristics

Questions Response 5.1 Do you have other jobs (income) apart from being a smallholder or landowner? 1 = Yes 0 = No

5.2 Do you own a small business? 1 = Yes 0 = No 5.3 House structure: 1 = Permanent 2 = Semi-permanent 3 = Traditional material (thatch roof) 5.4 What year was your house built?

5.5 In terms of the presence of the company in the area, how do you compare GPPOL with SIPL? 1 = GPPOL is better than SIPL 2 = SIPL is better than GPPOL 3 = They are both the same 5.6 In terms of the rents and royalty payments (or earnings from outgrowing), how do you compare GPPOL and SIPL? 1 = GPPOL is better than SIPL 2 = SIPL is better than GPPOL 3 = They are both the same 5.7 Do you feel you have more money now than during the SIPL time? 1 = Yes 0 = No 5.8 Do you think the current arrangement with GPPOL promotes peace in the community? 1 = Yes 0 = No 5.9 Do you think GPPOL provides most of the things you and the community need such as schools, clinics, etc? 1 = Yes 0 = No 5.10 Do you think you are involved more in income generating activities than the days of SIPL? 1 = Yes 0 = No 5.11 Factors preventing you from increasing your income: (List at least 3 factors)

5.12 How much do you spend in one week on basic goods such as food, cloth, shelter, kerosene, soap, sugar, etc…. 5.13 How much do you spend in one week on luxury goods such as alcohol, tobacco, betel nuts, etc? 5.14 How much do you spend in one year for school fees? 5.15 How much do you save in one month? 114

5.11.5 Peace variables Peace is the central focus of this study. Its inclusion in the questionnaire is important. Given its socially constructed nature, measuring it by means of a quantifiable number of conflict-related deaths, imprisonment, arrest, etc… will do injustice to those who still feel threatened, but reserve to express this publically for fear of retribution. Instead, this study uses perception to measure peace. It uses a Likert-scale to measure peace, and also uses some dichotomous and cardinal scale questions. The existence and /or presence of GPPOL in the Guadalcanal plains depend very much, among other things, on the level of peace in the area. This study quantifies peace (perception) as one of the explanatory variables to determine household income, which is proxied for output. It attempts to investigate the impact of peace on income, and household income is used to proxy household output. In so doing, we estimate an index for peace.85 Table 5.6 below shows the questions are pre-coded with numbers 1 to 5, with 1 indicating very safe (i.e. close 100% safe), 4 being less safe while 5 represents ‘not safe at all’. Further calibrations of these data are detailed in Chapter 6 to derive the peace variable.

For purposes of reliability we also adopted a few of the questions (marked with asterisk “*”) from the RAMSI’s ‘People Survey’ research, which is conducted annually, to gauge the people’s opinion on a wide range of issues since RAMSI’s intervention in July 2003. Among other things, the People Survey also asks about respondents’ perception of changes to peace. More information about the People Survey can be found by following this link: http://www.ramsi.org/solomon-islands/peoples-survey.html.

85 The Institute for Economics and Peace (IEP) calculates a Global Peace Index (GPI) based on various variables. This study is restricted to calculating a Peace Index from a microeconomic perspective (in particular household), hence the proxies used to measure peace are based solely on how peace is perceived and what peace means to this locality. 115

Table 5.6: Current peace environment

4.14 Law and order in your community overtime since 2003: 1 = very safe 2 = safe 3 = average safe 4 = less safe 5 = not safe at all 4.15 To what extent do you feel safe in your community? 1 = very safe 2 = safe 3 = average safe 4 = less safe 5 = not safe at all

4.16 Which time of the day do you feel safer?* 1 = During the day 2 = During the night 3 = No difference 4.17 Do you feel safe in your household?* 1 = very safe 2 = safe 3 = average safe 4 = less safe 5 = not safe at all

4.18 Do you feel safe when you go to Honiara?* 1 = very safe 2 = safe 3 = average safe 4 = less safe 5 = not safe at all

4.19 How much confidence do you have in the Police: (rank by using percentage; e.g. 100% confidence, or 60% confidence, etc…)

4.20 What type of security measures do you use? 1 = Fencing 2 = Security Guard 3 = Community policing (village security) 4 = Other, Specify

4.21 How much do you pay, in a month, for the above?

4.22 How safe are you if RAMSI leaves tomorrow? 1 = very safe 2 = safe 3 = average safe 4 = less safe 5 = not safe at all

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4.23 Do you think RAMSI should scale down as soon as law and order is restored? 1 = Yes 2 = No 3 = Don’t know 4.24 How long do you think RAMSI should remain in the country? 1 = Should leave now 2 = Should remain until work completes 3 = Should remain for another ten years 4 = Should remain for more than ten years 4.25 If peace and security is guaranteed, are you willing to lease out more of your land, (or are you willing to increase your smallholder plantation)? 1 = Yes 0 = No 4.26 By how many more hectares?

5.12 Data input process The completed questionnaires for any particular day were inputted directly (on that same day) into an excel spreadsheet. The hardcopies of the responses to the questionnaires and the signed consent forms were then stored in a locked suitcase. The inputted data was then further plotted into Microsoft excel to identify outliers. Only then was the data transferred to the Eviews software for econometric analysis.

Undertaking a structured interview in this study, the author is again reminded of the benefits echoed by McCall (1984: 277), who stresses that such instruments (i.e. structured interviews) ‘provides more reliable information about events; greater precision in the timing, duration, and frequency; greater accuracy in the time ordering of variables; and more accurate and economical reconstructions of large-scale social episodes’. The author also notes that there are certain elements of reliability and validity that researchers face. Some of these elements are explained in the next section, along with the steps that this study undertook to solve these reliability and validity concerns.

5.13 Reliability Reliability refers to ‘the consistency of a measure of a concept’ (Bryman and Bell, 2011: 158) were that concept to be repeated in research in the future, either with a different or the same sample (Veal, 2005). This definition entails upholding stability, 117

inter-observer consistency and internal reliability. Stability requires a particular measure administered over time that should have little variation in the results, while inter- observer consistency considers the extent between two or more similar observers are consistent in their coding of that behaviour on the observation schedule (Bryman and Bell, 2011). These two factors require more than one time period, and are not relevant for this study. However, the internal reliability factor is used to assess the reliability of this study.

5.13.1 Internal reliability This factor determines whether or not ‘respondents scores on any one indicator tend to relate to their scores on the other indicators’(Bryman and Bell, 2011: 158). A widely used measurement, the Cronbach alpha coefficient, is calculated to indicate the reliability of the variables through multiple-indicator measures. A Cronbach alpha of 1 indicates a perfect internal reliability while 0 indicates no internal reliability. A ‘rule of thumb’ for an acceptable internal reliability is mixed in the literature, with some suggesting 0.8 (Bryman and Bell, 2011: 159) while others suggest 0.7 (Schutte et al., 2000, George and Mallery, 2003, Kaplan and Saccuzzo, 1982). Nunnally (1967: 226), on the other hand, concedes that a Cronbach alpha from 0.5 to 0.6 can be relevantly suitable for a preliminary investigation. However, Murphy and Davidshofer (1988: 89) show that anything below 0.6 is unacceptable. Following Cronbach (1951: 299), the alpha coefficient is calculated using the following formula;

푛 1−∑ 𝜎i2 훼 = × … … … … … … … … … … … … … … . … … … (5.6)⁡ 푛−1 𝜎t where n is the number of components or items (i.e. number of conditions that contribute 2 to a total score), σi is the variance item scores, σt is the variance of total test scores.

Applying the formula in equation 5.6, the calculated coefficient for the latent construct peace, revealed a Cronbach’s alpha of 0.63 as shown below.

3 1 − ∑ 1.292 훼 = ∗ = 0.6297 3 − 1 2.226

Murphy and Davidshofer (1988) believe that 0.63 is acceptable, implying that there is internal consistency with the latent constructs. In addition to the Cronbach’s alpha, this

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study also undertook other measures to test for reliability. One such measure was that the sample was randomly selected (using PPS technique), avoiding participant and observer bias.

Internal consistency can also be maintained by adopting previous questionnaires that have already been used in similar studies (Bryman and Bell, 2011). Such an approach provides further assurance of the reliability of the research. Indeed, Saunders et al. (2008) concede that such an approach tests the consistency, precision and repeatability of the constructs applied in a study. This study likewise adopted some of the questions, in particular the latent variables, from previous research, as mentioned above.

5.14 Validity With validity, the concern is whether or not a set of indicators constructed to gauge a concept actually measures that concept (Bryman and Bell, 2011: 159), or to what extent does the data collected truly reflects the phenomena in question (Veal, 2005). It is important to note though that validity presumes reliability and not vice versa. This means that an unreliable measure cannot be valid (Bryman and Bell, 2011: 161), but a valid measure may not always be reliable.

There are two issues that can affect the validity of any measure; (i) the extent to which the indicator(s) truly reflect(s) the concept, and (ii) errors that may emerge during the research process when applying the concept in question (Bryman and Bell, 2011: 280). However, for this study to be credible the measures (indicators) must be valid and reliable. Thus, the two validity issues are addressed below, in conjunction with the validation techniques employed in this study.

For the first issue, regarding whether or not the indicator(s) truly measure the concept, this study employed the following validation approaches; face validity, concurrent validity and construct validity. The choice of these validity measures are purely determined by the context and environment this study was undertaken in.

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5.14.1 Face validity Face validity concerns with whether or not the indicator actually reflects the content of the concept to be measured (Bryman and Bell, 2011: 160). Intuitively, face validity can be verified through asking other people to establish if there is any relationship between the indicator and the concept to be measured. In this vein, during the pre-testing phase of the questionnaires of this study, the selected sample of individuals were asked to also comment on whether the variables (indicators) really reflect the lived experience. Consequently, the feedback from this pre-testing exercise provided reassurance on face validity.

5.14.2 Internal validity Internal validity establishes the likelihood of causality between variables (Bryman and Bell, 2011: 715). That is, it is concerned with whether or not the variation in the dependent variable is due only to the variation in the explanatory variables (Veal, 2005). This study, which investigates the impact of peace on income, revealed that changes in peace (as an independent variable) does cause changes in the dependent variable, income. This result is statistically significant. Furthermore, the fact that this study employed a random selection of the sample households certainly reduces the likelihood of bias in sample selection (Onwuegbuzie, 2000).

5.14.3 External Validity External validity refers to the extent the conclusion can be generalised (Lund Research Ltd, 2012). This study undertook a household survey in one of the rural communities on Guadalcanal. Given the settings and environment, this study can also be used in other parts of the Solomon Islands. Furthermore, it is possible to generalize this study to similar post-conflict small island states.

5.14.4 Concurrent validity Concurrent validity refers to relating a “criterion on which cases (e.g. people) are known to differ and that is relevant to the concept in question” (Bryman and Bell, 2011: 120

160). The coded interview data in this study, gathered to gauge the peace and livelihood constructs, were found to be concurrently valid. For instance, the degree of law and order and the extent of security in the community were among the measurements for the peace construct. The correlation between these two measurements was positive, as indicated by a coefficient of 0.38 compared to the weak correlation coefficient ‘rule of thumb’ of 0.2.

5.14.5 Construct validity Construct validity traditionally concerns “the degree to which a test measures what it claims, or purports, to be measuring” (Brown, 1996: 231). In other words, concepts and/or hypotheses are constructed from a theory (Bryman and Bell, 2011: 160), which means the constructs actually reflect the assumptions of that particular theory (or the generally accepted ideologies). Thus, the construction of the latent variable, peace, in this study investigates the peace-income theory that exists in the post-conflict economies literature. This is the most problematic of the measures in the survey.

5.15 Conclusion This chapter has presented the research design and methodology of the study. Informed by the theoretical underpinning of the study, this study adopted a mixed methods research approach, with an emphasis on the deductive approach. It can therefore be called ‘quantitative mixed methods research’.

Mixed methods research combines quantitative and qualitative research techniques. The rise and use of mixed methods began in the early the 1980s, and became increasingly popular. Despite the strong opposition to blending the traditional research techniques, mixed methods research continued to expand. This was evident with the establishment of two specialist journals specifically for this research approach.

Therefore, the study design adopted in this study illustrates how mixed methods research can be an alternative research method. This study applied both quantitative and qualitative technique in the research design, data collection, and data analysis stages, hence the mixed method. At a research design stage, a qualitative approach was pursued to assist in the sample selection of 312 households, which were randomly selected.

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The sample size was randomly selected through a two-stage probability proportional to size (PPS) sampling technique where the first stage was to select the villages. The second stage was then to select the households. All selections used the PPS method. The main features of the questionnaires are: Demography; Land tenure; livelihood characteristics; and Peace characteristics. Finally, the research method was also tested for reliability and validity. The next chapter provides the results of this survey.

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CHAPTER 6

RESULTS AND ANALYSES OF THE IMPACT OF PEACE: A PARTIAL EQUILIBRIUM ANALYSIS

6.1 Introduction This chapter presents the descriptive statistics results and analyses of the household survey. It is presented in two parts. Part I presents the descriptive results from the household survey, and Part II provides the derivation and econometric analyses of peace. This chapter attempts to address the following sub-research questions:

 How can peace be quantified?  What impact does peace have on the likelihood that GPPOL will remain in the Solomon Islands?  What impact does peace have on the likelihood that landowners will own businesses?  What is the impact of peace on household income?

The remaining sections are organised in the following manner. Section 6.2 presents the descriptive statistics results.86 The descriptive statistics categories are demographic and household, peace, land tenure arrangements for oil palm, and livelihood characteristics. The classification of the descriptive results into these four broad categories rationalizes our attempt to identify and measure the key construct relevant for this study. Section 6.3 quantifies peace by deriving a Peace Perception Index (PPI). This quantification of peace is the pillar to our analysis, and in fact is our contribution to this body of knowledge. Section 6.4 analyses the impact of peace using econometric analysis. We conclude this chapter in section 6.5.

86 Note that not all the variables from the questionnaires will be analysed, as some are used to derive other variables important for the analysis. 123

PART I

6.2 Results from the household survey In examining the results, some variables are cross-tabulated to establish whether there are any distinct patterns. The ⁡푃푒푎푟푠표푛′푠⁡푐ℎ푖 − 푠푞푢푎푟푒푑⁡(훸2) test was used to do this. The test determines the probability that the independence of the variables is not due to chance, which implies that the study can be replicated with a different sample using the same underlying population, and should deliver similar results.

6.2.1 Geographic, demographic and household characteristics The main characteristics in this section include household location (LOCATION); age (AGE); gender (SEX); marital status (MARITAL_STATTUS); educational level (EDUC_LEVEL); the number of people in the household (HOUSEHOLD_SIZE); the number of males in the household (MALES_NUMBER); the number of people less than 15 years old (UNDER_15); the number of people above 65 years old (ABOVE_65); the number of people attending school (SCHOOL_NUMBER); and the number of disabled people aged between 15 and 65 years old (DISABLED_15_65).

Question one (Q.1) identifies the geographical location of the sampled households. The location details were recorded using a handheld global positioning system (GPS) gadget. These details include the latitude and longitude coordinates of the household, as well as altitude above sea level. These coordinates are relevant to deriving the distance of the household from the main GPPOL head office and mill factory. The distance is one of the explanatory variables used in our model.87 Thus, the survey revealed that the average distance was found to be 5.84 km, with maximum and minimum distances being 14.87 km and 1.17 km respectively. The standard deviation was 2.91. The rest of the interview results are presented below.

Four variables (age, gender, marital status, and educational level) describing personal details can be conveniently used to tabulate with other (subsequent) variables to explain a number of relationships. The results are tabulated in Tables 6.1 and 6.2 below.

87 Further details on this variable are explained in Section 5.4.2 below. 124

Table 6.1 Tabulation of AGE and the Households characteristics

Mean Max Min Std. Dev. Observations

AGE 43.4 90 18 12.858 312

HOUSEHOLD_SIZE 6.1 15 1 2.716 309

MALES_NUMBER 3.2 12 0 1.911 312

UNDER_15 2.1 13 0 2.057 312

ABOVE_65 0.1 2 0 0.386 312

DISABLED_15_65 0.06 3 0 0.294 312

The mean age of the respondents was 43 years (see Table 6.1), with a maximum and minimum age of 90 and 18 years respectively. Three respondents aged 75, 80, and 90 years old were on the high end of the age range; two of whom were widowed while one was single. Purging these three high end ages, the mean age remains unchanged at 43 years. The average peak productive mean age ranges from 33 to 55 years old (Gobel and Zwick, 2009, Veen, 2008, Schneider, 2007) depending on the physical health of individuals. Thus, the mean age in this study resides well within the mean age range in the literature. This suggests that the respondents were generally the relevant target group for this study.

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Table 6.2 Tabulation of AGE, Marital Status, and Educational level by sex of household heads88

PATTERNS BY SEX

Male = 1 Female = 0 Total Total percent Number

(%) % count % count

Age

18 - 42 63.5 99 36.5 57 50 156

43 and over 74.4 116 25.6 40 50 156

Total 69 215 31 97 100 312

Marital Status

1 = Married 56.4 176 25.0 78 81.4 254

2 = Single 8.3 26 2.6 8 10.9 34

3 = Divorced 1.3 4 1.0 3 2.2 7

4 = Widowed 2.6 8 2.6 8 5.1 16

5 = De facto 0.3 1 0.0 0 0.3 1

Educational Level**

1 = University 4.5 14 1.0 3 5.5 17

2 = College (incl. 9.3 29 2.6 8 11.9 37 vocational training)

3 = Secondary 18.9 59 8.7 27 27.6 86

4 = Primary 29.2 59 9.9 31 39.1 122

88 The percentages were rounded off, so sometimes they may not add up to 100 percent. 126

5 = Didn’t complete 4.8 15 5.5 17 10.3 32 primary

6 = Never attended primary 2.2 7 3.5 11 5.8 18

Total 312

Source: Household Survey ** Significant X2. Degree of freedom =5; Value = 25.59319; prob = 0.0001.Therefore, at a 5% level, this is significant.

We split up the sample in to two age groups, using the mean age obtained from Table 6.1 as the cut-off point, to establish any relationships between the genders. As shown in Table 6.2 above, the first age group (18 – 42 years) indicates that males have a proportional size of almost twice than that of the females (that is, 63.5 percent against 36.5 percent). This disproportion tripled in the second age group, (i.e. 74.4 percent against 25.6 percent). However, combining the gender respondents between the age groups, both the age groups have an equal proportion of respondents (50 percent each).

On the question of the highest education level attained, the survey revealed that 39 percent (majority) of the respondents’ highest level of education was primary school (see Table 6.2 above).89 Of this group, around 29 percent were males and 10 percent were females. With respect to the rest of the other respondents, approximately 6 percent of respondents attained a university education, of which 5 percent were males and 1 percent females. Around 12 percent reached the college (including vocational training) level, with the gender distribution being 9 percent males and 3 percent females. For the secondary education level, about 28 percent of the respondents reached the secondary level; 19 percent of whom were males and 9 percent were females. Many of the respondents that reached the secondary level went as far as form 5 (or year 11). About 10 percent did not complete their primary school education, with both genders (male and female) each accounting for 5 percent. Finally, 6 percent of the total respondents never attended primary school at all. Of this 6 percent, the male respondents account for

89 The 2007 RAMSI People’s Survey also found that the majority (44.7 percent) reached only primary school level (R.A.M.S.I. 2007. People's Survey 2007. Canberra: ANU Enterprise.) 127

2 percent while 4 percent were females. Effectively around 83 percent of the total respondents did not reach college (including vocational training).

In terms of the household unit, the survey shows that the average household membership was around six individuals per household (see Table 6.1 above). Three households at the upper end were way off the distribution, so they were considered outliers. In these three households (outliers) the total number of individuals was 18, 24, and 27. This is extraordinary, and obviously such households are considered too large by any standard. I discovered during the interviews that these three households are relatively wealthy and accommodate under-privileged children, most of whom are disabled. The standard number of individuals in a household however ranges from 1 to 15 people. The average household size found in this survey is close to the national rural average household size of 6.0 found in the 2005/06 Household Income and Expenditure Survey (HIES) (Solomon Islands Government, 2006).

Of the average number of six people per household, the proportion of males and females are equal, with an average of two people considered dependent. These dependents are defined as not being able to contribute to production to earn income. The dependency age in this study is defined as those under the age of 15, those above 65, and those disabled and between the age of 15 and 64 years of age. The higher the number of dependents in a household suggests that income in that household (or labour input into production) will be lower.

6.2.2 Land arrangement and oil palm characteristics Regarding land arrangements, 46 percent of the total respondents were from the Ghaubata tribe, 15 percent were from the Thimbo tribe, 7 percent from Lathi tribe, 27 percent belong to the Thogo tribe, and 5 percent were from the Nekama tribe (see Table 6.3 below).90 Of the total respondents, 51 percent lease their land to GPPOL, 23 percent prefer to grow their own oil palm and sell the fruits to GPPOL (that is, smallholders or out-growers), 19 percent both lease and grow oil palm, and 7 percent do not have any arrangements with GPPOL.

90 Ghaubata is the largest tribe on Guadalcanal while Nekama and Lathi are the smallest tribes. 128

Table 6.3 Tabulation of tribe and land arrangements (EVIEWS OUTPUT)

Count LAND_ARRANGEMENT % Total 1 2 3 4 Total 1 71 26 30 18 145 22.76 8.33 9.62 5.77 46.47

2 26 8 12 0 46 8.33 2.56 3.85 0.00 14.74

3 5 13 5 0 23 TRIBE 1.60 4.17 1.60 0.00 7.37

4 48 22 11 2 83 15.38 7.05 3.53 0.64 26.60

5 10 2 2 1 15 3.21 0.64 0.64 0.32 4.81

Total 160 71 60 21 312 51.28 22.76 19.23 6.73 100.00

KEY TRIBE: 1 = Ghaubata; 2 = Thimbo; 3 = Lathi; 4 = Thogo; 5 = Nekama. KEY LAND_ARRANGEMENT: 1 = Lease to GPPOL; 2 = Out-grower; 3 = Both 1 & 2; 4 = None

Of the total 46 percent of respondents that belongs to Ghaubata tribe, around 23 percent lease land to GPPOL, 8 percent were out-growers (smallholders), 10 percent both lease land to GPPOL and were also out-growers at the same time, while 6 percent do not have any arrangement with GPPOL, nor do they grow any oil palm. For the Thimbo tribe, 8 percent lease land to GPPOL, 3 percent are out-growers, and 12 percent both lease land to GPPOL and were out-growers. From the Lathi tribe 2 percent of respondents lease land to GPPOL, 4 percent were out-growers and another 2 percent both lease and were out-growers. Amongst members of the Thogo tribe 15 percent lease to GPPOL, 7 percent were out-growers, 4 percent both leased and were out-growers, and one percent did not have any arrangement with GPPOL. Finally, members of the Nekama tribe had 3 percent that leased land to GPPOL, one percent were out-growers, another one percent both leased and were out-growers, and 0.3 percent do not engage in any of the arrangements.

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6.2.3 Peace perception characteristics As regards the peace characteristics, the most important variable is the perception of the general law and order situation. The scores are tallied from 1 to 5, with 1 being very safe and 5 being not safe at all. As shown in Table 6.4 below, 66 percent (the majority) of respondents believed that the law and order situation had improved, with a score of two (2). This is equivalent to around 61 – 80 percent peaceful.91 Around 20 percent thought that the law and order situation has been fully restored, with a score of one (1). However, about 12 percent of respondents believed that the general law and order situation is somewhere in the middle, with a score of three (3) – the minimum threshold level of peacefulness. Only two percent believed that law and order is lower, with a score of 4 (i.e. less safe). Interestingly, none of the respondents believed that the environment is not at all safe. This suggests that the general law and order situation has indeed improved since the arrival of RAMSI, although perceptions of the level and extent of improvement varies.

Table 6.4 Tabulation of the Peace characteristics

Very safe Safe (61% - Satisfactory Less safe Not safe all (81% – 80%) = 2 safe (41% - (21% - 40%) (0% - 20%) = 100%) = 1 60%) = 3 = 4 5

% count % count % count % count % count

Law and 20.2 63 66.0 206 11.5 36 2.2 7 0 0 Order

Community 51.9 162 42.3 132 5.8 18 0 0 0 0 safety

Household 76.3 238 20.8 65 1.9 6 1.0 3 0 0 safety

Honiara 34.0 106 43.3 135 18.0 56 3.2 10 1.6 5 safety

In terms of the level of safety in the community, 52 percent of respondents perceived that their communities have improved to become very safe, with a score of one (1), equivalent to 81 percent - 100 percent peacefulness. Forty-two (42) percent felt that the

91 These scores were converted to percentages for clarity. The computation of the percentage of peacefulness is shown in Section 5.3. 130

safety in their communities is between 61 – 80 percent safe (with a score of 2) while six percent believed that their community is only 41 – 60 percent safe, with a score of 3 (see Table 5.4 above). None of the respondents ranked the peacefulness of their communities below a score of three (3).

As for the safety at the household level, the majority (76 percent) of respondents believed that their households are very safe (81 – 100 percent peaceful), with a score of one. Around 21 percent felt that the safety of their households is equivalent to a score of two (2). Two percent believed that the safety in their households is in the minimum safety threshold (that is, score of 3) while one percent thought their households were less safe (score of 4), that is below the minimum safety threshold.

Communities were also asked about their perception of personal safety in Honiara. Thirty four percent believed that Honiara is very safe, with a score of one (1), while 43 percent gave a score of two (2). Eighteen percent gave three (3), three percent gave four (4), and 2 percent thought Honiara to be not safe at all with a five (5).

Table 6.5 below compares respondents’ perception of safety in the community (that is, the GPPOL area) to Honiara. Of the total 52 percent who felt that the community is very safe with a score of 1 (or 81 percent – 100 percent peaceful), 21 of them believed that Honiara is also very safe with a score of 1 (or 81 percent – 100 percent peaceful). Another 22 percent thought that Honiara is safe, with a score of 2 (or 61 percent – 80 percent peaceful). Eight (8) percent felt that it is satisfactory with a score of 3 (or 41 percent – 60 percent peaceful). Another two percent thought that Honiara is less safe with a score of 2 (or 21 percent – 40 percent peaceful), and only 0.32 percent mentioned that Honiara is not safe at all with a score of 5 (or 0 percent – 20 percent peaceful).

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Table 6.5 Tabulation of the safety in the community and Honiara

Test Statistics df Value Prob Pearson X2 8 46.54364 0.0000 Likelihood Ratio G2 8 28.66962 0.0004

Count SAFETY_HONIARA % Total 1 2 3 4 5 Total 1 64 67 24 6 1 162 20.51 21.47 7.69 1.92 0.32 51.92

2 40 64 23 4 1 132 SAFETY_COMM UNITY 12.82 20.51 7.37 1.28 0.32 42.31

3 2 4 9 0 3 18 0.64 1.28 2.88 0.00 0.96 5.77

Total 106 135 56 10 5 312 33.97 43.27 17.95 3.21 1.60 100.00

KEY: 1 = very safe; 2 = safe; 3 = satisfactory; 4 = less safe; 5 = not safe at all

Analysing respondents’ response between the safety in the community and the safety in Honiara, a total of 42 percent of respondents (see Table 6.5 above) scored the level of safety in the community to be two (2) (61 percent – 80 percent peaceful). Of this, 13 percent said that Honiara is very safe with a score of 1 (or 81 percent – 100 percent peaceful). Twenty-one percent felt that Honiara is 61 percent – 80 percent peaceful (scored 2), seven percent perceived it to be 41 percent – 60 percent peaceful (scored 3) while one percent of respondents said that it is between 21 percent – 40 percent peaceful (with a score of 4). Only 0.32 percent perceived Honiara is not safe at all, with a score of 5 (or 0 percent – 20 percent peaceful).

Finally, of the six percent that perceived the community was 41 percent – 60 percent peaceful (that is, a score of 3), one percent revealed that Honiara was very safe with a score of 1 (or 81 percent – 100 percent peaceful), another one percent said Honiara was 61 percent – 80 percent peaceful, with score of 2, three percent admitted Honiara to be 41 percent – 60 percent peaceful (scored 3), and another one percent perceived Honiara not to be safe at all with a score of 5 (that is, 0 percent – 21 percent peaceful).

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A question was also asked whether safety during the day is different from that at night. Table 6.6 below revealed that 78 percent (majority) felt that they feel safe both during the day and at night time. Twenty-two percent admitted that they only feel safe during the day while only 0.32 percent (i.e. one respondent) revealed that he does feel safe during the night time only. Further analysis reveals that of the 76 percent that believed in 81 percent – 100 percent peaceful (scored 1) in their households, about 57 percent of which revealed that they feel safe during both the night and the day (see Table 6.7 below).92 As well, 19 percent of them felt 100 percent safe only during the day time (see Table 6.7 below).

Table 6.6 Tabulation of day and night safety

Value Count Percent 1 69 22.12 2 1 0.32 3 242 77.56 Total 312 100.00

Key: 1 = safe during Day; 2 = safe during night; 3 = safe both day and night

92 From Table 5.6 read row one and column 3 133

Table 6.7 Tabulation of safety in the household and safety during the day/night

Tabulation Summary

Test Statistics df Value Prob Pearson X2 6 10.03309 0.1233 Likelihood Ratio G2 6 10.13711 0.1190

Count SAFETY_DAYNIGHT % Total 1 2 3 Total 1 59 0 179 238 18.91 0.00 57.37 76.28

2 7 1 57 65 2.24 0.32 18.27 20.83

SAFETY_H OUSEHOL D 3 2 0 4 6 0.64 0.00 1.28 1.92

4 1 0 2 3 0.32 0.00 0.64 0.96

Total 69 1 242 312 22.12 0.32 77.56 100.00

6.2.3.1 Other variables related to peace perception characteristics Responding to how they would feel if RAMSI left the country immediately (RAMSI_LEAVE), 34 percent of respondents said that it would be less safe, with a score of 4 (see Table 6.8 below). Interestingly, some nine percent of respondents said that they would not feel safe at all, with a score of 5, if RAMSI were to leave. Four percent felt that they will be very safe (score of 1) and 29 percent thought they would be safe (scored 2), while 24 percent scored three (3), implying satisfactory peace.

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Table 6.8 Tabulation of RAMSI_LEAVE and SEX

Tabulation Summary

Test Statistics df Value Prob Pearson X2 4 12.57895 0.0135 Likelihood Ratio G2 4 12.59464 0.0134

Note: Expected value is less than 5 in 10.00% of cells (1 of 10).

Count SEX % Total 0 1 Total 1 3 8 11 0.96 2.56 3.53

2 19 70 89 6.09 22.44 28.53

3 23 53 76 RAMSI_LEA VE 7.37 16.99 24.36

4 46 61 107 14.74 19.55 34.29

5 6 23 29 1.92 7.37 9.29

Total 97 215 312 31.09 68.91 100.00

KEY: 1 = very safe (81% - 100%); 2 = safe (61% - 80%); 3 = satisfactory (51 - 60%; 4 = less safe (21% - 50%); 5 = not safe at all (0% - 20%).

Moreover, the survey also asked whether the interviewees thought RAMSI should remain (RAMSI_REMAIN), with scores explained at the bottom of Table 6.9. Consequently, the survey found that the majority (49 percent) of the respondents would prefer RAMSI to remain until their work is completed, with a score of 2 (see Table 6.9 below). Some 29 percent of the respondents felt that RAMSI should remain for more than ten years (scored 4). This suggests that there is still a sense of insecurity at the community level despite improvement in the law and order situation. This insecurity perhaps stemmed from the public’s low level of confidence in the local police force, as shown in Tables 6.9 and 5.10 below. Twenty percent, on the other hand, believed that RAMSI should remain for another 10 years (scored 3). Only two percent felt that

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RAMSI should leave immediately. Tellingly, the respondents who constituted this two percent admitted to their homes having been raided by the RAMSI police.

In effect, 98 percent of the respondents preferred for RAMSI to remain, even after their ten year presence in the country. The latest RAMSI’s 2013 Peoples’ Survey also finds that the majority (86 percent) of the respondents supported the presence of RAMSI (Regional Assistance Mission to Solomon Islands, 2013). This suggests that RAMSI’s work program should not be time bound. Regardless, RAMSI have already withdrawn most of their major operations on the 10th anniversary of their arrival in 2003. The duration of RAMSI intervention conforms to the ten-year exit proposal advocated by Collier and Hoeffler (2004a), as shown elsewhere in Chapter two.

Table 6.9 Tabulation of RAMSI_REMAIN and RAMSI_SCALEDOWN

Tabulation Summary

Test Statistics df Value Prob Pearson X2 6 28.15318 0.0001 Likelihood Ratio G2 6 30.33780 0.0000

Count RAMSI_SCALEDOWN % Total 1 2 3 Total 1 5 0 0 5 1.60 0.00 0.00 1.60

2 91 50 11 152 29.17 16.03 3.53 48.72

RAMSI_REM AIN 3 22 38 3 63 7.05 12.18 0.96 20.19

4 33 55 4 92 10.58 17.63 1.28 29.49

Total 151 143 18 312 48.40 45.83 5.77 100.00

Key to RAMSI_REMAIN: 1 = leave now; 2 = remain until work completes; 3 = remain for another 10 years (after 10th anniversary); 4 = remain for more than 10 years (after 10th anniversary). Key to RAMSI_SCALEDOWN: 1 = Yes; 2 = No; 3 = don’t know

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Concerning whether or not RAMSI should scale down (RAMSI_SCALEDOWN), 48 percent thought so, while 46 percent felt otherwise (see Table 6.9 above). Only about 6 percent ‘do not know’. Further analysis revealed that many of the respondents who would prefer for RAMSI to stay longer than 10 years argued that RAMSI should (after 10 years of duty) focus only on advisory roles.

Gauging the confidence of the respondents in the police force, the descriptive statistics in Table 6.10 below indicates an average confidence level of 54 percent, with around 33 percent (highest) of the respondents revealing that their confidence in the police is within the 50 percent boundary (see Table 6.11 below). Some of the respondents revealed that most of the cases reported to police were dealt with slowly or never followed up. Some even mentioned that they are scared to report the crimes to police as some police officers were perceived to be dishonest. For example, few local police officers were seen as being involved in breaking the law, drinking and being disorderly in public places; some were even known to have been involved in criminal activities (such as threatening and demanding money from people) during the height of the civil conflict. Needless to say this could be the reason for the persistence of a feeling of insecurity in the community.

Table 6.10 Descriptive Statistics for Police Confidence

Mean Max Min Std. Dev Obs.

Police 54.077 100.00 0.00 21.104 312 Confidence

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Table 6.11 Tabulation of POLICE_CONFIDENCE

Value Count Percent 0 4 1.28 2 2 0.64 10 4 1.28 20 12 3.85 25 1 0.32 30 12 3.85 40 53 16.99 50 104 33.33 60 59 18.91 65 2 0.64 70 8 2.56 75 3 0.96 80 16 5.13 88 1 0.32 90 4 1.28 100 27 8.65 Total 312 100.00

6.3.1 Livelihood Characteristics The survey categorised the livelihood variables into financial and non-financial characteristics.

6.3.1.1 Financial livelihood characteristics For the financial characteristics, expenses, savings and school fees payments were included to investigate the respondents’ financial position. In the questionnaire, weekly expenses were requested, and disaggregated into basic and luxury goods. Monthly expenses (EXP_BASMTH for basic monthly goods and EXP_LUXMTH for monthly luxury goods) were calculated by multiplying the weekly expenses by 52 weeks for a year, and divided by 12 to obtain monthly expenses. Monthly savings data were also collected, as well as data on annual school fee payments. The survey thus found that the average total spending per month on basic goods was SBD$3,017.3693 local currency, with a maximum amount spent of $21,666.67 and minimum of $216.67 per month (see Table 6.12 below). Spending on luxury goods averaged $1,269.80, with SBD$17,333.33 being the highest amount spent per month. Average monthly savings, on the other hand, were $2,233.846 per month, with the highest savings found to be $30,000 per month. The average school fees paid per year were revealed to be $2798.97, with the highest

93 Unless specified the unit of currency is Solomon Islands Dollars (SBD$), SBD$1 = US$0.13. 138

fees paid being $25,000 per annum.94 One respondent revealed that he is paying $90,400 per annum for his child to study high school overseas. These results suggest that there is significant cash income accruing to these households.

Table 6.12 Tabulation of descriptive statistics for the financial livelihood characteristics

Basic Goods Luxury Savings Total Income School ($monthly) Goods ($monthly) ($monthly) Fees ($monthly ($Annual)

Mean 3045.14 1269.80 2233.85 6521.00 2798.97

Max 21666.67 17333.33 30000 44833.33 25000.00

Min 216.67 0 0 433.33 0

Std. Dev. 3054.24 1843.604 4385.407 6747.408 3964.437

Obs. 312 312 312 312 311

Aggregating the total monthly spending and monthly savings yields an estimated total monthly income (see Table 6.12 above). Accordingly, the average income is $6,521.00 per month. Interestingly, this average income is comparable to the average gross (before tax) income of the government’s most senior public servants.95 In addition, this average income level is comparable to urban income, which is four times higher than the average rural income (Solomon Islands Government, 2006: 30)

6.3.1.2 Non-financial livelihood characteristics In terms of whether the respondents have other incomes apart from the land rents, royalties, and dividends they receive from GPPOL, and/ or the proceeds they receive from the sale of the fresh fruit bunches, 93 percent revealed that they do have other incomes (see Table 6.13 below). Table 6.13 below also shows that about 58 percent do not own any small businesses. Of the total that has other incomes, about 40 percent own

94 I found that either the households paying very high fees sent their children to very expensive schools in town (Honiara), or their children are studying overseas, mainly in Fiji and Australia. The distribution of the school fee variable is vastly dispersed and skewed to the higher end. 95 The author has good knowledge of the Solomon Island Government’s payroll structure. The most senior public servants’ (i.e. from Under Secretary Level and up) receive average incomes of around $6,000 per month (or $3,000 per fortnight). Furthermore, public servants’ after tax salary is less than the above figure, whereas the local communities received the full amount because their (local farmers) income is not taxed. 139

small businesses. The small businesses include retail shops, transportation services, security services, and contractors.

Table 6.13 Tabulation of the non-financial livelihood characteristics

Yes = 1 No = 0

(%) Count (%) Count

Own Business 42.2 130 57.8 178

Other Income 93.2 287 6.8 21

Easy Money96 93.9 293 6.1 19

Despite their low educational background, the local communities have shown great determination to improve their quality of life by engaging in various income generating activities. Economic theory has it that the advantage of the presence of large investments be it foreign or locally owned, is that there are economic spin-offs, or what is known in economics as externalities. Similarly, the presence of GPPOL has significant economic spin-offs that facilitate economic opportunities for local households and communities. In the survey it was revealed that 94 percent of the respondents have found it easy to make money as a result of the presence of GPPOL (see Table 6.13 above). This is interesting because it implies that GPPOL’s presence facilitate economic opportunities for households and/or local communities that allow them to improve their livelihoods. Such improvements in economic opportunities are likely to reduce the potential for unwanted and untoward activities that may trigger a return to conflict. For instance, the security services for the GPPOL properties are provided by the villagers, as is the maintenance of the feeder roads to the company’s oil palm plots.

Furthermore, the overwhelming 94 percent majority revealed that they can now easily earn money (income) than the pre-conflict period. Respondents revealed that before the civil conflict, they earned income less than the other ethnic groups settling in the plains.

96 This refers to whether or not the current environment (post-conflict) is better or it is easier for the households to earn income than the pre-conflict period. 140

After the conflict, settlers have moved away from the plains, which gave the indigenous landowners to expand economic opportunities.

In terms of the house characteristics, most houses (62 percent) were built after RAMSI arrived, with 2004 being the average year of construction. Around 44 percent of the houses are permanent, 13 percent are semi-permanent, and 43 percent are traditional (i.e. thatched roof) houses (see Table 6.14 below). The oldest house was built in 1968, with the newest one being built in the beginning of 2014. In theory, households with higher incomes tend to have better and permanent housing. The fieldwork revealed that despite having higher incomes, some respondents were housed in either thatched roof or semi-permanent homes. I was told that some households are not keen to build better houses due to fear of being targeted through ‘black magic’.

Table 6.14 House structure

Year House Built House Structure

(%) Count

Mean 2004.5

Max 2014

Min 1968

Std. Dev. 7.262

1 = Permanent 44.2 138

2 = Semi-permanent 12.8 40

3 = Traditional 42.9 134 (thatch roof)

6.3.1.3 Landowners that leased land to GPPOL The respondents who leased their land to GPPOL receive three types of direct financial rewards; these are royalties on production, land rents from leasing their land, and dividends from owning 20 percent equity in GPPOL. The annual distribution of these financial benefits is tabulated in Table 6.15. For royalty payments, 19.2 percent of the respondents were paid $500 or less per year. Around 34 percent received royalty of more than $5000, while 29 percent did not receive anything because they are not

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landowners. For land rentals, 40 percent of the respondents received land rentals from the SBD$0 – SBD$500 range, while 7 percent received payment in the range of SBD$5000 and over. With dividend payments, 6 percent of the respondents were paid in the SB$0 – SBD$500 range, while 41 percent were in the SBD$5001 and over range. As explained in Chapter 3, these payments are paid to land trustees. Each of these five main tribes have several sub-tribes, and each sub-tribe appoints one trustee to represent them in the main tribe. The funds are paid to the trustees’ account, who in turn shares the money out to their fellow tribe’s people. In some instances, trustees were perceived to be unfair in the distribution of the money, and these distributive conflicts pose a threat to peace.

Table 6.15 Direct financial benefits to the Landowners

Royalty payments Land rent (annual) Dividends (annual) (SBD$annual)

Count % Count % Count %

0 – 500 60 19.2 125 40.1 18 5.8

501 – 1000 1 0.3 20 6.4 22 7.1

1001 – 1500 16 5.1 18 5.8 4 1.3

1501 – 2000 5 1.6 26 8.3 10 3.2

2001 – 2500 19 6.1 2 0.6 2 0.6

2501 – 3000 2 0.6 2 0.6 13 4.2

3001 – 3500 0 0.0 3 1.0 0 0.0

3501 – 4000 9 2.9 2 0.6 6 1.9

4001 – 4500 1 0.3 1 0.3 1 0.3

4501 – 5000 3 1.0 1 0.3 16 5.1

5001 and Over 105 33.7 21 6.7 129 41.3

NA 91 29.2 91 29.2 91 29.2

Total 312 100 312 100 312 100

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Note that the majority of the respondents receive little income from this source. The monthly average income obtained in Section 6.2.4.1 is very high, compared to what most people earn from royalties, rents and dividends. This suggests that most of the communities earn income from other activities, as will be shown below.

6.3.1.4 Smallholders (out-growers) The outcome of the random selection of the total sample saw 130 smallholders (out- growers) being selected for interview. It should be noted that some out-growers are also landowners who leased their land to GPPOL. Table 6.16 below presents the responses of this sample based on the dichotomous (Yes/No) questions. The questions ask whether or not they use certain inputs for their oil palm.

Table 6.16 Tabulation of the responses on smallholder inputs

Question Responses

1 = Yes (%) 0 = No (%)

Fertilizer 76 58.46 54 41.54

Herbicide 30 23.08 100 76.92

Hired Labour 115 88.46 15 11.54

Wheelbarrow 86 66.15 44 33.85

Tractor 22 16.92 108 83.08

Hand tools 105 80.77 25 19.23

Knapsack sprayer 46 35.38 84 64.62

Technical advice 101 77.69 29 22.31

Training 86 66.15 44 33.85

Share proceeds 39 30 91 70.00

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As can be seen from Table 6.16 above, the use of inputs are mixed among the respondents. For example, with the input fertilizer 58 percent of the sample uses fertilizer, while 42 percent do not. This contrasts with the 23 percent who use herbicide (chemical), while 77 percent do not use weedicide at all. With regards to labour, 88 percent hire labour, while 12 percent use only family members. Sixty-six (66) percent have wheelbarrows, while only 17 percent own or use a tractor. About 81 percent of the respondents have hand tools97, while 35 percent own or use knapsack sprayers. Around 78 percent received technical advice from GPPOL on how to plant and maintain the oil palm trees. Some 66 percent of the respondents also received training (both from GPPOL and the Government) about oil palm cultivation. Finally, 70 percent report that they share their proceeds from the sale of FFB with their immediate extended family members.

In addition, GPPOL also provides interest free loans to the smallholders for them to purchase inputs such as fertilizer and weedicide. However, many of the farmers expressed that these inputs (fertilizer and chemicals) are very expensive, and consumed a large proportion of their sales. Some smallholder farmers felt that these inputs should be provided free to them by GPPOL, given that they are imported duty free – suggesting a considerable misunderstanding of the term ‘duty-free’. Indeed, this is an indication of mismatch of information and perceptions between GPPOL and the communities (smallholders) that could create conflict.

Having established the results from the survey, the second part of the chapter explains the formal derivation of a new measure of peace, called the Peace Perception Index (PPI), and then analyses the impact of peace (as measured by PPI) on household income.

97 These include bush knives, axes, harvesting tools such as a long pole with a sickle, chisel, etc… 144

PART II

6.3 Derivation of a peace perception index (PPI) This section discusses a new measurement for peace, called the peace perception index, or PPI. Drawing from the framework constructed by Anders and Ohlson (2014) called legitimate peace, this study computes the peace perception index (PPI) to gauge the level of peacefulness at the community level. Considering the socially constructed nature of peace, the PPI is computed from people’s perceptions about peace, and is based on an interpretive epistemology. The methodological appeal is not lost when applied deductively. Furthermore, the PPI employs a ‘bottom-up’ perspective that reflects practically the reality of the social entity (households and/or communities). The novelty of this method is that it is in practice simple to measure the (perceived) level of peace, and thus can be applied to explain the variations in household incomes.

Derivation of the PPI is imperative to achieve four chief outcomes in this study. First, it is used to calculate a direct measure of peace and investigate the level of peacefulness. This addresses the first sub-research question presented in section 6.1. Second, the PPI is used to examine the effect of peace on the likelihood of GPPOL continuing to operate in the Solomon Islands. This addresses the second sub-research question in section 6.1 above. Third, it informs my investigation of the effect of peace on the likelihood for households to own a small business, which addresses the third sub-research question in section 6.1 above. Finally, it is essential in investigating the impact of peace on household income, which addressed the fourth sub-research question in section 6.1 above. All of these questions effectively address the role of peace on the economy at the micro level. These outcomes will also allow us to analyse the transmission mechanisms of peace to income. They also enable us to assess the different effects of peace with and without the presence of GPPOL. In effect, I quantify both the direct and the indirect impact of peace on income. The indirect effect of peace is particularly important, because it rationalises the role of GPPOL in sustaining the economy through the

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benefits accrued to the landowners and surrounding communities. Finally, another important reason for deriving the PPI is to allow the computation of the elasticity (or responsiveness) of income to changes in the level of peace. The derivation of the ‘peace elasticity’ is important, because it will be applied in the calibration of the data on the peace industry for the computable general equilibrium (CGE) model that will be presented in Chapter 8.

The analyses in this chapter are concerned with micro level interpretation of peace, using a partial equilibrium analysis. Significantly, it also shows that the measurement of peace in this study is directly based on “what peace is”, as opposed to “what violence is not”, or the “absence of violence” as seen in the measurement of peace in Chapter 2. In other words, the derivation and measurement of the construct peace in this chapter is subjectively oriented but analysed deductively. In Chapter 8, we extend the derivation and measurement of peace to become deductively oriented, before investigating the economy-wide perspective of the impact of peace on the economic aggregates using the CGE framework.

The respondents’ perception of peace is measured by three key variables, which were identified in Chapter 5 using the Cronbach’s alpha coefficient. It is important to note that the level of peace measured in this study focuses on the household and community levels. Conceptually this works from the deduction that the household unit is the central point for this bottom-up data aggregation. Recognising the sentiments of the communities shows whether the post-conflict community is at risk. Furthermore, perceptions and sentiments provide clues as to the likely risk of a recurrence in conflict, as the saying goes, “where there is smoke, there is fire”. Thus, aggregating the perceptions of all the households in the community derives the community’s PPI. A national PPI can be calculated by running similar exercises in other communities in the country. Similarly, a more representative sample of communities could be used to infer the extent of peace at the national level, which raises the question of external validity.

Applying our data on the communities on the Guadalcanal plains (or for the area of operations for GPPOL), we are able to derive a PPI. Drawing from the Cronbach’s

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alpha coefficient obtained in Chapter 4 the three variables: household safety, community safety, and general law and order, proved to be the true measures of the perception on the level of peace. These three variables were also subjected to a principal component analysis (PCA) to ensure that they reflect the true measure of peace. The principal axis method was employed to extract the components using Eviews 8 student version software. At the outset PCA is a linear combination of observed variables. A simple assumption in the use of PCA entails that the score and loading vectors corresponding to the largest eigenvalues contain relevant information that relates to the underlying construct under investigation (Wold et al., 1987: 42). The scores or values for the principal components (new variable) can be computed using either factor score

(i.e. weighted scores) or factor–based score (i.e. simple averaging) (ibid).

Given the simplicity of the latter, this study applies the factor-based score where the scores of each variable determined in the PCA are averaged to derive new scores for the new variable. Computing the PCA, the three factors (variables) mentioned above have higher loading vectors (see Table 6.17 below).98 Thus, we called our new variable (which is the principal component 1 in Table 6.17 below), peace. To obtain the scores for each observation in the peace variable, we simply take the mean scores of the three observed variables mentioned above for each observation. The other variables, which were also meant to measure peace but turned out to be unreliable, were perception on safety in Honiara, and the question of RAMSI’s exit. Clearly, these two variables are not related to perceptions of peacefulness in the wider community.

98 The three observed variables with higher loading (eigenvector) are marked with the asterisks (***) under PC1 (Principal component 1). Their correlation (lower section of the table) also showed that the three observed variables are highly correlated. PC1 also has an eigenvalue variance (the top section of the table) greater than 1 (which is the minimum ‘rule of thumb’ threshold for acceptance), that is the eigenvalue for PC1 is 1.93. Our focus is on PC1, hence the other principal components are ignored. 147

Table 6.17 Eviews Output: Principal Components Analysis

Computed using: Ordinary correlations Extracting 5 of 5 possible components

Eigenvalues: (Sum = 5, Average = 1) Cumulativ Cumulative e Number Value Difference Proportion Value Proportion

1 1.930273 0.858877 0.3861 1.930273 0.3861 2 1.071396 0.173803 0.2143 3.001669 0.6003 3 0.897593 0.227254 0.1795 3.899261 0.7799 4 0.670338 0.239938 0.1341 4.569599 0.9139 5 0.430401 --- 0.0861 5.000000 1.0000

Eigenvectors (loadings):

Variable PC 1 PC 2 PC 3 PC 4 PC 5

PEACE_COMM 0.608620*** 0.006175 -0.123563 -0.169111 -0.765296 PEACE_HHOLD 0.541428*** 0.273065 -0.216946 -0.500631 0.578441 PEACE_LAWORDER 0.487399*** -0.297154 -0.213895 0.750982 0.253806 PEACE_HON 0.267247 -0.500306 0.792826 -0.186749 0.121757 RAMSI_LEAVE 0.165706 0.766022 0.513170 0.349173 -0.022051

Ordinary correlations: PEACE_LAW RAMSI_LEA PEACE_COMM PEACE_HHOLD ORDER PEACE_HON VE PEACE_COMM 1.000000 PEACE_HHOLD 0.528162 1.000000 PEACE_LAWORD ER 0.425623 0.275263 1.000000 PEACE_HON 0.203786 0.071529 0.177786 1.000000 RAMSI_LEAVE 0.110505 0.174690 -0.013134 -0.004805 1.000000 Source: Eviews Output

The scores on the Likert scale in the peace characteristics were reverse coded so that 5 becomes very safe (i.e. scale between 81% - 100%), 4 is safe (between 61% - 80%), 3 is satisfactory (between 41% -60%), 2 is less safe (21% – 40%), and 1 is not safe at all (0% - 24%). Recall that the original questionnaire showed one (1) as being very safe with 5 being not safe at all. The reverse in scale was done to make it easier to compute the Cronbach’s alpha coefficient. In addition, it made the interpretation of variables from the econometric regression more straightforward. That is, the positive sign in the coefficient of the explanatory would entail a corresponding increase in the dependent variable or vice versa.

Also, notice that percentages are assigned to the scores. The allocation of percentages to the respective scores was done for purposes of ease of interpretation. These were 148

computed from the scores one to five. For example, a score of one (1), would mean 20 percent (i.e. 1/5), implying that the maximum peacefulness for this score is 20 percent; so we ranged peacefulness for this score from between 0 – 20 percent. A score of 2 would mean 40 percent (2/5) maximum peacefulness, thus, ranging between 21 – 40 percent. For a score of 3 peacefulness would be between 41 - 60 percent (3/5); a score of 4 entails peacefulness ranging from 61 – 80 (4/5) percent; and finally a score of 5 implies peacefulness to be between 81 – 100 percent (5/5). Because the new variable, peace, constitutes the mean scores of the three observed variables mentioned earlier on, it is possible to have a score with decimal points. The approach is demonstrated further below.

Formally, the level of peace (perception) for each observation, i, is derived as;

ℎ표푢푠푒ℎ표푙푑⁡푠푎푓푡푒푦 + 푐표푚푚푢푛푖푡푦⁡푠푎푓푒푡푦 + 푙푎푤푎푛푑표푟푑푒푟 푃푒푎푐푒 = 푖 푖 푖 … … … (6.1) 푖 3 where Peacei is the peace perception for each observed individual. The overall peace score for the entire sample is calculated by the sum of all individual observations divided by the sample size.

1 ⁡푃푒푎푐푒 = ⁡ ∑ 푃푒푎푐푒i⁡ (i = 1,2,3...) ...... 6.2 표푣푒푟푎푙푙 푛 where Poverall is the overall average peace score, Peacei are the individual peace scores, and n is the sample size.

Applying this to our data, the overall average peace score was calculated to be 4.413462 (or ≈ 4.41 – see Table 6.18 below). The 4.41 peace score is between the top score 5 and the second highest score 4 of peacefulness. Converting this overall average score into percentage gives 88.27 (4.4135/5 x 100), which rounds off to 88 percent. This suggests that a broadly defined idea of peace in the communities of Guadalcanal Plains has been perceived to improve by an average of 88 percent. According to RAMSI’s various People’s Survey results, the perception level of respondents has steadily increased over the years.99 Its latest findings showed that people thought that peace had continued to improve (R.A.M.S.I, 2013).100 Unfortunately, we cannot report on the temporal changes

99 Various People’s Surveys by RAMSI: 2007, 2008, 2009 2010, 2011, 2012. 100 RAMSI results on the level of peace perception are not quantified, so, we do not know how positive people’s perception of peace is. This is why the methodology for computing peace perception is advantageous, because it quantifies peace perceptions, and one can actually make comparisons from one period to another. 149

in the perception of peace due to the absence of a baseline: this survey data therefore provides a baseline. Nevertheless, we can confidently say that this 88 percent peace (perception) result is a significant improvement from 2003.

Table 6.18 Descriptive statistics for peace perception

Likert score Percent (%)

Mean 4.4135 88.269

Max 5.0000 100.0000

Min 3.0000 60.0000

Std. Dev. 0.4532 9.0647

Obs. 312 312

6.4 Conceptual Analysis of the Impact of Peace Having derived the PPI, the next step is to investigate the impact of peace on household income, which I do in this section, with the application of econometric models to address the three sub-research questions from the beginning of this chapter. At the outset, we want to find out whether or not peace contributes to income, through either direct or indirect mechanisms. For the indirect mechanisms, we investigate two possible channels: through (i) the presence of GPPOL; and (ii) the households owning small businesses. Thus, model 1 examines the effect of peace on the likelihood of GPPOL’s presence (that is, not to withdraw from the country). Model 2 investigates whether or not peace has any impact on households owning small businesses. Model 3 investigates the direct impact of peace on income. The indirect transmission mechanism is important because the presence of GPPOL, I have assumed, determines the household income both directly and indirectly, potentially to a great extent. Conceptualising the above narratives, Figure 6.1 below demonstrates the transmission of peace to income.

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Figure 6.1 Transmission Mechanism of Peace to income

Presence of local investment business Peace Income

Presence of foreign Investment Source: Author’s conceptualisation

6.4.1 Model Specification for Models 1 and 2 Models 1 and 2 entail two dichotomous models with values of the dependent variables ranging between one and zero, and the results in the form of a probability. Thus, the probability of the dependent variable occurring is explained by some independent factors (or variables). Drawing from (Greene, 1993: 636), the general framework of a probability model of this type is:

푃푟(푒푣푒푛푡푗⁡표푐푐푢푟푠) = 푃푟(푌 = 푗) = 퐹(푟푒푙푒푣푎푛푡⁡푒푓푓푒푐푡푠: 푝푎푟푎푚푒푡푒푟푠) … … . . (6.3) where F is a function with values strictly between zero and one: 0 < 퐹(푥′훽) < 1, for all real numbers. In addition, the probability of the event occurring is conditional on the independent factors. Thus,

Pr(푦 = 1|푥) = 퐹(푥′훽) … … … … … … … … … … … … … … … … … … … … … … … … . (6.4) where β is a set of parameters that reflect the impact of changes in x on the probability.

For the two models that are estimated, the dependent variable for the first model is whether or not GPPOL will remain in operation, which is identified as GREM. This is proxied by the services101 GPPOL provides to the communities. For model 2, the

101 Such services include, but are not limited to, the provision of better: roads, health and education for the communities, water supplies and sanitation, drainage system, financial credit lines for out-growers, provision of scholarship schemes, good relations between GPPOL management and the communities, and improved dialogue between GPPOL management and the communities.

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dependent variable is whether the landowner owns a small business; this is identified as BUS. Below specifies the dependent variables for both models.

Model 1

Pr(퐺푅퐸푀 = 1|푥) = 퐹(훽0 + 훽1퐿푁푃퐶퐸 + 훽2퐿푁퐼푁퐶 + 훽3푃푂퐿퐼퐶퐸 + 훽4퐷퐼푆푇퐴푁퐶퐸 +

훽5퐵푈푆) … … (6.5);

Model 2

Pr(퐵푈푆 = 1|푥) = 퐹(훽0 + 훽1퐿푁푃퐶퐸 + 훽2퐿푁퐼푁퐶 + 훽3푃푂퐿퐼퐶퐸 + 훽4퐷퐼푆푇퐴푁퐶퐸 + 훽5퐺푅퐸푀 … . (6.6) where x represents the explanatory variables on the right hand side. The rationale for the choice of the explanatory variables are explained in the data section below.

The objectives of models 1 and 2 are to test the respective hypotheses that; (i) peace is irrelevant for GPPOL’s presence; and (ii) peace does not matter for enterprise amongst the landowners.

6.4.2 Data for Models 1 and 2 The data for both models are derived from the household survey results. For model 1 we assumed that the continuous provision of much needed services to the communities will deter the communities from disturbing the operations of the company.102 In the questionnaire (Q5.9), the respondents supply information about whether GPPOL provides the essential services for their communities. A ‘yes’ (coded 1) answer to the question implies that the communities are happy, and it is very likely that the households (communities) will not disturb the operations of GPPOL, and ‘no’ (coded 0) for otherwise. From the survey result, around 44 percent of the sample revealed that GPPOL has provided the necessary services for their communities, implying that they

102 In 2006 (after one year of operation) some disgruntled landowners burnt down GPPOL’s administrative head office because these landowners perceived that GPPOL was not treating the landowners fairly by providing them with proper services such as better roads, schools, etc... Also, there were pockets of criminal activities and disturbances in and around the Company vicinity, causing disruptions to the operations. 152

are happy with GPPOL. This implies that GPPOL will not withdraw because there will be no disruptions to their operations. Conversely, 56 percent believed that GPPOL is not providing the necessary services, which implies that these people (56 percent) are not happy and may resort to violence again, that could result in disruption, causing GPPOL to withdraw.

Interviews with the GPPOL’s General Manager had also confirmed that, while the peace environment has improved, there are still pockets of untoward behaviours that could trigger the closure of GPPOL’s operations. Some of the triggers identified as major disruptions that could lead to closure are burnings of any major company properties, including the administration office, and any killings of expatriate employees. It was revealed that should any of these major disruptions occur the company will have no option but to totally withdraw from the country. It is therefore important to investigate the predicted probability for such an eventuality.

Model 2 assumes that, given the large amount of money circulating in and around the Guadalcanal plains,103 owning a small business is one way to keep individuals (in particular youths in the households) engaged in meaningful economic activities, thus reducing their time for disruptive activities. In the questionnaire (Q5.2) respondents were asked whether or not they own a business. The results showed that 43 percent (134) of the respondents own small businesses while 57 percent do not own any business. The explanatory variables for both models are explained below.

Peace (Pce): The construction of this variable has already been explained, in section 6.3 ‘derivation of the PPI’, above. Since this variable is the focus of the study, determining its effects are imperative. Thus, a positive association with the dependent variables in both models is expected.

Income (Inc): This is the average monthly income of the households. This variable was constructed using the data for consumption expenses. The calculation of this variable

103 This was found in a market survey conducted by the author (the same time that this survey was conducted) for and on behalf of the landowners. 153

was explained above. Care was taken when calculating this variable to ensure that expenses using incomes from GPPOL are not included. The continuous presence of GPPOL implies that the indirect spin-offs from the company provide inducements for household income to rise. The main sources of household income are from selling vegetables, root crops, fruit crops, small income generating activities, and rents from GPPOL.104. We assumed that when a household unit engages in income generating activities, there will be minimal (or no) disturbance to the GPPOL operation. Thus, a positive (+ve) sign is expected for this variable.

Police: This variable measures the confidence of the respondents in the local police force. It is assumed that the more effective the local police are in addressing reported crimes, the safer it is for households to engage in income generating activities, as well for GPPOL to remain within the community. Thus, a positive association is expected from both models.

Distance: This variable measures the distance (in kilometres) between the household and GPPOL’s main administration centre (as explained above). The distance entails that those households remote from the centre tend to have a low predicted probability for GPPOL’s presence to be felt, and a low predicted probability for landowners to own a business. This variable was derived from the GPS coordinates taken for each household. It was calculated using the tool in www.movable-type.co.uk/scripts/latlong.html. A negative sign is therefore expected for both models.

The conceptual framework for the model is outlined in Appendix A6.1a. The next section presents the analyses and results from the data.

6.4.3 Results for Model 1 and 2 Table 6.19 below presents the summary econometric results of the two probit regression models. The interpretations of the estimated coefficients are not straight forward as in the OLS. The only thing that can be resolved from the estimated probit regression models are the signs of the coefficients. In other words, the coefficients provide the ‘signs of the partial effects of each explanatory variable on the response probability’

104 In the market survey, also conducted by the author revealed that only 2 percent of the respondents rely on GPPOL for income. 154

(Wooldridge, 2013: 596). In our models, the variable of interest, peace, has a positive coefficient, implying that an improvement in peace entails a positive probability or likelihood of the dependent variable occurring.

Table 6.19 Summary results of the probit regression models 1 and 2 Dependent variable: GREM Dependent Variable: BUS

Probit Model 1 Probit Model 2

1 2 3 4 5 6

Coeffici Standard Marginal Coeffici Standard Marginal ents errors Effects ents errors Effects

Constant -17.621 (3.7100) -6.9032 -15.2506 (3.6625) -5.965

LNPCE 3.6026 (0.823)** 1.4114 2.3631 (0.8052)** 0.9242

LNINC 0.2279 (0.0973)* 0.0893 0.5319 (0.0992)** 0.2080

DISTANCE -0.0836 (0.02803)** -0.0327 -0.006153 (0.02705) -0.00241

POLICE -0.3332 (0.3669) -0.1305 -0.03917 (0.3692) -0.0153

BUS 0.1280 (0.1601) 0.018052

GREM 0.10931 (0.1595) 0.0126

Pseudo R2

LR statistic 49.7441 50.653

Prob (LR statistic 0.0000 0.0000

Log likelihood -189.308 -187.823

Note: double asterisks (**) denotes statistically significant at 5 percent level, single asterisk (*) means statistically significant at 10 percent level. The numbers in parenthesis are standard errors at household level.

To further investigate the impact of the explanatory variables on the response probability, we examine their marginal effects. The marginal effect is the derivative of the expected value of yi with respect to the j-th variable in xi. That is,

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휕퐸(푦푖|푥푖, 훽) ′ = 푓(−푥푖 훽)훽푗 … … … … … … … … … … … … … … … … … (6.7) 휕푥푖푗

Using the Eviews 8 student version software we evaluate, at the mean values of our data, the marginal effects. The results are recorded in columns 3 and 6 in Table 6.19 above. For model 1, our variable of interest, peace, shows that holding other factors constant, a one percentage point improvement in the log of the peace index induces a 1.4 percent increase in the likelihood of GPPOL remaining. This is statistically significant at the 5 percent level of significance. Hence, we reject the null hypothesis that the presence of GPPOL is irrelevant for peace. Intuitively, this result suggests that peace is a very important factor to ensure the continued presence of GPPOL. This is vitally important for the communities’ livelihood, because the government does not seem able to provide many essential services. More importantly, the result suggests that peace is important for investment in local communities. In order for GPPOL to continue providing such services, peace must continue to prevail. Thus, it is in the best interests of the communities to ensure that peace is maintained at all times. In addition, the communities also need to understand more about GPPOL’s operations, so that unnecessary and untoward behaviours, such as damaging the Company’s properties, are avoided.

The variable lninc (log income from other sources) is positive as expected. Holding other factors constant, a one percent increase in income increases the likelihood GPPOL remaining by 0.09 percent. The result is statistically significant at a 10 percent level. Intuitively, this result implies that as locals earn more income, the likelihood for them to disrupt GPPOL operations reduces, and they are assumed to be less likely to engage in criminal activities, as they have improved access to income generating activities. A qualification is in order: while income affects peace, the reverse is also true.105 Such, gives confidence to GPPOL to expand its operations, which the GPPOL management is now currently doing.

105 This variable (lninc) was also subjected to the endogeneity test (see Appendix A6.0) for suspected simultaneity. 156

In terms of the distance, the sign of the coefficient is as expected – i.e. negative. Evaluating at the mean value, an additional kilometre in distance of a household from GPPOL’s head office reduces the probability of GPPOL providing the necessary services by 0.03 percent, when controlling for other variables. Obviously, the further away a household is the less that household will benefit from the services provided by GPPOL. This implies a reduced sense of belonging to the ‘GPPOL family’. The result is statistically significant at the 5 percent level. This was evidenced during the fieldwork; remote households or villages that are located away from the main operations of GPPOL have roads that are in very poor condition. In addition, villagers and children from these remote communities commute long distances to get medical services and/or to attend schools.

With confidence in the local police, the sign of the coefficient is negative, contrary to our expectation. The negative results arose from the communities’ low level of confidence in the police, due to the perception of their ineffective and slow response to crimes that are reported, as was revealed during the interviews. This result however, is statistically insignificant.

Finally, the dummy variable, bus (i.e. whether landowners owning a business) have had an impact on the probability of GPPOL remaining. Taking into account the ceteris paribus effects, owning a business increases the probability of GPPOL remaining and providing the necessary services by 0.05 percent. This is, however, not statistically significant.

For model 2, analysing the likelihood that the landowners own a business, for consistency we use the same variables as in model 1. First, investigating the impact of peace, the marginal effect, controlling for the other variables, revealed that a one percent improvement in the level of peace raises the probability for the landowners to own a business to increase by close to one percent (0.9 percent). The result is statistically significant at the five percent level. Again, as in Model 1, the result implies that peace is a vital determinant for household income. Therefore, we reject the null

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hypothesis of peace does not matter for enterprising. During the interview, some households admitted that they are keen to start small businesses, but they feel that it was not yet safe for them to do so. One of the reasons given was that the negative behaviour caused by alcohol intoxication was inevitably worrisome, coupled with the ineffectiveness of the local police response to reported crimes, which exacerbated their fear.

In terms of income, the effect of log income, ceteris paribus, suggests that a one percent increase raises the probability of owning a business by 0.2 percent. This is also statistically significant at five percent level. As discussed above, many households are well endowed with financial resources. In terms of distance, the analysis showed that an additional increase of one kilometre, holding other factors constant, casues the probability to own a business to reduce by 0.002 percent. This is however, not statistically significant. Similarly, the level of confidence in police reduces the probability of owning a business by 0.02 percent. Again, the result is not statistically significant. Finally, the presence of GPPOL (i.e. GREM) entails a 0.04 percent increase in the probability of owning a business, ceteris paribus, although this is not statistically significant.

6.4.4 Robustness tests of the probit model To ensure consistency in the parameters of the two models, there is a need to check for the presence of heteroskedasticity. To do that we refer to the tests by Davidson and MacKinnon (1993). Employing Eviews 8 student version econometric software, the results are reported in Appendix A5.1b. The test reveals that our models are free from heteroskedasticity.

6.4.5 Model 3 specification: impact of peace on income Having examined the likelihood of GPPOL remaining in operation, and the likelihood of the landowners owning a business, I investigate the contribution of peace to income. I hypothesise that the presence of GPPOL and the landowners owning a business indirectly impacts the income of the households of the Guadalcanal Plains. However, in

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order for GPPOL to remain and for landowners to own businesses, peace must prevail. In saying that, the extent of the impact of peace on household income was investigated.

The peace – income nexus draws from the general empirical framework the peace – growth, for example see (Asteriou and Price, 2001, Polachek and Sevastianova, 2012, Butkiewicz and Yanikkaya, 2005, Santhirasegaram, 2008, Alesina and Perotti, 1996, Venieris and Gupta, 1986, Alesina et al., 1996, Barro, 1991, O'Reilly, 2014, Hou and Chen, 2013, Shahbaz et al., 2013). This study differs from the above studies in two ways. First, the above studies focus on the impact of peace on national income growth (that is, GDP growth), whereas this study investigates the impact of peace on household income. Second, peace in the above studies is measured indirectly through proxies like democracy, while this study directly measures peace through the perception of individuals.

The subsequent chapters analyse the impact of peace at the macro level. We incorporate the result in this section (micro level) to our macro level analysis in the next chapter. Such an approach attempts to contribute to the lacuna identified in the literature review. The novelty in such micro level analysis is that it assists policy makers to make informed decisions, and target specific policies to address specific problems, such as improving livelihood opportunities in particular communities. Thus, model 3 follows that:

푙푛푖푛푐푖 = 훾0 + 훾1푙푛푝푐푒푖 + 훾2푙푛푔푝푖푛푐푖 + 훾3푙푛푑푒푝푒푛푑푒푛푡푖 + 훾4푔푟푒푚푖 + 훾5푏푢푠푖

+ 훾6푛푚푎푙푒푖 + 훾7푝푐푒푔푟푒푚푖

+ 휀푖 … … … … … … … … … … … … … … … … … … … … . . (6.8) where 푙푛푖푛푐푖 is the logarithm income from other sources; lnpce is the logarithm peace; lngpinc is logarithm income from GPPOL; lndependent is the logarithm number of dependents in a household; grem is the binary variable for the presence of GPPOL; bus is the binary variable for a household owning a business; pcegrem is the interaction 106 term between lnpce and grem; and 휺풊is an error term.

106 Details of the explanatory variables are explained further below. 159

Assumptions: In evaluating equation 6.8, we assume that the dependent variable (lninc) and the explanatory variables are linear in the parameters, and that the value of lninc is determined for each value of the explanatory variables. In order to hold the above assumption true, it also follows that 퐸[푌푖|푋푖] = 푥′훽, wherein the conditional expectation of Yi, given the explanatory variable is equals to the right hand side of equation 6.17 (absent the error term). Furthermore, the conditional expectation of the error term is zero(푡ℎ푎푡⁡푖푠, 퐸[휀푖|푥푖] = 0), and that the error term is assumed to be uncorrelated with the explanatory variables (that is, equation 6.8 above, ɛi must not be related to any of the explanatory variables, in the left hand side) – no serial correlation. In the model, it is also assumed that the variance of the error term is constant over different observations. Similarly, the variances of the error term and the dependent variable must also be 2 constant. That is, [푣푎푟(휀푖) = 푣푎푟(푦푖) = 휎 ]. It is also assumed that the covariance of any pairs of error terms is zero, that is, 푐표푣푎푟(휀푖, 휀푗) = 푐표푣푎푟(푦푖, 푦푗) = 0, 푤ℎ푒푟푒⁡푖 ≠ 푗.

6.4.6 Data for Model 3 Again, the data for model 3 are derived from the household survey. The dependent variable, log income (lninc), has already been explained above. Note that this variable represents income from other sources (other than income received from GPPOL through royalties, land rental, and dividends).

The selection of the explanatory variables is drawn mainly from the underlying observed variables, in particular, the peace and livelihood characteristics, as well as human capital theory, which basically entails that everything that affects a person’s productivity will affect a person’s income. Based on the survey, the explanatory variables include the following; peace (pce): - This variable was derived as explained in 4.3 above. The sign is expected to be positive. gpinc: This variable refers to the economic rents received by the respondents from the operation of GPPOL, both through royalties (for those landowners who lease their land

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to GPPOL) and proceeds from the sale of fresh fruit bunches (for those landowners who grow their own oil palm – smallholders or out-growers).107 Note also that income from GPPOL is not included in the income variable above. Income from GPPOL is mainly spent on school fees and/or investments goods. Careful calibration to separate income from GPPOL and income from other sources was undertaken first. It is expected that this variable will have a positive sign. A high income from this source implies individuals (i.e. landowners) will be happy with GPPOL, thus there is no reason to disturb the company’s operations. grem: This is a dichotomous variable (already explained in 4.3 above). A positive sign is expected. bus: This is a binary variable obtained from the survey by asking whether or not the interviewee owned a small business. The answers are dichotomous with 1 = Yes, and 0 = No. The sign is expected to be positive. nmale: This is the number of males in a household. Since income is earned through physical work, a larger number of males in a household is preferred, as they are stronger, and contribute to higher (agricultural) production, which in turn increases income. Hence, a positive association is expected. pcegrem: This is the interaction term of peace and grem to take into account the different effects of peace on income (suppose GPPOL decides whether to withdraw).

The objective of model 3 is to address the central research question from a micro level perspective. Improvement in peace re-enforces confidence for GPPOL to continue investing in the country. Similarly, having a higher level of peace encourages households (landowners) to involve in income generating activities, thereby earning them higher income from sources other than GPPOL.

6.4.7 Tests for endogeneity Before the regression analysis is run we want to ensure that our model is free from the endogeneity problem. This is particularly important for our suspected variables – the logarithm of peace (lnpce) and the logarithm of income received from GPPOL

107 Note that some landowners receive royalties and are out-growers as well. 161

(lngpinc). We employ the Haussmann Test to determine the possible endogeneity in these two explanatory variables. Endogeneity problem occurs when the error term correlates with the explanatory variable, violating one of the OLS’ principles⁡(푖. 푒; 푐표푣(휀푖, 푥푖) = 0). We begin with our variable of interest, peace, and employ the Haussmann test to check for endogeneity. The results presented in Appendix A6.2a revealed that our suspected explanatory variables in the model are exogenous (that is not correlated with the error term). This means that we can estimate model 3 using OLS. The results are presented in the next section.

6.4.8 Results for Model 3 The results of the OLS estimation of model 3 are tabulated below (Table 6.20). Two additional estimation results are added for comparison purposes. The rest of the regression results can be found in Appendix A6.2b.

Table 6.20 Results of the OLS estimations

MODEL 3.1 MODEL 3.2 MODEL 3.3

VARIABLES108 LNINC LNINC LNINC

Coefficient SE Coefficient SE Coefficient SE

CONSTANT 1.9756 2.0048 2.4651 2.1856 0.0147 2.696

LNPCE 1.0931** 0.4490 0.9630* 0.4888 1.3874** 0.6061

LNDISTANCE -0.20991** 0.0992 -0.20997* 0.10429

LNGPINC 0.2586** 0.0382 0.2384** 0.0401 0.2365** 0.0391

LNDEPENDENT -0.2302** 0.0911 -0.0963 0.0908

NMALE 0.1328** 0.0313 0.0898** 0.0307

GREM 3.5491** 1.0911

BUS 0.4133** 0.10008

PCEGREM -0.0363** 0.0105

Note: * means significant at 10 percent level; ** means significant at 5 percent level.

108 Descriptions of the variables are explained in Section 5.4.6 above. 162

The results from the three OLS models above revealed that the variable under investigation, peace, is statistically significant at 5 percent level in models 3.1 and 3.3, and 10 percent level in model 3.2. Furthermore, the elasticity for peace in all three models is very high, suggesting that income is highly sensitive to changes in peace. Model 3.3 is our main model for analysis, hence our focus.

Our variable of interest, LNPCE, exhibited a positive sign as expected, and statistically significant at 5 percent level of significance. More importantly, it has an elasticity of greater than 1 (1.387 to be exact). The results indicate that a 10 percent improvement in peace induces a 13.87 percent increase in income, conditional on the other variables remaining unchanged. In other words, a 100 percent improvement in peace will cause income to increase by 138.7 percent. This high elasticity implies that income is very sensitive to peace, and that should there be changes to the level of peace, income will respond accordingly. Therefore, we reject the null hypothesis of peace is irrelevant for income. As seen in section 6.3 above, the level of peace or PPI was calculated to be 88 percent. To achieve the desired (100 percent) level of peacefulness the perceived risks prevalent in the communities must be addressed, as they could readily trigger a relapse into conflict. One of the major risks identified during the interview was the low level of confidence in the local police force. As already mentioned above, the average 54 percent confidence in the local police revealed during the interview is worrying. The RAMSI support to the local police force has yet to see results in terms of public confidence in the force. There is still distrust in the local police. The survey results revealed that one of the main factors why the respondents do not feel safe in their own communities is the ineffectiveness of the police to respond to crimes when they are reported. Many crimes were never reported to police for fear of retribution because the police cannot guarantee the safety of the informant.

Analysing the different effects of peace with and without GPPOL (i.e. grem) is important to explain the association between peace, GPPOL (grem), and income. This is shown by the interaction term, lnpcegrem. In the model, a one percentage point increase

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in log peace, without GPPOL (grem), induces the log income to increase by1.39 percent (as above), ceteris paribus. However, with GPPOL (grem), a one percent increase in log peace raises the log income by 1.35 percent, holding other factors constant.109 Both results are statistically significant at five percent level, and the negative sign in the interaction term, lnpcegrem, was contrary to our expectation. Notice that income (lninc) tends to be 0.036 basis points higher when without GPPOL (grem) than otherwise. This at first glance is contrary to the prior results. The intuition as to what is behind this could be that households switch to relying more on income from GPPOL (gpinc) rather than from other sources when the company is operating locally. Nonetheless, the difference is negligible, and does not cause any noticeable variation. Of particular note is the fact that the elasticity from both with and without GPPOL (grem) is large enough to cause significant movements. This result reaffirms that peace is indeed a very important factor to raising (household) income. This particular finding is important, because it means that if peace improves, the standard of living improves because of increased income. As the living standard improves, it then implies that poverty reduces, thereby increasing the welfare of the society.

The results for the other variables are as follows. Accounting for the ceteris paribus effects, a 10 percent increase in log gpinc (i.e. income from GPPOL) increases log income by 2.4 percent. The result is statistically significant at the five percent level of significance. The elasticity for this variable is relatively low because income from this source is fixed. This is because income from GPPOL, such as royalties and land rentals, are based on the MOU and rarely change. Despite this, some two percent of respondents depend on GPPOL as their major source of income.

For the dependent variable, a 10 percent increase in the number of (log) dependents in a household reduces log income by 0.96 percent. The result shows a negative association as expected, however, it is not statistically significant.

109 This is calculated as follows: LNINC 1.3878PCE + 3.5491GREM – 0.0363PCE*GREM 164

For the NMALE (the number of males in a household), the coefficient displays a positive sign as expected, which implies that income is positively associated with the number of males in a household. Thus, an additional male member in a household induces log income to rise by 0.09 percent, when controlling for other factors. The result is statistically significant at the five percent level of significance.

In terms of owning a business, BUS, households that own small businesses tend to earn income 0.45 percent higher than those that do not own a business, holding other factors constant. The difference is statistically significant at the five percent level of significance.

Analysing the three models together, the results revealed that peace transmits both indirectly and directly to income. The indirect mechanisms are through the presence of GPPOL (grem) and owning a business (bus). In all the three models, the variable peace has a significant effect on the dependent variables, as well as being statistically significant. Intuitively, this means that improving the level of peace increases the opportunity for people to improve their livelihood directly, by providing inputs to other businesses and by providing other services to the community. Thus, understanding these mechanisms is critical to framing informed policies.

6.4.9 Diagnostic Tests Following the empirical results obtained in Model 3 above, it is necessary to check to ensure that the principle OLS assumptions are not violated. First, I checked for the stability of the coefficients in the model, and examined whether the parameters of our model are stable across our sample data. I use the standard test – the recursive least squares estimates. I employed the following three tests: CUSUM, CUSUM of squares, and recursive coefficients (see Appendix A6.2c). The Ramsey RESET test was also applied, to test for model misspecification (see Appendix A6.2d). The results generally indicate that our model is stable with the correct functional form.

Given that the data is cross-sectional, it is necessary to test the stability in the residuals of our models for heteroskedacity. Under the normal OLS assumptions the error term is 165

constant for all observations [E(εi) = 0], thus giving rise to unbiased coefficient estimates. Applying the Breusch-Pagan test we find no heteroskedacity at the one percent level (see Appendix A6.2e).

6.5 Conclusion This chapter presented the results and analyses on the impact of the perception of peace on household income. It does so by calibrating the results obtained from the household survey. This chapter began with the descriptive results from the household survey, followed by a discussion of the derivation and quantification of the index for the perception of peace. With quantification, this study found that peace (in the plains of Guadalcanal households and communities) has been perceived to have improved by 88 percent. This is a remarkable improvement since the arrival of RAMSI in 2003. The chapter attempts to contribute to the peace literature by way of addressing the main research question.

The objective of this chapter was to quantify the impact of peace on (i) the likelihood that GPPOL remains in operation; (ii) the likelihood of households to own businesses; and (iii) household income. Evaluating the marginal effects at the mean, the result in model 1 suggests that peace does have an impact on the probability that GPPOL will continue its investment in the area. Its predicted probability is around 1.4 percent for a one percent change (i.e. improvement) in perceptions of peace. Similarly, model 2 finds that the predicted probability that households would own a small business increases to 0.9 percent when there is a one percent improvement in peace. Both the parameter estimates are statistically significant at the five percent level of significance.

Model 3 also revealed that peace is important (statistically significant) in determining income, with a high elasticity, of 139 percent. The important message from these results though is that peace is vital for production and thereby for sustaining income. It is therefore imperative that peace is maintained, so that people can freely engage in income generating activities to improve their standard of living. These findings generally corroborate with the notion of positive association of the peace – income nexus. 166

Finally, the results also find that peace can transmit to income both directly and indirectly. The indirect mechanisms are through the presence of GPPOL (grem), as well as through the households owning businesses (bus). Intuitively, these two mechanisms reinforce each other, meaning that for landowners to own businesses they depend on the presence of GPPOL, and for GPPOL to continue its operations, they depend on the land owners being engaged in other private enterprises. The latter means that it is important to have landowners owning small businesses and operating in a symbiotic relationship with the GPPOL operations. Furthermore, knowing that the transmission mechanisms of peace to income are both direct and indirect, suggests that peace is fundamental to improving the standard of living, which in turn reduces poverty.

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CHAPTER 7

THE COMPUTABLE GENERAL EQUILIBRIUM FRAMEWORK

7.1 Introduction The previous chapter developed an analysis of the impact of peace on income at the micro (household) level, employing a partial equilibrium framework. Such framework rests on the ceteris paribus principle when investigating a variation of a variable of interest, in this case peace, on the dependent variable. The partial equilibrium framework ignores the potential feedbacks of the changes in the variable under investigation on the economy. That is, it disregards the effects of changes in a policy variable to all sectors of the economy. Under normal circumstances, however, the changes in any one variable have multiple feedbacks and simultaneous economy-wide implications wherein markets, commodities, and industries are all affected. A general equilibrium analysis is required to capture all of the corresponding inter-linkages. Specifically, a computable general equilibrium (CGE) can be applied to model such economy-wide effects. It enables the changes in the variable to be traced throughout all sectors of the economy. This chapter therefore presents the CGE modelling framework for analysing the economy-wide impact of peace, and applies the framework to the Solomon Islands context.

As far as the literature is concerned, and to the best of the author’s knowledge, there has not been a CGE model constructed for the Solomon Islands. Furthermore, there are no CGE analyses of the impact of peace on any economy. This research fills these gaps. The construction and implementation of the CGE framework undoubtedly requires a considerable amount of time and effort. However, the well-known and respected ORANI-G framework developed by the Centre of Policy Studies at Victoria University110, Australia has indeed streamlined this arduous task. The ORANI-G

110 The ORANI and its variant ORANIG was developed by the Centre of Policy Studies (CoPS) back in the 1970s as part of the ‘IMPACT Project’ with the Australian Government to model the impact of policies in the Australian economy. Since then it has become popular, and been applied in many other countries. CoPS is currently hosted by Victoria University, Melbourne, Australia. For more information on ORANI, 168

framework adapts well to the input-output table structure, but maintains its microeconomic foundations. This study therefore adopts this framework to construct the CGE model for the Solomon Islands to analyse the impact of peace on the economy.

The rest of the chapter is organised as follows. Section 7.2 discusses the modern CGE model, with particular focus on the ORANIG and its variants, and some of the criticisms levelled against the theory of general equilibrium, to provide another perspective on the theory. It also explores the different schools of CGE models to gauge the extent of the usage of the CGE model thus far. Section 3.3 presents the conceptual framework employed to guide the analysis of the CGE simulation results. A methodology to quantify peace and a framework for peace to guide the analysis in the CGE simulation results is also discussed.111 In section 7.4, it outlines a theoretical framework of the ORANIG, with specific reference to the Solomon Islands context. It is worth noting that this framework employs a comparative static CGE model through which analyses and interpretations focus on what happens at the end of a particular period rather than analysing the time paths (i.e. dynamic CGE). Hence, the traditions under which the assumptions applied are that of ‘static’ conditions. This section also discusses the key features of the model (the SIORANIG) used in this study for the Solomon Islands. Section 7.5 elaborates on the database, structure, industry and commodity classifications, and the equations. For the database, we describe the input- output (IO) data for Solomon Islands, and the calibration procedures we use for the SIORANIG framework. Section 7.6 provides the equations for the model. In section 7.7, it outlines the variables, coefficient, and parameters. Section 7.8 presents the model closure and modelling processes, and identifies and separates the exogenous variables from the endogenous variables. Section 7.9 provides the model solution by specifying the technical processes involved in how to reach a solution (new equilibrium). Finally, section 7.10 concludes the chapter.

follow the link http://www.copsmodels.com/oranig.htm . The full citation can be found in the reference list. 111 Note that the quantification of peace in this chapter extends the peace variable measured in chapter 5. That is, the peace coefficient (elasticity) econometrically estimated in Chapter 5 is employed to compute a monetary value for peace in chapter 7 for purposes of our CGE model. 169

7.2 The modern computable general equilibrium (CGE) The computable general equilibrium (also known as Applied General Equilibrium) framework derives from the theory of general equilibrium. The theory dates back to the pioneering work of Walras (1874) and perhaps Edgeworth (1881). It seeks to explain the economy-wide interaction behaviour of many markets in terms of supply, demand, and prices (Walras, 1874). This interaction entails that at the end of a particular time period an established set of prices will result in an overall equilibrium. In other words, the general equilibrium analysis necessitates that supply and demand must be equal across all of the interconnected markets in the economy. The interactions conceptually capture all commodities, the factor markets and the decision making agents, which make up the economy-wide general equilibrium framework (Bandara, 1991).

For more than four decades, the theory of equilibrium went into the doldrums until Keynes (1924) and Fisher and League (1933) revived the debate by offering strong critical arguments,112 after which it went silent again for two decades. However, the ground-breaking work of Arrow and Debreu (1954) and Debreu (1959) who developed a formal derivation for the theory, reinvigorated the debate. The theory gained momentum when Scarf (1967) introduced a formal framework – the Applied General Equilibrium (AGE) - to solve the Arrow-Debreu (1954) General Equilibrium system in numerical terms. Combined with the work of Shoven and Whalley (1972) who analysed the effect of taxation on income from capital in the US, the theory of general equilibrium rose to new heights. These pioneers’ work gained wide acceptance, and have indeed simplified the abstract representation of the Walrasian theoretical equilibrium into some rational models of reality (Shoven and Whalley, 1984). While it can be argued that the general equilibrium framework is far from reality, as will be shown in section 7.2.1 below, the analyses carried out under this framework however, have provided better insights into how the variables and/or markets in an economy interact with each other.

The seminal work by Johansen (1960) on the multi-sectoral study of economic growth was perhaps one of the first contributions to the branch of computable general

112 See Section 7.2.1 below for the criticisms. 170

equilibrium modelling.113 His contribution has certainly proved ‘popular and versatile’ (Dixon and Rimmer, 2010: 3), and the incorporation of the input-output (IO) table into the CGE model was a breakthrough, and simplified what could be a strenuous task. The input-output model which was developed by Leontief (1936) for the US economy (with the inclusion of multi-sector) captures the interactions between the different sectors of the economy. The IO model attempts to simultaneously equate together the users (demanders) and the providers (supply) of resources of an economy. The IO model is also an application of the general equilibrium theory. The CGE model however, further streamlined the application/framework for ease of analysis. For example, the IO model assumes inputs such as labour to be fixed whereas the CGE model emphasises on the role of price; that is the wage rate adjusts accordingly. Furthermore, the IO model does not accommodate the supply constraint, rather it focuses more on the demand side, and hence the Leontief fixed coefficient technology is the prominent assumption.

Several variants of CGE model computations are currently in use; the extent of which elements in the general equilibrium are to be applied depends on the modellers’ preference, the structure of the economy, and the availability of the data, especially for developing countries. For example, one computational approach requires that the conditions for equilibrium can be solved from a set of simultaneous nonlinear equations by computing for the values of endogenous variables employing a fixed point algorithm (Scarf and Hansen, 1973, Broadie, 1983). Another approach, which this study adopts, is the piecewise linear approximations employing Johansen’s method, where nonlinear derivatives are linearized (Dixon et al., 1984).114

The other approach is to classify a CGE model through its structural framework. This framework can be distinguished into ‘neoclassical and eclectic non-neoclassical structures’ (Clarete and Roumasset, 1986: 1213). The neoclassical structure underscores the microeconomic foundations, entailing the optimization principle, where firms produce output by minimizing costs and households maximise utility subject to budget constraint. Firms operate under perfect competition, with prices exogenously

113 For more details on the link between Johansen multi-sectoral growth models and CGE models see Bergman, L. 1984. Extensions and Applications of the MSG Model: A Brief Survey, North-Holland, Amsterdam. 114 Section 4 provides further details on the framework. 171

determined, preventing them from earning supernormal profit. This structure is more ‘transparent and policy-specific’, thus ‘facilitates intuitive understanding of any adjustments in the economy’ (ibid., p.1213). Moreover the processes and/or the mechanics of how the model operates are known (ibid).

The eclectic non-neoclassical CGE model, on the other hand, requires grafting a macroeconomic model onto the microeconomic general equilibrium, resulting in a reduced form model. Such superimposition can create a lot of problems when analysing a specific scenario (McKinnon, 1984: 274), and it is not transparent (Clarete and Roumasset, 1986: 1213) because the mechanics of the model are unknown. Furthermore, such superimposition can also subject the CGE model to a range of theories. Once a CGE model becomes subjected to a variety of theories the structure becomes rather arbitrary (ibid). This study adopts the neoclassical approach, employing the Australian ORANIG structure.

7.2.1 Criticisms of the general equilibrium theory Despite the widespread appeal of the theory of general equilibrium with its CGE application, there are criticisms from within the economics discipline, especially from the Keynesian and Post-Keynesian schools of thought. In the main, the critiques strongly reject that a general equilibrium exists, especially in the long term. This is because the long run is a ‘misleading and useless guide to any current economic events’ (Keynes, 1924: 80). Having to wait for an equilibrium in the long run is indeed a ‘slow and painful process’ (De Sismondi and Hyse, 1991), and consequently, ‘there is absolutely nothing like perfect equilibrium’ (Fisher and League, 1933: 339).

In addition, equilibrium economics is ‘impractical and irrelevant to analyse the market forces’ (Kaldor, 1972: 1237),115 and Kaldor argues that equilibrium economics is under- represented by the economic systems of the developed economies. Accordingly, the promoters of the general equilibrium theory ‘have become a major obstacle to the development of economics as a science’ (ibid), and it is ‘impossible to make any real progress’ without dismantling the conceptual framework altogether (ibid., p.1240).

115 Kaldor coined the term equilibrium economics to refer to the theory of value. 172

In spite of Kaldor’s (1972) aggressive call to dismantle the general equilibrium theory, his ‘ideas are fragmented and lack philosophical underpinnings’ (Boylan and O'Gorman, 2008: 4). Thus, in a subtle but moderated tone, Hahn and Petri (2013) point out that despite the lack of perfect guidance of the actual behaviour of market economies, there is currently no theoretically adequate alternative.116 In other words, the general equilibrium theory cannot be as easily demolished as Kaldor (1972) would have liked, because as we will see in the next section the application of the theory is gaining momentum. Besides, the application of the theory has in fact expanded, so much so that several variants of the modern CGE software applications are becoming much easier to analyse the economy-wide implications of policy variables. Thus, complete removal of the theory seems far-fetched.

7.2.2 Use of CGE models and the different approaches Notwithstanding the critiques, the CGE model usage and application has steadily increased. Its modelling capability has extended from the macroeconomic impacts to tracing the sector-specific effects. That is, the use of the CGE application to model the economic implications of a shock (change) in a variable on the entire economy is now possible, and easier than would have been the case without the CGE framework. This innovation has simplified the abstract and complex interactions of the market. The wide acceptance of the CGE framework has seen its increasing use by academics and development practitioners, see for example (Asafu-Adjaye and Mahadevan, 2013, Dixon et al., 2010, Dixon, 2008, Asafu‐Adjaye, 2007, Asafu-Adjaye, 1996, Narayan, 2004, Narayan and Prasad, 2007, Bandara, 1991, de Melo, 1988, Clarete and Roumasset, 1986, Cooper et al., 1985, Horridge et al., 1993, Berrittella et al., 2007, Dahmardeh et al., 2012, de Miguel and Manresa, 2008, Fraser and Waschik, 2013).

Moreover, CGE application has not only been confined to analyses of the developed economies, but has also gained momentum in developing countries as well. It has, for instance, been used in analysing the impact of such policies as land reforms in Papua

116 For a full survey on critiques on General Equilibrium, see Hahn, F. & Petri, F. 2013. General Equilibrium: Problems and Prospects, Taylor & Francis. 173

New Guinea (Fairhead et al., 2010); investments in Papua New Guinea (Dixon et al., 2010) and in Fiji (Narayan, 2004); financial liberalisation in China (McCleery and Paolis, 2012); agricultural policy in PNG (Asafu-Adjaye, 1996) and in Mexico (Taylor et al., 1999) and in fifteen other developing countries (Jensen et al., 2010); trade policy in a survey of developing countries (de Melo, 1988); and water policy in the global context (Berrittella et al., 2007). The CGE framework can also analyse economy-wide impacts of shocks such as coups (Narayan and Prasad, 2007) in Fiji and crime (Levantis, 1998) in Papua New Guinea.

In its earlier versions, the CGE framework focuses mainly on macroeconomic analyses in developing countries (de Melo, 1988: 469); that is not the case anymore. CGE modellers are now able to capture the sectoral impacts of (policy) shocks. By 1988, some 70 CGE applications focusing on developing countries had been identified in the economic literature (Bandara, 1991). Using the ProQuest Central database, between 1989 to June 2014, I identified at least 251 CGE applications focusing on developing countries. This study adds to the usage of the CGE application in a developing country, focusing on the Solomon Islands.

The increasing usage of the CGE model stemmed from its ability to address the interactions of the market in an economy in totality. For example, with the assistance of software like GEMPACK, CGE can analyse both the macroeconomic and sectoral effects. The CGE framework is a set of simultaneous equations that describes the behaviour of private economic agents along with their environment. The CGE framework considers the economic inter-linkages, interactions, and interdependencies of sectors and industries within an economy. There are of course limitations, but the one specific to the Solomon Islands is the estimation of most of the parameters. That is, some of the parameters used for the Solomon Islands CGE are obtained from the literature, which raises questions of their relevance to this particular context. In some other cases, parameters are guestimated, where data from developing countries are problematic. Analysing developing countries using CGE models is often difficult, due to poor data quality and the availability of appropriate and relevant data.

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7.2.3 Different schools of CGE A closer review of the extent of the application of the CGE framework revealed that there are generally three schools of CGE usage in the literature. Bandara (1991: 12 - 13) identifies these three schools as the ‘World Bank tradition’ promoted by the likes of (Grais, 1981, Dervis et al., 1982, de Melo, 1988); the ‘Yale tradition’ promoted by authors like (Serra-Puche, 1984, Kehoe and Serra-Puche, 1983, Clarete and Roumasset, 1986); and the ‘Johansen tradition’ which was linked to the work of (Vincent, 1982, Mayer, 1982, Gupta, 1983).

While the features of CGE models in these three schools may vary significantly, the obvious ones are highlighted. For the World Bank CGE tradition, the supply side is characterised by a generalised Leontief production function along with a non-linear expenditure system (Grais, 1981). The Yale tradition assumes the supply side to have nested production functions with Leontief and CES nest while the demand functions features a Cobb-Douglas utility function (Serra-Puche, 1984, Kehoe and Serra-Puche, 1983). For the Johansen tradition, the supply side entails a nested production function including Leontief, and CRESH CES, with the demand functions characterised by additive utility functions with CES commodity nests (Vincent, 1982, Gupta, 1983)..

The Australian ORANI framework which this study adopted applies the Johansen tradition. This is done via the use of the GEMPACK software to model the Australian economy, and has been adopted in over twenty other countries.117 The framework was initially hosted by the University of Melbourne, and subsequently at La Trobe University, and Monash University, and as of 2014 was hosted at Victoria University.

7.3 Methodology for quantifying peace and a conceptual framework for peace in the CGE analysis This section develops a methodology to value (quantify) peace, as well as to provide a conceptual framework for the CGE analysis. First, I develop a methodology to quantify the monetary value of peace. After that, I establish a framework for the CGE that guides

117 See the CoPS website http://www.copsmodels.com/oranig.htm for countries that have used ORANI or its variant. 175

the analysis in chapter eight. The developments of the methodology and the conceptual framework for peace have been derived following the identification a knowledge gap in the literature, for which this study contributes to fill that lacuna.

7.3.1 Quantifying the output value for peace We begin by developing a methodology to quantify peace in the context of the model. The approach forms the basis for computing a value for the output of peace into our input-output database. The computed value is the monetary value for the output of peace, produced by the peace industry. This value appears in the production output table (i.e. the MAKE matrix table) shown in Section 7.5.1 below.

Given that peace is a social construct, its quantification entails subjective interpretations, with the need to superimpose some conditions so that the analysis becomes deductive and objective. In so doing, we employ the peace industries concept developed by the Institute of Economics and Peace (2007)118, which was explained in Chapter 2. I contextualise this peace industry concept in the Solomon Islands economy by identifying the industries that were seriously affected and/or closed down during the civil conflict between 1999 and 2003, and reopened again when peace (that is, law and order) was restored (explained in Chapter 3).119 To legitimise the peace industry in our model, we assume that such industries exist only during peace times. Thus, apart from operating their usual lines of businesses, the fact that they only operate during peacetimes means that they are also industries for peace. Thus, the causation runs both ways. That is, we establish that peace industries operate because of the presence of peace. In turn, now that the industry is in operation, we further establish that their presence also contributes to promoting peace. This is made clearer in section 7.3.2.2 below.

Consequently, this study quantifies the value of peace by drawing from the theory of ‘compound interest’, which states that the present value of an investment is equal to the

118 For more details, See Institute for Economics and Peace 2008. The Study of Industries the Prosper in Peace - the 'Peace Industry'. Discussion Paper. 119 These industries are further discussed in section 7.4. 176

principle (initial investment) amount (i.e. capital) plus the rate of interest accrued for t years. With a bit of manipulation, equation 7.1a below formally represents the basic computation for the value of peace:

푛 t 퐷푃푉 = ∑ 퐼푖 (1 + 훿푖) … … … … … … … … … … … … … … … … … … … … … … … (7.1푎) 푖=1 where;

DPV = dollar peace value at current prices

Ii = Initial investment (outlay) value for industry i since the onset of peace.

δi = peace premium for industry i.

t = number of years (since reopening of the firm until now or today)

Equation 7.1a above states that the dollar value of peace (DPV) today is equal to the sum of the initial investment value of the affected industries plus the peace premium (0<δ<1) accrued for t years. The rationale behind this formulation stemmed from the fact that the restoration of peace was one of the main deciding factors for a particular industry to re-open. Hence, the initial value of investment during the re-opening year can be inferred as the volume of peace. Note also, that peace onset in this particular setting refers to the time (or year) the industry re-opens in the post-conflict period.

The volume (initial investment value) is then added to the peace premium (δ), which is between zero and one. The peace premium, δ, is recognised as a reward or bonus for having the confidence in reopening the industry in a post-conflict environment. Summing up the DPV of all the affected industries gives us the total value of peace produced by the peace industries.

In terms of peace, as an intermediate and final demand, the following expression formalises our conceptualisation:

1 푃퐶푖 = 푋푖 − ( ) ∗ 푋푖 … … … … … … … … … … … … … … … … … … … … … … … … (7.1푏) 1 + 훿푖 177

where;

PCi = consumption of peace by user i

Xi = total cost of production for industry i / total cost of consumption for final demanders

δ = peace discount (0<<1)

We assumed that the commodity peace is a public good consumed by all industries as an intermediate input. The extent of consuming peace depends on how large or small δ is in each industry. Some will require less (or even zero) of the good, peace, than other industries. Therefore equation 7.1b entails that the demand for peace is equal to the total cost of consumption by user i less the peace discount. Section 7.3.2.1 below explains the consumption nature of peace and how to account for peace in our analysis.

The coefficient δ for each industry is econometrically estimated. For this study, we were only able to compute the coefficient for the oil palm industry, the underlying industry for this study, using the household survey. The previous chapter, which computed the elasticity of peace on (household) income, is employed as a proxy for the elasticity for the oil palm industry. Due to limited time and financial resources I was unable to estimate the elasticities for the other affected industries in the Solomon Islands. Consequently, in the absence of the industries’ elasticities, the author guestimated the elasticities for the other affected industries based on my knowledge of the local economy.

7.3.2 Conceptual framework for investigating the impact of peace Having outlined the methodology for computing the value of peace, I next developed a conceptual framework to analyse the impact of peace on the economy. In the partial equilibrium analysis performed in the previous chapter, we find that peace transmits both directly and indirectly to (household) income. The analysis found that one of the indirect mechanisms was transmitted through the presence of the Guadalcanal Plain Palm Oil Ltd (GPPOL). In this section, I expand on this proposition through a CGE application by developing a framework to underpin the analysis of the CGE model

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results in Chapter 8. Figure 7.1a below illustrates the framework, followed by a discussion on the mechanisms that influence the presence of peace on the economy.

7.3.2.1 Consumption of the commodity peace As briefly mentioned above, there is an assumption that peace is a public good. Thus, its ‘consumption’ is assumed to have similar axiomatic principles as that of any other public good. That is, its provision is available to all regardless of whether or not we pay for it. It has the public good property of being non-rival, meaning that its consumption by one party does not reduce the quantity available to another party. Furthermore, once provided, no one can be excluded from enjoying the benefits of peace. However, in order for peace to be continuously available, someone has to pay for it. This study therefore argues that the presence of investments like GPPOL provides the impetus for the continuous availability of peace. The mechanism is its stimulus to the surrounding communities to engage in economic activities, thereby reducing the likelihood for major violent conflicts. The very act of producing oil palm requires the presence of peace to ensure the product reaches the market. Individual producers reliant on the commodity for their livelihood have a vested interest in maintaining peace.

In the CGE model itself, the contribution of peace is modelled in the form of ‘technical progresses’ of the ‘peace industry’. Strictly, technical progress refers to new and better ways of doing things that improves output without actually increasing input costs. Similarly, this study refers to ‘peace innovation’ as technical progress. In addition, peace innovation refers to the intrinsic value of peace that creates the space for confidence for investment activities to flourish during peace times. This intrinsic value is derived from the presence of peace providing the confidence for investors to invest, while its absence (or the lack thereof) instils fear and loss of investor confidence. Another assumption is that the consumption of peace as a commodity provides similar utility (satisfaction) as that of any other real commodities.

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Figure 7.1a Schematic diagram of the impact of peace innovation on the economy

Oil Palm Sector Expand (I, L, K)

Loop7d Loop7c

Other Sectoral Loop 3b Impact Loop 4

Peace Innovation National (technical progress) Loop 1 Income (GDP)

Loop7b Loop7a

Macroeconomic Loop 5 Impact Source: Author’s conceptualization

7.3.2.2 Effect of peace innovation Having established the channels through which peace can bring about changes to the economy, I then analyse the feedback mechanisms or impact of these changes on the economy. I do this by separately analysing the feedback mechanisms, both at the macroeconomic level and the sectoral level. Using Figure 7.1a above, I explain the interactions of markets buoyed by the shock (improvement) of peace innovation. In essence, I attempt to address the research question, what is the contribution of peace to the rebound in the economy?

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Macroeconomic Impact First, we analyse the macroeconomic impact. The macroeconomic impact of peace can be transmitted both directly and indirectly. Below both the direct and indirect transmission mechanisms are discussed separately, with Figure 7.1a illustrating the process.

Direct Impact By shocking the peace innovation (technical progress) the direct impact on the GDP is through the technical progress component of the GDP identity, from the income approach, which is shown in Figure 7.1b below (as TECH CHANGE). Thus, a positive shock (improvement) in peace innovation induces expansion of the GDP. Loop 1 of Figure 7.1a above illustrates this. The income side of the GDP identity comprises the wages, rate of return on capital, technical progress, and indirect taxes.

In addition, with the general framework in Figure 7.1a above, further analyses of short- run and long-run causation are possible. Figure 7.1b below illustrates the short-run causation. The arrows indicate a plausible direction of causation between variables. This framework illustrates how the components of GDP are affected. The rectangles illustrate the exogenous variables while the ovals depict the endogenous variables. Under a ‘business as usual’ scenario, changes to the GDP are caused by variations in any of the variables in the ovals in Figure 7.1b below. For example, from the expenditure side, an increase/decrease in GDP could result from a change in the trade balance surplus/deficit. That is, an increase in exports would induce GDP to expand and vice versa.

Similarly, from the income side, a change in GDP is generated by a change in the labour market effects, which stem from a change in aggregate employment (while the real wage is exogenous). Hence, growth in aggregate employment due to increased economic activities will cause the GDP to expand. Increase in employment can also result from an expansion in the trade balance surplus. For example, a widening trade balance surplus entails an increase in exports, thereby inducing firms to hire more

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workers, shown by the dotted arrow running from trade balance to employment in the Figure 7.1b.

Figure 7.1b The Short-run causation

KEY : RATE OF Endogenous REAL RETURN WAGE

Exogenous

TECH CHANGE CAPITAL EMPLOYMENT STOCK

GDP CONSUMPTION INVESTMENT GOVERNMENT TRADE BALANCE

Adopted from Horridge (2000)

Under a policy or exogenously induced shock, whether from the income or expenditure side of the GDP, a change in GDP would occur. Given the short-run, the change triggers variations in the oval shapes, namely the trade balance, rate of return to capital and aggregate employment. This scenario is illustrated in the dotted arrow going from ‘technological change’ to the trade balance and employment in Figure 7.1b above.

In addition, the short-run labour market effect arising from the exogenously induced shock can be represented by the following expression:

푊 푃 퐾 = ∗ 퐴 ∗ 퐹퐿 ( ) … … … … … … … … … … … … … … … … … … … … . . (7.1) 푃푐푝푖 푃푐푝푖 퐿

Where W is the nominal wage, P is the price of locally produced goods, Pcpi is the

consumer price index, A is the technology, and FL(K/L) is the marginal product of

labour, MPL. Since the short-run timeframe runs from one to three years, the real wage

(W/Pcpi) cannot be easily adjusted in such a timeframe hence, it is constant. Similarly, 182

technology (A) and capital (K) take more than three years to adjust; hence, they have to be determined outside the model (exogenous). The relative price (P/Pcpi) and employment (L), however, can be adjusted within a short space of time, and so they endogenous. The magnitude and degree of the overall variation depends on the extent of the shock. Chapter 8 therefore analyses the extent of a 12 percent improvement in peace innovation and a 100 percent expansion in the oil palm industry.

As regards the long-run causation, again it depends on the kind of closure one adopts. However, generally, capital stock is allowed to adjust, as the timeframe is long enough to make adjustments. For this study, aggregate investment is allowed to vary by following the movement in capital (See Figure 7.1c below arrow running from investment to capital). The rates of return on capital are determined exogenously. In terms of the external sector, the trade balance is determined exogenously, constraining the GDP (from the expenditure). To ameliorate this constraint, we allow private consumption and government expenditure to move together (See Figure 7.1c below). Finally, for the labour market effects, the real wage is allowed to vary because the timeframe is long enough for the changes to have an effect while aggregate employment is exogenous.

Figure 7.1c Long-run causation

KEY: Endogenous RATE OF REAL RETURN WAGE Exogenous

TECH CHANGE CAPITAL EMPLOYMENT STOCK

CONSUMPTION TRADE GDP AND INVESTMENT BALANCE GOVERNMENT

Source: Adopted from Horridge (2000)

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Indirect Impact The indirect mechanisms operate in several ways. First, it affects national income (GDP) via the macroeconomic variables (Loops 2 and 5 in Figure 7.1a above).This can also be shown in Figure 7.1b above, where a shock to peace innovation can indirectly induce employment to increase, thus effecting GDP, or via the increase in the trade balance, as shown by the arrows (going from trade balance to employment). Note that this mechanism does not work in isolation, as can be seen from Figure 7.1a above, Loop 7a also support to drive up the macroeconomic variables. The second indirect transmission goes from ‘other sectors’, then through to the macroeconomic variables (that is from Loop 3b to Loop 7a to Loop 5) before causing GDP to change. For example, in the long-run, a shock (improvement) to peace innovation will cause capital to vary, so that investments in the ‘other sectors’ increases and causes GDP to expand (See Figure 7.1c above).

The third indirect effect goes from the oil palm industry (our industry of interest) to ‘other sectors’, then to macroeconomic variables. This is represented by going from Loop 3a to Loop 7d to Loop 7a, and finally to Loop 5. This is similar to the second transmission, except that the variation comes through a specific sector, the oil palm industry. The fourth indirect transmission mechanism goes from macroeconomic to other sectors, represented by Loop 2 to loop 7b, and then to Loop 4. As we can see, each of the transmission causes a change in GDP, it simply follows the logic established by Walras (1874) that there is always an interaction of the market.

Sectoral Impact The sectoral impact of peace is explained next. An improvement in peace innovation induces ‘other sectors’ to vary. Some sectors will expand, other sectors decline, while other sectors do not change, depending on the sensitivity of each sector to peace (as represented by the value of). The change in ‘other sectors’ then induces GDP to change. This is illustrated by Loop 3b and Loop 4 in Figure 7.1a above. Another way of impacting GDP is going from other sectors, then to the macroeconomic variables, before influencing GDP. This is shown in Figure 7.1a above as going from Loop 3b to Loop 7a to Loop 5.

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Concerning our industry of interest, the oil palm industry, the improvement in peace causes the oil palm industry to change before impacting the GDP. This is shown by Loop 3a and Loop 6 in Figure 7.1a above. It also goes from Loop 3a to Loop 7d (‘other sectors) before influencing the GDP (Loop 4).

7.3.2.3 Impact of increase in the oil palm industry Having established the impact of peace on the oil palm industry, we then investigate the impact of oil palm expansion on the economy, using the updated data derived from the peace innovation simulation. As mentioned elsewhere in section 7.3.1 above, the reverse causality in this would be to investigate the role of the private sector (investment) in promoting peace. Expansion in the oil palm industry can be triggered by any of the following: (i) an increase in investment demand (I), (ii) acquiring more land for oil palm cultivation (L), or (iii) increase in capital stock (K). The transmission mechanisms are similar to the above peace simulations, except that the impact analysis begins with a shock to the oil palm sector rather than the peace innovation. Thus, the direct impact of the expansion of the oil palm industry on national income (GDP) is shown by Loop 6. The direct impact derives from the fact that the variables, investment (I), land (L), and capital (K) that triggers the oil palm industry to expand are all components of GDP. A change in any of these variables will directly impact the GDP. The other sectoral impact is illustrated by going from Loop 7d to Loop 4 in Figure 7.1a above. The final transmission goes from oil palm to ‘other sectors’, to macroeconomic variables and then to GDP. This is shown in Figure 7.1a above as going from Loop 7d to Loop 7a, and then to Loop 5.

In summary, this section presented the valuation methodology for quantifying peace. It derives a conceptual framework for the transmission of peace, and the expansion in the oil palm industry, through changes in either investment, land, capital or a combination of any two or all of the three variables. The framework showed that the transmission mechanisms can be both direct and indirect, having an impact both at the macroeconomic level and at the sectoral level.

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The next section below outlines the CGE framework this study has used. As mentioned previously, this study follows the ORANIG framework developed by the Centre of Policy Studies (CoPS) at Victoria University, Australia.120

7.4 The theoretical framework of SIORANIG The SIORANIG draws closely from the ORANI-G framework introduced by Horridge et al. (1993). The ORANI-G is a generic version of ORANI designed to construct new models based on pre-determined CGE datasets. The novelty of the generic version is that it is designed to allow for adaptation to any single country context (Horridge, 2000).

The theoretical structure of ORANIG is an example of an applied general equilibrium describing the economy at some point in time. Under this framework, the Solomon Islands economy is modelled under the ‘Walrasian’ general equilibrium assumptions. This entails that production and imports equal the sum of intermediate inputs, total consumption, total investment, government consumption, and exports. In such a model the equations of the framework define: producers’ demand for produced inputs and primary factors; producers’ supplies of commodities; demands for inputs to capital information; household demands; export demands; government demands; relationship of basic values to production costs and to purchasers’ prices; market-clearing conditions for commodities and primary factors; numerous macroeconomic variables and the price indices (Horridge, 2000).

The framework also underpins the microeconomic foundations under a perfect competitive market, such that the demand and supply equations for private agents are derived from the ‘constraint optimization of neoclassical production and utility functions’ (Bandara, 1991: 12). Thus firms choose inputs to minimize cost and households maximize utility subject to budget constraint. In such market conditions, firms are prevented from earning supernormal profits as they take prices as given. The

120 Details on the ORANI-G related material can be accessed at http://www.copsmodels.com/oranig.htm 186

factor market assumes a constant elasticity substitution (CES) functional form with the labour market divided into skilled and unskilled labour. The combinations of the labour types are also aggregated by a CES production function. Furthermore, workers can change jobs where their skills allow them to move. For commodities, imported goods are assumed to have imperfect substitution so that the demand of imported goods does not displace the consumption of locally produced goods (Armington, 1969). The commodities are also aggregated by a CES function.121

The essential pillar of the data required for the explanation of the Solomon Islands economy is depicted in Figure 7.2 below. The model is essentially divided into the following demanders: domestic producers divided into I industries; investors divided into I industries: a single representative household; an aggregate foreign purchaser of exports; government Demands; and changes in Stock.

121 A schematic diagram of the above description is shown in Figure 7.3 under Section 7.6.1. 187

Figure 7.2: The ORANI-G Flows Database

Absorption Matrix 1 2 3 4 5 6

Producers Investors Household Export* Government Stocks

Size I I H 1 1 1

Basic Flow V1BAS V2BAS V3BAS V4BAS V5BAS V6BAS domestic C

Basic Flow V1BAS V2BAS V3BAS V4BAS V5BAS V6BAS Imported C

Margins CxSxM V1MAR V2MAR V3MAR V4MAR V5MAR

Taxes CxS V1TAX V2TAX V3TAX V4TAX V5TAX

Labour O V1LAB C = Number of Commodities I = Number of Industries

Capital 1 V1CAP S = 2: Domestic, Imported O = Number of Occupation Types

Land 1 V1LND M = Number of Commodities used as Margins

Other 1 V1OCT H = 1: Number of Household Costs Types

Production 1 V1PTX Tax * Note: Export column is for domestic goods only.

Make Import Matrix Duty Size I Total Size 1

C MAKE C V0TAR = sales row totals = col total Total absorption Source: Adopted from Horridge (2000).

188

The entries in each column show the structure of the purchases made by the agents identified in the column heading. Each of the C commodity types identified in the model can be obtained locally or imported from overseas. The source-specific commodities are then used by industries as inputs to current production and capital formation (investment). They are also consumed by households and governments, are exported, or are added to or subtracted from inventories (stocks). For the exports column, only domestically produced goods are recorded. M of the domestically produced goods are used as margin services (trade and transport) which are required to transfer commodities from their sources to their users. Commodity taxes are payable on the purchases. As well as intermediate inputs, current production requires inputs of three categories of primary factors: labour (divided into O occupations), fixed capital, and agricultural land. Production taxes include output taxes or subsidies that are not user-specific. The 'other costs' category covers various miscellaneous taxes on firms, such as municipal taxes or charges.

Each cell in the illustrative absorption matrix in Figure 7.2 above contains the name of the corresponding data matrix. The cells in rows one and two display the C commodities demanded by the users. For example, the cells in row one such as V1BAS is a 3- dimensional array displaying the basic value on the flows of C commodity, domestically produced and sourced, for use in the current production of industry i. The second row are the basic values of the flows of goods to the same users as in row one, except that these commodities are imported. With the exception of exports, all the other users can use both domestically produced goods and imported goods. It is assumed that exports only use domestically produced goods.

Row three in Figure 7.2 above shows demand for the margin commodities by the users. The margin commodities in this regard are trade and transport. Therefore, V1MAR is a 4-dimensional array indicating the cost of margin M commodities on the flows of C commodities, both produced domestically and imported (S), to industry i. Similarly,

189

V2MAR is a 4-dimensional array showing the cost of M margins services on the flows of C goods, both domestically produced and imported (S), to I investors, and so forth.

Row four of Figure 7.2 above depicts the taxes applied to the purchase of goods as intermediate, investment, or final consumption. For example, V1TAX is an array showing the cost of tax on the flows of goods, produced both locally and imported, to industry i in the current production. Rows five to seven displays the values of the factor inputs and other associated costs used by the industries in the current production. For instance, V1LAB is the cost of labour by occupation used in industry i in the current production. V1CAP is the value of fixed capital rental by industry i in the current production. In addition, V1LND is the value of land rent by industry i in the current production. The final two rows V1OCT and V1PTX respectively depict an array of ‘other costs’ such as business licenses, basic rates paid by industries in current production and an array showing costs such as tax on production or subsidies paid or received by the industries in the current production.

The satellite matrixes are the MAKE matrix and the import duties matrix. The satellite MAKE matrix (see bottom of Figure 7.2 above) shows the value of output of each commodity produced by each industry. In principle, each industry is capable of producing any of the C commodity types. Two possibilities can occur. First, an industry produces only one commodity, and only one commodity can be produced by an industry. This means that the off diagonal elements in the matrix will have values of zeros. The second possibility is that an industry can produce more than one commodity, and a commodity can be produced by more than one industry. In this case, the off diagonal matrix will have non-zero values. The second option applies to this study, in particular reference to our variable of interest, peace, where it can be produced by all industries.

The other satellite matrix, the import duties, shows the tariffs on imports. These are assumed to be levied at rates which vary by commodity but not by user. The revenue obtained is represented by the tariff vector V0TAR.

190

7.5 Data calibrations and Input-Output (IO) Tables This section discusses the data and database that forms the simulations for the SIORANIG CGE model. At the outset, like other developing countries, the coverage and the quality of data in the Solomon Islands remain problematic. Thus, the data provided in this study were albeit, carefully calibrated from very rudimentary statistics, there may be still issues with the quality. Thus, the simulation results must be interpreted with caution. As already mentioned in Chapter 5 the principal data sources for this CGE framework were the Central Bank of Solomon Islands (CBSI) and the Solomon Islands National Statistics Office (SINSO) at the Ministry of Finance and Treasury.122

With a bit of data mining, the author constructed an input-output (IO) table, tailored to suit the representation of the SIORANIG framework.123 The format of the newly constructed IO table follows closely the naming styles of sectors and commodities in the SIORANIG framework. The published national accounts comprised twenty sectors. In this study, we disaggregated the Solomon Islands economy into 30 sectors (industries) and 32 commodities (30 x 32). Table 7.1 below compares our version of the sectors to that of the national accounts published by SINSO. The sector (industry) names in the national accounts correspond to our version, which harmonizes with the SIORANIG nomenclature. Table 7.1 below displays the commodities and the industries used in the SIORANIG model.

122 The author is very thankful to these two institutions for providing the data and their assistance in directing the author to relevant information. 123 An input-output (IO) table for the Solomon Islands was first constructed by Powell (1987). Since then that IO table has never been updated; nor was it utilized by the authorities. 191

Table 7.1 Commodities and Industries for the SIORANIG model

SIORANIG SINSO naming Commodities (= naming (Industries) i) Details Industries (= j) Agriculture & 1 OilPalm Fresh Fruit Bunch 1 OilPalm hunting Crude Palm Oil, Palm Kernal Oil, Palm Agriculture & 2 OilPalmPro Kernal Meal, 1 OilPalm hunting Agriculture & 3 Cocoa Cocoa 2 Cocoa hunting Agriculture & 4 Copra Copra 3 Copra hunting Agriculture & 5 CoconutOil Coconut oil, Coconut expeller 3 Copra hunting Forestry & 6 ForestryLog Round logs, and timber 4 ForestryLog Logging 7 Fishing Fishing, and other marine products 5 Fishing Fishing Vegetables, root crops, fruit crops, nuts, Agriculture & 8 AgriFood etc... 6 AgriFood hunting Kava, Vanilla, Coffee, and other Agriculture & 9 OtherAgric Agriculture products 7 OtherAgric hunting 10 Manufactured food, beverages and 8 Manufacturing FoodBevManuf tobacco FoodBevManuf Mining & 11 MiningQuar Mining and quarrying 9 MiningQuar Quarrying 12 10 HandcraManuf Handmade Handicrafts HandcraManuf 13 OtherManuf Other manufacturing 11 OtherManuf Manufacturing 14 ChemFert Chemical, fertilizer 12 ChemFert 15 ClothingFtw Clothing and footwear 13 ClothingFtw 14 Electricity & 16 ElectWatEner Electricity, water, energy, gas ElectWatEner Water 17 Construction Construction 15 Construction Construction 18 MachnEqp Machine equipment 16 MachnEqp 19 Trade Retail and wholesale, and distribution 17 Trade Trade Hotel, village stay, restaurant, and other Hotel & 20 HotelRestaur tourism related goods 18 HotelRestaur restaurants Transport & 21 Transport Land, sea, and air transport 19 Transport Storage Communication and information Communication 22 CommIT technology 20 CommIT 192

Financial 23 FinancInterm Financial intermediaries 21 FinancInterm Intermediation 24 Insurance Insurance 22 Insurance Insurance Real Estate & 25 RealEstate Real estate 23 RealEstate renting Owner occupied 26 OwnDwelling Own owner occupied dwelling 24 OwnDwelling dwellings 27 BusineServ Business services 25 BusineServ Business services 26 Public Admin 28 PubAdminDef Public, administration, and defence PubAdminDef Defense 29 Educ Education 27 Educ Education 30 Health Health 28 Health Health 31 Peace Peace 29 Peace 32 OtherServ Other services 30 OtherServ Other services Source: Constructed by the author.

The disaggregation of the sectors and commodities in this study certainly requires disaggregated data. Unfortunately, there is a very limited amount of disaggregated data available. As such, the author employed a mixed approach to disaggregate the available data. Using the published national accounts (top-down) and the CBSI’s business survey data (bottom-up), the author compiled the sectoral data and constructed an input-output (IO) table.124 This study uses the 2013 data for the analyses. Data on government operations (i.e. tax revenues and expenditures) were obtained from the Ministry of Finance and Treasury (MOFT) through CBSI. The data on household consumption was collected from SINSO published data. The last household income and expenditure survey (HIES) was conducted in 2006, thus the 2013 household consumption data was computed by extrapolation using the 2006 figures. Data on the financial sector was sourced from CBSI. Where the relevant data is missing, we extrapolate to arrive at the requisite information.

We also best guestimate the data if and only if there is no clear clue or nuance to lead us to any better estimation. Where guestimation was used, the author carefully used his

124 The country’s main statistics office, SINSO has been (and still is) seriously under-resourced, thus they cannot produce most of the economic data. However, the Central Bank of the Solomon Islands has been collecting some of these main data, except for employment data. Having worked with CBSI, I had the privilege to cross-examine data from both institutions and came up with some compromises. 193

knowledge of the economy to arrive at a reasonable figure. Many CGE users have also employed Guestimate approach (Bandara, 1991), although it should only be applied where it is absolutely necessary. Furthermore, the elasticity and parameter estimates are borrowed from the literature since Solomon Islands does not have any.125 Therefore, the true values of these estimates may not reflect their hypothesised values, so the simulation results should be interpreted as likely effects of the shock instead of the true estimates of outcomes.

The main data compiled include: the MAKE matrix; aggregated USE table; Imports; Import duties; Exports; Export duties; Value added; wage relativity; and estimated number of people employed. These data were not in the form adaptable to the SIORANIG framework. The next section describes further calibrations of the data to fit it to the SIORANIG framework.

7.5.1 The IO Table The most common tables in the IO table are the USE and the MAKE matrix tables. Given the rudimentary nature of the data, the IO table was constructed to ensure the balancing conditions were met. The first step was to check that the total value of commodities produced by industry i (in the MAKE matrix) is equal to the total cost of production in industry i (in the USE table) and value added in industry i. We do not have enough information on the value for each factor input (labour, capital, and land), but we do have the estimated values for the valued added for each industry. Thus, we let the data on the value added be determined residually so as to meet the balancing conditions. We then check to ensure that the total final demanders in the USE table equals to the total value added. We balance this by letting the change in stock (inventory) adjust accordingly.

In the next step the value added row of the USE table is split into the primary factor inputs (i.e. labour, capital, and land). First, I divided the wages into each industry, and

125 I search the literature for studies that have focussed on economies that are relatively similar to the Solomon Islands, in terms of structure. In particular, I focussed on studies of PNG and Fiji. 194

maintained the economy-wide wage in the Solomon Islands to be 55 percent of the value added, as estimated by Powell (1992).126 Then I calculated the estimated relative wage rate by dividing wage by the estimated numbers employed. Next, I estimated the value of capital and land, the sum of which is the gross operating surplus (GOS). That is, the difference between value added and wage is GOS. In terms of capital-land ratio, the current economic structure of the Solomon Islands economy is such that capital has higher value than land. Thus, I assumed that the capital-land ratio is 60/40. Then I checked to ensure the balancing condition is met. This is done by ensuring that the sum of factor input and total intermediate input cost of industry i (in the USE table) less the total production in industry i (in the MAKE matrix) is zero (or tiny).

Next, I computed the margins matrix, separating the margin component from the basic values. This study identifies trade and transport as the margin commodities. However, not all industries use margin commodities, as some produce their own transport. Industries like mining, oil palm, utilities, fishing, logging, communications and information technology produce their own transport.

The data provided by the authorities in the USE table are not disaggregated into source types. Our next task, therefore, is to separate the domestically produced goods from the imported ones. The Solomon Islands relies heavily on imported goods, thus the import component constituted a large share, 87 percent127 to be exact, of the total USE table. Similarly, the majority of investments are import-intensive, and are hence estimated at 80 percent; so was household consumption. Again, I ran a check to ensure that the balancing condition is maintained.

The final step was to split out investment according to industry. In doing so, I distinguish the service investment industries from the non-service investment industries. The non-service industries are mainly the industries that produce tangible output. Using the import share mentioned above for investment, I computed the investment matrix, and checked for the balancing condition.

126 Powell (1987) found the economy-wide wage to be 55 percent of value added. 127 Derived from the value of total imports to total value added. 195

Having explained the input-output data for the CGE model, I next discuss the equations of the model that will utilize this data.

7.6 Equations of the Model The equations of a CGE framework provide a formal description and algebraic specification of the model attempting to represent the interactions of the economy. Such interactions are aimed at achieving equilibrium. The equations are combined based on the functional form for production, consumption, aggregation, and transformation. Importantly, the functional form determines the implementation of the CGE models through the calibration principle. The most common functional forms include: Cobb- Douglas (CD) function; Leontief production function; the constant elasticity of substitution (CES); constant elasticity of transformation (CET); and the linear expenditure system (LES)128. The choice of the functional form may vary, but in general the functional form should meet the Walras general equilibrium condition, which displays a continuous functions with homogenous of degree zero (Shoven and Whalley, 1984). Besides this condition, the choice also depends on the sectors and/or characteristics of the economy under investigation, along with the values of various related elasticities. A standard approach used in the ORANIG framework, which this study uses, is a combination of the last four functional forms in the various stages of the input and output combinations in the nesting structures.

In a typical Johansen model the equations are grouped into five main groups, describing household and other final demands for commodities; industry demands for primary factors and intermediate inputs; price equations; market clearing equations for primary factors and commodities; and miscellaneous equations (Dixon et al., 1997: 13). In this study, the SIORANIG model has 143 equations, organised into blocks, each representing the main structures of the model. The equation blocks are presented in Table 7.2 below. These equations are based on the Tablo file information of the SIORANIG. The actual number of equations (which is equal to the number of endogenous variables) in the SIORANIG model is nonetheless greater than 143, due to the fact that some variables

128 Cobb-Douglas and Leontief production functions are variants of CES functions. 196

are dimensioned by industry (j), commodity (i), factor type (f), skill type (o), and sources (s).

Table 7.2 Equations of the SIORANIG model. Blocks Number of Description Equations

1 17 Equations for production inputs

2 7 Equations for production output

3 3 Equations for investment demands

4 11 Equations for household demands

5 4 Equations for export demands

6 2 Equations for government demands

7 2 Equations for inventory demands

8 5 Equations for margin demands

9 12 Equations for market clearing

10 24 Price equations

11 3 Equations for Labour market

12 53 Miscellaneous equations and other equations

Total 143

Note: Sourced from the Tablo file, sioranig.TAB

The descriptions below regarding the equation blocks in Table 7.2 above are sourced from Horridge (2000). Broadly, this study provides only the core equations for the application of the model. More details on the equations can be found in (Horridge, 2000) and the general details of the equations can be found in (Dixon et al., 1997), or what is normally referred to as ‘the green book’ by users of ORANI.

197

7.6.1 Input-Output multi production Under the production structure in this framework, industry i produces one or more commodities by combining intermediate inputs from domestically produced or imported goods and using a mixture of factor inputs (i.e. labour skill types, capital, and land) (Horridge, 2000). Such input-output multi production assumes a series of separability assumptions, which entails a general production function of;

퐹(푖푛푝푢푡푠, 표푢푡푝푢푡푠) = 0 … … … … … … … … … … … … … … … … … (7.2)129

This can be written as

퐺(푖푛푝푢푡푠) = 푋1푇푂푇 = 푍(표푢푡푝푢푡푠) … … … … … … … … … … … (7.3)130, where X1TOT is an industry activity index. Furthermore, the export destined commodities are separated from the commodities meant for local use. Equation 7.3 above shows the relationship between the input and output multi production. This relationship is depicted in a diagrammatic form in Figure 7.3 below. Thus, the Z function in equation 7.3 above corresponds to the two nested constant elasticity of transformation (CET) aggregation functions shown at the top of Figure 7.3. The G function, on the other hand, is disaggregated into a series of four nests. The ‘top nest’ (in Figure 7.3) combines the commodity composites, a primary factor composite, and ‘other costs’ using a Leontief production function combination. Such Leontief production function combinations entail a proportional input to X1TOT. This is expressed as;

푂푢푡푝푢푡 = 퐹(푖푛푝푢푡푠) = 퐹(퐿푎푏표푢푟, 퐶푎푝푖푡푎푙, 퐿푎푛푑, 푑표푚푒푠푡푖푐⁡푔표표푑푠, 푖푚푝표푟푡푒푑⁡푔표표푑푠, 표푡ℎ푒푟⁡푐표푠푡푠) … … (7.4)

129 Adopted from Horridge (2000). 130 Ibid. 198

Figure 7.3 Structure of Production

Local Export Local Export market Market Market Market CET CET

Good 1 Good 2 Good G

CET

Activity Level Top nest

Leontief

Intermediate Intermediate Primary ‘Other Factors Costs’ Good 1 Good G

CES CES CES Primary factor nest

Domestic Imported Good 1 Good 1 Domestic Imported Good G Good G Land Labour Capital Armington nest

CES Source: Adopted from Horridge (2000).131 Skill nest

Skilled Unskilled Labour Labour

The general representation of the CES nests production function for input demands are 132 expressed as follows. We choose inputs Xi(i = 1 to N), to minimise cost ∑PiXi of producing output Z, subject to the CES production function:

131 The origin of the above figure is traced back to: Horridge, J. M., Parmenter, B. R. & And Pearson, K. R. 1993. A General Equilibrium Model of the Australian Economy. Economic and Financial Computing, 3. 132 This is reproduced from Horridge (2000). See Appendices in Horridge (2000) for more details. 199

1 − 𝜌 −𝜌 133 푍 = (∑ 훿푖 푋푖 ) … … … … … … … … … … … … … … … … … … … … (7.5) 푖

Where; Z = Output

X = input combinations

δ = parameter (constant)

ρ = constant elasticity of substitution

After computing the first order conditions, and with further manipulations, we arrived at the following input demand functions:

1 1 − 푃 𝜌+1 𝜌+1 푘 134 푋푘 = 푍훿푘 [ ] … … … … … … … … … … … … … … … … … … … . (7.6) , 푃푎푣푒

1 휌 (𝜌+1)/𝜌 휌+1 휌+1 135 Where 푃푎푣푒 = (∑푖 훿푖 푃푖 ) … … … … … … … … … … … … … … … … … (7.7)

The nests in the diagram (Figure 7.3 above) need to be elaborated. I begin with the intermediate input. The separability assumption renders to simplify the production structure into;

푂푢푡푝푢푡 = 퐹(푝푟푖푚푎푟푦⁡푓푎푐푡표푟⁡푐표푚푝표푠푖푡푒, 푐표푚푝표푠푖푡푒⁡푔표표푑푠) … … … … … … . . (7.8)

The commodity composite is a constant elasticity of substitution (CES) function of both domestically produced and imported commodities (Armington nest, See Figure 7.3 above). That is,

퐶표푚푝표푠푖푡푒⁡푔표표푑(푖) = 퐶퐸푆(푑표푚푒푠푡푖푐⁡푔표표푑(푖), 푖푚푝표푟푡푒푑⁡푔표표푑⁡(푖)) … … … … … . . (7.9)

The primary factor composite is also a CES function combining land, capital, and labour composite (primary factor nest, See Figure 6.3 above). That is;

푃푟푖푚푎푟푦⁡푓푎푐푡표푟⁡푐표푚푝표푠푖푡푒 = 퐶퐸푆(퐿푎푏표푢푟, 퐶푎푝푖푡푎푙, 퐿푎푛푑) … … … … … … … … … . (7.10)

The labour input can be further split into occupational types (skilled and unskilled). The occupational types are then combined with a function. This is expressed as;

133 Reproduced from Appendix A1 of Horridge (2000). 134 Reproduced from Appendix A7 of Horridge (2000). 135 Reproduced from Appendix A8 of Horridge (2000). 200

퐿푎푏표푢푟 = 퐶퐸푆(푆푘푖푙푙푒푑, 푈푛푠푘푖푙푙푒푑) … … … … … … … … … … … … … … … . . (7.11)

The production structure in all industries is similar, but the input proportion and parameter behaviours for each industry are different.

7.6.2 Investment demand equation The investment equation renders the production of new fixed capital. The equation is obtained from the investors’ two nested production structure as shown in Figure 7.4 below. The cost minimizing problem embraces two levels. At the bottom level of the nest minimizes cost subject to the CES production function;

푂푢푡푝푢푡⁡표푓⁡푐표푚푝표푠푖푡푒⁡푔표표푑(푖) = 퐶퐸푆(푑표푚푒푠푡푖푐⁡푔표표푑푠(푖), 푖푚푝표푟푡푒푑⁡푔표표푑푠(푖)) … … … … … … … … … … (7.12)

Figure 7.4 Structure of Investment Demand

New Capital for Industry i

Leontief

Good 1 Good C

CES CES

Domestic Imported Domestic Imported Good 1 Good 1 Good C Good C

Adopted from Horridge (2000)

The commodity composite at the top level of the production function, on the other hand, is minimised subject to the Leontief production function:

201

푂푢푡푝푢푡⁡표푓⁡푛푒푤⁡푐푎푝푖푡푎푙(푖) = 퐿푒표푛푡푖푒푓푓푢푛푐푡푖표푛(푐표푚푝표푠푖푡푒⁡푔표표푑(푖)) … … … … (7.13)

The equations for the demand for source-specific inputs and for composite goods are similar to those of the intermediate demand equations described in equation 7.6 above, except that there is no primary factor input directly involved.

7.6.3 Household demand equation The nesting structure of the household demand is similar to the investment demand discussed above, except that the commodity composites in this one are combined by a Klein-Rubin (K-R) utility function (Klein and Rubin, 1947). This aggregation by the K- R utility function leads to a linear expenditure system (LES). Figure 7.5 below shows the nesting structure for the household demand. Thus, the equations for the lower nest (import and domestic goods) are similar to the equation of the intermediate and investment goods as;

푂푢푡푝푢푡(푖) = 퐶퐸푆(푑표푚푒푠푡푖푐⁡푔표표푑(푖), 푖푚푝표푟푡푒푑⁡푔표표푑(푖) … … … … … … … … . . (7.14) while the top level nest aggregated by this expression;

푈푡푖푙푖푡푦 = 퐾푅(퐶표푚푝표푠푖푡푒⁡푔표표푑(푖)) … … … … … … … … … … … … … … … … . (7.15).

Thus the LES function entails maximizing;

∏ (푋 −휑 )훽푐⁡ 푈 = 푐 푐 푐 … … … … … … … … … … … … … … … … … … … … … … … … … … . . (7.15)136, 푄⁡

subject to budget constraint;

푌 = ∑ 푃푐푋푐 … … … … … … … … … … … … … … … … … … … … … … … … … … … … … . (7.16)

Where; U = utility

Q = number of households

Xc = consumption of good c

ᴪc = committed (subsistence) consumption

βc = marginal budget shares, 0<β<1.

136 Reproduced from Horridge (2000) 202

Figure 7.5 Structure of Household Demand

Household utility

Klein- Rubin

Good 1 Good C

CES CES

Domestic Imported Domestic Imported Good 1 Good 1 Good C Good C

Adopted from Horridge (2000)

7.6.4 Export demand equation Commodities in the export demand equation are separated into individual exports and collective exports. Individual export commodities exhibit an inverse relation between the commodity’s price and the foreign demand. Collective exports, conversely, entail an inverse relationship between the average price of all collective export goods and the foreign demand.

푃∗ 𝜎 푖 137 푄푖 = 푞4 ( ) … … … … … … … … … … … … … … … … … … … … … … … … . . (7.17) 휀푖 ∗ 푃푖

Where; Qi = export volume for commodity i

q4 = quantity (right) shift in export demands

137 Reproduced from Horridge (2014).Also see Dixon, P. B., Parmenter, B. R., Sutton, J. & Vincent, D. P. 1997. ORANI: A Multisectoral Model of the Australian Economy, New York, North-Holland Publishing Company. 203

P* = foreign price

ε = exchange rate, local currency/world currency

P = price (upward) shift in export demand schedule

σ = constant elasticity of demand

For collective exports the commodity composite is aggregated by the Leontief form, exogenising the collective exports. That is,

푄 = 푥4 ∗ 푃푎푣푒 … … … … … … … … … … … … … … … … … … … … … … … … … … … . . (7.18)

Where; Q = collective export demand

x4 = quantity, collective export aggregate

Pave = price, collective export aggregate

7.6.5 Government demand equation The level and composition of government consumption is determined exogenously.

푂푢푡푝푢푡 = 퐹(푐표푚푝표푠푖푡푒⁡푔표표푑푠푖, 표푣푒푟푎푙푙⁡⁡푠ℎ푖푓푡⁡푣푎푟푖푎푏푙푒) … … … … … … … … … . (7.19)

The equation 6.19 above means that composite goods and the overall shift variable are shift variables determined exogenously. In this set up, government consumption does not move with household consumption (Horridge, 2000).

7.7 Variables, coefficients, and parameters

This section presents the variables, coefficients, and parameters of the SIORANIG model based on the equations discussed above. The SIORANIG model has a total of 191 variables, 99 coefficients, and 12 parameters. The possible maximum variables in the SIORANIG executable program model reached 46,758 variables, comprising 665 exogenous variables and 46,093 endogenous variables. This is due to the fact that some variables have more than one dimension. The actual number of exogenous and endogenous variables however, depends on the actual type of closure that one runs.

With the total 191 variables, 78 of which are macroeconomic variables. The rest of the remaining variables are shared between price variables (23), tax variables (18), 204

commodity variables (37), primary factor variables (12), and technical change and shift variables (23). The SIORANIG model Tablo language defines the variables mainly in ‘percentage change’ identified by lower cases. Other variables use ‘ordinary change’ and often start with the letters “del”.138

The coefficients are determined by the model when running a simulation. Of the identified 99 coefficients, 58 are quantity and value coefficients, price coefficient (8), tax coefficient (15), share coefficients (9), and other coefficients (9). In the SIORANIG Tablo language, the coefficients are identified in upper case.

The parameters, on the other hand, comprised constant elasticity of substitution, demand and supply elasticity. The choice of parameters is critical as they determine the simulation results in the model. The computations of relevant parameters can be derived from econometric estimations. However, as (Bandara, 1991) points out that it is difficult to estimate the required parameters because of the unavailability of data, especially in developing countries. Even if the data are available, parameters estimated through econometric estimations have also proven to be unreliable in the simulations due to their low elasticity (Dixon, 2008). In the absence of any econometrically estimated parameters, as well as the implausibility in the low elasticity from the econometrically estimated parameter, most CGE modellers employ parameters from the literature. In extreme cases, other studies have also employed guestimates or best guess parameters where possible (Bandara, 1991: 18).

7.8 Model Closure and Modelling Process 7.8.1 Model Closure To run the simulation, the model needs to be closed with some underlying assumptions on the exogenous and endogenous variables (Bandara, 1991: 16). To do so, the modeller chooses which variables are to be exogenous, and which endogenous. The choice of closure depends on the modeller’s taste and his/her views on the circumstances: as Dewatripont and Michel (1987:68) concede ‘there is no clear-cut theoretical justification for the choice of a particular closure’.

138 The naming system of the SIORANIG follows the ORANIG template, which can be found in Horridge (2000). 205

In the closure, the number of endogenous variables must equal the number of equations. The SIORANIG model showed that the number of variables (191) is higher than the number of equations (143). This implies that the difference between the numbers of variables and equations must then be the exogenous variables. The model’s 143 equations and 191 variables are arranged in by their dimensions as shown in Table 7.2 below.

Table 7.2 Tally of variables and Equations

1 2 3 4

Dimensions Variable Count Equation Count Exogenous Count

Scalar (Macro) 78 63 15

IND 36 23 13

COM 25 19 6

EXPMAC 1 1 0

OCC 3 2 1

COM*IND 8 6 2

COM*SRC 14 11 3

COM*MAR 2 1 1

IND*OCC 3 2 1

COM*FANCAT 1 1 0

COM*DESTPLUS 1 1 0

COM*SRC*IND 10 8 2

COM*SRC*MAR 4 2 2

COM*SRC*DEST 1 1 0

COM*SRC*IND*MAR 4 2 2

191 143 48

Source: Extracted from the TABmate SIORANIG file: sioranig13.CLO

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The first column in Table 7.2 shows the dimensions or the various combinations of the set of indices that occur in the model. Column 2 indicates the number of variables each combination has. For instance, the dimension COM and SRC has 14 variables. The third column displays the number of equations in a dimension. For example, the COM and SRC dimension has 11 equations. Column 4 is the difference between column 2 and column 3, which is not explained by the model. Hence, they must be the exogenous variables, which are determined outside the model. In other words, the difference shows how many variables of that dimension would be exogenous.

Another way to locate the exogenous variables is by locating them in the Tablo input file. The Tablo input file construction adopted in this study follows that of the generic ORANI naming system, where each equation is named after the variable it seemed to explain. Thus, the variables in column 4 had no equation named after them, and they are definitely candidates for exogenous variables. Such variables can be identified as:

 technical change variables, beginning with the letter ‘a’;  tax rate variables, beginning with ‘t’;  shift variables, beginning with ‘f’;  land endowments, x1lnd, and the number of households, q;  industry capital stocks, x1cap;  foreign prices, pf0cif, and the investment slack variable, invslack;  inventory to sales ratios, fx6;  the exchange rate, phi, serving as the numeraire; and  household above-subsistence expenditure, w3lux. The above closure can be seen as a base case simulation set up. Hence, Table 7.3 below provides the list of exogenous variables in the base case closure. The other results have already been tabulated in Table 7.2 above. The definitions of the exogenous variables (column 2) are presented in Appendix A7.0.

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Table 7.3 Tally of Exogenous variables

1 2

Dimensions Exogenous Variables in the base closure

Scalar (Macro) capslack, f1lab_io, f1tax_csi, f2tax_csi, f3tax_cs, f4p_ntrad, f4q_ntrad, f4tax_ntrad, f4tax_trad, f5tax_cs, f5tot2, invslack, phi, q, w3lux

IND a1cap, a1lab_o, a1lnd, a1oct, a1prim, a1tot, a2tot, delPTXRATE, f1lab_o, f1oct, x1cap, x1lnd, x2tot

COM a3_s, f0tax_s, f4p, f4q, pf0cif, t0imp

EXPMAC

OCC f1lab_i

COM*IND a1_s, a2_s

COM*SRC a3, f5, fx6

COM*MAR a4mar

IND*OCC f1lab

COM*FANCAT

COM*DESTPLUS

COM*SRC*IND a1, a2

COM*SRC*MAR a3mar, a5mar

COM*SRC*DEST

COM*SRC*IND*MAR a1mar, a2mar

Source: SIORANIG.CLO in the TABmate.

This closure can be used as a base case simulation to determine the Solomon Islands economy under the assumptions of ‘business as usual’. Once the shock and simulation is completed then the updated database is used to run the simulation.

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7.8.2 Modelling Process As mentioned above, the Solomon Islands CGE model, the SIORANIG, is a comparative static CGE model. The ‘comparative static’ nature implies that the model offers a solution at one point in time in the future, whenever that year in the future may be (called the ‘solution year’), when the Walrasian equilibrium is attained. The model ensures that the economy adjusts after the initial shock. Figure 7.6 below displays these concepts. The vertical axis indicates the percentage change in the variables, for example investment, plotted against time, t, on the horizontal axis.

Figure 7.6 Comparative static interpretations of results Investment

(%) C

Change

t0 T

Source: Adopted from Horridge (2000)

In this setting, at t0, which is the base period (year), A is the level of investment. Under ‘business as usual’ conditions, that is, no shocks or no policy changes, the economy would, in the future, achieve the level of investments at B. However, suppose a shock of, say a 20 percent increase in investment occurs, then at T period in the future, the economy would have attained the C level of investments, ceteris paribus.

The underlying growth path of investment is characterised by the movement from A to B, which is business as usual conditions, without any policy variations. The comparative-static analysis is thus only concerned with the gap between B and C; it tells us nothing about the adjustment path taken to reach B or C as indicated by the dotted 209

lines. Consequently, a comparative-static simulation might generate the change (i.e. C – B), percentage change (100(C – B)/B) or both in investments, showing how investments would be affected by the shock, in T period.

Furthermore, comparative static simulations such as the SIORANIG model can also analyse both the short-run and long-run effects of shocks or policy changes. For the short-run effects, capital stocks are normally held at their pre-shock levels (Horridge, 2000). The short-run equilibrium has been econometrically estimated to be around up to two years, i.e. T = 2 (Cooper et al., 1985). However, depending on the underlying economic fundamentals, the short-run equilibrium could be up to three years. The long- run effects, on the other hand, assume that capital stocks will have adjusted to restore the rates of return on capital, which can take anything from 10 to 20 years, i.e. T = 10 or 20.139 For this study, we investigate both the short-run and long-run effects of peace innovation. From the short-run simulation on peace innovation, we use the updated database to simulate the long-run effect of increasing investment and land drawn into production in the oil palm industry.

7.9 Model Solution Given that this study follows the generic ORANIG framework, it adopts a linearised solution method following Johansen (1960), whose work made it possible for CGE modellers to solve a model through a series of linear equations in relation to percentage changes in model variables’ (Horridge, 2000). The other approach is to solve the model on levels. Model solutions such as these mean solving the equations in level forms (Dixon and Rimmer, 2010).

The GEMPACK, Release 11, licensed to the School of Business, UNSW Canberra is used in this study. With the GEMPACK software, the simulations can be run through either the source-code executable program or the GEMSIM executable program. The former requires a FORTRAN compiler to run the simulations. This study runs the simulation using the former. Figure 7.7 below illustrates how the initial solutions data

139 Ibid. 210

are simulated to generate a final solution, using the fortran compiler. Figure 7.7 uses actual file names used in this study.

Figure 7.7a Building a model-specific EXE file

SIORANIG.TAB

TABLO SIORANIG.STI SIORANIG.FOR program

FORTRAN compiler

SIORANIG.EXE SIORANIG.AXT SIORANIG.AXS

Figure 7.7b Using the model-specific EXE to run a simulation

Legend: SIORANIG.AXS CMF file: auxiliary Closure Shocks Solution method Binary File

SIORANIG.AXT auxiliary Program

Data2.HAR SIORANIG.EXE Pre-simulation Text File (base) data

Note: SIORANIG is the file name for the SIORANIG model, Summary of Post-simulation using the 2013 data base data SL4 solution files (updated) data of simulation results

ViewHAR to examine VieSOL to data examine results Adapted from Horridge (2000).

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The simulation process begins with the conversion of the SIORANIG.TAB and SIORANIG.STI files into a FORTRAN source file, SIORANIG.FOR, using the TABLO program (see Figure 7.7a above). The SIORANIG.FOR contains the model specific code needed for a solution program (Horridge, 2000). The SIORANIG.FOR is then combined by the FORTRAN compiler along with other special purpose code to produce the executable program, SIORANIG.EXE. This SIORANIG.EXE (executable program) solves the model specified by the user in the TAB and STI files by simulating the inputs. The inputs include the pre-simulation base data containing the input-output data and behavioural parameters, and the data file containing the initial equilibrium (or the initial solutions). This is labelled as Data2.HAR in Figure 7.7b above. Furthermore, inputs include the CMF file specifying the exogenous variables, shocks to exogenous variables, number of steps in the computations, names of input and output files, and details of the solution process. A final solution file, SL4, which is a binary file, is produced in each simulation (see the lower portion of Figure 7.7b above).

7.10 Conclusion This chapter has presented the computable general equilibrium (CGE) framework employed in the following chapter to analyse the impact of peace on the economy. The chapter began by elucidating the modern CGE against the criticisms that have been levelled against the underlying theory (i.e. theory of equilibrium) that underlie the framework. Despite the criticisms, the CGE framework has gained momentum in its application, especially in the developing countries. It has been shown that the CGE framework has had extensive applications, not only in the advanced economies, but also in the developing economies. It has been applied to policy areas such as trade, climate change, investment, extraction of natural resources, and has also been applied to examine the impact of shocks like coups and financial and economic crisis on an economy.

A major innovation in this study, highlighted in this chapter, is the derivation of a conceptual framework for quantifying peace, as well as a theoretical innovation explaining the economy-wide transmission of peace. The value of peace was derived by drawing from the theory of ‘compound interest’. For the conceptual framework, it showed that peace transmits through direct and indirect channels to affect changes in 212

GDP. The indirect transmission is through two mediums, with the first being through macroeconomic variables and the second through microeconomic variables. Both transmission mechanisms have various mediums through which peace can impact national income. For example, the transmission can either go from macroeconomic variables to GDP or from macroeconomic to other sectors before affecting GDP. It can also go from the microeconomic (other sectors) to macroeconomic level before affecting GDP.

Another innovation to this study is the inclusion of peace as an industry in the list of industries or sectors for the Solomon Islands. The concept of a peace industry was drawn from the Institute for Economics and Peace. The impact of peace in this framework is analysed through the variations in peace innovation, which was defined as the intrinsic value of peace that creates the space for investor confidence. Conceptually, peace is treated as a public good that is essential for production but is non-rival to and non-excludable from consumption.

Following the elucidation of the conceptual framework, the data and data calibration processes were explained. As a result, an input-output (IO) table is constructed for Solomon Islands, and tailored to suit the SIORANIG CGE framework. Again, this is another innovation. The SIORANIG model was constructed in accordance to the generic ORANI framework developed by the Centre of Policy Studies at Victoria University, Melbourne. Using the TABLO program to develop the SIORANIG model, the program was able to retrieve the number of variables, coefficients, and parameters. The simulation was made possible by the GEMPACK software (Release 11.0). Consequently, a total of 143 equations have been derived, with 191 variables (48 of which are exogenous), 99 coefficients, and 12 parameters. Finally, the chapter also elaborates on the processes for closing the model, specifying which variables to be exogenised, as well as how the model implements the solution.

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CHAPTER 8

ANALYSIS OF THE CGE SIMULATION RESULTS

8.1 Introduction Having developed the conceptual framework for the analyses with the SIORANIG CGE model in Chapter 7, this chapter presents the actual results and analyses of the simulations on the impact of peace on the economy. The investigation and analyses of the results in this chapter employs the conceptual framework developed in section 7.3.2 in the previous chapter. Consequently, there are four model closures and simulations carried out in this chapter to investigate the impact of peace on the economy. The first one simulates the short-run impact of peace on the economy. In this closure, we simulate the effects of the shock of a ‘peace innovation’ on the macroeconomic variables, as well as on the sectoral level, allowing for the factor input, labour, to vary. The second closure simulates the long run impact of peace on the economy. This examines what happens when all factor inputs are allowed to vary. These simulations attempt to address the research questions posed in Figure 5.2 of Chapter 5. In both simulations, the contribution of peace innovation to the rebound on the oil palm sector (our industry of interest) is emphasized. This is to identify further whether the rebound in the oil palm industry plays a role in sustaining peace.

The third simulation analyses the short-run impact of an expansion in the oil palm industry, as a result of improved peace, on the economy. As in the short run closure for peace above, the short run closure for oil palm is to investigate the impact of the expanded oil palm investment, allowing the labour input to vary. The final model closure simulates the long-run impact of improved oil palm investment on the economy. Again, the assumption is that under the long run scenario all factor inputs vary. This simulation is crucial to detect whether any indirect enhancements of peace by the oil palm industry have flow-on effects to the wider economy (that is, GDP). It is equally imperative to know whether the oil palm industry has a role in promoting and sustaining peace in the country. To carry out this simulation, we utilize the updated database from the previous simulations done on peace.

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In this model, an expansion in the oil palm industry comes from the expected increase in the investment of the Guadalcanal Plain Palm Oil Limited (GPPOL) company. Two sources are likely to have caused this expansion: (i) an increase in agricultural land for further oil palm planting; and (ii) an increase in capital. In the context of our model, and with regard to circumstances surrounding the GPPOL Company, the former is the most likely option, as has been discussed in the second part of Chapter 3. The simulations on oil palm attempts to address the sub-research question; what is the impact of the expected increase in the oil palm industry on the economy?

The remaining sections of the chapter are organized as follows. Section 8.2 tests the data for the homogeneity principle, to ensure that the model conforms to the assumptions imposed in Section 7.6 of the previous chapter. Section 8.3 analyses the short-run impact of a 12 percent improvement in peace innovation, and simulates the impact of improvements in peace innovation on the oil palm industry, the macro economy, and other sectors. The rationale for imposing a 12 percent shock to peace innovation will be made clearer in this section. In section 8.4, it presents the long-run simulation results of a 12 percent improvement on peace innovation. Again, the impact on the industry of interest, oil palm, is analysed separately. Section 8.5 simulates the short-run impact of an expansion of 150 percent in land utilization within the oil palm industry on the economy, separately analysing the macroeconomic and the sectoral impacts. Again, the rationale for the 150 percent shock will be explained in this section. Section 8.6 is a long-run simulation of the 150 percent increase in oil palm expansion. The conclusions follow in section 8.7.

8.2 Testing for homogeneity assumption One of the assumptions in this CGE model is that of homogeneity, where output changes by the same proportion as that of inputs. Thus, the homogeneity assumption entails that supposing all inputs were to increase by 10 percent, output is expected to increase by 10 percent. Prior to conducting the simulation, we therefore test, to ensure that the SIORANIG CGE model exhibits the homogeneity assumption. We begin with the nominal variables. To do so, it is useful to shock the numeraire. In our model, the exchange rate is the numeraire; thus, we shock the exchange rate, phi, and the supernumerary household expenditure, w3lux, by one percent. The outcome is an

215

increase, by one percent of all the nominal variables. Table 8.1a below displays the results of selected variables from the homogeneity simulation of the nominal variables.

Table 8.1a Homogeneity simulation results of selected nominal macroeconomic variables: shocked by one percent

Nominal percentage Descriptions of the Nominal Variables Variables change (%) C.I.F. local currency value of imports w0cif_c 1.0 Nominal GDP from expenditure side w0gdpexp 1.0 Nominal GDP from income side w0gdpinc 1.0 Nominal total household consumption w3tot 1.0 Local currency border value of exports w4tot 1.0 Aggregate nominal value of government demands w5tot 1.0 Source: Simulation results, SIORANIG

Similarly, we also test for homogeneity in the real variables. To do this, we shock the following variables: real government expenditure (x5tot); real investment expenditure (x2tot_i); real private consumption (x3tot); all sectoral agricultural land (x1lnd); all sectoral capital (x1cap); individual exports (f4q(TRADEXP)); collective exports (f4q_ntrad); and the number of households (q). All these variables are shocked by one percent. Table 8.1b below shows the simulation results of selected real variables. As the results show, the model passes the homogeneity test.

Table 8.1b Homogeneity simulation results of selected real macroeconomic variables: shocked by one percent

Real Percentage Descriptions of Real Variables Variables change (%) Real GDP from expenditure side x0gdpexp 1.0 Decomposition of real GDP from income side x0gdpinc 1.0 Real GNE from expenditure side x0gne 1.0 Import volume index, duty-paid weights x0imp_c 1.0 Aggregate capital stock, rental weights x1cap_i 1.0 Aggregate land stock, rental weights x1lnd_i 1.0 Aggregate effective primary factor use x1prim_i 1.0 Quantity, collective export aggregate x4_ntrad 1.0 Export volume index x4tot 1.0 Source: Simulation results, SIORANIG

216

Having informed and conformed to the homogeneity assumption, the next step is to proceed with the simulations of variables of interest. The variables under investigations are peace and oil palm production; that is we investigate the impact of peace, as well as the impact of oil palm production on the economy. These variables are analysed separately below.

8.3 Modelling the improvement in peace innovation in the short-run This section analyses the short-run impact of an improvement in the externally augmented technical change in peace (i.e. ‘peace innovation’). In undertaking the simulation, we shock the exogenous peace innovation variable a1tot(“peace”) by 12 percent. The 12 percent figure was derived from the gap in the level of peace found in chapter 5. That is, in section 5.3 of chapter 5, we found that the level of peace achieved in the community of Guadalcanal plains was estimated at 88 percent.140 This implies that the community perceives peace to be short by 12 percent of the desired level of peacefulness.

Therefore, we analyse the short-run effects by adopting the framework developed in section 7.3.2 of Chapter 7. In this study, the short-run analysis assumes that any change to the GDP (from the expenditure side) stems from variations in the trade balance as real consumption, real investment, real government consumption, and changes to inventory are assumed to be exogenous. That is, an increase in the trade surplus/ (deficit) will induce GDP to increase/ (decrease).141 From the income side, any change to the GDP emanates from a variation in either the level of employment or the rate of return to capital while the wage rate, technological change, and capital are assumed to be exogenous.142

Furthermore, expansion in the trade surplus results in increased output in the exporting industries. This induces the demand for labour to increase while maintaining the real

140 This 88 percent is employed as a proxy for the country-wide peacefulness. The People Survey conducted by RAMSI, although it did not quantify peace, noted that since 2003 peace had improved markedly. They also noted that there are still issues that need to be addressed. Thus, my estimation (quantification) that the level of peace is around 88 percent is roughly reasonable. The pending issues that need to be addressed are explained elsewhere in Chapter 6. 141 This is illustrated in Figure 7.1a in Chapter 7 (from Loop 2 to Loop 5). As well, Figure 7.1b of Chapter 7 demonstrated the trade balance as a component of GDP from the expenditure side. 142 Again, see Figures 7.1a and 7.1b in Chapter 7. 217

wage fixed. As a result, aggregate employment increases, inducing the GDP to expand from the income side. In order to see how these changes occur, we need to amend a few variables to suit this short-run framework. Table 8.2 below illustrates the actions undertaken:

Table 8.2 Short-run actions for new exogenous variables

Old exogenous New Description exogenous Swap f5tot = x5tot Disconnect government and household consumption Swap invslack = x2tot_i Fixed aggregate investment

Swap w3lux = x3tot Fixed household consumption

Swap fx6 = delx6 Fixed inventory changes

Swap f1lab_io = realwage Fixed average real wage

8.3.1 Macroeconomic Impact We first analyse the macroeconomic effects. Table 8.3 below presents the result of the short-run simulations of the 12 percent improvement (positive shock) in peace innovation on selected macroeconomic variables (i.e. key components of GDP from the expenditure side). Accordingly, overall GDP expands by 1.7 percent. This means that if peace were to improve more by 12 percent, GDP would improve by another 1.7 percent. From the expenditure side, with the domestic absorption (consumption, investment, government, and change in inventory) determined outside of the model, the increase in GDP stems from an expansion in the contribution of the trade balance surplus (contBOT) to GDP, which rose by the same proportion, 1.7 percent. The increase in the contBOT can be attributed to a 1.51 percent rise in the contribution of exports to GDP, due to a 4.1 percent increase in the volume of exports (x4tot). Similarly, the contribution of imports to GDP also rose, by 0.18 percent. These are activity effects.

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Table 8.3 Short Run Simulation of Peace on selected Macroeconomic Variables

Macroeconomic Variables Percentage change (%)

Real GDP 1.6970

Real Consumption 0

Real Investment 0

Real Government demands 0

Change in Stocks 0

Exports (contribution to GDP) 1.5135

Imports (contribution to GDP) 0.1835

Employment 2.6487

Source: Simulation results, SIORANIG. Note: The GDP components are their respective contribution to GDP.

With the impact of the shock on the endogenous macroeconomic variables, such as the trade balance, the economy-wide relative price movements can be traced as well. The relative price movements provide an insight that helps to interpret the industry results, which is done through the labour market effect. Subsequently, the expansion in the output of the exporting industries generates the demand for more workers. This is illustrated in chapter 7, section 7.3.2 Figure 7.1b, and is shown by the dotted arrow running from trade balance to employment. In addition, equation 7.1 in the previous chapter explains this relationship. The simulation result thus shows aggregate employment rising by 2.6 percent.

8.3.2 Sectoral Impact Note two things about the sectoral impacts. First, labour inputs to various sectors will be affected. Second, output in the various sectors will be affected, not only because of labour input, but also because of peace being an input into all the sectors. In the simulation analysis, we focus more on how the change in labour due to the 12 percent 219

shock in the exogenous peace innovation affects the output of each industry. Table 8.4 and Figure 8.1 below display the simulation results of the main industries affected. The results show the main winners and losers143 of the improvement in peace. We begin by separately analysing the impact of peace on the industry of interest, the oil palm industry.

8.3.2.1 Impact of peace on the oil palm industry A positive shock to peace innovation will cause an increase in the output of the oil palm industry. This is illustrated in Figure 7.1a (Loop 3a). The increase in oil palm output is caused by a rise in the labour factor input, as capital and land are fixed in the short run. Labour increased as it is assumed that workers (mainly unskilled) can easily move around industries, and the real wage is fixed (exogenous). Consequently, a 12 percent improvement in peace innovation causes output in the oil palm industry to increase by 8.2 percent. The expansion stemmed from a 44 percent surge in the factor input labour. This is mainly due to workers (mainly unskilled) moving around in search of employment. One company, the Guadalcanal Plains Palm Oil Limited (GPPOL) (discussed in Chapter 4), currently dominates the oil palm industry. Its reopening after the conflict has provided employment for many unskilled locals, who could not otherwise have an opportunity to engage in wage labour. In section 8.5 and 8.6, we quantify the short run and long run contribution of the Oil palm industry (or GPPOL) to employment and income. Our estimates take into account the direct and indirect contribution of GPPOL to GDP and to peace.

143 The industry names are as they appeared in the Tablo file. For description of the industries, refer to Table 6.1 of chapter 6. 220

Table 8.4a Main winners of a short run peace simulation

Percentage change Percentage change Industry (%) in output (%) in labour input Oilpalm 8.225 44.019 ForestryLog 1.266 3.178 Mining & 16.286 31.008 Quarrying Construction 7.929 28.223 HotelRestaur 4.487 85.239 Educ 2.654 1.431 Health 2.338 1.393 Peace 11.926 -67.521 OtherServ 5.154 2.581 Source: Simulation results, SIORANIG

Table 8.4b Losers of a short run peace simulation

Percentage change Percentage change Industry (%) in output (%) in labour input AgriFood -0.000 -1.561 RealEstate -0.021 -13.361 PublicAdminDef -0.022 -0.010 Source: Simulation results, SIORANIG

221

Figure 8.1 Sectoral impact of a short-run peace simulation

100

-20

-40

-60

-80

Output Labour input

Source: Simulation results, SIORANIG

222

8.3.2.2 Impact of peace on the ‘Other sectors’ The analysis of ‘Other sectors’ follows the Loop 3b of Figure 7.1a in Chapter 7. Table 8.4 above, shows the main winners (apart from oil palm) and losers in ‘other sectors’. In general, all but three industries received positive impacts for their outputs, resulting in a positive net effect in the overall output (i.e. GDP). Tables 8.4a and 8.4b displays only the industries with major changes. Figure 8.1 shows all the industries with their impacts.

Major Winners

Forestry, Timber and Logging (ForestryLog) sector With the 12 percent improvement in peace, the output in the forestrylog sector expanded by 1.3 percent. The expansion came from a 3.2 percent increase in the labour input. As noted in Chapter 3, the forestry and logging sector dominates growth in the local economy; therefore the improvement in peace, however small it may be, will have a significant impact on the economy. Although most logging operations are outside of Honiara, the marketing and exports of the round logs take place in Honiara. Thus, peace in Honiara is essential for the continuation of commercial activities that support the trade (export and import) activities.

Mining and Quarrying (MiningQuar) sector The output in the mining and quarrying (MiningQuar) sector increased by 16.3 percent, as a result of a 12 percent improvement in peace innovation. The increase stemmed from a 31.0 percent surge in the factor input, labour. One mining company, Gold Ridge Mining Limited (GRML), currently dominates the industry. There are two other major prospecting mining companies, namely Sumitomo (Japanese owned) and Axiom Ltd (Australian owned). Like GPPOL, GRML is also one of the major employers in the Solomon Islands. GRML reopened operations in 2011 and an increase in output is expected.

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Construction (construction) sector For the construction sector, output increased by 7.9 percent, owing to a 28.2 percent rise in the factor input labour. The improvement in the construction industry stems from the fact that this industry provides the stimulus during the peace onset as noted by Collier (2008). The revitalization of an economy, in the aftermath of conflict, usually takes impetus from the construction of infrastructure and other necessities so this result is expected.

Hotel, accommodation, tourism, and restaurants (HotelRestaur) sector For the HotelRestaur sector, output rose by 4.5 percent as a result of an increase in the labour input of 85.2 percent. Performance in the sector has been hampered by several constraints including inadequate facilities and infrastructure, poor transportation, shortage of qualified human resources, accommodation shortage, and insufficient financial and technical support from the government (Central Bank of Solomon Islands., 2005). Despite these constraints, the sector is slowly improving. The rise in labour stems from the construction of a major additional hotel144 and some restaurants.

Education (Educ) sector For the education sector, output rose by 2.7 percent, which stems from an increase in the labour input, by 1.4 percent. The education sector is one of the sectors that absorb most of the university graduates in to entry-level jobs before they venture into other sectors. Hence, in the recent post-conflict years, university graduates have been seen entering teaching jobs at secondary schools, as highlighted by the gross enrolment rate (GER) of students in Figure 7.2 below (Ministry of Education and Human Resources., 2015). Furthermore, education is a major recipient of funds from the national budget, accounting for more than 28 percent of the total budget.145 During the conflict, this sector was severely affected.

144 The Heritage Park Hotel. 145 The 2013 preliminary figures provided by the Ministry of Finance and Treasury. 224

Figure 8.2 Gross Enrolment Rate (GER)

Source: http://www.mehrd.gov.sb/images/graphs/GER_Secondary.png

Health (Health) sector In the health sector, output expanded by 2.3 percent as a result of the 12 percent improvement in peace. A 1.4 percent upturn in the labour factor input was behind this. The health sector is the second largest recipient of funds from the national budget, accounting for more than 14 percent of the total budget. The health sector, like education, was severely affected during the conflict period, so much so that health centres and clinics in the rural villages were closed due to the lack of drugs and appropriate equipment.

Peace (Peace) sector With regards to the peace industry, output surged by 11.9 percent, despite the significant fall in the labour input by 67.5 percent. The decline in the factor labour input suggests that the increase in output was triggered by an exogenous variable (which in this case is ‘peace innovation’) that in turn led to a reallocation of labour away from peace. Consequently, as more peace is produced, demand for labour in the sector diminishes. This frees up labour for use in other industries. The net effect of peace on GDP in this particular simulation is that it causes GDP (via its impact on other sectors) to expand by 1.7 percent as shown in Table 8.3 above.

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Other Services (OtherServ) sector The other services sector registered a 5.2 percent increase in output because of the 12 percent shock in peace. This stemmed from a 2.6 percent rise in the labour input factor. The sector includes any service sector industry not included elsewhere in our list of industries. The positive impact on this sector implies that peace has had a general positive impact on the economy, as shown by the expansion in the overall GDP.

Losers Improvements in peace leave only a few sectors adversely affected. Those that were affected are mainly those sectors that do not require much peace. Technically, these are the sectors with lower elasticity. They include the agricultural food (agrifood), real estate (realEstate), and the public administration and defence (pubAdminDef) sectors. Notably, the negative impacts are negligible, as shown by the simulation results.

Agricultural Food (AgriFood) sector Accordingly, the impact of improvement in peace on the output in the agricultural food sector was virtually (minus) zero; with the factor input labour falling by 1.6 percent. The agricultural food sector mainly refers to the informal (rural) sector. As the civil conflict took place mainly on Guadalcanal, the rural villages outside of Guadalcanal were relatively peaceful. In other words, the peace elasticity in the agricultural food sector is low, and in this study guestimated it to be 0.1. The drop in the labour input suggests that these mainly unskilled labourers have moved to paid jobs.

Real Estate (RealEstate) sector Similarly, output for the real estate sector was also virtually (minus) zero, with a minimal decline of 0.021 percent. The factor input labour dropped by 13.4 percent. Again, the peace elasticity for this sector is also guestimated at 0.1, as the sector was not dramatically affected by the conflict. This implies that this sector is not sensitive to peace.

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Public Administration and Defense (PubAdminDef) sector Finally, the public, administration and defence sector recorded a minimal adverse impact of 0.022 percent, stemming from a marginally 0.010 percent decline in the factor input labour. This sector mainly refers to the government sector, excluding education and health.

In summing up, the short-run simulation of the improvement in peace induces improvements in almost all of the sectors’ output, thereby generating a positive short- run net effect on the overall economy. The next section analyses the long-run impact of the positive shock in peace innovation.

8.4 Modelling the increase of peace innovation in the long-run This section simulates the long-run impact of an improvement in the externally augmented technical change in ‘peace innovation’. As for the short-run simulation, the exogenous peace innovation variable a1tot(“peace”) is shocked by 12 percent, but with the factor input, capital, allowed to adjust, as well as the real wage. That is, the long-run model closure allows capital to vary, such that it induces the variations in the investment component of GDP. In allowing adjustment in the capital stock, the rates of return on capital are allowed to be exogenous.

Another amendment required for the long-run simulation is to identify the adjustments in the labour market. The assumption is such that the long run is long enough to address the rigidities in the labour market; hence, the real wage adjusts accordingly. In other words, we assume full employment, and therefore it is fixed. Such a mechanism means that, in the long-run, the labour force and the employment rate represents the non- accelerating inflation rate of unemployment (NAIRU). Furthermore, consumption of households and the government move together, with the external sector also allowed to vary. This implies that the trade deficit is financed both by consumption as well by external sources.

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Subsequently, we perform the following amendments to our model closure. The current capital stock (x1cap) is swapped with the gross sectoral rates of return (gret) to allow the latter to be exogenous. The real wage (realwage) adjusts while aggregate employment (employ_i) is fixed, thus swapping f1lab_io with employ_i allows the latter to be exogenous.

8.4.1 Macroeconomic Impact For the macroeconomic impact, Table 8.7a below provides the long-run simulation results of the 12 percent shock (improvement) to peace innovation on selected macroeconomic variables (GDP components from the expenditure side). The simulation result finds that real GDP increases by 0.31 percent. The expansion in GDP stems from increases in real investment and exports, with their contribution to GDP growing by 0.044 percent and 0.87 percent respectively. The increase in the contribution to GDP in these two components was due to 1.6 percent and 2.4 percent expansions in aggregate investments and exports respectively. Also, the increase in the contribution to GDP in exports more than offset the 0.4 percent decline in the contribution to GDP in imports, resulting in a positive growth of the trade balance (by 0.47 percent). As for the contribution of private and government consumptions, both fell by 0.084 percent and 0.11 percent respectively. The falls reflected the decline in both components, each by 0.55 percent.

For other macroeconomic results, aggregate capital increased by 0.087 percent while real wages went up by 6.2 percent. The increase in the real wage reflects the 7.5 percent surge in the nominal wage and a 1.2 percent rise in the consumer price index (CPI).

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Table 8.7a Long-run simulation results on selected macroeconomic variables Macroeconomic Variables Percentage change (%)

Contribution to GDP

Real GDP 0.3140

Real Consumption -0.0841

Real Investment 0.0439

Real Government Consumption -0.1139

Change in Stocks 0

Exports 0.8709

Imports -0.4028

Other macroeconomic results:

Aggregate capital 0.0864

Real wage 6.1940

Average nominal wage 7.4741

CPI 1.2055

Source: Simulation results, SIORANIG

8.4.2 Sectoral Impact In terms of the sectoral impacts, again similar to the short-run simulation, two things should be noted. First, unlike the short-run, factor inputs, labour and capital into the various sectors will be affected. Second, output in the various sectors will be affected not only due to labour and capital inputs, but also because peace was also an input into these sectors. In the simulation analysis, we focus more on how the change in labour and capital, due to the 12 percent shock in the exogenous peace innovation, affects the output of each industry. Table 8.7b and Figure 8.2 below display the simulation results of the main industries affected. The results are tabulated for the main winners and

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losers146 as a result of the improvement in peace. We begin by separately analysing the impact of peace on the industry of interest, the oil palm industry.

8.4.2.1 Impact of peace on the oil palm industry The effect of the 12 percent shock (improvement) on peace innovation reveals that output in the oil palm industry (or specifically, for GPPOL output) increases by 23.7 percent. The expansion reflects increases in the factor inputs, labour and capital by 35.4 percent and 39.0 percent respectively. These increases are significant, implying that peace is indeed necessary for GPPOL’s long-term investment.147 Therefore, the long- term sustainability of peace is important for GPPOL’s continuous operations. In order to show why it is important to ensure that peace is enhanced and sustained we simulated the oil palm (GPPOL) investment on peace and other sectors. This is done in sections 8.5 and 8.6.

Table 8.7b Main winners of a long-run peace simulation

Percentage (%) Percentage Percentage (%) change in output (%) change in change in capital Industry labour stock 23.697 35.410 39.027 Oilpalm 24.087 29.217 32.668 MiningQuar 3.057 2.466 5.203 ChemFert 3.976 4.011 6.789 ClothingFtw 8.552 10.445 13.395 Construction 2.599 1.918 4.640 Trade 16.366 25.466 28.818 HotelRestaur 2.802 2.036 4.761 RealEstate 11.654 -5.353 -2.825 Peace Source: Simulation results, SIORANIG

146 The industry names are written as they appear in the Tablo file. For descriptions of the industries, refer to Table 6.1 of chapter 6. 147 GPPOL apparently is labour intensive, however the company started from scratch as the factory mill and other heavy machines and equipment were destroyed. As such, GPPOL heavily invested in these machines, hence the reason why capital stock increased. 230

8.4.2.2 Impact of peace on the ‘Other sectors’ In terms of the sectoral impact, as in section 8.3.2.2 above we focus on the industry outputs and the primary factor inputs, but in the long-run. Again, referring to Figure 7.1a in Chapter 7, the transmission is from peace to other sectors through Loop 3b. In this analysis, we focus on how the supply side effects, arising from the 12 percent shock in the exogenous peace innovation, affect the output of each industry. We focus on the main winners and losers from the shock.

Major Winners

Mining and Quarrying (MiningQuar) sector For the mining and quarrying (MiningQuar) sector, the improvement in peace causes the valued added to increase by 24.1 percent, which stems from increases in capital and labour use of 29.2 percent and 32.7 percent respectively. Given the significant operational costs in the mining industry, demand for both factor inputs is necessary. The continuing operations of the mining industry are important for the long-term economic growth of Solomon Islands. However, recent events concerning Gold Ridge mining seems to suggest that the company is facing more problems.148

Chemical and Fertilizer (ChemFert) sector The ChemFert is a small sector, but contributes to the operations of larger industries such as oil palm, mining, and fishing. Thus, the result of the 12 percent improvement in peace raised output in the ChemFert sector by 3.1 percent. This reflects increases in both the labour input and capital use by 2.5 percent and 5.2 percent respectively.

Clothing and Footwear (ClothingFtw) sector Output in the clothing and footwear (ClothingFtw) industry went up by 4.0 percent because of increases in labour use of 4.0 percent and capital use of 6.8 percent. Although, the sector is currently relatively small, the long-term impact from the improvement in peace reveals the potential for the industry.

148 See Chapter 3 for details. 231

Construction (Construction) sector For the construction sector, output rose by 8.6 percent, due to the 12 percent improvement in peace. The increase was because of an expansion in labour use by 10.4 percent and capital use by 13.4 percent. The expansion in the sector was expected as it corresponds with the growth of the economy. As the economy grows, it requires more infrastructure, hence there is an increase in opportunities for the construction sector .

Trade (Trade) sector In terms of the trade sector, output goes up by 2.6 percent, reflecting a 1.9 percent increase in labour use, as well as a 4.6 percent rise in capital use. Again, a growing economy means more trading activities; hence the increase in this sector is expected in the long-term.

Hotel, accommodation, tourism, and restaurants (HotelRestaur) sector With the hotel and tourism industry, the long-run effect of the 12 percent improvement in peace causes the industry output to surge by 16.4 percent. The increase comes on the back of a 25.5 percent increase in labour usage and a 28.8 percent surge in capital stock. In the long-run, given the improvement in peace, the number of tourists is expected to increase as the infrastructure in this industry improves. The increase in the capital input usage is probably due to the construction of more accommodation, as well as the categorizations of the hotel’s food and beverages as capital.

Real Estate (RealEstate) sector For the real estate sector, output rose by 2.8 percent, owing to increases in the factor inputs labour and capital, which rose by 2.0 percent and 4.8 percent respectively. The real estate sector is a growing sector with the potential to expand to meet potential demands for housing and commercial buildings stemming from the growing economy.

8 Peace (Peace) sector The final winner of the sectors is the peace industry itself. Given the exogenously augmented increase in the peace innovation (productivity), this led to an expansion in 232

output for the peace sector by 11.7 percent. As peace improves, the need for the factor inputs in the industry diminishes, freeing up more labour and capital to be used in the productive sectors. Consequently, labour input dropped by 5.4 percent and capital by 2.8 percent.

Major Losers

Forestry, Timber and Logging (ForestryLog) sector The long-run impact of the 12 percent improvement in peace causes the output in the forestry sector to fall by 5.6 percent. This is attributed to the fall in labour use and capital use by 8.4 percent and 6.0 percent respectively. As noted in Chapter 3, the rate of harvesting in the forestry sector is unsustainable, so that in the long run output must fall. The fall in output is due to the decrease in factor inputs, as they are absorbed by other sectors, especially the oil palm, mining, construction, and tourism industries.

Owner occupied dwelling (OwnDwelling) sector The other major loser in the 12 percent positive shock of peace is the owner occupied dwelling sector. As the name implies, this sector is comprised mainly of privately owned and occupied residential properties. The improvement in peace shows that the output in this sector fell by 6.6 percent, stemming from the decline in capital use, by 10.5 percent. This suggests that less people reside in their own homes, as peace makes it easier to access rental accommodation. In other words, people built homes mainly for rental investment, which is possible because the real estate sector is expanding. Although the OwnDwelling do not usually use labour, there are instances that individuals built their own properties without using construction companies. Therefore, this sort of labour use declines, by 9.6 percent. This suggests that the construction firms construct new rental residential properties.

Education (Educ) sector For the education sector, the 12 percent improvement in peace led to a decline in output by 11.3 percent, in the long-term. This stems from the decline in the labour use by 7.9 percent and the capital stock by 5.4 percent. In the Solomon Islands, employment in this 233

sector does not attract high remuneration. Even the top echelons of education are highly skewed in terms of senior and well-paying positions. Consequently, over the years highly qualified teachers have pursued employment opportunities in other sectors. Hence, the long-run improvement in peace is potentially detrimental to the education sector.

Health (Health) sector In the health sector, the 12 percent improvement in peace in the long-run leads to an 8.2 percent fall in output. The drop is attributed to declines in labour use and capital use by 5.8 percent and 3.3 percent respectively. Just as in the education sector, the current remuneration, especially for doctors and nurses, are not competitive enough to retain employees. Most qualified specialists have sought employments elsewhere, including overseas. Thus, with the expectation that the economy will continue to grow in the long- term, the potential for the health sector to lose skilled and professional workers is real.

Table 8.7c Main losers of a long-run peace simulation

Percentage (%) Percentage Percentage (%) change in output (%) change in change in capital Industry labour stock -5.648 -8.408 -5.961 ForestryLog -6.578 -9.632 -10.503 OwnDwelling -11.349 -7.873 -5.412 Educ -8.215 -5.843 -3.328 Health

Source: Simulation results, SIORANIG

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Figure 8.3 Sectoral impact of the long-run peace simulation

-10

-20

Output Labour input Capital stock

Source: Simulation results, SIORANIG

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In summary, the short-run and long run impact of the 12 percent improvement in peace innovation has shown that the net effect of peace on the economy is positive. The short- run effect showed a higher net effect of a 1.7 percent growth in GDP while the long-run net effect showed a 0.31 percent growth in GDP. The sectoral impacts show that the majority of sectors have been positively influenced. Overall, improvement in peace is good for the economy. The sections below extend the simulation to analyse the impact of the expansion of the oil palm industry as a result of improved peace.

8.5 Modelling the short-run impacts of the expansion in the oil palm industry Having demonstrated the role/impact of peace on the economy, the next step is to investigate the impact of the oil palm sector on the economy given that peace has improved. That is, we take into account the improved peace simulated in the above sections. To account for the improved peace, we use the updated database created in the short-run simulation. Again, we closely follow the framework developed in Chapter 7, section 7.3.2, as per Figure 7.1a of the that chapter, which shows the shock to the oil palm sector, and the transmissions to GDP are indicated directly by Loop 6 and indirectly by Loop 7d to Loop 4, as well as from Loop 7d to Loop 7a to Loop 5. In addition, since this is a short run simulation, the interactions between the macroeconomic variables are as demonstrated in Figure 7.1b of Chapter 7, section 7.3.2.

Again, the setting and short-run closure is similar to the closure for peace (see section 8.3.1) above. I held discussions with the GPPOL (oil palm) management, who told me the company is envisaging expanding the oil palm plantation by at least a factor of three from its current size.149 In addition, as noted in Chapter 4, the current oil palm plantation (in terms of land size) is just above seven thousand hectares. Tripling the current plantation land size means a 300 percent increase in the land size. However, allowing for uncertainties, as well as taking into account the instability the 300 percent

149 Initial groundwork, such as community awareness for the process of land acquisition, has already started. 236

increase may have caused to the model given the large shock, we take the following steps to closely reflect the reality. In the short-run, we increase (shock) the agricultural land for oil palm plantation by 100 percent. Then, in the long run, we increase (shock) the agricultural land for oil palm plantation by 200 percent. In doing so, we take into account the expected expansion. Better still, this seems the way GPPOL is going to proceed, i.e. acquiring land may be done as a one-off thing, but the planting of oil palm trees may be staggered to take into account the amount of work involved. Thus, we shock x1lnd(“OilPalm”) by 100 percent for the short-run and 200 percent for the long- run simulations. The results are discussed below, with the macroeconomic and sectoral implications analysed separately.

8.5.1 Macroeconomic Impact In terms of macroeconomic impact, Table 8.8a below presents the results of a short-run 100 percent (positive shock) increase in agricultural land acquisition for the (GPPOL) oil palm plantation expansion on selected macroeconomic variables. Similar to the short-run simulation in section 8.3.2 above, the impact of the shock on the endogenous macroeconomic variable, the trade balance, and the economy-wide relative price movements, can be traced. The relative price movements give us an insight into the industry results. However, first we focus on the activity effect. The impact of the shock thus triggers GDP to expand by 0.44 percent. Having the domestic absorption (consumption, investment, government, and change in inventory) as given, the increase in GDP stems from an expansion in the trade balance (contBOT) surplus, by the same proportion, 0.44 percent. The improvement in the trade balance surplus is due mainly to a 0.51 percent increase in export contribution to GDP, outweighing the 0.08 percent decline in the import contribution to GDP. The marginal increase in the GDP attributed to the offsetting effects arising from the declines in other export commodities such as cocoa and copra. These two commodities are directly competing with oil palm in terms of land space.

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Table 8.9a Short-run simulation results of selected macroeconomic variables

Macroeconomic Variables Percentage change (%)

Real GDP 0.437

Real Consumption 0

Real Investment 0

Real Government demands 0

Change in Stocks 0

Exports (contribution to GDP) 0.512

Imports (contribution to GDP) -0.075

Employment 0.09

Real devaluation150 0.547

Terms of trade -0.269

GDP at factor cost 0.481

Source: Simulation result, SIORANIG

The price effect is that the 100 percent expansion in land acquisition results in a 0.55 percent increase in real devaluation (p0realdev). Note that this variable does not refer to the exchange rate; rather it is the ratio of the import price index (cif) to the GDP price index. Given that the Solomon Islands is a price taker in the world market, import prices are exogenously determined. Thus, the positive real devaluation implies less foreign export demand while there are high import demands. Consequently, the terms of trade (p0toft) further deteriorates by 0.27 percent, outweighing the 1.4 percent increase in the export volume.

For the labour market effect, the expansion in the output of the exporting industries triggered by the 100 percent expansion in the agricultural land for the oil palm

150 Note, this variable does not refer to the exchange rate. It does however refer to the ratio of import price index, cif to GDP price index. 238

plantation generates demand for more workers. In other words, in the short-run, it creates more employment because the real wage does not change. In Chapter 7, section 7.3.2 Figure 7.1b illustrates this transmission, shown by the dotted arrow running from the trade balance to employment. In addition, equation 7.1 in the previous chapter explains this relationship. The simulation result therefore finds that aggregate employment (employ_i) goes up by 0.09 percent. The increase in aggregate employment influences movement in the components of real value added or the GDP at factor cost (xgdpfac) to increase by 0.48 percent. Both GDP at factor cost and GDP at current market have increased, with the former rising by more than the latter. The difference between the two aggregates is the indirect taxes. The dominant effect on aggregate output is the variation in aggregate employment, which causes the increase in GDP.

8.5.2 Sectoral Impact As in section 8.3.3 above, this section provides the final output of each sector or industry, as well as the labour market effect. Again, using Figure 7.1a in Chapter 7, the transmission occurs along Loop 7d to Loop 4. We trace the short-run impact of the 100 percent expansion in the oil palm plantation on sectoral labour, which in turn triggers the movement in the sectoral outputs. Tables 8.9b and 8.9c below display the main winners and losers from the policy action. Figure 8.4 displays all the sectoral simulation results.

Table 8.9b Main winners of a short run oil palm simulation Percentage change Percentage change Industry (%) in output (%) in labour input Oilpalm 20.329 3.498 ElectWatEner 0.304 0.367 Construction 0.196 0.702 Insurance 0.0329 2.0532 Peace 1.330 146.454 OtherServ 2.584 1.278 Source: Simulation results, SIORANIG

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8.5.2.1 Main Winners

Oil palm (Oilpalm) sector Since the shock was from within the oil palm sector, output in the sector is expected to increase, apart from the labour factor input. Consequently, output surged significantly by 20.2 percent while labour input rose by 3.5 percent. Obviously, the expansion of the agricultural land for the oil palm plantation will require more inputs such as labour, which attracts resources from other competing sectors, and thus invoking the Rybczynsky effect.151

Electricity, Water, Energy, and Fuel (ElectWatEner) sector The 100 percent expansion in the oil palm plantation land also causes output in the ElectWatEner sector to increase by 0.30 percent, with the labour input going up by 0.37 percent. The increase is to be expected, as the oil palm company (i.e. GPPOL) produces their own electricity, and use fuel for their operation. Thus, an expansion of the land size for its plantation means an increase usage in power and other material inputs, including labour.

Construction (Construction) sector Complementing the above is the construction sector. This sector also expands as a result of the growth of the oil palm sector. Consequently, output in construction rose by 0.20 percent, with labour input increasing by 0.7 percent. Again, expanding the oil palm sector means increasing the physical size, including the need for more buildings, hence the flow on effect for the construction industry.

Insurance (Insurance) sector With more properties or assets accumulated due to the increase in production in the oil palm sector, the need for insurance rises. Accordingly, output in the insurance sector

151 Attributed to Tadeaus Rybczynski (1955), the theory entails that an increase in an endowment will result in increased output from a sector using that endowment intensively, and reduced output for another sector, assuming that prices are constant. 240

expands by 0.033 percent. This stems from a 2.1 percent increase in the factor labour input.

Peace (Peace) sector In the model we assume that peace is a public good, therefore consumed by all the sectors, including the peace sector itself, albeit the extent of consumption depends on each sector’s elasticity. At the same time, peace is also produced by each sector; how much is produced depends on how sensitive peace is to each sector’s requirements. For the oil palm industry, peace is important; hence its output subsequently expands by 1.3 percent as a result of the 100 percent expansion in the oil palm plantation land hectares. This resulted from a marked surge in the labour input by 146.5 percent. Because peace is a public good, hence everyone who is advocating for peace is seen as employed in the peace industry. Thus, the increase of labour reflects the voluntary involvement of people promoting peace in their communities.

Other services (OtherServ) sector This sector contains the residual industries, so the impact can move either way, depending on whether or not the shocked variable has any connection with the industries in this category. In this instance, the ‘other services’ sector, output rose by 2.6 percent, owing to a 1.3 percent increase in the labour input.

8.5.2.2 Main Losers

Mining and Quarrying (MiningQuar) sector In terms of the sectors that would experience adverse effects because of the 100 percent expansion in the oil palm plantation land size, the mining sector is one of the sectors that would be immediately affected. This is because the only mining company (GRML) operating in the country is also located in close proximity to GPPOL, and draws on the same pool of workers and capital. Therefore, an improvement and/or expansion in any particular investment will definitely have an adverse impact on the others, especially when both companies are competing for the same (skilled and unskilled) workers. Consequently, the mining and quarrying sector output dropped by 0.73 percent due to a fall in the factor labour input by 1.4 percent. The drop reflects the movement of 241

workers, mainly unskilled, to the oil palm sector. This is another example of the Rybczynski effect.

Hotel, tourism, and restaurant (HotelRestaur) sector In terms of the HotelRestaur sector, output fell by 0.17 percent stemming from a drop in the labour input by 3.2 percent. The decline in the output of this sector is probably due to it does not having any link (either direct or indirect) with the oil palm sector. The only possible link here is the movement of unskilled workers to the oil palm sector.

Education (Educ) and Health (Health) sectors For the education sector, output dropped by 0.16 percent as a result of a 0.09 percent decrease in the labour input. Similarly, output in the health sector also declined, by 0.15 percent, owing to a fall in the labour input by 0.09 percent. The declines in health and education are due to the fact that health workers and teachers are switching jobs to become administrators in other sectors. These two sectors are the least attractive jobs in the Solomon Islands in terms of salary.

Table 8.9c Main Losers of a short-run oil palm simulation

Percentage change Percentage change Industry (%) in output (%) in labour input MiningQuar -0.734 -1.389 HotelRestaur -0.174 -3.200 Educ -0.163 -0.088 Health -0.146 -0.087 Source: Simulation results, SIORANIG

242

Figure 8.4 Graphical presentations of the short-run simulation results

160

140

120

100

-20

Output Labour input

Source: Simulation results, SIORANIG

243

8.6 Modelling the long-run impact of the expansion in the oil palm industry This section analyses the long-run impact of an expansion in the exogenous (positive) shock of agricultural land for expansion of the oil palm plantation. Like in the short-run simulation, the exogenous agricultural land variable for the oil palm x1lnd(“oilpalm”) is shocked. In line with the intention of the oil palm industry (GPPOL) to expand by 300 percent, we shock the long-run simulation by 200 percent, as 100 percent has already been taken care of in the short-run. Similar to section 8.4 above, the long-run model closure allows capital to vary, such that it influences the variations in the investment component of GDP. In allowing adjustment to the capital stock, the rates of return on capital are allowed to be determined outside the model (exogenous).

In terms of the labour market, total employment is assumed to have reached its potential capacity in the long-run (that is full employment), hence, it is fixed. Thus, the real wage becomes endogenous in the long run. Again, as mentioned in section 8.4 above, all the components of GDP, from the expenditure side, are allowed to vary to reflect the openness and vulnerability of such a small open economy.

The next sections below present the long-run simulation results of the 200 percent expansion in the oil palm plantation agricultural land. As in the above simulation results, the results are presented according to their macroeconomic and sectoral impacts.

8.6.1 Macroeconomic Impact The simulation results of selected macroeconomic variables are presented in Table 8.10a below. We begin the analysis by examining the GDP and its components. The impact of the shock raised the GDP by 0.21 percent. In terms of the expenditure side, the increase in the GDP stems from increases in consumption by the private and public sectors, as well as a positive outcome for the trade balance. The negligible increase in GDP reflects the declines in the major sectors (as will be seen in the sectoral analysis below), more than offsetting the increases in the other sectors. 244

Table 8.10a Long-run simulation results of selected macroeconomic variables Macroeconomic Variables Percentage change (%) Contribution to GDP Real GDP 0.208

Real Consumption 0.039

Real Investment -0.029

Real Government Consumption 0.048

Change in Stocks 0

Exports 0.300

Imports -0.150

Other macroeconomic results:

Aggregate capital -1.028

Real wage -0.683

Average nominal wage -1.130

CPI -0.450

Source: Simulation result, SIORANIG

Consequently, the contribution of real private consumption to GDP increases by 0.04 percent. The increase stemmed from an increase in the consumption of agriculture and food (agrifood),manufactured food and beverages (foodbevmanuf),clothing and footwear (clothingftw), electricity, water, and energy (electwatener),hotel, restaurant, and tourism (hotelrestaur),owner own dwelling (owndwelling), education (educ), and health (health). Similarly, the government’s real expenditure grew by 0.5 percent, because of consumption in electwatener, trade, hotelrestaur, financial and intermediaries (financinterm), public administration and defence (pubadmindef), educ, and health. The other sectors recorded no change in both components of the GDP.

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In terms of investment, the 200 percent increase in the oil palm plantation land caused a decline in the overall real investment as a contribution to GDP, by 0.03 percent. The decrease in the real investment is further analysed in the next (sectoral) section below. This section (section 8.6.2) highlights the sectors that win and lose as a result of the shock of the 200 percent expansion in the oil palm plantation.

The trade balance, on the other hand, recorded an increase by 0.15 percent as a contribution to GDP. The increase comes from a 0.3 percent rise in the contribution to GDP from exports, more than offsetting the 0.15 percent decline in imports. In volume terms, exports rose by 0.80 percent while imports rose by 0.28 percent.

Another GDP component often neglected in the macroeconomic textbooks is the stock (that is, change in inventory). In the simulation results, the change in stock is zero, which reflects the closure assumption of fixed stock. As mentioned elsewhere in Chapter 7, in our database we do not have concrete information on the structure of stock; hence it was allowed to adjust in order for the data to meet the initial equilibrium condition.

Analysing GDP from the income side, the factor inputs have also registered increases as a result of the 200 percent expansion in the oil palm plantation. Capital, which varied with investments, goes down by 1.0 percent. The drop reflects decline in the capital stock of all the sectors, more than offsetting the increases in the oil palm industry, construction, insurance, real estate, and peace. With the labour market effect, real wage, which is the ratio of average nominal wage and the consumer price index, goes down by 0.68 percent. Apparently, the long-run impact of the oil palm sector has caused adverse effect for the other real sectors, as they are also competing for the same input resources. The drop in the real wage is due to the fall in the consumer price index, which stemmed from the price declines of sectors adversely affected by the shock.

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The next section discusses the impact of the shock on each individual sector, highlighting the winners and losers.

8.6.2 Sectoral Impact Table 8.10b below tabulates the simulation results of the 200 percent increase in the oil palm plantation agricultural land. We begin by first analysing the winners from this development. The sectors that benefited (winners) from the shock apparently have connections, either directly or indirectly, with the oil palm industry.

8.6.2.1 Main Sectoral Winners

Oil palm (Oilpalm) For the oil palm industry, the expansion of the oil palm plantation causes the final output in oil palm to increase by 35.4 percent. The expansion reflects increases in both capital and labour use, with the former surging by 6.2 percent and the latter by 9.6 percent. The significant improvement in the oil palm industry directly reflects the GPPOL Company’s intention. Unfortunately, the expansion in the oil palm plantation land has had negative effects on the other agricultural sectors. The main reason is that land has been acquired for oil palm expansion, leaving the other crops with limited alluvial land for agriculture.

Electricity, Water, Energy, and Fuel (ElectWatEner) sector Output for the Electwatener sector registered an increase of 0.62 percent. The increase attributes to labour usage, which rose by 1.0 percent while capital input fell by 2.1 percent. As the oil palm sector produces its own electricity, hence the expansion in the oil palm means that electricity demand will also increase.

Construction (Construction) sector Similarly, the construction sector also benefits from the shock; it rose by 1.1 percent. The improvement is attributed to increases in both labour and capital inputs, by 3.5

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percent and 0.37 percent respectively. Again, this sector complements the expansion of the oil palm sector.

Insurance (insurance) The expansion in the output of oil palm also raises output in the insurance sector, by 0.48 percent. The increase stems from a 3.9 percent and 0.71 percent increase in labour and capital inputs respectively. Apparently, expansions in these sectors reflect the growing demand for insurance services from growth in the oil palm sector.

Real Estate sector (RealEstate) For the real estate sector, output went up by 1.9 percent, owing to a 6.4 percent and 3.2 percent rise in the labour and capital usage respectively. With a growing economy the demand for real estate to accommodate the increasing work force will rise.

Public, Administration, and Defense (PubAdminDef) sector The public administration and defence (pubadmindef) sector also registered an improvement in output, by 0.05 percent. Unfortunately, the small increase arises from other sources than the labour and capital inputs, as these two factor inputs recorded declines, of 1.6 percent and 4.6 percent respectively.

Education (Educ) and Health (Health) sectors For the social service sectors, output in the education and health went up by 6.0 percent and 4.3 percent respectively. Increase in the former stems from a 3.2 percent rise in labour input while capital input fell by a negligible 0.008 percent. Similarly, for health, the increase also came from the increase in labour input, by 2.3 percent, as capital input dropped by 0.8 percent.

Peace (peace) Again, reiterating the discussion in section 8.5.2.1 above of the peace sector, the model assumes that peace is a public good. Thus, it is an input to (or consumed by) all the 248

sectors including the peace sector itself. However, the extent of consumption depends very much on the elasticity of the commodities in each sector. On the production side, peace is also produced by each sector; how much is produced depends on the output elasticity for peace. Therefore, for this instance, output in peace surged by 47.3 percent, due to increases in the labour input and capital stock of 117.6 percent and 110.9 percent respectively.

Other Services (OtherServ) sector Finally, the other services sector also expanded in their output, by 5.0 percent. This stemmed from the increase in the factor labour input, as capital input fell by 1.0 percent.

Table 8.10b Winners of long-run oil palm simulation

Percentage (%) Percentage Percentage (%) change in output (%) change in change in capital Industry labour stock 35.428 9.557 6.194 Oilpalm 0.621 1.018 -2.083 ElectWatEner 1.135 3.549 0.370 Construction 0.481 3.902 0.712 Insurance 1.888 6.442 3.175 RealEstate 0.055 -1.607 -4.627 PubAdminDef 6.025 3.159 -0.008 Educ 4.340 2.334 -0.807 Health 47.354 117.615 110.936 Peace 4.973 2.104 -1.030 OtherServ Source: Simulation result, SIORANIG

8.6.2.2 Main Losers Table 7.10c below displays the main sectors that lose out because of the 200 percent increase in the oil palm plantation size. We only focus on the main losers from this expansion. Figure 8.5 below, however, shows the simulation results of all the sectors.

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Cocoa (cocoa) The cocoa sector recorded a 3.2 percent fall in output. The decrease came from declines in labour and capital use, by 2.5 percent and 5.5 percent respectively. The cocoa sector competes directly with the oil palm sector in terms of available alluvial land. Thus, any land expansion for the oil palm industry means that less land is available for cocoa. Additionally, the cocoa sector also competes with the oil palm sector for unskilled labour.

Copra (copra) Similarly, copra output also dropped, by 2.6 percent, mirroring the falls in both labour and capital use, with the former down by 1.4 percent and the latter by 4.4 percent. As for cocoa, the copra sector also competes with the oil palm industry for labour. Thus, the expansion of the oil palm industry would likely see movement of (both skilled and unskilled) workers away from other sectors to the oil palm industry.

Forestry, timber, and log (ForestryLog) sector The forestry and logging (forestrylog) sector also recorded a decline of 2.2 percent in output because of the expansion in the oil palm sector. This reflects declines in labour usage of 1.5 percent, as well as capital usage by 5.5 percent.

Fishing (Fishing) sector The fishing sector also registered a fall, by 1.8 percent, reflecting the 2.5 drop in capital. This more than offset the 0.61 percent increase in labour use.

Mining and Quarrying (MiningQuar) sector The MiningQuar also becomes one of the significant losers in the oil palm expansion, with output increasing by 2.1 percent. The drop reflects declines in the labour input by 1.5 percent and capital by 4.5 percent. Again, the decline in labour use reflects the unskilled labour (from Gold Ridge Mining Ltd) moving mainly to the oil palm industry (GPPOL Company). The GPPOL Company and Gold Ridge Mining Company are located adjacent to each other on Guadalcanal Island.

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Handcraft Manufacturing (HandcraManuf) and Clothing and Footwear (ClothingFtw) sectors For the HandcraManuf sector, output fell by 1.3 percent, stemming from the drop in labour use by 2.2 percent, despite a 0.9 percent increase in capital. Similarly, the ClothingFtw sector also declined, by 2.7 percent, as a result of a 1.8 percent drop in labour input and capital use by 4.8 percent.

Hotel, accommodation, tourism, and restaurants (HotelRestaur) sector The expansion in the oil palm sector also causes a reduction in the HotelRestaur sector, with output falling by 1.3 percent. The decline came on the back of a 2.3 percent drop in capital, more than offsetting the 0.8 percent in labour.

Transport (Transport) sector The transport sector’s output declined by 1.0 percent, as a result of a 1.2 percent drop in the factor input capital. This is despite an increase of 1.0 percent in labour use. Although, it was expected that this sector would increase, the decline in the sector indicated that the sectors that have increased output utilize transport as margin goods. In other words, those sectors that have experienced a fall in their output use their own transport; hence, they do not use the transport sector.

In summary, the impact of the 200 percent expansion in the oil palm plantation has had winners and losers in the economy, both in the short and the long run. In aggregate, however, the shock has an overall positive effect on economic growth, as shown by the increase of the GDP. The expansion of oil palm production also has a positive effect on aggregate employment in the short-run, as well as increased intra-sectoral employment. Furthermore, the expansion also has a positive impact on the Solomon Islands trade balance, with expected favourable terms of trade effects.

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Table 8.10c Losers of a long-run oil palm simulation

Percentage (%) Percentage Percentage (%) change in output (%) change in change in capital Industry labour stock -3.202 -2.470 -5.464 Cocoa -2.646 -1.372 -4.400 Copra -2.236 -1.464 -4.488 ForestryLog -1.182 0.610 -2.478 Fishing -2.098 -1.492 -4.515 MiningQuar -1.324 0.858 -2.238 HandcraManuf -2.709 -1.803 -4.818 ClothingFtw -1.269 0.813 -2.282 HotelRestaur -1.019 1.003 -2.097 Transport

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Figure 8.5 Losers and Winners of a long-run oil palm simulation

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8.7 Conclusion This chapter employed the newly developed computable general equilibrium framework of the previous chapter to analyse two fundamental phenomena. The first part empirically analyses the impact of peace on economy-wide aggregates – GDP, inflation, employment, and the trade balance. Under the SIORANIG framework, the shocked variable is a1tot(“peace”). The simulation traces the transmission mechanisms of peace through the economy. This closely follows the theoretical framework developed in Chapter 7 section 7.3.2. The second simulation investigates the role of the private sector (investment) on the economy; that is, investigating the likely impact of a rise in investment (oil palm in this case) on the Solomon Islands economy. Because the focus is on peace, we incorporated the peace simulation results conducted in the first analysis into the oil palm (second) analysis. To do this, we employed the updated database from the short-run peace simulation to the oil palm analysis.

The simulation results from both analyses display the overall positive impact of an improvement in peace on the economy. With the peace simulation, the 12 percent improvement in peace induces a 1.7 percent growth in GDP in the short-run. From the expenditure side, the growth stems from the improvement in the trade balance, while increase in the use of labour led to the expansion of the GDP from the income side. Similarly, the long-run simulation finds that improvement in peace causes GDP to grow by 0.44 percent, with the expenditure side showing an upsurge in all the components, except for private and government consumption. The income side of the GDP saw the aggregate real wage increasing by 6.2 percent, as was aggregate capital, by 0.08 percent.

In terms of the sectoral impact, the improvement in peace led to increases in the activity levels of most of the sectors, with significant improvements seen in the OilPalm, ForestryLog, foodbevmanuf, MiningQuar, ClothingFtw, HotelRestaur, OwnDwelling, Educ, health, and peace sectors. Intuitively, the improvement for the majority of sectors implies that peace is fundamental for there to be a rebound in these economic activities. This reaffirms the argument from chapter two that ‘peace is necessary for economic prosperity’.

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For the oil palm simulation, the expansion of the oil palm plantation size stimulates the economy to grow by 0.44 percent in the short-run and 0.21 percent in the long-run. In the short-run, the growth comes on the back of an increase in the trade balance stemming from increased production of export commodities. Aggregate employment recorded an expansion, by 0.1 percent, which also assists the growth in GDP. Long-run growth, on the other hand, can be attributed to increases (from the expenditure side) in investments and exports, despite the decline in the contribution of private and government consumptions to GDP.

The sectoral impact of the oil palm plantation expansion has mixed results; with the sectors that have links with the oil palm industry expanding in tandem with the oil palm industry. These sectors are mainly the services sectors while the other agriculture sectors experience declines in their output as a result of direct competition for labour input. Weighing the pros and cons, the benefits of an expansion of the oil palm plantation outweigh the costs in terms of aggregate income and employment.

Putting the results into perspective, it was claimed in chapter two that the presence of an investment (in this case GPPOL) in a community (or in a country for that matter) helps to improve and sustain peace, thereby reduces the risk of conflict reoccurring. The results from the CGE model also confirm this argument, as they are similar to the results of the partial equilibrium analysis. Both the short-run and the long-run simulations show significant improvement in the peace industry. The fact that these industries have re-opened and /or scaled up operations after the peace onset is a consequence of improved peace. Building on this the continuous presence of these sectors or companies in the post-conflict societies or communities re-enforces the prevalence of peace. This means that the private sector has a role to play in sustaining peace.

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CHAPTER 9

CONCLUSION AND POLICY IMPLICATIONS

9.1 Overview of the Study This thesis has investigated the economic benefits of (post-conflict) peace, examined the channels that peace can transmit to the economy, and investigated the extent to which the private sector promotes peace, using the post-conflict Solomon Islands as a case study. It was motivated by the question, what is the contribution of peace to the economic recovery in a post-conflict country, which stemmed from the identification of three knowledge gaps in the literature. The existing empirical measurement of peace has been informed by the ‘absence of personal and structural violence’ definition that engendered two continua; on the one hand, ‘negative peace’ (the minimalist view) and the other, ‘positive peace’ (the maximalist view). This thesis has therefore established a middle ground for the empirical definition of peace between the two continua. Given the socially constructed nature of peace, this study quantified peace by deriving a peace perception index (PPI) based on the perceptions of people about peace. The CGE framework evaluated the contribution of peace to the macro- economy, which fills the other knowledge gap. The final knowledge gap identified was the fulfilment to constructing and implementing a CGE model for the Solomon Islands economy.

The empirical analyses employed two methodologies. First, the impact of peace on (household) income was investigated in Chapter 6 by econometric analysis using household level data. As a result, the peace perception index (PPI) was computed, which was then employed as a variable for peace in the econometric analysis. The second is the construction of the CGE model for the Solomon Islands to address the impact of peace on the economy as a whole. Chapter 7 describes its features. That chapter also provides the model closure and solution process, along with the conceptual framework that guided the analysis of the CGE simulation results in Chapter 8. The analyses of the results of both the econometric and CGE model addressed the central research questions, and thereby help to fill the knowledge gaps previously identified.

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9.2 Main empirical findings This section synthesises the main empirical findings, and answers the research questions based on the findings from both the household survey (and econometric) and CGE analyses.

9.2.1 Main findings from the analysis of the household survey (econometric analysis) Chapter 6 was concerned with quantifying peace and investigating the impact of peace on household income, as well as presenting the descriptive statistics from the household survey. It also investigated the transmission mechanism of peace to income. In quantifying peace, this study draws from the notion of legitimate peace (Anders and Ohlson, 2014), which focuses on perception rather than on the minimalist or maximalist view of peace. The information/data to construct the PPI uses data from the household survey conducted by the author on the plains of North Guadalcanal. The PPI was calculated to be 88 percent, which implies that the level of peacefulness in the community has improved to 88 percent since the onset of peace.

Having quantified peace, our variable of interest, the next step was to investigate the impact of peace on income. In doing so, we investigated the transmission mechanisms of peace to income. Applying the conceptual framework to peace, the chapter evaluated whether or not peace transmits directly and indirectly to income. For the indirect mechanisms, two models were tested. Model 1 examines the effect of peace on the likelihood of GPPOL’s continued presence, and Model 2 investigates whether peace matters for ownership of small businesses by households. The probit regression model was used to derive estimates for these two models.

The results from Model 1 indicated that peace supports the likelihood of GPPOL’s continuing presence. The elasticity is very high (141 percent), suggesting that peace is vital for the presence of GPPOL. For model 2, the results also showed that peace is important if households are to operate small businesses, with an elasticity of 92 percent. These results suggest that peace is indeed necessary for economic activities, both at a corporate and household level. That is, peace contributes to the growth of enterprises, which in return triggers economic growth.

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The final model investigates the impact of peace on income. Among other explanatory variables, including peace, the dependent variable in models 1 and 2 are also included. Model 3 was tested using an OLS regression and the result suggested that peace raises income. Directly, peace causes income to increase by a very high elasticity (139 percent), implying that income is very sensitive to peace. Indirectly, as shown through the two models above, peace also transmits to income. Importantly, the results also showed that the presence of GPPOL is important for causing income to increase. This finding corroborates with the literature on the positive association of the peace – income nexus. It thus contributes to the literature on peace – income nexus at the micro level. Therefore, the micro results from the household survey indicated that peace is necessary for sustaining household income. The contribution of peace to income is large enough to effect improvement in the household income, as shown by the high elasticity of 139 percent.

9.2.2 Main findings from the CGE Analysis In terms of the findings from the CGE analysis, the conceptual frameworks developed in Figure 7.1 of Chapter 7 guided the fulfilment of the knowledge gaps identified in the literature, which were addressed in the form of sub-research questions. Two major policy changes were the highlights of this simulation; they are the impact of the improvement in peace and the impact of the expansion in the oil palm industry on the economy. Both of these variables were analysed for their short run and long run impacts.

The first simulations were conducted using the 12 percent improvement in peace innovation. The short-run findings revealed that were peace to improve by 12 percent, GDP would expand by 1.7 percent. In other words, of the average 7.3 percent growth rates seen over the post-conflict years in the Solomon Islands, peace contributed 1.7 percent. This outcome was driven largely by the external sector as the domestic absorption (components of GDP) was assumed to be exogenous. Obviously, the arrival of RAMSI and the influx of donor funds were the main contributing factors to this outcome. Also, during this time (short-run) employment surged, as a result of Solomon Island Government request for donors to, as much as possible, utilize locals in many of the donor-funded projects. In terms of the sectoral impact, the improvement in peace causes positive impacts in all the sectors, except for those

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sectors that have minimal requirements for peace. Note that peace was considered an input into all the sectors of production. The consumption of peace by each sector, however, depends on the sector’s sensitivity to peace. Evidently, agricultural food (Agrifood), real estate (Realestate), and public administration and defence (PubAdminDef) were the only sectors with low requirements for peace. This was expected, as the former is basically the subsistence sector, which does not require much peace in order to produce its output, given that most of the conflict occurred on Guadalcanal. The impacts on the second and third sectors however were very minimal, as they are confined to urban Honiara which was relatively secure during the tensions.

In the long-run, the impact of improvements in peace were also positive, although the expansion on GDP is a far lower than in the short-run. This is expected, because in the long run the demand for peace naturally diminishes as peace becomes normalised. Thus, the contribution of peace to growth diminishes over time. This means that over time, as the continuation of peace becomes of little concern, long-term growth is driven by factors other than the variable peace. In the long run, all the components of GDP are allowed to vary. Consequently, GDP increased by 0.31 percent, owing mainly to expansions in real investment and the trade balance. In terms of the sectoral impact, the findings are mixed; the sectors with guestimated high peace elasticity showed positive impacts, while those with low guestimated elasticity indicated negative impacts. Obviously, the oil palm sector was one of the sectors that showed a positive impact.

Overall, the results from the short-run and long run simulations of the improvement in peace re-enforces the legitimate role of peace in stimulating and sustaining economic growth in post-conflict countries. It strongly suggests that peace is necessary for economic prosperity. Peace is measured differently in this study; however, the findings support the consensus on the positive nexus between peace and income.

Having established the importance of peace to the economy, the thesis then examines the impact of the expansion of the oil palm industry on the economy. This particular simulation attempts to investigate the contribution of the private sector to a post-conflict economy.

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Thus, the simulation focused on the impact of expanding the land size of the oil palm plantation by 300 percent, in line with GPPOL’s current plan. Given that a 300 percent increase will have a long lead up time, the increase was treated as two stages of a 100 percent in the short-run, and a 200 percent increase in the long-run. The short-run simulation results suggest a positive impact on the overall economy, with a 0.44 percent increase in GDP. Again, the domestic components of GDP are exogenous, thus the increase stemmed from the positive trade balance’s contribution to GDP. In terms of the sectoral impact, the results are mixed, with the major industry winners being the oilpalm, electwatener, construction, insurance, peace, and other sectors. Apparently, these sectors have connections to the oil palm industry, and benefit from flow-on effects from the expansion of oil palm production. The major losers were the minigquar, hotelrestaur, education, and health sectors. These sectors appear not to depend on the oil palm industry. For the mining sector, the decline reflected the Rybczynsky effect; the only mining company in the country and GPPOL are operations adjacent to each other, which mean workers can change employers whenever it is useful for them to do so. Importantly, the labour input to the peace industry shot up quite remarkably, suggesting that the majority of labourers promote peace within their own sphere of influence.

The long-run simulation for the impact of expanding the oil palm plantation also indicated a positive impact on income (or GDP). While real investment registered a decline, the other components of GDP expanded, with the export sector dominating the effect. The fall in aggregate investment can be explained by the sectoral impacts, where many of the productive sectors were negatively impacted. In contrast, the sectors that recorded positive impacts were mainly the service sectors, which depend on the productive sectors.

Overall, the expansion in the oil palm sector provides impetus for the economy to expand. More importantly, the peace industry expands faster than any other sector, suggesting that such investments as GPPOL (located within the community in the form of a ‘nucleus’ enterprise) has a role to play in promoting peace within the local community. Given that this is a post-conflict community, and GPPOL provides employment and essential services to the local community, their presence seems to be an essential ingredient for promoting peace.

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9.3 Theoretical and policy implications I now turn to the theoretical and policy implications arising from these findings. The next section attempts to situate this study in the broad literature about the peace – income nexus, as well as finds its position in the policy sphere.

9.3.1 Theoretical implications The theory of peace has treated peace as the ‘absence of violence’. This ‘absence of violence’ proposition has led to the measurement of peace using definitions based on ‘negative peace’ or ‘positive peace’. The empirical literature has tended to categorise and therefore measure peace as either ‘negative’ or ‘positive’, encompassing a ‘minimalist’ or ‘maximalist’ view of peace. This study argues that this method of categorisation and measurement of peace has ignored the existence of emotionally maimed individuals and minority groups in societies once riven by conflict. This is a significant problem for the existing literature. Drawing from Anders and Ohlson (2014), this study develops an alternative way of measuring peace by focusing on the ‘perceptions’ of individuals. Accordingly, in line with Anders and Ohlson’s theorisation of peace, this study actually quantifies peace based on the perception of individuals. Subsequently, this study derived what we have termed the peace perception index or PPI. Given the socially constructed nature of peace, this PPI has qualitative and quantitative theoretical underpinnings.

A great deal of the empirical analyses of peace have increasingly been carried out in contradistinction to conflict or violence, implying the ‘absence of violence’ (or what violence is not) instead of ‘what peace is’. As such, this study has quantified peace by obtaining individuals’ views on ‘what they think peace is’. In doing so, it is possible to study peace directly and empirically without referring to an ‘absence of violence’. Another novelty of this measurement is that it is micro level oriented, and readily complements work carried out on the institutional or macro level.

Consequently, the empirical results (i.e. the partial equilibrium analysis) discussed in Chapter 6 showed that the way peace was measured is relevant to explaining the peace – income nexus at the micro (household) level. The theoretical implication for such a measurement is

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that it can be used to analyse the relationship between peace and household income. This study thus contributes to re-defining the construct ‘peace’ as a variable for empirical analysis.

Furthermore, the existing empirical literature on peace has mainly analysed peace using the partial equilibrium framework. There is a void in such analysis, as the construct ‘peace’ has multiple linkages and market interactions with the underlying economy. Thus, this study contributes to stimulating the empirical analysis of peace by employing a computable general equilibrium (CGE) model upon which future empirical work on the construct ‘peace’ can be carried out.

9.3.2 Policy Implications The empirical findings have various policy implications for the Solomon Islands. There are three relevant policy implications of the partial equilibrium analysis (Chapter 6). First, the evidence indicated that peace increases the likelihood of GPPOL to remain in the country. Since GPPOL’s operations started, it has become one of the largest employers and the fourth largest foreign exchange earner in the country. Therefore, if GPPOL are to continue their investment in the Solomon Islands, ensuring peace where they conduct their operations is imperative. The Government must therefore do all in its power to maintain peace where GPPOL operates at all times. The presence of the Tetere Police Station within the centre of the Guadalcanal Plains, where the most intense violence occurred at the height of the conflict, is positive, but the impact of this presence can be maximised only if the police are seen as being impartial. At the micro level, GPPOL also has an important role to play in ensuring peace. GPPOL provides such basic essential services as roads, schools, clinics, and sanitation. Similarly, it is in the best interest of the local communities to maintain peace in the area if they wish to continue to receive these services.

Second, the presence of GPPOL in the local community has provided impetus for the communities in the plains to better utilize the benefits they accrued. These benefits vary, but the common ones are the economic rents they receive from GPPOL, as well as the positive externalities arising from GPPOL’s presence. As such, there is evidence of the communities having a surprising degree of financial liquidity. The problem, however, is that in terms of

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financial management the community lacks relevant skills, experience and knowledge. The Government should therefore provide training in financial literacy and financial management for the local communities.

Finally, the evidence on the peace – household income relationship shows that while the presence of GPPOL provides ease of access to funds (directly and indirectly) by the locals, the government should ensure that special incentives are accorded to local communities who continue to maintain peace, in order to empower local level initiatives. For example, incentives such as a tax free zone for communities with businesses that employ young people. This is where the ‘doing different things’ policy can be applied. By doing this the government can create opportunities for young people to earn money and gain skills. In the post-conflict context, there is the added benefit of making them less likely to involve themselves in activities that might renew the previous conflict. In this regard, diversifying economic activities to other crops would not only increase livelihoods of the communities, but also potentially reduces the likelihood of conflict.

Three policy implications arise from the CGE simulations. First, the short-run evidence on the impact of improvements to peace showed that peace significantly contributes to higher economic growth. This suggests that post-conflict government policies should urgently prioritise economic policies that support the expansion of the private sector during the peace onset phase. Doing so supports the sustainability of peace through the available economic instruments.

The second policy implication draws from the long-run evidence, which showed that the contribution of peace to economic growth diminishes over time, though it remains positive. This result implies that the government should strategically manage economic policies, so that a fall in the contribution of peace (stemming from a combination of a natural catch-up and support from donor partners) to growth is counter-balanced by improvements in productivity and expansion of the productive sectors. This means that if economic policies are synchronized, when short-term policies cease to be effective, long-term policies take effect without interrupting economic growth.

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Having been able to synchronize and align economic policies to enhance peace, the third form of evidence focuses on the need to recognise and support the peace industries. This has demonstrated that the contribution of the expansion in the oil palm industry makes a positive impact on the economy. We saw above that the presence of GPPOL in the communities of Guadalcanal affords the local communities some leverage to improve their standard of living. Therefore, the investment model adopted by GPPOL is one that may have lessons for the rest of the Solomon Islands, and possibly beyond. In light of this investment model, the government should emphasise that any foreign investor willing to invest in the country must undertake to give local landowners, whose land will be used for production, equity in the investment. The payment of shares in this equity will be in the form of land that will be freed up for investment. In this way, two complementary things happen. First, it gives a sense of ownership to the local landowners, and promotes pride in the economic relationship, as well as making it a simple act of self and collective interest to protect the investment. Hence, peace is maintained. Second, it empowers the local communities economically, thereby improving their living standard and reducing the likelihood of conflict recidivism.

9.4 Study Limitations and Future Research Areas Like in any other research project, there were limitations to this research. This section highlights some of the limitations that this study has experienced, and suggests possible means of rectifying them. Given the employment of two data sources (i.e. household data and the macroeconomic data for CGE) in this study, I will discuss problems with each of these data sources separately. A survey conducted on households in the targeted area gathered household data. The main problem encountered during the survey was logistical. The households are not located close to each other, so that some time was lost in just moving from one household to another. Coupled with limited funds, this meant I was not able to complete the targeted sample, albeit the completed sample was sufficient to provide the data for the regression analysis.

The other limitation concerns the measurement of the underlying variable, peace. As can be seen from the analyses, three variables representing the perceptions of the interviewees

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constituted ‘peace’. While this measurement was sufficient for this study, it would have been useful to explore other research instruments, such as focus groups and/or longitudinal data. A longitudinal study would be more appropriate to gauge the level of peace in the community over time. The data collected as part of the household survey includes GPS coordinates, allowing for future longitudinal surveys.

In addition to the measurement issue, while this study was able to compute a proxy for the peace elasticity for the oil palm sector, it could not do the same for the other sectors analysed in the CGE model. This is due to the relevant data not being available. In order to collect this data another survey would be required, which is well beyond the reach of available resources.

In terms of the macroeconomic data for the CGE model, two major problems are noted. First, there was no suitable input-output (IO) table available for the Solomon Islands. As such, this study had to start from scratch by constructing one. Second, the data needed for the IO table was virtually non-existent. Even the published macroeconomic data from the national accounts were insufficient to make an IO table. As well, the data on the published national accounts were highly aggregated. Thus, this study employed data from very rudimentary sources, mainly the Central Bank of Solomon Islands (CBSI) and the Solomon Islands National Statistics Office (SINSO), to build the IO table. Accordingly, most of the data was crudely estimated, and in some cases guestimated. Furthermore, all the parameters used in the CGE model are borrowed from the literature. These deficiencies would have to be addressed as part of future research.

In light of the above problems, five possible areas are available for further research. First, there is a need for further research into the proper definition and measurement of peace, taking into account standard models, as well as the framework unveiled in this study. My judgement is that a proper quantification of the construct peace is paramount. The current framework in this study has opened up some lines of inquiry for future research.

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Second, the incorporation of peace as an industry needs further thought. This study has used the peace industry definition developed by the Institute for Economics and Peace (2008) and incorporated peace elasticity, which was econometrically estimated in Chapter 6 for use in the CGE model. However, it would be useful to explore the peace industry as a stand-alone sector, just like any other sector.

Third, the long-run simulation of the impact of peace has showed that its contribution fell over time. The simulation cannot show whether the contribution of peace to growth has reached its peak. All we know was that in the short-run the contribution of peace to growth was higher than in the long run. Therefore, it would be useful to know the time path of the contribution of peace.

Fourth, there is a need to further disaggregate the number of sectors and include the nine provinces (of the Solomon Islands) in the CGE model. The further disaggregation is necessary, as it allows the effects of a shock to be analysed in detail, both in terms of industries and space/regions. In doing so, it will give policy makers the opportunity to tailor policies to the context.

Finally, after addressing the above future study would consider developing a dynamic CGE model, given its advantages over the static model.

9.5 Concluding Statement This study has investigated the contribution of peace to the rebound in the post-conflict economy. It was set to test the peace – income nexus by employing microeconomic and macroeconomic analysis. The micro level analysis used household survey data to perform a partial equilibrium analysis. The CGE framework was used for the analysis of the macro level data. The aims of the study were achieved, despite the limitations, and the study’s findings fill a (small) void in the knowledge gaps identified in the literature. This study contributes to the empirical literature on the relationship between peace and income by identifying the positive contribution of peace to the economy, both at the micro and macro level. It has also contributed to the way policy makers can incorporate targeted economic policies that can help avert the resumption of conflict.

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Appendix A5.0 Research Permit

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Appendix A6.0a Table A6.0a Monthly royalty from household survey

Cumulative Cumulative Monthly Royalty Number of ($) observations Percent (%) Count Percent 0 60 27.15 60 27.15 50 1 0.45 61 27.60 100 15 6.79 76 34.39 120 1 0.45 77 34.84 150 5 2.26 82 37.10 170 1 0.45 83 37.56 180 1 0.45 84 38.01 200 17 7.69 101 45.70 220 1 0.45 102 46.15 250 1 0.45 103 46.61 300 9 4.07 112 50.68 350 1 0.45 113 51.13 400 3 1.36 116 52.49 500 24 10.86 140 63.35 600 10 4.52 150 67.87 700 1 0.45 151 68.33 800 4 1.81 155 70.14 900 2 0.90 157 71.04 1000 16 7.24 173 78.28 1200 3 1.36 176 79.64 1300 2 0.90 178 80.54 1500 2 0.90 180 81.45 1600 1 0.45 181 81.90 2000 12 5.43 193 87.33 2500 1 0.45 194 87.78 2700 1 0.45 195 88.24 3000 8 3.62 203 91.86 3500 1 0.45 204 92.31 4000 1 0.45 205 92.76 5000 1 0.45 206 93.21 6000 1 0.45 207 93.67 10000 4 1.81 211 95.48 10300 1 0.45 212 95.93 12000 1 0.45 213 96.38 13000 2 0.90 215 97.29 15000 1 0.45 216 97.74 16000 1 0.45 217 98.19 17000 1 0.45 218 98.64 19000 1 0.45 219 99.10 40000 1 0.45 220 99.55 50000 1 0.45 221 100.00 Total 221 100.00 221 100.00

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Appendix A6.0b Table A6.0b Quarterly land rentals from household survey

Cumulative Cumulative Quarterly rental Number of ($) observations Percent (%) Count Percent 0 93 42.08 93 42.08 30 2 0.90 95 42.99 50 3 1.36 98 44.34 65 1 0.45 99 44.80 100 26 11.76 125 56.56 150 3 1.36 128 57.92 170 1 0.45 129 58.37 200 12 5.43 141 63.80 250 4 1.81 145 65.61 300 17 7.69 162 73.30 340 1 0.45 163 73.76 400 9 4.07 172 77.83 500 17 7.69 189 85.52 600 2 0.90 191 86.43 700 2 0.90 193 87.33 800 3 1.36 196 88.69 900 1 0.45 197 89.14 1000 1 0.45 198 89.59 1100 1 0.45 199 90.05 1200 1 0.45 200 90.50 1300 2 0.90 202 91.40 1350 1 0.45 203 91.86 1500 3 1.36 206 93.21 1600 1 0.45 207 93.67 2000 1 0.45 208 94.12 2400 1 0.45 209 94.57 2500 1 0.45 210 95.02 2600 1 0.45 211 95.48 3000 2 0.90 213 96.38 3500 1 0.45 214 96.83 4000 1 0.45 215 97.29 12000 3 1.36 218 98.64 13000 1 0.45 219 99.10 15000 1 0.45 220 99.55 22000 1 0.45 221 100.00 Total 221 100.00 221 100.00

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Appendix A6.0c Annual dividend from household survey Number of 13800 1 0.45 144 65.16 Annual dividend observations Cumulative Cumulative 14000 2 0.90 146 66.06 ($) (count) Percent (%) Count Percent (%) 15000 2 0.90 148 66.97 0 11 4.98 11 4.98 16000 2 0.90 150 67.87 100 1 0.45 12 5.43 18000 2 0.90 152 68.78 200 2 0.90 14 6.33 20000 8 3.62 160 72.40 500 4 1.81 18 8.14 23000 1 0.45 161 72.85 24000 1 0.45 162 73.30 1000 22 9.95 40 18.10 25000 1 0.45 163 73.76 1200 1 0.45 41 18.55 28000 2 0.90 165 74.66 1300 1 0.45 42 19.00 30000 15 6.79 180 81.45 1500 2 0.90 44 19.91 30800 1 0.45 181 81.90 1700 1 0.45 45 20.36 32000 6 2.71 187 84.62 2000 9 4.07 54 24.43 36000 2 0.90 189 85.52 2300 1 0.45 55 24.89 40000 1 0.45 190 85.97 2500 1 0.45 56 25.34 44000 1 0.45 191 86.43 2600 1 0.45 57 25.79 45000 1 0.45 192 86.88 3000 12 5.43 69 31.22 50000 8 3.62 200 90.50 4000 6 2.71 75 33.94 51000 2 0.90 202 91.40 4100 1 0.45 76 34.39 52000 3 1.36 205 92.76 5000 16 7.24 92 41.63 56000 1 0.45 206 93.21 5100 1 0.45 93 42.08 60000 3 1.36 209 94.57 5800 1 0.45 94 42.53 70000 3 1.36 212 95.93 6000 5 2.26 99 44.80 76000 1 0.45 213 96.38 6800 2 0.90 101 45.70 80000 2 0.90 215 97.29 7000 5 2.26 106 47.96 96000 1 0.45 216 97.74 8000 5 2.26 111 50.23 100000 1 0.45 217 98.19 9000 1 0.45 112 50.68 130000 1 0.45 218 98.64 10000 24 10.86 136 61.54 145000 1 0.45 219 99.10 11800 2 0.90 138 62.44 190000 1 0.45 220 99.55 12000 1 0.45 139 62.90 200000 1 0.45 221 100.00 12800 1 0.45 140 63.35 13000 2 0.90 142 64.25 Total 221 100.00 221 100.00

13500 1 0.45 143 64.71

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Appendix A6.1a: Framework for the binary dependent variable Given the dichotomous nature of the dependent variable or rather technically called a binary variable where it only takes the values of zero and one, the use of OLS would be inappropriate. The OLS regression of such model with the binary variable as the dependent variable is known as the linear probability model. OLS would be inappropriate because the implied model of the conditional mean places inappropriate restrictions on the residuals of the model. Given the dependent variable takes only the values of 0 and 1, so does the error term, εi, and so it is not normally distributed (Verbeek, 2008: 190).

For example, suppose Yi = 1 with probability πi, then

휀푖 = 1 − 퐸(푌푖) = 1 − (훼 + 훽푋푖) = 1 − 휋푖 … … … … … … … … … … … … … … . (퐴6.1)

Contrast this when Yi = 0, with probability 1 – πi,

휀푖 = 0 − 퐸(푌푖) = 0 − (훼 + 훽푋푖) = 0 − 휋푖 = −휋푖 … … … … … … … … … … … … (퐴6.2)

Also, the inappropriate use of OLS is due to the non-constant in the error variance (i.e. the problem of heteroskedacity ). That is, the E(εi) = 0 has been violated as can be seen below;

2 2⁡ V(εi)=πi(1‐πi) +(1‐πi)(‐πi) =πi(1‐πi) … … … … … … … … … … … … … … (퐴6.3)

Furthermore, the fitted value of the dependent variable from the OLS is not restricted to lie between zero and one. Thus, it does not make any sense to interpret numbers outside of the probability interval.

To overcome these problems, a proper specification is employed so that the regression function is interpreted as predicted probability. That is, we want to ensure π is restricted to lie between zero and one. To do this, we need a positive monotone function that transforms the linear predictor 훾 = 훼 + 훽푋 into the zero – one interval. The binary dependent variable regression is thus interpreted as modelling the probability that, the dependent variable, yi, equals to one (i.e. yi = 1).

292

Two popular models that have been used for nonlinear (binary) models for which this study employs are the probit and logit functions. Drawing from (Stock and Watson, 2007, Greene, 2008, Johnston and DiNardo, 1997, Maddala, 1983, Wooldridge, 1997, Verbeek, 2008), the conceptual framework of the functional modelling of the dependent variable is described below. These two models have similar results except that they vary in their distribution function. The probit model exhibits a standard normal cumulative distribution function (c.d.f) while the logit model displays a standard normal logistic distribution function. Because of their similarity, as well as the assumed normality of the error term in the probit model, we are going to use the probit model. The conceptual framework for the probit model is specified below.

The cdf for the probit model is expressed as an integral:

푥′훽 Pr(푦 = 1) = ∫ 훷(푣)푑푣 = 훷(푥′훽) … … … … … … … … … … … … … … … … … (퐴6.4) −∞

Where Φ(v) is the standard normal density

푣2 훷(푣) = (2휋)−1/2 exp (− ) … … … … … … … … … … … … … … … … … … . . (퐴6.5)⁡ 2

Therefore, the probability model is a regression:

퐸[푦] = 0[1 − 퐹(푥′훽)] + 1[퐹(푥′훽)] = 퐹(푥′훽) … … … … … … … … … … … … (퐴6.6) where F is a continuous, strictly increasing function that takes a real value and returns a value between zero and one. We assume a standard convention where the index specification is linear in the parameters so that it takes the form⁡푥′훽.

Maximum Likelihood Estimation The coefficients of the above specifications can be estimated using the method of maximum likelihood estimation (MLE), which is given by;

푛 ′ ′ 152 푙(훽) = ⁡ ∑ 푦푖 log(1 − 퐹(−푥푖 훽)) +⁡(1 − 푦푖) log(퐹(−푥푖 훽)) … … … … … … … … … (퐴6.7) 푖=0

152 For details on MLE, See for example, Wooldridge, J. M. 2013. Introductory Econometrics: A Modern Approach (5th ed.), USA, South Western, Cengage Learning. Also, see Greene, W. H. 1993. Econometric Analysis (2nd.ed.), USA, Macmillan Publishing Company. 293

The binary model is often driven by some underlying behavioural assumptions, which are mainly motivated by a latent variable specification. As for this study, peace is a latent variable assumed to have an impact on the presence of GPPOL, as well as on whether or not * landowners own businesses. The unobserved latent variable yi is assumed to have a linear additive relationship with x (explanatory variables), so that;

* ′ 푦푖 ⁡ = 푥푖 훽 +⁡휀푖 … … … … … … … … … … … … … … … … … … … … … … … . . (퐴6.8), where 휀푖 is a random disturbance. We then assume that GPPOL will not be disrupted (and landowners owning businesses) if the utility difference exceeds a threshold value which is set to zero;

* 1⁡푖푓⁡푦푖 > 0 푦푖 = { * … … … … … … … … … … … … … … … … … … … … (퐴6.9) 0⁡푖푓⁡푦푖 ⁡ ≤ 0

* Consequently, we observe yi = 1 (for the event to occur) only if yi > 0 and yi = 0 (otherwise). Accordingly, we have

′ Pr(푦푖 = 1|푥푖, 훽) = Pr(푦푖 ∗> 0) = Pr(푥푖 훽 + 휀푖 > 0) ⁡⁡⁡⁡⁡⁡⁡⁡⁡ ′ = 1 − 퐹(−푥푖 훽) … … … … … … … … … … … … … … … … . . (퐴6.10), where F is the cumulative distribution function of 휀.

294

Appendix A6.1b Tests for Heteroskedasticity for Model 1 and Model 2 We use the LM tests using the artificial regression method described by Davidson and Mackinnon (1993). We test the null hypothesis of homokedasticity against the alternative of heteroskedasticity of the form:

′ 푣푎푟⁡(푢푖) = exp(2푧푖 훾) … … … … … … … … … … … … … … … … … … … … . . (퐴6.11) where γ is an unknown parameter. From the artificial regression, the explained sum of squares derives the test statistics. The regression is of the form:

′ ̂ ′ ̂ ′ ̂ (푦푖 − 푝̂푖) 푓(−푥푖 훽) ′ 푓(−푥푖 훽)(−푥푖 훽) ′ = ⁡ ⁡푥푖 푏1 + ⁡푧푖 푏2 + 푣푖 … … … … … … … . (퐴6.12) √푝̂푖(1 − 푝̂푖) √푝̂푖(1 − 푝̂푖) √푝̂푖(1 − 푝̂푖) The above equation is asymptotically distributed as a chi-squared (푋2) with degree of freedom equal to the number of variables in z.

Heteroskedasticity tests for Model 1

Step 1: Estimate and Save the residual of Model 1

Step 2: Forecast the fitted probabilities 푝̂푖 and the fitted index⁡푥′훽 in Model 1. Save the forecasted⁡푝̂푖 p and⁡푥′훽. Step 3: Run the artificial OLS regression and obtain the fitted values. Step 4: Take the sum of squares of these fitted values gives us the LM test statistics. The LM test statistics is 9.48958. Compare this with the critical value from the chi-square table with five degree of freedom.

H0: γ = 0 (homoscedasticity or no heteroskedasticity) H1: γ ≠ 0 (heteroskedasticity)

Decision rule: Reject H0 if test statistics is greater than the critical value. The chi-square critical value of five degree of freedom at five percent is 11.07. Given that the test statistics (9.49) is less than the critical value (11.07), therefore we fail to reject H0. This means that our model is free from the hetereoskedasiticity problem.

295

Heteroskedasiticity tests for Model 2

Step 1: Estimate and Save the residual of Model 2

Step 2: Forecast the fitted probabilities 푝̂푖 and the fitted index⁡푥′훽 in Model 2. Save the forecasted⁡푝̂푖 p and⁡푥′훽. Step 3: Run the artificial OLS regression and obtain the fitted values. Step 4: Take the sum of squares of these fitted values gives us the LM test statistics. The LM test statistics is 3.33. Compare this with the critical value from the chi-square table with five degree of freedom. H0: γ = 0 (homoscedasticity or no heteroskedasticity) H1: γ ≠ 0 (heteroskedasticity)

Decision rule: Reject H0 if test statistics is greater than the critical value. The chi-square critical value of five degree of freedom at five percent is 11.07. Given that the test statistics (3.33) is less than the critical value (11.07), therefore we fail to reject H0. This means that our model is free from the heteroskedasticity problem.

296

Appendix A6.2a Haussmann test for endogeneity

Model 1:

Pr(퐺푅퐸푀 = 1|푥) = 퐹(훽0 + 훽1퐿푁푃퐶퐸 + 훽2퐿푁퐼푁퐶 + 훽3푃푂퐿퐼퐶퐸 + 훽4퐷퐼푆푇퐴푁퐶퐸 + 훽5퐵푈푆);

LNINC

Step 1: Estimate LNINC as a function of its instrumental variables, which in our survey were: whether locals businesses (bus); and the number of males in a household (nmale) and the rest of the other variables in the structural equation.

Step 2: Save the residuals as RESID01_LNINC.

Step 3: Re-estimate model 1 including RESID01_LNINC as an additional explanatory variable.

Pr(퐺푅퐸푀 = 1|푥)

= 퐹(훽0 + 훽1퐿푁푃퐶퐸푖 + 훽2퐿푁퐼푁퐶푖 + 훽3푃푂퐿퐼퐶퐸푖 + 훽4퐷퐼푆푇퐴푁퐶퐸푖 + 훽5퐵푈푆푖

+ 훽6푟푒푠푖푑01−푙푛푖푛푐푖 + 휖푖)

Step 4: Hypothesis Test

Null hypothesis (H0): β6 = 0 (i.e. Coefficient of RESID01_LNINC = 0). The null hypothesis entails that there is no simultaneity between LNINC and GREM, implying LNINC is exogenous.

Test statistic: the coefficient of RESID01_LNINC on a two-tailed z-test is equals to (- 0.120995) with a z-statistics of (-0.3091). The critical z-value for a two-tailed test at the 5 percent level is ±1.9996.

Decision and conclusion: The null hypothesis is rejected if the z-statistics is greater than the z-critical value. Therefore, since the z-test statistic is less than the z-critical value, we do not reject the null hypothesis. The variable, LNINC does not exhibit endogeneity, rather it is exogenous.

297

Appendix A6.2b Haussmann Test for endogeneity: Model 3

Structural Model:

퐿푛푖푛푐푖 = 훼 + 훽1푙푛푝푐푒푖 +⁡훽2푙푛푑푖푠푡푎푛푐푒푖 + ⁡훽3푙푛푔푝푖푛푐푖 +⁡훽4푙푛푑푒푝푒푛푑푒푛푡푖 + 훽5푛푚푎푙푒푖

+⁡훽6푔푟푒푚푖 +⁡훽7푏푢푠푖 +⁡⁡훽8푝푐푒푔푟푒푚푖 + 휖푖

LNPCE

Step 1: Estimate LNPCE as a function of its instrumental variable, which in our survey were confidence in police and whether or not RAMSI should leave (RAMSI_LEAVE), and the rest of the explanatory variables.153

Step 2: Save the residuals as RESID01_LNPCE.

Step 3: Re-estimate model 3 including RESID01_LNPCE as an additional explanatory variable.⁡

퐿푛푖푛푐푖 = 훼 + 훽1푙푛푝푐푒푖 + ⁡훽2푙푛푔푝푖푛푐푖 +⁡훽3푙푛푑푒푝푒푛푑푒푛푡푖 + 훽4푛푚푎푙푒푖 +⁡훽5푔푟푒푚푖

+⁡훽6푏푢푠푖 +⁡⁡훽7푝푐푒푔푟푒푚푖 +⁡⁡훽8푟푒푠푖푑01_푙푛푝푐푒푖 + 휖푖

Step 4: Hypothesis Test

Null hypothesis (H0): β8 = 0 (i.e. Coefficient of RESID01_LNPCE = 0). The null hypothesis entails that there is no simultaneity between LNPCE and LNINC, implying LNPCE is exogenous.

Test statistic: the coefficient of RESID01_LNPCE on a two-tailed t-test is equals to (- 1.736) with a t-statistics of (0.4968). The critical value for a two-tailed test at the 5 percent level with 106 degrees of freedom (i.e. t1-α/2,df = t0.975,106) is ±1.9996.

Decision and conclusion: The null hypothesis is rejected if the t-statistics is greater than the t-critical value. Therefore, since the t-test statistic is less than the critical value, we do not reject the null hypothesis. The variable, LNPCE does not exhibit endogeneity, rather it is exogenous. This implies that OLS is appropriate to use, and that we do not need to use instrumental variables in the model.

153The variable Police reflects the confidence in police to uphold law and order. The variable RAMSI_LEAVE is how peacefulness will it become if RAMSI leaves now. These two variables correlate with and indirectly affect peace in the underlying communities (i.e. GPPOL communities). 298

LNGPINC

Step 1: Estimate LNGPINC as a function of its instrumental variables, which in our survey were: number of persons is a household (household_number); the land arrangement type (land_arrangement),154as well as the rest of the other variables in the structural equation.

Step 2: Save the residuals as RESID01_LNGPINC.

Step 3: Re-estimate model 3 including RESID01_GPPOL as an additional explanatory variable.

퐿푛푖푛푐푖 = 훼 + 훽1푙푛푝푐푒푖 + ⁡훽2푙푛푔푝푖푛푐푖 +⁡훽3푙푛푑푒푝푒푛푑푒푛푡푖 + 훽4푛푚푎푙푒푖 +⁡훽5푔푟푒푚푖

+⁡훽6푏푢푠푖 +⁡⁡훽7푝푐푒푔푟푒푚푖 +⁡⁡훽8푟푒푠푖푑01_푔푝푝표푙푖 + 휖푖

Step 4: Hypothesis Test

Null hypothesis (H0): β8 = 0 (i.e. Coefficient of RESID01_LNGPPOLINC = 0). The null hypothesis entails that there is no simultaneity between LNGPPOLINC and LNINC, implying LNGPPOLINC is exogenous.

Test statistic: the coefficient of RESID01_LNGPINC on a two-tailed t-test is equals to (0.06497) with a t-statistics of (0.7255). The critical value for a two-tailed test at the 5 percent level with 106 degrees of freedom is ±1.9996.

Decision and conclusion: The null hypothesis is rejected if the t-statistics is greater than the t- critical value. Therefore, since the t-test statistic is less than the critical value, we do not reject the null hypothesis. The variable, LNGPINC does not exhibit endogeneity, rather it is exogenous. This implies that OLS is appropriate to use, and that we do not need to use instrumental variables.

154 These instrumental variables correlates with the income landowners receive from GPPOL, although they are not directly related to the income earn from other sources. The higher the number of individuals in a household (household_number) the higher the income they receive from royalties and dividends. The more land is leased to GPPOL (land_arrangement), the more royalties one receives. 299

Appendix A6.3: Eviews Output Results for Model 3.1 Dependent Variable: LNINC Method: Least Squares Date: 04/02/15 Time: 17:08 Sample: 1 312 Included observations: 251

Variable Coefficient Std. Error t-Statistic Prob.

LNPCE 1.093085 0.449011 2.434430 0.0156 LNDISTANCE -0.209914 0.099180 -2.116490 0.0353 LNGPINC 0.258606 0.038175 6.774243 0.0000 C 1.975605 2.004803 0.985436 0.3254

R-squared 0.202981 Mean dependent var 8.410848 Adjusted R-squared 0.193300 S.D. dependent var 0.841838 S.E. of regression 0.756109 Akaike info criterion 2.294546 Sum squared resid 141.2102 Schwarz criterion 2.350728 Log likelihood -283.9655 Hannan-Quinn criter. 2.317155 F-statistic 20.96820 Durbin-Watson stat 1.736892 Prob(F-statistic) 0.000000

Eviews Output Results for Model 3.2 Dependent Variable: LNINC Method: Least Squares Date: 04/02/15 Time: 17:08 Sample: 1 312 Included observations: 206

Variable Coefficient Std. Error t-Statistic Prob.

LNPCE 0.963044 0.488811 1.970178 0.0502 LNDISTANCE -0.209969 0.104290 -2.013322 0.0454 LNGPINC 0.238415 0.040146 5.938717 0.0000 LNDEPENDENT -0.230218 0.091128 -2.526330 0.0123 NMALE 0.132761 0.031334 4.237013 0.0000 C 2.465097 2.185591 1.127886 0.2607

R-squared 0.283094 Mean dependent var 8.425853 Adjusted R-squared 0.265171 S.D. dependent var 0.832028 S.E. of regression 0.713232 Akaike info criterion 2.190674 Sum squared resid 101.7401 Schwarz criterion 2.287603 Log likelihood -219.6395 Hannan-Quinn criter. 2.229876 F-statistic 15.79531 Durbin-Watson stat 1.750567 Prob(F-statistic) 0.000000

300

Eviews Output Results for Model 3.3

Dependent Variable: LNINC Method: Least Squares Date: 08/03/15 Time: 12:58 Sample: 1 312 Included observations: 206

Variable Coefficient Std. Error t-Statistic Prob.

C 0.014729 2.694653 0.005466 0.9956 LNPCE 1.387366 0.606050 2.289193 0.0231 LNGPINC 0.236460 0.039142 6.041017 0.0000 LNDEPENDENT -0.096310 0.090849 -1.060101 0.2904 NMALE 0.089799 0.030752 2.920083 0.0039 GREM 3.549064 1.091105 3.252725 0.0013 BUS 0.413260 0.100084 4.129121 0.0001 PCEGREM -0.036291 0.012051 -3.011468 0.0029

R-squared 0.364292 Mean dependent var 8.490575 Adjusted R-squared 0.341818 S.D. dependent var 0.843506 S.E. of regression 0.684323 Akaike info criterion 2.117288 Sum squared resid 92.72302 Schwarz criterion 2.246526 Log likelihood -210.0806 Hannan-Quinn criter. 2.169556 F-statistic 16.20914 Durbin-Watson stat 1.720450 Prob(F-statistic) 0.000000

301

Appendix A6.4: CUSUM Test The cumulative sum of the recursive residuals (CUSUM) test plots the cumulative sum along with the 5 percent critical lines (Brown et.al 1975). Suppose the cumulative sum goes outside the band area between the two critical lines, the parameters are said to be not stable. Formally, this CUSUM test is as follows:

푡

푊푡 = ∑ 푤휏/푠, ⁡⁡푡 = 푘 + 1, … , 푁 … … … … … … … … … … … … … … … … … (퐴6.13) 푟−푘+1

Where w is the recursive residual155 that has to be within the plus and minus two standard error band in order for the parameter to be stable, and s is the standard error of the regression fitted to all N sample points. Suppose the vector of coefficients is constant all throughout the period, then the expected value of the recursive residual should be zero (i.e. E[Wt] = 0), but

Wt diverges from the zero mean value line if the coefficients change. Any movement away from the zero line can be assessed against a pair of 5 percent lines. The 5 percent significance lines can be found by joining the points

1 1 [푘 ± 0.948(푁 − 푘)2] ⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡푎푛푑⁡⁡⁡⁡⁡⁡⁡⁡⁡ [푁 ± 3 ∗ 0.948(푁 − 푘)2] … … … … … … … … (퐴6.14)

Departing of Wt outside of the critical lines implies that the coefficients are not stable, and so as the model. Applying this to our model using the Eviews 8 Student version, Wt is within the 5 percent critical line, suggesting that our model is stable (See CUSUM Figure below).

CUSUM of Squares test The CUSUM of square test plots the standard error (Brown et.al, 1975) s against observers, t, with a pair of 5 percent critical lines. The test is based on the test statistics

푡 푁 2 2 푆푡 = ∑ 푤푟 ∑ 푤푟 … … … … … … … … … … … … … … … (퐴6.15 푟=푘+1 푟=푘+1

′ 155 푦푡−푥푡푏푡−1⁡ The recursive residual is defined as: 푤푡 = 1/2 , where 푤푡is the recursive residual; 푏푡−1 is ′ ′ −1 (1+푥푡(푋푡−1푋푡−1⁡) 푥푡) ⁡⁡ ′ the estimated coefficient vector for the period t-1;⁡푥푡 is the vector of observations on the regressors in period t; 푦푡 is the vector of observations on the dependent variable; and 푋푡−1 is the k matrix of the regressors from ′ period t-1. Together 푦푡 − 푥푡푏푡−1 is the forecast error (see Brown et.al 1975). 302

Under the hypothesis of constant parameters, the expected value of S is

푡 − 푘 퐸[푆 ] = … … … … … … … … … … … … … … … … … … … … … . (퐴6.16) 푡 푁 − 푘 where it ranges from zero (at t = k) to one (t = N). Two parallel straight lines with a 5 percent significance are drawn to enclose the expected values. As with CUSUM, any departure of S from the expected values (that is, moving outside of the critical (parallel) lines entails instability in the parameters or variance. Applying this to our model, the CUSUM of squares lies within the parallel lines, suggesting that the parameters and variance in our model is stable (See CUSUM of squares below).

-20

-40

-60

105

113

126

139

148

157

166

176

196

209

224

235

247

258

275

288

CUSUM 5% Significance

303

1.2

1.0

0.8

0.6

0.4

0.2

0.0

-0.2

14 24 36 45 56 64 74 84 94

105 113 126 139 148 157 166 176 196 209 224 235 247 258 275 288

CUSUM of Squares 5% Significance

30 4 1.0

0.8 20 2

0.6 10 0 0.4 0 -2 0.2

-10 -4 0.0

-20 -6 -0.2

2 3 2 4 5 6 0 0 2 5 0 5 5

0 1 3 4 5 6 8 0 2 3 5 6 8

14 26 40 53 64 78 90

1 1 1 1 1 1 1 2 2 2 2 2 2

Recursive C(1) Estimates Recursive C(2) Estimates Recursive C(3) Estimates ± 2 S.E. ± 2 S.E. ± 2 S.E.

0.8 .8 8

0.4 .6 4

0.0 .4 0 -0.4 .2

-4 -0.8 .0

-1.2 -.2 -8

2 3 2 4 5 6 0 0 2 5 0 5 5

0 1 3 4 5 6 8 0 2 3 5 6 8

14 26 40 53 64 78 90

1 1 1 1 1 1 1 2 2 2 2 2 2

Recursive C(4) Estimates Recursive C(5) Estimates Recursive C(6) Estimates ± 2 S.E. ± 2 S.E. ± 2 S.E.

1.5 .12

1.0 .08

0.5 .04 0.0 .00 -0.5

-.04 -1.0

-1.5 -.08

2 3 2 4 5 6 0 0 2 5 0 5 5

0 1 3 4 5 6 8 0 2 3 5 6 8

14 26 40 53 64 78 90

1 1 1 1 1 1 1 2 2 2 2 2 2

Recursive C(7) Estimates Recursive C(8) Estimates ± 2 S.E. ± 2 S.E.

304

Appendix A6.5 Eviews Output: Ramsey RESET Test

Equation: EQ01_MAIN Specification: LNINC C LNPCE LNGPINC LNDEPENDENT NMALE GREM BUS PCEGREM Omitted Variables: Squares of fitted values

Value df Probability t-statistic 1.103823 197 0.2710 F-statistic 1.218425 (1, 197) 0.2710 Likelihood ratio 1.270165 1 0.2597

F-test summary: Mean Sum of Sq. df Squares Test SSR 0.569957 1 0.569957 Restricted SSR 92.72302 198 0.468298 Unrestricted SSR 92.15307 197 0.467782

LR test summary: Value df Restricted LogL -210.0806 198 Unrestricted LogL -209.4455 197

Unrestricted Test Equation: Dependent Variable: LNINC Method: Least Squares Date: 08/03/15 Time: 16:44 Sample: 1 312 Included observations: 206

Variable Coefficient Std. Error t-Statistic Prob.

C 12.28905 11.44131 1.074094 0.2841 LNPCE -2.724051 3.773636 -0.721864 0.4712 LNGPINC -0.490534 0.659776 -0.743486 0.4581 LNDEPENDENT 0.214509 0.295861 0.725032 0.4693 NMALE -0.191133 0.256358 -0.745572 0.4568 GREM -7.308738 9.896807 -0.738495 0.4611 BUS -0.856253 1.154447 -0.741699 0.4592 PCEGREM 0.074755 0.101319 0.737814 0.4615 FITTED^2 0.180986 0.163962 1.103823 0.2710

R-squared 0.368200 Mean dependent var 8.490575 Adjusted R-squared 0.342543 S.D. dependent var 0.843506 S.E. of regression 0.683946 Akaike info criterion 2.120830 Sum squared resid 92.15307 Schwarz criterion 2.266223 Log likelihood -209.4455 Hannan-Quinn criter. 2.179632 F-statistic 14.35095 Durbin-Watson stat 1.737553 Prob(F-statistic) 0.000000

305

Appendix A6.6 Breusch-Pagan-Godfrey Test for Heteroskedasticity

Heteroskedasticity Test: Breusch-Pagan-Godfrey

F-statistic 2.098600 Prob. F(7,198) 0.0453 Obs*R-squared 14.22812 Prob. Chi-Square(7) 0.0473 Scaled explained SS 16.80270 Prob. Chi-Square(7) 0.0187

Test Equation: Dependent Variable: RESID^2 Method: Least Squares Date: 08/03/15 Time: 17:22 Sample: 1 312 Included observations: 206

Variable Coefficient Std. Error t-Statistic Prob.

C -0.453735 2.789039 -0.162685 0.8709 LNPCE 0.135158 0.627278 0.215467 0.8296 LNGPINC 0.027408 0.040513 0.676527 0.4995 LNDEPENDENT 0.077538 0.094032 0.824592 0.4106 NMALE -0.036130 0.031829 -1.135102 0.2577 GREM 2.857008 1.129323 2.529841 0.0122 BUS 0.270422 0.103590 2.610505 0.0097 PCEGREM -0.030826 0.012473 -2.471453 0.0143

R-squared 0.069069 Mean dependent var 0.450112 Adjusted R-squared 0.036157 S.D. dependent var 0.721456 S.E. of regression 0.708293 Akaike info criterion 2.186143 Sum squared resid 99.33248 Schwarz criterion 2.315381 Log likelihood -217.1728 Hannan-Quinn criter. 2.238412 F-statistic 2.098600 Durbin-Watson stat 1.563779 Prob(F-statistic) 0.045327

306

Appendix A7.0: GEMPACK names of the original exogenous variables

Exogenous Variable Size a1 ; COM*SRC*IND Intermediate basic tech change a1cap ; IND Capital-augmenting technical change a1lab_o ; IND Labour-augmenting technical change a1lnd ; IND Land-augmenting technical change a1mar ; COM*SRC*IND*MAR Intermediate margin tech change a1oct ; IND "Other cost" ticket augmenting technical change a1prim ; IND All factor augmenting technical change a1tot ; IND All input augmenting technical change a1_s ; COM*IND Tech change, intmdiate imp/dom composite a2 ; COM*SRC*IND Investment basic tech change a2mar ; COM*SRC*IND*MAR Investment margin tech change a2tot ; IND Neutral technical change - investment a2_s ; COM*IND Tech change, investment imp/dom composite a3 ; COM*SRC Household basic taste change a3mar ; COM*SRC*MAR Household margin tech change a3_s ; COM Taste change, household imp/dom composite a4mar ; COM*MAR Export margin tech change a5mar ; COM*SRC*MAR Governmnt margin tech change capslack ; 1 Slack variable to allow fixing aggregate capital delPTXRATE ; IND Change in rate of production tax f0tax_s ; COM General sales tax shifter f1lab ; IND*OCC Wage shift variable f1lab_i ; OCC Occupation-specific wage shifter f1lab_io ; 1 Overall wage shifter f1lab_o ; IND Industry-specific wage shifter f1oct ; IND Shift in price of "other cost" tickets f1tax_csi ; 1 Uniform % change in powers of taxes on intermediate usage f2tax_csi ; 1 Uniform % change in powers of taxes on investment f3tax_cs ; 1 Uniform % change in powers of taxes on household usage f4p ; COM Price (upward) shift in export demand schedule f4p_ntrad ; 1 Upward demand shift, collective export aggregate f4q ; COM Quantity (right) shift in export demands f4q_ntrad ; 1 Right demand shift, collective export aggregate

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f4tax_ntrad ; 1 Uniform % change in powers of taxes on nontradtnl exports f4tax_trad ; 1 Uniform % change in powers of taxes on tradtnl exports f5 ; COM*SRC Government demand shift f5tax_cs ; 1 Uniform % change in powers of taxes on government usage f5tot2 ; 1 Ratio between f5tot and x3tot fx6 ; COM*SRC Shifter on rule for stocks invslack ; 1 Investment slack variable for exogenizing aggregate investment pf0cif ; COM C.I.F. foreign currency import prices phi ; 1 Exchange rate, local currency/$world q ; 1 Number of households t0imp ; COM Power of tariff w3lux ; 1 Total nominal supernumerary household expenditure x1cap ; IND Current capital stock x1lnd ; IND Use of land x2tot ; IND Investment by using industry

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