Arts, Faculty of
Political Science, Department of
DSpace
UBCV
Rojas Lozano, Daniel
Doctor of Philosophy - PhD
University of British Columbia
Conventional wisdom is that redistribution in Latin America is state-driven and increases with electoral competition or left-wing governments. Contrary to this established understanding, I develop a theory and provide evidence that business elites and right-wing governments also redistribute as a strategy to thwart social unrest or dampen support for left-wing parties, respectively. My research lies at the intersection of comparative politics and political economy, offering insights into unconventional responses to redistributive threats in Latin America. My work relies on a mixed-method approach, incorporating observational and experimental empirical strategies, qualitative interviews, natural language processing techniques, and using administrative and original data sources. The first part of my dissertation argues that business elites respond to redistributive claims when expressed via violent social unrest by coordinating through business associations and creating jobs because they fear future destruction and economic losses. They are likely to do so when the state is too slow or incapable of addressing popular demands. I connect research on the political economy of development, which sees economic elites as pivotal actors in guaranteeing social stability, and those on contentious politics in developing democracies, which see violent unrest as an expression of low state capacity. In contrast to extensive work on state-led redistribution, this part of my theory spotlights the role of business elites as non-state actors who can use employment as a form of redistribution and who respond more directly to violent protests as opposed to electoral dynamics. My empirical findings show that redistribution might happen through non-state actors. Yet, in the second part of my dissertation, I also examine the conditions under which political elites are likely to provide state-led redistribution. I argue that conservative parties have strong incentives to pursue redistribution when confronted with a credible leftist threat–namely, an electorally strong left-wing party with credible redistributive promises. Furthermore, I contend, and provide empirical evidence, that the wealthy are more likely to support redistributive policies when they are advanced by right-wing governments rather than by left-wing ones because they perceive it as less uncertain, more efficient, and less prone to create macroeconomic instability.
Strategic ConcessionsElites, Redistribution, and Social Stability in LatinAmericabyDaniel Rojas LozanoM.A., Facultad Latinoamericana de Ciencias Sociales - Mexico, 2018B.A., Icesi University, 2015A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFDOCTOR OF PHILOSOPHYinThe Faculty of Graduate and Postdoctoral Studies(Political Science)THE UNIVERSITY OF BRITISH COLUMBIA(Vancouver)October 2025© Daniel Rojas Lozano 2025The following individuals certify that they have read, and recommend to the Faculty ofGraduate and Postdoctoral Studies for acceptance, the dissertation entitled:Strategic Concessions: Elites, Redistribution, and Social Stability in Latin Americasubmitted by Daniel Rojas Lozano in partial fulfillment of the requirements forthe degree of Doctor of Philosophyin Political ScienceExamining Committee:Alan Jacobs, Professor, Department of Political Science, UBCSupervisorMichael Weaver, Assistant Professor, Department of Political Science, UBCCo-supervisorAlisha Holland, Professor, Government Department, Harvard UniversitySupervisory Committee MemberYang-Yang Zhou, Assistant Professor, Government Department, Dartmouth CollegeSupervisory Committee MemberClaudio Ferraz, Professor, Vancouver School of Economics, UBCUniversity ExaminerCallan Hummel, Assistant Professor, Department of Political Science, UBCUniversity ExamineriiAbstractConventional wisdom is that redistribution in Latin America is state-driven and increaseswith electoral competition or left-wing governments. Contrary to this established un-derstanding, I develop a theory and provide evidence that business elites and right-winggovernments also redistribute as a strategy to thwart social unrest or dampen support forleft-wing parties, respectively. My research lies at the intersection of comparative poli-tics and political economy, o↵ering insights into unconventional responses to redistributivethreats in Latin America. My work relies on a mixed-method approach, incorporating ob-servational and experimental empirical strategies, qualitative interviews, natural languageprocessing techniques, and using administrative and original data sources.The first part of my dissertation argues that business elites respond to redistributiveclaims when expressed via violent social unrest by coordinating through business associ-ations and creating jobs because they fear future destruction and economic losses. Theyare likely to do so when the state is too slow or incapable of addressing popular demands.I connect research on the political economy of development, which sees economic elites aspivotal actors in guaranteeing social stability, and those on contentious politics in develop-ing democracies, which see violent unrest as an expression of low state capacity. In contrastto extensive work on state-led redistribution, this part of my theory spotlights the role ofbusiness elites as non-state actors who can use employment as a form of redistribution andwho respond more directly to violent protests as opposed to electoral dynamics.My empirical findings show that redistribution might happen through non-state actors.Yet, in the second part of my dissertation, I also examine the conditions under whichpolitical elites are likely to provide state-led redistribution. I argue that conservative partieshave strong incentives to pursue redistribution when confronted with a credible leftistthreat–namely, an electorally strong left-wing party with credible redistributive promises.Furthermore, I contend, and provide empirical evidence, that the wealthy are more likely tosupport redistributive policies when they are advanced by right-wing governments ratherthan by left-wing ones because they perceive it as less uncertain, more ecient, and lessprone to create macroeconomic instability.iiiLay SummaryIn Latin America, redistribution (e.g., job creation or social benefits) is usually seen assomething only left-wing governments do. My research challenges this idea. I show thatbusiness and conservative leaders also support redistribution, but for di↵erent reasons:to prevent protests or stop left-wing parties from gaining power. I studied this usinginterviews, experiments, and data analysis. In the first part of my work, I found thatwhen protests turn violent, business leaders sometimes step in to create jobs because theyfear damage to the economy, especially if governments are too slow to act. In the secondpart, I show that conservative politicians may expand social programs when they facestrong competition from the political left. Wealthy people are also more likely to supportredistribution if it comes from the political right, which they see as more stable. Overall,my work shows that redistribution can come from unexpected places.ivPrefaceThis dissertation comprises original work. Online surveys and fieldwork interviews wereapproved by the UBC Behavioral Research Ethics Board (applications #H21-03702 and#H23-01179 ). No Generative Artificial Intelligence tools were used in the research process,development, or writing of the thesis.Chapter 3 was co-authored with Dr. Fabio Resmini (Ph.D. UBC, 2022), an AssistantProfessor in the Department of Social Sciences at the Universidad Cato´lica del Peru´ inLima. We contributed equally to the development of the relevant research questions andresearch design. I programmed the online surveys, cleaned, and analyzed the data fromthe resulting datasets. Moreover, I generated the tables and graphics for the chapter. Iwrote the bulk of the research design and data section, as well as the results section. Wecontributed equally to the introduction, theory, and conclusion. This chapter was presentedat the annual conference of the Midwest Political Science Association (MPSA) in April2023, the annual conference of the Red para el Estudio de la Economı´a Pol´ıtica de Ame´ricaLatina (REPAL) in June 2023, and the annual conference of the American Political ScienceAssociation (APSA) in September 2023. The article version of the chapter was publishedin the Political Science Research and Methods journal in July 2025 [Resmini F, Rojas D.Government ideology and support for redistribution among the wealthy. Political ScienceResearch and Methods. Published online 2025:1-17. doi:10.1017/psrm.2025.10029].Chapters 1, 2, and 4 of this dissertation were constructed entirely by me. I formulatedthe research questions, discussed the relevant literature, and collected and analyzed thedata. Earlier versions of the chapters were presented at the annual conferences of MPSA,REPAL, APSA, the Latin American Studies Association (LASA), the European PoliticalScience Association (EPSA), and the Society for Political Methodology (PolMeth).vTable of ContentsAbstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iiiLay Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ivPreface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vTable of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viList of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ixList of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiAcknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiiiDedication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 How Do Business Elites Respond to Social Protests? . . . . . . . . . . . 42.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42.2 Elites’ Responses to Social Unrest: Existing Literature . . . . . . . . . . . 62.3 Theory: Business Elites’ Concessions to Violent Unrest . . . . . . . . . . . 72.4 Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102.5 Research Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112.5.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112.5.2 Empirical Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . 162.6 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172.6.1 Robustness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202.6.2 Placebo Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212.7 Mechanism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 242.7.1 Economic Uncertainty and Costs . . . . . . . . . . . . . . . . . . . . 252.7.2 Coordination among Elites . . . . . . . . . . . . . . . . . . . . . . . 262.7.3 Elites’ Concessions . . . . . . . . . . . . . . . . . . . . . . . . . . . 262.8 Alternative Explanations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29viTable of Contents2.9 Scope Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 302.10 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 303 Support for Redistribution among the Wealthy. . . . . . . . . . . . . . . 323.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323.2 Preferences for Redistribution in Wealthy Individuals . . . . . . . . . . . . 343.2.1 Class-based Explanation . . . . . . . . . . . . . . . . . . . . . . . . 353.2.2 Partisanship Explanation . . . . . . . . . . . . . . . . . . . . . . . . 363.2.3 Argument. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 363.3 Research Design and Data . . . . . . . . . . . . . . . . . . . . . . . . . . . 383.3.1 Cross-Country Evidence . . . . . . . . . . . . . . . . . . . . . . . . 383.3.2 Survey Experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . 393.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 443.4.1 Study 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 463.4.2 Heterogeneous Treatment E↵ects . . . . . . . . . . . . . . . . . . . 473.4.3 Non-Redistributive Policies . . . . . . . . . . . . . . . . . . . . . . . 503.5 Alternative Explanations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 503.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 514 Leftist Threat and Post-Election Transfers . . . . . . . . . . . . . . . . . . 534.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 534.2 Strategic Expansion of Social Policies . . . . . . . . . . . . . . . . . . . . . 554.2.1 Bringing Ideology to the Strategic Expansion . . . . . . . . . . . . . 554.3 The Colombian Case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 564.3.1 Familias en Accio´n . . . . . . . . . . . . . . . . . . . . . . . . . . . 584.4 Empirical Strategy: Data and Model . . . . . . . . . . . . . . . . . . . . . 594.4.1 Empirical Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 604.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 614.6 Discussion: Plausible Mechanisms . . . . . . . . . . . . . . . . . . . . . . . 644.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 655 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71AppendicesA How Do Business Elites Respond to Social Protests? . . . . . . . . . . . 83A.1 Descriptive Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83A.2 Validation Check . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84A.3 Testing Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85viiTable of ContentsA.4 Complete Sample - Coding Scheme . . . . . . . . . . . . . . . . . . . . . . 86A.5 Additional E↵ect of Violence . . . . . . . . . . . . . . . . . . . . . . . . . . 87A.6 Heterogeneous Treatment E↵ects . . . . . . . . . . . . . . . . . . . . . . . . 87A.7 Robustness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90A.7.1 Excluding Epicenter of Unrest . . . . . . . . . . . . . . . . . . . . . 90A.7.2 Alternative Specifications . . . . . . . . . . . . . . . . . . . . . . . . 91A.7.3 Geographic Matching . . . . . . . . . . . . . . . . . . . . . . . . . . 93A.7.4 Spatial Donuts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94A.7.5 Synthetic Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96A.8 State Concessions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97A.9 Concessions in Control Group . . . . . . . . . . . . . . . . . . . . . . . . . 99A.10 Quantitative Text Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 100A.10.1 News Articles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100A.10.2 Business Statements . . . . . . . . . . . . . . . . . . . . . . . . . . . 101A.11 Mechanism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102A.11.1 Firm-level Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102A.11.2 Interviews . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105B Support for Redistribution among the Wealthy. . . . . . . . . . . . . . . 107B.1 Descriptive Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107B.2 External Validity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109B.3 Vignettes and Survey Questions . . . . . . . . . . . . . . . . . . . . . . . . 110B.4 Equivalence Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112B.5 Regression Results - Survey Experimental Data . . . . . . . . . . . . . . . 115B.5.1 Alternative Explanation . . . . . . . . . . . . . . . . . . . . . . . . 118B.5.2 Adjusted p-values . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120B.5.3 Heterogeneous E↵ects . . . . . . . . . . . . . . . . . . . . . . . . . . 121B.6 Subgroup Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126B.7 Non-redistributive Policies . . . . . . . . . . . . . . . . . . . . . . . . . . . 128B.8 Regression Results - Observational Data . . . . . . . . . . . . . . . . . . . 130B.9 Cross-country Regressions . . . . . . . . . . . . . . . . . . . . . . . . . . . 132C Leftist Threat and Post-Election Transfers . . . . . . . . . . . . . . . . . . 135C.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135C.2 Checking Parallel Trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138C.3 Alternative Functional Forms . . . . . . . . . . . . . . . . . . . . . . . . . . 139viiiList of Tables2.1 Description of Firms in Sample . . . . . . . . . . . . . . . . . . . . . . . . . 143.1 Testing Theoretical Explanations - Study 1 . . . . . . . . . . . . . . . . . . 463.2 Testing Theoretical Explanations - Study 2 . . . . . . . . . . . . . . . . . . 474.1 E↵ect of Left-Wing’s Voteshare. . . . . . . . . . . . . . . . . . . . . . . . . . 624.2 E↵ect of 2014 Runner-Up’s Voteshare. . . . . . . . . . . . . . . . . . . . . . . 634.3 E↵ect of 2015 Left-Wing’s Voteshare. . . . . . . . . . . . . . . . . . . . . . . 64A.1 Summary Statistics - Municipalities . . . . . . . . . . . . . . . . . . . . . . 83A.2 Summary Statistics - Firms . . . . . . . . . . . . . . . . . . . . . . . . . . . 84A.3 ACLED - Validation Check . . . . . . . . . . . . . . . . . . . . . . . . . . . 85A.4 Testing Assumption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86A.5 Treatment Variables - Coding Scheme . . . . . . . . . . . . . . . . . . . . . 86A.6 Top 5 Nearest Neighbors for ‘protest*’ . . . . . . . . . . . . . . . . . . . . . 101A.7 E↵ect of Most Important Problem on Likelihood of Increasing Number ofEmployees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104B.1 Equivalence Table (Study 1) - T0 vs T1 . . . . . . . . . . . . . . . . . . . . 112B.2 Equivalence Table (Study 2) - T0 vs T1 . . . . . . . . . . . . . . . . . . . . 113B.3 Regression Results - Study 1 (without covariates) . . . . . . . . . . . . . . . 115B.4 Regression Results - Study 2 (without covariates) . . . . . . . . . . . . . . . 116B.5 E↵ect on Uncertainty (Alternative coding) . . . . . . . . . . . . . . . . . . . 118B.6 E↵ect of Redistributive Proposal on Trust . . . . . . . . . . . . . . . . . . . 120B.7 Adjusted p-values - Study 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . 121B.8 Adjusted p-values - Study 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . 121B.9 Heterogeneous E↵ects (Ideology = Right) - Study 1 . . . . . . . . . . . . . 122B.10 Heterogeneous E↵ects (Ideology = Right) - Study 2 . . . . . . . . . . . . . 123B.11 Heterogeneous E↵ects (Vote Intention = Right-wing) - Study 1 . . . . . . . 123B.12 Heterogeneous E↵ects (Vote Intention = Right-wing) - Study 2 . . . . . . . 124B.13 Testing Theoretical Explanations in Subgroup - Study 1 . . . . . . . . . . . 127B.14 Testing Theoretical Explanations in Subgroup - Study 2 . . . . . . . . . . . 128B.15 Regression Results - Left-Wing Candidate . . . . . . . . . . . . . . . . . . . 130ixList of TablesB.16 Regression Results - Cross-country Analyses . . . . . . . . . . . . . . . . . . 132C.1 Summary Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136C.2 E↵ect of Left-Wing’s Voteshare on Number of FA Beneficiaries . . . . . . . 140C.3 E↵ect of Left-Wing’s Voteshare on IHS of Number of FA Beneficiaries . . . 140xList of Figures2.1 Municipalities Exposed to Nonviolent Protests. . . . . . . . . . . . . . . . . . 122.2 Municipalities Exposed to Violent Protests. . . . . . . . . . . . . . . . . . . . 132.3 Overtime Exposure to Protests . . . . . . . . . . . . . . . . . . . . . . . . . 162.4 E↵ect of Protests. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192.5 E↵ect of Protests - Complete Sample. . . . . . . . . . . . . . . . . . . . . . . 202.6 E↵ect of the 2019 Protests. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 222.7 Media Coverage of Protests. . . . . . . . . . . . . . . . . . . . . . . . . . . . 232.8 E↵ect of Violent Protests on Government Job Vacancies. . . . . . . . . . . . 242.9 Main Problems among Firms. . . . . . . . . . . . . . . . . . . . . . . . . . . 252.10 E↵ect of Most Important Problem. . . . . . . . . . . . . . . . . . . . . . . . . 282.11 Elites’ Concessions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283.1 Support for Redistribution in Latin America. . . . . . . . . . . . . . . . . . . 393.2 Preferences for Redistribution in Wealthy Colombians (S1). . . . . . . . . . . 453.3 Preferences for Redistribution in Wealthy Colombians (S2). . . . . . . . . . . 473.4 Ideological Distribution of Respondents. . . . . . . . . . . . . . . . . . . . . . 483.5 Heterogeneous Treatment E↵ects (Preferences). . . . . . . . . . . . . . . . . . 493.6 Heterogeneous Treatment E↵ects (Expectations). . . . . . . . . . . . . . . . . 504.1 Vote Share of Left-wing Candidate. . . . . . . . . . . . . . . . . . . . . . . . 574.2 Ideological Placement of Top Candidates. . . . . . . . . . . . . . . . . . . . . 58A.1 E↵ect of Protests on Job Vacancies (Interaction). . . . . . . . . . . . . . . . 87A.2 Estimated E↵ect of Protests by Type of Economy. . . . . . . . . . . . . . . . 88A.3 Estimated E↵ect of Protests by Level of Education. . . . . . . . . . . . . . . 89A.4 Estimated E↵ect of Protests by Level of Experience. . . . . . . . . . . . . . . 89A.5 Estimated E↵ect of Protests by Salary. . . . . . . . . . . . . . . . . . . . . . 90A.6 Estimated E↵ect (excluding epicenter). . . . . . . . . . . . . . . . . . . . . . 91A.7 Estimated E↵ect (alternative FEs). . . . . . . . . . . . . . . . . . . . . . . . 92A.8 Estimated E↵ect (working age pop.). . . . . . . . . . . . . . . . . . . . . . . 92A.9 Estimated E↵ect (alternative Covid-19 control). . . . . . . . . . . . . . . . . 93A.10 Estimated E↵ect (geographic matching) . . . . . . . . . . . . . . . . . . . . 94A.11 Estimated E↵ect (100km donut). . . . . . . . . . . . . . . . . . . . . . . . . . 95xiList of FiguresA.12 Estimated E↵ect (200km donut). . . . . . . . . . . . . . . . . . . . . . . . . . 95A.13 A Synthetic Control Approach (MC estimator). . . . . . . . . . . . . . . . . 96A.14 A Synthetic Control Approach (IFE estimator) . . . . . . . . . . . . . . . . 97A.15 Estimated E↵ect on Public Contracts. . . . . . . . . . . . . . . . . . . . . . . 98A.16 Estimated E↵ect on Logged Public Contracts. . . . . . . . . . . . . . . . . . 99A.17 Overtime Vacancies in Control Group. . . . . . . . . . . . . . . . . . . . . . . 100A.18 Estimated Vacancies in Control Group. . . . . . . . . . . . . . . . . . . . . . 100B.1 Descriptive Statistics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108B.2 Perception of Herna´ndez’ Ideology Position. . . . . . . . . . . . . . . . . . . . 109B.3 External Validity. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109B.4 Dependent Variables in Original Scales - Study 1. . . . . . . . . . . . . . . . 116B.5 Dependent Variables in Original Scales - Study 2. . . . . . . . . . . . . . . . 117B.6 Ideological Placement and Fear. . . . . . . . . . . . . . . . . . . . . . . . . . 119B.7 Heterogeneous Treatment E↵ects (preferences, two groups). . . . . . . . . . . 124B.8 Heterogeneous Treatment E↵ects (expectations, two groups). . . . . . . . . . 125B.9 Heterogeneous Treatment E↵ects (preferences, three groups). . . . . . . . . . 125B.10 Heterogeneous Treatment E↵ects (expectations, three groups). . . . . . . . . 126B.11 Preferences (subgroup) - Study 1. . . . . . . . . . . . . . . . . . . . . . . . . 127B.12 Preferences (subgroup) - Study 2. . . . . . . . . . . . . . . . . . . . . . . . . 127B.13 Non-Redistributive Policies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129B.14 Non-Redistributive Policies - Heterogeneous Treatment E↵ects. . . . . . . . . 130C.1 Distribution of log FA New Beneficiaries. . . . . . . . . . . . . . . . . . . . . 137C.2 FA New Beneficiaries over Time. . . . . . . . . . . . . . . . . . . . . . . . . . 137C.3 Potential FA Demand in 2017. . . . . . . . . . . . . . . . . . . . . . . . . . . 138C.4 Logged FA New Beneficiaries. . . . . . . . . . . . . . . . . . . . . . . . . . . 139xiiAcknowledgementsThis dissertation marks the end of a beautiful and challenging academic journey. Firstly, Iwould like to thank Dr Alan Jacobs for giving me the opportunity to study at UBC. Alanbelieved in me and provided me with several resources to strengthen my academic training.I am grateful for the way in which Alan taught me to think more deeply.I would also like to thank Dr Yang-Yang Zhou for mentoring me. She has been a rolemodel, inspiring me with her empirical rigour and research creativity over the years. Shehas been a mentor, a co-author and a friend. I am also grateful to Dr Michael Weaverfor giving me the tools and advice I needed to turn my ideas into reality. Michael wasalways willing to share his insights and motivate my productivity. Similarly, I would liketo thank Dr Alisha Holland for kindly accepting my invitation to join the committee, andfor always encouraging me to be more ambitious in my theoretical statements. Alisha’swork has shaped my thinking and inspired my academic work. It has been an honour tohave her on my committee.The PhD journey can be long and lonely. However, I was fortunate to meet and workwith great scholars who made these years memorable: Margaret Peters, Nata´lia Bueno,Daniel Masterson and Antonella Bandiera. They have been more than co-authors; theyhave been mentors and friends. Fabio Resmini, a dear friend and co-author of one of mychapters, deserves a special mention. I would also like to thank several friends who mademy days enjoyable and provided emotional and mental support without even realising it:Francesco, Alessio, Adria´n, Zara´ı, Sebastia´n and Valentina.I would also like to thank my family: My parents, Jaime and Alexandra, whose love,wisdom and unwavering dedication support me every day. They have passed on to me thestrength and motivation to pursue my dreams. And my sister, Paola, whose adventurouslife keeps me entertained.Finally, I would like to thank my wife, Johana, for encouraging me to see the worldbeyond books and for keeping my ideas grounded. Thank you for being with me throughoutmy academic journey and for sticking by my side despite the numerous relocations andsacrifices involved. This dissertation is dedicated to you.xiiiA Johana – a su amor y compan˜´ıa incondicional.xivChapter 1IntroductionWhen, why, and how do elites respond to redistributive demands? While the dominantview in the political economy of Latin America holds that redistribution is primarily state-led and increases under conditions of electoral competition or when left-wing governmentsare in power, this dissertation challenges that conventional understanding. I argue thateconomic elites and right-wing governments can and do redistribute, strategically and se-lectively, to thwart social unrest, co-opt demands from below, and prevent political threatsfrom the left. This elite-led redistribution is not driven by ideological commitment to equal-ity, but rather by an e↵ort to maintain political stability, protect material interests, andsecure regime survival. The dissertation thus rethinks who redistributes, when, and why.This project lies at the intersection of comparative politics, distributive politics, and thepolitical economy of inequality, with a regional focus on Latin America. It makes the casethat redistribution does not always follow the path laid out by canonical models, in whichthe poor vote for redistribution and the state implements it under pressure from the me-dian voter (Meltzer and Richard, 1981; Romer, 1975). Instead, redistribution may emergethrough non-electoral, elite-led mechanisms: when unrest threatens private interests, orwhen redistribution by right-wing actors is perceived as a tool to neutralize the politicalleft. Scholars have long explored the conditions under which governments expand redis-tribution. In this tradition, structural models of political economy argue that economicelites generally oppose redistribution and capture the state to protect their wealth (Boix,2003a; Acemoglu and Robinson, 2006; Albertus and Menaldo, 2014). Redistribution, whenit occurs, is typically attributed to democratization, electoral competition, or left-wing rule(Huber and Stephens, 2012; Garay, 2016). These models assume elites act to block redis-tributive pressures by weakening fiscal capacity or undermining state institutions. Parallelliteratures in protest politics and contentious politics argue that redistribution is drivenby mass mobilization (Piven and Cloward, 1978; Tilly, 1999), but these accounts tend tofocus on state responses, such as social spending or repression, and not on elite responsesbeyond the state. Recent work shows that violent protest tends to backfire, generatingbacklash among the public or prompting government repression (Chenoweth and Stephan,2011; Wasow, 2020), but does not examine whether economic elites might respond directly.Meanwhile, the growing literature on business elites and inequality has documented howeconomic elites shape tax regimes, resist labor reforms, or capture policymaking (Fairfield,2015; Schneider, 2009; Puente and Schneider, 2020), but typically assumes their poweris exercised through the state. Studies of redistribution in Latin America, for instance,1Chapter 1. Introductionemphasize how left governments expand social policy (De La O, 2015), and how businesspower constrains them (Flores-Macias, 2019). What is missing is a theoretical frameworkthat explains how elites might themselves initiate redistribution when their interests arethreatened, especially in weak institutional environments. Recent research begins to com-plicate these assumptions. Fairfield and Garay (2017), for instance, show that elites maysupport progressive taxation under the Right. Macchi and Stalder (2024) argue that firmsin developing contexts use employment as an informal redistributive tool. Yet, we lack anintegrated framework that links elite agency, redistributive threats, and non-state responsesacross di↵erent political contexts. This dissertation addresses this gap by proposing thatelite-led redistribution can be both reactive and strategic. Business elites may redistributedirectly—outside of the state—when social unrest imposes real economic costs and thestate lacks capacity. Likewise, right-wing governments may expand social transfers or pur-sue progressive tax reform, not due to ideological commitment, but to neutralize electoralthreats from the left or to control redistribution on their own terms. I argue that theseresponses are conditional, shaped by the nature of the threat (violent or electoral), thecredibility of the opposition, and the capacity for elite coordination. This dissertationconsists of three papers that collectively theorize and test elite responses to redistributivedemands in Colombia, a high-inequality, low-capacity democracy that provides ideal varia-tion on protest, elite behavior, and electoral competition. The first paper, How Do BusinessElites Respond to Social Protests?, explores how business elites respond to violent unrest.Contrary to the expectation that violent protest reduces support for protester demands,I show that in areas with fixed capital and weak state capacity, violent unrest increaseslabor demand, as business elites use employment as a redistributive concession to restoreorder. Drawing on an event study design, firm-level data, and elite interviews, I find thatjob creation increased in municipalities a↵ected by violent protest, and that business asso-ciations coordinated these concessions. This paper challenges the idea that redistributiononly happens through the state and shows how elites redistribute privately under threat.The second paper, Government Ideology and Support for Redistribution among the Wealthy(with Fabio Resmini), uses survey experiments to investigate when the wealthy supportredistributive policies. We find that the ideology of the government enacting redistribu-tion matters more than the policy content: wealthy individuals are more supportive of taxincreases and redistributive programs when they are proposed by right-wing governments.Respondents perceive redistribution under the Right to be more ecient and less likelyto generate macroeconomic instability. This suggests that elite support for redistributionis contingent on ideological trust and partisan alignment, not just material self-interest.The third paper, Leftist Threat and Post-Election Transfers, shows that right-wing incum-bents in Colombia strategically expanded social transfers after elections, particularly inmunicipalities that supported left-wing candidates. Using a di↵erence-in-di↵erences designand administrative data on a major cash transfer program, I find that the Leftist Threatbetween the incumbent and the runner-up shaped post-election redistribution. Right-winggovernments redistributed more to left-leaning constituencies, suggesting a strategic use of2Chapter 1. Introductionsocial policy to dampen future opposition. Together, these papers show that redistributioncan emerge not only from the ballot box or leftist parties, but also through strategic eliteconcessions in response to political threats. Whether through job creation, selective sup-port for redistribution, or post-electoral transfer expansion, elites act to preempt demandsfor more radical change. This dissertation recasts elites not simply as veto players, but asactive political agents capable of shaping redistributive outcomes.3Chapter 2How Do Business Elites Respondto Social Protests?2.1 IntroductionOn April 28, 2022, Cali, Colombia witnessed an unprecedented social protest triggered by aregressive tax reform during a period of rising poverty and inequality. The protest quicklyescalated to roadblocks, looting, and widespread destruction of private and public property(BBC, 2021a). Remarkably, in the weeks following the unrest, business elites announcedthe creation of thousands of jobs as a response to the demands raised during the unrest.1Elites’ direct concession is surprising as violent protests tend to backfire and discourageeconomic activities.Existing theories in political economy and contentious politics suggest that responsesto social unrest typically come from the state, taking the form of repression, policy changesor social programs (Piven and Cloward, 1978; Tilly, 1999; Madestam et al., 2013; Hummel,2022). Governments are expected to address citizens’ grievances, while economic elites areassumed to pursue their material interests and rely on the state to ensure social order (Boix,2003a; Acemoglu and Robinson, 2006). Furthermore, scholarship on protest dynamics oftenemphasizes that violent protests reduce support for protesters’ demands and harm economicconditions by discouraging investment and employment (Alesina and Perotti, 1996; Luders,2006; Collins and Margo, 2007; Chenoweth and Stephan, 2011; Beber, Roessler and Scacco,2014; Lupu and Wallace, 2019; Wasow, 2020). Yet, these expectations do not align withthe pattern observed in Colombia: why did violent unrest prompt direct, redistributiveaction by business elites?The existing literature sheds little direct light on this type of question. Despite thegrowing scholarship in social protests, many recent contributions focus on the e↵ects ofprotests on political elites and public opinion, including congressional voting behavior, levelof support for social spending, racial attitudes and attitudes toward police (Madestam et al.,2013; Mazumder, 2018; Enos, Kaufman and Sands, 2019; Wasow, 2020; Reny and Newman,2021; Eady, Hjorth and Dinesen, 2023). However, the literature pays less attention tobusiness elites2 and their capacity to address economic demands. This lack of research is1See https://tinyurl.com/yc87266h for the coverage of the announcement in Spanish.2While political and economic elites often overlap, this paper specifically focuses on economic elites,treating them as conceptually di↵erent from political elites.42.1. Introductionsurprising given the significant political influence of business elites and their vulnerabilityto violent protests: roadblocks, boycotts, riots, and other forms of violent protests disrupteconomic activities and can lead to declines in stock market valuations of firms, increasedinsurance costs, reduced market consumption, or even business failures (Collins et al., 2004;Luders, 2006; Acemoglu, Hassan and Tahoun, 2018).This article introduces a novel theoretical framework that examines the role of economicelites in responding to social unrest in contexts where state capacity is limited and capitalmobility is constrained. I argue that violent, economically driven unrest generates economicuncertainty and tangible costs for business elites who own fixed capital, such as factories orphysical assets, making them unable to shield their investments through relocation. Whenthe state lacks the capacity to mitigate social discontent, elites have strong incentives torespond directly to unrest. Specifically, I posit that violent protests—unlike nonviolentones—compel elites to act collectively through business associations and use job creationas a strategic tool to address redistributive demands, di↵use tensions, and restore socialorder.This argument departs from prevailing theories in two key ways. First, while much ofthe literature assumes that economic elites primarily influence redistribution through thestate, I highlight their capacity to act independently by leveraging their control over thelabor market. Second, I challenge the notion that violent protests are uniformly detrimentalto protesters’ goals. Instead, I demonstrate that under conditions of low state capacity,violent unrest can elicit concessions from economic elites, particularly when the unrestthreatens their physical assets and disrupts local economies.I test my theory in Colombia, a developing democracy that experienced an unprece-dented wave of violent and nonviolent protests that started in April 2021 and lasted forseveral weeks. Using an event study research design and administrative data on labordemand, I find that municipalities exposed to violent protests experienced an increase injob vacancies per capita after April 2021 compared to those without protests. In contrast,the e↵ect of nonviolent protests is negligible. Heterogeneous treatment e↵ect analyses sug-gest that elites’ concessions are concentrated in areas with labor-intensive economies andare focused on individuals with secondary education, those with a year or less of priorwork experience, and low-paid labor. My main findings are highly robust and hold acrossadditional tests, including synthetic controls and placebos.To further substantiate my findings, I collect firm-level data and conduct interviewswith business elites from Cali, Colombia, the epicenter of the violent unrest. Analyses ofthe firm-level data show that while the 2021 violent protest was the primary challenge mostcompanies faced that year—surpassing crisis such as the COVID-19 pandemic—it was theonly event positively associated with job creation. Moreover, several interviews highlightthe pivotal role of business associations in coordinating elites’ responses.By focusing on the interplay between social unrest and elite behavior, this article makesseveral substantive contributions. First, it advances theories of democracy and redistribu-tion. Existing theories argue that high levels of inequality persist in many democracies,52.2. Elites’ Responses to Social Unrest: Existing Literaturedespite the predicted redistribution of canonical models (see, e.g., Romer, 1975), becauseeconomic elites e↵ectively block redistribution. They do so by undermining political pro-cesses, capturing institutions, and weakening the state’s fiscal capacity (Boix, 2003a; Ace-moglu and Robinson, 2008; Albertus and Menaldo, 2014; Hollenbach and Silva, 2019). Iextend this literature by demonstrating the conditions under which economic elites deviatefrom their expected anti-redistribution preferences and use their influence in the labor mar-ket to concede to extra-electoral redistributive demands. My theory, therefore, highlightsthat redistribution in democracies can occur through actors typically assumed to opposeredistribution and via private mechanisms.Relatedly, my study complements the burgeoning scholarship on business elites’ rolein politics (Castan˜eda, 2017; Flores-Macias, 2019; Puente and Schneider, 2020; Bogliacciniand Madariaga, 2024). Existing research focuses on how elites interact with governmentsto shape politics and protect their interests. However, it largely overlooks the conditionsunder which economic elites can a↵ect socioeconomic processes and protect their localinterests without relying on the state. This article illustrates how the role of business elitesin democracies might not necessarily require an alignment or overlap with political elites.I also extend the literature on contentious politics by illustrating why violent protestsdo not necessarily fail. The consensus in the literature is that violent protests backfire,as they make citizens less supportive of rioters’ goals (Chenoweth and Stephan, 2011;Beber, Roessler and Scacco, 2014; Lupu and Wallace, 2019; Wasow, 2020). However,existing research has mainly examined how these events influence voting behavior andpublic opinion, largely ignoring other outcomes that violent protests can a↵ect. By focusingon labor demand, I demonstrate how violent protests can sometimes succeed. Specifically,I show that when the state has limited capacity to address the economic demands drivingviolent social unrest, elites are more likely to o↵er jobs as concessions to these demands.Finally, my study contributes to the emerging literature on employment as a form ofinformal redistribution (Alby, Auriol and Nguimkeu, 2020; Macchi and Stalder, 2024). Re-cent research suggests that firms in low-income countries use employment as a redistributivemechanism influenced by kinship ties or workfare considerations. I develop a theoreticalframework and provide evidence for an alternative mechanism through which social andpolitical factors drive elites’ strategic behavior.2.2 Elites’ Responses to Social Unrest: Existing LiteratureEmpirical research demonstrates that nonviolent protests influence elites’ behavior. Specif-ically, protests can shift elites’ stances in favor of protesters’ demands (McAdam and Su,2002; Madestam et al., 2013; Gillion and Soule, 2018; Mazumder, 2018). Protests can a↵ectbehavior by signaling previously unrevealed policy preferences (Lohmann, 1993, 1994) ortransmitting political views (Madestam et al., 2013). Additionally, exposure to news cov-erage of protests and the presence of cohesive crowds articulating coherent and consistent62.3. Theory: Business Elites’ Concessions to Violent Unrestdemands are mechanisms through which protests influence elites (Wasow, 2020; Mueller,2024).Scholars examining the e↵ect of violent protests3 consistently find that such protestsbackfire, reducing support for the demands articulated during the unrest (Chenoweth andStephan, 2011; Beber, Roessler and Scacco, 2014; Lupu and Wallace, 2019).4 Indeed,Wasow (2020) empirically demonstrates that violent protests increase public support forsocial control measures rather than concessions to rioters’ demands. Furthermore, violentprotests impact not only political attitudes but also economic activities. They typicallyinvolve the destruction of physical capital and generate perceptions of insecurity and socialdisorder, which, in turn, discourage economic demand and investment (Collins and Margo,2007; Acemoglu, Hassan and Tahoun, 2018).Although recent research has primarily focused on the impact of protests on politicalelites and public opinion, often overlooking economic elites, seminal theories of democra-tization provide insights into how economic elites might respond to social unrest. Thesetheories claim that in contexts where citizens can threaten social disorder, elites makeconcessions to prevent unrest and maintain social stability (Boix, 2003a; Acemoglu andRobinson, 2006). Elites in power make concessions by establishing democratic institutions,while elites without de jure power do so by accepting wealth redistribution. These theoriesoften assume that states have the capacity to implement elites’ concessions and addresssocial demands. Likewise, they suppose that a substantial proportion of economic eliteshold mobile capital, enabling them to protect their investments from disruptive events byrelocating assets. What happens, however, when economic elites hold fixed rather thanmobile capital and cannot rely on the state to address social unrest? Canonical theoriesoverlook the impact of unexpected shocks that might weaken the state’s capacity to addresssocial demands.Furthermore, they underestimate the political and economic relevance of elites whodepend on fixed capital and are more vulnerable to social unrest. In the next section, Ipropose a theory that addresses current limitations in the existing literature and explainsthe conditions under which economic elites concede to violent unrest.2.3 Theory: Business Elites’ Concessions to Violent UnrestI define business elites as private actors who can influence the formal labor market bycreating jobs. I assume that business elites own small or big firms with physical assetsand are di↵erent from mere holders of financial capital. This di↵erence is conceptuallyrelevant because the latter hold mobile capital and can prevent the adverse e↵ects of social3I focus on violence by rather than against demonstrators. See the literature on protest policing for theimplications of violence against protesters (Hibbs et al., 1973; McAdam and Su, 2002; Lawrence, 2017).4In contrast to existing evidence, Enos, Kaufman and Sands (2019) find that violent protests promptsupport for protesters’ demands. However, the authors focus on public opinion rather than elites’ behavior.72.3. Theory: Business Elites’ Concessions to Violent Unrestunrest on their wealth by moving it abroad (see Boix, 2003a). However, business elites,as I characterize them, face sunk costs; firms are less mobile than financial capital, andmoving their production abroad takes time.Given these characteristics, business elites struggle to operate amid social unrest be-cause it increases costs of operations and production, a↵ects perceived security, and con-tracts consumption (Collins and Margo, 2007; Hadzi-Vaskov, Pienknagura and Ricci, 2023).In fact, firms perform better in places with stable political and social environments (Alesinaand Perotti, 1996).Elites might prefer to avoid social unrest and locate their economic activities in stableenvironments. However, there are major shocks such as epidemics or financial crises thatweaken state capacity, exacerbate existing poverty and inequality (Bottan, Ho↵mann andVera-Cossio, 2020; Bodea, Houle and Kim, 2021), and trigger social unrest (Censolo andMorelli, 2020; van der Zwet et al., 2022). As the literature shows, increasing poverty andinequality sparks protests (Kurer et al., 2019; Ponticelli and Voth, 2020).Protests work as mechanisms to obtain attention and concessions (Lipsky, 1968; Pivenand Cloward, 1978), and usually target corporations and employers when driven by eco-nomic hardship (Ortiz et al., 2022). Although some protests are nonviolent, violent onesare particularly disruptive for business elites, who face looting, vandalism, destruction ofphysical capital, and higher insurance and production costs (Collins and Margo, 2007).Importantly, violent protests are more likely to happen when there is a low state capacityto concede to social demands (Machado, Scartascini and Tommasi, 2011; Sullivan, 2019,2024). Then, business elites face an additional hurdle because they cannot rely on the stateto appease social discontent. Specifically, elites fear that ongoing violence and destructionmay escalate due to the limited state capacity to attenuate social discontent. Importantly,economic elites face economic uncertainty since violent unrest could lead to unexpectedeconomic losses for firms and potential bankruptcies.Provided this scenario, elites can decide between ignoring, repressing, or conceding tothe unrest. Although elites may choose to ignore the unrest, this option implies enduringviolence and destruction. I argue that the economic costs and uncertainty created by theviolence make elites search for autonomous solutions to social unrest.Business elites have di↵erent tools and capacities from political elites. They controlmaterial resources and have market positions that allow them to a↵ect the labor marketvia wages, human capital investment, and labor demand. Moreover, business elites cana↵ect their environment (i.e., the labor market) without enduring political bargaining andelectoral competition. While political elites have distinct resources, they often undergoextended bargaining processes and face several veto players to modify the status quo,particularly when they have low capacity (Tsebelis, 2002). Therefore, business elites havetools and incentives to address the social unrest without relying on the state.Business elites must decide between repressing or conceding to violent unrest. Re-pression may involve tactics like union busting, worker surveillance, or the use of private82.3. Theory: Business Elites’ Concessions to Violent Unrestsecurity services to quell the violent unrest.5 However, repression may spark more protests(see Hager and Krakowski, 2022) and fail over time as they do not address the central mo-tivation behind the unrest: economic hardship in the population. Because violent protestsin my theory are economically driven, i.e., triggered by economic hardship, business elitesdecide to make concessions to encourage people to drop out and prevent further unrest.Elites’ concessions can be of di↵erent types. Elites can decide to focus on their la-bor force and increase wages, improve working conditions and benefits, or be open to theunionization of employees. Elites can also address demands raised by people outside theirlabor force. I argue that elites center their response in the latter group because it isimpoverished individuals with low opportunity costs who are more likely to engage in vio-lent protests (Chassang and i Miquel, 2009; Blattman and Miguel, 2010; Dube and Vargas,2013); protesting is costly in terms of time and resources, yet impoverished individuals haveless to give up (e.g., labor income) by participating in violent protests. Hence, elites mightconsider it unlikely that their labor force is joining violent protests and deem concessionsto this group unnecessary, especially when protesters demand economic opportunities.Elites then can concede by providing jobs or investing in human capital among un-employed people.6 Yet, these responses involve di↵erent time horizons. While the formerrepresents a fast and concrete response, the potential benefits of investing in human capitalrequire a longer commitment by elites and recipients alike. Given the reactive nature ofelites’ concessions and the pressing scenario elites face, I posit that elites choose job creationas their preferred strategy. Elites might also decide to invest in human capital. However,I argue that such a strategy will follow a preemptive rather than reactive logic given thetime horizon elites must consider. Specifically, elites might see economically driven futureunrest as more unlikely if people are more qualified to overcome economic hardship.I therefore argue that elites decide to bear the cost of the violent unrest by providingjobs. In doing so, elites can directly address one of the main motivations behind theprotests (i.e., lack of economic opportunities) and lessen social unrest. While elites couldinvest in state capacity (e.g., paying more taxes) to let the state address the social unrest,they prefer to use their distinct capacities to directly address economic demands becausepossible policy reforms to meet economic hardship might take months. However, businesselites can respond faster and more credibly by providing jobs.Concessions are economically costly for elites. While some elites might have earnedsurplus and liquidity to concede to violent unrest, others may face budget constraints.However, the costs of keeping firms closed or operating at reduced capacity might behigher than conceding to participants’ demands through employment or human capital5However, strategies such as union busting and worker surveillance are unlikely to be e↵ective in contextscharacterized by high labor informality.6Elites could focus their strategy on riot leaders. Specifically, they might try to buy them out throughjob o↵ers, thereby attempting to disarticulate the violent unrest by co-opting them. Conceptually, however,this buyout approach would fall under a broader concession strategy.92.4. Contextinvestment,7 especially when protesters are low-skilled and can be hired at low cost.8Likewise, collective action problems might prevent elites from conceding. However,business elites are a small group by definition and can solve collective action problemseasier than larger groups (Olson Jr, 1971), especially when they face violent threats (Ace-moglu and Robinson, 2006; Slater, 2010; Rodr´ıguez-Franco, 2016). Moreover, businesselites have homogeneous economic preferences (i.e., profit-maximization), which favor col-lective action by making individuals more inclined to interact with each other (Barbera andJackson, 2020). Another feature that promotes collective action among business elites is theprotest itself. A protest is a signal that facilitates coordination among elites by providingthem with aggregated information about individuals’ demands (Casper and Tyson, 2014).Importantly, business elites usually belong to associations (e.g., federations, chambers ofcommerce, etc.), which promotes coordination (Schneider, 2009; Castan˜eda, 2017; Gottlieb,2022). I argue that these associations facilitate coordination among elites. Specifically, Iposit that these collective environments allow elites to exchange information among them-selves, evaluate possible concessions, and produce a consistent response to social unrest.I assert that when protests are nonviolent, the dynamic just described does not hold.Nonviolent protests do not have the same disruptive e↵ect as violent ones on business elites.Therefore, I hypothesize thatH1: Business elites concede to economically driven violent protests by providing jobs.H2: Business elites are unresponsive to economically driven nonviolent protests.2.4 ContextWhile the Colombian Constitution of 1991 guarantees the right to protest, social protestsin Colombia have been rare. The first salient protest was the National Agrarian Strike in2013, where the main actors were farmers, who demanded investment in rural areas, landownership, and government measures to reduce production costs (Acemoglu and Robinson,2019). Mobilizations before the 2013 protest were limited to local or regional strikes andfocused on demands related to the armed conflict (Archila, 2006; Archila et al., 2020).In fact, before the peace agreement between the Colombian government and the FARC-EP guerrilla group in 2016, actors demanding redistribution used to be stigmatized andexcluded from the political arena through violence. However, after the 2016 agreement,social protests started raising redistributive demands (Garc´ıa-Montoya, Gu¨iza-Go´mez and7As Luders (2006) explains, economic actors concede to protesters when disruption costs outweigh con-cession costs. Luders (2006, p. 967) defines disruption costs as “[t]he losses resulting directly and indirectlyfrom movement actions” and concession costs as “[t]he actual or anticipated losses resulting from movementsuccess”.8While civic protests are considered a middle-class phenomenon (Welzel, 2013), I assume and providesuggestive empirical evidence in Appendix (A.3) that lower-class people are more likely to engage in violentprotests. Violent protests are costly signals, and those with low opportunity costs might be more prone tojoin them.102.5. Research DesignSa↵on, 2023). Indeed, at the end of 2019, Colombia experienced national protests ledby labor unions and university students and motivated by social discontent with Duque’sright-wing government. Specifically, protesters opposed economic and social policies thatDuque’s government was planning to implement (e.g., eliminating the minimum wage, in-creasing taxes on labor, privatizing pensions and health). Additionally, protesters raiseddemands related to police violence, corruption, gender discrimination, and the implemen-tation of the peace agreement signed in 2016. At the beginning of 2020, protests stoppeddue to COVID-19 restrictions.In 2021, there was a new wave of protests. The demonstrations were organized by theNational Strike Committee (CNP in Spanish), a civil organization formed by students andunions from di↵erent sectors across the country. Although strikes are usually organizedby committees, the CNP was a nationwide organization recognized by the governmentand media as the main interlocutor in the 2021 protests. These protests sparked witha tax reform proposal that leaned on the middle class and generated outrage among thepopulation, which had been enduring the economic burden of the pandemic. The CNPretook the unaddressed demands raised in 2019 and emphasized the economic distressand inequality the pandemic had aggravated. Moreover, the CNP demanded universalbasic income and free university tuition, among other demands. Many people joined theprotests organized by the CNP due to the shared discontent created by increasing poverty,unemployment, and inflation rates. Besides the motivations and prominent role of theCNP, the 2021 protests had other unprecedented features. There were intense and frequentviolent protests that led to the destruction of private property and public goods, as wellas clashes between police, protesters, and civilians (BBC, 2021b).9 Moreover, a nationwidewave of roadblocks caused economic damage and a shortage of food and fuel in severalcities.2.5 Research Design2.5.1 DataI construct the independent variable using data from the Armed Conflict Location andEvent Data Project (ACLED) (Raleigh et al., 2010). ACLED provides daily counts ofprotests and riots based on local, regional, and national newspapers. Both types of eventsare classified separately, with riots including violence perpetrated by participants. I use thiscategorization to create my Nonviolent Protest and Violent Protest variables, respectively.ACLED identifies the actors involved in each event, allowing me to focus on violent andnonviolent protests where the CNP participated, either alone or with other actors (e.g.,farmers and ethnic groups). With a high level of disaggregation, ACLED’s data enable meto measure the occurrence of these events across 1,101 municipalities and over time, with9According to ACLED data, the number of violent events in the first month of protests was 250, whilein the 2019 protests, it was 24.112.5. Research Designdaily data since 2018. I use the data to construct discrete treatment variables. Figures2.1 and 2.2 illustrate the geographic distribution of treated and untreated municipalitiesby type of protest. The treatment variable Violent protest equals 1 when a municipalityreports a violent protest involving the CNP and 0 when it reports no protest (neitherviolent nor nonviolent) involving the CNP. I build the Nonviolent protest variable in asimilar way, assigning 1 to municipalities reporting nonviolent protests involving the CNPand 0 to those unexposed to protests involving the CNP. This coding scheme allows me tohave a subset of municipalities as a pure control group.Figure 2.1: Municipalities Exposed to Nonviolent Protests.Note. This map shows municipalities exposed and unexposed to nonviolent protests beforeand after the national strike (April 2021). Only protests where the CNP participated areconsidered.122.5. Research DesignFigure 2.2: Municipalities Exposed to Violent Protests.Note. This map shows municipalities exposed and unexposed to violent protests beforeand after the national strike (April 2021). Only protests where the CNP participated areconsidered.One important limitation of ACLED’s data is the lack of information about the sizeof the demonstrations. However, as ACLED relies on newspaper reports, I assume, basedon existing evidence (e.g., McCarthy, McPhail and Smith, 1996; McPhail and McCarthy,2004), that the size of registered demonstrations in the database was significant enough todraw media attention. For example, the Global Protest Tracker by the Carnegie Endow-ment for International Peace reports that, at the country level, more than 10,000 peopleparticipated in the 2021 protests, roughly ten times more than the number of people whojoined the 2020 protest contesting police brutality.Because ACLED relies on newspaper reports to classify events, there might be concernsabout measurement bias in ACLED’s classification. To validate ACLED’s classification, Iconducted two checks. First, I manually coded a random selection of 200 reports whereACLED classified events as violent. Based on my coding scheme, ACLED accurately clas-sified 94.5% of violent events. Second, I collected data from the Superintendence of Surveil-lance and Private Security, a public institution overseeing private security and surveillanceservices in Colombia.10 The data include reported o↵enses by private security organiza-tions at the municipality-month level. To run the validation check, I focus on o↵enses thatthe Oce of the Attorney General of Colombia classified as related to protests. If ACLEDcorrectly classified events, I should observe that municipalities exposed to violent protestsare more likely to have protest-related reported o↵enses relative to municipalities without10Private security services refer to activities provided to individuals or private organizations by legalfirms. In contrast to vigilante or self-defense organizations, these firms can only conduct activities with agovernment license and are supervised by a government institution.132.5. Research Designthese events. In fact, I find that municipalities with violent protests are more likely to havereported o↵enses than municipalities without protests ( = 0.41, p-value < 0.01). Reas-suringly, the likelihood of reporting o↵enses is lower among municipalities with nonviolentprotests than municipalities without protests ( = 0.06, p-value < 0.01).11In my theory, I argued that business elites concede to violent protests by providingjobs. While employment would be the ideal outcome variable, such data is unavailableat the municipal level. Therefore, I rely on labor demand data. Specifically, I collectedadministrative data from the Employment Service Unit (ESU) at the Ministry of Labor inColombia to create my dependent variable. ESU records job vacancy data, a standard mea-sure of labor demand, by municipality and month since January 2015.12 Given ACLED’sdata time coverage, I used ESU’s data since 2018. I divide these data by population at themunicipal-year level. Additionally, I constructed a similar variable by dividing number ofjob vacancies by the number of working-age population (15 to 64).13Since the municipal-level data do not specify whether the employer is public or private,there may be validity concerns with my dependent variable. It could capture not onlythe response of business elites but also governments’ response through public job creation.However, according to information privately provided by the Special Administrative Unit ofthe Public Employment Service, 97.8% of employers posting vacancies on the ESU platformare private firms, 1.4% are public firms, and 0.8% are public-private companies. Table 2.1presents the description and distribution of the private employers generating the labordemand I use as the outcome.Table 2.1: Description of Firms in SampleSize Proportion Num. EmployeesBig 0.09 > 200Medium 0.17 51 - 200Small 0.30 11 - 50Micro 0.44 2 - 10Another concern might be that employers posting vacancies could be systematicallydi↵erent from those not reporting them. However, existing law requires all employers topost job vacancies on the ESU platform.I use a set of control variables to address concerns that variation in my outcome is drivenby an overall post-pandemic or post-unrest economic reactivation and also investigate the11The validation check focuses on infractions reported during April and May 2021. See regression tablein Appendix A.3.12Importantly, the data include short-duration vacancies, i.e., those filled in less than a month.13Although unemployment data is available for fewer than 3% of municipalities, I find reassuringly thatthe number of job vacancies is negatively correlated with the unemployment rate (r = 0.15, p-value< 0.01), which alleviates concerns about the validity of the outcome.142.5. Research Designrobustness of my findings.14 One potential concern is that variation in labor demand,measured as job vacancies per capita, might be driven by rehires or a general post-unrestor post-pandemic economic recovery. To address this, I control for the number of jobs lostin the private sector during the year leading up to the April 2021 unrest, using private datafrom Colombia’s Ministry of Health and Social Protection. Additionally, I control for thebaseline (logged) number of COVID-19 cases recorded by the National Institute of Healthin Colombia.I employ municipality-year level data published by the National Department for Plan-ning (DNP) to control for (logged) urban population, high-school education coverage,central transfers per capita, homicide rate per 100,000 inhabitants, share of Venezuelanmigrants in the population, share of internal migrants in the population, value added percapita as a percentage of the national average, and (logged) number of people classified aspoor by the System for the Identification of Potential Beneficiaries of Social Programs (SIS-BEN). I also include as covariates the number of police stations (per capita) reported bythe National Police of Colombia, the left-wing vote share in the 2018 presidential elections,available in the National Civil Registry, the labor supply (per working age population)reported by the ESU. Importantly, the hypothesized e↵ect of violent protests might beconfounded by the predominant type of industry in the municipality. Municipalities thatexperienced violent protests may be the ones that have the industries that create morejobs. Although data at the municipal level for the number of jobs created by industry isunavailable, I use data on the percentage of value added by economic activity, as reportedby the DNP. The value added measures the higher value created in the production processdue to the combination of factors, including labor. Therefore, I control for the percent-age of value added by the service sector, which is the one that contributed the most tomunicipalities’ total value added during the period under study.I measure covariates at baseline, i.e., before the national strike, to prevent post-treatmentbias. I interact these variables with an indicator (at the month level) to allow their e↵ectsto change over time.14I log variables that are not in rates or normalized by population (e.g., per capita) to correct the skewnessof the data.152.5. Research Design2.5.2 Empirical Strategy0.00.10.20.30.42018 2019 2020 April 2021 2022Treated units (share)Protest Violent NonviolentFigure 2.3: Overtime Exposure to ProtestsTo test the e↵ect of violent and nonviolent protests on private labor demand, I exploit thefact that the CNP called for a national strike on a specific date: April 28, 2021. As Figure2.3 illustrates, no protest involving the CNP occurred before this date, and there was nostaggered treatment. Because of this particular setting, I employ an event study researchdesign using municipal-month variation. This empirical strategy allows me to compare thechange in the outcome of interest before and after the national strike between treated anduntreated municipalities. Given that some municipalities were exposed to violent protests(6.1% municipalities), others to nonviolent protests (17.2% municipalities), and othersremained untreated (76.7% municipalities), I estimate two di↵erent models to ensure thatI compare units treated with violent/nonviolent protests against untreated units.yit = ↵i + t +kX⌧=k⌧ [Violent Protesti] +kX⌧=k⌘⌧ [Zi] + ✏it, (2.1)yit = ↵i + t +kX⌧=k⌧ [Nonviolent Protesti] +kX⌧=k⌘⌧ [Zi] + ✏it, (2.2)where y is number of posted vacancies per capita in municipality i and month t.Violent Protesti and Nonviolent Protesti are discrete variables equal 1 when a munici-pality reports a violent/nonviolent protest involving the CNP and 0 when it reports nooccurrence of protest (i.e., neither violent nor violent).15 The indicator variable ⌧ rangesfrom 39 months away from the national strike to 18 months after this event. c is a vector ofcontrols measured at baseline and interacted with ⌧ . ↵i are municipality fixed e↵ects, t areyear-month fixed e↵ects. The former allows me to control for time-invariant characteristics15I use a discrete rather than a continuous treatment variable because the latter requires a strongerparallel trends assumption (see Callaway, Goodman-Bacon and Sant’Anna, 2021).162.6. Resultsof each municipality, and the latter accounts for potential time-varying confounders thatmight a↵ect municipalities in a similar way. I follow Cameron, Gelbach and Miller (2011)and cluster the error term ✏it two-way by municipality and department-year-month. Inthis way, I allow for autocorrelation of the error term within each municipality and spatialcorrelation within the same department and month in a given year. The coecients ofinterest are ⌧ and ⌧ . These coecients capture the e↵ect of protests on labor demand.The identifying assumption in equations 2.1 and 2.2 is that absent the national strike,the di↵erence in outcomes between treated and untreated units, i.e., municipalities exposedto violent/nonviolent protests and unexposed to either, would not have changed after thenational strike, conditional on baseline controls and fixed e↵ects. If the parallel trendassumption holds, I should observe that the coecients ⌧ before the event are statisticallyindistinguishable from 0.The main specifications are equations 2.1 and 2.2. However, I also estimate a modelincluding the two main independent variables (i.e., violent and nonviolent protest) in thesame regression, thus using the full sample of municipalities. Although this approachprevents me from having a pure control group,16 it allows me to formally show the dif-ference between the e↵ect of violent and nonviolent protests. Using the complete set ofmunicipalities, I estimate the following model:yit = ↵i + t +kX⌧=k1⌧ [Violent Protesti]+kX⌧=k2⌧ [Nonviolent Protesti] +kX⌧=k⌘⌧ [Zi] + ✏it (2.3)This model keeps the same definitions as the models explained above. However, nowViolent Protesti equals 0 when a municipality reports no occurrence of protest or exposureto nonviolent protests, and Nonviolent Protesti equals 0 when a municipality reports nooccurrence of protest or exposure to violent protests. Here, the expectations are that⌧ > 0, ⌧ = 0, and ⌧ > ⌧ post-treatment.2.6 ResultsOverall, I find results consistent with my theory. Figure 2.4 reports results from the mainspecifications (equations 2.1 and 2.2). Panels (A) and (C) show point estimates and 95%confidence intervals from models that exclude the vector of covariates and only control16To avoid perfect collinearity between the two variables, the control group in the violent protest treatmentmust include municipalities with nonviolent protests. Similarly, the control group in the nonviolent protestmust contain municipalities exposed to violent protests as well as municipalities with violent and nonviolentprotests. I report the coding scheme in Table A.5 in the Appendix172.6. Resultsfor the logged number of COVID-19 cases, a relevant confounder not captured by thefixed e↵ects given the dynamic evolution of the disease across municipalities and over time.Panel (A) shows a clear increase in vacancies per capita after the protest in April 2021. Thepoint estimate of every period after the event is positive, and most of them are statisticallysignificant. Although the plotted regression coecients are small, the e↵ect of violentprotests is sizeable when compared with labor demand at baseline (0.0006). For example,a regression coecient of 0.0003 indicates a 50% increase in vacancies per capita relative tovacancies created at baseline. In contrast, panel (C) shows negligible e↵ects of nonviolentprotests on labor demand.Importantly, before the event (April 2021), the di↵erence in vacancies per capita be-tween municipalities treated and untreated with violent/nonviolent protests was statisti-cally insignificant for most periods, thus attenuating concerns related to parallel trendsassumption and possible seasonal e↵ects driving the results. Because the parallel trendsare a key assumption in my main empirical models, I provide further empirical evidence inAppendix (A.7.5) supporting it. Specifically, I use a generalized synthetic control methoddesigned to address and mitigate plausible violations to the parallel trends (see Xu, 2017).This method addresses potential outcomes as a missing data problem and imputes counter-factuals for each treated unit using information from the control group (i.e., municipalitieswithout protests), thus conditioning on well-matching pre-trends. This approach providesmore reliable estimates of the average treatment e↵ect on the treated (ATT) when thereare unobserved cross-unit time-varying confounders (Liu, Wang and Xu, 2024). Reassur-ingly, results from the generalized synthetic control are consistent with y main findings andsupport the parallel trends assumption behind my empirical strategy (see figures A.13 andA.14).Panels (B) and (D) of Figure 2.4 show results from the main empirical specification, thusincluding the vector of controls. Results are consistent with those presented in panels (A)and (C), and the point estimates are more precisely estimated. In the Appendix, I show thatelites’ concessions are concentrated in municipalities with labor-intensive economies (FigureA.2). Notably, subgroup analyses show that post-treatment labor demand in municipalitiesexposed to violent protests focuses on people with secondary education (Figure A.3), thosewith a year or less of previous work experience (Figure A.4), and on low-paid labor (FigureA.5).182.6. ResultsFigure 2.4: Estimated E↵ect of Violent/Nonviolent Protests on Job Vacancies Per Capita.Note. Estimated e↵ect of event (violent or nonviolent protest) on job vacancies per capita.Regressions include municipality and year-month fixed e↵ects. Models for panels (A) and(C) control for logged number of COVID-19 cases interacted with ⌧ . Models for panels (B)and (D) include the complete vector of controls at baseline interacted with ⌧ . Standarderrors clustered by municipality and department-year-month. Confidence intervals: 95%Figure 2.5 reports results from a regression model estimated following equation 2.3, thususing the complete sample of municipalities. It shows the expected post-treatment positivee↵ect of violent protests (⌧ > 0), the null e↵ect of nonviolent protests (⌧ = 0), andthe statistically significant di↵erence between the e↵ect of violent and nonviolent protestson labor demand post-treatment (⌧ > ⌧ ). In the Appendix (Figure A.1), I furtherdemonstrate that violent protests rather than the combination of violent and nonviolentprotests increase labor demand.192.6. ResultsFigure 2.5: Estimated E↵ect of Violent/Nonviolent Protests on Job Vacancies Per Capita- Complete Sample.Note. Estimated e↵ect of event (violent or nonviolent protest) on job vacancies per capitausing all municipalities. The regression includes controls at baseline interacted with ⌧ andmunicipality and year-month fixed e↵ects. Standard errors clustered by municipality anddepartment-year-month. Confidence intervals: 95%2.6.1 RobustnessIn the Appendix, I present additional tests to demonstrate the robustness of my find-ings. First, Figure A.6 demonstrates that results are virtually the same when I excludethe municipality considered the epicenter of the violent protests from the analysis. Sec-ond, Figure A.7 reports results using department-month fixed e↵ects and shows they arerobust to time-varying regional changes. Third, Figure A.8 shows that results are un-changed when using vacancies per working age population rather than per capita as theoutcome variable. Fourth, as a more conservative approach to estimating the e↵ect ofviolent/nonviolent protests, I construct a geographic matching design that allows me tocontrol for potential (unobserved) confounders that might change with distance (e.g., levelof development). Figure A.10 shows that the substantive findings hold. Fifth, I addressconcerns about the potential spillover e↵ects of the treatment by conducting spatial donutanalyses. Specifically, I remove control municipalities within 100km and then 200km of agiven treated municipality, thus dropping from the analyses municipalities that are mostlikely to be exposed to spatial spillover. The results are consistent with the main findings(figures A.11 and A.12 in the Appendix). Sixth, I implement a synthetic control method,which provides more reliable estimates of the average treatment e↵ect on the treated (ATT)when there are unobserved cross-unit time-varying confounders. Results are in line withmy main findings (figures A.13 and A.14).202.6. Results2.6.2 Placebo TestsTo further validate my findings, I conduct two types of placebo tests (see Eggers, Tun˜o´nand Dafoe, 2023). Specifically, I estimate the e↵ect of the national protest of November2019 on vacancies per capita (placebo treatment test) and the e↵ect of the national protestof April 2021 on labor demand in the public sector (placebo outcome test). In both tests, Iuse the event study design specified in equations 2.1 and 2.2, thus keeping the key featuresof the analyses to avoid violating the core assumptions of the research design.17As explained in the Context section, the 2019 and 2021 protests had related socialdemands. However, the latter had higher levels of violence and media coverage. In fact,Panel (A) in Figure 2.7 shows that the number of newspaper articles that includes theword protest and its variations (e.g., protesters) was higher in April 2021 than in November2019. Importantly, Panel (B) illustrates that the word ‘violence’ becomes a more distinctivefeature in the 2021 articles relative to other years, keeping constant newspaper outlets andnumber of words per article.18 Reassuringly, Figure 2.6 shows that, while the e↵ect ofthe 2019 violent protest on vacancies per capita has a positive direction, it does not reachstatistical significance. Likewise, Figure 2.6 shows a null e↵ect of nonviolent protests.17I adjust the vector of controls in the placebo treatment test to account for the di↵erent year and avoidcontrolling for post-treatment variables like the number of COVID-19 cases.18I apply a recent method developed by Rodriguez, Spirling and Stewart (2023) to understand what aword means in di↵erent contexts. Here, I utilize this method to identify changes in the understanding ofprotests in the Colombian context across the years. In the Appendix (A.10), I detail the application of thismethod.212.6. ResultsFigure 2.6: Estimated E↵ect of the 2019 Violent/Nonviolent Protests on Job VacanciesPer Capita (Placebo Treatment).Note. Estimated e↵ect of 2019 event (violent or nonviolent protest) on job vacancies percapita. Regressions include municipality and year-month fixed e↵ects as well as baselinecontrols interacted with time. Standard errors clustered by municipality and department-year-month. Confidence intervals: 95%222.6. ResultsFigure 2.7: Media Coverage of Protests.Note. Panel (A) shows the number of articles that include words starting with protest*published in printed national newspapers between January 2018 and December 2022. Panel(B) plots the scalars (Norm of ˆs) obtained from an embedding regression with theirbootstrapped 95% CIs. Higher values mean the word ‘violence’ is more embedded innewspaper articles.My theory contends that violent protests threaten business elites and hypothesizesconcessions by them. If my theory correctly predicts that business elites, rather than thestate, concede to violent protests by increasing labor demand, I should observe a null e↵ectof violent protests on job vacancies in the public sector. At the same time, business elitesshould not rely on state institutions to channel their concessions because the state has alow capacity to address the unrest. Since my theory makes no predictions about the stateresponse to social discontent expressed through nonviolent protests, the placebo outcometest focuses on violent protests.To conduct the placebo outcome test, I collected administrative data from the ESU tobuild the placebo outcome: government job vacancies per capita. Due to data availability, Irestrict the analysis to 2020-2022. Given that the data is disaggregated at the municipality-month level, the restricted period I analyze does not undermine the statistical power ofthe analysis. In line with my theory, Figure 2.8 shows null e↵ects of violent protests ongovernment job vacancies per capita.232.7. MechanismFigure 2.8: Estimated E↵ect of Violent Protests on Government Job Vacancies Per Capita(Placebo Outcome).Note. Estimated e↵ect of event (violent protest) on government job vacancies per capita.Regressions include municipality and year-month fixed e↵ects as well as baseline controlsinteracted with time. Standard errors clustered by municipality and department-year-month. Confidence intervals: 95%2.7 MechanismWhy does violent social unrest increase labor demand? In my argument, I proposed acausal mechanism that implies a positive e↵ect of violent protests on labor demand: vi-olence creates economic uncertainty and costs to elites, who decide to coordinate theirresponse through business associations and concede by providing jobs because they cannotrely on the state and fear further destruction. To validate this mechanism and illustrate mytheory, I conducted 22 semistructured interviews with business elites situated in treatedand untreated Colombian municipalities, as defined in my database. Elites were selectedutilizing non-probability sampling methods in municipalities that are capital cities andhave business associations.19 To complement the qualitative evidence, I collected 50 busi-ness statements from 13 business associations located in municipalities exposed to violentprotests (I provide details on the data collection in the Appendix).Interviews conducted in Cali, Colombia, the epicenter of the social unrest, lay out thee↵ects of violent protests. Cali is Colombia’s third-largest city and an industrial center. Itis located between the western and central cordilleras. This particular location has isolatedCali from other urban centers like Bogota´, the capital of Colombia. Given the politicalcentralism of the Colombian state, Cali’s development has historically relied on businesselites, who have usually acted as local technocrats and excluded popular sectors fromeconomic growth (O↵ner, 2019). During the national strike of April 2021, Cali became thefocal point of social unrest,20 and protests became one of the main concerns among firms,as Figure 2.9 shows. Anecdotal evidence suggests that poverty and inequality, exacerbated19In the Appendix A.11.2, I present the questions I used to structure the interviews.20https://tinyurl.com/8vjdfpcz242.7. Mechanismby the COVID-19 pandemic, played a crucial role in making Cali the epicenter of theviolent protests (Chang and Garc´ıa-Montoya, 2021). In fact, between 2019 and 2020, thepopulation of Cali living in poverty increased by 67.3%, and the level of inequality increasedby 12.5% (DANE, 2022).Figure 2.9: Main Problems among Firms.Note. This figure shows the main problems among firms in Cali, Colombia, and the pro-portion of firms reporting a given problem as the main one. Panel (A) reports the overalldistribution of main problems for 2014-2022. Panel (B) shows the distribution of mainproblems by year.2.7.1 Economic Uncertainty and CostsIn my theory, I argue that violent protests create economic uncertainty and costs. More-over, I argue that these events are more likely to happen in places with low state capacity.In fact, during the social unrest, Esteban Piedrahita, the then Cali Chamber of Commerce(CCC) president, warned that the region would lose the equivalent of 4% of its GDP dueto violent protests. He also alerted that 47% of firms were operating at reduced capacity(Garc´ıa Sierra, 2021). In my own interviews, several respondents claim that the state wasinsucient to address the unrest. For example, the CEO of a large firm in Cali said that“there was a feeling of fear, even at the personal level. We fear for our personal safety be-cause of the magnitude of the situation [. . . ] We had a mayor completely disconnected from252.7. Mechanismthe city [...], and the president never connected with the country. President Duque nevermanaged to connect with the needs of the country; we did not feel him close.” (Onlineinterview, June 28, 2023).212.7.2 Coordination among ElitesIn the theoretical section, I posit that elites can more easily solve collective action prob-lems when they face violent threats. Indeed, as one interviewee stated, “no businessmancould ignore the situation because everyone was being a↵ected.” (Online interview, May24, 2023). Importantly, my theory explains that business associations ease coordinationamong elites. Business associations allow elites to overcome collective action problems andcoordinate a response. Several interviews illustrate this point. One business elite recountedthat a group of businessmen was concerned about the interrupted economic activity causedby the violent protests. The group decided to meet to discuss the situation and join e↵ortsto overcome it. Another businessman explained that after the meeting, they decided to gethelp from Propac´ıfico22 to appeal to more businessmen. Indeed, the Executive Director ofPropac´ıfico explained that elites approached them to coordinate the response because theyunderstood that they needed to have a sizeable impact: “If they had gone their separateways, they would not have been able to add up and make an impact.” (Online interview,July 5, 2023).2.7.3 Elites’ ConcessionsDid violence cause elites’ concessions? My theoretical prediction is that the violent unrestleads elites to concede by increasing labor demand. One of the CEOs I interviewed explicitlystated that the violent dimension of the protests motivated their response: “It was theviolence what made us respond. When you see deaths and that even the upper classes getarmed, you realize it is some sort of civil war. We hit rock bottom and realized that wehad to do something di↵erent [. . . ] Had we not done anything, the violence would haveescalated.” (Online interview, June 23, 2023). Similarly, another interviewee stated that“lots of protesters demanded employment, and amid despair, business elites decided toopen job vacancies.” (Online interview, May 16, 2023). Although some business ownersdeclared that creating more jobs was unfeasible due to the limited operation capacity, andothers had to reduce the number of workers, a common opinion among the intervieweeswas that elites must address violent social unrest by creating jobs. As the CEO of a firmthat has been operating for 48 years put it, “the social unrest is solved by creating jobs.”(Online interview, September 20, 2023).21After applying natural language processing to the business statements, I found that “fear” is the mostsalient emotion revealed in the context of protest-related terms. (see Appendix A.10.2).22Propac´ıfico is an association that articulates business elites operating in Cali.262.7. MechanismTo validate the collected qualitative evidence suggesting that business elites concedeto violent protests, I use survey data at the firm-level privately provided by the CCC andestimate a repeated cross-section model. The CCC is the only chamber of commerce inColombia with biannual measures of expected increase in workers and main problem facedby firms from 2014 to 2022. I use these data to estimate the association between the mostimportant problem (e.g., taxes or protests) and expected increase in workers, before andafter the 2021 event.23 Figure 2.10 shows estimates from four di↵erent regressions, wherethe outcome variable is likelihood of increasing number of employees and the independentvariable is the given problem. It shows that pre-2021 national strike event, firms reportingprotests as their main problem were less likely to expect an increase in the number ofemployees the following semester. Yet, the direction of this association becomes positiveand significant after the 2021 national strike. While the estimated positive e↵ect of protestspost-event could be driven by an economic recovery after the pandemic, regressions controlfor employment separation in the previous semester (i.e., layo↵s) and expected increase ingross sales, thus ensuring that protests, rather than the anticipation of economic recovery,a↵ect the likelihood of increasing the number of employees. Importantly, protests are theonly firms’ self-reported problem that a↵ect their likelihood of increasing the labor force.My argument states that elites’ concessions are reactive. Yet, elites might consider pre-emptive long-term strategies such as investing in human capital. Using data reported byCompromiso Valle (Valley Commitment), an NGO created to respond to the 2021 socialunrest in Cali, I present suggestive evidence on elites’ preemptive strategies. CompromisoValle relies on private donations to run social programs focused on low-income populations.Figure 2.11 illustrates the increasing private donations to this NGO (panel A). Indeed, asof May 2023, more than 400 firms contribute to Compromiso Valle (Figure 2.11, panel B),whose ongoing programs include food security, employment, and education. In an inter-view with Compromiso Valle’s director, she explained the reasoning behind this initiative:“What Cali experienced was so strong and intense that elites understood they had to dothings di↵erent [. . . ] Firms cannot do well in an environment that is not well [. . . ] We areall responsible.” (Online interview, May 16, 2023).23In the Appendix, I specify the model and variables I employ in the reported analyses.272.7. MechanismFigure 2.10: E↵ect of Most Important Problem on Likelihood of Increasing Number ofEmployees.Note. This figure uses CCC firm-level data and shows the association between the salienceof a given problem (i.e., protests, demand, COVID-19, or competition) and the probabilitythat firms increase the number of employees, pre- and post-2021 National Strike. OLS mod-els include control variables. Robust standard errors are clustered at the sector-semesterlevel. Confidence intervals: 95%. Regressions reported in table A.7 in SI.Figure 2.11: Elites’ Concessions.Note. Panel (A) shows the cumulative private donations in billions (COP) made to theNGO Compromiso Valle. Panel (B) plots the cumulative number of corporate donors inthis NGO. The dotted vertical line indicates the month when the event occurred.My theory claims that while increasing employment is costly to elites, this cost might bebearable because rioters are likely low-skilled, i.e., the hiring cost is low. In fact, a specialreport by Cali’s municipal government documents the socioeconomic profile of protestersand shows that they tended to be young (between 18 and 40 years old), without post-secondary education, and unemployed (Observatorio de Paz y Cultura Ciudadana, 2022).Consistently, the subgroup analyses reported in Appendix A.6 show that after the socialunrest, the labor demand in municipalities exposed to violent protests focused on people282.8. Alternative Explanationswith low levels of education and labor experience, and was therefore low-paid. Theseresults are consistent with what two interviewees from well-known companies explained:Due to security concerns, firms used to have a hiring protocol that excluded people fromspecific neighborhoods from the recruitment process. However, after the social unrest in2021, firms relaxed this protocol: “Senior management reached out to human resourcesdepartments to make the selection process more flexible and provide opportunities to thispopulation.” (Online interview, August 24, 2023).2.8 Alternative ExplanationsAn alternative explanation for increasing labor demand after the violent unrest is an overallpost-pandemic economic recovery. Since the unrest occurred at the end of a global pan-demic, the estimated positive e↵ect of violent protests might be driven by the reactivationof economic activities. It is well known that the pandemic disrupted economic activities.Hundreds of people stopped working due to the virus, and several jobs were destroyed asan e↵ect of the pandemic. Hence, an alternative plausible explanation of my findings isthat once the pandemic started waning, business elites decided to increase labor demandto anticipate or respond to an economic recovery, which should be higher in places morea↵ected by the pandemic. Three pieces of evidence are consistent with my theory ratherthan the post-pandemic recovery account. First, controlling for number of COVID-19 casesat baseline does not a↵ect the direction of the estimated post-unrest event, as my mainresults show (Figure 2.4). Second, results are virtually the same when I control for numberof COVID-19 cases among the working-age population (see A.9). Third, and perhaps moreimportantly, my findings hold when I include a proxy for job loss during the pandemicas a control in the regression analyses. Specifically, I use private data provided by theMinistry of Health and Social Protection of Colombia on the number of all formal workerscontributing to health and pension schemes in private firms. Using these data, I estimatethe number of jobs lost between April 2020 and April 2021. The positive e↵ect of violentprotests on labor demand remains significant when controlling for jobs lost, mitigating con-cerns that the observed e↵ects are driven by rehiring or a mechanical economic recovery(Figure 2.4).A second alternative is that the government concedes to violent protests through busi-ness elites. The literature shows that governments can use non-state actors such as businessorganizations to channel resources to specific areas (Palmer-Rubin, 2016). Therefore, myfindings could be capturing government concessions via private firms rather than demon-strating the theorized business elites’ behavior. Specifically, the government could assigncontracts to private actors who can further hire individuals to perform the contracted tasks.However, I provide empirical evidence that rules out this possibility. I use data from theElectronic System for Public Procurement (SECOP in Spanish) on the number and valueof contracts assigned by the State to private actors and demonstrate that the government292.9. Scope Conditionsdoes not concede to nonviolent or violent protests via contracts (see Figure A.15), and thevalue of the assigned contracts does not increase in response to nonviolent/violent protests(see Figure A.16).2.9 Scope ConditionsMy theory regarding the positive e↵ect of violent protests on labor demand depends crit-ically on the redistributive e↵ect of the shock. Specifically, the shock must a↵ect peoplein the lower part of the income distribution in particular, thus exacerbating poverty andinequality. Yet, several conditions make elites’ concessions to violent protests most likely.First, elites must belong to business associations to coordinate their response. Suchassociations must be able to bring elites together and provide them with a space for ex-changing information and creating commitments among them. This condition is more likelyin democratic countries (Martin and Swank, 2008; Castan˜eda, 2017). Second, elites musthold physical (immobile) rather than financial (mobile) capital. Elites must find it hard toprotect their physical assets against protests and be credibly threatened by violent events.This condition is more likely in low- and middle-income settings, but there is variation evenwithin these environments (Schneider, 2009). Third, the state must have a low capacityto address economic hardship among people. Elites’ strategic response requires that stateslack policies to attenuate financial distress in the population. This condition is more likelyin developing democracies (Mares, 2005). Finally, violence and destruction are necessaryfor my theory to operate; violence imposes real costs on elites and disrupts their economicactivities.2.10 ConclusionUnder what conditions do economic elites concede to social unrest? Violent unrest dis-rupts the economic activities of business elites. While these elites have tools to addressand respond to economically motivated unrest, most existing literature has focused on howpolitical elites and public opinion respond to protests. Despite the growing political influ-ence of business elites, their responses to social unrest remain understudied. In this article,I have proposed a theory of elites’ concessions to violent unrest in developing democracies.I argued that violent social unrest driven by economic discontent generates economic un-certainty and imposes costs on business elites. Because elites cannot rely on the state dueto its limited capacity to address the violent unrest, they intervene to restore social orderand prevent further destruction and disruption to their economic activities. Specifically, Ihypothesize that elites respond to rioters’ economic demands by o↵ering jobs as a meansof appeasing violent social unrest.Using Colombian administrative data from the 2018–2022 period, I find that labordemand increased in municipalities a↵ected by the 2021 violent social unrest but not in302.10. Conclusionthose where the unrest was nonviolent. Consistent with my theory, I find that firms highlya↵ected by the 2021 violent protests were more likely to hire additional workers afterthe unrest but not beforehand. Qualitative accounts of business elites complement thesefindings and support the uncertainty and cost mechanism. Together, this evidence suggeststhat business elites can compensate for the limited state capacity to address socioeconomicdemands. Specifically, elites can leverage their distinctive ability to influence the labormarket to create jobs and restore social order.This article focuses on protests as a widespread expression of social unrest and providesevidence for a single country. Future research could revisit my argument by exploring busi-ness elites’ concessions to violent social unrest across other developing democracies. Suchresearch could complement my evidence and, perhaps, uncover new causal mechanisms.Likewise, future research should explore whether economic elites claim credit for the con-cessions they provide and try to capitalize on them in the political arena.My findings have important implications for understanding how economic elites actas pivotal actors in mediating between civil society and the state. While the state oftenstruggles to address socioeconomic grievances, especially in developing democracies withlimited institutional capacity, business elites emerge as key agents in responding to un-rest. By leveraging their economic power and influence over the labor market, they cancreate immediate and visible solutions that di↵use tensions. However, this raises norma-tive questions about the role of unelected actors in shaping redistributive policies and thesustainability of elite-led solutions in the absence of broader structural reforms.Finally, my findings suggest that the dynamics of social unrest and elite responses mayvary based on the type of unrest and the economic context. While this study highlights theunique role of violent protests in eliciting concessions from elites, further work could explorewhether nonviolent protests could achieve similar outcomes under di↵erent institutionalarrangements or economic conditions. Additionally, understanding the long-term e↵ects ofelite concessions on inequality, employment stability, and public trust in institutions couldprovide valuable insights into the broader societal impact of these dynamics.31Chapter 3Government Ideology and Supportfor Redistribution among theWealthywith Fabio Resmini3.1 IntroductionIn Spring 2021, ProBogota´ – a business organization including important private companiesin Colombia – publicly urged the right-wing government of Iva´n Duque to pass a moreprogressive tax reform to raise social spending and alleviate the economic hardships facedby low-income citizens during the COVID-19 pandemic (El Espectador, 2021). Similarly,the president of the National Business Association of Colombia (ANDI) –the country’smost important private sector association– recommended the Duque government that thetax reform be designed to make the wealthy pay more, without a↵ecting other social classes(El Tiempo, 2021). However, a year later these same representatives of business and high-income sectors were quick to oppose a progressive tax reform under newly elected left-wingpresident Gustavo Petro. Surprisingly, Petro’s reform was largely in line with what businessorganizations had advocated for a year before. What explains these di↵erent postures inthe face of similar policy proposals? When and why do the wealthy support redistribution?In this paper, we examine the e↵ect of who is in power on the wealthy’s preferences forredistribution. Two bodies of scholarship provide leads for answering this question. On theone hand, canonical political economy models assume that individuals are self-interestedand aim to maximize their wealth (see Meltzer and Richard, 1981; Alesina and Rodrik,1994): redistributive preferences are a function of material self-interest and decrease withincome. One the other hand, partisan motivated reasoning theory suggests that parti-sanship is a crucial factor shaping preferences over redistribution (Bartels, 2008; Bafumiand Shapiro, 2009), especially when increased taxation targets the a✏uent (Ballard-Rosa,Martin and Scheve, 2017). These e↵ects have been detected even in weak partisan contexts(Samuels and Zucco Jr, 2014).We lean on the literature that shows that the wealthy support redistribution under theRight (Fairfield and Garay, 2017; Flores-Mac´ıas, 2022) to bridge the two bodies of schol-323.1. Introductionarship.24 Consistent with a material self-interest explanation, we posit that, in contexts ofhigh and increasing inequality, the wealthy might be in favor of some form of controlledredistribution to avoid potentially bigger losses in the future, such as more radical formsof redistribution. Drawing on the partisan e↵ects literature, we further argue that thewealthy expect redistribution to be controlled (i.e., less costly, more reliable, and morepredictable) when a right-wing government is in power, and they are thus more likely tosupport redistribution under the Right than under the Left.We theorize three possible considerations that might explain why the wealthy see re-distribution under the Right as a more “controlled” form of redistribution (vis-a`-vis whenthe Left enacts it) and thus might motivate them to support it: (1) uncertainty - i.e., thewealthy are less uncertain about the upper bound of redistributive policies when the Rightis in power vis-a`-vis the Left; (2) eciency - i.e., the wealthy believe that a right-winggovernment is more likely than a left-wing one to actually redistribute to the poor thetaxes collected from the rich; and (3) instability - i.e., the wealthy consider the Right lesslikely than the Left to generate macroeconomic instability while implementing redistribu-tive policies.To test our theory, we conducted pre-registered online vignette survey experiments withwealthy individuals in Colombia during the 2022 presidential election. This case study isan ideal setting to test our argument for four reasons. First, inequality in Colombia hasbeen increasing over the last five years and it has been a central issue in the political debatesince the 2021 protests.25 Second, the electoral campaign made the Left-Right cleavagemore salient, which makes the ideological positioning of the prospective government a keyconsideration in respondents’ minds. Third, Colombia has a socioeconomic stratificationsystem that allows us to sample wealthy individuals. Fourth, Colombia is an extremelylow partisan identification context, which allows us to test the limits of the partisan e↵ectstheory.Results are consistent with our theory and undermine the material self-interest assump-tion of seminal political economy models. We find that who is in power (i.e., the Rightor the Left) a↵ects preferences for redistribution among the wealthy, even in low partisan-identification contexts. In particular, we show that the a✏uent are more likely to supportredistribution under the Right than the Left. We find that expectations of eciency andeconomic instability are higher and lower, respectively, with a prospective right-wing gov-ernment compared to a left-wing one. We find similar e↵ects for non-redistributive policies:wealthy individuals are more likely to support restrictive migration policies and the expan-sion of the police force when these policies are proposed by a right-wing government. Theideologically diversified composition of our sample also allows us to test our argument onthe left-wing wealthy as well. We find heterogeneous treatment e↵ects for preferences and24We use “Right” as a broad category encompassing various types of right-wing politicians (e.g., author-itarians or libertarians) who adhere to democratic rules of electoral competition. For a discussion of thedi↵erent cases of right-wing actors in the region we study, see (Kestler, 2022).25See: https://tinyurl.com/4k5ewp4x333.2. Preferences for Redistribution in Wealthy Individualsexpectations of eciency across the Left-Right cleavage: respondents are much more likelyto support policies promoted by the candidate closer to their ideological position and ex-pect him/her to be much more ecient in redistributing to the poor the taxes collectedfrom the rich. We find similar e↵ects for non-redistributive policies. Expectations of eco-nomic instability constitute an exception, as we find that left-wing wealthy do not expect aright-wing government to create macroeconomic instability when enacting redistribution.While our findings are largely consistent with a standard party/leader cueing argu-ment, what distinguishes our work from previous cueing experimental studies on policypreferences (see, e.g., Samuels and Zucco Jr, 2014; Barber and Pope, 2019) is the fact thatthe redistributive policies we use in our experiments are explicitly and directly financiallycostly for all of our respondents. Redistribution (and particularly increasing taxation onthe rich) is a policy area where partisan/leader motivated reasoning and protection of one’sown self-interest might clash in the minds of wealthy respondents. Thus, our experimen-tal design allows us to determine whether partisan/leader motivated reasoning can leadrespondents to accept bearing the cost of a policy promoted by their preferred candidate,hence setting aside their immediate material self-interest. In a way, we are testing thelimits of party/leader cueing e↵ects: will respondents be willing to support a policy that iscostly for them if their preferred candidate is the one enacting it? This is something thatother cueing studies cannot do, either because they simply do not include raising taxes onthe rich as a policy in their experimental items (Samuels and Zucco Jr, 2014) or becausetheir sample includes respondents from social strata that would not be financially harmedby a tax increase on the rich (Barber and Pope, 2019).This article makes two important contributions. First, it contributes to the literature onpolicy preferences of wealthy individuals. We add to that body of scholarship by showingthat a✏uent people might support redistribution depending on the ideological orientationof the government in charge of implementing it. In particular, we find that the wealthy tendto be more willing to go against their immediate self-interest and support redistributionif the Right is enacting it. This article also provides evidence suggesting that the wealthyhave no fixed policy preferences in the realm of taxation and redistribution, which has adirect bearing on their immediate self-interest. Second, this article joins a recent bodyof scholarship on taxation in Latin America that explores the political factors behinddi↵erent levels of taxation (Flores-Macias, 2019; Flores-Mac´ıas, 2022). As we show, politicalpreferences and expectations of eciency and instability a↵ect support for tax increasesamong the wealthy.3.2 Preferences for Redistribution in Wealthy IndividualsDespite the abundance of research examining the redistributive preferences of the generalpublic, there is a scarcity of empirical studies that focus on the preferences of a✏uent indi-viduals. Limited data availability and survey underreporting have prevented scholars from343.2. Preferences for Redistribution in Wealthy Individualsinvestigating the policy preferences of this group. We consider the lack of studies on thepolicy preferences of the wealthy —and particularly on redistribution-– to be problematicfor two main reasons. First, we know that policy usually reflects the preferences of thewealthy (Gilens, 2012; Page, Bartels and Seawright, 2013; Persson and Sundell, 2024), yetwe rarely know what these preferences are, given the dearth of studies sampling the richbeyond the US. Second, we know that in the current historical context of great and increas-ing wealth inequality (Piketty, 2014; Zucman, 2019; Ho↵mann, Lee and Lemieux, 2020)the political clout of the wealthy is only likely to grow, making it even more necessary toinquire into their preferences. In particular, what behooves us is to discover under whatconditions they are more likely to accept and even support redistribution, given that theiropposition is often enough to prevent the adoption of such policies (Page and Seawright,2023).Two strands of literature provide insights into the redistributive preferences of thewealthy, generating opposite theoretical predictions. On the one hand, the class-basedexplanation grounded in the political economy literature predicts that the wealthy opposeredistribution because it goes against their material interests. On the other hand, thepartisanship explanation predicts that individuals’ partisanship determines their policypreferences, with left-wing individuals supporting redistribution and right-wing individualsopposing it. The wealthy are interesting to study because it is the group where the twoperspectives might collide: do the wealthy reject redistribution no matter what or doestheir partisanship play a role in shaping their preferences in such a sensitive policy area?In this section, we review the two dominant explanations and present our theory as analternative.3.2.1 Class-based ExplanationThe very common assumption in political economy models is that the wealthy opposeredistribution because it negatively a↵ects them materially (Boix, 2003b; Acemoglu andRobinson, 2006). A recent body of scholarship provides support for this assumption. Page,Bartels and Seawright (2013) find that a✏uent respondents prefer less regulation, fewersocial welfare programs, and oppose government redistribution. Using a larger sampleof a✏uent Americans, Suhay, Klasˇnja and Rivero (2021) present descriptive informationpointing in the same direction: the rich are more economically conservative than the have-nots. Lopez et al. (2020) focus on the Global South and conduct interviews with politicaland economic elites in Brazil, South Africa, and Uruguay. They find that the wealthyoppose redistribution because they see the poor as irrational and manipulable and redis-tribution towards them as a waste of money.Although the abovementioned findings suggest that the wealthy prefer less redistribu-tion, another set of studies shows that the rich do not always oppose it. Dimick, Ruedaand Stegmueller (2016) show that increases in inequality boost support for redistributionamong the rich. Rueda and Stegmueller (2016) zoom in on this association and demon-353.2. Preferences for Redistribution in Wealthy Individualsstrate that high levels of inequality trigger fear of crime among the rich, who then supportredistribution to curb inequality. Berens and von Schiller (2017) find that when trust ininstitutions increases, a✏uent people are more likely to favor progressive income taxes. De-spite these important findings, we still know relatively little about the e↵ect of incumbents’ideological orientation on support for redistribution among the rich. This paper seeks tofill this gap.3.2.2 Partisanship ExplanationThe literature on partisan-motivated reasoning provides another promising lead to answerour question. We know that the wealthy have heterogeneous partisanship preferences(Gilens, 2012; Gilens and Thal, 2018; Broockman, Ferenstein and Malhotra, 2019) andthat partisanship can influence economic behavior (McConnell et al., 2018) and individualpreferences in many policy areas, including redistribution (Bartels, 2008; Gilens, 2012).Scholarly work has shown that partisanship can even trump ideological commitments topolicy preferences (Barber and Pope, 2019). However, our knowledge on the e↵ects ofpartisanship on policy preferences is built mostly on insights from the United States, acontext with traditionally strong partisanship identification. Samuels and Zucco Jr (2014)found partisan e↵ects also in the case of Brazil, a country with comparatively lower levelsof partisan identification. What happens in the quite common case of even lower partyidentification, such as Colombia?26 This paper seeks to answer this question as well.3.2.3 Towards a Theory of Wealthy’s Support for RedistributionThe theory we put forward lies at the intersection between the two explanations justdiscussed. Our goal is, indeed, to bridge them. First, we argue that class matters andthat the material conditions of the wealthy a↵ect their calculations about whether tosupport redistribution. As the political economy literature shows, self-interested calculationcan lead the wealthy to support redistribution in order to avoid potentially bigger losses.This logic applies to contexts of high and increasing inequality, where the wealthy aremore open to conceding to redistribution to avoid social unrest or the election of radicalgovernments less prone to protect their interests (Acemoglu and Robinson, 2006).27 Weposit that, to prevent these scenarios, the wealthy support controlled redistribution, i.e.,redistribution with well-established limits that they believe would not put individual andnational economic stability at risk.26According to LAPOP 2018, 73.9% of Colombians do not identify with a party. This proportion waseven higher in 2023, arriving at 87.1%27We depart from the existing explanation that inequality increases fear of crime and therefore the wealthysupport redistribution (see Rueda and Stegmueller, 2016). As Altamirano, Berens and Ley (2020) explain,this logic implies that states have the capacity to reduce crime, which is not necessarily true in developingdemocracies.363.2. Preferences for Redistribution in Wealthy IndividualsSecond, we argue the wealthy hold di↵erent expectations about the material costs thatthey will pay from redistribution depending on the ideological orientation of the governmentenacting the redistributive process. These expectations constitute the wealthy’s concep-tion of “controlled” redistribution. We build on recent work by Flores-Mac´ıas (2022), whoargues that government ideology matters for the willingness of the wealthy to supportredistribution. Right-wing governments, he argues, enjoy greater credibility among eco-nomic elites, which contributes to overcoming the usual mistrust between the a✏uent andthe government and provides guarantees that the interests of the wealthy will be protectedduring the redistributive process. Specifically, we argue that the wealthy are more likelyto support redistribution under the Right rather than the Left because they see the formeras a more reliable option to address socioeconomic disparities.Indeed, there are several examples of redistribution under the Right in Latin Amer-ica. In Colombia, right-wing president A´lvaro Uribe (2002–2010) implemented a wealthtax in 2002 to enlarge the military and increase the provision of social services. Dur-ing his inaugural speech, Uribe declared that the wealthiest and the businessmen wouldneed to take on their shoulders new tax responsibilities. In fact, the wealthy tax wasrenewed in 2003, 2006, and 2009, and a↵ected 7000 individuals and some corporations(Rodr´ıguez-Franco, 2016). In Mexico, right-wing presidents Vicente Fox (2000–2006) andFelipe Caldero´n (2006–2012) extended health care, income support, and pensions programsto the low-income population. Importantly, Caldero´n enacted tax reform in 2007 targetingbusiness elites. In Chile, former president Sebastia´n Pin˜era (2010–2014 and 2018–2022)increased a corporate tax from 17% to 20% despite business’ opposition; it was the largesttax increase in three decades (Fairfield and Garay, 2017). In Argentina, Mauricio Macri(2015 - 2019) expanded the coverage of the conditional cash transfer program Asignacio´nUniversal por Hijo (Universal Family Allowance) to curb poverty increase (Niedzwieckiand Pribble, 2017).There are three reasons behind our argument. First, we claim that the rich are lessuncertain about the upper limit of the redistributive policy under the Right relative to theLeft. Although some left-wing governments have kept their redistributive promises, othershave exceeded them and even threatened property rights. As a consequence, the richmight be uncertain about the scope of the redistributive policy once a left-wing politicianis elected. Imagine that both the left- and right-wing candidates make the campaignpromise of increasing a progressive tax rate now standing at C by r percentage points,where C is the existing rate, and r is the proposed raise. We posit the di↵erent level ofsupport for the same policy proposal across candidates may be explained by the value thewealthy assign to r under the right-wing government vis-a`-vis the left-wing government.While the wealthy expect r to be a positive number in both cases, they do not know howmuch higher r could actually be under a left-wing government compared to a right-wingone. In other words, we expect the wealthy to be less uncertain about the upper bound ofa redistributive policy when advanced by a right-wing politician vis-a`-vis a left-wing one.Second, as recent research suggests, the wealthy value government eciency in deliver-373.3. Research Design and Dataing social policies (Holland, 2018). At the same time, the Right is usually associated witheciency given its market-oriented approach to social policies (De La O, 2015). Therefore,we argue that the wealthy are more likely to support redistribution under the Right be-cause a right-wing government increases expectations of state eciency in delivering socialpolicies.Third, the rich value macroeconomic stability (Page, Bartels and Seawright, 2013) be-cause a stable macroeconomic environment allows them to grow their wealth without majorrisks. Given that the Right prioritizes economic stability more than the Left (Sattler, 2013),we argue that the rich support redistribution under the Right because its commitment tostability limits potential costs associated with redistributive policies (e.g., inflation andincrease in public debt). Specifically, we argue that the a✏uent believe it is less likely thatthe Right put economic stability at risk when advancing redistributive policies.On the basis of these three theoretical logics, we propose the following hypotheses:H1 (Progressive taxation): Right-wing governments increase the wealthy’s support forraising progressive taxes relative to left-wing governments.H2 (Subsidies): Right-wing governments increase the wealthy’s support for social as-sistance relative to left-wing governments.H3 (Uncertainty): Right-wing governments decrease the wealthy’s level of uncertaintyabout the upper bound of a capital tax relative to left-wing governments.H4 (Eciency): Right-wing governments increase the wealthy’s expectation of stateeciency in delivering social policies relative to left-wing governments.H5 (Instability): Right-wing governments decrease the wealthy’s expectation that socialpolicies will produce economic instability relative to left-wing governments.3.3 Research Design and Data3.3.1 Cross-Country EvidenceWe start testing our theory using data from LAPOP 2018/9. We analyze whether dif-ferences in government ideology a↵ect preferences for redistribution in Latin Americancountries.28 This survey wave asks respondents whether they agree or disagree that it isfine for the rich to pay a lot in taxes but receive little in state services. As opposed to thecommon question about redistributive demand,29 this question highlights the redistributivedimension of taxation. Responses are measured on a scale from “strongly disagree” (1)to “strongly agree” (7). We follow the standard practice and dichotomize the questionto indicate if a respondent strongly supports redistribution (6 or above) or does not. Wecollect these data along with data on demographics and asset ownership. We use the lat-28See online SI with list of countries.29E.g., The State must implement strong policies to reduce income inequality between the rich and poor.To what extent do you agree or disagree with this statement?383.3. Research Design and Datater to build income deciles. In addition, we collect data from the Database of PoliticalInstitutions to classify governments’ ideologies.If our theory is correct, we should observe that the wealthy are more likely to supportredistribution when there is a right-wing government in charge. While we cannot exper-imentally manipulate the ideology of the government advancing the redistributive policy,we can compare preferences for redistribution between individuals in countries governedby the Right vis-a`-vis the Left while keeping several confounding factors constant.Results are consistent with our theory. Figure 3.1 shows that the rich (i.e., people in the10th decile of the income distribution) oppose redistribution under left-wing governments.However, their support for redistribution under right-wing governments is positive andstatistically significant. This di↵erence in preference only applies to the wealthy, as wefound no significant results for the other nine deciles.While consistent with our theory, results reported in Figure 3.1 do not identify causalrelations, as several confounding variables might a↵ect the observed associations. The nextsection presents an experimental design to identify the causal e↵ect of the government’sideological position on preferences for redistribution.Figure 3.1: Support for Redistribution among the Wealthy in Latin America.Note. This figure uses LAPOP 2018/9 data and shows the moderator e↵ect of governmentideology on the probability respondents from 14 Latin American countries across di↵erentincome deciles support progressive redistribution. Government ideological positions areextracted from the Database of Political Institutions. OLS models include control variablesand country fixed e↵ects. Standard errors are clustered at the municipal level. Confidenceintervals: 95%. Regression reported in table B.16 in the online supporting information.3.3.2 Survey ExperimentTo test our hypotheses, we conducted online survey experiments with wealthy Colombiansin April 2022, right before the presidential election. Colombia is a presidential republic,where President and Congress are elected in the same year, but elections are independent393.3. Research Design and Dataof each other, with legislative elections happening before the presidential ones. By the timewe conducted the survey, Congress had been already elected, with no party reaching anabsolute majority in either house. However, the left-wing obtained the highest proportionof seats in recent history (17% in the Senate and 18% in the House of Representatives).30Colombia o↵ers several advantages for the purpose of our study. First, inequality hasbeen increasing since 2016 (Solt, 2020), placing the country among the most unequal inLatin America–the most unequal region in the world. Indeed, redistributive issues becamea salient topic among Colombians after the wave of social protests during the summerof 2021, which put inequality at the center of the national agenda.31 Second, presidentialelections heightened the existing Left-Right cleavage in Colombian politics. By the time westarted fielding the surveys (April 11, five weeks before Election Day), Gustavo Petro andFederico Gutie´rrez, the left- and right-wing candidates, respectively, had the highest voteintention, which made the Left-Right cleavage particularly salient.32 In addition, runningthe study during a presidential campaign increased the validity of our experiment, as theissues we asked about were at that moment very much present in the public debate andlikely in the minds of respondents, making the scenario we constructed in our vignettesparticularly realistic. Third, the Colombian government has a stratification system thatallows straightforward identification of wealthy individuals. The population is divided intosix socioeconomic strata (SEL) based on household features and real estate value: 1 is thelowest and 6 is the highest. A✏uent people live in SEL 5 and 6 and correspond to 2% ofthe Colombian population. SEL determine service prices and access to utility subsidies;people living in SEL 1 pay lower utilities than those in SEL 6. While households with lowSEL have poor infrastructure and high levels of poverty, households with high SEL areusually in gated communities and enjoy private services. Fourth, Colombia is a countrywhere partisan identification is currently extremely low, much lower than in other contextswhere partisan e↵ects have been tested.33 This particular feature of the Colombian caseallows us to test for partisan e↵ects in a least likely scenario.A✏uent people are, by definition, a small population. They are also hard to reach inface-to-face surveys because many live in gated condominiums. To overcome these di-culties, we used NetQuest opt-in online panel to survey wealthy Colombians. NetQuestrecruits panelists through online ads posted on websites like Facebook. Panelists partici-pation in surveys is paid and no panelist can take more than three surveys per month. Torecruit wealthy respondents, we leaned on the stratification system explained above and30Of note is that our theory does not consider possible moderator e↵ects of the composition of Congress.Yet, given the context we study, we lack reasons to expect the composition of Congress to negativelya↵ect the credibility of the redistributive policies. If anything, given the increased strength of the left, theresults of the legislative elections made the implementation of redistributive policies under the prospectivepresident (whatever their ideological stance) a more credible possibility for respondents.31https://tinyurl.com/4k5ewp4x32https://tinyurl.com/2fb29ner33While in Colombia it stands at 12.9% (LAPOP, 2023), in the United States and Brazil, for instance, itstands at above 50% (Gallup, 2022) and 23% (LAPOP, 2023), respectively.403.3. Research Design and Datasampled respondents that reported living in SEL 5 or 6. Although the survey firm hadrespondents’ SEL beforehand, we included a screening question to control the authenticityof the sample. Of note is that there was no incentive to report living in SEL 5 or 6. In orderto have enough statistical power, we did not employ quotas but invited all panelists in SEL5 or 6 to take the survey. Given the short length of the survey (an average completion timeof six minutes), we only employed one attention check. We collected 799 quality responsesbetween April 11 and 22. Our survey seems to have successfully captured a✏uent respon-dents. Descriptive statistics indicate that 56.5% respondents have at least a universitydegree, 75% respondents are business owners, formal salaried employees, or self-employed,and the modal respondent is 55 years old or older (see Figure B.1 in the online SI).We employ data from a recent nationally representative survey to check how our samplecompares to the population in SEL 5 or 6. Specifically, we use data from the 2021 PoliticalCulture Survey (ECP) by the National Administrative Department of Statistics (DANE,2021). Figure B.3 shows that our respondents are similar to the broad upper-class popu-lation in terms of gender (p > 0.55), modal age (p > 0.64), and marital status (p > 0.07).However, our sample is more educated (p < 0.01) and less catholic (p < 0.01). Impor-tantly, the ECP measures self-reported ideology using a 10-point scale, where 1 representsleft-wing and 10 represents right-wing. We recode the ECP ideology variable to compareit with the 5-point scale ideology variable used in our survey. Specifically, for the ECPvariable, we collapse pairs of subsequent values into a single value (e.g., values 1 and 2 arecoded as 1). In the ECP, as in our sample, we classify respondents with ideological scoresof 4 or 5 as right-wing.34 As Figure B.3 illustrates, our sample is more right-leaning (p <0.01).The literature shows that education correlates negatively with support for redistribu-tion; people invest in education with prospects of upward mobility, thus becoming averseto redistribution (Alesina and Giuliano, 2011). Scheve, Stasavage et al. (2006) argue thatreligion works as a substitute mechanism of insurance, and as a result, religiosity is nega-tively correlated with support for redistribution. However, the authors find no significantassociation between Catholicism and redistributive preferences. Also, it is well known thatpolitical ideology is associated with redistributive preferences, with right-leaning peoplehaving less progressive ones (Ballard-Rosa, Martin and Scheve, 2017). Provided the evi-dence about the e↵ect of these three variables on support for redistribution, our samplemight tend to prefer more redistribution than the general population. However, we showthat our results are virtually the same with covariate adjustment. Therefore, we believe34There might be concerns about what people understand by Left or Right when they self-place in theideological spectrum. Although we do not measure preferences for redistribution pre-treatment, we havepre-treatment metrics of political predispositions. The correlation between such predispositions and right-wing ideology attenuates validity concerns regarding what we capture as right-wing. Specifically, right-wingideology positively correlates with vote intention for the right-wing candidate (r = 0.6, p < 0.001) and trustin the military (r = 0.4, p < 0.001), and negatively correlates with vote intention for the left-wing candidate(r = -0.5, p < 0.001).413.3. Research Design and Dataour sample is a good approximation of the a✏uent class in Colombia, and we lack reasonsto think our respondents are unique in ways that undermine our ability to draw conclusionsabout preferences in wealthy individuals.Experimental DesignIn the survey experiment, we asked respondents about their level of support for two di↵erentredistributive policies, randomly assigning the candidate proposing each one. Then, wetested three possible reasons behind respondents’ preferences. We used vignettes where weclearly indicated who stood to lose or gain from the proposed policies. The information weused in the vignettes was realistic, as we confirmed in a pilot survey where we checked howcredible the policy proposals were. We also provided essential information about policycontent to correct for potential misperceptions or lack of knowledge among respondents.The policies we asked about, such as fiscal reform, were salient in the existing context.The first policy proposed to increase taxes on the rich and thus focused on the “takingfrom the rich” side of redistribution. The second policy proposed to subsidize the util-ity spending of low-income strata and thus focused on the “giving to the poor” side ofredistribution. We randomized the candidate proposing each policy and measured respon-dents’ level of support on a scale from 1 to 5, where 1 indicated strong disagreement and5 indicated strong agreement. The control group read the policy as advanced by the left-wing candidate (T0 = Gustavo Petro), and the treatment group read the same policy butadvanced by the right-wing candidate (T1 = Federico Gutie´rrez). The wording of the vi-gnettes implied temporality: the implementation of a policy occurs after the election of thecandidate. With this wording, we aimed to alleviate concerns about reverse causality (e.g.,respondents support an existing policy, and therefore they prefer a candidate). Since bothcandidates were widely known and clearly linked to specific ideologies, the expectation wasthat their names would make a strong treatment. Our outcome variables are ProgressiveTaxation and Subsidies.35 The value 1 of these variables indicates that respondents either“somewhat agree” or “totally agree” with the policy, and 0 indicates otherwise.Progressive Taxation: In Colombia, income tax is progressive: people withhigher incomes pay more taxes. For example, a person earning 2.5 million permonth pays an income tax of 10%, while a person earning 15 million per monthpays an income tax of 24.5%. To what extent do you agree or disagree withthe income tax for this second group going from 24.5 to 29.5 under a [T0/T1]government, i.e., that those earning 15 million would go from paying 3.7 to 4.4million in taxes?35We acknowledge that taxation is di↵erent from redistribution and might be regressive. However, weargue that taxation is a necessary condition for redistribution. Consistently, in the vignette, we primerespondents with the progressive dimension of the income tax.423.3. Research Design and DataSubsidies: To what extent do you agree or disagree with implementing a tax forpeople living in strata 5 and 6 to subsidize 10% of the utility spending of peopleliving in strata 1 and 2 under a government of [T0/T1]?To explore the reasons behind the level of support for redistribution we measured re-spondents’ expectations around specific redistributive policies. To test for our ‘uncertainty’hypothesis (H3), we asked respondents about their level of uncertainty regarding the upperbound of a progressive wealth tax. The tax rate proposed by the candidate was 13%, threepercentage points higher than the existing wealth tax at the time of the survey. Respon-dents used a slider to indicate whether they expected the actual tax rate after the reformto be lower or higher than 13%. The range of the tax rate in the slider we utilized tomeasure uncertainty went from 3% to 23%, so 10 percentage points lower and higher thanthe proposed new tax rate. Again, we randomly varied the candidate proposing the taxadjustment. In the analysis, our outcome variable Uncertainty is rescaled and takes onvalues from -10 to +10.Uncertainty: Several candidates have considered implementing taxes to solvethe State’s fiscal deficit. One of the proposals is to increase the tax on incomereceived by corporate shareholders. Currently this tax is 10%. Imagine that[T0/T1] proposes to increase this tax to 13%, if [T0/T1] is elected president,do you expect him to implement exactly the tax he proposes, or do you expectthe tax implemented to be lower or higher than the one proposed?To test H4 and H5, we asked respondents two separate questions about the expectedeciency in delivering social policies and the impact of such policies on macroeconomicstability. Specifically, we asked respondents how likely it was that the money to helpthe poor would actually be spent on the poor under the (Left or Right) candidate andhow likely it was that the social policies of the (Left or Right) government would putColombia’s economic stability at risk. In both questions, response options ranged from“not at all likely” (1) to “very likely” (3). The outcome variables Eciency and Instabilityare dichotomized measures where 1 indicates that respondents consider the outcome to bevery likely, and 0 indicates otherwise.Eciency: How likely do you think it is that the money to help the poor willactually be spent on the poor under [T0/T1]’s government?Instability: How likely do you think it is that the social policies of [T0/T1]’sgovernment will put Colombia’s economic stability at risk?Before collecting the experimental evidence, we measured a set of pre-treatment vari-ables to control for potential sources of varied preferences: demographics, exposure to433.4. Resultscrime, guerrilla-threat perception, perception of social resentment, intention to migrate,trust in institutions, ideological self-placement, and vote intention.To identify our treatment e↵ects, we estimate the following OLS model:yi = ↵ + 1Di + 1Xi + ✏i, (3.1)where yi is the given outcome,Di is the treatment variable (0 for the left-wing candidate,1 for the right-wing candidate), and Xi is the vector of pre-treatment variables.363.4 ResultsOverall, we find that who is in power a↵ects the redistributive preferences of the wealthy.We begin by reporting results for H1 and H2. Figure 3.2 shows the di↵erence between theprobability of supporting redistributive policies advanced by the right-wing candidate (T1= Federico Gutie´rrez) and the probability of supporting the same policies but advancedby the left-wing candidate (T0 = Gustavo Petro). Consistent with H1, we find wealthyrespondents are 9.4 percentage points (pp) significantly more likely to support an increasein a progressive tax rate if proposed by the right-wing candidate ( = 0.489, SE = 0.026)relative to the left-wing candidate ( = 0.395, SE = 0.025). We also find the hypothesizedtreatment e↵ect with subsidies (H2): respondents are 4.2 pp more likely to support atax on them to subsidize utility spending in low-income households (T0:  = 0.196, SE= 0.02; T1:  = 0.239, SE = 0.022). However, this result does not reach statisticalsignificance. Importantly, results hold even when we control for respondents’ ideology37 andvote intention. In the SI, we show that results have the hypothesized direction and reachstatistical significance also when we use the 5-point scale dependent variables (FigureB.4).36In the pre-analysis plan we considered clustering standard errors at the city level. However, the numberof cities in the final sample is below 20. As Angrist and Pischke (2008) demonstrate, a small number ofclusters (i.e., fewer than 42) leads to biased standard errors. Therefore, we use robust standard errorswithout clustering.37We measure ideological position using a 5-point scale ranging from 1 (left-wing) to 5 (right-wing).Respondents above the average ideological score of the sample were coded as 1, while those below theaverage were coded as 0. Respondents who did not self-identify with a particular ideology were removedfrom the analyses, thus a↵ecting the size of the confidence intervals in the results with covariate adjustment.443.4. ResultsFigure 3.2: Preferences for Redistribution in Wealthy Colombians - Study 1.Note. This figure shows the estimated change in the probability respondents supportredistributive policies proposed by the right-wing (T1) vs left-wing (T0). OLS modelswith robust standard errors. Confidence intervals: 95%. Regressions reported in table B.3in the Appendix.Why are the rich more likely to support redistribution under the Right? When weanalyze the logic behind the di↵erent levels of support for redistributive policies, we find twoof the three theorized reasons at work. Table 3.1 shows that expectations of eciency andinstability explain support for redistribution among the a✏uent under a prospective right-wing government. We also find the hypothesized negative treatment e↵ect on expectationsof uncertainty. However, the coecients are imprecisely estimated (p < 0.1).38 Specifically,consistent with H4, we find that respondents are 16.9 pp significantly more likely to expectsocial policies to be actually spent on the poor under a right-wing government, as the thirdcolumn in Table 3.1 reports.39 In the fifth column of the same table, we show that thewealthy are 25.9 pp less likely to expect that social policies under a right-wing governmentwill put the stability of the national economy at risk. This result is statistically significantand confirms our theoretical expectation (H5). In the SI, we show that the results havethe hypothesized direction and are statistically significant also when we use the dependentvariables in their point scale.38Additional analyses reported in the SI suggest that treatment e↵ects on Uncertainty are sensitive todi↵erent coding schemes (see B.5).39We argued that the expectation of eciency relies on market-oriented approaches to policies. However,we note that such expectations could stem from various considerations, including perceived corruption,competence, and politicians’ reputation more generally. The reasons behind these di↵erent expectations ofeciency lie outside the scope of this paper and should be the subject of further research. Regardless ofwhat generates these di↵erent expectations, our argument still holds: The Right and the Left have di↵erente↵ects on the wealthy’s expectations about how the budget for social policies will be spent.453.4. ResultsTable 3.1: Testing Theoretical Explanations - Study 1Uncertainty Eciency Instability1 2 3 4 5 6Base category(T0 = Leftwing Candidate) 1.143⇤⇤⇤ 1.872 0.164⇤⇤⇤ 0.113 0.518⇤⇤⇤ 0.126(0.271) (1.610) (0.019) (0.136) (0.026) (0.153)T1 = Rightwing Candidate 0.649⇤ 0.607 0.169⇤⇤⇤ 0.175⇤⇤⇤ 0.259⇤⇤⇤ 0.286⇤⇤⇤(0.365) (0.412) (0.031) (0.035) (0.034) (0.038)Covariate Adjustment No Yes No Yes No YesNum. obs. 723 565 764 606 746 594RMSE 4.939 4.732 0.424 0.428 0.470 0.461⇤⇤⇤p < 0.01; ⇤⇤p < 0.05; ⇤p < 0.1. OLS models with robust standard errors.3.4.1 Study 2In the first round no candidate reached 51% of votes. As opposed to what opinion pollssuggested when we fielded the survey, the right-wing candidate Gutie´rrez was not amongthe two most voted candidates. Instead, the left-wing candidate Gustavo Petro advanced tothe second round along with businessman Rodolfo Herna´ndez, an independent candidate.Although unknown to the political establishment, Herna´ndez, a former mayor of a smallcity in north-central Colombia, cultivated wide popular support on social media runningon an anti-establishment and anti-corruption platform. After Gutie´rrez did not make it tothe run-o↵, the Right decided to support Herna´ndez, who promised to manage the state asa private firm and create a parallel cabinet of business elites to advise the president. Hispledges placed him to the right of the left-wing candidate in the economic dimension.We exploit the unexpected electoral outcome to do a follow-up survey. In the ques-tionnaire, we replaced the defeated right-wing candidate’s name with Rodolfo Herna´ndez.Then we fielded the same survey between May 30 and June 7, 2022. We collected 833responses.40 Given Herna´ndez’s conservative position on redistributive policies, our theo-retical expectations did not change.Figure 3.3 shows that results in our second study hold. Wealthy respondents are 8.9pp significantly more likely to support an increase in a progressive tax rate if proposed bythe right-wing candidate (T0:  = 0.38, SE = 0.024; T1:  = 0.469, SE = 0.025). Supportfor subsidies has the expected positive direction but does not reach statistical significance(T0:  = 0.181, SE = 0.019; T1:  = 0.229, SE = 0.021). Yet, the e↵ects are significantand follow the expected direction when we use the dependent variables in their original40We did not pre-register the second survey because we measured identical outcomes on the same sampleand tested the same hypotheses.463.4. Resultspoint scales rather than their binary versions, as we show in the SI (Figure B.5).Figure 3.3: Preferences for Redistribution in Wealthy Colombians - Study 2.Note. This figure shows the estimated change in the probability respondents supportredistributive policies proposed by the right-wing (T1) vs left-wing (T0). OLS modelswith robust standard errors. Confidence intervals: 95%. Regressions reported in table B.4in the Appendix.Table 3.2 indicates that the reasons to support redistribution also move in the sametheorized direction: expectations of eciency increases and expectations of an unstableeconomy decreases under the right-wing candidate. Although the level of uncertainty is inthe expected negative direction, it is not statistically significant.Table 3.2: Testing Theoretical Explanations - Study 2Uncertainty Eciency Instability1 2 3 4 5 6Base category(T0 = Leftwing Candidate) 1.121⇤⇤⇤ 2.306⇤ 0.193⇤⇤⇤ 0.277⇤ 0.568⇤⇤⇤ 0.465⇤⇤⇤(0.239) (1.327) (0.020) (0.151) (0.026) (0.151)T1 = Rightwing Candidate 0.320 0.246 0.179⇤⇤⇤ 0.171⇤⇤⇤ 0.337⇤⇤⇤ 0.346⇤⇤⇤(0.316) (0.341) (0.031) (0.036) (0.033) (0.037)Covariate Adjustment No Yes No Yes No YesNum. obs. 832 643 804 638 798 634RMSE 4.560 4.264 0.444 0.448 0.458 0.455⇤⇤⇤p < 0.01; ⇤⇤p < 0.05; ⇤p < 0.1. OLS models with robust standard errors.3.4.2 Heterogeneous Treatment E↵ectsConventional wisdom assumes that the wealthy are predominantly right-wing. However,in Colombia, we observe a sizeable proportion of left-wing wealthy (see Figure 3.4). Thisfeature of our sample allows us to test our argument among the left-wing a✏uent as well.473.4. ResultsSpecifically, we conduct heterogeneous treatment e↵ect analyses to examine how govern-ment ideology a↵ects preferences and expectations across left- and right-wing ideologicalgroups.41Figure 3.4: Ideological Distribution of Respondents.Note. This figure shows the proportion of left- and right-wing respondents, defined as thosewho are below or above the median in the 5-point ideological scale, respectively.To conduct heterogeneous e↵ects analyses, we use respondents’ self-ideological place-ment and classify respondents as left-wing if they are below the median value of the ideolog-ical scale and right-wing if they are above, thus excluding respondents who self-place in thecenter.42 Figure 3.5 shows that the e↵ect of right-wing government ideology varies acrossideological subgroups: it increases support for redistributive policies among the right-wingwealthy and decreases it among the left-wing wealthy.41We did not pre-register the analyses reported in this section as we did not expect to have a considerablenumber of left-wing respondents.42In Section B.5.3, we report results using diverse coding schemes for ideological self-placement. Thefindings remain consistent across di↵erent criteria.483.4. ResultsFigure 3.5: Heterogeneous Treatment E↵ects (Preferences).Note. This figure shows the estimated change in the probability respondents support redis-tributive policies proposed by the right-wing (T1) vs left-wing (T0) by ideology. Respon-dents self-identified as centrists are excluded from this analysis. OLS models with robuststandard errors and without covariate adjustment. Confidence intervals: 95%. Regressionsestimating di↵erences in means are reported in tables B.9 and B.10 in the online SI.We turn to our theorized mechanisms to further explore these heterogeneous treatmente↵ects. Figure 3.6 shows that a right-wing government leads to contrasting expectationsregarding the eciency of the social policies across ideological subgroups.43 However, itdoes not trigger expectations of macroeconomic instability among the left-wing wealthy(Figure 3.6). Results are similar when we use vote intention rather than ideological self-placement to classify respondents, as we report in tables B.11 and B.12 in the online SI.43In Table B.9, we report results on uncertainty, which are statistically insignificant.493.5. Alternative ExplanationsFigure 3.6: Heterogeneous Treatment E↵ects (Expectations).Note. This figure shows the estimated change in the probability respondents expect e-ciency/instability under the right-wing (T1) vs left-wing (T0) by ideology. Respondentsself-identified as centrists are excluded from this analysis. OLS models with robust stan-dard errors and without covariate adjustment. Confidence intervals: 95%. Regressionsestimating di↵erences in means are reported in tables B.9 and B.10 in the online SI.3.4.3 Non-Redistributive PoliciesDoes the ideology of the prospective government have an e↵ect on wealthy’s preferencesin other policy areas as well? Results from two pre-registered hypotheses suggest that thistheory may even apply to non-redistributive policies. Specifically, we tested whether whois in power (i.e., the Left or Right) a↵ects support for increasing police force in urbanareas and implementing a restrictive migration policy. As we explained in the SI (SectionB.7), we were agnostic about the e↵ect of who is in power on non-redistributive policiesand expected null e↵ects. However, we found that the right-wing treatment increases thewealthy’s support for these policies (Figure B.13). We also find heterogeneous treatmente↵ects in line with the ones we uncover in the redistributive policies (Figure B.14). Theseresults contradict the assumption that the e↵ect of who is in power is limited to redis-tributive policies and they might indicate that our theorized expectations operate in otherdomains. Future research can examine the mechanisms behind the wealthy’s support fornon-redistributive policies.3.5 Alternative ExplanationsWe address three possible alternative explanations for our results. First, the fact that theleft-wing candidate is a polarizing figure and could be driving the e↵ect of our treatments.Second, the argument that the truncated nature of welfare states diminishes expectations503.6. Conclusionof redistribution (Holland, 2018). Third, the possibility that respondents consider theredistributive proposals cheap talk. A full discussion of the three explanations is in the SI(Section B.5.1).3.6 ConclusionResearch on wealthy individuals has become more relevant in a period of rising economicinequality and redistributive pressures on governments. We known that the rich haveoutsized political influence, but we know little about their attitudes toward redistribution,especially in Latin America. In this article, we hypothesized that the rich are more likelyto support redistributive policies under the Right rather than the Left. We theorized threereasons that explain such support: uncertainty about the upper bound of the redistributivepolicy, government eciency in delivering policies, and concerns about the e↵ect of socialpolicies on macroeconomic instability.We conducted pre-registered online survey experiments with wealthy individuals duringthe 2022 presidential election in Colombia. This setting allowed us to leverage the existingLeft-Right cleavage in the country. Our findings, supported by a second study conducted inthe run-o↵ election, show that the wealthy are more likely to support redistribution underthe Right because they expect greater eciency in delivering policies and less economicinstability when a right-wing government enacts redistributive policies. In addition, sinceour sample had a sizable share of left-wing individuals, we explored whether our theorizedmechanisms work for them too. Overall, we find that the left-wing wealthy oppose redis-tribution under the Right. However, they do not expect more macroeconomic instabilityas a consequence of right-wing redistribution.Our findings challenge standard political economy models, which assume that redis-tributive preferences are a function of objective material conditions. We have providedcausal evidence showing that the government’s ideology has an important e↵ect on prefer-ences for redistribution among the wealthy. In particular, we have shown when and whythe government’s ideology a↵ects preferences for redistribution among the wealthy anddemonstrated that ideologically driven expectations, rather than income position, explainredistributive preferences in the a✏uent. The fact that left-wing wealthy do not exclude thepossibility of macroeconomic instability under the Left yet still support the redistributivepolicies that they think might cause it constitutes even stronger evidence of the limitationsof the material self-interest assumption. As a matter of fact, our results show that thewealthy are willing to pay the cost of redistribution for ideological reasons, even if thatentails not only an immediate, direct cost (i.e., the increased tax burden on them) but alsoa more di↵used, generalized one (i.e., macroeconomic instability for everyone).With respect to partisan motivated reasoning theory, results from studies 1 and 2 arelargely consistent with it. Indeed, results are similar across both studies, even when theright-wing/treatment candidate varied. Despite this change, treated respondents supported513.6. Conclusionredistributive policies and had similar expectations. This consistency provides strong ev-idence that elite cues trump material interests, even in challenging contexts where thewealthy have vested interests.Our results have several implications for the political economy literature and currente↵orts to increase taxation in developing countries. First, the rich seem to have no fixedpolicy preferences in the realm of redistribution and taxation, as who is implementingthe policy is a predictor of their preferences rather than the policy content itself. Second,right-wing governments might have a political advantage when implementing redistributivepolicies since the wealthy are more open to supporting right-wing redistribution in contextsof increasing inequality.Cross-country experimental evidence on the e↵ect of government ideology on wealthy’sredistributive preferences was beyond the scope of our study. We believe that futureresearch could examine whether our findings hold in countries where the wealthy havebeen more frequently exposed to left-wing governments. Likewise, future scholarship couldinvestigate whether the policy preferences of other social strata (e.g., middle class, popularsectors, etc.) are as susceptible to ideological expectations related to general beliefs aboutthe Left and the Right in government.52Chapter 4Leftist Threat and Post-ElectionTransfers4.1 IntroductionThe literature on distributive politics consistently finds that central governments use trans-fers to reward allied constituencies, punish the opposition, and maximize future vote shares(Cox and McCubbins, 1986; Schady, 2000; Brollo and Nannicini, 2012); compared to oppo-sition constituencies, allied ones get more transfers. In line with seminal theoretical work,recent empirical research shows that such reward is provided before elections (Brollo andNannicini, 2012; Bueno, 2021). However, scholars find no increase in government transfersto allied constituencies after Election Day. Existing scholarship suggests that the cycle oftransfers is explained by pre-election credit claiming and post-election government budgetshrinkage. Yet, the literature tends to overlook who the incumbent won to.In this article, I argue that examining the ideological stance of the incumbent in relationto the defeated candidate can shed light on how the incumbent allocates resources inthe months following the election, as ideologically di↵erent contenders may pose di↵erentfuture risks to the establishment. For example, a narrow electoral victory can presentgreater challenges for the elected government if the defeated candidate holds an opposingideological position. In this case, the incumbent may need to adjust their policies in orderto appease opposition voters and prevent backlash. While formal and empirical researchprovides compelling arguments about the pre-election reward to allied constituencies, theydo not fully explain if and when opposition constituencies are rewarded as well. This articlefocuses on the left-right divide and advances an argument that explains the expansion ofsocial transfers to left-wing constituencies by right-wing governments.One of the organizing principles in politics is the left-right divide. The left has uniquefeatures that make it the prevalent adversary of the right. In contrast to the right, theleft promises redistribution, social investment, and advocates for the poor (Hibbs, 1977;Cameron, 1978; Huber and Stephens, 2012). Left-wing policies might create redistributivethreats as they can involve fiscal populism and expropriations (Levitsky and Roberts,2011). These policies can have legacies such as massive debt, high inflation, and militantlabor movements contrary to austerity (Ondetti, 2021). Therefore, the left can producefundamental threats to existing political and economic arrangements preferred by the right.Existing work demonstrates that the right fears the left and employs various strategies534.1. Introductionto prevent its electoral success. The right may resort to violence to intimidate voters andhinder their support for the left (Elazar, 2000; Acemoglu et al., 2022). Similarly, the rightmay grant concessions with the same aim of prevention. Notably, several redistributiveand welfare policies implemented by right-wing governments in Western Europe during theCold War aimed to inhibit support for the left, thereby safeguarding the existing politicalorder (see, e.g., Obinger and Schmitt, 2011; Sant’Anna and Weller, 2020). Although mostextant work on the consequences of the leftist threat focuses on established democracies, re-cent evidence demonstrates that the right employs repressive strategies to prevent supportfor the left in developing democracies, particularly in Latin America (Arjona, 2016; Fer-gusson et al., 2021). However, it remains unclear whether right-wing governments extendconcessions to left-wing opponents in these contexts.In order to test whether the ideological dimension of the electoral competition is relevantin understanding the post-election expansion of social transfers, I leverage the overtimevariation in the left-right electoral competition in Colombia, where the 2018 presidentialelection marked the first time the establishment faced a credible left-wing candidate in thepolitical arena. I use election data at the municipal level on the 2014 and 2018 presidentialelections and collect administrative records of Familias en Accio´n (FA), a conditional cashtransfer (CCT) program aimed at alleviating intergenerational poverty.The empirical analysis uses a continuous di↵erence-in-di↵erence research design andshows that the left-wing vote share is positively associated with the proportion of newFA beneficiaries enrolled after elections. Placebo tests provide suggestive evidence thatthe ideological stance of the incumbents in relation to the defeated candidates, as wellas the electoral strength of candidates, may explain how incumbents allocate resourcesin the months following an election: the 2014 runner-up’s vote share had no e↵ect on FAenrollments after elections. Notably, the candidate was ideologically close to the right-wingincumbent. Similarly, the poor electoral performance of left-wing candidates in the 2015local elections had no e↵ect on FA enrollment.My article contributes to the literature on distributive politics by emphasizing the rel-evance of the ideological dimension in the electoral competition. Previous studies haveshown that right-wing governments, which are often closely aligned with business interestsand advocate smaller government, expand social policies (Schady, 2000; De La O, 2015;Garay, 2016; Fairfield and Garay, 2017; Conover et al., 2020). While the prevailing ex-planations correctly identify electoral competition as a driver of the expansion of socialpolicies, they neglect the ideological aspect of the competition. I present a theoreticalframework and empirical evidence suggesting that right-wing incumbents consider how farto the left their main opponents are in order to strategically increase social spending afterelections.544.2. Strategic Expansion of Social Policies4.2 Strategic Expansion of Social PoliciesSeveral developing countries started implementing and expanding social policy programsover the last decades. During this period, governments have introduced anti-poverty pro-grams like CCTs and extended access to social services for low-income people (Barrientos,2013; De La O, 2015). Countries in Latin America, Asia, Sub-Saharan Africa, and theMiddle East have conditional cash transfers programs (Fiszbein and Schady, 2009). In the1990s, governments moved toward a cost-e↵ective approach to social policies following rec-ommendations from organizations like the World Bank. This approach aimed at optimallyallocating transfers given insucient government budget, thus avoiding inflation-prone poli-cies and reducing political manipulation (Dı´az-Cayeros and Magaloni, 2009; Barrientos,2013).Although governments adopted technical criteria for the distribution of social programs,research suggests that governments expand social policies strategically. Schady (2000)shows that in Peru, former president Fujimori increased spending on a social fund aimedat creating employment, alleviating poverty, and improving access to social services inprovinces where he had a larger vote share. Garay (2016) argues that electoral competitionto obtain votes from the informal sector, rural workers, and unemployed explains theexpansion of social policies in Argentina, Brazil, Chile, and Mexico in the 1990s and 2000s.Similarly, Fairfield and Garay (2017) qualitatively show how electoral competition andsocial mobilizations motivated the expansion of redistributive policies under right-winggovernments in Chile and Mexico. Fried (2012) finds that political criteria a↵ect thedistribution of the CCT program Bolsa Famı´lia in Brazil. In particular, the author findsthat one standard deviation shift in voting results in elected incumbent increasing coveragein core municipalities 3.4%. More recently, Bueno (2021) investigates the Brazilian caseand demonstrates that incumbents increase the number of new CCT beneficiaries beforeelections. The author exploits Brazilian rules banning credit claiming before electionsand finds that politicians expand the CCT programs in the period when they can claimthe credit. However, incumbents contract expansion of programs when credit claiming isforbidden.4.2.1 Bringing Ideology to the Strategic ExpansionDi↵erent political opponents entail di↵erent risks to the government. In particular, theleft has unique features that make it the prevalent adversary of the right. In contrast tothe right, the left promises redistribution, social investment, and advocates for the poor(Hibbs, 1977; Cameron, 1978; Huber and Stephens, 2012). Importantly, left-wing policiesmight create redistributive threats as they can involve fiscal populism and expropriations(Levitsky and Roberts, 2011). These policies have legacies such as massive debt, highinflation, and militant labor movements contrary to austerity (Ondetti, 2021). Therefore,the left can produce substantial changes that political opponents ideologically closer to the554.3. The Colombian Caseright, like centrist parties, are unlikely to generate. In fact, recent empirical work suggeststhat the right fears the rise of the left (see, e.g., Fergusson et al., 2021; Acemoglu et al.,2022)Although there is a recent increasing number of anti-establishment and anti-elite right-wing politicians (e.g., Bolsonaro in Brazil, Milei in Argentina, and Herna´ndez in Colombia),I contend that such politicians are unlikely to threaten right-wing governments as left-wingpoliticians do since they share the same ideology and economic interests. In fact, severalright-wing governments in the context I study have openly supported anti-establishmentright-wing politicians.44The distinctive features of the left have two implications. First, the right has strongincentives to prevent the election of a left-wing government. The changes executed by theleft can empower labor movements and damage the macroeconomic stability of the country,thus making it more costly for the right to advance its political agenda (e.g., by limitingaccess to financial markets and investors). Second, the logical support for left-wing partiesshould come from the poor, provided they aspire to have better socioeconomic conditions.Then, right-wing governments could expand social transfers to this group of people toattenuate support for the left.There are di↵erent types of social transfers governments can implement. Yet, the mostpopular ones to reduce poverty in developing countries are the CCT programs (Fiszbein andSchady, 2009). CCT programs are government social assistance programs that target low-income families with school-age children. CCTs aim to invest in human capital growth andprovide a safety net to low-income people by paying families over the entire education cycleof children (e.g., from 9 to 18 years old). Transfers are conditional on school attendance andregular health checkups for the recipient families (Barrientos, 2013). Although CCTs havespecific assignment criteria (e.g., low-income and school-age children in the household),governments in developing countries can strategically allocate transfers, as reviewed above.Therefore, I expect right-wing governments to expand these types of social transfers to left-wing constituencies when the runner-up in the previous election was a left-wing contender.4.3 The Colombian CaseIn this paper, I use data on the 2014 and 2018 presidential elections and administrativerecords of the Colombian CCT program Familias en Accio´n (FA) to empirically evaluatewhether right-wing governments reward left-wing constituencies when they have faced a44E.g., Federico Gutie´rrez, the right-wing candidate in the 2022 Colombian election, openly endorsedRodolfo Herna´ndez, the anti-establishment right-wing politician; Gutie´rrez said “We do not want to losethe country and we do not want to put Colombia’s future at risk. For that reason, Rodrigo [the vicepresidential candidate] and I will vote for Rodolfo” (El Tiempo, 2022). More recently, the anti-elite right-wing politician Javier Milei, elected as the president of Argentina in 2023, allied with Mauricio Macri, theformer right-wing president, to keep the left-wing out of power (Stefanoni, 2024).564.3. The Colombian CaseFigure 4.1: Vote Share of Left-wing Candidate.Note. Share of vote for left-wing candidate (presidential elections first-round).credible left-wing electoral threat. Several features of this case make it an ideal setting fortesting this association.Colombia is a unitary state divided between 32 departments (administrative states)that group over 1000 municipalities. There are democratically elected ocials at each levelof political authority. The people directly elect the president every four years throughgeneral elections. A candidate wins the presidential race with half plus one of the ballots,or in a run-o↵ election between the two most voted candidates if no one reaches the 50%plus one vote in the first round. Since the Political Constitution of 1991, the electoralsystem opened to political competition beyond the two traditional parties. This democraticopenness derived into a multiparty system and allowed new political forces to compete foroce.Colombia has had a long armed conflict between the state and insurgent groups, in-cluding left-wing and right-wing armed groups. Yet, there is evidence of collusion betweenright-wing paramilitary organizations and the government to combat left-wing guerrillas(Dube and Vargas, 2013; Fergusson et al., 2021), particularly under the administration ofA´lvaro Uribe (2002-2010). In 2016, president Juan Manuel Santos signed a peace agree-ment with the FARC. This group was the oldest and strongest left-wing guerrilla in thecountry. The long history of armed conflict makes the left-right divide particularly salientin Colombia.The role of the armed and unarmed left is particularly important for the period underanalysis. Before the 2018 presidential election, the left was largely relegated to the extra-electoral arena, and there was no credible left-wing opposition to incumbents at the nationallevel. However, Colombia finally began to exhibit meaningful left-right competition in the574.3. The Colombian Case2014 Election: L RC1C2C32018 Election: L RC1C2 C3Figure 4.2: Ideological Placement of Top 3 Presidential Candidates.Note. Ideological position of the three most voted candidates (C) in the 2014 and 2018presidential elections. The ideological scores are based on Baker and Greene (2011). Scoresare rescaled to go from 1 to 10.electoral arena in the 2018 election. Specifically, a former left-wing guerrilla combatant wasa front-runner candidate in the 2018 presidential election, thus generating a salient left-rightpolitical cleavage. This feature of the Colombian case provides overtime variation in voteshares for opposition parties that are de facto in the opposite direction of the government’sideological position, as figures 4.1 and 4.2 illustrate. Therefore, the Colombian contextprovides a setting to identify clearly the leftist threat.4.3.1 Familias en Accio´nThe Colombian government created FA in 1999, a CCT program funded by the govern-ment, the World Bank, and the Inter-American Development Bank. FA aims to alleviateand reduce intergenerational poverty by targeting low-income households with kids. Ben-eficiaries receive monthly payments conditional on children’s recurrent medical check-upsand school attendance.The program was initially rolled out to 350 pilot municipalities and subsequently ex-tended to another 691 in 2002. Eligible municipalities had to have fewer than 100,000inhabitants, enough education and health infrastructure to supply the program services, abank to process FA’s beneficiaries payments, and available information on potential benefi-ciaries, based on their economic status. Potential beneficiaries were the poorest householdswith children aged 0-17 in qualifying municipalities. Selected families received a monthlytransfer approximately similar to 15% of the minimum wage. In 2012, the governmentinitiated the third phase of FA and expanded it to all municipalities.The government uses the System for the Identification of Potential Beneficiaries ofSocial Programs (SISBEN) to select households that meet the inclusion criteria (i.e., low-income households with kids). The SISBEN is a survey anyone can take and update.The responses provided in the SISBEN survey classify individuals based on their economicneeds. The government relies on such surveys to allocate social programs like FA.45 Al-45The third phase, which I use to test my argument, concluded in 2019. There were no enrollments in2020 because the program transitioned from phase III to IV. The government pre-selected beneficiaries inphase IV using a new version of the SISBEN, which prevents me from comparing the overtime programexpansion.584.4. Empirical Strategy: Data and Modelthough anyone can take the SISBEN survey, people must be pre-selected by the governmentto enroll in the FA program; this pre-selection is conditional on the SISBEN classification.Once a household is pre-selected, it must register with the Department of Social Prosper-ity (DPS), a government agency formulating social policies for vulnerable populations, toreceive the benefits. Therefore, households have the discretion to enroll in the program.While a household might decide not to enroll in the program, that is unlikely to be thecase; in an interview with the author, the national director of the FA program claimed thatmost families enroll in the program (Online interview, August 9, 2022).Notably, the number of eligible beneficiaries typically exceeds the number of peoplethe government can enroll given its budgetary constraints. The average di↵erence acrossmunicipalities between the number of households eligible to enroll in the CCT program andthe number of households enrolled is 9,692. This excess demand allows the government topre-select beneficiaries across municipalities at di↵erent rates. In fact, a former bureaucratat the DPS explained that “although the qualifying score is defined, the territorial criterionis more flexible and manipulable” (Online interview, February 2, 2024). Consistently, Nupia(2018) demonstrates that while the incumbent government follows the selection criteria toexpand the FA program, it also manipulates such expansion.Alcald´ıas (municipal governments) must ensure the infrastructure for education andhealth services. Although alcald´ıas enroll households in the program and assume theresponsibility for the program operation, they have limited discretion to a↵ect benefitsand enrollment. First, families do not need to update their status with alcald´ıas; families’enrollment lasts until kids become adults and end their participation in the program.Second, beneficiaries’ information and participation in the program is cross-validated byseveral actors. For example, schools and hospitals report to the central government kids’attendance at school and medical check-ups. Third, the central government does not handout grants to mayors or any other elected local authority; it pays beneficiaries directly witha debit card.4.4 Empirical Strategy: Data and ModelTo conduct the empirical analysis, I collected administrative data on the number of FAbeneficiaries at the municipal level. Specifically, to build my dependent variable, I aggre-gated the number of new beneficiaries enrolled in the program by quarter and municipality.I use the logged aggregated data as the dependent variable. The data on beneficiaries comefrom the DPS and is publicly available.The independent variable in my main empirical specification (Eq. C.1) is the voteshare of the left-wing runner-up candidate in the 2018 presidential election. Specifically,Left Voteshare is a continuous variable [0-1]. Additionally, I collected data on vote sharesof the right-wing runner-up candidate in 2014 and left-wing candidates in the 2015 localelections to conduct placebo tests explained below. The data is available in the National594.4. Empirical Strategy: Data and ModelCivil Registry.I use as control variables the criteria the government employs to select beneficiaries:number of households registered in the SISBEN, number of educational establishments,number of public healthcare centers, number of school-aged children, displaced peoplearrived at the municipality, and displaced people from the municipality. I collected the datafrom the Centro de Estudios sobre Desarrollo Econo´mico (CEDE) at Los Andes Universityand log-transformed all variables. Additionally, I control for sociodemographic municipal-level characteristics: poverty, population, number of students in preschool, primary, andsecondary education, share of population living in rural areas, and voter turnout. I alsoinclude an index of fiscal performance, number of homicides, violent threats, and presenceof coca crops.Moreover, I control for informal criteria the government might use to expand the CCTprogram. For instance, Niedzwiecki (2018) shows that the alignment between local author-ities and the central government increases coverage of CCT programs. Therefore, I buildtwo binary indicators to control for governors’ and mayors’ alignment with the centralgovernment, respectively. These indicators take on 1 when the governor/mayor belongs tothe same incumbent party and 0 otherwise. In cases where the mayor/governor belongsto a multiparty coalition, the alignment indicator takes on 1 if at least one party in thecoalition is the incumbent party.46Another element that can a↵ect both opposition vote shares and CCT expansion is vote-buying. While collecting this information at the municipal level is unfeasible, I employ datafrom the Misio´n de Observacio´n Electoral (MOE) and build a binary indicator of electoralrisk. This variable takes on 1 in municipalities that reported electoral fraud or politicalviolence around Election Day.I use lagged values of all control variables but voter turnout and electoral risk to at-tenuate concerns of endogeneity and prevent post-treatment bias.4.4.1 Empirical ModelMy main analysis employs a continuous di↵erence-in-di↵erence research design and usesmunicipality as the unit of analysis. The independent variable is left-wing vote sharesin the 2018 presidential election.47 Unlike binary di↵erence-in-di↵erence estimators, allmunicipalities had some left-wing vote share. Therefore, I estimate the e↵ect of di↵erentshares of vote for the left-wing candidate on the within-municipality change in new FAbeneficiaries.I run the following OLS regression interacting left-wing vote shares and control variables46The coalitions I focus on are electoral alliances. Therefore, those coalitions formed in the run-o↵elections are post-treatment controls because the main independent variable is left-wing vote share in thefirst round.47The presidential election happens in the second quarter of the year. The first round was on May 27,and the run-o↵ was on June 17.604.5. Resultswith a dummy and including municipality and quarter fixed e↵ects, with robust standarderrors clustered by municipality:yiq = ↵i + ⇣q + Left Votesharei · Electionq + Xit1 · Electionq + ✏i, (4.1)where y is logged number of new FA beneficiaries in municipality i and quarter q.↵i are municipality fixed e↵ects, ⇣q are quarter fixed e↵ects. Left Votesharei is the voteshare of the left-wing candidate in municipality i in the first round of the 2018 presidentialelection. Electionq is a dummy variable that is equal to 1 from the quarter in whichthe 2018 elections were held onwards, and equal to 0 otherwise.48 The parameter  isthe slope indicating the direction of the association. Because the dependent variable isin logged numbers, the coecient  indicates changes in percentage points in number ofbeneficiaries relative to the pre-2018 election period. Xit1 is the matrix of covariatesmeasured pre-treatment and interacted with the Electionq dummy. This interaction allowsme to address the possibility that left-wing vote shares are significantly correlated withseveral attributes of municipalities.4.5 ResultsOverall, I find results consistent with my argument: vote shares for left-wing candidatesare positively associated with the right-wing government’s expansion of CCT beneficiarieswithin left-wing constituencies. Because I use a logged outcome variable in the mainanalysis, results from Table 4.1 indicate that a 10% increase in the vote share for the left-wing candidate is associated with a 4.87% increase of FA beneficiaries after elections. Inthe online SI, I report an event study showing modest deviations from the parallel trendsassumption (Figure C.4). Although the overall findings are consistent across researchdesigns, the violation of the parallel trends assumption cautions against a strict causalinterpretation.The expansion of the FA program under Iva´n Duque, the 2018 right-wing president,is well known. Duque’s Plan Nacional de Desarrollo (National Development Plan)49 em-phasized expanding FA. In fact, Duque accounted for 33% of the total expenditure on theFA program since 2001. In comparison to preceding governments, Duque implemented themost extensive expansion of the FA program (DPS, 2022). Naturally, the FA expansionfocused on poorer municipalities, where the left-wing vote share is likely to be higher. Yet,results reported in columns 2 and 3 in Table 4.1 control for socioeconomic variables thatcould be biasing the theorized association, thus attenuating concerns that the FA expansionfollowed a mere development rather than political logic.48My dataset covers 23 quarters away from the 2018 presidential election to 5 quarters after the election.49The Plan Nacional de Desarrollo is a document all presidents are mandated to elaborate. The documentsets the government strategies and goals for the overall term in oce.614.5. ResultsRecent literature highlights potential validity issues in di↵erence-in-di↵erences usinglogged dependent variables, especially when it is sometimes zero (see, e.g., Roth andSant’Anna, 2023). In the online SI, I show that my findings are robust to di↵erent func-tional form assumptions (see Section C.3).Table 4.1: E↵ect of Left-Wing’s Voteshare on Logged Number of FA Beneficiaries1 2 3L Voteshare X Post-election (2018) 0.487⇤⇤⇤ 0.529⇤⇤⇤ 0.589⇤⇤⇤(0.087) (0.091) (0.095)Municipality FE Yes Yes YesQuarter FE Yes Yes YesControls x Event No Yes YesCoalition dummy No No YesNum. obs. 26128 21888 21888Num. groups: municipality id 1107 903 903Num. groups: quarter 25 25 25⇤⇤⇤p < 0.01; ⇤⇤p < 0.05; ⇤p < 0.1. Robust standard errors clustered at the municipalitylevel.Placebo TestsI argue and provide evidence consistent with the idea that right-wing governments increasesocial transfers within left-wing constituencies after they have faced left-wing runner-upcontenders in elections. If it is the Leftist Threat between the right-wing incumbent andthe main contender what drives the post-election expansion of social transfers, we shouldnot observe the same expansion when the runner-up candidate is ideologically close to theright-wing incumbent, as the candidate does not pose a leftist threat. Similarly, we shouldnot observe any e↵ect when left-wing candidates are electorally weak.In the 2014 presidential elections in Colombia, O´scar Iva´n Zuluaga, the right-wingrunner-up candidate, was close to the ideological position of Juan Manuel Santos, therunning and then re-elected incumbent (Figure 4.2). If my argument is correct, I shouldnot observe a positive association between Zuluaga’s vote share and the post-election loggednumber of FA beneficiaries.To conduct the placebo test, I run the same regression specified in Eq. (C.1), butusing as independent variable the vote shares of Zuluaga and setting the reference periodin the quarter where the 2014 elections were held. Reassuringly, Table 4.2 shows no sta-624.5. Resultstistically significant association between opposition vote shares and logged number of FAbeneficiaries after the 2014 election.Table 4.2: E↵ect of 2014 Runner-Up’s Voteshare on Logged Number of FA Beneficiaries1 2 3Voteshare X Post-election (2014) 0.095 0.135 0.089(0.077) (0.085) (0.090)Municipality FE Yes Yes YesQuarter FE Yes Yes YesControls x Event No Yes YesCoalition dummy No No YesNum. obs. 25317 21447 21174Num. groups: municipality id 1108 906 895Num. groups: quarter 25 25 25⇤⇤⇤p < 0.01; ⇤⇤p < 0.05; ⇤p < 0.1. Robust standard errors clustered at themunicipality level.As I explained above, most left-wing political activity was excluded from the electoralarena before 2018. Right-wing governments had no credible left-wing opponents untilthis year. For example, in the 2015 local elections, the left-wing Polo Democra´tico partywon fewer than 2% Alcald´ıas. Indeed, left-wing candidates obtained votes in fewer than47% of municipalities, with an average vote share of 18.4%. As the incumbent right-winggovernment faced no threat from the left, we would not expect to see a positive correlationbetween left-wing vote share and FA enrollments after 2015.To provide empirical evidence showing the null e↵ect of the left-wing vote share inthe 2015 local elections, I collected electoral data and recently published data on the localcandidates’ political ideology (see Cabra-Ruiz et al., 2023) and estimated a model followingEq. (C.1). Table 4.350 shows the expected null association between left-wing vote sharesin the 2015 local elections and new FA beneficiaries.50Results from regression models reported in Table 4.3 have fewer observations because Cabra-Ruiz et al.(2023)’s database has missing information on the ideological position or vote shares of local politicians inseveral municipalities.634.6. Discussion: Plausible MechanismsTable 4.3: E↵ect of 2015 Left-Wing’s Voteshare on Logged Number of FA Beneficiaries1 2L Voteshare X Post-election (2015) 0.001 0.101(0.112) (0.107)Municipality FE Yes YesQuarter FE Yes YesControls x Event No YesCoalition dummy No NoNum. obs. 12584 11081Num. groups: municipality id 517 449Num. groups: quarter 25 25⇤⇤⇤p < 0.01; ⇤⇤p < 0.05; ⇤p < 0.1. Robust standard errors clustered atthe municipality level.4.6 Discussion: Plausible MechanismsThe results suggest that the ideological position of the main contenders can explain theexpansion of social transfers after the election, particularly when there is a clear left–rightdivide. To demonstrate why right-wing governments implement this strategy would requireme to elicit the incumbent’s political motivations. While this is beyond the scope of thisarticle, I discuss two mechanisms that may explain the expansion of social transfers toleft-wing constituencies under a right-wing government.The expansion of social transfers by right-wing governments might follow a conversionstrategy, where the right-wing incumbent aims to win over potential left-wing voters andincrease future votes. Likewise, it might correspond to a demobilization strategy, wherethe right-wing government expands transfers to the poor to satisfy a demand (i.e., bettersocioeconomic conditions) that might lead them to support the left, thus aiming to tamefuture support for the left-wing opponent. This strategy di↵ers from the conversion onebecause the incumbent does not expect to win over the potential left-wing voters. Instead,the incumbent aims to prevent their support for the left. Here, a necessary assumptionis that once the poor satisfy a prevailing material need, they might be less inclined toprefer left-wing candidates with extreme ideological positions, i.e., candidates ideologically644.7. Conclusionsfarther from the right.Building on canonical work (see Cox and McCubbins, 1986; Dixit and Londregan, 1996),I assume that the preference for the conversion strategy decreases with the Leftist Threatbetween the voter and the government since ideologically distant voters are more costlyto convince. Therefore, it would be more plausible that right-wing governments applydemobilization rather than the conversion strategy on left-wing supporters.514.7 ConclusionsThis article examines whether the incumbent’s ideological stance in relation to the defeatedcandidate influences their allocation of resources in the months following the election. Thearticle assumes that contenders with di↵erent ideologies pose di↵erent future risks to theestablishment. In particular, I analyze how the right-wing responds to support for a cred-ible left-wing contender–that is, a candidate who posed an ideological and electoral threatduring the election campaign. I argue that left-wing redistributive promises, macroeco-nomic impact and political legacies pose a greater threat to right-wing governments thancentrist or even right-wing anti-establishment parties do.I test my argument in Colombia, a developing democracy where the left-right politicaldivide is salient. I use data on two presidential elections and administrative records of acash transfer program. I find suggestive evidence that ideology matters when governmentsdecide how to expand social transfers. Specifically, I find that the electoral support for aleft-wing runner-up during a presidential election is positively associated with enrollmentsin a social program under a right-wing government. Importantly, results from placebotests suggest that right-wing governments do not implement this targeted expansion whenthe runner-up is ideologically closer or when left-wing candidates perform poorly in localelections.Due to the diculty of directly observing the motivations of incumbents behind their useof social policies, I discuss two plausible mechanisms that could explain the uncovered as-sociation between left-wing support and the right-wing expansion of social policies. I arguethat right-wing governments might aim to win over left-wing voters (conversion strategy)or prevent them from supporting future left-wing candidates (demobilization strategy). Iclaim that, under specific assumption, the latter strategy might be more plausible.This article proposes that empirical scholars working on distributive politics shouldincorporate the dimension of politicians’ ideology in future work. It can inform emergingscholarship on the timing of transfer allocations (Bueno, 2021) by showing that, undercertain conditions, governments can extend them after rather than before elections. Futureresearch must examine the post-election strategies adopted by left-wing governments when51Another necessary assumption here is that voters can choose from a pool of at least three candidateswith di↵erent ideological positions. The demobilization strategy would therefore aim to encourage votersto choose a centrist candidate over a left-wing one.654.7. Conclusionsthey have faced a right-wing runner-up.66Chapter 5ConclusionsThis dissertation has examined how and under what conditions economic and politicalelites respond to redistributive demands. Contrary to dominant accounts in the politicaleconomy literature that treat redistribution as a product of state-led initiatives underelectoral pressure or left-wing governance, my research shows that elites can and do initiateredistribution strategically to mitigate political threats and stabilize social order. Acrossthree empirical studies, I demonstrate that business elites and right-wing governments inColombia have engaged in redistributive behavior to forestall violent unrest, defuse electoralchallenges from the left, and manage expectations among the a✏uent. These findingsbroaden our understanding of elite agency and expand the conceptual and empirical terrainof redistributive politics in Latin America.The central theoretical contribution of this dissertation is to reconceptualize economicelites as actors who can address socio-economic grievances independently of the state.While the extant literature typically presents business elites as political veto players whouse state institutions to block redistribution (Boix 2003; Acemoglu Robinson 2006), I arguethat under conditions of state incapacity or political uncertainty, elites have incentives toengage directly in redistribution. This insight has particular relevance in contexts of weakstate capacity, where the cost of inaction may be higher than the cost of concession. Empir-ically, I show that such elite-led redistribution can take the form of job creation followingviolent protest, support for redistributive policies when promoted by ideologically proxi-mate governments, and the strategic allocation of transfers to left-leaning constituenciesfollowing elections.These arguments and findings make several contributions to political science. First,they o↵er a corrective to the dominant models of redistribution, which emphasize electoralmechanisms and state-centered solutions. By highlighting non-electoral, elite-driven chan-nels of redistribution, this dissertation shows how elites respond to redistributive pressuresthrough means that are under-theorized in current scholarship. Second, it contributes tothe growing literature on elite behavior in developing democracies, particularly in LatinAmerica, by demonstrating how elites engage in strategic concessions, not only to shapepolicy outcomes but also to preempt systemic threats to their interests. Third, it bridgesliteratures on protest, redistribution, and elite power, o↵ering an integrated framework tounderstand how private actors respond to social unrest and political polarization.My research also speaks to longstanding debates in the literature on violent protestand elite responses. While most studies suggest that violent protest is counterproduc-67Chapter 5. Conclusionstive, leading to public backlash or repression (Wasow 2020; Chenoweth Stephan 2011),I show that under specific conditions (i.e, low state capacity and fixed capital exposure)violent protest can prompt redistributive concessions from business elites. In this way, mywork challenges the prevailing assumption that private sector actors respond only to stateincentives or public opinion and instead foregrounds their autonomous strategic behavior.The methodological strength of this dissertation lies in its innovative use of data andmulti-method approach. I rely on administrative labor demand data, firm-level surveys,in-depth interviews with business elites, and original survey experiments with wealthy in-dividuals. These sources allow me to capture both behavioral responses and subjectiveexpectations, o↵ering a well-rounded account of elite decision-making. In doing so, I con-tribute to the growing empirical literature on elite politics, which has historically facedchallenges in measurement and access.Nonetheless, this research is not without limitations. The most significant constraintis external validity. All three studies are based on evidence from Colombia, a case markedby high inequality, weak state capacity, and a polarized political system. While Colombiao↵ers fertile ground for theory development, the extent to which these findings generalizeto other Latin American or Global South contexts remains an open question. Furthermore,the empirical period overlaps with the Covid-19 pandemic—a major socioeconomic shockthat likely influenced both elite behavior and redistributive demand. While I have takensteps to account for this context, the confounding e↵ects of such a large-scale event cannotbe fully ruled out.Despite these limitations, the findings of this dissertation have important practicalimplications. For donors and development actors, the results suggest that private sectorconcessions, especially those coordinated through business associations, may serve as ef-fective, if underutilized, tools for appeasing socio-economic grievances. For policymakers,the research underscores that right-wing governments may have “windows of opportunity”to implement redistributive policies that are not available to the left, precisely becausethe wealthy view redistribution under the Right as more stable and ecient. Understand-ing this dynamic can inform the design and timing of social policy reforms in polarizeddemocracies.This dissertation opens several promising avenues for future research. One immediateextension is to explore whether economic elites capitalize politically on the redistributiveconcessions they make in response to social unrest. While the current work focuses on thestrategic logic behind elite concessions, an important next step is to examine their down-stream political consequences. Do firms or business associations gain preferential accessto policymaking, regulatory relief, or reputational benefits in exchange for job creation orconcessions? This line of inquiry would deepen our understanding of the political returnsto redistribution from above. To pursue this, I plan to link firm-level responses (e.g., labordemand or investment decisions) to subsequent changes in lobbying behavior, campaign do-nations, or participation in state-corporate councils. A possible empirical strategy involvesmerging administrative employment or tax data with government registries of political68Chapter 5. Conclusionscontributions and lobbying activities. An event-study or di↵erence-in-di↵erences frame-work could then assess whether firms that respond generously to unrest later enjoy greaterpolitical access or state contracts. This research could reveal whether elite redistributionis not only strategic in terms of pacification, but also instrumental in building long-termpolitical capital.A second path for future research involves extending the theory of strategic elite con-cessions to other Latin American contexts, such as Mexico and Chile. These countries varymeaningfully from Colombia in terms of state capacity, institutional robustness, and thedensity of elite-political networks, o↵ering a rich basis for comparative analysis. Mexico’scentralized but fragmented elite structure, and Chile’s historically technocratic business-government ties, may shape both the likelihood and form of elite-led redistribution. Totest how my theory travels, I plan to construct a comparative dataset of protest events,private-sector responses (e.g., employment, philanthropy, or social investments), and gov-ernment capacity indicators. Fieldwork involving elite interviews and business associationdocuments in these countries would complement the quantitative work, enabling a cross-case comparative design that isolates how context shapes elite calculations. In doing so, Iaim to refine the theory’s scope conditions and generate a typology of elite responses thataccounts for variation in institutional and political embeddedness across Latin America.A third important direction is to investigate how di↵erent types of exogenous shockstrigger elite responses, especially in contexts of limited state capacity. In this disserta-tion, I leverage the Covid-19 pandemic as a shock that intensified inequality and unrest,prompting elite redistribution. But a growing empirical and normative challenge for po-litical economy is to understand how climate-related shocks, such as floods, droughts, orextreme weather events, a↵ect elite strategies. These events disproportionately harm vul-nerable populations and are increasingly tied to social unrest. My future work will examinewhether economic elites respond to climate shocks in ways that mirror or di↵er from re-sponses to economic shocks. This would involve combining georeferenced climate eventdata with firm-level employment or investment responses, as well as variation in protestactivity. A quasi-experimental approach, such as a regression discontinuity in space (RDsp)or spatial di↵erence-in-di↵erences, could identify causal e↵ects of climate shocks on elitebehavior. This research would bridge political economy, environmental politics, and in-equality studies, and o↵er timely insights into how elites adjust to redistributive pressuresunder global ecological stress.Together, these research directions build on the core theoretical framework developedin this dissertation, that elites are not merely passive defenders of the status quo but ac-tive, strategic agents who respond to perceived threats with calculated concessions. Eachproposed extension deepens, contextualizes, and tests this framework under di↵erent con-ditions, reinforcing the broader contribution of this research to the study of elite behavior,redistribution, and democratic resilience in the Global South.In sum, this dissertation provides a new lens through which to understand the politicsof redistribution in developing democracies. It demonstrates that elites are not merely veto69Chapter 5. 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Transfers per capita (COP) 63,800 814,460.9 355,186.2Covid-19 (deaths) 63,858 4,271.7 44,756.5Police Stations 63,858 1.1 1.183A.2. Validation CheckTable A.2: Summary Statistics - FirmsStatistic N Mean St. Dev.Expect Increase in Workers (share) 8,297 0.2 0.4Main Problem = Protests 8,297 0.04 0.2Main Problem = Taxes 8,297 0.2 0.4Main Problem = Competition 8,297 0.1 0.3Main Problem = COVID-19 8,297 0.1 0.3Main Problem = Demand 8,297 0.1 0.3Investment (share) 8,297 0.3 0.5Expected Gross Sales (share) 8,297 0.5 0.5Previous Separations (share) 8,297 0.2 0.4Economic Sector = Tertiary 8,297 0.2 0.4Note: Share refers to the proportion of firms.A.2 Validation CheckIn this section, I report results from a validation check of ACLED data. In the main text,I explain that I use data from the Superintendence of Surveillance and Private Security, apublic institution overseeing private security and surveillance services in Colombia. To runthe validation check, I focus on o↵enses that the Oce of the Attorney General of Colombiaclassified as related to protests in the Colombian Criminal Code: Violence against publicservant (Article 429); use or throwing of dangerous substances or objects (Article 359);disturbance in public transportation service - collective or ocial (Article 353); arson(Article 350); violence against a means of transport or causing damage to public roads orplaces (Article 218B); damage to another’s property (aggravating circumstances) (Article266); shooting a firearm at a vehicle (Article 356); damage to another’s property (Article265); obstruction of public roads that a↵ect public order (Article 353A).I restrict the validation check to April-May 2021, when the social unrest occurred,thus running a cross-section comparison rather than a panel analysis. Specifically, in thestatistical test, I compare municipalities exposed to violent/nonviolent protests againstmunicipalities without any type of protest. Moreover, I create a binary variable Protest-related Report that takes on 1 when municipalities had at least one protest-related reportand 0 otherwise. Table A.3 presents results from a simple linear regression and showsthat municipalities exposed to violent protests are 42% more likely to have protest-relatedreports relative to municipalities without protests. Importantly, column 2 shows that thelikelihood of having protest-related reports is lower in municipalities exposed to nonviolentprotests.84A.3. Testing AssumptionsTable A.3: ACLED - Validation CheckProtest-related Report1 2Violent Protest 0.413⇤⇤⇤(0.019)Nonviolent Protest 0.058⇤⇤⇤(0.010)Num. obs. 897 1027⇤⇤⇤p < 0.01; ⇤⇤p < 0.05; ⇤p < 0.1A.3 Testing AssumptionsMy theory assumes that lower-class people are more likely to engage in violent protests.Directly testing this assumption is challenging due to the lack of available data. Moreover,social desirability might prevent respondents from revealing their participation in violentprotests. However, LAPOP provides individual-level survey data for Colombia for indi-rectly testing my theoretical assumption. Specifically, between 2010 and 2014, LAPOPincluded a question measuring the level of support for roadblocks as a form of protest (Towhat extent do you approve or disapprove that people block streets or highways as a formof protest? ). Therefore, LAPOP data allow me to use a repeated cross-section regressionmodel to test my assumption.There might be two concerns regarding the validity of this empirical approach. First,the survey question does not directly refer to violence. Second, it does not inquire aboutprevious participation or the likelihood of joining a roadblock. However, the Colombianpenal code classifies roadblocks as violent forms of protests (Uprimny and Sa´nchez, 2023),and the indirect questioning attenuates concerns of social desirability bias (see Fisher,1993).I use the above question to build my dependent variable, Support for Violent Protests,which I utilize as a proxy to test my assumption. Respondents’ approval of roadblocksas a form of protest goes from 1 (disapprove) to 10 (approve). I create a binary variablethat takes on 1 for responses above 7 and 0 otherwise. Additionally, I collect data onasset ownership to build income deciles using principal component analysis and createthe independent variable, Lower Class, which takes on 1 for individuals in deciles 1, 2,or 3, and 0 otherwise. I include as control variables respondents’ sex, age, race, level ofeducation, marital status, ideology, number of kids, employment status, and geographiclocation. Moreover, I include the survey year on the right-hand side of the regression tocontrol for time trends.85A.4. Complete Sample - Coding SchemeTable A.4 shows the expected association between income position and support forviolent protests. Column 1 indicates that respondents who are lower class are more likelyto support violent protests. This association reaches conventional levels of statistical sig-nificance once I include the control variables (column 2).Table A.4: Testing AssumptionSupport for Violent Protests1 2Lower Class 0.018⇤ 0.022⇤⇤(0.010) (0.009)Controls No YesNum. obs. 6725 6030N Clusters 55 55⇤⇤⇤p < 0.01; ⇤⇤p < 0.05; ⇤p < 0.1. OLS models regress-ing support for violent protests on income position. Robuststandard errors clustered by region-year and a binary vari-able indicating whether the respondent is in an urban area.A.4 Complete Sample - Coding SchemeThe preferred models (equations 2.1 and 2.2) use subsets of municipalities to ensure thattreated units are compared against a pure control group. However, I also report resultsfrom a single model that includes the two main variables and uses the complete sampleof Colombian municipalities (equation 2.3). To utilize a unified regression framework, Iapply a coding scheme that allows me to include all municipalities in the same dynamicdi↵erence-in-di↵erence analysis:Table A.5: Treatment Variables - Coding SchemeViolence (Yes) Violence (No)Nonviolence (Yes) Violent protest = 1 Violent protest = 0Nonviolent protest = 0 Nonviolent protest = 1Nonviolence (No) Violent protest = 1 Violent protest = 0Nonviolent protest = 0 Nonviolent protest = 086A.5. Additional E↵ect of ViolenceA.5 Additional E↵ect of ViolenceIn the main text, I show that violent protests increase labor demand while nonviolentprotests have a null e↵ect on this outcome. Additionally, I demonstrate that the di↵erente↵ect is statistically significant. Here, I provide an additional test to demonstrate furtherthat violence leads to concessions. Specifically, I extend equation 2.3 by including aninteraction between Violent Protesti and Nonviolent Protesti. If the joint e↵ect of violenceand nonviolence increases labor demand, I should observe post-treatment positive andsignificant estimates of the interaction term. Figure A.1 plots the dynamic e↵ects of violentand nonviolent protests and their joint e↵ect on vacancies per capita. Reassuringly, FigureA.1 shows that it is the former what positively a↵ects labor demand.Figure A.1: Estimated E↵ect of Violent/Nonviolent Protests on Job Vacancies Per Capita- Interaction.Note. Estimated e↵ect of event on job vacancies per capita by type of economy (labor- orcapital-intensive). Regressions control for COVID-19 cases at baseline interacted with ⌧and municipality and year-month fixed e↵ects. Standard errors clustered by municipalityand department-year-month. Confidence intervals: 95%A.6 Heterogeneous Treatment E↵ectsMy findings demonstrate that labor demand increases in municipalities exposed to violentprotests. In this section, I present subgroup analyses to explore under which conditions wewould expect higher concessions among treated municipalities.First, I explore whether concessions are higher in municipalities with labor-intensiveeconomies or in those with capital-intensive economies. To conduct this heterogeneoustreatment e↵ect analyses, I collected administrative data on the value added by economicsectors within municipalities. The value-added measures the higher value created in theproduction process due to the combination of factors, including labor and capital. I clas-sified municipalities as labor-intensive when the sector that contributes the most to the87A.6. Heterogeneous Treatment E↵ectsvalue-added was labor-insensitive (e.g., the service sector), based on the National Admin-istrative Department of Statistics (DANE) data. I followed the same process to classifymunicipalities as capital-intensive. Figure A.2, panel (A) shows that concessions concen-trate in municipalities with labor-intensive economies.Figure A.2: Estimated E↵ect of Violent/Nonviolent Protests on Job Vacancies Per Capitaby Type of Economy.Note. Estimated e↵ect of event on job vacancies per capita by type of economy (labor- orcapital-intensive). Regressions control for COVID-19 cases at baseline interacted with ⌧and municipality and year-month fixed e↵ects. Standard errors clustered by municipalityand department-year-month. Confidence intervals: 95%Second, I investigate the types of vacancies posted between April 2019 and September2022. These analyses use data privately provided by the Employment Service Unit (ESU).For each municipality and month, the ESU records the level of education and experiencea vacancy requires, as well as the o↵ered salary.52 Results show that labor demand (i.e.,vacancies) in municipalities exposed to violent protests focuses on people with secondaryeducation (Figure A.3) and with a year or less of previous work experience (Figure A.4).Importantly, the o↵ered salary is low, around 360 USD per month (Figure A.5).52Since firms are not mandated to report this information when posting a vacancy, my analyses rely ona subset of vacancies for which this data is available. Therefore, results must be treated with caution.88A.6. Heterogeneous Treatment E↵ectsFigure A.3: Estimated E↵ect of Violent/Nonviolent Protests on Job Vacancies Per Capitaby Level of Education Required.Note. Estimated e↵ect of event on job vacancies per capita by level of education required(primary, secondary, or tertiary). Regressions control for COVID-19 cases at baselineinteracted with ⌧ and municipality and year-month fixed e↵ects. Standard errors clusteredby municipality and department-year-month. Confidence intervals: 95%Figure A.4: Estimated E↵ect of Violent/Nonviolent Protests on Job Vacancies Per Capitaby Level of Experience Required.Note. Estimated e↵ect of event on job vacancies per capita by level of experience required(no experience, less than a year of experience, or more than a year of experience). Re-gressions control for COVID-19 cases at baseline interacted with ⌧ and municipality andyear-month fixed e↵ects. Standard errors clustered by municipality and department-year-month. Confidence intervals: 95%89A.7. RobustnessFigure A.5: Estimated E↵ect of Violent/Nonviolent Protests on Job Vacancies Per Capitaby Level of Salary O↵ered.Note. Estimated e↵ect of event on job vacancies per capita by level of salary o↵ered (low,middle, or high). Regressions control for COVID-19 cases at baseline interacted with ⌧and municipality and year-month fixed e↵ects. Standard errors clustered by municipalityand department-year-month. Confidence intervals: 95%A.7 RobustnessA.7.1 Excluding Epicenter of UnrestIn the main text, I explain that Cali, Colombia, was the epicenter of the violent socialunrest. Protests in Cali were particularly violent and prominent. However, I demonstratethat results are not driven by this municipality. In fact, Figure A.6 shows that results arevirtually the same when I exclude Cali from the analysis.90A.7. RobustnessFigure A.6: Estimated E↵ect of Violent Protests on Job Vacancies Per Capita.Note. Estimated e↵ect of event on job vacancies per capita excluding Cali from the analysis.The regression includes the complete vector of controls at baseline interacted with ⌧ andmunicipality and year-month fixed e↵ects. Standard errors clustered by municipality anddepartment-year-month. Confidence intervals: 95%A.7.2 Alternative SpecificationsThe main empirical specification includes year-month fixed e↵ects to account for potentialtime-varying confounders that might a↵ect municipalities in a similar way. Figure A.7shows that results are unchanged when I use department-year-month fixed e↵ects, whichaccount for time-varying regional changes.The outcome variable I use in the main analyses is job vacancies per capita. However,Figure A.8 shows that results hold when I use job vacancies per working age population(15 - 64 years old).91A.7. RobustnessFigure A.7: Estimated E↵ect of Violent/Nonviolent Protests on Job Vacancies Per Capita.Note. Estimated e↵ect of event (violent or nonviolent protest) on job vacancies per capita.Regressions include controls at baseline interacted with ⌧ and municipality and department-year-month fixed e↵ects. Standard errors clustered by municipality and department-year-month. Confidence intervals: 95%Figure A.8: Estimated E↵ect of Violent/Nonviolent Protests on Job Vacancies per workingage pop.Note. Estimated e↵ect of event (violent or nonviolent protest) on job vacancies per workingage population. Regressions include municipality and year-month fixed e↵ects as wellas baseline controls interacted with ⌧ . Standard errors clustered by municipality anddepartment-year-month. Confidence intervals: 95%The main analyses control for logged number of COVID-19 cases. Figure A.9 shows thatresults hold when I control for logged number of COVID-19 cases among the working-age92A.7. Robustnesspopulation instead.Figure A.9: Estimated E↵ect of Violent/Nonviolent Protests on Job Vacancies per capita.Note. Estimated e↵ect of event (violent or nonviolent protest) on job vacancies per capita.Regressions include municipality and year-month fixed e↵ects as well as baseline controlsinteracted with ⌧ . Standard errors clustered by municipality and department-year-month.Confidence intervals: 95%A.7.3 Geographic MatchingI implement a geographic matching design to control for potential unobserved confoundersthat might change with distance (e.g., historical origins, level of development, and politicalpreferences). Specifically, I match (without replacement) a given municipality exposed toviolent/nonviolent protests to the closest municipality unexposed to protests and conductthe event study analysis. This empirical strategy reduces the subset of municipalities usedto analyze the e↵ect of violent protests from 897 to 129. Likewise, it reduces the subsetemployed in the analysis of nonviolent protests from 1050 to 347. Figure A.10 demonstratesthat the substantive findings hold. Panel (A) shows that municipalities exposed to violentprotests experience an increase in labor demand after the event. Panel (B) shows that thee↵ect of nonviolent protests is smaller and closer to zero.93A.7. RobustnessFigure A.10: Estimated E↵ect of Violent/Nonviolent Protests on Job Vacancies per capita- Geographic Matching.Note. Estimated e↵ect of event (violent or nonviolent protest) on job vacancies per capitausing a geographic matching strategy. Regressions include municipality and year-monthfixed e↵ects. Standard errors clustered by municipality and department-year-month. Con-fidence intervals: 95%A.7.4 Spatial DonutsI use a spatial donut approach to assuage concerns related to spillovers in treatment. First,I drop from the analysis control municipalities within 100km of a given treated municipality.I repeat this procedure utilizing a 200km area. I reestimate equations 2.1 and 2.2 (excludingcovariates) and find e↵ects consistent with my theory. Figure A.11 shows a clear positivee↵ect of violent protests on jab vacancies per capita and a smaller e↵ect of nonviolentprotests. Importantly, the e↵ect of the former is larger and well precisely estimated evenwhen the sample size is smaller than the one I use to assess the e↵ect of nonviolent protests.Figure A.12 shows highly similar results.94A.7. RobustnessFigure A.11: E↵ect of Violent/Nonviolent Protests - 100km Donut.Note. This figure presents the e↵ect of violent/nonviolent protests on job vacancies percapita after removing control municipalities within 100km of a given treated municipality.Regressions include municipality and year-month fixed e↵ects. Standard errors clusteredby municipality and department-year-month. Confidence intervals: 95%.Figure A.12: E↵ect of Violent/Nonviolent Protests - 200km Donut.Note. This figure presents the e↵ect of violent/nonviolent protests on job vacancies percapita after removing control municipalities within 200km of a given treated municipality.Regressions include municipality and year-month fixed e↵ects. Standard errors clusteredby municipality and department-year-month. Confidence intervals: 95%.95A.7. RobustnessA.7.5 Synthetic ControlTo further demonstrate the theorized e↵ect of violent and nonviolent protests on labordemand, I implement a generalized synthetic control method (see Xu, 2017). This methodaddresses potential outcomes as a missing data problem and imputes counterfactuals foreach treated unit using information from the control group (i.e., municipalities withoutprotests), thus conditioning on well-matching pre-trends. This approach provides morereliable estimates of the average treatment e↵ect on the treated (ATT) when there areunobserved cross-unit time-varying confounders (Liu, Wang and Xu, 2024). I estimate thesynthetic control utilizing a matrix completion (MC) estimator, which reduces the risk ofoverfitting and relies on nonparametric inference (see Athey et al., 2021). Consistentlywith my main results, Figure A.13 shows that violent protests have the expected positiveimpact on vacancies per capita while nonviolent protests have a null e↵ect.Figure A.13: E↵ect of Violent/Nonviolent Protests on Job Vacancies Per Capital - ASynthetic Control Approach (MC Estimator).Note. This figure presents the e↵ect of violent/nonviolent protests on job vacancies percapita. The e↵ects are estimated using a synthetic control method with a MC estimator.Period equals 0 for the event (April 2021). Standard errors are based on nonparametricbootstraps (blocked at the municipality level) of 1,000 times.Results from the synthetic control method are virtually the same when I use an inter-active fixed e↵ects (IFE) estimator. In contrast to the MC estimator, the IFE one relieson parametric inference and involves hard imputation, i.e., it directly fills in missing val-96A.8. State Concessionsues (counterfactuals) with specific estimated values rather than assigning a distribution ofpossible values, as the MC estimator does (see Xu, 2017).Figure A.14: E↵ect of Violent/Nonviolent Protests - A Synthetic Control Approach (IFEEstimator).Note. This figure presents the e↵ect of violent/nonviolent protests on job vacancies percapita. The e↵ects are estimated using a synthetic control method with an IFE estimator.Period equals 0 for the event (April 2021). Standard errors are based on parametricbootstraps (blocked at the municipality level) of 1,000 times.A.8 State ConcessionsMy argument states that private elites rather than the State concede to violent protests.In the main text, I demonstrate that the government does not concede by increasing labordemand in the public sector. However, the government could concede via private employers.Specifically, the government could assign contracts to private actors who can further hireindividuals to perform the contracted tasks. In this section, I provide empirical evidencethat rules out this possibility. I use data from the Electronic System for Public Procurement(SECOP in Spanish) on the number and value of contracts assigned by the State to privateactors. The data includes all the activities the government outsources to private actors.I focus on contracts assigned from 2018 to 2022 and conduct the dynamic di↵erence-in-di↵erence analyses specified in equations 2.1 and 2.2 in the main text. Figure A.15demonstrates that the government does not concede to nonviolent or violent protests via97A.8. State Concessionscontracts. Furthermore, Figure A.16 shows that the value of the assigned contracts doesnot increase in response to nonviolent/violent protests.Figure A.15: Estimated E↵ect of Violent/Nonviolent Protests on Logged Number ofAwarded Contracts.Note. Estimated e↵ect of event (violent or nonviolent protest) on logged awarded con-tracts. The regression includes controls at baseline interacted with ⌧ and municipality andyear-month fixed e↵ects. Standard errors clustered by municipality and department-year-month. Confidence intervals: 95%98A.9. Concessions in Control GroupFigure A.16: Estimated E↵ect of Violent/Nonviolent Protests on Logged Value of AwardedContracts.Note. Estimated e↵ect of event (violent or nonviolent protest) on logged value of awardedcontracts. The regression includes controls at baseline interacted with ⌧ and municipalityand year-month fixed e↵ects. Standard errors clustered by municipality and department-year-month. Confidence intervals: 95%A.9 Concessions in Control GroupMy theory explains that elites can concede by implementing reactive and preemptive mea-sures. My data only allow me to provide evidence for the former. However, I present sugges-tive qualitative evidence of preemptive measures in the main text (see Figure 2.11). Here,I further explore whether elites implement preemptive measures. Specifically, I present theover-time evolution of vacancies per capital in control municipalities. If elites are imple-menting measures to inhibit future violent unrest, I should observe an increase in labordemand in control municipalities. However, Figure A.17 shows no clear upward trend invacancies per capita. A more rigorous analysis, reported in Figure A.18, shows that va-cancies per capital tend to decrease in control municipalities. Therefore, elites might tendto react to rather than prevent violent unrest.99A.10. Quantitative Text AnalysisFigure A.17: Evolution of Vacancies per capita in Untreated Municipalities.Note. Average number of vacancies per capita over time across municipalities without anytype of protestsFigure A.18: Estimated Evolution of Vacancies per capita in Untreated Municipalities.Note. Estimated average number of vacancies per capita within municipalities without anytype of protests. The OLS estimation controls for (logged) COVID-19 cases at baselineinteracted with ⌧ and municipality and year-month fixed e↵ects. Standard errors clusteredby municipality and department-year-month. Confidence intervals: 95%A.10 Quantitative Text AnalysisA.10.1 News ArticlesIn the paper, I applied a novel natural language processing (NLP) method developed byRodriguez, Spirling and Stewart (2023) that allows me to identify the embeddedness ofthe word ‘violence’ in news articles in Colombia from 2018 to 2022 applying the regressionmachinery, i.e., I can test the hypotheses that the distinctiveness o ‘violence’ is systemati-100A.10. Quantitative Text Analysiscally di↵erent in 2021 relative to other years. I used the database Factiva to collect printedarticles that included words starting with protest*. After collecting the articles, I created acorpus using the R package quanteda (Benoit et al., 2018). In this corpus, I trained a setof word embeddings53 using the Word2Vec algorithm and the R package text2vec (Ro¨der,Both and Hinneburg, 2015). I followed the standard practice and set vector dimensional-ity to length 300, used a window size of 6, and set the maximum number of iterations fortraining the embedding layer to 100. I trained the embedding layer in words that appear atleast 5 times across all the documents. I then computed the transformation matrix to runthe embedding regression using the R package conText (Rodriguez, Spirling and Stewart,2023). This matrix reweighs words that appear with high frequency in the corpus. I thenran an embedding regression using conText to compare the embeddedness of the word‘violence’ in news articles across years, keeping constant the number of words per articleand the newspaper outlet. In the regression, I used permutation for hypothesis testingand obtained bootstrapped 95% CIs. As Rodriguez, Spirling and Stewart (2023) explain,this regression produces norms of ˆs, i.e., the regression coecients are scalars (distances)between groups (e.g., years).Table A.6: Top 5 Nearest Neighbors for ‘protest*’Year2019 protesta, pac´ıfica, marchas, ciudadanos,calles (ENG: protest, peaceful, demonstrations, citizens, streets)2021 protesta, manifestaciones, protestas, marchas,violencia (ENG: protest, rallies, protests, demonstrations, violence)A.10.2 Business StatementsTo collect the 50 business statements I used in the text data analysis, I searched the websitesof all Colombian chambers of commerce (n = 47) and other well-known business associations(n = 8) located in treated municipalities. I scraped statements related to the 2021 protestproduced by the business associations. Specifically, I scraped documents that includedprotest-related words (protests, strikes, demonstrations, mobilizations, marches, blockades)and posted in 2021. I omitted statements with unavailable publication dates. Importantly,I applied the same search criteria to business associations in control municipalities andfound no statements related to the 2021 protests.I analyzed the 50 statements using natural language processing to identify salient emo-tions in the statements. To do so, I followed the standard procedure in text analysis: Idefined a list of stemmed terms related to protests, removed stop words, and extracted53Word-embedding techniques keep the context and order of the text, thus allowing the exploration ofsemantic associations between words. Table A.6 gives the 5 nearest neighbors of protest* in 2019 and 2021.As I would expect, this term is mentioned in the context of protest-related words.101A.11. Mechanismprotest terms and their textual context using a window size of ten. I then applied a dic-tionary of sentiments to the specified context and estimated the term frequency using aboolean weighting scheme. Here, I depart from the commonly utilized tf-idf weighting (seeGrimmer, Roberts and Stewart, 2022). The tf-idf weighting down weights frequent emo-tions across documents, which is what I want to capture. As expected, the most salientemotion is “fear”.A.11 MechanismA.11.1 Firm-level DataIn the main text, I presented repeated cross-section models with firm-level data from theChamber of Commerce of Cali (CCC). The CCC surveys a random sample of managersand sta↵ in charge of the financing area from aliated firms via telephone. Here, I specifythe variables I use and the model I estimate (see summary statistics in Table A.2).To build the Problem variables, I use the following survey question: “What was yourcompany’s main problem during the semester for the normal development of its activities?”I coded answers related to a given issue, e.g., protests, as 1 and 0 otherwise, thus creatingfive discrete variables, one per issue (taxes, protests, demand, COVID-19, competition).54I build the outcome variable Likelihood of Increasing Number of Employees as a binaryvariable that takes on 1 when the employer states that “The number of employees in his/hercompany will increase in the next six months compared to the current semester” and 0otherwise. Because the CCC only repeats a subset of questions across survey waves, I onlyuse questions repeated across waves to create control variables. Specifically, I use previousinvestments in fixed capital (Investment), expected increases in gross sales (Expected GrossSales), and employment separation in previous semester (Previous Separations) as controls.Investment is a binary variable that relies on the survey question “Did you make anyinvestments in your company during the semester, such as the purchase of machinery,equipment, or expansion of production capacity?”. I code positive responses (i.e., “yes”)as 1 and 0 otherwise. Expected Gross Sales takes on 1 when a firm states that “The totalvalue of the company’s sales in the current six-month period compared to the previoussix-month period” will increase and 0 otherwise. Previous Separations equals 1 when afirm reports that “The number of employees in the company during the six-month periodcompared to the previous six-month period” decreased and 0 otherwise. Finally, I use yearas a control for time trends. With these variables, I estimate the following model, one perproblem:yi = ↵+ 1 Problemi + ⇣1Zi + 1 Eventi+ 1 Problemi ⇥ Eventi + 2 Zi ⇥ Eventi + yeari + ✏i (A.1)54There are more issues reported by firms. However, I focus on the issues most firms referred to in 2021.102A.11. Mechanismwhere y is a binary variable that equals 1 if firm i expects to increase the labor forcein the following semester. Problemi is the given most important problem (taxes, protests,demand, COVID-19, or competition). Zi is the vector with control variables. I interactthese variables with Eventi, an indicator equal to one for all semesters since 2021-1. Ialso include yeari in the estimation to control for time trends. I cluster the errors bysector-semester. This clustering prevents me from creating a few clusters, which wouldgenerate unreliable estimates. Moreover, it allows autocorrelation of the error term withinthe economic sector and semester. The coecient of interest is 1, which captures thee↵ect of a given problem on the likelihood of increasing the number of employees beforeand after the April 2021 national strike.103A.11. MechanismTable A.7: E↵ect of Most Important Problem on Likelihood of Increasing Number ofEmployeesLikelihood of Increasing Number of Employees1 2 3 4 5(Intercept) 0.031 0.034 0.035 0.035 0.031(0.017) (0.108) (0.018) (0.017) (0.017)(Main Problem = Protests) 0.156⇤⇤(0.052)(Main Problem = Protests) x Event 0.172⇤⇤(0.054)(Main Problem = Taxes) 0.002(0.125)(Main Problem = Taxes) x Event 0.006(5.902)(Main Problem = Competition) 0.027⇤(0.013)(Main Problem = Competition) x Event 0.071(0.062)(Main Problem = Demand) 0.027⇤(0.014)(Main Problem = Demand) x Event 0.039(0.035)(Main Problem = COVID-19) 0.018 0.018 0.019 0.025 0.018(0.021) (0.021) (0.021) (0.021) (0.021)(Main Problem = COVID-19) x Event 0.035 0.028 0.027 0.036 0.028(0.065) (142.718) (0.065) (0.068) (0.066)Investment 0.081⇤⇤⇤ 0.082 0.080⇤⇤⇤ 0.079⇤⇤⇤ 0.080⇤⇤⇤(0.011) (0.037) (0.011) (0.011) (0.011)Investment x Event 0.018 0.019 0.020 0.017 0.019(0.029) (41.783) (0.030) (0.030) (0.029)Expected Gross Sales 0.340⇤⇤⇤ 0.339⇤⇤ 0.339⇤⇤⇤ 0.340⇤⇤⇤ 0.340⇤⇤⇤(0.009) (0.011) (0.009) (0.009) (0.009)Expected Gross Sales x Event 0.028 0.026 0.027 0.028 0.026(0.031) (27.852) (0.030) (0.029) (0.030)Previous Separations 0.066⇤⇤⇤ 0.068 0.066⇤⇤⇤ 0.069⇤⇤⇤ 0.067⇤⇤⇤(0.015) (0.039) (0.015) (0.015) (0.015)Previous Separations x Event 0.060 0.061 0.061 0.058 0.061(0.051) (5.177) (0.051) (0.050) (0.050)Time Trend Yes Yes Yes Yes YesNum. obs. 8297 8297 8297 8297 8297N Clusters 34 34 34 34 34⇤⇤⇤p < 0.01; ⇤⇤p < 0.05; ⇤p < 0.1. OLS models interacting main problem and controls with the Event dummy (0 for semestersbefore 2021-1 and 1 otherwise). Robust standard errors clustered by economic sector-semester. 104A.11. MechanismTable A.7, column 1, shows the expected positive and statistically significant e↵ect ofprotests after the 2021 event. This e↵ect holds when I control for previous investments infixed capital and time trends (column 2). The estimated e↵ect of protests nets the impactthat other issues a↵ecting firms could have on the likelihood of increasing labor demand,such as COVID-19 or taxes. Substantively, these results indicate that protests, rather thanother concerns, motivated firms to plan more hiring.A.11.2 InterviewsI conducted interviews over approximately six months between the summer and fall of2023. I used selective and snowball (nonprobability) sampling. I employed the former tocontact visible and accessible business elites whose contact information was available onwebsites. Once I contacted the first interviewees, I used the snowball technique to findadditional elites and to build credibility and trust among them. The interviewees includebusiness owners, chief executive ocers, managers, NGO directors, executive directors ofbusiness associations, and presidents of chambers of commerce.I did all interviews remotely, via Zoom or phone, and used a semi-structured formatdesigned to probe the theorized mechanism. These interviews helped me understand howelites experienced the period of social unrest, their concerns, how they perceived the state,and how they decided to act. Importantly, these interviews allowed me to understandthe pivotal role that business associations played in coordinating responses and shed lighton types of concessions that are hard to capture with existing quantitative data (e.g.,relaxation of hiring protocol).Interviews usually lasted around 40 minutes. I recorded interviews after requestingpermission. All interviewees granted their informed consent. The data collection receivedinstitutional review board (IRB) approval from the University of British Columbia.I structured the interviews around the following questions:• How did the company/business sector experience the 2021 social unrest?• Was the business sector a↵ected? If so, how?• Was the business sector involved in the issue? If so, how?• Did business elites go to any government institution?• What do you think were the motivations behind these protests?• Why do you think some protests turned violent? / Why do you think there were noviolent protests there?• What was the response of the private sector to these protests?• What was your company’s response?105A.11. Mechanism• Was there any coordination among companies to respond to social unrest? If so, howdid they manage to coordinate a group e↵ort?• Why did the business sector respond in this manner?• More generally, how do you think the protests a↵ect business?• What needs to be done to reduce the protests and calm the population?• How did you experience the period of protests?• What fears did you have?• What decisions did you consider taking?• What do you consider your role as a businessman in the face of these outbreaks ofsocial unrest? [passive/active role]• How did you feel the local government responded to the protests?• How did you feel the national government responded to the protests?• Do you think that a social outburst like the one in 2021 could happen again?• What do you think the business sector could do to avoid a new wave of protests?• What do you think the State could do to avoid a new wave of protests?106Appendix BGovernment Ideology and Supportfor Redistribution among theWealthyB.1 Descriptive StatisticsIn this section, we present descriptive data of our samples. Sample 1 refers to respondentssurveyed before the first election round (April 2022). Sample 2 contains respondents sur-veyed during the run-o↵ (June 2022). Overall, the distribution of both samples is similarin several characteristics since we recontacted most respondents during the second survey.In figure B.2, we report respondents perception of Herna´ndez’s ideological placement.107B.1. Descriptive StatisticsFigure B.1: Descriptive StatisticsNote. This figure shows the distribution of demographics characteristics and vote intentionin samples 1 and 2.108B.2. External ValidityFigure B.2: Respondents’ Perception of Herna´ndez’ Ideology Position.Note. This figure shows how respondents from Study 2 perceived the ideological positionof Rodolfo Herna´ndezB.2 External ValidityWe employ data from a recent nationally representative survey to check how our samplecompares to the population in SEL 5 or 6. Specifically, we use data from the 2021 PoliticalCulture Survey (ECP) by the National Administrative Department of Statistics (DANE,2021). Figure B.3 shows that our respondents are similar to the broad upper-class popu-lation in terms of gender (p > 0.55), modal age (p > 0.64), and marital status (p > 0.07).However, our sample is more educated (p < 0.01) and less catholic (p < 0.01).Figure B.3: External Validity.Note. This figure compares respondents from our online sample (grey) and a nationallyrepresentative sample from SEL 5 or 6 (black) based on several sociodemographic charac-teristics. All estimates are presented with 95% CIs.109B.3. Vignettes and Survey QuestionsB.3 Vignettes and Survey QuestionsIn this section, we present the vignettes, questions, and response options we employed inthe experimental module of the survey. To avoid repetition, we use [T0/T1] to indicatethe part we randomized in the vignettes. T0 refers to the left-wing candidate, GustavoPetro. T1 refers to the right-wing candidate: Federico Gutie´rrez in Study 1 and RodolfoHerna´ndez in Study 2.Progressive Taxation:In Colombia, income tax is progressive: people withhigher incomes pay more taxes. For example, a person earning 2.5 million permonth pays an income tax of 10%, while a person earning 15 million per monthpays an income tax of 24.5%. To what extent do you agree or disagree withthe income tax for this second group going from 24.5 to 29.5 under a [T0/T1]government, i.e., that those earning 15 million would go from paying 3.7 to 4.4million in taxes?Response options: 1 strongly disagree - 5 strongly agreeSubsidies:To what extent do you agree or disagree with implementing a tax forstrata 5 and 6 to subsidize 10% of the utility spending of strata 1 and 2 undera government of [T0/T1]?Response options: 1 strongly disagree - 5 strongly agreeUncertainty:Several candidates have considered implementing taxes to solvethe State’s fiscal deficit. One of the proposals is to increase the tax on incomereceived by corporate shareholders. Currently this tax is 10%. Imagine that[T0/T1] proposes to increase this tax to 13%, if [T0/T1] is elected president,do you expect him to implement exactly the tax he proposes, or do you expectthe tax implemented to be lower or higher than the one proposed?Response options: Please, use the slider to indicate the tax you expect thecandidate to implement55.Eciency:How likely do you think it is that the money to help the poor willactually be spent on the poor under [T0/T1]’s government?Response options: 1 not at all likely - 3 very likelyInstability:How likely do you think it is that the social policies of [T0/T1]’sgovernment will put Colombia’s economic stability at risk?Response options: 1 not at all likely - 3 very likely55The slider goes from 3 to 23110B.3. Vignettes and Survey QuestionsWe also included questions to measure the following pre-treatment covariates:AgeFemaleCapital: In what city do you live?. Coded as 1 if respondent lived in a capitalcity.White: Do you consider yourself a white, mestizo, indigenous, Afro-descendant,mulato or other? Coded as 1 if respondent answered ’white’, 0 otherwise.Catholic: If you are a member of any religion, please indicate which one.Coded as 1 if respondent answered ’catholic’, 0 otherwise.Married: What is your marital status? Coded as 1 if respondent answered’married’.University degree: What is the last grade of education you completed? Codedas 1 if respondent answered ’college (university)’, 0 otherwise.Employed: What is your occupation? Coded as 1 if respondent answered’business owner’, ’salaried worker’ or ’self-employed’, 0 otherwise.Exposure to crime: Have you been a victim of robbery, assault, extortion,threats or any other type of criminal act in the last 12 months? Coded as 1 ifrespondent answered ’yes’, 0 otherwise.Guerrilla threat: Which of the following groups represents the greatest threatto your security? Please indicate all that apply. Coded as 1 is respondent’sselection included ’guerrilla’, 0 otherwise.Intention to migrate: Do you intend to move to live or work in anothercountry in the next three years? Coded as 1 if respondent answered ’yes’, 0otherwise.Trust in institutions: How much confidence do you have in the Congress/politcalparties/armed forces? We built an index by averaging responses from the threequestions.Vote intention: For whom do you plan to vote?Ideology: Nowadays when talking about political trends, many people talk aboutthose who sympathize more with the left or with the right. Depending on whatyou mean by the terms ’left’ and ’right’ when you think about your politicalviews, would you describe yourself as right-wing, center-right, center, center-left, or left-wing? Response options went from 1 (Left) to 5 (Right).Resentment: Please indicate how much you agree or disagree with the follow-ing statement: There is a lot of social resentment in the country. Here, highervalues indicate a higher perception of social resentment (from 1 to 5).Trust Herna´ndez: How much confidence do you have in Rodolfo Herna´ndezas a possible president of Colombia? Higher values indicate a higher trust inthe candidate (from 1 to 3).111B.4. Equivalence TestsB.4 Equivalence TestsIn this section, we show results from equivalence tests. These tests allow us to check ifrespondents exposed to the left-wing candidate (T0) and those exposed to the right-wingcandidate (T1) are equivalent across several covariates. Because we use 5 experimentalvignettes in both studies, we report 10 equivalence tests. In these analyses, p-values indicatewhether we reject the null of nonequivalence between treatment groups. Specifically, wereport equivalence in terms of age, gender (female), place of residence (capital city), race(white), religion (catholic), marital status (married), education (university degree), andemployment (employed).Table B.1: Equivalence Table (Study 1) - T0 vs T1Variable Mean Di↵erence CI (+/-) p-valProgressive Taxationage -0.120 0.269 0.000female 0.005 0.047 0.005capital 0.054 0.107 0.245white -0.027 0.085 0.037catholic -0.045 0.099 0.156married -0.021 0.077 0.027university degree 0.017 0.073 0.020employed -0.039 0.090 0.179right ideology -0.026 0.090 0.050Subsidiesage 0.022 0.150 0.000female -0.048 0.106 0.122capital 0.038 0.091 0.118white 0.033 0.092 0.055catholic -0.010 0.058 0.015married -0.023 0.080 0.033university degree -0.071 0.129 0.311employed 0.023 0.073 0.072right ideology 0.002 0.002 0.003Uncertaintyage 0.072 0.228 0.000female 0.021 0.081 0.032capital 0.037 0.094 0.127white -0.052 0.113 0.153catholic -0.100 0.157 0.738112B.4. Equivalence Testsmarried -0.044 0.104 0.117university degree 0.032 0.093 0.063employed 0.047 0.100 0.254right ideology 0.018 0.083 0.036Eciencyage 0.059 0.206 0.000female -0.001 0.001 0.001capital 0.027 0.080 0.063white -0.041 0.100 0.084catholic 0.016 0.069 0.027married 0.014 0.068 0.016university degree -0.039 0.097 0.079employed -0.009 0.055 0.023Instabilityright ideology 0.010 0.065 0.014age -0.004 0.004 0.000female -0.049 0.107 0.127capital 0.022 0.075 0.046white -0.001 0.001 0.001catholic -0.015 0.068 0.025married 0.000 0.000 0.000university degree 0.009 0.060 0.009employed 0.023 0.073 0.072right ideology 0.011 0.069 0.017Table B.2: Equivalence Table (Study 2) - T0 vs T1Variable Mean Di↵erence CI (+/-) p-valProgressive Taxationage -0.082 0.226 0.000female 0.025 0.081 0.030capital 0.009 0.057 0.011white -0.012 0.065 0.011catholic -0.011 0.060 0.018married -0.054 0.110 0.160university degree -0.002 0.002 0.002employed -0.002 0.002 0.004right ideology 0.023 0.084 0.035113B.4. Equivalence TestsSubsidiesage -0.046 0.187 0.000female -0.059 0.116 0.193capital 0.054 0.108 0.202white 0.029 0.086 0.039catholic 0.035 0.088 0.098married -0.004 0.034 0.003university degree 0.053 0.110 0.151employed -0.032 0.080 0.130right ideology 0.062 0.123 0.225Uncertaintyage -0.013 0.116 0.000female 0.050 0.106 0.126capital -0.033 0.087 0.071white -0.068 0.125 0.263catholic 0.040 0.092 0.124married 0.028 0.084 0.040university degree -0.013 0.066 0.012employed -0.003 0.026 0.007right ideology 0.022 0.083 0.033Eciencyage 0.144 0.288 0.000female 0.075 0.132 0.344capital 0.009 0.057 0.011white 0.025 0.081 0.029catholic 0.004 0.038 0.006married 0.045 0.101 0.104university degree -0.047 0.104 0.113employed -0.033 0.081 0.140Instabilityright ideology -0.018 0.077 0.024age 0.093 0.237 0.000female -0.044 0.101 0.095capital 0.005 0.041 0.005white 0.025 0.082 0.031catholic -0.043 0.096 0.150married -0.045 0.101 0.104university degree 0.026 0.083 0.034114B.5. Regression Results - Survey Experimental Dataemployed -0.045 0.093 0.248right ideology 0.002 0.002 0.002B.5 Regression Results - Survey Experimental DataTable B.3: Regression Results - Study 1 (without covariates)Pro. Tax. Subsidies Uncertainty Eciency InstabilityBase category(T0 = Leftwing Candidate) 0.395⇤⇤⇤ 0.196⇤⇤⇤ 1.143⇤⇤⇤ 0.164⇤⇤⇤ 0.518⇤⇤⇤(0.025) (0.020) (0.271) (0.019) (0.026)T1 = Rightwing Candidate 0.094⇤⇤⇤ 0.042 0.649⇤ 0.169⇤⇤⇤ 0.259⇤⇤⇤(0.036) (0.030) (0.365) (0.031) (0.034)R2 0.009 0.003 0.004 0.038 0.071Adj. R2 0.008 0.001 0.003 0.037 0.070Num. obs. 760 773 723 764 746RMSE 0.495 0.412 4.939 0.424 0.470⇤⇤⇤p < 0.01; ⇤⇤p < 0.05; ⇤p < 0.1. Robust standard errors.115B.5. Regression Results - Survey Experimental DataFigure B.4: Dependent Variables in Original Scales - Study 1.Note. This figure shows the e↵ect of the treatment (proposal by the right-wing candidate)on the dependent variables at their original scales. DVs in a 5-point scale go from stronglydisagree (1) to strongly agree (5), DVs in a 3-point scale go from not at all likely (1) tovery likely (3), and the DV in a 20-point scale goes from 3 to 23. OLS models with robuststandard errors. Confidence intervals: 95%.Table B.4: Regression Results - Study 2 (without covariates)Pro. Tax. Subsidies Uncertainty Eciency InstabilityBase category(T0 = Leftwing Candidate) 0.380⇤⇤⇤ 0.181⇤⇤⇤ 1.121⇤⇤⇤ 0.193⇤⇤⇤ 0.568⇤⇤⇤(0.024) (0.019) (0.239) (0.020) (0.026)T1 = Rightwing Candidate 0.089⇤⇤ 0.048⇤ 0.320 0.179⇤⇤⇤ 0.337⇤⇤⇤(0.035) (0.028) (0.316) (0.031) (0.033)R2 0.008 0.004 0.001 0.039 0.119Adj. R2 0.007 0.002 0.000 0.038 0.118Num. obs. 797 819 832 804 798RMSE 0.493 0.403 4.560 0.444 0.458⇤⇤⇤p < 0.01; ⇤⇤p < 0.05; ⇤p < 0.1. Robust standard errors.116B.5. Regression Results - Survey Experimental DataFigure B.5: Dependent Variables in Original Scales - Study 2Note. This figure shows the e↵ect of the treatment (proposal by the right-wing candidate)on the dependent variables at their original scales. DVs in a 5-point scale go from stronglydisagree (1) to strongly agree (5), DVs in a 3-point scale go from not at all likely (1) tovery likely (3), and the DV in a 20-point scale goes from 3 to 23. OLS models with robuststandard errors. Confidence intervals: 95%.Table B.5 reports treatment e↵ects on Uncertainty, alternatively coded.56 Specifically, wecoded the outcome variable so that it takes a value of -1 when respondents expected alower tax rate, 0 when they expected the same, and 1 when they expected a higher rate.Results are statistically significant and consistent with our theoretical predictions.56This analysis was not pre-registered.117B.5. Regression Results - Survey Experimental DataTable B.5: E↵ect on Uncertainty (Alternative coding)Study 1 Study 21 2 3 4Base category(T0 = Leftwing Candidate) 0.244⇤⇤⇤ 0.331 0.240⇤⇤⇤ 0.497⇤⇤(0.047) (0.312) (0.043) (0.252)T1 = Rightwing Candidate 0.154⇤⇤ 0.171⇤⇤ 0.114⇤ 0.121⇤(0.066) (0.074) (0.059) (0.065)Covariate Adjustment No Yes No YesNum. obs. 694 543 804 625RMSE 0.866 0.840 0.833 0.811⇤⇤⇤p < 0.01; ⇤⇤p < 0.05; ⇤p < 0.1. OLS models with robust standard errors.B.5.1 Alternative ExplanationPetro is a former guerrilla member and has been often linked to former radical-left Venezue-lan president Hugo Cha´vez. Petro’s candidacy in 2018 and 2022 triggered fears of potentialproperty rights violations and expectations of macroeconomic mismanagement given thecandidate’s promises of social spending and public sector expansion. Therefore, our resultsmight be about fears of and opposition to Petro rather than general opposition to the Left.Since Petro was the only left-wing candidate in the 2018 and 2022 presidential elections, wecould not test whether results held with a di↵erent left-wing candidate. We thus use exist-ing survey data and present evidence that suggests that people oppose the Left in generalrather than Petro in particular. We take advantage of a survey conducted by LAPOP inColombia 2018, the presidential election year. This survey asked respondents about theirperceived candidates’ ideological position on a left-right scale (from 1 to 10). Likewise,the survey asked respondents how often they felt fear of something Gustavo Petro (theleft-wing candidate) did during the political campaign. Respondents answered the samequestion for the right-wing candidate, Iva´n Duque. Response options for these questionswere: never, sometimes, half of the time, most of the time, and always. We use questionsabout fear to build the outcome variables, which take on 1 when people felt fear towardthe candidate (Petro or Duque) most of the time or always and 0 otherwise. We use theideological placement questions to build our independent variables: Petro - Left takes on1 when the candidate is perceived as being between 1 and 3 in the 1-10 ideological scaleand 0 otherwise. Petro - Right takes on 1 when people perceive Petro as being between 8and 10 in the ideological spectrum and 0 otherwise. To conduct a placebo test, we buildDuque - Left following the same approach we used with Petro - Left.118B.5. Regression Results - Survey Experimental DataIf what triggers fears among Colombians is the figure of Petro himself rather than hisideological position is what triggers fears among Colombians, we should observe that feartoward Petro is positive regardless of Petro’s ideological placement. Likewise, if peoplefear the Left itself, we should see a positive association between Duque - Left and feartoward Duque. Figure B.6 shows that the Left rather than Petro is what triggers fearsamong Colombians. These results do not support the alternative explanation that negativeattitudes towards Petro are driving the results.Figure B.6: E↵ect of Candidate’s Ideological Placement on Fear of the Candidate.Note. In this figure, we use LAPOP data and show the probability of fearing the 2018 pres-idential candidate (Petro or Duque) as a function of the perceived candidate’s ideologicalplacement. OLS models include control variables and robust standard errors clustered atthe municipal level. Confidence intervals: 95%. Regression reported in table B.15 in theonline SI.The existing scholarship demonstrates that welfare states in this region tend to havean upward bias. Specifically, social policies disproportionately target urban formal workerswhile excluding poor informal sectors (Holland and Schneider, 2017). As Holland (2018)argues, these truncated welfare states diminish expectations of redistribution. Therefore,our findings could be driven by these diminished redistributive expectations: the a✏uentsupport redistribution because they expect social spending to be limited and biased intheir favor. However, as Ballard-Rosa, Martin and Scheve (2017) highlight, redistributioninvolves spending and taxation. While the empirical evidence about the truncated welfarestates focuses on the former, our experimental evidence refers to the latter. In particu-lar, our survey vignettes Progressive Taxation and Subsidies measure expectations abouttaxation and emphasize that the wealthy are the target of the increased taxation.If our respondents support redistribution under the Right because they consider itcheap talk, we should observe that they do not trust the right-wing candidate presentingthe redistributive policies. We use our survey data collected during the run-o↵ election totest this implication. In the survey, we asked respondents about their level of trust in theright-wing candidate (Rodolfo Herna´ndez) as a possible president of Colombia. Becausethis is a post-treatment measure and the vignettes showing redistributive proposals underthe Right/Left were randomly assigned, we can estimate the average causal e↵ect of aright-wing redistributive proposal on trust in the right-wing candidate. We assume that119B.5. Regression Results - Survey Experimental Datapeople do not trust candidates whose proposals are perceived as cheap talk. Table B.6shows that Herna´ndez’s redistributive proposals do not a↵ect respondents’ trust in him(p > 0.05). Therefore, there is no evidence to consider the cheap talk as a valid alternativeexplanation of our findings.Table B.6: E↵ect of Redistributive Proposal on Trust1 2 3 4(Intercept) 1.980⇤⇤⇤ 1.305⇤⇤⇤ 2.047⇤⇤⇤ 1.321⇤⇤⇤(0.038) (0.177) (0.038) (0.176)Tax (T1) 0.033 0.027(0.055) (0.044)Subsidy (T1) 0.105⇤ 0.003(0.055) (0.044)Covariate Adjustment No Yes No YesNum. obs. 787 631 787 631RMSE 0.769 0.544 0.768 0.545⇤⇤⇤p < 0.01; ⇤⇤p < 0.05; ⇤p < 0.1. OLS models with robust standard errors.B.5.2 Adjusted p-valuesSince we test multiple hypotheses, we control the false discovery rate (FDR) using theBenjamini-Hochberg procedure and report the adjusted p-values in this section57.57The FDR correction is a less conservative approach than the Bonferroni correction.120B.5. Regression Results - Survey Experimental DataTable B.7: Adjusted p-values - Study 1Estimate Unadjusted FDRProgressive Taxation 0.0937223 0.009 0.016Subsidies 0.0424166 0.154 0.154Uncertainty -0.6486714 0.076 0.089Eciency 0.1688425 0.000 0.000Instability -0.2591360 0.000 0.000Police 0.0830918 0.013 0.018Visa 0.0899280 0.007 0.016Table B.8: Adjusted p-values - Study 2Estimate Unadjusted FDRProgressive Taxation 0.0886047 0.011 0.027Subsidies 0.0481207 0.089 0.124Uncertainty -0.3203190 0.311 0.311Eciency 0.1787202 0.000 0.000Instability -0.3372355 0.000 0.000Police 0.0474136 0.138 0.161Visa 0.0571197 0.082 0.124B.5.3 Heterogeneous E↵ectsIn this section, we report regression tables for the heterogeneous analyses presented in themain text (Figure 3.5 and 3.6). In the interaction terms, ‘Ideology (Right)’ is a discretevariable that equals 1 when respondents self-identified as right-wing (i.e., values 4 or 5in the 5-point ideological scale) and 0 when respondents self-identified as left-wing (i.e.,values 1 or 2 in the 5-point ideological scale), thus excluding centrist respondents. We alsoreport results using vote intention rather than ideology as the moderator. In the analysesusing vote intention, ‘Vote (Right)’ equals 1 when a respondent intends to vote for the121B.5. Regression Results - Survey Experimental Dataright-wing candidate and 0 otherwiseAdditionally, we present results using di↵erent coding schemes for ideology. First, in-stead of excluding centrist respondents, we do a data-driven classification. Specifically,we classify as left-wing/right-wing those respondents below/above the average ideologicalscore.58 Second, we use three ideological groups, left-, center-, and right-wing, correspond-ing to values 1-2, 3, and 4-5 in the 5-point ideological scale, respectively.Figures B.7 and B.8 show results consistent with the heterogeneous e↵ects reportedin the main text. Figures B.9 and B.10 suggest that the e↵ects found in the pooledanalyses are driven by center- and right-wing respondents. Specifically, we find that centristrespondents, similarly to those self-identified as right-wing, are more likely to supportredistribution under the Right.Table B.9: Heterogeneous E↵ects (Ideology = Right) - Study1Pro. Tax. Subsidies Uncertainty Eciency InstabilityBase category(T0 = Leftwing Candidate) 0.765⇤⇤⇤ 0.493⇤⇤⇤ 0.500 0.528⇤⇤⇤ 0.219⇤⇤⇤(0.047) (0.058) (0.393) (0.059) (0.049)T1 = Rightwing Candidate 0.279⇤⇤⇤ 0.249⇤⇤⇤ 0.113 0.504⇤⇤⇤ 0.142⇤⇤(0.075) (0.075) (0.759) (0.062) (0.072)T1 X Ideology (Right) 0.610⇤⇤⇤ 0.460⇤⇤⇤ 0.103 1.063⇤⇤⇤ 0.683⇤⇤⇤(0.089) (0.085) (0.918) (0.073) (0.085)Ideology (Right) 0.564⇤⇤⇤ 0.424⇤⇤⇤ 2.599⇤⇤⇤ 0.504⇤⇤⇤ 0.474⇤⇤⇤(0.056) (0.061) (0.583) (0.060) (0.061)R2 0.162 0.113 0.060 0.360 0.232Adj. R2 0.157 0.108 0.053 0.356 0.227Num. obs. 500 498 462 497 489RMSE 0.457 0.400 4.715 0.364 0.427⇤⇤⇤p < 0.01; ⇤⇤p < 0.05; ⇤p < 0.1. OLS models with robust standard errors.58In both studies, the average ideological score is 3.4. Therefore, the data-driven classification includescentrist respondents in the left-wing group.122B.5. Regression Results - Survey Experimental DataTable B.10: Heterogeneous E↵ects (Ideology = Right) -Study 2Pro. Tax. Subsidies Uncertainty Eciency InstabilityBase category(T0 = Leftwing Candidate) 0.771⇤⇤⇤ 0.438⇤⇤⇤ 1.014⇤⇤ 0.608⇤⇤⇤ 0.347⇤⇤⇤(0.046) (0.056) (0.467) (0.057) (0.055)T1 = Rightwing Candidate 0.278⇤⇤⇤ 0.150⇤ 1.697⇤⇤ 0.531⇤⇤⇤ 0.030(0.076) (0.077) (0.673) (0.065) (0.078)T1 X Ideology (Right) 0.526⇤⇤⇤ 0.265⇤⇤⇤ 1.483⇤ 1.022⇤⇤⇤ 0.561⇤⇤⇤(0.089) (0.085) (0.826) (0.076) (0.090)Ideology (Right) 0.585⇤⇤⇤ 0.373⇤⇤⇤ 2.902⇤⇤⇤ 0.567⇤⇤⇤ 0.384⇤⇤⇤(0.054) (0.058) (0.612) (0.059) (0.065)R2 0.170 0.111 0.053 0.297 0.210Adj. R2 0.165 0.106 0.047 0.293 0.205Num. obs. 515 518 520 514 513RMSE 0.449 0.371 4.504 0.389 0.441⇤⇤⇤p < 0.01; ⇤⇤p < 0.05; ⇤p < 0.1. OLS models with robust standard errors.Table B.11: Heterogeneous E↵ects (Vote Intention = Right-wing) - Study 1Pro. Tax. Subsidies Uncertainty Eciency InstabilityBase category(T0 = Leftwing Candidate) 0.540⇤⇤⇤ 0.304⇤⇤⇤ 0.682⇤⇤ 0.284⇤⇤⇤ 0.355⇤⇤⇤(0.033) (0.032) (0.317) (0.031) (0.034)T1 = Rightwing Candidate 0.111⇤⇤ 0.119⇤⇤⇤ 0.838⇤ 0.225⇤⇤⇤ 0.020(0.049) (0.041) (0.495) (0.035) (0.047)T1 X Vote (Right) 0.462⇤⇤⇤ 0.357⇤⇤⇤ 0.437 0.861⇤⇤⇤ 0.528⇤⇤⇤(0.069) (0.058) (0.733) (0.050) (0.065)Vote (Right) 0.338⇤⇤⇤ 0.236⇤⇤⇤ 1.046⇤ 0.278⇤⇤⇤ 0.358⇤⇤⇤(0.046) (0.037) (0.561) (0.031) (0.049)R2 0.075 0.054 0.012 0.315 0.152Adj. R2 0.071 0.051 0.008 0.312 0.149Num. obs. 760 773 723 764 746RMSE 0.479 0.402 4.927 0.359 0.450⇤⇤⇤p < 0.01; ⇤⇤p < 0.05; ⇤p < 0.1. OLS models with robust standard errors.123B.5. Regression Results - Survey Experimental DataTable B.12: Heterogeneous E↵ects (Vote Intention = Right-wing) - Study 2Pro. Tax. Subsidies Uncertainty Eciency InstabilityBase category(T0 = Leftwing Candidate) 0.598⇤⇤⇤ 0.323⇤⇤⇤ 0.410 0.416⇤⇤⇤ 0.339⇤⇤⇤(0.038) (0.037) (0.324) (0.038) (0.037)T1 = Rightwing Candidate 0.110⇤⇤ 0.108⇤⇤ 0.668 0.318⇤⇤⇤ 0.014(0.055) (0.048) (0.483) (0.045) (0.052)T1 X Vote (Right) 0.337⇤⇤⇤ 0.259⇤⇤⇤ 0.570 0.842⇤⇤⇤ 0.563⇤⇤⇤(0.070) (0.059) (0.634) (0.056) (0.065)Vote (Right) 0.368⇤⇤⇤ 0.234⇤⇤⇤ 1.209⇤⇤⇤ 0.393⇤⇤⇤ 0.412⇤⇤⇤(0.046) (0.041) (0.466) (0.040) (0.047)R2 0.078 0.045 0.012 0.250 0.214Adj. R2 0.075 0.042 0.009 0.248 0.211Num. obs. 797 819 832 804 798RMSE 0.476 0.395 4.541 0.392 0.433⇤⇤⇤p < 0.01; ⇤⇤p < 0.05; ⇤p < 0.1. OLS models with robust standard errors.Figure B.7: Heterogeneous Treatment E↵ects (Preferences).Note. This figure shows the estimated change in the probability respondents supportredistributive policies proposed by the right-wing (T1) vs left-wing (T0) by ideology. Re-spondents are classified in two ideological groups: left- and center-wing respondents areincluded in group L, and right-wing respondents in group R. OLS models with robust stan-dard errors and without covariate adjustment. Confidence intervals: 95%.124B.5. Regression Results - Survey Experimental DataFigure B.8: Heterogeneous Treatment E↵ects (Expectations).Note. This figure shows the estimated change in the probability respondents expect e-ciency/instability under the right-wing (T1) vs left-wing (T0) by ideology. Respondentsare classified in two ideological groups: left- and center-wing respondents are included ingroup L, and right-wing respondents in group R. OLS models with robust standard errorsand without covariate adjustment. Confidence intervals: 95%.Figure B.9: Heterogeneous Treatment E↵ects (Preferences).Note. This figure shows the estimated change in the probability respondents support redis-tributive policies proposed by the right-wing (T1) vs left-wing (T0) by ideology. Respon-dents are classified in three ideological groups: left-, center-, and right-wing. OLS modelswith robust standard errors and without covariate adjustment. Confidence intervals: 95%.125B.6. Subgroup AnalysisFigure B.10: Heterogeneous Treatment E↵ects (Expectations).Note. This figure shows the estimated change in the probability respondents expect e-ciency/instability under the right-wing (T1) vs left-wing (T0) by ideology. Respondentsare classified in three ideological groups: left-, center-, and right-wing. OLS models withrobust standard errors and without covariate adjustment. Confidence intervals: 95%.B.6 Subgroup AnalysisIn this section, we replicate our main analyses, focusing on the subgroup of respondentslikely to be the most a✏uent in our sample.59 Although we did not measure income, wecollected data on the level of education. We assume that education is a good proxy forsocioeconomic status, and that respondents with post-graduate education are more a✏uentthan those without it.Figures B.11 and B.12 show that the direction of the e↵ects holds for this subgroup.However, given the reduced sample size, the e↵ects are less precisely estimated. TablesB.13 and B.14 show consistent results in terms of expectations.59The analyses reported in this section were not pre-registered.126B.6. Subgroup AnalysisFigure B.11: Preferences for Redistribution in Wealthy Colombians (Subgroup) - Study 1.Note. This figure shows the estimated change in the probability respondents with post-graduate education support redistributive policies proposed by the right-wing (T1) vs left-wing (T0). OLS models with robust standard errors. Confidence intervals: 95%.Figure B.12: Preferences for Redistribution in Wealthy Colombians (Subgroup) - Study 2.Note. This figure shows the estimated change in the probability respondents with post-graduate education support redistributive policies proposed by the right-wing (T1) vs left-wing (T0). OLS models include with robust standard errors. Confidence intervals: 95%.Table B.13: Testing Theoretical Explanations in Subgroup - Study 1Uncertainty Eciency Instability1 2 3 4 5 6Base category(T0 = Leftwing Candidate) 1.313⇤⇤ 1.257 0.196⇤⇤⇤ 0.093 0.539⇤⇤⇤ 0.752⇤⇤(0.552) (4.647) (0.038) (0.415) (0.050) (0.337)T1 = Rightwing Candidate 0.023 0.259 0.187⇤⇤⇤ 0.186⇤⇤ 0.321⇤⇤⇤ 0.399⇤⇤⇤(0.733) (0.972) (0.060) (0.086) (0.063) (0.073)Covariate Adjustment No Yes No Yes No YesNum. obs. 212 132 219 138 221 140RMSE 5.386 5.353 0.445 0.456 0.457 0.406⇤⇤⇤p < 0.01; ⇤⇤p < 0.05; ⇤p < 0.1. OLS models with robust standard errors.127B.7. Non-redistributive PoliciesTable B.14: Testing Theoretical Explanations in Subgroup - Study 2Uncertainty Eciency Instability1 2 3 4 5 6Base category(T0 = Leftwing Candidate) 0.260 5.290⇤ 0.180⇤⇤⇤ 0.242 0.600⇤⇤⇤ 0.454(0.458) (2.808) (0.035) (0.355) (0.047) (0.289)T1 = Rightwing Candidate 0.819 1.729⇤⇤ 0.227⇤⇤⇤ 0.224⇤⇤⇤ 0.397⇤⇤⇤ 0.484⇤⇤⇤(0.586) (0.744) (0.056) (0.080) (0.058) (0.075)Covariate Adjustment No Yes No Yes No YesNum. obs. 258 133 252 132 248 131RMSE 4.722 4.242 0.445 0.447 0.445 0.425⇤⇤⇤p < 0.01; ⇤⇤p < 0.05; ⇤p < 0.1. OLS models with robust standard errors.B.7 Non-redistributive PoliciesIn this section, we report results of the e↵ect of who is in power on two policies wherematerial implications might be less salient to the wealthy: increasing the police forcein urban places and implementing visas for Venezuelans. As in the main analyses, werandomized the candidate (left- or right-wing) proposing the policy: the control groupread the policy as advance by the left-wing candidate, and the treatment group read thesame policy, but proposed by the right-wing candidate.First, we measure the level of support for increasing police force in urban areas. Crim-inal activity in Latin America signals the state’s failure to provide public security andreduces the demand for such services (Altamirano, Berens and Ley, 2020). Consistently,instead of relying on public security, the wealthy tend to invest in private security services(Phillips, 2017). We therefore expect the wealthy’s support for public security to be low,regardless of the ideological position of the government proposing the policy. Importantly,one could argue that security policies vary by government ideology. The right-wing mightpursue ‘iron fist policies’, and the left-wing might prefer social investments as a preemptiveapproach. However, we remind the reader that respondents read the same specific securitypolicy: increasing police force in cities.Second, we measure the level of support for a restrictive migration policy: implement-ing visas for Venezuelans. For this policy, we expect null treatment e↵ects for severalreasons. Despite the large number of Venezuelan migrants in Colombia, migration remainsa low-salience issue for Colombians.60 Existing evidence suggests that the low salienceof immigration negatively correlates with support for it (Kustov, 2023). This association60LAPOP 2021 shows that less than 2% of the Colombian public ranks immigration as the most importantissue in the country.128B.7. Non-redistributive Policiesholds across countries, regardless of the ideological position of their governments. There-fore, given the likely low-salience of immigration among our respondents61, we expect themto oppose the visa policy, regardless of the ideological position of the candidate presentingthe policy. An additional reason to expect null e↵ects is that the material implications ofthe visa policy for the wealthy are less clear, making the preferences unlikely to operateunder our theorized expectations (uncertainty, eciency, and instability).Contrasting our expectations, Figure B.13 shows that who is in power also a↵ectspolicies with less salient redistributive implications. The probability that the wealthysupport these policies increases when it is the right-wing rather than the left-wing whoimplements them. Figure B.14 shows significant heterogeneous e↵ects as well.These unexpected results indicate that the wealthy are sensitive to the ideological po-sition of the government when it comes to supporting policies with or without redistribu-tive implications. Future research should explore whether government ideology a↵ects thewealthy’s preferences across policy domains. Likewise, more work is needed to understandwhy the government ideology a↵ects perceptions of state failure in providing security andsupport for anti-migration policies, even in contexts where migration is a low-salience issue.Figure B.13: Non-Redistributive Policies.Note. This figure shows the estimated change in the probability respondents support non-redistributive policies proposed by the right-wing (T1) vs left-wing (T0). OLS models withrobust standard errors. Confidence intervals: 95%.61In our survey, we did not measure the salience of immigration. However, we can use our question onthe intention to migrate as a proxy. We assume that issue salience is likely low among those who do notplan to migrate. Reassuringly, 70% of respondents did not plan to migrate in the next three years.129B.8. Regression Results - Observational DataFigure B.14: Heterogeneous Treatment E↵ects (Preferences).Note. This figure shows the estimated change in the probability respondents support non-redistributive policies proposed by the right-wing (T1) vs left-wing (T0) by ideology OLSmodels with robust standard errors. Confidence intervals: 95%.B.8 Regression Results - Observational DataIn this section, we report complete regression tables of the analyses conducted to rule outthe left-wing candidate’s figure as an alternative explanation of our findings.Table B.15: Regression Results - Left-Wing CandidateFear of Petro Fear of Petro Fear of Duque(Intercept) 0.146⇤ 0.219⇤⇤ 0.226⇤⇤(0.077) (0.077) (0.083)Petro Left 0.104⇤⇤⇤(0.021)Petro Right 0.097⇤⇤⇤(0.030)Duque Left 0.158⇤⇤⇤(0.036)Low income 0.007 0.007 0.065⇤(0.026) (0.027) (0.035)Stable income 0.026 0.030 0.034(0.019) (0.018) (0.024)Pop. size 0.007 0.001 0.007130B.8. Regression Results - Observational DataFear of Petro Fear of Petro Fear of Duque(0.037) (0.037) (0.035)Urban 0.026 0.022 0.024(0.034) (0.035) (0.033)Big city 0.023 0.030 0.002(0.035) (0.036) (0.033)Female 0.009 0.010 0.053⇤⇤(0.027) (0.026) (0.020)Age 0.002⇤ 0.002⇤ 0.002⇤(0.001) (0.001) (0.001)Education 0.011⇤⇤ 0.010⇤⇤ 0.007⇤⇤(0.004) (0.004) (0.003)Catholic 0.026 0.025 0.073⇤(0.036) (0.034) (0.036)Religiosity 0.017 0.017 0.050⇤(0.025) (0.025) (0.024)Employed 0.006 0.009 0.018(0.027) (0.028) (0.030)Married 0.059⇤⇤ 0.065⇤⇤ 0.041(0.024) (0.025) (0.041)Race 0.008 0.009 0.010(0.007) (0.007) (0.008)Num. kids 0.022⇤⇤ 0.023⇤⇤ 0.001(0.009) (0.009) (0.010)Sec. perception 0.029 0.032 0.017(0.029) (0.029) (0.032)Victim del. 0.011 0.013 0.012(0.026) (0.026) (0.024)Media consumption 0.025 0.026 0.012(0.025) (0.025) (0.027)Media exposure 0.025⇤⇤ 0.025⇤⇤ 0.034⇤⇤(0.011) (0.011) (0.012)Trust Cong. 0.082⇤ 0.075⇤ 0.005(0.041) (0.041) (0.045)Ideology 0.034 0.032 0.042(0.031) (0.032) (0.033)Interest in pol. 0.001 0.006 0.018(0.025) (0.025) (0.021)Machismo 0.020 0.016 0.001131B.9. Cross-country RegressionsFear of Petro Fear of Petro Fear of Duque(0.016) (0.016) (0.013)R2 0.085 0.079 0.065Adj. R2 0.069 0.063 0.048Num. obs. 1320 1320 1334RMSE 0.410 0.412 0.395N Clusters 47 47 47⇤⇤⇤p < 0.01; ⇤⇤p < 0.05; ⇤p < 0.1. Standard errors clustered at the municipal level.B.9 Cross-country RegressionsHere, we report regression results for the cross-country analysis. Countries in our sample:Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Ecuador, El Salvador, Guatemala,Honduras, Mexico, Paraguay, Peru, Uruguay.Table B.16: Regression Results - Cross-country AnalysesModel 1 Model 2Decile 2 0.015 0.018(0.023) (0.022)Decile 3 0.012 0.013(0.028) (0.028)Decile 4 0.000 0.002(0.025) (0.025)Decile 5 0.011 0.015(0.026) (0.026)Decile 6 0.001 0.003(0.025) (0.025)Decile 7 0.005 0.006(0.025) (0.025)Decile 8 0.021 0.023(0.027) (0.027)Decile 9 0.000 0.003(0.024) (0.024)Decile 10 0.068⇤⇤⇤ 0.066⇤⇤(0.025) (0.025)Pop. size 0.003 0.009132B.9. Cross-country RegressionsModel 1 Model 2(0.009) (0.011)Urban 0.004 0.012(0.010) (0.011)Big city 0.002 0.006(0.011) (0.012)Female 0.019⇤⇤ 0.018⇤⇤(0.007) (0.007)Age 0.001⇤⇤⇤ 0.001⇤⇤⇤(0.000) (0.000)Education 0.003⇤⇤⇤ 0.002⇤⇤(0.001) (0.001)Catholic 0.004 0.006(0.008) (0.008)Sec. perception 0.021⇤⇤ 0.029⇤⇤⇤(0.010) (0.010)Religiosity 0.018⇤⇤ 0.027⇤⇤⇤(0.008) (0.008)Employed 0.007 0.009(0.008) (0.008)Married 0.001 0.003(0.008) (0.008)Race 0.009⇤⇤⇤ 0.005⇤(0.003) (0.003)Media consumption 0.014⇤ 0.008(0.008) (0.008)Victim del. 0.008 0.006(0.008) (0.008)Stable income 0.010 0.004(0.007) (0.007)Trust Cong. 0.012 0.011(0.013) (0.013)Trust parties 0.056⇤⇤⇤ 0.057⇤⇤⇤(0.017) (0.017)Trust president 0.018 0.005(0.011) (0.011)Gov. assistance 0.004 0.025⇤⇤⇤(0.009) (0.009)Stable cnt econ 0.026⇤⇤⇤ 0.021⇤⇤(0.008) (0.008)133B.9. Cross-country RegressionsModel 1 Model 2Stable ind econ 0.008 0.011(0.008) (0.008)Voted 0.012 0.005(0.009) (0.009)Decile 2 X Right 0.013 0.015(0.031) (0.031)Decile 3 X Right 0.018 0.017(0.034) (0.035)Decile 4 X Right 0.005 0.006(0.034) (0.034)Decile 5 X Right 0.019 0.019(0.034) (0.034)Decile 6 X Right 0.029 0.029(0.033) (0.033)Decile 7 X Right 0.015 0.017(0.034) (0.034)Decile 8 X Right 0.010 0.006(0.034) (0.034)Decile 9 X Right 0.048 0.049(0.034) (0.034)Decile 10 X Right 0.084⇤⇤ 0.089⇤⇤⇤(0.034) (0.034)Country FE Yes NoR2 0.032 0.013Adj. R2 0.028 0.010Num. obs. 16415 16415RMSE 0.454 0.458N Clusters 963 963⇤⇤⇤p < 0.01; ⇤⇤p < 0.05; ⇤p < 0.1. Standard errors clusteredat the municipal level.134Appendix CLeftist Threat and Post-ElectionTransfersC.1 DataIn this section, I present the summary statistics of the variables used in the regressionanalyses conducted using quarterly and annual data. In addition, I plot the distributionof logged Familias en Accio´n (FA) beneficiaries, the evolution of FA enrollments in rawnumbers, and the map with the potential demand for the FA program.135C.1. DataTable C.1: Summary StatisticsMean SDFA Beneficiaries 1071.04 1788.46Sisben (Households) 8940.82 40353.10Students (primary) 4031.73 20921.73Students (secondary) 4922.04 26369.65Health Institutions 4.99 10.65Population 45112.46 278692.98Displaced Pop. (arrived) 122.72 701.68Displaced Pop. (expelled) 143.13 712.87Education Coverage 54.19 108.89Rurality Index 0.54 0.24Unsatisfied Basic Needs 44.78 20.42Coca Crops 1.17 0.37Fiscal Performance Index 68.69 7.22Homicides 11.76 51.87Violent Threats 38.50 120.39Abstention 0.50 0.10Electoral Risk 0.16 0.37Mayor Alignment 0.55 0.50Governor Alignment 0.62 0.48Leftwing Vote Share 2018 (1st round) 0.24 0.19Opponent Vote Share 2014 0.35 0.17Leftwing Vote Share 2015 (local elections) 0.18 0.20136C.1. DataFigure C.1: Distribution of log FA New Beneficiaries.Figure C.2: FA New Beneficiaries over Time - Quarterly data.137C.2. Checking Parallel TrendsFigure C.3: Potential FA Demand in 2017.Note. Di↵erence between number of households in SISBEN and number of households inthe FA program).C.2 Checking Parallel TrendsI use the same data as in Table 4.1 to assess pre-election parallel trends, estimating thefollowing model:yiqt = ↵i + ⇣q +kX⌧=k⌧ · Left Votesharei+kX⌧=k⌧ · Xit1 + ✏iqt, (C.1)where y is logged number of new FA beneficiaries in municipality i, in quarter q andyear t. ↵i are municipality fixed e↵ects, ⇣q are quarter fixed e↵ects. Left Votesharei isthe vote share of the leftwing candidate in municipality i in the first round of the 2018presidential election. The parameter ⌧ is the treatment e↵ect. Because the dependentvariable is in logged numbers, coecients ⌧ indicate changes in percentage points innumber of beneficiaries relative to the reference period. ⌧ = 0 is the omitted coecientand corresponds to the quarter in which elections were held. ⌧ ranges from 23 quartersaway from the 2018 presidential election to 5 quarters after the election. Xit1 is the matrixof pre-treatment covariates interacted with quarters ⌧ .Figure C.4 shows modest deviations from parallel pre-trends in the period before the138C.3. Alternative Functional Forms2018 election. This cautions against a strict causal interpretation, but the post-electionpositive regression estimates remain consistent with the proposed argument.Figure C.4: Logged FA New Beneficiaries.Note. Estimated e↵ect of vote shares of left-wing candidate in first round of 2018 presi-dential election (May 27) on the logged number of FA beneficiaries. Regressions includemunicipality and quarter fixed e↵ects as well as controls interacted with quarter. Standarderrors clustered at the municipal level. Confidence intervals: 95%C.3 Alternative Functional FormsRecent literature demonstrates potential validity issues in di↵erence-in-di↵erences designsusing logged dependent variables, especially when it is sometimes zero (see, e.g., Roth andSant’Anna, 2023). Here, I present results using di↵erent functional forms of the outcomevariable: the raw number of beneficiaries (Table C.2) and the inverse hyperbolic sine (IHS)transformation of number of beneficiaries (Table C.3). In general, the post-election positivee↵ects hold regardless of the functional form imposed in the outcome.139C.3. Alternative Functional FormsTable C.2: E↵ect of Left-Wing’s Voteshare on Number of FA Beneficiaries1 2 3L Voteshare X Post-election (2018) 25.804⇤⇤⇤ 14.934 14.517(6.774) (10.750) (10.816)Municipality FE Yes Yes YesQuarter FE Yes Yes YesControls x Event No Yes YesCoalition dummy No No YesNum. obs. 26128 21888 21888Num. groups: municipality id 1107 903 903Num. groups: quarter 25 25 25⇤⇤⇤p < 0.01; ⇤⇤p < 0.05; ⇤p < 0.1. Robust standard errors clustered at the municipalitylevel.Table C.3: E↵ect of Left-Wing’s Voteshare on IHS of Number of FA Beneficiaries1 2 3L Voteshare X Post-election (2018) 0.548⇤⇤⇤ 0.571⇤⇤⇤ 0.638⇤⇤⇤(0.094) (0.098) (0.101)Municipality FE Yes Yes YesQuarter FE Yes Yes YesControls x Event No Yes YesCoalition dummy No No YesNum. obs. 26128 21888 21888Num. groups: municipality id 1107 903 903Num. groups: quarter 25 25 25⇤⇤⇤p < 0.01; ⇤⇤p < 0.05; ⇤p < 0.1. Robust standard errors clustered at the municipalitylevel.140
Thesis/Dissertation
eng
Political Science
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Jacobs, Alan M. (Alan Michael)
Weaver, Michael
Strategic concessions : elites, redistribution, and social stability in Latin America
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