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Why Businesspeople Become Politicians in Russia David Szakonyi Submitted in Partial Fulfillment of the R Renting Elected Office: Why Businesspeople Become Politicians in Russia David Szakonyi Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Graduate School of Arts and Sciences COLUMBIA UNIVERSITY 2016 © 2016 David Szakonyi All rights reserved ABSTRACT Renting Elected Office: Why Businesspeople Become Politicians in Russia David Szakonyi Why do some businesspeople run for political office, while others do not? Sending directors into elected office is one of the most powerful but also resource-intensive ways firms can influence policymaking. Although legislative bodies are populated with businesspeople in countries worldwide, we know little about which firms decide to invest in this unique type of nonmarket strategy. In response, I argue that businesspeople run for elected office when (1) they cannot trust that the politicians they lobby will represent their interests and (2) their firms have the resources avail- able to contest elections. My theory predicts the probability of politician shirking (reneging on their promises) depends on whether rival firms have representatives in parliament and political parties are capable of enforcing informal quid pro quo agreements. Evidence to test my arguments comes from an original dataset of 8,829 firms connected to candidates to regional legislatures in Russia from 2004-2011. I find that both greater oligopolistic competition and weaker political parties incen- tivize businessperson candidacy, while the ability to cover campaign costs depends on the level of voter income and firm size. Do firms with directors holding elected political office then benefit from political connections? Using the same dataset but restricting the analysis to elections in single-member districts, I next employ a regression discontinuity design to identify the causal effect of gaining political ties, comparing outcomes of firms that are directed by candidates who either won or lost close elections to regional legislatures. I first find that a connection to a winning politician can increase revenue by roughly 60% and profit margins by 15% over their time in office. I then test between different mechanisms potentially explaining the results, finding that connected firms improve their performance by gaining access to bureaucrats and reducing information costs, and not by signaling legitimacy to financiers. Finally, winning a parliamentary seat is more valuable for firms where democratization is greater, but less valuable when firms face acute sector-level competition. This finding suggests that the intensity of economic rivalry, rather than the quality of political institutions, best explains the decision to send a director into public office. Contents List of Tables iv List of Figures vi 1 Introduction 1 1.1 Puzzle and Theoretical Arguments . 6 1.2 The Case of Russia . 13 1.3 Contributions to the Literature . 18 1.4 Plan of Work . 22 2 Chapter 2: Businesspeople in Politics 24 2.1 The Determinants of Corporate Political Activity . 26 2.2 Enlarging the Menu of Political Strategies . 32 2.3 Direct Strategies: Running for Political Office . 39 2.4 When do Businesspeople Run for Political Office? . 43 2.5 Returns to Corporate Political Activity . 49 2.6 Ways Forward . 54 3 Chapter 3: The Determinants of Businessperson Candidacy 55 i 3.1 Direct versus Indirect Corporate Political Strategies . 58 3.2 The Problem of Politician Shirking . 61 3.3 Market Environment, Political Parties and Politician Shirking . 63 3.4 The Costs of Candidate Entry . 67 3.5 Data and Empirical Strategy . 69 3.6 Results . 80 3.7 Conclusion . 83 4 Chapter 4: Party and Ballot Choice Among Businesspeople 92 4.1 Theoretical Arguments . 95 4.2 Data and Empirical Strategy . 111 4.3 Results . 113 4.4 Conclusion . 118 5 Firm-level Returns from Businesspeople Becoming Politicians 123 5.1 Data and Empirical Strategy . 126 5.2 Balance Checks . 134 5.3 RDD Results . 136 5.4 Causal Mechanisms . 138 5.5 Heterogeneous Treatment Effects . 141 5.6 Out of Sample Performance Effects . 146 5.7 Discussion and Concluding Remarks . 149 6 Conclusion 164 6.1 Summary of Findings . 164 6.2 A Further Agenda . 167 6.3 Future of Businessperson Candidacy . 172 Bibliography 176 ii Appendix 193 Data Appendix . 193 Robustness Appendix . 200 iii List of Tables 1.1 Percentage of Businesspeople in National Legislatures . 6 2.1 Antecedents of Corporate Political Activity . 27 3.1 Summary Statistics . 85 3.2 Correlation Matrix . 86 3.3 Firm Summary Statistics Subset by Businessperson Candicacy . 87 3.4 Determinants of Businessperson Candidacy . 90 4.1 Ballot Choice of Businessperson Candidates . 120 4.2 Party Choice of Businessperson Candidates . 121 5.1 Political Connections and Firm Revenue . 156 5.2 Political Connections and Firm Profit Margin . 157 5.3 Political Connections and Underlying Mechanisms . 158 5.4 Heterogeneous Treatment Effects - Institutional Variables . 159 5.5 Heterogeneous Treatment Effects - Competition-Related Variables . 160 5.6 Heterogeneous Treatment Effects - Economic and Sectoral Variables . 161 5.7 Matching: Winning Firms and Firm Total Revenue . 162 5.8 Matching: Winning Firms and Firm Net Profit . 162 iv 5.9 Matching: Losing Firms and Firm Total Revenue . 163 5.10 Matching: Losing Firms and Firm Net Profit . 163 B1 Party Choice of Businessperson Candidates - Subset by Ballot Choice . 201 B2 Placebo Checks - Candidate Covariates . 203 B3 Placebo Checks - Firm Covariates (1) . 204 B4 Placebo Checks - Firm Covariates (2) . 205 B5 Determinants of Competitive Elections . 208 B6 Summary Statistics . 213 B7 Political Connections and Firm Revenue, Only Directors . 216 B8 Political Connections and Firm Profit, Only Directors . 216 B9 Political Connections and Firm Revenue, Only SMD Candidates . 217 B10 Political Connections and Firm Profit, Only SMD Candidates . 217 B11 Covariate Balance in Full and Matched Samples, Winning Firms - Band- width = 0.1 . 220 B12 Covariate Balance in Full and Matched Samples, Winning Firms - Band- width = 0.2 . 220 B13 Covariate Balance in Full and Matched Samples, Winning Firms - Band- width = 1.0 . 221 B14 Covariate Balance in Full and Matched Samples, Losing Firms - Band- width = 0.1 . 222 B15 Covariate Balance in Full and Matched Samples, Losing Firms - Band- width = 0.2 . 222 B16 Covariate Balance in Full and Matched Samples, Losing Firms - Band- width = 1.0 . 223 v List of Figures 3.1 Sectoral Distribution of Candidate Firms . 88 3.2 Sectoral Concentration in Russia . 89 3.3 Candidacy Broken down by Sector: Random Effects . 91 4.1 Party Affiliation by Sector . 122 5.1 Percentage of Total SMD Elections Decided by Less Than 10%, by Region 152 5.2 McCrary (2008) Density Tests - Winning Margin . 153 5.3 Balance Statistics . 154 5.4 RD - Graphical Illustrations . 155 A1 Example of SPARK Connections Page . 196 B2 Multiple Thresholds - Total Revenue . 210 B3 Multiple Thresholds - Change in Profit Margin . 211 B4 Candidate Margin of Victory (%) . 214 vi Acknowledgments This dissertation would not have come together without unending support and encouragement from Tim Frye. From the very beginning, Tim kept the faith in me when, in his words, “the wheels were spinning but not finding traction.” Between emboldening to me to pull the plug on my first (admittedly mediocre) dissertation idea to furnishing a multiyear research fellowship in Russia to helping put the finishing touch on chapters, he has been there for me every step of the way. The best decision I ever made in graduate school was made before I even got there: coming to Columbia to work with Tim. Yotam Margalit pushed me hard to express my ideas more concretely and accept the challenge of identification head on, and I thank him greatly for it. His clear advice vastly improved how I built and framed the analysis. Working with Johannes Urpelainen taught me so much about the challenges of crafting quality political science research and making the leap from vague hypotheses to polished, defensible results. I was also very fortunate to have taken a class with Jonas Hjort, who perhaps unknowingly reinvigorated my interest in Russian political economy through our multiple collaborations. I continue to learn from him how to identify what’s most important in one’s research agenda and then sell it to an audience. The work also greatly benefitted from invaluable feedback from a number of vii friends and colleagues. I owe a debt of gratitude to Quintin Beazer, Michael Best, Noah Buckley-Farlee, Bo Cowgill, Olle Folke, Scott Gehlbach, Shigeo Hirano, Phil Keefer, Yegor Lazarev, Jeffrey Lenowitz, Eddy Malesky, Israel Marques, Suresh Naidu, Will Pyle, and Camille-Strauss Kahn. A very special thanks goes to John Reuter, who encouraged me early on to keep the focus on businesspeople and leg- islatures and was a vital sounding board during all stages of the project. Early versions of chapters were improved based on comments received at the 2014 Amer- ican Political Science Association Conference, the 2014 and 2015 Annual ISCID conferences, the 2015 Academy of Management Annual Conference and the 2014 and 2015 ASEEES Conferences. I also thank seminar participants at Columbia, Florida State, Duke, MIT, George Washington, University of Washington, UCSD, and the Coase Institute. Funding and institutional support from a variety of sources made possible the data collection and fieldwork in Russia. This dissertation was prepared within the framework of the Basic Research Program at the National Research Univer- sity Higher School of Economics (HSE) and supported within the framework of a subsidy granted to the HSE by the Government of the Russian Federation for the implementation of the Global Competitiveness Program. I want to especially thank Andrei Yakovlev for giving me an academic home in Moscow for the years it took to complete the project. I would not have been able to build the datasets without HSE as well as help and friendship from my wonderful colleagues there, including Alexei Baranov, Nina Ershova, Guzel Garifullina, Olga Masyutina, Eugenia Nazrullaeva, and Michael Rochlitz.
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