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UNIVERSITY of CALIFORNIA Los Angeles Do Criminal UNIVERSITY OF CALIFORNIA Los Angeles Do Criminal Politicians Deliver?: Evidence from India’s Employment Guarantee and Hindu Holidays A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Political Science by Galen Patrick Murray 2020 c Copyright by Galen Patrick Murray 2020 ABSTRACT OF THE DISSERTATION Do Criminal Politicians Deliver?: Evidence from India’s Employment Guarantee and Hindu Holidays by Galen Patrick Murray Doctor of Philosophy in Political Science University of California, Los Angeles, 2020 Professor Daniel Posner, Chair In India, politicians facing criminal charges are routinely elected at higher rates. In this dis- sertation, I investigate three primary questions to better understand criminal politicians’ electoral success and performance in office: 1) Do criminal politicians deliver superior access to social wel- fare programs relative to clean politicians? 2) Do criminal politicians target benefits to co-partisans at higher rates than clean politicians? 3) Do voters reward criminal politicians for delivering more constituency service than clean politicians? On the one hand, powerful dons may be less responsive to voters’ needs, banking on clout to keep voters in line. On the other hand, previous literature and my fieldwork suggest a more Machiavellian strategy, where criminal politicians use both violence and deep pockets to distribute resources to voters. I present two key arguments to explain criminal politicians’ distributive advantages. First, I contend that criminal politicians core assets of money, muscle and networks make them particularly suited to both deliver more state benefits and target co-partisans. Second, I identify a trade-off that candidates face between accruing enough capital to fund campaigns and remaining rooted in the constituency to provide personalized service to voters. I argue that criminals’ muscle-power allows ii them to sidestep this trade-off and optimize on both dimensions. Muscle enables criminals to establish lucrative protection rackets in their home constituencies. In effect, protection rackets turn muscle into money. To protect this money, criminals invest in networks for delivering resources to voters. Constituent service networks help criminal politicians maintain political power, which proves useful for protecting their illegal enterprises. To measure criminal politicians’ in-office performance, I focus on how India’s state legislators influence the delivery of the world’s largest public works program, India’s National Rural Em- ployment Guarantee Scheme (NREGS). Specifically, to determine if criminal politicians translate their assets of money, muscle and networks into superior social welfare delivery, I construct and combine three original datasets. First, to measure criminality, I scraped self-disclosed affidavits listing 87,000 candidates’ criminal charges. The dataset details the criminal histories, wealth, and electoral results of all state legislative candidates in India between 2003 and 2017 (N = 87,000). To measure criminal politicians’ benefit distribution, I combine the candidate dataset with origi- nal data on the geo-locations of over 20 million NREGS local public works projects. Finally, to determine if criminal politicians are more likely to target resources to co-partisans, I map the geo- tagged NREGS projects to over 400,000 polling stations. Methodologically, I use causal inference and machine learning techniques to analyze this data and strengthen the validity of my estimates. Overall, I find that criminal politicians deliver more NREGS benefits in safe seats, though not necessarily in competitive constituencies. Second, I find suggestive evidence that criminal politicians target welfare benefits to co-partisans at higher rates relative to clean politicians. By remaining embedded in the constituency, I argue that criminals are better positioned to identify, and then meet, supporters needs. Finally, and perhaps unsurprisingly, I find criminals’ core advan- tage derives from their capacity for violence. Both qualitative and quantitative evidence speak to criminal muscle as a necessary input for improved constituency service and benefit delivery. Em- pirically, I find that criminals with violent charges are associated with increased NREGS delivery. Whereas, non-violent criminals are not. iii The dissertation of Galen Patrick Murray is approved. Jennifer Bussell Miriam Golden Michael Ross Daniel Posner, Committee Chair University of California, Los Angeles 2020 iv For my mother, Sharon. v Contents 1 Introduction 1 1.1 Setting the Stage . .1 1.1.1 Focus on India’s National Rural Employment Guarantee Scheme . .4 1.2 The Argument . .5 1.2.1 Money . .7 1.2.2 Muscle . .8 1.2.3 Networks . .9 1.3 Research Design and Chapter Overviews . 12 1.3.1 Chapter 2: Do Criminals Deliver NREGS? . 12 1.3.2 Chapter 3: Do Criminals Target Co-partisans? . 14 1.3.3 Chapter 4: Do Criminals Deliver Constituency Service? . 17 1.4 Why NREGS and MLAs? . 18 1.4.1 NREGS Background and Political Interference . 19 1.5 Contributions . 21 2 Do Criminal Politicians Deliver? Evidence from NREGS 25 2.1 Introduction . 25 2.2 Criminal Politicians and Service Delivery in India . 29 2.3 Research Design . 31 2.3.1 Population and Sampling Frame . 33 vi 2.3.2 NREGS Background and Data . 34 2.4 RDD Validity . 42 2.4.1 Balance Tests and Controls . 46 2.5 Results . 49 2.5.1 Main Results Without Controls . 49 2.5.2 Main Results With Controls . 54 2.5.3 Sensitivity Analysis . 56 2.5.4 Serious Criminals Analysis . 59 2.6 Corruption . 61 2.7 Conclusion . 67 Appendices 70 2.A Controls . 70 2.B Sensitivity Analysis for Models Including All Charges . 70 2.B.1 Varying Bandwidth Selectors . 70 2.B.2 Varying Local Polynomial Order . 72 2.B.3 Varying Global/Parametric Polynomials . 76 2.C Serious Charges . 77 2.C.1 Serious Charges Balance Tests . 77 2.C.2 Serious Chrages Varying Polynomials for Non-parametric models . 80 2.C.3 Serious Charges Bandwidth Sensitivity . 83 2.C.4 Serious Charges Placebo Tests . 84 2.D State-Years in RD Sample . 85 2.E Maps . 86 2.F Unlogged Estimates . 87 2.G Variation in NREGS Outcomes by State and MLA type . 88 2.H Estimates from Various R Packages . 93 vii 2.I Estimates from Varying Bandwith Sizes . 94 2.J Estimates from Varying Local Polynomials . 96 2.K Placebo Tests . 100 2.L Financial Years . 102 2.M Wages and Employment per Project . 104 3 Criminal Crosshairs: Do Criminal Politicians Target Co-partisans? 107 3.1 Introduction . 107 3.1.1 Primary Results . 110 3.1.2 Mechanisms Investigation . 111 3.1.3 Overview of Empirical Strategy . 112 3.2 Context: NREGS Targeting and Criminal Politicians in India . 114 3.2.1 MLAs and NREGS Targeting . 114 3.2.2 Criminal Politicians and NREGS Targeting . 116 3.3 Data Construction . 123 3.3.1 Primary Outcomes . 123 3.3.2 Measuring Political Support . 124 3.3.3 Identifying Criminality: Constructing the Candidates Dataset . 126 3.3.4 Control Variables . 127 3.3.5 Summary Statistics . 128 3.4 Descriptive and Graphical Analysis . 129 3.4.1 Political Competition in State Assembly Elections . 129 3.4.2 Political Competition and Criminality . 131 3.5 Empirical Estimation . 133 3.5.1 Kernel Regularized Least Squares . 137 3.6 Results . 139 3.6.1 Criminal Politicians and NREGS Targeting Results . 142 viii 3.7 Discussion . 145 3.7.1 Money . 145 3.7.2 Muscle . 148 3.7.3 Networks: Contractor Raj? . 151 3.7.4 Assembly Constituency Political Competition . 154 3.7.5 Comparing Model Predictions on Test Data . 159 3.8 Conclusion . 159 Appendices 163 3.A Coding Ruling and Opposition Parties . 163 3.B NREGS Projects . 165 3.C Data Matching and Merging . 166 3.C.1 Match Rates for TCPD Candidate Results and ADR Criminal Charges . 166 3.D Political Competition . 167 3.D.1 Graphical Analysis of Political Competition and NREGS Distribution . 167 3.D.2 Urban and Rural Polling Stations . 171 3.D.3 Rural Polling Stations Only . 175 3.E Multiverse Analysis . 178 3.F Supplemental Results . 180 3.F.1 KRLS . 180 3.F.2 Muscle Mechanism Test . 182 3.F.3 Networks Mechanism Test . 185 3.F.4 Assembly Constituency Political Competition . 189 4 Wedding Service: Do Criminal Politicians Deliver Constituency Service? 196 4.1 Introduction . 196 4.1.1 The Role of Constituency Service in Winning Elections . 197 ix 4.1.2 The Role of Money in Winning Elections . 199 4.2 Criminals in the Constituency . ..
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