Arts, Faculty of
Vancouver School of Economics
DSpace
UBCV
Ricca, Federico
Doctor of Philosophy - PhD
University of British Columbia
Chapter 2 investigates how the racial and ethnic demographic composition of U.S. municipalities is priced in the municipal bonds market. Using high-frequency data on municipal bonds issued between 2004 and 2019, I find that cities with higher non-White population shares face higher borrowing costs. This diversity premium holds controlling for maturity structure, credit ratings, income, population trends, and city finances. Causal estimates are obtained using shift-share instruments for Black and Latino population shares. The findings are consistent with discrimination in the primary market for municipal bonds, independent of credit ratings or underwriter influence. Chapter 3 examines the underrepresentation of minority voters in U.S. city councils. Disproportionality in the representation of Minority groups is widespread, but stronger when racial or ethnic minorities are electorally pivotal, a phenomenon we define strategic underrepresentation. For the period 1981-2020, levels of strategic underrepresentation of African American, Asian, and Latino voters in U.S. city politics are substantial and tied to the strategic choice of institutional features like electoral rules, council size, and form of government. We provide causal evidence of the effects of these practices using the U.S. Supreme Court's narrow decision of Shelby County v. Holder (2013). The ruling invalidated Section 4(b) of the Voting Rights Act, which had required preclearance for electoral changes in a specific subset of U.S. jurisdictions, enabling systematic disenfranchisement. Chapter 4 explores voter registration as a potential mechanism behind the underregistration results. We use proprietary data on the universe of registered voters to show evidence of underregistration—the gap between the population share of Minority voters and their share of registered voters—in U.S. cities. This pattern closely matches that of underrepresentation of minorities in city-councils, suggesting that underregistration is a key channel. We then show that the removal of voting rights protection following Shelby County v. Holder (2013) had negative effects on the registration of Minority voters relative to White voters, especially in jurisdictions with above-median population shares of minorities, highlighting the strategic nature of the disenfranchisement efforts.
Essays on Local Political EconomybyFederico RiccaM.Sc., Bocconi University, 2018B.Sc., Bocconi University, 2015A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THEREQUIREMENTS FOR THE DEGREE OFDOCTOR OF PHILOSOPHYinTHE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES(Economics)THE UNIVERSITY OF BRITISH COLUMBIA(Vancouver)April 2025© Federico Ricca, 2025The following individuals certify that they have read, and recommend to the Facultyof Graduate and Postdoctoral Studies for acceptance, the thesis entitled:Essays on Local Political Economysubmitted by Federico Ricca in partial fulfillment of the requirements for the degreeof Doctor of Philosophy in Economics.Examining Committee:Kevin Milligan, Professor, Vancouver School of Economics, UBCCo-SupervisorTerry Moon, Assistant Professor, Vancouver School of Economics, UBCSupervisory Committee MemberClaudio Ferraz, Professor, Vancouver School of Economics, UBCUniversity ExaminerChristopher Kam, Professor, Department of Political Science, UBCUniversity ExaminerAdditional Supervisory Committee Members:Francesco Trebbi, Professor, Haas School of Business, UC BerkeleyCo-SupervisorMatilde Bombardini, Professor, Haas School of Business, UC BerkeleySupervisory Committee MemberiiAbstractChapter 2 investigates how the racial and ethnic demographic composition of U.S.municipalities is priced in the municipal bonds market. Using high-frequency dataon municipal bonds issued between 2004 and 2019, I find that cities with highernon-White population shares face higher borrowing costs. This diversity premiumholds controlling for maturity structure, credit ratings, income, population trends, andcity finances. Causal estimates are obtained using shift-share instruments for Blackand Latino population shares. The findings are consistent with discrimination in theprimary market for municipal bonds, independent of credit ratings or underwriterinfluence.Chapter 3 examines the underrepresentation of minority voters in U.S. city coun-cils. Disproportionality in the representation of Minority groups is widespread, butstronger when racial or ethnic minorities are electorally pivotal, a phenomenon wedefine strategic underrepresentation. For the period 1981-2020, levels of strategicunderrepresentation of African American, Asian, and Latino voters in U.S. city politicsare substantial and tied to the strategic choice of institutional features like electoralrules, council size, and form of government. We provide causal evidence of the effectsof these practices using the U.S. Supreme Court’s narrow decision of Shelby County v.Holder (2013). The ruling invalidated Section 4(b) of the Voting Rights Act, whichhad required preclearance for electoral changes in a specific subset of U.S. jurisdictions,enabling systematic disenfranchisement.Chapter 4 explores voter registration as a potential mechanism behind the under-registration results. We use proprietary data on the universe of registered voters toshow evidence of underregistration—the gap between the population share of Minor-ity voters and their share of registered voters—in U.S. cities. This pattern closelyiiimatches that of underrepresentation of minorities in city-councils, suggesting thatunderregistration is a key channel. We then show that the removal of voting rightsprotection following Shelby County v. Holder (2013) had negative effects on theregistration of Minority voters relative to White voters, especially in jurisdictions withabove-median population shares of minorities, highlighting the strategic nature of thedisenfranchisement efforts.ivLay SummaryThis thesis examines how racial and ethnic diversity impacts public finances andpolitical representation in U.S. cities, revealing three key findings. Chapter 2 showsthat cities with higher non-White populations face higher borrowing costs in municipalbond markets, suggesting potential discrimination. Chapter 3 finds that racial andethnic minorities are underrepresented in city councils, especially where their votesare pivotal. This strategic underrepresentation results from institutional factors likeelectoral rules and governance structures. Based on the evidence in Chapter 4, lowvoter registration rates among minorities contribute significantly to their limitedpolitical influence. Chapters 3 and 4 show that the U.S. Supreme Court ruling inShelby County v. Holder (2013), which weakened voting rights protections, furtherreduced minority registration and representation. By combining financial and politicalanalyses, this study highlights how racial disparities persist in governance and markets.vPrefaceThe research in Chapter 2 is an original, unpublished, independent work by the authorof this thesis, Federico Ricca. Chapters 3 and 4 are based on original, unpublished, jointwork with Professor Francesco Trebbi (Haas School of Business, University of California,Berkeley). The authors collaborated on the definition of the research questions.Federico conducted the data collection, processing, and empirical analysis. Theauthors contributed equally to the design of the estimation strategy, the interpretationof the results, and the writing.The use of generative artificial intelligence (ChatGPT and Copilot) in the preparationof this dissertation was minimal and limited to the revision of original text writtenby the author. The purpose of this revision was purely to improve the clarity andreadability of the text.viTable of ContentsAbstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iiiLay Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vPreface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viTable of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viiList of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xList of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiiiAcknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvDedication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvi1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 The Price of Diversity: Evidence from Municipal Bonds . . . . . . 52.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52.2 Municipal Bonds Background . . . . . . . . . . . . . . . . . . . . . . 112.3 Measuring Borrowing Costs . . . . . . . . . . . . . . . . . . . . . . . 132.4 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172.5 Empirical Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202.6 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 252.7 Mechanisms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 302.8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 353 Minority Underrepresentation in U.S. Cities . . . . . . . . . . . . . 373.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37vii3.2 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 433.3 Patterns of Minority Underrepresentation . . . . . . . . . . . . . . . . 463.4 An Anatomy of Underrepresentation . . . . . . . . . . . . . . . . . . 523.4.1 Electoral Rules . . . . . . . . . . . . . . . . . . . . . . . . . . 523.4.2 Form of Government, Council Size, Paid Council Members . . 583.4.3 Turnout . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 593.5 Causal Inference: Shelby . . . . . . . . . . . . . . . . . . . . . . . . . 603.5.1 Background on Shelby County v. Holder . . . . . . . . . . . . 603.5.2 Shelby Effects on Underrepresentation . . . . . . . . . . . . . 633.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 684 Behind Minority Underrepresentation: Voter Registration . . . . 704.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 704.2 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 734.2.1 Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 764.3 Underregistration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 774.4 Shelby Effects on Underregistration . . . . . . . . . . . . . . . . . . . 844.4.1 Modelling Registration Dynamics . . . . . . . . . . . . . . . . 854.4.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 864.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 905 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94A Appendix to Chapter 2 . . . . . . . . . . . . . . . . . . . . . . . . . . 106A.1 Effective Tax Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106A.2 Tables and Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108B Appendix to Chapter 3 . . . . . . . . . . . . . . . . . . . . . . . . . . 121B.1 Measures of underrepresentation . . . . . . . . . . . . . . . . . . . . . 121B.1.1 Some properties of u . . . . . . . . . . . . . . . . . . . . . . . 122B.2 Tables and Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124C Appendix to Chapter 4 . . . . . . . . . . . . . . . . . . . . . . . . . . 145viiiC.1 Estimating Changes in Population Dynamics from a Cross-Section ofSurvivors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145C.1.1 Representing Population Dynamics . . . . . . . . . . . . . . . 145C.1.2 Detecting Changes in the Leslie Matrix from Cross-Sectional Data146C.1.3 Estimating the Treatment Effect . . . . . . . . . . . . . . . . . 147C.2 Tables and Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149ixList of TablesTable 2.1 Bonds Summary Statistics . . . . . . . . . . . . . . . . . . . . 18Table 2.2 Accounts Summary Statistics . . . . . . . . . . . . . . . . . . 19Table 2.3 Census Summary Statistics . . . . . . . . . . . . . . . . . . . 20Table 2.4 Minority and Yield-Spread . . . . . . . . . . . . . . . . . . . . 26Table 2.5 Shift-Share IV Estimates . . . . . . . . . . . . . . . . . . . . . 27Table 2.6 Underwriters and Potential Discrimination . . . . . . . . . . . 34Table 3.1 Summary Statistics . . . . . . . . . . . . . . . . . . . . . . . . 45Table 3.2 Minority Underrepresentation . . . . . . . . . . . . . . . . . . 50Table 3.3 Minority Underrepresentation by Electoral Rule . . . . . . . . 55Table 3.4 Endogenous Choice of Electoral Rule . . . . . . . . . . . . . . 57Table 3.5 Pre/Post Mean Shelby Changes and Means Comparison Test . 62Table 3.6 Minority Underrepresentation Pre/Post Shelby (2013) - Effects 65Table 4.1 Summary Statistics - Registration (2020) . . . . . . . . . . . . 75Table 4.2 Registered Voters and CVAP Shares . . . . . . . . . . . . . . 76Table 4.3 Average Registration Rates . . . . . . . . . . . . . . . . . . . . 77Table 4.4 Minority Underregistration . . . . . . . . . . . . . . . . . . . . 81Table 4.5 Effects of Shelby (2013) on Minority and White Registration . 87Table A.1 Bonds Summary Statistics . . . . . . . . . . . . . . . . . . . . 110Table A.2 Means Balance . . . . . . . . . . . . . . . . . . . . . . . . . . 111Table A.3 Blacks, Latinos, and Yield-Spread . . . . . . . . . . . . . . . . 113Table A.4 Property Crime Robustness . . . . . . . . . . . . . . . . . . . 114Table A.5 Pension Liabilities Robustness . . . . . . . . . . . . . . . . . . 115Table A.6 Alternative Mechanisms Robustness . . . . . . . . . . . . . . . 116Table A.7 Black and Latino Shares, Alternative Mechanisms Robustness 117Table A.8 Issuance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120xTable B.1 Variables Definitions . . . . . . . . . . . . . . . . . . . . . . . 124Table B.2 Latino Underrepresentation . . . . . . . . . . . . . . . . . . . 126Table B.3 African American Underrepresentation . . . . . . . . . . . . . 127Table B.4 Asian Underrepresentation . . . . . . . . . . . . . . . . . . . . 128Table B.5 Minority Underrepresentation by Rep/Dem Leaning . . . . . . 130Table B.6 Latino Underrepresentation by Electoral Rule . . . . . . . . . 132Table B.7 African American Underrepresentation by Electoral Rule . . . 133Table B.8 Asian Underrepresentation by Electoral Rule . . . . . . . . . . 134Table B.9 Mayor-Council v. Council-Manager . . . . . . . . . . . . . . . 135Table B.10 Council Size . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136Table B.11 Paid Council Members . . . . . . . . . . . . . . . . . . . . . . 137Table B.12 Minority Underrepresentation Pre/Post Shelby (2013) - Param-eters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139Table B.13 Minority Underrepresentation Pre/Post Shelby (2013) - Estimates140Table B.14 Minority Underrepresentation Pre/Post Shelby (2013) - Argmax 141Table B.15 Minority Underrepresentation Pre/Post Shelby (2013) - Re-stricted (200 miles) . . . . . . . . . . . . . . . . . . . . . . . . 142Table B.16 Minority Underrepresentation Pre/Post Shelby (2013) - Re-stricted (100 miles) . . . . . . . . . . . . . . . . . . . . . . . . 143Table B.17 Minority Underrepresentation Pre/Post Shelby (2013) - Effects,by Rep/Dem Leaning . . . . . . . . . . . . . . . . . . . . . . . 144Table C.1 Variables Definitions - Registration . . . . . . . . . . . . . . . 149Table C.2 Minority Underregistration by Rep/Dem Leaning . . . . . . . 152Table C.3 Effects of Shelby (2013) on Minority and White Registration(Below Median Minority VAPS) . . . . . . . . . . . . . . . . . 154Table C.4 Effects of Shelby (2013) on Minority and White Registration(Above Median Minority VAPS) . . . . . . . . . . . . . . . . . 155Table C.5 Effects of Shelby (2013) on Minority and White Registration(Restricted to 200 Miles) . . . . . . . . . . . . . . . . . . . . . 156Table C.6 Effects of Shelby (2013) on Minority and White Registration(Below Median Minority VAPS, Restricted to 200 Miles) . . . 158Table C.7 Effects of Shelby (2013) on Minority and White Registration(Above Median Minority VAPS, Restricted to 200 Miles) . . . 159xiTable C.8 Effects of Shelby (2013) on Minority and White Registration(Restricted to 100 Miles) . . . . . . . . . . . . . . . . . . . . . 160Table C.9 Effects of Shelby (2013) on Minority and White Registration(Below Median Minority VAPS, Restricted to 100 Miles) . . . 162Table C.10 Effects of Shelby (2013) on Minority and White Registration(Above Median Minority VAPS, Restricted to 100 Miles) . . . 163Table C.11 Effects of Shelby (2013) on Minority and White Registration(Dem. Leaning) . . . . . . . . . . . . . . . . . . . . . . . . . . 165Table C.12 Effects of Shelby (2013) on Minority and White Registration(Rep. Leaning) . . . . . . . . . . . . . . . . . . . . . . . . . . 166xiiList of FiguresFigure 2.1 Bonds Holders, 2004-2022 ($ Millions) . . . . . . . . . . . . . 12Figure 2.2 Yield Curves, 2004-2019 . . . . . . . . . . . . . . . . . . . . . 16Figure 2.3 Ratings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18Figure 2.4 Rating Probabilities (LPM, 2SLS) . . . . . . . . . . . . . . . . 33Figure 3.1 Minority Underrepresentation . . . . . . . . . . . . . . . . . . 47Figure 3.2 Minority Underrepresentation . . . . . . . . . . . . . . . . . . 48Figure 3.3 Minority Underrepresentation by Electoral Rule . . . . . . . . 53Figure 3.4 Minority Underrepresentation Pre/Post Shelby (2013) . . . . . 66Figure 3.5 Pre-trend analysis of Shelby (2013) effect of Minority Underrep-resentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67Figure 4.1 Minority Underregistration . . . . . . . . . . . . . . . . . . . . 79Figure 4.2 Minority Underregistration . . . . . . . . . . . . . . . . . . . . 80Figure 4.3 Minority Registration Rate . . . . . . . . . . . . . . . . . . . . 82Figure 4.4 Minority Registration Gap . . . . . . . . . . . . . . . . . . . . 82Figure 4.5 Minority Underrepresentation and Underregistration . . . . . 83Figure 4.6 Effects of Shelby (2013) on Minority v. White Registration . . 88Figure 4.7 Effects of Shelby (2013) on Minority v. White Registration(Above/Below Median Minority) . . . . . . . . . . . . . . . . . 89Figure A.1 Yield Curves by Maturity . . . . . . . . . . . . . . . . . . . . 108Figure A.2 Spread by Maturity . . . . . . . . . . . . . . . . . . . . . . . . 109Figure A.3 Ratings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112Figure A.4 Rating Probabilities (OLS) . . . . . . . . . . . . . . . . . . . . 118Figure A.5 Rating Probabilities (OLS), Black, Latino, and Other Shares . 119Figure B.1 Underrepresentation by Race/Ethnicity . . . . . . . . . . . . . 125Figure B.2 Minority Underrepresentation by Rep/Dem Leaning . . . . . 129xiiiFigure B.3 Minority Underrepresentation in State Legislatures (2020) . . 131Figure B.4 VRA Coverage at the time of Shelby (2013) . . . . . . . . . . 138Figure C.1 Registered Voters and Citizen Voting Age Population . . . . . 150Figure C.2 Minority Underregistration by Rep/Dem Leaning . . . . . . . 151Figure C.3 Minority Registration Rate by Rep/Dem Leaning . . . . . . . 153Figure C.4 Minority Registration Gap by Rep/Dem Leaning . . . . . . . 153Figure C.5 Effects of Shelby (2013) on Minority v. White Registration(Restricted to 200 Miles) . . . . . . . . . . . . . . . . . . . . . 157Figure C.6 Effects of Shelby (2013) on Minority v. White Registration(Restricted to 100 Miles) . . . . . . . . . . . . . . . . . . . . . 161Figure C.7 Effects of Shelby (2013) on Minority v. White Registration (byRep./Dem. Leaning) . . . . . . . . . . . . . . . . . . . . . . . 164xivAcknowledgementsThis journey would not have been possible without the unwavering support of myfamily. Thank you to Giulia, for being with me every step of the way and remindingme every day of which things really matter. I would like to thank my mom, my dad,Giovanni, and Aaron. They believed in me more than I did. I am grateful for theirlove and encouragement wherever I was and whatever I was doing.Thank you to Francesco, Chiara, and Aaron for flying over an ocean and a continentto share a summer of memories with me. And thank you to Ghibo, Peo, and myfriends at home, for being there every time I went back and making me feel like Inever left.Thank you to the people I have met along the way, for making Vancouver feel a littlebit more like home. To my colleagues, for sharing this path. To Allen, Pacho, Elyse,Sebastian, Valentina, Max, and Julian for the best moments.Finally, special thanks to my advisors Francesco Trebbi, Kevin Milligan, MatildeBombardini, and Terry Moon for their invaluable mentorship, patience, and encour-agement.xv—To my family: Giulia, mamma, papà, Giovanni, and Aaron.xviChapter 1IntroductionThis thesis comprises three chapters exploring the interplay of race, economics, andpolitics in local governments. Specifically, it studies the relationships between thedemographic composition of U.S. municipalities, their finances, and the political repre-sentation of minorities. Chapter 2 investigates how the racial and ethnic demographiccomposition of U.S. cities affects their cost of issuing debt through municipal bonds.Chapter 3 examines how non-White minorities are underrepresented in city councils.Finally, Chapter 4 focuses on the dynamics of voter registration as a key mechanismbehind the underrepresentation of Minority voters in local governments.Chapter 2 studies how the racial and ethnic demographic composition of municipalissuers is priced into the interest that cities pay on municipal bonds. Municipalitiesprovide key public goods at the local level, and they raise capital to invest with bonds.The market for municipal bonds amounts to $4 trillion in outstanding securities,equivalent to 13% of the U.S. federal debt or just under half the amount of outstandingcorporate bonds.I use proprietary high-frequency financial data covering the universe of municipalbonds issued between 2004 and 2019 provided by SDC (2021), combined with detailed,municipality-level sociodemographics from the U.S. Census. The empirical specificationcontrols for a number of economic fundamental drivers of risk, including income,population trends, and city finances. It also accounts for the prevailing marketconditions at the time of issuance, the maturity structure, and credit rating of bonds.I address the potential endogeneity of Minority population using shift-share instrumentsfor the shares of Black and Latino population (among others, see Calderon et al. (2023)1and Mayda et al. (2022)). These capture plausibly exogenous demographic variationacross municipalities, and are made robust to the recent methodological advancementsin Borusyak et al. (2022); Borusyak and Hull (2023).The results show that cities with higher population shares of non-White populationpay significantly higher interests on their bonds. One standard deviation increase inthe population shares of Black and Latinos increases the yield-spread by up to 10 basispoints. This is equivalent to 3.8% higher total interest costs on the average bond inmy sample. This is an economically meaningful diversity premium that cities have tofinance their investments. The results are consistent with the discrimination of racialand ethnic minorities in the municipal bonds market. Notably, more diverse cities arenot discriminated in terms of credit ratings on their debt, nor by the underwritersinvolved in the issuance process—typically investment banks. This diversity premiumis a primary market result.Overall, the chapter contributes to the growing literature on diversity and bonds(Rugh and Trounstine, 2011; Bergstresser et al., 2013; Dougal et al., 2019; Eldemire-Poindexter et al., 2022; Smull et al., 2023) with a comprehensive analysis of race inthe municipal bonds market focused on U.S. cities. This study also fits in the broaderliterature on the political economy of public goods provision (Alesina et al., 1999;Battaglini and Coate, 2007, 2008; Bouton et al., 2020; Trounstine, 2016; Beach andJones, 2017). Understanding how cities get the money to spend on local public goods,and how much they pay for it and why, is crucial to understanding their investmentdecisions.Chapter 3 turns the attention to how racial and ethnic minorities are representedin local governments. The 1965 Voting Rights Act established measures to protectMinority voters from disenfranchisement. In 2013, a ruling of the U.S. Supreme Courtundermined a key provision of the Act, which required jurisdictions with a history ofracial discrimination to seek federal approval before implementing any change in theelectoral process. Today, race remains a fundamental theme of American politics.1We find evidence of significant underrepresentation of non-White minorities in citycouncils as difference between their share in the population and the share of Minoritycouncil members. The highly nonlinear shape of this disproportionality tells us more.Minority voters are mostly underrepresented—up to 36 percentage points—when they1https://www.brookings.edu/articles/race-remains-the-ugly-underbelly-of-american-politics/.2account for 55% to 60% of the total voting age population. Minority voters appearmost affected when they account for a sizeable, potentially majoritarian, part of theelectorate. We explore the mediating effects of several institutional features, includingelectoral rules, council size, and form of government. We find that different electoralrules are deployed strategically to minimize the electoral power of Minority votersdepending on their population share: At-Large elections when minorities are relativelysmall, and single-member districts as they approach 50% of the population. Ourfindings confirm and extend the results of Trebbi et al. (2008) to a much more recentperiod (1981–2018).We then show the effects of the Shelby County v. Holder (2013) ruling on under-representation at the local level. We compare municipalities that required federalpreclearance before the ruling, to municipalities never covered by the Act, before andafter the ruling. Removing voting rights protection made underrepresentation worseby up to 6 percentage points, de facto cancelling the progress in representation madesince the VRA (Cascio and Washington, 2014; Ang, 2019). Our results confirm thestrategic nature of the disenfranchisement of Minority voters in U.S. cities.Chapter 4 focuses on the dynamics of voter registration as a key mechanism behindthe underrepresentation of Minority voters in local governments. Voter registration isa key step in the electoral process, and it is a necessary condition for voting.We use proprietary data from Aristotle (2021) covering the universe of registeredvoters in the U.S. to uncover patterns of underregistration that match our results onunderrepresentation in Chapter 3. We find that Minority voters are most underreg-istered relative to their population share when they account for 45% to 50% of thetotal voting age population. The nonlinearity of underregistration and where it peaksare consistent with the patterns of underrepresentation in city councils and pointtowards registration as a key mechanism. Minority voters also show lower registrationrates relative to the pool of potential voters, when compared to White voters. Theregistration rate gap peaks at Minority population shares between 20% and 30%.We then study the effects of Shelby County v. Holder (2013) on voter registration.We develop an estimation strategy compatible with data covering only currentlyregistered voters with information on when they registered. We compare the dynamicsof registration of Minority voters relative to White, in municipalities previously coveredby the VRA relative to those never covered. We find that registration is lower for3both Minority and White voters in municipalities that lost voting rights protection.However, the effect of Shelby County v. Holder (2013) is stronger and more negative forMinority voters. On top of that, the negative effect for Minority voters is concentratedin jurisdictions with above-median Minority population shares. This is in line withanecdotal evidence on renewed disenfranchisement attempts in the wake of the ruling.2This chapter contributes to the literature on the effects of Shelby County v. Holder(2013) on registration and turnout—so far showing mixed results—and provides credi-ble evidence of a key mechanism behind the underrepresentation of Minority voters inlocal governments.2See https://www.washingtonpost.com/news/the-fix/wp/2014/07/07/how-has-voting-changed-since-shelby-county-v-holder/, and Chapter 4 for an in-depth discussion.4Chapter 2The Price of Diversity: Evidencefrom Municipal Bonds2.1 IntroductionLocal governments in the U.S. are key providers of public goods (Trounstine, 2020).These include essential infrastructure like roads, sewage, and waste treatment facilities,local and regional transit, housing, fire and police protection. These goods and servicesare paid for with a combination of taxes and debt. Municipal bonds are the primarysource of capital, a $4 trillion market (MSRB, 2023). Understanding how municipalbonds are priced and what drives the borrowing costs of local governments is thereforecritical to understand their investment decisions and the resulting provision of publicgoods.A lot of attention has been given to how local expenditures are affected by preferencesand composition of the electorate. Dating back at least to the seminal work of Alesinaet al. (1999), a vast literature in political economy has studied how the ethnic diversityof local communities influences public spending. Much less attention has been paidto how local governments finance their expenditures, and to what explains the costof providing public goods. This chapter studies the effect of the racial and ethnicdemographic composition of U.S. municipalities on the borrowing costs they face inthe municipal bonds market.Most of the literature suggests that diversity in some form—ethnic fractionalization,5racial segregation, political representation of minorities, or simply the share of non-White population—is associated with the underprovision of public goods (Alesinaet al., 1999; Hopkins, 2009; Trounstine, 2016; Beach and Jones, 2017).1 The intuitionbehind this result is that different racial and ethnic groups have heterogeneous prefer-ences for public goods, in terms of quantity and type, creating gridlock. Even underthe assumption of homogeneous preferences, different groups might dislike paying forothers having access to shared goods. This conflict of interests results in disagreementover spending, and ultimately in underprovision. The mechanisms identified areprimarily focused on the demand for public goods by citizens (Habyarimana et al.,2007).This chapter takes a step back and estimates the effect of diversity—measured asnon-White share of the population—on the borrowing costs paid by municipalitieswhen issuing bonds. The presence of a diversity premium could explain, at least inpart, the underprovision of public goods as a result of higher costs for raising capitalto invest.2The goal of this chapter is to test empirically whether more diverse municipalitiespay higher borrowing costs on their debt. Conceptually, we are interested in whethermore diverse municipalities are perceived as riskier borrowers beyond their economicfundamentals. This would be consistent with statistical discrimination—if bonds issuedby more diverse municipalities were on average riskier investments—or taste-baseddiscrimination—if investors demand higher interests from more diverse communitiesregardless of their economic fundamentals and their statistical riskiness (see Bartlettet al. (2022) for a recent example of discrimination in financial markets). Controllingfor economic fundamentals is the focus of my empirical specification, including controlsand fixed effects to capture the role of local wealth, city finances, and market conditions.It is also the reason behind a battery of robustness checks that rule out a number ofalternative mechanisms, discussed below.I collect data on the universe of municipal bonds issued between 2004 and 2019 by1Gisselquist (2014) warns that the negative correlations reported by Alesina et al. (1999) are quitesensitive to the specification used, and depend on the specific categories of spending considered. Inthis respect, my research is relevant specifically for capital spending categories.2Throughout the chapter, I use the term premium in a negative connotation, to indicate morecostly—for municipalities, and in terms of yields—debt. It is unrelated to whether the actual bondsare issued at a price premium or discount.6cities and towns in the U.S. (SDC, 2021). I measure borrowing costs as yield-spreads,the difference between the tax-adjusted yield-to-maturity of a bond and a risk-freebenchmark (Schwert, 2017; Babina et al., 2021; Garrett et al., 2023). The spreadaccounts for the varying tax advantages of bonds across states, and for differences inthe risk structure of the bonds issued.I find that municipalities with higher non-White shares of population pay significantlyhigher costs on their debt. The effect is economically meaningful: one standarddeviation increase in the population share of Blacks increases the yield-spread by 10basis points on average (+6%), or the total interest costs on the average bond by3.8%; a one standard deviation increase in Latinos increases the yield-spread by 4.6basis points and the total interest costs by 1.8% on average.My empirical strategy controls for a number of economic fundamental drivers of risk -sociodemographics of the issuers, their revenues, expenditures, and outstanding debt -as well as prevailing market conditions, the maturity structure, and credit ratings ofthe bonds.There may be concerns about endogeneity due to unobserved fundamentals, in partic-ular risk factors positively correlated with Minority shares and spread. To accountfor these potential sources of endogeneity, I use a novel implementation of shift-shareinstruments for the share of minorities in the population. I rely on the Great Migra-tion flows of 1940-1970 for the share of Blacks (Boustan, 2010; Derenoncourt, 2022;Calderon et al., 2023), and international migration flows for the shares of Blacks andLatinos (Mayda et al., 2022, 2023).3 The instruments capture an arguably exogenouscomponent of shares of minorities predicted by migration flows. I adapt the instru-ments to the most recent advances in the shift-shares literature based on Borusyaket al. (2022) and Borusyak and Hull (2023). By recentering the instruments control-ling for the sum-of-shares used to construct them, my IV analysis relies only on theplausible exogeneity of the flows for identification, while allowing the pre-determinedshares to be endogenous. This is possibly one of the first studies implementing thesenew robustness strategies in the context of shift-share instruments for populationcomposition.The controls included in the main specification rule out a number of mechanisms. The3A shift-share IV similar to Mayda et al. (2022) and Mayda et al. (2023) has been recently appliedto municipal bonds by Gordon and Guerron-Quintana (2024). They show that municipalities moreexposed to migration issue more debt, suggesting this is because population growth makes futurerepayments easier.7diversity premium is not driven by income effects or population trends—more diversemunicipalities being poorer or undergoing periods of population decline, affectingtheir riskiness as borrowers—nor by municipal revenues, outstanding debt, or taxcapacity of the issuers—more diverse municipalities relying more heavily on debtoverall, or having a smaller tax base to service debt. Additional robustness checksshow that the effect of diversity is not confounded by crime, other liabilities like publicpension obligations, state-level market conditions at the time of issuance, nor politicalrepresentation of minorities in city councils. Instead, the results are consistent withthe discrimination of racial and ethnic minorities in the primary market for municipalbonds.An important limitation is that I cannot disentangle whether discrimination is statis-tical or taste-based. In contrast to existing papers on discrimination, municipal bondsdo not offer an objective benchmark of whether they are priced fairly. For example,in the case of motor vehicle searches (Knowles et al., 2001), we can observe whetherthe stop found a violation. For mortgages (Berkovec et al., 1998), we can measureloan performance based on timely payments. In the case of municipal bonds, however,defaults are extremely rare (Moody’s, 2022), and I do not have information on smallerfailures like delayed payments. It is worth pointing out that these events are recordedby the rating agencies and would presumably enter the credit rating assigned to bonds,which I control for. As a result, more diverse municipalities could be statistically riskierborrowers on average and differences in expected risk could explain the occurrence ofstatistical discrimination. On the contrary, if the premium in yields is the result of adislike of investors for diverse communities, with no justification in terms of risk, thediscrimination would be taste-based. Market yields are an endogenous equilibriumoutcome, and the absence of an objective measure of risk for municipal bonds makesit impossible to disentangle the two types of discrimination. Moreover, if either formof discrimination happens at the market level, looking at secondary market tradingwould present the same limitations.4While I cannot disentangle statistical and taste-based discrimination, I can test wherethis discrimination may be happening during the issuance process. The evidencesuggests that there is no discrimination in credit ratings, and that it does not occur4On the topic of discrimination, Jenkins (2021) offers an insightful case study on debt and themaking of San Francisco in the second half of the 20th century, and how debt has been used for thedevelopment of White neighborhoods to the detriment of minorities.8because of the underwriters—usually investment banks—involved in the issuanceprocess. Municipalities with higher non-White shares of population are not more likelyto receive worse credit ratings for otherwise equivalent municipal bonds. Includingfixed effects for the underwriters of each bond does not cancel our the effect of diversity.Nor do underwriters make higher profits in dealing with issues from more diversecommunities. The diversity premium originates in the primary market for bonds.To the best of my knowledge, this chapter is one of the most comprehensive studieson diversity and the municipal bonds market with a focus on U.S. cities. Rughand Trounstine (2011) show that more diverse communities see fewer bond elections.Conditional on being proposed, these bonds are larger presumably because of theneed to satisfy more diverse preferences, and are more likely to be approved as aresult. Bergstresser et al. (2013) county-level analysis shows a positive correlationbetween the religious and ethnic fractionalization of an issuer and the offering yieldof bonds. Dougal et al. (2019) show that historically black colleges and universitiespay higher underwriting fees on school bonds. Similarly, Eldemire-Poindexter et al.(2022) confirm the positive correlation between the share of Black population at thecounty level and yields on municipal bonds of Bergstresser et al. (2013). They showthis correlation is stronger in states with higher racial resentment, arguing that this isthe result of discrimination. Recently, Smull et al. (2023) report a similar positivecorrelation between the share of Black population and yield-spreads on a cross-sectionof municipal bonds in 2022.I contribute to this emerging literature by focusing on U.S. cities, rather than poolingtogether different levels of local jurisdictions, and by showing a robust effect of diversityon a precise measure of borrowing costs. Relative to the existing research, my measureof borrowing costs takes into account the tax advantages and a credible risk-freebenchmark of municipal bonds: the yield-spread allows for a clean comparison ofbonds across a panel of issuers, without confounding the estimates with nominaldifferences in taxation or maturity structure. Importantly, diversity matters not onlyin terms of non-White population share, but also in terms of individual Black andLatino shares considered separately. Finally, this is the first study providing a crediblecausal estimate of the effects of diversity on yield-spreads by implementing validatedshift-share instruments.This chapter relates to the broader literature on municipal bonds. A considerable9amount of research looks at factors determining the supply of capital in this market(Bergstresser and Orr, 2014; Dagostino, 2022; Yi, 2021; Adelino et al., 2023). Severalpapers discuss risk factors of municipal bonds such as defaults and liquidity (Schwert,2017; Gao et al., 2019a; Abott et al., 2021), politics (Gao et al., 2019b; Dagostino andNakhmurina, 2023; Ren and Zhao, 2023), and climate change (Goldsmith-Pinkhamet al., 2023). Babina et al. (2021) and Garrett et al. (2023) study how to accuratelymeasure the tax advantages of municipal bonds and the implications for this market,including a high degree of segmentation by state. Giesecke et al. (2023) offer acomprehensive and detailed analysis of the financial position of U.S. municipalities,covering revenues, expenditures, holdings, and liabilities. Dippel (2022) focuses onthe specific case of public pension liabilities, for which the data is unfortunately quitelimited and makes calculation of forward looking obligations particularly challenging.Carlson et al. (2022) tackles the problem of municipal issuance from a game-theoreticalperspective.5 Demographic diversity is an understudied factor in the pricing ofmunicipal bonds, and I contribute to fill this gap.This chapter also relates to the political economy literature on taxation, debt, andpublic goods provision(Alesina and Passalacqua, 2016). Most of this literature is pri-marily theoretical (Battaglini and Coate, 2007, 2008; Bouton et al., 2020) or calibratedto aggregate U.S. level data (Bassetto, 2006; Azzimonti et al., 2016). A notable recentexception is Janas (2023): U.S. cities greatly expanded their debt during the 1920s;those more financially exposed when the Great Depression hit were forced to cutexpenditures, in particular capital investments. Political representation at the locallevel also can play an important role in shaping public investments (Trebbi et al., 2008;Ricca and Trebbi, 2022). I aim to further our understanding of public goods provi-sion focusing on the cost of public investments through debt, and its underlying drivers.The rest of the chapter is organized as follows. Section 2.2 provides backgroundinformation on municipal bonds. Section 2.3 explains how to measure borrowingcosts, taking into account their tax advantages and comparing them to a meaningfulrisk-free benchmark. Section 2.4 describes the data used in the chapter. Section 2.5presents the empirical strategy, including the main specification and a discussion ofthe shift-share instruments. Section 2.6 reports the main results. Section 2.7 discussesthe mechanisms. Section 2.8 concludes.5For a more general review of the municipal bonds literature, see Cestau et al. (2019).102.2 Municipal Bonds BackgroundU.S. municipalities play a crucial role in providing public goods such as roads andinfrastructure, fire and police protection, sewage and waste management facilities.These services are funded through an operating budget, which includes tax revenuesand current expenditures. Virtually all local governments in the U.S. must adhereto balanced budget rules, meaning total revenues and expenditures in the operatingbudget should match (NLC, 2023; Poterba, 1995).Many of the services provided necessitate substantial capital investments that exceedthe scope of the operating budget. To fund these investments, municipalities issue debtin the form of municipal bonds. However, bonds can only be used to finance capitalexpenditures. The servicing and repayment of this debt are part of the operatingbudget and must be balanced with tax revenues. Therefore, municipal bonds cannotbe used to cover operating deficits. The reliance of U.S. municipalities on debt forcapital investment is substantial. According to my data, the average ratio of capitaloutlays in a year to the amount of outstanding debt is approximately 25%.The market for municipal bonds in the U.S. is substantial. As of 2021, the totaloutstanding municipal debt amounted to over $4 trillion, with an average $435 billionin new debt issued every year since 2015.6 To put things into perspective, this isequivalent to 14% of the U.S. federal debt. However, it is important to note thatmunicipal debt is distinct from federal debt: the federal government bears no liabilityfor municipal bonds, the responsibility rests solely with the issuing local government.The municipal bonds market is traditionally illiquid, with an average $9 billion tradeddaily. Individual investors, who typically adopt a buy-and-hold strategy until maturity,hold over 40% of bonds (Figure 2.1).7 The remainder of the debt is primarily held bybanks, insurance companies, and funds.6These statistics include bonds issued by states and all other local governments. Source: MSRB(2023).7The tax-exemption of municipal bonds from federal and, in most cases, own-state income taxes is thereason why the municipal bonds market is segmented by state. Investors have a strong preferencefor holding bonds issued by jurisdictions in their own state, and enjoy the tax benefits. It is also thereason why individuals are the most common holders, rather than tax-exempt entities like pensionfunds. However, there are no explicit incentives for investors to hold bonds issued by their own city.To the best of my knowledge, most of the market segmentation happens at the state-level. Theresults presented here are unlikely explained by which households actually hold the bonds, whetherWhite or Minority households. Unfortunately, I do not have information of who owns these bondsspecifically. This would certainly be an extremely interesting avenue for future research.11Figure 2.1: Bonds Holders, 2004-2022 ($ Millions)Share of holders of municipal bonds (from all local governments, States and below) by group. Source: MSRB (2023).When a municipality decides to issue bonds, it follows an underwriting process. Themunicipality deals directly with an underwriter, usually an investment bank, whichpurchases the entire issuance. The underwriter then sells these bonds to investors,which could be individuals or other financial institutions. The municipality and theunderwriter agree on the price and yield at which the underwriter pledges to sell thebonds on the primary market. They also agree on the gross underwriting spread,which is the difference between the price at which the underwriter buys from themunicipality and sells to investors. This spread represents the underwriter’s profit.The municipality and underwriter can engage in a negotiated process, collaborating todesign the bonds in terms of maturity structure and other financial features. Alterna-tively, the municipality can design the entire issuance and then submit it to potentialunderwriters. These underwriters then compete in an auction, bidding on the totalinterest cost. This cost reflects both the gross underwriting spread asked and the priceand yield of the bonds.12Municipalities are responsible for the timely payment of coupons on these bonds,usually every six months and almost always at a fixed rate predetermined at issuance.At maturity, the issuers repay the principal amount to the bondholders.The funds used for these repayments can come from different sources. So-calledGeneral Obligation (GO) bonds are backed by the full faith and taxing capacity ofa municipality to the repayment of a bond. Technically, the municipality is legallyrequired to raise taxes if necessary to meet its obligations. Because of the importantliabilities they generate, GO bonds often require voter approval to be issued. On theother hand, Revenue bonds are repaid from a specific, identified source of income,often related to the project the bonds are financing. This is the case, for example, ofa parking garage whose parking fees cover the interests and principal repayment ofthe bonds issued to finance its construction.Municipal bonds have low default rates. According to Moody’s (2022) and MSRB(2023), default rates on investment-grade municipal bonds between 1970 and 2020were 0.1%, compared to 2.24% for corporate obligations. Most bonds receive a creditrating by rating agencies, reflecting the ability of the issuer to make timely interestsand principal payments.Beyond default risk, investors consider other risk factors. Municipal bonds are exemptfrom federal income taxes and often state income taxes as well (see Section 2.3 fordetails). Changes in tax rates affect returns and market value of bonds. Bonds canbe called by municipalities, usually in periods when they can refinance their debt atbetter conditions, adding to the uncertainty. Finally, municipal bonds are just onetype of security available on the financial markets. Their value is a function of theprevailing interest rate, with U.S. Treasury bonds being regarded as an importantrisk-free benchmark. Interest rate fluctuations are particularly important for illiquidassets like municipal bonds.2.3 Measuring Borrowing CostsThe goal of this chapter is to study the effect of the racial and ethnic demographiccomposition of U.S. municipalities on the borrowing costs they face in the municipalbonds market. In order to do so, one of the first and most important questions to13answer is how to measure borrowing costs in a precise and consistent way.The yield-to-maturity of a bond measures the overall returns to an investor holding itfrom issuance to maturity, taking into account both the recurring coupon paymentsas well as the possible initial pricing at a discount below or premium above the facevalue. The yield is also a good candidate measure of the borrowing cost for the issuingmunicipality. Formally, the yield-to-maturity of a bond of price p is y such that:p =T∑t=1coupont(1 + y)t +face value(1 + y)T , (2.1)that is, the rate that equates the present-discounted cash flow generate by the bondto its price at issuance.To get the most accurate measure of borrowing costs, it is necessary to make twoadjustments. First, all municipal bonds are exempt from the federal income tax.Bonds are also most often exempt from own-state income taxes: tax-residents of astate do not pay income tax on bonds issued in that state. Babina et al. (2021) showthat, because of these tax advantages, the market for municipal bonds is segmentedby state: investors have a preference to hold bonds from their own state and enjoythe corresponding tax benefits. The assumption to adjust bond yields for their taxadvantage is therefore safe and intuitive. The tax advantages of bonds are pricedinto their yields, which means that the exact same bond (in terms of payment andmaturity structures), issued by identical municipalities in different states, displaydifferent yields simply because of differences in tax rates and nothing else. To makebonds from all municipalities comparable, the first step is to adjust their yields forthe different tax advantages.I follow Schwert (2017) and Garrett et al. (2023) to compute state-specific and time-varying tax advantages and the corresponding tax-adjusted yields. State taxes arealways deductible from federal taxes. Deductibility of federal taxes from state taxesvaries by state. In states without deduction of federal taxes, the effective tax rate is:τ = τf (1− τs) + τs;where τf and τs are the federal and state income tax rates respectively. In states that14allow deduction of federal taxes, the effective tax rate is:τ = τf (1− τs) + τs(1− τf )1− τfτs .Given the effective tax rate τ , the tax-adjusted yield is:y˜ = y1− τ . (2.2)See Appendix A.1 for derivation.The second adjustment is to make all municipal bonds comparable in terms of riskstructure and market conditions at the time of issuance. As seen in Equation 2.1,the yield of a bond is a function of the coupon payments, the price at issuance (atpremium or discount), and the maturity structure (when coupons and principal arepaid). We do not want to compare the yield of a long-term bond issued in a volatileyear to that of a short-term bond issued in a stable period, but rather take thesemechanical drivers of risk explicitly into account. The natural comparison is thenrelative to a risk-free benchmark, rather than directly across different bonds: howmuch would it cost to generate the same cash flow of a given bond using risk-freesecurities? U.S. Treasury bonds are often assumed risk-free by the finance literature(Longstaff et al., 2005). An investor can guarantee the same cash flow of a municipalbond by purchasing the right amount of treasury bonds of specific maturities. Forinstance, suppose a municipal bond pays a coupon of $100 in six months and another$100 coupon and principal of $1,000 at maturity in one year. An investor can ensurethe same payments by purchasing a six-months treasury bond that pays $100 atmaturity and a one-year treasury bond that pays $1,100 at maturity. Each of thesetreasury bonds has their own price. The sum of these prices is the price of a risk-freesynthetic security generating the exact same cash-flow of a risky municipal bond.Using this synthetic risk-free price in Equation 2.1 gives a corresponding syntheticrisk-free yield. The difference between the actual yield of a municipal bond and thecorresponding risk-free yield is the yield-spread, which is the main outcome variableof interest about bonds used in the rest of the chapter.In practice, U.S. Treasury bonds have their own specific maturity and paymentstructures, the simple $100 payment security described above often does not exist.15The Federal Reserve Board of Governors and the U.S. Treasury publish daily estimatesof a zero-coupon equivalent yield curve for treasury bonds.8 The curve is estimatedmonthly for maturities of less than a year, and yearly after that. I interpolate thecurve to the monthly level to better match the timing of the coupon payments ofmunicipal bonds. Let yrft be the estimated zero-coupon equivalent risk-free yield of atreasury bond maturing at time t. We can use Equation 2.1 to compute the price prfof a synthetic risk-free security generating the same cash flow of any municipal bond:prf =T∑t=1coupont(1 + yrft )t+ face value(1 + yrft )T. (2.3)Using prf in Equation 2.1 allows to recover a unique risk-free yield for the entire bondyrf , and to calculate the tax-adjusted yield-spread as:s = y˜ − yrf . (2.4)Note that the treasury curves are estimated daily, meaning that we know the risk-freeFigure 2.2: Yield Curves, 2004-20190200400600800Yields (bp)1 3 5 7 10 15 20Maturity (years)Nominal Risk-free Tax-adjustedYield-to-maturity (at issuance) curves estimated on the bond level data used in the chapter, covering the universe of municipal bondsissued by cities between 2004 and 2019. Local means regression. The nominal curve is based on the nominal yield of bonds, observed rawin the SDC (2021) data. The tax-adjusted curve is estimated on the bond yields adjusted according to Equation 2.2. The risk-free curveis based on the equivalent risk-free yield in Equation 2.3 built on U.S. Treasury bills and matched to each municipal bond in the data.8Federal Reserve Yield Curve data available here (link) and U.S. Treasury Bill Rates data availablehere (link).16equivalent yield for each bond at the precise time of issuance, which accounts forthe prevailing market conditions at the time. Together with the tax-adjustment, thisfinally makes the yield-spread the most precise measure to compare municipal bonds.Figure 2.2 shows the three curves of the nominal, tax-adjusted, and risk-free yieldsestimated on the bonds used in the chapter. Additional yield and spread curves bymaturity and year are illustrated in Figure A.1 and Figure A.2.2.4 DataThe chapter combines three main sources of data on the universe of municipal bondsissued by municipalities, their sociodemographics, and their financial accounts. I relyon proprietary data on the universe of municipal bonds issued between 2004 and 2019by municipalities in the U.S, based on the SDC Global Public Finance module ofThomson Reuters. The data includes detailed information on bonds at the time ofissuance.Out of the 6,899 unique cities or towns with at least 2,500 inhabitants existing inthe contiguous U.S. during the sample period, 3,514 issued at least one bond, atleast once between 2004 and 2019.9 Conditional on issuing at least once, the averagemunicipality issued bonds 5.7 times. The median municipality issued bonds 3 times.Based on these numbers, a coverage of approximately 50% relative to the populationof municipalities is reasonable. Issuing bonds is no small feat, especially for smallermunicipalities. It is therefore plausible that a significant number of municipalities donot end up in the sample of issuers not because they do not rely on bonds, but simplybecause they did not do so during this period.Table 2.1 reports summary statistics for 256,185 bonds issued by 3,514 unique munici-palities. The average bond has a spread of approximately 173 basis points with respectto its risk-free benchmark discussed in Section 2.3. Bonds are worth on average justover $1 million and mature in 8.5 years. 72% of bonds are GO, that is backed by thefull faith and taxing capacity of a municipality. The rest are Revenue bonds. Newbonds are issued either competitively or through a negotiated process. Finally, over9Having removed outliers, the universe of potential issuers is 6,032 municipalities. Almost 60% ofthose, or 3,514 are issuers in the data used throughout the chapter. This is a result of an extensivedata cleaning, including probabilistic name matching (95% match-rate) and trimming of outliers.This is therefore a lower-bound number of issuers, with some not being included most likely due toimperfections of the raw data.17Table 2.1: Bonds Summary StatisticsMean Std. Dev. Min. Max. Obs.Yield-spread (bp) 172.871 86.054 9.700 519.300 256,185Par Amount ($1mm) 1.139 3.306 0.003 284.003 256,185Maturity (years) 8.495 5.143 1.000 20.000 256,185GO (indic.) 0.722 0.448 0.000 1.000 256,185Competitive (indic.) 0.493 0.500 0.000 1.000 256,185Rated (indic.) 0.856 0.351 0.000 1.000 256,185The yield-spread is measured in basis points (0.01%). The par amount is the facevalue or principal of the bond. Values deflated to 2010 dollars. maturity is thelength of the bond, from date of issuance to the date the principal will be repaidand the bond extinguished. GO bonds commit the full faith and taxing capacityof the issuing municipality to the repayment of the bond, as opposed to revenuebonds with their own dedicated cash flow. Bonds can be issued through competitiveauctions between underwriters or through a negotiated process with one specificunderwriter. Bonds can be rated by the main rating agencies similar to any othersecurity.85% of bonds receive the rating of a credit rating agency. Table A.1 in Appendix A.2reports additional summary statistics at the bonds and issue levels.Figure 2.3: Ratings0510152025Percentnot rated BB BB+ BBB- BBB BBB+ A- A A+ AA- AA AA+ AAARatingShare of bonds in the sample assigned to each rating category.18As mentioned in Section 2.2, municipal bonds have extremely low default rates. Thisis reflected in their high-quality ratings, shown in Figure 2.3. Over 60% of bonds arerated double- or triple-A. The rest is either not rated (15%) or almost entirely ratedat least A-. All bonds in the sample are considered investment-grade.Information on the city finances is based on the U.S. Census of Governments, aquinquennial survey of all local governments in the country, conducted in yearsending in 2 and 7. The surveys offer a detailed breakdown of municipal revenues andexpenditures, as well as a snapshot of the stock of outstanding debt. To each newbond issuance, I attach the data of the previous, most recent Census. Table 2.2 offersa brief description of these accounts. On average, issuing municipalities raise andspend a little less than $2,000 per capita, per year. Following the discussion in Section2.2, it is not surprising that they keep a balanced budget, on average. At the sametime, these municipalities bear a considerable amount of long-term outstanding debt,at just over $2,000 per capita, or 112% of their annual revenues. I combine these withreal estate data from Zillow (2023) to proxy for tax capacity: on average, over 55% oftax revenues for municipalities in the sample come from property taxes.10Table 2.2: Accounts Summary StatisticsMean Std. Dev. Min. Max. Obs.Total Revenues ($1mm) 154.208 1,977.600 0.234 100,306 7,865Revenues per capita ($) 1,966.897 1,517.144 70.140 32,441 7,865Total Expenditures ($1mm) 154.331 2,002.740 0.075 102,446 7,865Expenditures per capita ($) 1,956.071 1,533.695 16.983 31,821 7,865Outstanding Debt ($1mm) 201.635 2,561.266 0.000 125,092 7,865Outstanding Debt per capita ($) 2,033.746 2,130.468 0.000 72,916 7,865Deficit/Revenues -0.001 0.184 -0.979 1.901 7,865Debt/Revenues 1.119 0.805 0.000 5.009 7,865Based on the quinquennial Census of Governments, run in years ending in 2 and 7, covering theuniverse of local governments in the U.S. Values deflated to 2010 dollars. Summary statistics for the3,514 unique issuing municipalities in the sample, over the 2004-2019 sample period.10Zillow Home Value Index (ZHVI) for All Homes Including Single-Family Residences, Condos, andCO-OPs. Measured as weighted average of the middle third (mid-tier) of homes in a given location.It should be interpreted as the typical, not median, home value.19Finally, information on the sociodemographics of the municipalities is based on U.S.decennial censuses from Manson et al. (2022), linearly interpolated to match thetiming of the U.S. Census of Governments. These variables include the population sizeand its composition in terms of non-White minorities overall, Blacks (not of Hispanicorigin), and Hispanic or Latinos. In addition, the controls of the main specificationinclude measures of the median household income, the share of over 65 population,the unemployment rate, and two measures of education, the shares of populationholding at least some degree or a graduate degree. Summary statistics for the Censusinformation are reported in Table 2.3.Table 2.3: Census Summary StatisticsMean Std. Dev. Min. Max. Obs.Population 52,041 237,339 2,501 8,615,473 7,865Minority (frac.) 0.242 0.202 0.005 0.980 7,865Black (frac.) 0.080 0.125 0.000 0.929 7,865Latino (frac.) 0.107 0.142 0.002 0.978 7,865Median household Income ($) 56,328 23,617 17,122 232,901 7,865Over 65 (frac.) 0.140 0.050 0.011 0.670 7,865Unemployment (rate) 0.070 0.031 0.007 0.381 7,865Some Degree (frac.) 0.268 0.088 0.046 0.589 7,865Graduate Degree (frac.) 0.108 0.078 0.006 0.571 7,865Based on decennial U.S. Census. All municipality-level info based on Manson et al. (2022). Cen-sus observations are linearly interpolated to match the Census of Governments data in Table 2.2.Racial and ethnic composition shares computed relative to the voting-age population. Valuesof median household income deflated to 2010 dollars. Summary statistics for the 3,514 uniqueissuing municipalities in the sample, over the 2004-2019 sample period.2.5 Empirical StrategyThe goal is to estimate the effect of the racial and ethnic composition of a municipalityon the borrowing condition it faces when issuing a bond. The dependent variable ofinterest is the yield-spread, the difference between the tax-adjusted yield of a bondand a risk-free benchmark, introduced in Section 2.3.The main independent variable of interest is the share of Minority population, measuredas non-White population. This can naturally be expanded into shares of single groups,20such as Black and Latino shares. In practice, these shares are computed relativeto the voting age population of a municipality, to better capture their political andeconomic relevance (Ricca and Trebbi, 2022). To account for potential endogeneity andomitted risk factors, I employ a novel implementation of shift-share instruments forthe share of minorities in the population. This is discussed in detail below, followingan explanation of my measurement choices and the basic empirical specification.The tax adjusted yield measure offers two better qualities compared to a simple yield,that makes comparisons between bonds more precise and economically meaningful.First, the tax-adjustment allows to abstract from spurious differences in taxationover time and across locations. Second, the comparison with a risk-free benchmarkaccounts for the specific cash flow of each bond, while at the same time capturing theprevailing market conditions at the time of issuance.11The main OLS specification is the following:spreadbmt = βminoritymt+γ′cXcmt+γ′aXamt+γ′fXf bt+δs(m)+δty(b)+δr(b)+εbmt, (2.5)where b indicates attributes of a specific bond, issued by municipality m in year t. Allspecifications, unless otherwise noted, include three main fixed effects. The first isa fixed effect for the state of the issuer s(m), to control for time-unvarying marketcharacteristics for each state, and differences in budgetary rules by state. The sampleperiod from 2004 to 2019 is arguably too short of a time frame to rely on municipalityfixed effects. In addition, the demographic data available at the local level cannotmatch the high-frequency of new bonds issues, without relying on extreme interpola-tion. Nonetheless, robustness checks with municipality fixed effects are reported in therobustness checks. The second is a year-of-issue by maturity fixed effect, essentiallyto capture the time varying yield and spread curves for bonds of different durations,illustrated in Figure A.1 and Figure A.2. Finally, it includes a fixed effect for the rat-ing group of the bond r(b), to control for the assessed risk of the bonds (see Figure A.3).The specification can include three sets of controls. Xcmt indicates a set of sociode-mographics derived from the Census: the log of the population size, the log of the11The frequency of bond issues of the median municipality in my sample is too low to explore volatilitymeasures. Similarly, the secondary market for municipal bonds, which is not considered within thescope of this chapter, is known for its low liquidity (Cestau et al., 2019), making it unsuitable tostudy municipal bonds volatility.21median household income, the share of population over 65, the unemployment rate,and the shares of population holding at least some degree or a graduate degree. It alsoincludes the population growth rate in the previous 10 years. All of these measuresare designed to account for the overall size and structure of the issuer, and its longerterm trend.Similarly, Xamt includes key economic indicators of the financial health of a municipal-ity: the log of total revenues and expenditures, the log of the outstanding debt at thetime of issuance, the log of mid-tier house values and its growth rate over the previousyear. Both these sets of controls are designed to measure the overall taxing potentialof issuers and their credit worthiness based on economic fundamentals. Essentially,information observable to investors that can explain the yield-spread on municipalbonds.An additional set of controls Xf bt includes several financial features of the bonds: thelog of the face value, the log amount of the entire issue, indicators for whether thebond is callable, GO, issued competitively, used for new money or refinancing, creditenhanced, bank qualified, and whether a sink fund exists. While these are interestingfeatures, and they are often included in the finance literature on municipal bonds,they are also inherently endogenous to the financial conditions faced by an issuer. Forinstance, how much a municipality decides to issue can be a function of what theyexpect to pay for it. In the same way, whether they decide to issue it competitively orthrough a negotiated process, or whether to purchase credit enhancements or not, canbe a result of the conditions they expect on the market, which in turn are a functionof economic fundamentals and their sociodemographics structure. These would bebad controls, which I occasionally add for robustness purposes.The specification in Equation 2.5 aims to control for the main observable drivers ofspread, and to isolate the effect of the Minority share. However, while the set offixed effects and controls included can arguably capture most economic fundamentalsfactors, they cannot rule out definitely the presence of omitted variables. In particular,we may worry about omitted risk variables that are positively correlated with both theMinority share and the spread on bonds. This is the case, for instance, if minoritiessort into high-spread municipalities. Or, vice versa, if White population sorts into lowspread municipalities. This would bias the OLS estimates in Equation 2.5 upwards.To tackle this concern, I construct a shift-share instrument for the share of minorities22in the population, similar to that used by Boustan (2010) and Mayda et al. (2022)(see also Derenoncourt (2022), Calderon et al. (2023), and Mayda et al. (2023)). Iadapt this instrument following the most recent advances in the literature on shift-share instruments and their identifying assumptions, in particular based on Borusyaket al. (2022) and Borusyak and Hull (2023). This allows me to relax one of the keyassumptions for validity, and rely exclusively on the exogeneity of the shifts used inconstructing the instrument.The idea behind a shift-share instrument is to predict exogenous shares for any groupof interest, by combining predetermined shares of that group with plausibly exogenousshifts in its population. By measuring only the exogenous component of the shares,the instrument would clean the minorities estimates from biases due to the possiblepositive or negative correlation of minorities shares with omitted risk variables. If theminorities shares are positively correlated with omitted risk, the IV strategy wouldcorrect the estimates downwards. If the minorities shares are negatively correlatedwith omitted risk, explaining the selection into the sample of issuers, the IV strategywould correct the estimates upwards.The instrument is constructed as follows. For any location c in which we want topredict a valid exogenous population shares of a group, let Bjc be the number of peopleof that group born in state or country j and living in c (out-of-state or country) in agiven pre-period. The total number of people born in j and living out of j is ∑k Bjk,for all locations k not in j. Define sjc = Bjc/∑k Bjk, the share of people in c out ofthe total born in and living out of j.This is the predetermined share of the group inc used in the instrument. Let Gj be the total number of migrants in the group ofinterest out of j, during a given time period. This is the shift of the instrument. Thepredicted share of the group in c is then:zc =∑j sjcGjpc, (2.6)where pc is the total population of c in the pre-period. I follow Boustan (2010) andothers in relying on the Great Migration of Blacks out of the U.S. South between 1940and 1970 as plausibly exogenous shift to instrument for Black shares out of South.1212Southern states, following the Census definition, include: West Virginia, Virginia, North Carolina,South Carolina, Georgia, Kentucky, Tennessee, Alabama, Mississippi, Arkansas, Louisiana, Okla-homa, Texas. Maryland, Delaware, and Florida are net receivers of Black migrants between 194023The migration data are based on Gardner and Cohen (1992) and Bowles et al. (2016).The 1940 shares are computed from the 1940 full count Census (Ruggles et al., 2023).Because of the origin of the shifts used, this instrument is only available for locationsout-of-South.I augment these instruments with insights from the international migration literature(Mayda et al. (2022)). To instrument for Black shares, I add migration flows to theU.S. between 1980 and 2000 from the Caribbean Islands and non-North Africa.13 Thepre-period shares for these flows are based on the 1980 Census.To instrument for the share of Latinos, I use shifts between 1980 and 2000 from Mexico,Cuba, Dominican Republic, Central America, South America. These are the country oforigin used by the U.S. Census for its Hispanic or Latino classification. The pre-periodshares for these flows are also based on the 1980 Census. The instruments for Blackand Latino shares are available at the county level. In addition, an instrument for theshare of Asian is possible, using shifts from China, India, Japan, Korea, Philippines,and Vietnam between 1980 and 2000. However, this instrument performs particu-larly poorly in terms of first-stage, and is not used in the empirical analysis that follows.The identifying assumption behind a shift-share instrument is that, conditional oncontrols, omitted variables affecting the yield-spread must not be simultaneouslycorrelated with both the pre-period shares sjc and shifts Gj. In the context of thisstudy, exogeneity of the shares may not be tenable assumption. Despite being setin a very early pre-period, either 1940 or 1980, they might still be correlated withthe omitted risk variables that motivate the IV strategy in the first place. It turnsout, thanks to the recent advancements of Borusyak et al. (2022) and Borusyak andHull (2023), that shift-share IVs can be valid even with exogenous shifts only. Theyshow that a shift-share design can be reshaped into an aggregate shift-level regressionrelying simply on shift-level instruments and their exogeneity and producing equivalentestimates. This equivalence can be achieved by controlling for the sum of shares usedto build the shift-share instruments in the usual specification. The simple intuition isto control for the part of the instrument that is mechanically generated by the shares,and 1970 and are therefore not included in the shifts.13The Caribbean islands include Jamaica and other (non-Hispanic) West Indies. Migrants from thesecountries are predominantly classified as Black or African-American by the U.S. Census. The flowis calculated as the difference in foreign-born people from these countries between the 1980 and the2000 Census.24not necessarily exogenous, and not by the shifts. Locations with high pre-determinedshares show higher values of the instrument for any given amount of shift. In my case,this means controlling for: ∑j sjcpc, (2.7)or the sum of shares rescaled by the pre-period population. This control is includedin all 2SLS results that follow. The shift-level strategy developed by Borusyak et al.(2022) also allows to cluster the standard errors by shift, something that is not possiblein the usual bond level setup. Shift-level equivalent estimates are also reported forall 2SLS results. The robust IV strategy developed by Borusyak et al. (2022) doesnot accommodate an over-identified setup with different shares. That is, a setup inwhich shift-share instruments for different groups are combined to predict an aggregateMinority share. This is why the IV result will focus on Black and Latino sharesinstrumented separately. To the best of my knowledge, this is a novel implementationof Borusyak et al. (2022); Borusyak and Hull (2023) robust IV strategies in the contextof shift-share instruments for population shares.2.6 ResultsThis section discusses the main results of the chapter. First, it presents an overviewof the correlation between Minority shares and yield-spread, ruling out a number ofalternative mechanisms. I present next the results using the IVs just described inSection 2.5, which provide more causally robust estimates of the effect of the ethnicand racial composition of municipalities on the borrowing costs. Finally, I try toquantify the economic significance of these results.Table 2.4 reports the OLS results of the main specification in Equation 2.5 for theMinority share coefficient. The Minority share is standardized, so the coefficient canbe interpreted as the effect of one standard deviation increase in shares, and the unitsof measure are basis points. All columns include the full set of fixed effects describedin Equation 2.5: state, year of issuance by maturity, rating group. The columnsprogressively add the three set of controls. The preferred specification includes thecensus and accounts control, and it is reported in column (5). The coefficient ofMinority share in column (5) of Table 2.4 is positive and significant: a one standard25Table 2.4: Minority and Yield-Spread(1) (2) (3) (4) (5) (6)Spread (bp) Spread (bp) Spread (bp) Spread (bp) Spread (bp) Spread (bp)Minority (sd) 6.431∗∗∗ 1.888∗∗ 2.320∗∗∗ 3.518∗∗∗ 2.528∗∗∗ 2.138∗∗∗(0.648) (0.753) (0.633) (0.551) (0.744) (0.661)Census Controls - X - - X XAccounts Controls - - X - X XFinancial Controls - - - X - XMean Dep. Var. 172.871 172.871 172.871 172.871 172.871 172.871SD Dep. Var. 86.054 86.054 86.054 86.054 86.054 86.054Obs. 256,185 256,185 256,185 256,185 256,185 256,185Municipalities 3,514 3,514 3,514 3,514 3,514 3,514Standard errors clustered at the municipality level in parentheses. ∗ Significant at 10%, ∗∗ significant at 5%, ∗∗∗ significantat 1%. OLS estimates. The observations are at the bond level. Spread (yield-spread in Equation 2.4) is measured in basispoints (0.01%). The Minority fraction is the share of non-White voting age population, and it is standardized to mean 0 andstandard deviation 1. All columns include state, year×maturity, and rating fixed effects. The census controls are city-level andinclude: (log) population, (log) median household income, share of over-65 population, shares of college and graduate educated,unemployment rate, population growth rate over 10 years. The accounts controls are city-level and include: (log) revenues,(log) expenditures, (log) outstanding debt at the time of issuance, (log) mid-tier house values, mid-tier house values growthrate over 1 year. All columns include state, year×maturity, and rating fixed effects. The financial controls are bond level andinclude: (log) amount of the bond, (log) amount of the issue, and indicators for callable, GO, competitively issued, new money,credit enhanced, bank qualified, and sink fund bonds.deviation increase in the Minority share is associated with a 2.5 basis points increasein the yield-spread. Municipalities with higher Minority share face higher borrowingcosts. This is equivalent to a 1.4% increase relative to the average spread, or 3% ofits standard deviation. For context, the standard deviation of the Minority share is0.2. As an example, consider an average $1 million bond issued at face value, with anaverage maturity of 8 years and average yield to maturity and coupon rate of 2.62%($210k of total interests). A one standard deviation increase in the Minority share isequivalent to a 0.95% increase in interests, or an extra $2,000.Estimates of Equation 2.5 unpacking the previously aggregate Minority share intoshares of Black, Latino, and other minorities are reported in Table A.3 of AppendixA.2. The results are consistent, with both the shares of Blacks and Latinos positivelycorrelated with the yield-spread. Other minorities display a smaller correlation, andnot significant.26The shift-share instruments designed and discussed in Section 2.5 can identify causalestimates, in particular by ruling out bias from omitted risk variables. Column (1)in Table 2.5 reports the baseline OLS estimates for reference, which include the fullTable 2.5: Shift-Share IV Estimates(1) (2) (3) (4)Spread (bp) Spread (bp) Spread (bp) Spread (bp)Second Stage:Black (sd) 1.166∗∗ 3.059∗∗ 10.289∗∗ 10.289∗∗∗(0.543) (1.241) (5.223) (2.128)Latino (sd) 2.190∗∗∗ 0.292 4.687∗ 4.687∗∗∗(0.725) (0.854) (2.674) (1.360)First Stage:Black IV (sd) 0.272∗∗∗ 0.063∗∗(0.066) (0.027)Latino IV (sd) 0.534∗∗∗ 0.414∗∗∗(0.033) (0.034)Method OLS Reduced 2SLS Shift lvl.F-stat 19.775 / 42.878 5.383 / 147.691Mean Dep. Var. 172.871 172.871 172.871SD Dep. Var. 86.054 86.054 86.054Obs. 256,185 256,185 256,185 21Cluster Level County County County ShiftClusters 1,324 1,324 1,324 21Standard errors clustered at the county level in parentheses. ∗ Significant at 10%, ∗∗ significantat 5%, ∗∗∗ significant at 1%. The observations are at the bond level. Spread (yield-spread inEquation 2.4) is measured in basis points (0.01%). The Black and Latino fractions are sharesof non-White voting age population, and are standardized to mean 0 and standard deviation 1.All columns include census and accounts controls. The census controls are city-level and include:(log) population, (log) median household income, share of over-65 population, shares of collegeand graduate educated, unemployment rate, population growth rate over 10 years. The accountscontrols are city-level and include: (log) revenues, (log) expenditures, (log) outstanding debt atthe time of issuance, (log) mid-tier house values, mid-tier house values growth rate over 1 year.All columns include state, year×maturity, and rating fixed effects. The shift share instrumentsare constructed according to Equation 2.6 and standardized to mean 0 and standard deviation 1.Shifts are defined in Section 2.5. Column (2) is a reduced form specification using the instrumentsas regressors. Column (3) is recentered adding the sum of shares∑j sjc/pc as control (Borusyakand Hull, 2023). The first stage panel reports the first stage coefficients of each instrument ontheir corresponding endogenous variable. The reported F-stats are for the Black and Latino IVsfirst-stages respectively. Column (4) reports the shift-level equivalent estimates based on Borusyaket al. (2022). The effective sample sizes, computed as the inverse of the Herfindahl index of thesquared shares, are 12.7 and 4.4 for the Black and Latino IVs respectively.27set of fixed effects and the census and accounts controls. Minorities are consideredseparately as Black, Latino, and other population shares. Both Black and Latinoshares are positively and significantly correlated with spread, in line with the aggregateMinority results. Column (2) shows reduced-form estimates, using the shift-share IVsdirectly as regressors instead of the Black and Latino shares. Column (3) reports the2SLS coefficients of interest. A one standard deviation increase in the share of Blacksincreases yield-spread by 10 basis points. A one standard deviation increase in Latinoshares increases yield-spread by 4.6 basis points. These estimates are economicallymeaningful. Going back to the million dollar bond example, a one standard deviationincrease in the share of Blacks increases the total interest costs by 3.8%, or an extra$8,000. A one standard deviation increase in the share of Latinos increases theborrowing costs by 1.8%.This IV specification is necessary to rule out omitted variable bias. We worry aboutomitted measures of risk that positively correlate with spread and population sharesof minorities. This would bias the baseline OLS estimates upwards. The second stageestimates are robust and positive, attenuating this concern. The results do not appearconfounded by omitted risk. However, the fact that the 2SLS coefficients in column (3)are significantly larger in magnitude than the OLS estimates of column (1) warrantscaution when interpreting these effects.The instruments are relatively strong. The first stage is significant and works asexpected. The F-stats are above the customary threshold of 10 for both.14 This isunlikely an issue of weak IVs. Besides, weak IVs would bias 2SLS estimates closer toOLS.The second stage coefficients could be picking up some of the selection of municipalitiesinto the sample of issuers. I can only observe a spread for municipalities that decideand manage to issue bonds. In other words, the concern is that most higher-Minoritymunicipalities in the sample are those that can afford to issue bonds at lower spreads, forsome unobservable or omitted reason. This would bias the OLS estimates downwards.However, the IV specification is not designed to correct for this particular typeof selection, so this is unlikely to explain the difference between OLS and 2SLScoefficients.1514Angrist and Kolesár (2024) suggest that in a just-identified setting like the one of this study, theconventional threshold works well for inference.15Looking at a simple means balance test between issuers and non-issuers in Table A.2, it seems thatthat municipalities are selecting into issuers only marginally based on demographics. Similarly,28The difference between 2SLS and OLS coefficients could be hiding significant heteroge-neous treatment effects of Minority shares on spread. From a potential outcomes andLATE perspective, the second stage estimates measure the effect of Minority on spreadfor a subsample of compliers—municipalities that increase their Minority shares as aresult of the shifts. This combines with the fact that the shift-share instruments arehighly skewed to the right, doing a better job at predicting larger Minority shocks, butnot so much for small shares. If the diversity premium is heterogeneous and strongerin high-Minority municipalities, these effects would be weighted more heavily by the2SLS estimates.16Notably, the 2SLS estimates are reassuringly close to the coefficients from an alternativespecification including municipality fixed effects presented in Table A.6. This suggeststhat the IVs are effectively isolating the effect of exogenously higher Minority sharesfrom unobservable municipality-level characteristics.Column (4) estimates are based on the shift-level equivalence result in Borusyak et al.(2022). These are the result of a shift-level regression in which the original data isreshaped and weighted by the shares of the shift-share instrument, to obtain a samplewith as many observations as shifts used to construct the instrument, in this caseorigin locations. The endogenous variable can then be instrumented directly with thecorresponding shift. If the original location-level specification in column (3) correctlycontrols for the rescaled sum of shares in Equation 2.7, the estimates are equivalentto column (4), as in this case. All in all, this supports the fact that the results onlyrely on the exogeneity of the shifts as identifying assumption, without also requiringexogeneity of the shares. This is a considerably less demanding condition for validity.Up to 10 basis points increase per standard deviation increase of Black and Latinopopulation shares, or a 3.8% increase in total interests, may appear somewhat small.It is to benchmark these magnitudes against existing research on municipal bonds.preliminary results on issuance in Table A.8 suggest that more diverse municipalities—by a onestandard deviation increase—are approximately 1 percentage point less likely to issue bondsthroughout my sample period, relative to an average 38% probability of issuing.16Tentative explorations of heterogeneous effects do suggest that the diversity premium is strongerand concentrated in high-Minority municipalities, but the estimates are unreliably noisy. Shift-shareinstruments for demographic composition are notoriously skewed to the right. The solution adoptedby Derenoncourt (2022) is to use the percentiles of the predicted shock. While this attenuates theskewness, it also prevents to use the adjustments proposed by Borusyak et al. (2022); Borusyakand Hull (2023) to make sure the shift-shares rely only on the exogeneity of the shifts. The tradeoffis between a more linear first-stage and less demanding validity assumptions for the IV strategy.29Garrett et al. (2023) study the tax advantage of municipal bonds. They estimatethat 1 percentage point more of tax subsidy—taxes from which municipal bonds areexempt—decreases borrowing costs by 6.5 to 7 basis points. Goldsmith-Pinkhamet al. (2023) study the effects of climate change on the bonds market and riskiness ofmunicipal borrowers. They estimate that a 10 percentage point increase in the shareof properties exposed to a 6 ft sea level rise corresponds to a 5.3 basis points increasein spread on municipal bonds. These interesting comparisons show results in the sameorder of magnitude as my estimates of the effect of diversity. Much of the variationin municipal bonds spreads is explained by market level conditions. What is left hasnecessarily smaller effects but is nonetheless economically meaningful.2.7 MechanismsThe controls already included in Table 2.4 and Table 2.5 are carefully designed torule out several potential mechanisms. In particular, controlling for median householdincome and unemployment rate rules out the possibility that the diversity premiumis simply due to more diverse municipalities being poorer and therefore perceived asriskier borrower. I test an additional specification that adds the shares of population indifferent income brackets, to account for income inequality. The results are consistentand the effect of diversity holds.The population growth rate in the main controls excludes a population trend mechanismof more diverse municipalities undergoing periods of population decline. This couldexplain pessimistic forecasts of tax capacity in the long run and therefore higherspread. In fact, the opposite is true, with non-White minorities being by far the fastestgrowing groups in the population nation-wide.17The effect of diversity on spread is also not explained by the total amount of revenuesraised, nor by the amount of outstanding debt when issuing new bonds. The effectholds regardless of the overall reliance on debt to finance spending.As mentioned in Section 2.4, on average over 55% of tax revenues for municipalitiesin the sample come from property taxes. The real estate values and trends (mid-tierhouse values from Zillow (2023)) included in the controls are the best proxy to measurethe tax capacity of municipal issuers, ruling out risk driven by the potential tax baseavailable to service debt.17www.census.gov/newsroom/press-releases/2024/population-estimates-characteristics.html30I test for a number of additional mechanisms, most of which carry significant datalimitations which I discuss case by case.A recurring comment in the public discourse is that more diverse cities tend to displayhigher levels of crime.18 To examine whether crime can explain, at least in part, theeffect of diversity on spread, I collect and analyze crime reporting data from the FBIand the Department of Justice (NIBRS, 2024). I re-estimate the main specificationspresented in Table 2.4 and Table 2.5 adding reported property crimes per 1,000residents as controls. The results are shown in Table A.4 of Appendix A.2. Theeffect of Minority shares on spread remains positive and significant, robust to theinclusion of crime controls. The reporting system operates on a voluntary basis, witha less-than-ideal coverage of about a third of my sample. The first-stages of the 2SLSspecification are weaker, and the second-stage estimates insignificant, regardless ofthe inclusion of crime controls. The loss of precision is arguably due to the loss ofobservations rather than the added controls. This check holds also including crimesagainst persons and society, on top of property.In addition to outstanding bonds, public pension benefits are a significant source ofliabilities for municipalities. Just like outstanding debt, unfunded pension liabilitiescould affect the perceived credit-worthiness of municipal issuers. I assess whether theseadditional liabilities confound the diversity premium evidence by collecting publicpension data from the Annual Surveys of Public Pensions (ASPP, 2024). Ideally,we would want to measure future unfunded pensions. However, this information hasonly been collected starting with the 2017 wave. Instead, I use the ratio of totalbenefits paid to total contributions received and gains as proxy. Estimates with theadditional pension controls are reported in Table A.5 of Appendix A.2. The diversitypremium effect holds. Similar to the crime data, the coverage of ASPP (2024) surveysis limited to a fourth of my sample, in line with existing research using the samesource (Dippel, 2022). Once again, the instruments’ first-stages are weak and thesecond-stage estimates imprecise, whether or not the pension controls are included,due to the reduced observations.Robustness checks for additional specifications are presented in Table A.6 and Table A.718https://www.brennancenter.org/our-work/research-reports/myths-and-realities-understanding-recent-trends-violent-crime31of Appendix A.2. The diversity premium effect is robust to the inclusion of municipalityfixed effects, which rule out time-unvarying municipality-specific factors. Becauseof the tax advantage of municipal bonds discussed in Section 2.2, the bonds marketis highly segmented by state (Babina et al., 2021; Garrett et al., 2023). To betteraccount for state-level market conditions at the time of issuance, I can modify themain specification to include a time-varying state-by-year fixed effect. This capturesboth demand-side dynamics such as the number of potential investors interested inbonds, as well as supply-side factors such as debt issues from other local governmentsin the state.Finally, I test for the potential effect of city council composition and political represen-tation of non-White minorities. Council composition data is based on ICMA (2020)surveys, covering approximately 40% of the sample. The political representation ofminorities in city councils does not seem to have an effect on spread. On the otherhand, the population diversity effect remains, although estimates less precise becauseof the loss of observations due to missing council composition data.19Having ruled out an extensive list of alternative mechanisms, the diversity premiumresult remains consistent with potential discrimination of diverse municipalities in thebond market, in terms of higher borrowing costs. As introduced in Section 2.1, animportant limitation is that I cannot disentangle whether the larger yield-spreads aredue to statistical or taste-based discrimination in the bonds market. This is because ofa missing objective measure of performance of bonds in my data, contrary to existingliterature on discrimination. For instance, in the case of discrimination in motor vehiclesearches (Knowles et al., 2001), we can observe whether a stop found a violation. Inthe case of discrimination in loan approvals (Berkovec et al., 1998), we can measureloan performance based on timely payments. The intuition is that these features allowthe authors to measure the expected utility of searches and loan approvals for decisionmakers, and disentangle statistical from pure taste-based discrimination that goesbeyond a rational utility maximization behavior.In contrast, yields and spreads are endogenous market outcomes and cannot beconsidered objective performance indicators. Other measures such as defaults are19Additional specifications, not reported here, tested alternative standard errors to account forpotential spatial correlation at the state level. This is particularly relevant considering thesegmentation of bond markets by state. The statistical significance of the results remains strong.32extremely rare in municipal bonds (Moody’s, 2022), and I do not have informationon smaller failures like delayed payments. It is worth pointing out, however, thatthese events are recorded by rating agencies and would credibly enter the credit ratingassigned to bonds, which I control for. Finally, if either form of discrimination happensat the market level, looking at secondary market trading would present the samelimitations.While I cannot disentangle statistical and taste-based discrimination, I can test wherethis discrimination may be happening during the issuance process. The evidencesuggests that there is no discrimination in credit ratings, and that it does not occurbecause of the underwriters—usually investment banks—involved in the issuance.I estimate the probability of each bond being assigned a specific rating using simpleLinear Probability Models. The dependent variables are indicators for each ratingFigure 2.4: Rating Probabilities (LPM, 2SLS)-.2-.10.1.2Minorities (sd) coeff. on Rating Group (indic.)Not rated A+ or below AA- AA AA+ AAARating GroupsBlack LatinoC.I. 95%, standard errors clustered at the county level. Linear Probability Model estimates, plotting the second-stage coefficients ofBlack and Latino population shares. The observations are at the bond level. The dependent variables are indicators for each bond beingassigned a specific rating category. The Black and Latino fractions are shares of non-White voting age population, and are standardizedto mean 0 and standard deviation 1. The census controls are city-level and include: (log) population, (log) median household income,share of over-65 population, shares of college and graduate educated, unemployment rate, population growth rate over 10 years. Theaccounts controls are city-level and include: (log) revenues, (log) expenditures, (log) outstanding debt at the time of issuance, (log)mid-tier house values, mid-tier house values growth rate over 1 year. Including state and year fixed effects. The shift share instrumentsare constructed according to Equation 2.6 and standardized to mean 0 and standard deviation 1. Shifts are defined in Section 2.5.The specification is recentered adding the sum of shares∑jsjc/pc as control (Borusyak and Hull, 2023). OLS estimates available inFigure A.4 and Figure A.5 for reference.33group. The specification is otherwise identical to the main one in Equation 2.5 interms of controls, with state and year fixed effects. The main regressors of interest arethe population shares of Blacks and Latinos, instrumented with the usual shift-shares.Their second-stage coefficients are plotted in Figure 2.4. There is no clear evidencethat bonds issued by more diverse municipalities receive significantly worse ratings,once accounting for the economic fundamentals of the issuers.20Discrimination also does not occur because of the underwriters involved in the issuanceprocess. Usually, these are investment banks that help municipalities design the bonds,then buying the entire issuance from the city and selling the bonds to investors.Acting as intermediaries between the municipalities and investors, they could affectthe premium paid by diverse municipalities. I test for this possibility in columns (1)Table 2.6: Underwriters and Potential Discrimination(1) (2) (3) (4) (5) (6)Spread (bp) Spread (bp) Spread (bp) Gross-Spread Gross-Spread Gross-SpreadMinority (sd) 2.527∗∗∗ -0.025(0.717) (0.094)Black (sd) 1.149∗∗ 9.207∗ -0.076 0.989(0.578) (4.741) (0.077) (0.656)Latino (sd) 2.104∗∗∗ 4.517∗ -0.003 0.120(0.642) (2.465) (0.080) (0.343)Underwriter FE X X X - - -Method OLS OLS 2SLS OLS OLS 2SLSF-stat 22.373 / 42.549 15.948 / 34.859Obs. 256,163 256,163 256,163 9,962 9,962 9,962Cluster Level Municipality Municipality County Municipality Municipality CountyClusters 3,514 3,514 1,324 2,697 2,697 1,128Clustered standard errors in parentheses. ∗ Significant at 10%, ∗∗ significant at 5%, ∗∗∗ significant at 1%. Columns (1) to (3) are at thebond level. The dependent variable is Spread (yield-spread in Equation 2.4), measured in basis points (0.01%). Columns (4) to (6) are atthe issuance level. The dependent variable is the gross-spread profit of the underwriter, measured in $ per thousand of par value. TheMinority, Black, and Latino fractions are share of non-White voting age population, and it are standardized to mean 0 and standarddeviation 1. All columns include census and accounts controls. The census controls are city-level and include: (log) population, (log)median household income, share of over-65 population, shares of college and graduate educated, unemployment rate, population growthrate over 10 years. The accounts controls are city-level and include: (log) revenues, (log) expenditures, (log) outstanding debt at the timeof issuance, (log) mid-tier house values, mid-tier house values growth rate over 1 year. All columns include state and rating fixed effects.Columns (1) to (3) include year×maturity and underwriter fixed effects. Columns (4) to (6) include year-of-issuance fixed effects. Theshift share instruments are constructed according to Equation 2.6 and standardized to mean 0 and standard deviation 1. Shifts aredefined in Section 2.5. The 2SLS specifications are recentered adding the sum of shares∑j sjc/pc as control (Borusyak and Hull, 2023).20This is true with the exception of cities with higher shares of Blacks being less likely to receive atriple AAA rating ceteris paribus. These bonds, however, make up only about 7% of the market.34to (3) of Table 2.6. Adding fixed effects for the specific underwriters of each bonddoes not affect the coefficients on shares of minorities compared to the main results.The diversity premium is not driven by underwriters.Underwriters also make a profit on the transaction between issuers and primarymarket investors. This is measured in dollars per thousand of par value and it is calledgross-spread. Columns (4) to (6) check whether underwriters make higher profitson bonds issued by more diverse municipalities. The specification is at the issuancelevel, with gross-spread as dependent variable. Everything else is consistent with themain specifications seen so far. The estimates suggest that this is not the case, withinsignificant coefficients very close to zero. As a note of caution, the gross-spreadinformation is only available for about half of the issues in my sample. Reassuringly,repeating the estimates of columns (1) to (3) only on bonds and underwriters forwhich there is information on profits confirms the main results.Although I am not able to distinguish statistical from taste-based discrimination,this is, to the best of my knowledge, a first step towards better understanding wherediscrimination may be happening in the municipal bonds market. The diversitypremium found in this chapter originates in the primary market for bonds.2.8 ConclusionThis chapter studies the effect of the racial and ethnic demographic composition ofU.S. municipalities on the borrowing costs they face in the municipal bonds market.Understanding how these demographics are priced in the market for municipal debtis crucial to understanding the investment decisions of diverse municipalities, and tobetter interpret the evidence of underprovision of public goods in diverse communities.Borrowing costs are measured as yield-spreads, the difference between the yield-to-maturity of a bond and a risk-free benchmark. The spread is adjusted for the varyingtax advantages of bonds across states, and for differences in the risk structure ofthe bonds issued. The data on yields covers the universe of municipal bonds issuedbetween 2004 and 2019 by cities and towns in the U.S.The results show evidence of a diversity premium in bonds pricing. Municipalitieswith higher non-White shares of population pay substantially higher costs on theirdebt. The effect is economically significant: one standard deviation increase in the35population share of Blacks increases the yield-spread by 10 basis points on average,or the total interest costs on the average bond by 3.8%; a one standard deviationincrease in Latinos increases the yield-spread by 4.6 basis points and the total interestcosts by 1.8% on average.To rule out potential bias from omitted risk variables, I use shift-share instruments forthe share of minorities in the population, which rely on plausibly exogenous domesticand international migration shocks. Higher shares of minorities causally increase theborrowing costs of cities.The effect is not explained by economic fundamentals of cities, and holds controllingfor maturity structure and credit rating of the bonds. The diversity premium is notdriven by income, population trends, municipal revenues, amount of outstandingdebt, or tax capacity of the issuers. Despite my best efforts, I acknowledge thatat least part of the diversity premium could still reflect unobserved risk. Futureresearch could explore this further with richer panels, looking at past performance ofbonds and perhaps extremely valuable information of partial defaults. The resultsare consistent with the discrimination of racial and ethnic minorities in the primarymarket for municipal bonds. And while I am not able to disentangle statisticalfrom taste-based discrimination, I show that discrimination is not present in creditratings and does not occur because of the underwriters involved in the issuance process.Having proved the existence of this interest premium for municipalities with highershares of minorities, the next step will be to assess whether the higher cost hasimplications for the capital investment decisions of these municipalities. Capitalinvestments are in turn crucial drivers for the amount and quality of public goods andservices provided, arguably one of the most important outcomes of interest for theliterature on debt and public spending. The results in this chapter can help us betterunderstand the supply of public goods in local governments.36Chapter 3Minority Underrepresentation inU.S. Cities3.1 IntroductionSixty years after the Voting Rights Act (VRA) of 1965, the question of equal rep-resentation and equal access to the electoral franchise for minorities in the UnitedStates is still debated. To some, the discussion appears decoupled from empiricalfundamentals. For example, in the Shelby County v. Holder (2013) landmark ruling,a majority of Supreme Court justices "asserted that the federal oversight of electionswas no longer necessary in nine states, mostly in the South, because of strides made inadvancing voting rights since passage of the 1965 law."1 Others disagree. "A plethoraof reasons remain to justify aggressively monitoring voting practices under [VRA’s]current provisions", according to Lewis (2005).Evidence of disproportionality in descriptive representation is available.2 Non-Whiteracial or ethnic minorities account for approximately 40% of the U.S. population,while only 23% of the 117th Congress members belong to an ethnic minority. The1"House Passes Voting Rights Bill Despite Near Unanimous Republican Opposition" The New YorkTimes, Dec. 6, 2019.2With the term descriptive representation we mean here the mapping between certain measurabledemographic characteristics of the voting age population and the characteristics of their correspondingelected representatives. Substantive representation—the supply of policies demanded by certaindemographic subpopulations—is an issue only partially addressed in this work and only insomuch aspolicy catering to the interests of ethnic subpopulations correlates with our measures of descriptiverepresentation. Empirical evidence in support of this association is provided in Lowande et al.(2019); Broockman (2013); Griffin (2014); Wallace (2014), among others.37National Conference of State Legislatures (NCSL, 2021) reports an average gap of -13.48 percentage points between percent non-White representatives in state legislaturesin 2020 and percent non-White of state population in 2019. Certain groups stand out.In 2018, Latino elected officials represented 1.2% of the total of national and localelected officials, while Latinos make up 18.1% of the U.S. population.3While much of this debate is motivated by gaps in representation in U.S. nationalpolitics, the goal of this chapter is to provide a comprehensive analysis of the statisticalevidence of strategic Minority underrepresentation at the local level, defined as under-representation levels that are selectively higher in polities where ethnic minorities canbe electorally pivotal. The chapter focuses first on within municipality variation overtime in a panel data setting and then presents causal inference results stemming fromShelby County v. Holder (2013), which exploit the geographic variation in the liftingof the VRA coverage resulting from this Supreme Court ruling.This chapter’s focus on local politics is motivated by four main considerations. First,as Hajnal (2009) discusses (p.42), "local elections are fairly sharply divided by race andethnicity" and "there is a considerable gap between the vote of the White electorateon the one hand and the vote of the African American, Latino, and Asian Americanelectorate on the other". Hence, city governments represent a rich environment to studythe political economy of race.4 As municipal politics is also largely nonpartisan, thereis limited risk of party-specific confounders (Ferreira and Gyourko, 2009). Second, thefact that local governments often represent an entry point in political careers aimedat higher office highlights that impediments to local representation may reverberateat higher levels of government (Frendreis et al., 1990; Shah, 2014). Third, in theU.S. local governments are key suppliers of public goods (education, policing, publicinfrastructure, etc.) affecting the median voter (Coate and Knight, 2011; Trounstine,2020). This implies that welfare consequences associated with our findings couldbe large. Fourth, the vast amount of city demographic variation—the U.S. counts90,000 local governments (Trounstine, 2010; Warshaw, 2019; Trounstine, 2020) andour sample covers 7,687 cities over the 1981-2020 period—allows us to explore vastinstitutional heterogeneity across observations, which is unfeasible at the national3"Latinos make up only 1% of all local and federal elected officials, and that’s a big problem" USAToday, Jan. 6, 2020.4Throughout the chapter, the terms municipality, city, town, and alike are used interchangeably toindicate a general-purpose subcounty local government.38level, and provides suitable levels of statistical power (Trebbi et al., 2008; Trounstine,2020).This work begins by studying the relationship between Minority shares of the votingage population and the share of Minority city council members from the perspectiveof descriptive representation.5 Using data on municipalities from eight quinquennialInternational City/County Management Association (ICMA) surveys,6 spanning from1981 to 2018, combined with U.S. Census data from Manson et al. (2022), Section 3.3documents a sizable representation gap of minorities in city councils in terms ofdifference between the Minority population share and the share of council membersbelonging to a racial or ethnic Minority.7 On average, non-White minorities arecollectively underrepresented by approximately 8.4 percentage points in the sample.The results extends to the African American, Asian, and Latino minorities consideredseparately.Not only underrepresentation is a pervasive feature of U.S. municipal politics, but it isstronger in those cities where minorities may have a higher chance of being electorallypivotal: where non-White minorities are close to constituting a majority of the votingage population. More precisely, the representation gap is sharply nonlinear, withunderrepresentation being the highest when minorities account for 55% to 60% of thetotal voting age population. From the specificity of the shape of this nonlinearity, onecan begin to draw inference about the strategic nature of the underrepresentation.In a second contribution of this study, we trace an institutional anatomy of un-derrepresentation. We look at whether the choices of city political features appearsystematically designed to limit Minority representation and at which level of Minorityshares of the voting age population.8U.S. cities typically select electoral rules among a limited set of alternatives. At-Large5In the remainder of the chapter we will use the terms population and voting age populationinterchangeably, but with the understanding that our empirical analysis will only focus on votingage population in order to account for differential fertility and age profiles of different ethnic groups.The focus on voting age population to minimize mismeasurement is also an important novel featureof our empirical analysis relative to much of the literature.6Available at https://icma.org/.7City councils are on average composed by 6.2 seats in our sample, but much larger in populouscities.8Electoral laws and city council procedures are frequently endogenously chosen to effectively achievethe goals of incumbents. For a discussion of the general issue of strategically chosen politicalinstitutions see Aghion et al. (2004) and Acemoglu and Robinson (2006).39(i.e. city-wide) multi-member elections are common, while other cities allocate councilseats to geographic subdivisions in single-member districts races (SMD), or via a mixof SMD and At-Large. A substantial literature in political economy and politicalscience has shown that, for cities with geographically segregated ethnic groups, SMDelectoral rules play a crucial role in fostering Minority representation (Davidson andKorbel, 1981; Davidson, 1994; Sass and Pittman, 2000; Trounstine and Valdini, 2008;Marschall et al., 2010; Abott and Magazinnik, 2020), as White majorities are betterable to exclude Minority candidates from being elected when they control a city-widemajority of votes (thus diluting the weight of areas with a concentration of Minorityvoters). In our data we confirm that the estimated maximum underrepresentation ishigher in At-Large systems by 6 to 10 percentage points.To assess the strategic use of electoral rules in U.S. cities one can apply the simpletheoretical framework of Trebbi et al. (2008), which focuses on African Americanrepresentation around the passage of the VRA. They show how At-Large and SMDssystems are alternatively used to limit the political influence of minorities as a functionof their relative size. When minorities are small, At-Large system are better at dilutingthe vote of minorities. As minorities grow and approach 50% of the population, SMDsprevent them from potentially electing the whole council, as it might be the case underan At-Large rule. Consistently with this framework, we find that electoral rules areendogenously chosen to maximize minorities’ underrepresentation. At-Large systemsare more likely to be used in municipalities in which minorities are approximately25% of the population, large enough to potentially win in a SMD, but not enoughto threaten the White majority in At-Large elections, and less and less likely to bechosen as the Minority population grows.Besides electoral rules, city form of government, council size, and monetary retributionfor council members also display patterns consistent with underrepresentation ofminorities at points of the distribution where minorities can be electorally pivotal.In order to establish the causal mechanism behind the observed Minority under-representation patterns, a third contribution of this research is to exploit a recentruling of the U.S. Supreme Court, limiting a crucial provision of the VRA. Accordingto Section 5 of the VRA, certain jurisdictions, identified according to the coverageformula contained in Section 4(b), were required to obtain federal preclearance on anyproposed changes to their voting procedures. These covered jurisdiction, characterizedas having exhibited "entrenched racial discrimination in voting" before 1965, had to40prove that the proposed changes had no discriminatory purpose, nor deleterious effectswith respect to racial, ethnic, or language minorities before implementing any changes.Following the Shelby County v. Holder (2013) U.S. Supreme Court case, the coverageformula was ruled unconstitutional, with previously covered jurisdictions effectivelyno longer requiring federal approval in order to implement electoral changes. Thisallows us to perform a comparison of covered and uncovered municipalities, beforeand after the 2013 landmark decision.Our results confirm, in both covered and non-covered municipalities, a non-linearrelationship between minorities underrepresentation and population shares before andafter Shelby County v. Holder (2013), but with a crucial difference. Before the ruling,non-covered municipalities display higher Minority underrepresentation compared tocovered ones. After the ruling, the two groups of municipalities converge to similarlevels of underrepresentation for ethnic minorities. In essence, the results indicate thatthe VRA coverage was an imperfect, but effective tool in limiting the representation gapin cities requiring federal preclearance. Lifting the coverage systematically decreasedrepresentation of minorities in the councils of previously covered municipalities. Alleffects are statistically precise and robust to either using the entire contiguous U.S. orrestricting the analysis to areas in a neighborhood of 200 or 100 miles on either sideof the boundary of previously covered jurisdictions.In summary, the chapter presents three main sets of findings consistent with a strate-gic interpretation of the mechanisms behind Minority underrepresentation: (i) thepeculiarity of the shape of Minority underrepresentation as a function of voting agepopulation shares, which is maximal at a point where Minority voters are close to beingable to shift the political outcome of an election—that is, where electoral incentives ofan incumbent group to handicap the opposition are the strongest; (ii) evidence of thestrategic selection of certain voting procedures that matches established patterns ofMinority disenfranchisement around the VRA; (iii) the specific response in the after-math of Shelby County v. Holder (2013) and the effects of the removal of protectionsfor Minority voter blocs in areas with a proven past of Minority disenfranchisement.In terms of related literature, the role of VRA for minorities representation has been theobject of interest in a number of studies. At the local level, Shah et al. (2013) explorethe representation of African American minorities in city councils from 1981 to 2006,showing a positive effect of the VRA for Minority representation throughout the entire41period. At the national level, Schuit and Rogowski (2017) study the attitudes towardscivil rights legislation of Congress members elected by covered jurisdictions. Theirresults suggest that the representatives of covered jurisdiction are more supportive ofcivil rights proposals, in particular in districts where African American voters accountfor a larger fraction of the electorate. Ang (2019) recently examines the long-runeffects of the VRA and how it increased voter turnout at the state level thanks to theincreased participation of minorities. Such participation substantially affected politicalcompetition (Besley et al., 2010) and public goods provision (Cascio and Washington,2014). Looking at the consequences of minorities representation, the work of Facchiniet al. (2020) shows how the VRA and the enfranchisement of African American votershelped lowering the arrest rates for minorities, especially when combined with elected,rather than nominated, chief police officers. Bernini et al. (2023b) document increasingWhite voter registration as a reaction following the approval of the VRA. In a classiccontribution Alesina et al. (1999) link ethnic diversity in local governments with theprovision of public goods offering cross-sectional evidence from U.S. cities, highlightinga negative relationship. Relatedly, Beach and Jones (2017) use a regression disconti-nuity design applied to close municipal elections in California, and show that moreethnically diverse city councils provide less public goods. The authors suggest that thismight be due to increased hurdles in the legislative bargaining process in more diversecouncils, and overall lower levels of agreement. In a more recent contribution, Beachet al. (2024) show that electing non-White council members generates higher homevalue gains in Minority neighborhoods. The effect of Minority representation is notlimited to the differential provision of public goods but extends to private outcomes.Recent work on the effects of Shelby County v. Holder (2013) has focused on turnoutand registration, finding mixed results (Komisarchik and White, 2024; Billings et al.,2024). Feder and Miller (2020) find a significant increase in registered voter purgesfollowing the ruling. Chapter 4 studies voter registration as one of the main channelsof Minority underrepresentation.The rest of the chapter proceeds as follows. Section 3.2 describes the data on U.S.municipalities. Section 3.3 presents the evidence on systematic strategic underrep-resentation of racial and ethnic minorities in U.S. cities. Section 3.4 lays out theinstitutional features that contribute to underrepresentation. Section 3.5 reports causalinference originating from Shelby County v. Holder (2013). Section 3.6 concludes.423.2 DataThe analysis is based on two main data sources, combining institutional informationon U.S. municipalities with detailed demographics at the municipal level.The institutional data on form of government, electoral rules, and council compositionof U.S. municipalities are based on the Municipal Form of Government surveysmanaged by ICMA.9 The waves included are 1981, 1986, 1991, 1996, 2001, 2006,2011, and 2018. For reference, the 2018 edition of the survey was submitted to 12,761municipalities, and returned by 4,109, with a response rate of 32.2%. The surveysare representative of the universe of U.S. municipalities with a population of 2,500and over. The resulting dataset is therefore restricted to municipalities above thisthreshold located in the contiguous U.S., for a final panel of 29,974 observations, from7,687 unique local governments, each included on average 3.9 periods. The mainvariables of interest recovered from this source are summarized in Table 3.1.The top panel reports the summary statistics for the ethnic and racial breakdown ofcouncil members. From the ICMA surveys, it is possible to recover the fraction ofHispanic or Latino, Asian or Pacific Islander, Black or African American, and White(not of Hispanic origins) councilors. For notational purposes and with no intentionof diminishing the importance of any race or ethnicity, these fractions are codedas Latino, Asian, African American, and White respectively.10 Finally, a collectiveMinority fraction is defined as the reciprocal of White. Throughout the chapter, theterm Minority is used to refer to the latter measure, and instead the term minorities isused to refer to the collection of individuals belonging to one of the non-White racesor ethnicities listed above.The institutional details of U.S. municipalities are complemented by a correspondingset of sociodemographic data at the municipal level, based on the decennial U.S.Census data from Manson et al. (2022). The linkage between ICMA and census datais made through the Federal Information Processing System (FIPS) place classificationavailable in both sources.11 Because the census data are only available by decade, we9ICMA surveys have been used extensively in the Economics and Political Science literatures. Amongothers, see Trebbi et al. (2008); Coate and Knight (2011); Shah et al. (2013); Ang (2019); Abott andMagazinnik (2020). The survey are sent to city clerks, and most likely filled by them or their staff.We have no reason to believe that there could be endogenous racial classification or misreporting.10In an earlier version of this study, the terms Black and African American have been used inter-changeably and may still be in some parts of this draft.11Federal Information Processing Standard classification for places, including municipalities such43linearly interpolate the census data when necessary and match each survey-year tocensus measures from the year before. That is, for instance, the 2001 survey is matchedwith the 2000 census data, the 2006 survey is matched with 2005 interpolated data,and so on. The second panel of Table 3.1 summarizes the data on the ethnic and racialcomposition of the population at the municipal level. In parallel to the informationon council composition, the population fractions are reported for Hispanic or Latino,Asian or Pacific Islander (not of Hispanic origins), Black or African American (not ofHispanic origins), and White (not of Hispanic origins). Similarly to the ICMA councilmeasures, an aggregate Minority population fraction is computed as the reciprocalof White (not of Hispanic origins). Because of the classification used by the U.S.Census, the distinction between ethnicity and race is particularly relevant. In the restof the chapter, the Asian, African American, and White measures refer to people notof Hispanic or Latino origins. The population measures are computed with respectto the voting age population only,12 that is 18 years of age or older. The use ofvoting age populations is particularly relevant in order to account for the differentage distributions between minorities, traditionally younger and thus less likely to beeligible to vote, and White.Combining these two sources, in the third panel we report the summary statisticsfor underrepresentation in local governments, measured as the difference between theMinority population share and the share of members belonging to an ethnic Minority.The following two panels summarize the institutional features of interest of U.S.municipalities. The variable Mayor-Council is an indicator for municipalities adoptinga Mayor-Council form of government, with both a Mayor and a council elected directly,as opposed to a Council-Manager system in which voters elect the councilors, whichin turn appoint an administrative manager. U.S. municipalities also differ in terms ofthe electoral rules through which voters elect their council members. These includeAt-Large, SMD elections, or a combination of the two. The variable At-Large reportsthe fraction of council members elected via a city-wide system. The remaining twovariables of the first panel are Council-size, which simply reports the number seatsin a city council, and the indicator VRA covered for municipalities subject to theas cities, towns, villages, and townships. For a detailed description of the FIPS classification,see https://www.census.gov/programs-surveys/geography/guidance/geo-identifiers.html andhttps://nhgis.org/documentation/gis-data/place-points.12An analysis using total population is available upon request and confirms what reported in thechapter.44Table 3.1: Summary StatisticsMean Std. Dev. Min. Max. Obs.Council Composition (frac.):Latino 0.025 0.111 0.000 1.000 27,686Asian 0.003 0.030 0.000 1.000 27,686African American 0.051 0.128 0.000 1.000 27,686Minority 0.087 0.180 0.000 1.000 27,686White 0.913 0.180 0.000 1.000 27,686Voting Age Population (frac.):Latino 0.069 0.130 0.000 0.985 29,940Asian 0.019 0.040 0.000 0.693 27,884African American 0.070 0.127 0.000 0.982 29,847Minority 0.171 0.191 0.000 1.000 29,855White 0.829 0.191 0.000 1.000 29,931Underrepresentation:Latino 0.043 0.086 -0.996 0.833 27,656Asian 0.015 0.038 -0.991 0.505 25,688African American 0.020 0.079 -1.000 0.971 27,569Minority 0.084 0.136 -0.991 0.990 27,576Main Institutional Features:Mayor-Council 0.433 0.495 0.000 1.000 26,802Council Size 6.230 2.009 2.000 50.000 29,381At-Large (frac.) 0.716 0.413 0.000 1.000 28,199Paid Council Members 0.879 0.326 0.000 1.000 29,035VRA covered 0.171 0.377 0.000 1.000 29,957Additional Institutional Features:Partisanship 0.262 0.440 0.000 1.000 28,954Running Fees 22.923 105.453 0.000 5,000.000 15,904Voters per Councilor (thousands) 2.662 5.566 0.181 180.901 29,347Staggered Terms 0.807 0.395 0.000 1.000 29,046Term Length 3.390 0.904 1.167 8.000 28,584Term Limits 0.092 0.289 0.000 1.000 16,692Sociodemographics:Population 24,117.542 85,414.602 2,313 7,071,639 29,940Voting Age Population 18,043.860 63,780.398 1,472 5,306,172 29,940Over 65 (frac.) 0.144 0.058 0.001 0.708 29,922Rural (frac.) 0.075 0.206 0.000 1.000 29,621Foreign (frac.) 0.064 0.078 0.000 0.706 29,902Median Household Income 42,358.913 19,242.365 4,563.352 200,001.000 29,922Note that these are municipality-level summary statistics, and therefore reflect the characteristics of the averagemunicipality in our sample. This should be taken into consideration when comparing these measures withnation-wide statistics, especially for sociodemographics. Census race/ethnicity measures (excluding Latinos)are based on people not of hispanic or latino origin.45preclearance requirements of Section 5 of the VRA, at the time of the landmark rulingof the U.S. Supreme Court in the case of Shelby County v. Holder (2013).The data on the VRA coverage are collected directly from the U.S. Department onJustice.13 Covered jurisdictions are required to request and receive federal approvalbefore implementing any change to their electoral systems, and are explicitly requiredto prove that the proposed changes are not designed to, or result in, discriminatingvoters on the basis of their race or language. For a detailed discussion of the Section 5of the VRA and the Shelby County v. Holder (2013) ruling, see Section 3.5.1.Additional institutional variables include a partisanship indicator for local electionsin which the political party affiliation of candidates appear on the ballot, the fee (in$) to run for office, the number of voters represented by a council member, whetherthe council is elected in staggered elections, and finally the term length of councilmembers (in years), and an indicator for municipalities with term limits.The bottom panel reports a number of variables, all at the municipal level, used ascontrols in the empirical analyses. In addition to population and voting age populationmeasures, it includes U.S. Census data on the fraction of population over 65 yearsof age and older, the fractions of rural and of foreign populations, and the medianhousehold income.143.3 Patterns of Minority UnderrepresentationWe begin our analysis by studying (nonparametrically and parametrically) the rela-tionship between Minority council members share and the share of minorities in thevoting age population. Throughout, with the term underrepresentation we will refer tothe absolute difference between the fraction of the Minority voting age population andthe fraction of elected Minority council members. Formally, for a given municipality13We rely on the list of covered jurisdictions at the time of Shelby County v. Holder (2013) fromhttps://www.justice.gov/crt/jurisdictions-previously-covered-section-5, last accessed in November2021. The list accounts for early bailouts from coverage, until 1984. For simplicity, we do notconsider a number of bailouts occurring between 1997 and 2013, effectively involving only 16municipalities in our sample, and regard those too as covered at the time of Shelby County v.Holder (2013).14Deflated using Consumer Price Index data from the Bureau of Labor Statistics, using the year2000 as reference. LBS CPI series, all items, U.S. city average, annual averages. See https://data.bls.gov/cgi-bin/surveymost?cu.46m at time t, underrepresentation is defined as:umt = pmt − cmtwhere p is the Minority share of the voting age population and c is the Minority shareof the council members. A higher value of underrepresentation u corresponds to alarger disproportionality between population size and council representation in thecity. An absolute difference like u, expressed in percentage points of the VPA, is theappropriate for the political calculus—if the Minority is 52 percent of the VPA, 2percentage points of underrepresentation is all that is necessary to keep the Minoritybelow 50 percent. A more complete discussion of the appropriateness of u as a measureof underrepresentation is provided in Appendix B.1.Figure 3.1 presents a map of the geographic dispersion of underrepresentation. Under-representation appears more intense in all areas with large concentrations of ethnicminorities, particularly in the South Atlantic, East South Central, where minoritiestend to be African American, and the South West, where minorities tend to be Latino.Figure 3.1: Minority UnderrepresentationCounty-level average Minority underrepresentation. Color breaks by quintiles.47Figure 3.2 plots a binned scatter of Minority voters’ representation in city councilsand the size of minorities in the voting age population for our sample. The bins arecomputed by averaging the fraction of Minority council members for each percentagepoints (up to 80%, and 5 percentage points increments above that) of Minoritypopulation shares. The plotted line represents a nonparametric LOWESS smoothingfit of the underlying municipality-level observations. The figure also includes the 45degree line for reference, to represent perfect proportionality between Minority councilrepresentation and population, as a normative benchmark.Figure 3.2: Minority UnderrepresentationSolid line represents a LOWESS smoothing of the underlying municipality-level data. Markers represent population bins averages.Figure 3.2 displays a strongly non-linear relationship between Minority representa-tion and relative voting age population size. Minorities are invariably underrepre-sented throughout the whole range of p, constantly below the 45 degree line, butnot monotonically so. The largest representation gap (the vertical distance betweenthe nonparametric fit and the 45 degree line) peaks approximately in cities between55% and 60% Minority voting age population share. Similar relationships are alsopresent, in varying degrees, for each Minority group taken in isolation: Latino, AfricanAmerican, and Asian, as reported in Figure B.1. This is important, as it underlines acommon mechanism driving this set of empirical regularities across different parts of48the country.15 This is also consistent, for example, with White voters having strongerincentives to coalesce politically and to strategically manipulate voting procedures atthe point where they are at the highest risk of losing control of their local government.16This interpretation of the data is consistent with historical asymmetries in access tolocal institutions and administrations in U.S. city politics by White local majorities,17but needs additional corroboration, which we offer in Section 3.5.Table 3.2 complements the nonparametric evidence above with a parametric analysiscontrolling for fixed municipality-specific and time-specific confounders. Our preferredspecification looks at Minority underrepresentation as a quadratic function of itsrelative population size:umt = β0 + β1pmt + β2p2mt + δm + δt + x′mtα + εmt, (3.1)where δm and δt are municipality and year fixed effects, and xmt is a vector oftime varying controls, including log-population, log-median household income, andthe fractions of rural, foreign, and over 65 population. Importantly, Equation 3.1exclusively employs identifying variation stemming from within-municipality changes,so it is conservative.Given the quadratic specification in Equation 3.1 and the set of estimates βˆ0, βˆ1, and15In the data, the population shares of the different minorities tend to be little or negatively correlated.Considering municipalities with an overall Minority share above the median (0.09), Latinos andAfrican Americans are correlated by −0.27. Those with Minority share above the average (0.17),Latinos and African Americans are correlated by −0.43. This is consistent with the geographicsorting of African Americans in the South and Latinos in the West, by far the two most populousminorities.Notably, underrepresentation appears somewhat lower when considering individual races or ethnici-ties separately. One possible reason behind this could the different levels of coordination betweenMinority groups on common candidates. For instance, consider a city with a 5-seats council and20% Latino and %10 Black minorities coordinating on a common candidate of Latino ethnicity. Ifthe common candidate is elected, it will reduce underrepresentation of Latino voters to zero, whilethe underrepresentation of both minorities jointly would remain relatively high at 10 percentagepoints. Coordination of this kind could explain the occasional overrepresentation of individual racesor ethnicities. Unfortunately, our data do not provide information on candidates, and thereforewe do not investigate the candidacy behavior of Minority politicians. Existing evidence (Shah,2014) suggest that Black candidates to decide strategically whether to run or not, taking intoaccount the size of the Black electorate among other factors. Whether or not Minority voters doactually have the chance to vote for a Minority candidate is certainly one mechanism behind ourunderrepresentation result.16For evidence, see Trebbi et al. (2008) and Amy (2002).17See Alesina and Glaeser (2004).49βˆ2, the estimated max and argmax of Minority underrepresentation with respect tothe Minority population fraction p are given by:max = βˆ0 − βˆ214βˆ2; argmax = − βˆ12βˆ2. (3.2)In Equation 3.1 with fixed effects, the constant term is computed and can be inter-preted as the sample mean of the estimated fixed effects. Note that the estimatesfor the municipality fixed effects are unbiased, but not necessarily consistent unlessT → +∞, where T is the total number of periods observed for each municipality.In the specifications with controls, the constant term used to compute the max inEquation 3.2 also includes x¯′αˆ, where x¯ are the sample means of the control variables.Column (1) in Table 3.2 corresponds to a baseline model without fixed effects orcontrols, which is progressively augmented in columns (2)-(4) to include year fixedTable 3.2: Minority Underrepresentation(1) (2) (3) (4) (5)MinorityUnderrep.MinorityUnderrep.MinorityUnderrep.MinorityUnderrep.MinorityUnderrep.Regression estimates:Minority 0.801∗∗∗ 0.776∗∗∗ 1.238∗∗∗ 1.493∗∗∗ 1.384∗∗∗(0.018) (0.018) (0.029) (0.042) (0.048)Minority2 -0.727∗∗∗ -0.706∗∗∗ -1.060∗∗∗ -1.222∗∗∗ -1.195∗∗∗(0.030) (0.030) (0.049) (0.054) (0.055)Max/Argmax estimates:max 0.216∗∗∗ 0.211∗∗∗ 0.303∗∗∗ 0.364∗∗∗ 0.326∗∗∗(0.004) (0.004) (0.006) (0.009) (0.012)argmax 0.551∗∗∗ 0.550∗∗∗ 0.584∗∗∗ 0.611∗∗∗ 0.579∗∗∗(0.013) (0.013) (0.016) (0.015) (0.016)Population VAP VAP VAP VAP VAPYear FE - X - X XMunicipality FE - - X X XControls - - - - XObs. 27,576 27,576 26,205 26,205 26,065Municipalities 7,472 7,472 6,101 6,101 6,084Cluster level Municipality Municipality Municipality Municipality MunicipalityAdjusted R2 0.260 0.266 0.493 0.500 0.500The top panel reports regression estimates. Cluster robust standard errors in parentheses. Significance levels: *** p <0.01, ** p < 0.05, * p < 0.1. The dependent variable in all specifications is Minority underrepresentation, computedas the difference between the fraction of Minority population and the fraction of Minority council members. The mainindependent variables are the fraction of Minority population and its squared value. Population measures based on votingage population. The controls include the log-population, the log-median household income, and the fractions of rural,foreign, and over 65 population. The second panel reports Max/Argmax estimates based on the regression estimates.Standard errors in parentheses are asymptotic. Given a quadratic formula y = ax2 + bx+ c, the max is computed as c− b24a ,while the argmax is computed as − b2a .50effects, municipality fixed effects, and both. Column (5) adds time varying controls.All specifications show a clear nonmonotonic concave relationship between Minorityunderrepresentation and the Minority population share, which confirms the nonpara-metric relationship suggested by the graphical analysis of Figure 3.2. All coefficientsare strongly statistically significant. In terms of location, the argmax estimates suggestthat the representation gap for ethnic minorities is at the highest when its relativepopulation size is approximately between 55% to 61%, with corresponding max esti-mates ranging from 21 to 36 percentage points. Again, these results establish that theminorities are most underrepresented when they approach an electoral majority ofthe voting age population—close to the point where they could gain control of themajority of the city council.In Appendix B.2 we show that these results hold for Latino, Asian, and AfricanAmerican underrepresentation considered separately (see Tables B.2, B.3, B.4). Theargmax estimates are also relatively consistent with the previous results, ranging from40% to 55%, depending on the race or ethnicity considered. In synthesis, a similarrepresentation gap affects all the minorities considered, not just African Americans.As an additional check, in Appendix B.2, we show that these results are not driven bylocal partisanship.18 For each municipality we assign the corresponding county-levelvote shares in the most recent previous Presidential election to approximate its partisanleaning. Figure B.2 and Table B.5 indicate that underrepresentation is extensiveand not statistically different at standard significance levels in either Democratic orRepublican leaning municipalities.Finally, to support the external validity of our city-level findings of Figure 3.2, webriefly look at state legislatures. Figure B.3 reports evidence of the state-level mappingbetween state Minority share of the population in 2019 and state legislatures’ share of18According to our data, only 26% of the cities in our sample run partisan election, meaningthat the candidate display their partisanship affiliation on the ballot. This is the only pieceof information about parties contained in our data. We are therefore very limited in what wecan say about how local parties shape local elections and the representation of Minority voters.The effect of parties on representation could go either way. On the one hand, parties couldfacilitate the coordination of voters of different racial and ethnic groups towards the support ofa common candidate. On the other, to the extent that partisan ideology differs between votersof different groups, partisan elections could weaken Minority voters. One example would be thestark partisan difference in partisan affiliation of Cuban voters relative to other Latino voters (see,for instance, https://www.pewresearch.org/short-reads/2020/10/02/most-cuban-american-voters-identify-as-republican-in-2020/)51Minority representatives in 2020, based on data from NCSL (2021). Using a LOWESSfit, Figure B.3 shows in state legislatures a nonlinear pattern of underrepresentationsimilar to what observed in city councils. In the remainder of this study we will limitourselves to Minority representation in cities for data limitations and parsimony, butwith the caveat that higher levels of government may exhibit similar patterns.3.4 An Anatomy of UnderrepresentationIn order to support a claim that the nonmonotonic pattern of underrepresentationpresented in the previous section is strategic, one should be able to produce evidenceof a systematic manipulation of certain voting procedures targeted at increasing therepresentation of White voting blocs at the expense of Minority groups. This sectionpresents a first set of facts in this direction, finding that At-Large elections, Council-Manager form of government, smaller city council size, unpaid council positions areall tools correlating in various ways with Minority underrepresentation.For the case of At-Large elections in particular, we can also show that the patterns ofselection match those of Trebbi et al. (2008), which describes how electoral rules wereused around the passage of the VRA as tools for strategically reducing the electoralweight of African American voters after they were enfranchised by the Act.3.4.1 Electoral RulesU.S. cities differ in terms of the electoral rules through which voters elect theirmost important local representative body, the city council. In 2018 about 65% ofmunicipalities sampled by ICMA relied entirely on At-Large electoral systems. In At-Large elections all councilors run in a single multi-member district (the city). In mostcases, voters cast as many ballots as seats to be filled, thus allowing a simple majorityof voters to elect the entire council. In this sense, At-Large is a bloc voting systemdiffering significantly from a proportional representation rule. Single-member districtor SMD resembles more closely first-past-the-post single member elections. In 2018,20% of municipalities divided their geographic jurisdiction in several non-overlappingelectoral districts or wards, each electing one member of the city council by plurality.The remaining 15% of cities adopt a mix of the two systems, with a certain number ofmembers elected At-Large and the rest by SMD.52In our sample, about 14% of cities change their electoral rule over a period of fiveyears on average (8% not in combination with a change in number of seats) and theaverage number of seats in the council is 6.2.Figure 3.3 explores underrepresentation nonparametrically, differentiating betweenmunicipalities adopting an At-Large rule from those using SMD. The bins for munici-palities electing at least 50% of their council seats via SMD elections are representedby darker × markers, while the lighter circles indicate averages for municipalitieselecting a majority of seats At-Large. The lines represent the LOWESS smoothingfit for each electoral system separately. Consistently with the large literature on U.S.city representation cited in the Introduction, SMD municipalities display a smallerrepresentation gap, compared to At-Large systems. Geographic partitioning in SMDallows segregated minorities to express Minority candidates from their districts. In-stead, At-Large systems with their high premia for city-wide majorities dilute therepresentation of minorities. The dilution effect of At-Large systems is, however, non-linear and strongest in particular around a Minority population share of 30% to 40%.Figure 3.3: Minority Underrepresentation by Electoral RuleSolid lines represent LOWESS smoothing of the underlying municipality-level data. Markers represent population bins averages.Darker markers and line are based on municipalities electing a majority of council seats through Single Member Districts (SMDs)elections. Lighter markers and line are based on municipalities electing a majority of council seats through At-Large elections (city-widemulti-member races).53Furthermore, despite showing a smaller underrepresentation gap, SMD municipalitiesstill display a considerable amount of disproportionality and a non-linear relation-ship converging closer to proportionality only for very high or very low Minority shares.In terms of parametric estimates, the baseline specification Equation 3.1 can be modi-fied to include electoral rule differences. Let us denote ALmt = I (m At-Large in t),where I (·) is an indicator function taking value 1 for municipalities electing themajority of their council members via At-Large elections at t, and 0 otherwise. Byincluding its interactions with a Minority population fraction and its squared value,we obtain the modified specification:umt = β0+ β1pmt + β2p2mt + γ0ALmt + γ1 (ALmt × pmt) + (3.3)+γ2(ALmt × p2mt)+ δm + δt + x′mtα + εmt.Given the added interaction terms, the max and argmax parametric estimates of theunderrepresentation curve can be differentiated between At-Large and SMD systemsas follows:maxSMD = βˆ0 − βˆ214βˆ2;argmaxSMD = −βˆ12βˆ2;maxAL = βˆ0 + γˆ0 −(βˆ1 + γˆ1)24(βˆ2 + γˆ2) ;argmaxAL = −(βˆ1 + γˆ1)2(βˆ2 + γˆ2) ;where the constant term βˆ0 term includes the sample mean of the estimated fixedeffects and x¯′αˆ and where x¯ are sample means of the control variables.The estimates of Equation 3.3 are reported in Table 3.3. Column (1) reports theestimates of the model without fixed effects and controls. Columns (2) and (3) addyear fixed effects and municipality fixed effects, respectively. Column (4) includesboth fixed effects. Finally, column (5) is based on the full specification with both54the fixed effects and controls. Taking into account the added interaction terms,the first two coefficients are those corresponding to SMD systems. Throughout allspecifications, these coefficients display a strong concave relationship between Minorityunderrepresentation and population. The relationship is highly statistically significant.Table 3.3: Minority Underrepresentation by Electoral Rule(1) (2) (3) (4) (5)MinorityUnderrep.MinorityUnderrep.MinorityUnderrep.MinorityUnderrep.MinorityUnderrep.Regression estimates:Minority 0.543∗∗∗ 0.519∗∗∗ 0.990∗∗∗ 1.219∗∗∗ 1.124∗∗∗(0.028) (0.028) (0.041) (0.051) (0.056)Minority2 -0.495∗∗∗ -0.478∗∗∗ -0.765∗∗∗ -0.914∗∗∗ -0.892∗∗∗(0.046) (0.045) (0.066) (0.068) (0.068)At-Large -0.017∗∗∗ -0.016∗∗∗ -0.008 -0.009∗ -0.008(0.002) (0.002) (0.005) (0.005) (0.005)At-Large × Minority 0.427∗∗∗ 0.424∗∗∗ 0.373∗∗∗ 0.353∗∗∗ 0.345∗∗∗(0.035) (0.035) (0.046) (0.045) (0.045)At-Large × Minority2 -0.386∗∗∗ -0.377∗∗∗ -0.388∗∗∗ -0.378∗∗∗ -0.376∗∗∗(0.057) (0.056) (0.072) (0.071) (0.072)Max/Argmax estimates:max (SMD) 0.153∗∗∗ 0.148∗∗∗ 0.265∗∗∗ 0.324∗∗∗ 0.287∗∗∗(0.005) (0.005) (0.011) (0.014) (0.015)max (At-Large) 0.255∗∗∗ 0.250∗∗∗ 0.340∗∗∗ 0.387∗∗∗ 0.351∗∗∗(0.005) (0.005) (0.008) (0.009) (0.012)argmax (SMD) 0.548∗∗∗ 0.543∗∗∗ 0.647∗∗∗ 0.667∗∗∗ 0.630∗∗∗(0.027) (0.027) (0.034) (0.029) (0.029)argmax (At-Large) 0.551∗∗∗ 0.551∗∗∗ 0.591∗∗∗ 0.609∗∗∗ 0.579∗∗∗(0.013) (0.013) (0.018) (0.016) (0.018)Test max (p-value) 0.000 0.000 0.000 0.000 0.000Test argmax (p-value) 0.937 0.769 0.074 0.032 0.050Population VAP VAP VAP VAP VAPYear FE - X - X XMunicipality FE - - X X XControls - - - - XObs. 26,419 26,419 25,022 25,022 24,885Municipalities 7,401 7,401 6,004 6,004 5,987Cluster level Municipality Municipality Municipality Municipality MunicipalityAdjusted R2 0.293 0.299 0.506 0.511 0.511The top panel reports regression estimates. Cluster robust standard errors in parentheses. Significance levels: *** p <0.01, ** p < 0.05, * p < 0.1. The dependent variable in all specifications is Minority underrepresentation, computedas the difference between the fraction of Minority population and the fraction of Minority council members. The mainindependent variables are the fraction of Minority population and its squared value. Population measures based on votingage population. At-Large is an indicator for municipalities in which at least 50% of council seats are elected using anAt-Large electoral rule. The controls include the log-population, the log-median household income, and the fractions ofrural, foreign, and over 65 population. The second panel reports Max/Argmax estimates based on the regression estimates.Standard errors in parentheses are asymptotic. Given a quadratic formula y = ax2 + bx+ c, the max is computed as c− b24a ,while the argmax is computed as − b2a . We report the p-values of the tests for whether the max and argmax are differentbetween electoral rules.55At the same time, both interaction coefficients are statistically different from zero,suggesting a difference in the relationship between Minority underrepresentation andpopulation in At-Large systems compared to SMD, as shown visually in Figure 3.3. Thelocation estimates of the argmax for the two systems are similar across specifications,with SMD showing somewhat higher argmax estimates consistent with the constrainingeffects of this system for high Minority shares and the relatively better performanceat low shares.19 The max estimates indicate a maximum underrepresentation between6 and 10 percentage points higher in predominantly At-Large municipalities than inSMD. All max estimates are statistically different between the two electoral rulesat standard significance levels, while the differences between the argmax are not (orweakly) statistically significant, depending on the specification.Overall, the parametric analysis suggests that the representation gap is both presentand highly non-linear under the two electoral systems. While SMD systems appearto have some beneficial effect for Minority representation, in line with most of theexisting literature, they are not resolutive of underrepresentation.20Regarding the endogeneity of the electoral rule, Trebbi et al. (2008) show how, as aresponse to the massive enfranchisement of the African American electorate in theaftermath of the VRA of 1965, White majorities in the South selectively changedmunicipal electoral rules to maintain political control of their jurisdictions. Theauthors describe how the incentives to employ At-Large bloc voting increase as theMinority group fraction to the population increase, because the dilution At-Largeaffords is useful to prevent minorities from electing representatives to the council. Atthe same time, there are strong strategic incentives to decrease the At-Large fractionas the African American population approaches the critical 50% threshold, due to therisk for the White majority of losing control of the council.Table 3.4 shows that the same forces are at play in a larger sample than Trebbi et al.(2008) and are relevant to understanding underrepresentation of all Minority groups,19This is due to "packing", occurring when a large Minority population is concentrated in a fewdistricts.20Looking at ethnic groups separately, the differences between the two systems are particularlystrong for the African American minority, with above 10 percentage points gap between the twoestimated maximum underrepresentation (with At-Large being higher). The Asian estimates showno statistically significant difference between the two systems, while the results for the Latinominority lie somewhat in between the two, with an estimated maximum representation gap of 4percentage points higher under At-Large rules (see Tables B.6, B.7, and B.8).56not just African Americans. In this table the original baseline regression model ofEquation 3.1 is adapted using as dependent variable the fraction of council seatselected At-Large, while keeping the fractions of Minority population and its squaredvalue as main explanatory variables. Due to the inclusion of municipality fixed effects,these preferred specifications are identified by the relationship between within-citychanges in ethnic composition and changes in electoral rules. In columns (1)-(2) ofTable 3.4 we explore the entire sample, while in columns (3)-(4) we focus only onthose municipalities with shares of Whites in the voting age population above 50%(the subsample in which it is most plausible that Whites are selecting the electoralrule).All columns display a strong concave relationship between the At-Large fraction of seatsand the relative Minority population size, with an argmax consistently between 23%to 29%. The strategic deployment of SMD closer to the 50% threshold is particularlyTable 3.4: Endogenous Choice of Electoral Rule(1) (2) (3) (4)At-Large(frac.)At-Large(frac.)At-Large(frac.)At-Large(frac.)Regression estimates:Minority 0.298∗∗∗ 0.231∗∗∗ 0.811∗∗∗ 0.777∗∗∗(0.076) (0.086) (0.119) (0.132)Minority2 -0.510∗∗∗ -0.484∗∗∗ -1.693∗∗∗ -1.686∗∗∗(0.083) (0.084) (0.201) (0.201)Argmax estimates:argmax 0.293∗∗∗ 0.239∗∗∗ 0.240∗∗∗ 0.230∗∗∗(0.042) (0.062) (0.015) (0.022)Population VAP VAP VAP VAPSample Full Full ≤ 50% ≤ 50%Year FE X X X XMunicipality FE X X X XControls - X - XObs. 26,714 26,557 24,457 24,314Municipalities 6,151 6,130 5,756 5,736Cluster level Municipality Municipality Municipality MunicipalityAdjusted R2 0.766 0.766 0.773 0.773The top panel reports regression estimates. Cluster robust standard errors in parentheses. Signifi-cance levels: *** p < 0.01, ** p < 0.05, * p < 0.1. Columns (1) to (3) use the full sample, columns(4) to (6) are restricted to municipalities with Minority voting age population fraction less or equal to50%. The dependent variable in all specifications is the share of council seats elected At-Large. Themain independent variables are the fraction of Minority population and its squared value. Populationmeasures based on voting age population. The controls include the log-population, the log-medianhousehold income, and the fractions of rural, foreign, and over 65 population. The second panelreports Argmax estimate based on the regression estimates. Standard errors in parentheses areasymptotic. Given a quadratic formula y = ax2 + bx+ c, the argmax is computed as − b2a .57consistent with both the theory and with the argmax estimates placing the highestunderrepresentation levels at around 50% to 60% Minority population shares evenunder SMD. The patterns of selection of electoral rules in presence of changes inthe ethnic composition of the municipalities in our broad sample match the sameestablished patterns of African American underrepresentation around the passage ofthe VRA in Trebbi et al. (2008), pointing to similar forces being at play for the Latinoand Asian, as well as African American population.3.4.2 Form of Government, Council Size, Paid Council Mem-bersIn the previous section we have focused on electoral rules. This subsection exploresform of government, council size, and whether council members are paid for theirservice.U.S. municipalities can be broadly divided in two main forms of government. Citiescan adopt a so-called Mayor-Council system, in which voters elect both the councilmembers and a mayor directly. In these, the mayor holds significant administrativeautonomy over the council, which operates as the deliberative body. Alternatively,cities can adopt a Council-Manager structure, in which the constituents only electthe council, which in turn nominates a professional manager to carry out day-to-dayadministrative duties.21 This second approach attributes considerable more power tothe council members, while limiting the independence of the manager role.Using the same approach of Table 3.3 and progressively saturating the specifica-tions with more restrictive sets of fixed effects, Table B.9 shows that cities withCouncil-Manager form of government have statistically significantly higher levels ofunderrepresentation of minorities—their max estimates being 0.8 to 5.3 percentagepoints higher than for Mayor-Council cities. A possible explanation is that in Council-Manager municipalities the role and political value of each council seat is bigger, thusproviding stronger incentives to retain control of council seats.Similar deleterious effects to the representation of minorities are also found in com-paring cities with smaller councils (thus limiting access to small blocs of voters) and21ICMA, the source of the surveys used in part of this chapter, is the professional association of U.S.city managers.58cities with unpaid council members (thus increasing the opportunity cost of servingon the council). The results are reported in Table B.10 and Table B.11. Cities withcouncil size below the median have between 1.4 and 5.4 percentage points higherunderrepresentation of minorities at their max relative to cities above median. Citieswith unpaid council members have between 1.1 and 3.8 percentage points higherunderrepresentation of minorities at their max relative to cities with paid members.223.4.3 TurnoutThis section briefly addresses the issue of endogenous voter turnout by race. Given theabsence of reliable ethnic group level turnout data for city elections, this subsectionpresents a discussion of the possible role of voter turnout in our results.A potential confounding driver of Minority underrepresentation may be the differentialpropensity of voters belonging to different ethnic groups to strategically show upat the polling booth. Indeed, the differential voter turnout by ethnic group is anissue that has found active discussion in the literature (Fraga, 2016, 2018), albeitmostly for national level elections. As long as differential patterns of turnout (forexample White voters being more prone than Minority voters to turn out) are constantacross municipal demographic composition, this is not a concern affecting directlythe evidence in Figure 3.2 and Figure B.1 (nor our main parametric regressions).Systematically lower Minority turnout would take simply the form of a downwardparallel shift of the 45 degree line in Figure 3.2.While part of the political science literature underscores how "Whites tend both to bemore conservative and to be politically mobilized where Minority populations comprisea considerable size—around 40% of the district" (Griffin and Newman 2007 p.1034, andLublin, 2021),23 the nonlinearity presented in Sections 3 and 4 can not entirely originatefrom differential levels of mobilization at the local level—a form of ethnic backlash inlocal politics. To affect the shape of the nonlinear patterns of underrepresentationthat we report, differential turnout should be higher for Whites around the 50-50percent split between Whites and Minority voters than, for instance, in more ethnicallylopsided cities with a 80-20 White majority or a 20-80 White minority. This is where22Other institutional features, such as term limits, number of voters per seat, and staggered electoralterms for the council, which do not appear to systematically associate with higher levels ofunderrepresentation.23See also Washington (2006) and Ang (2019) for evidence of ethnic backlash.59standard predictors of voter turnout have difficulties in matching the moments inthe data. While it is known that close elections (around a 50-50 split) are a strongpredictor of turnout,24 it is unclear why this effect should be stronger for Whitesthan Minority. At a 50-50 ethnic split, both White and Minority groups are closer tobecoming electorally pivotal.Furthermore, to completely explain all variation, the endogenous propensity to turnoutof Whites should also respond to the institutional features presented in Section 3.4.1and Section 3.4.2, matching those precise patterns with a similar mechanism. It isnot clear then, following Figure 3.3, how Minority turnout could also be higher underSMD than At-Large. The reason is that SMD tends to reduce the closeness of districtlevel elections, as typically Minority groups are geographically segregated and packed,facing less competition from White candidates within their wards (Trebbi et al., 2008).Other drivers of voter mobilization, such as low stakes (suppressing turnout) andsmaller populations (increasing the likelihood of being pivotal and therefore theincentives to turn out), also appear ambiguously correlated with White and Minoritymobilization rates (Geys, 2006). As these factors should affect all groups symmetrically,they do not offer a clear interpretative key to our findings.3.5 Causal Inference: Shelby3.5.1 Background on Shelby County v. HolderThe general provision of the VRA of 1965 broadly prohibits the implementation ofany voting device that would result in the disenfranchisement of any Minority. Theprovision is intentionally quite generic and since 1982 it has been amended by Congressto focus specifically on the resulting discriminatory effects of any voting practice,regardless of the original intent. Because its general provision is relatively vague, theVRA contains a number of special provisions that prescribe additional limitations andprotections of the voting rights of minorities. Section 4(b) of the VRA contains theso-called coverage formula used to determine which jurisdictions are subject to thespecial provisions. Originally, were considered covered those jurisdictions that in 1964were employing any device restricting registration and voting and in which less thanhalf the eligible voters were registered to vote in the 1964 Presidential election, or24See Geys (2006) for a comprehensive discussion.60less than half the eligible voters voted in that election. The VRA coverage was lateramended to include those jurisdiction that met the same two requirements in 1968 and1972. Section 5 of the VRA, requires that all jurisdictions identified by the coverageformula in Section 4(b) must receive a federal approval, known as preclearance, beforeimplementing any change to their voting procedures. By change, the section refersto any major or minor change. The jurisdiction proposing the change is requiredto prove that said change does not have discriminatory purpose nor effects withrespect to race or language minorities. Over time, a number of U.S. Supreme Courtruling have established that the discriminatory effects must be evaluated with respectto the existing conditions. That is, in order to obtain preclearance, the proposedchange should not worsen the existing disenfranchisement. On top of limiting ex-antethe introduction of discriminatory practices, Section 5 of the VRA is also crucial inproviding legal ground to private plaintiffs to sue a jurisdiction that failed to obtainfederal approval.In 2013, the U.S. Supreme Court took a crucial stand on the constitutionality ofSection 4(b) of the VRA, in relation to the case of Shelby County v. Holder (2013).The Court upheld the special provisions of Section 5 as constitutional, but at the sametime ruled the coverage formula based on 1964 conditions unconstitutional. The rulinghas been extremely consequential. Without the formula, effectively no jurisdiction isany longer subject to the special provisions and the preclearance requirement untilCongress establishes a new formula.25Table 3.5 tabulates the change in institutional features at the city level when comparingthe two survey straddling Shelby County v. Holder (2013) (the 2011 and 2018 surveys).Following Section 3.4, we focus on changes in the share of seats elected At-Large,changes between Mayor-Council and Council-Manager forms of government, changes incouncil size, and finally changes in whether or not council members are paid. Lookingat raw differences, we do not see any specific pattern in institutional changes aroundShelby County v. Holder (2013). In a certain sense, this is expected and a cautionarytale. There are several reasons why we are not detecting any significant changes.First, our research shows that all these institutional features operate differently inreducing representation at different levels of Minority shares of the population and25See Figure B.4 for coverage of the VRA at the time of Shelby County v. Holder (2013).61that these institutions are adopted surgically and strategically. A simple comparisonof raw means cannot recover that. Second, the information on changes available fromour sources covers a limited number of features. Electoral rules, forms of government,and the like are also first-order institutional features, and changing these is no smallfeat. It is reasonable to expect local governments to pursue simpler and faster changesfirst. Even if these major changes were to happen, our sample period post-Shelbymay simply be not long enough to capture them. Easier pathways to discriminationfollowing Shelby County v. Holder (2013) include moving or closing polling locationsstrategically, with evidence of longer wait times for racial and ethnic minorities (Chenet al., 2022).26 While we cannot say more about the specific changes generating theconvergence in underrepresentation following Shelby County v. Holder (2013), inChapter 4 we explore one potential key mechanism behind it: voter registration.Table 3.5: Pre/Post Mean Shelby Changes and Means Comparison Test(1) (2) (3) (4)Full Sample VRAnon-coveredVRAcovered DifferenceAt-Large share change -0.003 0.000 -0.015 -0.015(0.219) (0.225) (0.186) (.)[-1.055]Mayor-Council change 0.020 0.019 0.025 0.006(0.344) (0.344) (0.341) (.)[0.231]Council size change 0.017 0.017 0.017 0.000(1.175) (1.277) (0.504) (.)[0.000]Paid council members change 0.021 0.016 0.044 0.028(0.277) (0.253) (0.362) (.)[1.105]Obs. 1,334 1,092 242 1,334Means and difference in means reported. Standard deviations in parentheses, t-statistics in brack-ets. Significance levels: *** p < 0.01, ** p < 0.05, * p < 0.1. The variables are changes in shareof seats elected At-Large, changes in the indicator for Mayor-Council municipalities, changes incouncil size, and changes in the indicator for municipalities with paid council members. Column(4) is the difference between column (3) and column (2), for each corresponding variable.26Nearly 1,200 polling locations have closed in the South since Shelby County v. Holder (2013):https://www.reuters.com/article/world/southern-us-states-have-closed-1200-polling-places-in-recent-years-rights-gr-idUSKCN1VV097/.623.5.2 Shelby Effects on UnderrepresentationThe purpose of this section is to exploit the Shelby County v. Holder (2013) ruling toestablish a causal link between strategic disenfranchisement and the underrepresenta-tion of minorities, comparing municipalities that were covered by the VRA formulato municipalities never covered by the VRA (referred to as covered and non-covered)before and after the Court ruling (referred to as pre and post periods).27 The aim isto compare both the shape of the relationship as well as the extent of the Minorityunderrepresentation.Relative to standard difference-in-differences estimators, here we are focusing ondifferences in nonlinear relationships. To this goal, let V RAm = I (m covered) be anindicator variable if municipality m was covered by the VRA before Shelby Countyv. Holder (2013). Let POSTt = I (t > 2013) be an indicator variable for postShelby County v. Holder (2013) observations. The specification that allows us toparametrically recover the patterns of underrepresentation is:umt = β0 + β1pmt + β2p2mt ++γ0V RAm + γ1(V RAm × pmt) + γ2(V RAm × p2mt) ++δ0POSTt + δ1(POSTt × pmt) + δ1(POSTt × p2mt) + (3.4)+η0(V RAm × POSTt) + η1(V RAm × POSTt × pmt) ++η2(V RAm × POSTt × p2mt) + εmt.Given estimates of Equation 3.4 and letting g(m, t) summarize the four groups of preand post, covered and non-covered municipalities, we can derive the estimates for theparameters of the quadratic formula:cˆg(m,t) = βˆ0 + γˆ0V RAg(m,t) + δˆ0POSTg(m,t) + ηˆ0(V RAg(m,t) × POSTg(m,t)),bˆg(m,t) = βˆ1 + γˆ1V RAg(m,t) + δˆ1POSTg(m,t) + ηˆ1(V RAg(m,t) × POSTg(m,t)),(3.5)aˆg(m,t) = βˆ2 + γˆ2V RAg(m,t) + δˆ2POSTg(m,t) + ηˆ2(V RAg(m,t) × POSTg(m,t)).27A complete list of jurisdictions covered by the VRA at the time of Shelby County v. Holder (2013)can be found here: https://www.justice.gov/crt/jurisdictions-previously-covered-section-5. Lastaccessed November 2021.63The estimated max and argmax for a given set of estimates in Equation 3.5 are givenby:maxg(m,t) = cˆg(m,t) −bˆ2g(m,t)4aˆg(m,t);argmaxg(m,t) = −bˆg(m,t)2aˆg(m,t).Finally, we can assess the effect of Shelby on Minority underrepresentation as:Shelby = (maxV RA,POST −maxV RA,PRE)− (maxNONV RA,POST −maxNONV RA,PRE) . (3.6)Table B.12 collects the estimated parameters for covered and non-covered municipali-ties, for the pre and post periods. The columns and estimates correspond to that ofTable B.13 and are based on the main specification in Equation 3.4 and the linearcombinations in Equation 3.5. Column (2) includes municipality fixed effects, column(3) adds controls as well.The parameters confirm a strong and significant non-linear relationship between under-representation and population shares, for all groups and periods. From Equation 3.6,we can measure the effect of Shelby by comparing the estimated max levels of un-derrepresentation between groups and periods. Table 3.6 shows the estimated maxfor all groups, as well as the effect of Shelby. The ruling had a strong and significanteffect on underrepresentation, with an increase of approximately 6 percentage pointsin previously covered municipalities.28Given the quadratic specification, it is easier to compare the four groups graphically.Figure 3.4 plots the estimated curves in Table B.12, dividing by pre and post periods.Before the ruling of Shelby County v. Holder (2013), non-covered municipalities had28As a reminder, the quadratic specification we adopt in this chapter is informed by the strongnon-linearity we observe in the raw data. The advantage of our parametric specification is toprovide a precise and intuitive measure of the effect of Shelby County v. Holder (2013), looking atthe difference in maximum underrepresentation. Following our discussion of alternative measuresin Appendix B.1, our measure of underrepresentation could be convex, concave, or neither, andour estimates could reflect any of these. Our results show a consistent concavity before and afterShelby County v. Holder (2013), for both treated and control municipalities. Other measures ofunderrepresentation, such as the percentage of Minority population left without representation,could be better suited for a more standard DiD estimation.64Table 3.6: Minority Underrepresentation Pre/Post Shelby (2013) - Effects(1) (2) (3)MinorityUnderrep.MinorityUnderrep.MinorityUnderrep.Max (Pre, VRA non-covered) 0.247∗∗∗ 0.327∗∗∗ 0.286∗∗∗(0.007) (0.011) (0.081)Max (Pre, VRA covered) 0.167∗∗∗ 0.236∗∗∗ 0.207∗∗(0.006) (0.020) (0.083)Max (Post, VRA non-covered) 0.255∗∗∗ 0.300∗∗∗ 0.261∗∗∗(0.008) (0.010) (0.081)Max (Post, VRA covered) 0.239∗∗∗ 0.272∗∗∗ 0.241∗∗∗(0.011) (0.020) (0.083)Shelby 0.063∗∗∗ 0.063∗∗∗ 0.058∗∗∗(0.015) (0.016) (0.015)Population VAP VAP VAPSample Full Full FullMunicipality FE - X XControls - - XObs. 27,562 26,190 26,050Clusters 7,470 6,098 6,081Cluster level Municipality Municipality MunicipalityAsymptotic standard errors in parentheses. Significance levels: *** p < 0.01, ** p < 0.05,* p < 0.1. The dependent variable in all specifications is Minority underrepresentation,computed as the difference between the fraction of Minority population and the fraction ofMinority council members. We report the estimates for municipalities covered/non-coveredby the VRA at the time of Shelby, and for pre/post Shelby periods. Estimates basedon the parameters in Table B.12. The controls are the log-population, the log-medianhousehold income, and the fractions of rural, foreign, and over 65 population. Given aquadratic formula y = ax2 + bx+ c, the max is computed as c− b24a , while the shelby effectis a diff-in-diffs estimate based on the max.significantly higher underrepresentation for almost all levels of Minority population.Following Shelby County v. Holder (2013), the covered and non-covered municipalitiesbecome virtually indistinguishable, with a convergence of previously covered citiesto the levels of uncovered municipalities. Essentially, once previously covered mu-nicipalities become free to adopt whatever changes in their voting procedures, theunderrepresentation of minorities converges to the same level of those municipalitiesthat were never constrained in their voting procedures to begin with. This resultclearly shows that the VRA preclearance formula was an effective tool in limitingMinority underrepresentation sixty years after its first implementation.2929This seems to support the intuition in the dissent to the ruling by Justice Ruth Bader Ginsburg:"throwing out preclearance when it has worked and is continuing to work to stop discriminatory65Figure 3.4: Minority Underrepresentation Pre/Post Shelby (2013)Figures based on column (1) of Table B.12.To add further support to the analysis, we investigate possible pre-trends compar-ing covered and non-covered municipalities by year. To do so, the specification inEquation 3.4 is modified to include yearly fixed effects and interactions as follows. LetY 1981t = I (t = 1981) be an indicator for observations in year t = 1981, and so on forall the remaining years in the sample: 1986, 1991, 1996, 2001, 2006, 2011, and 2018.changes is like throwing away your umbrella in a rainstorm because you are not getting wet."See https://www.documentcloud.org/documents/717244-supreme-court-decision-in-shelby-county-v-holder.html#document/p32 last accessed on November 2021.66Then, the yearly specification is given by:umt =∑T={1981,...,2018}Y Tt[β0,T + β1,Tpmt + β2,Tp2mt++γ0,TV RAm + γ1,T (V RAm × pmt) + γ2,T (V RAm × p2mt)]+ εmt. (3.7)Figure 3.5 displays the Shelby effect coefficients based on Equation 3.6 and estimatesof Equation 3.7 taking 2011 as reference period. For all the years before Shelby Countyv. Holder (2013), the non-covered and covered municipalities display no clear pre-trendand Shelby effects are never statistically significant at standard levels up until afterthe ruling.30Figure 3.5: Pre-trend analysis of Shelby (2013) effect of Minority Underrepresentation-.1-.050.05.1Shelby effect1981 1986 1991 1996 2001 2006 2011 2018YearShelby effect coefficients taking 2011 as reference period, and 95% C.I.Pre/Post Shelby 1981-2018 (VAP)Reporting estimates of Shelby effects similar to Table 3.6, including survey-year interactions to control for pre-trends. Survey-year 2011taken as reference.As a robustness check of our analysis, we also restrict our regressions to a morecontained set of control municipalities. Rather than taking the collection of allVRA non-covered municipalities in the country as control units, we focus on a morecomparable sample of covered and non-covered observations based on areas in aneighborhood of 200 or 100 miles from the boundary of any VRA covered jurisdiction.More specifically, we only use municipalities falling geographically within a narrow 20030Regrettably, we only have one ICMA survey wave following Shelby County v. Holder (2013).Future research should investigate the persistence of the effects in the longer term.67miles (or 100 miles) bandwidth around all VRA covered state and counties’ boundaries.This exercise has the advantage of creating a more comparable set of treatment andcontrol observations in the estimation, both in terms of observable and unobservablecharacteristics of control and treated units.Reassuringly, both for the case of the 200 miles in Table B.15 and the 100 miles analysisin Table B.16, the estimated parameters for covered and non-covered municipalities,for the pre and post periods, align consistently with the results of Figure 3.4 andTables B.12 and 3.6. All our main findings in terms of effects of VRA removalon underrepresentation appear robust to different bandwidths and our difference-in-differences results appear not likely to be driven by confounding dynamics withinparts of the country not comparable to VRA covered areas.The effect of Shelby County v. Holder (2013) on underrepresentation is substantial alsoconsidering Republican and Democratic leaning observations separately, as supportedby Table B.17. Republican municipalities seem to have been affected more negatively,but we can safely rule out any overwhelming role of partisanship in driving the results.3.6 ConclusionThis chapter studies empirically the issue of strategic Minority underrepresentationin municipal governments in the United States for the period 1981-2020. Usinga comprehensive panel of U.S. municipalities, we provide statistical evidence thatunderrepresentation relative to overall voting age population Minority shares is awidespread phenomenon that is particularly strong where racial or ethnic minoritiesare more likely to be electorally pivotal, pointing to a strategic origin of such patterns.We show that these patterns are common to Latino, African American, and Asianvoters alike. Selected features of the city institutional structure (electoral rule, formof government, council size, whether council members are paid) contribute the extentof these patterns.A difference-in-differences estimator, based on variation induced by Shelby Countyv. Holder (2013) Court’s decision of eliminating Section 4(b)’s formula to determinepreclearance coverage under the VRA of 1965, provides causal evidence of the abilityof local governments of adjusting municipal institutional features to limit the politicalcompetition and representation of Minority voters. Using a newly developed methodol-ogy, we also show that in the five years following the removal of preclearance conditions68requiring Washington’s approval for institutional changes that weakened Minorityrepresentation in covered jurisdictions, municipalities which were no longer coveredrevert to levels of underrepresentation similar to previously non-covered municipalities,eroding post-1965 gains. Again, we show that these results are driven by municipalitieswhere minorities are electorally more relevant. The response to Shelby County v.Holder (2013) points to patterns of underrepresentation consistent with strategicmanipulation of voting procedures.Within the context of this study, one of our contributions is to show the degree ofprecision with respect to electoral conditions with which voting procedures detrimentalto representation of minorities are applied. Future research should further investigatethe conditional use of surgical changes in voting procedures at other levels of theU.S. government. This will complement the necessarily partial picture that we couldprovide in this article and further inform the debate about voting rights in the country.In Chapter 4 we make a step in this direction by conducting an in depth study ofvoter registration, arguably one of the most important component of the democraticprocess in the U.S. and possibly the main channel shaping the underrepresentation ofminorities.69Chapter 4Behind MinorityUnderrepresentation: VoterRegistration4.1 IntroductionIn Chapter 3, we established the extensive underrepresentation of Minority voters inU.S. cities. Our results in Section 3.4 highlight the potential mediating role of severalinstitutional features, electoral rules in particular. Other mechanisms remain under-studied. This chapter digs deeper behind Minority underrepresentation by focusingon voter registration as a critical potential mechanism.We present novel evidence of nonlinearity in Minority voter registration rates usingproprietary information collected by Aristotle, a nonpartisan technology and voterinformation firm based in Washington DC. We show that Minority voters’ underregis-tration peaks at levels of Minority population shares between 45% and 50%, similarlyto what observed for underrepresentation. Minority voter registration rates have thelargest gap relative to White voter registration rates between 20% and 30% of thepopulation (where ethnic minorities would have a chance at being represented viaSMD elections). These patterns have implications not just for voter registration forlocal elections, but translate in underregistration for voting in national elections aswell. Indeed, work on the effects of Minority enfranchisement has shown access to the70electoral franchise to be highly consequential at the federal level.Using a new methodology developed specifically for the analysis of "live" datasets ofvoter registration information, we show that the removal of the 1965 Voting RightsAct protection following Shelby County v. Holder (2013) affected patterns of voterregistration of minorities vis-à-vis Whites. Municipalities that saw preclearancelimitations lifted after 2013 display relatively less registration of Minority votersthan Whites over time relative to non-covered municipalities (again suggesting thatdifferential obstacles to voter registration for Minority groups manifest once VRAprotections are removed). Existing anecdotal evidence confirms renovated efforts tointroduce previously blocked electoral reforms affecting voter registration and pollingstation shut downs following the Shelby County v. Holder (2013) ruling.1 Notably,new attempts to introduce potentially discriminatory legislation have been made byboth previously covered and non-covered jurisdictions. If also non-covered jurisdictionsperceived the Shelby County v. Holder (2013) ruling as a signal of a more permissiveapproach towards discriminatory electoral reforms, then the findings on the effects ofShelby County v. Holder (2013) on covered municipalities compared to non-coveredones should be interpreted as underestimates due to attenuation bias. On top of this,our estimates are particularly strong in areas with above-median Minority population,indicative of the fact that the disenfranchisement efforts following Shelby County v.Holder (2013) target Minority voters strategically where they matter electorally.Overall, we show evidence that underregistration of Minority voters matches thepatterns we found for underrepresentation in Chapter 3, which supports the intuitionof registration being a key mechanism behind our results. We also show that ShelbyCounty v. Holder (2013) had detrimental effects on Minority voters underregistration,confirming that at least part of the negative impact of Shelby County v. Holder (2013)on underrepresentation in Section 3.5.2 happened through the registration channel.This chapter falls under the broad umbrella of research on racial and ethnic disenfran-chisement. This review complements Section 3.1, with a focus on voter registrationand the effects of Shelby County v. Holder (2013). Much of the literature stems from1See, for instance, https://www.brennancenter.org/our-work/research-reports/election-2016-restrictive-voting-laws-numbers, https://www.washingtonpost.com/news/the-fix/wp/2014/07/07/how-has-voting-changed-since-shelby-county-v-holder/, and https://www.theatlantic.com/politics/archive/2018/07/how-shelby-county-broke-america/564707/. For its economic consequences also seeAneja and Avenancio-León (2019).71the exceptional re-enfranchisement effects of the Voting Rights Act of 1965 (see Berniniet al. (2024) for a review on the VRA research, and Jones et al. (2012) for a broaderdiscussion of disenfranchisement before the VRA). Washington (2012) and Cascioand Washington (2014) document an increase in Black voter registration immediatelyfollowing the passage of the VRA. Looking at the consequences of this, Bernini et al.(2023a) show increased Black office-holding, while Bernini et al. (2023b) point towardsincreased White voter registration as a reaction to the enfranchisement of minorities.Ang (2019) paper covers the long-run effects of the VRA coverage, documenting lastingeffects on turnout almost entirely driven by increased participation of Minority voters.This holds to the very last election before Shelby County v. Holder (2013) in 2012. Ourresearch confirms the positive effects of the VRA on Minority enfranchisement showinghow the removal of its protection had detrimental effects on Minority voter registration.The evidence on the more recent tightening of state ID laws appears not as settled. Asway of example, Cantoni and Pons (2021) find no effect of strict ID laws on registrationor turnout by race, nor any effect of ID laws on voter fraud or on curbing perceptionof electoral fraud, while there is limited evidence of the modest effects of strict ID lawson turnout (see Highton (2017) for a review of this literature). Part of the problemin reconciling some of these results may stem from the "almost surgical precision" ofhow certain institutional features are applied.2 These voting procedures may not havedetectable average effects, but can be fine tuned to precise local subconstituencies.The evidence in this article supports this intuition. Aside from the specific caseof strict ID laws, there is instead ample evidence of the negative effect of voterregistration requirements on turnout and disenfranchisement, such as the experimentalresults of Braconnier et al. (2017) in France, and Nickerson (2015) and Hajnal et al.(2017) in the U.S. The hurdle of voter registration and identification is particularlysalient for minorities, as shown by Atkeson et al. (2014) and White et al. (2015).Finally, the recent work of Cantoni and Pons (2022) reveals that contextual factors,including institutional features, explain about a third of the cross-state variation in2For instance, in 2016 in discussing a voter-I.D. law in North Carolina that an appeals courtfound the law could "target African-Americans with almost surgical precision". The appeals courtnoted that the North Carolina Legislature "requested data on the use, by race, of a numberof voting practices" and then based on the data "enacted legislation that restricted voting andregistration in five different ways, all of which disproportionately affected African Americans." Seehttps://www.ca4.uscourts.gov/opinions/published/161468.p.pdf. Last accessed November 2021.72turnout, registration, and party affiliation, as opposed to individual characteristics.Interestingly, place effects matter more for the registration of minority voters, but lessfor their differential turnout across states.While this chapter does not address directly the effects of voter ID laws, the concretepossibility that Shelby County v. Holder (2013) made it easier to pass and enforcesuch limitations shows why it is so important to assess its effects on Minority voterregistration, which we do here.A limited number of recent papers has focused on the effects of Shelby County v.Holder (2013), providing mixed results on registration and turnout. Billings et al.(2024) find a decrease in Black turnout relative to White voters in previously VRA cov-ered counties relative to never-covered ones. This holds conditional on registration. Onthe other hand, Raze (2022) and Komisarchik and White (2024) find no evidence of anincrease in registration nor turnout gap between Black and Hispanic voters relative toWhite following Shelby County v. Holder (2013). Finally, Feder and Miller (2020) findincreased registered voters purges in previously covered jurisdictions, although they donot assess whether these purges impact Minority voters differentially from White. Wecontribute to this growing literature showing that Shelby County v. Holder (2013) did,in fact, have a negative effect on the registration of Minority voters compared to White.The rest of the chapter is organized as follows. Section 4.2 describes and validatesthe voter registration data used in the analysis. Section 4.3 presents the evidenceon the underregistration of Minority voters in U.S. cities and how it relates to theirunderrepresentation in local governments. Section 4.4 investigates the effects of ShelbyCounty v. Holder (2013) on Minority voter registration. Section 4.5 concludes.4.2 DataThis chapter shares part of the data with Chapter 3. The sociodemographics are basedon the same 2010 and 2020 U.S. Census data from Manson et al. (2022). Contrary tothe ICMA surveys, the registration and Census data used in this chapter cover theuniverse of municipalities in the contiguous U.S. and with a population of 2,500 andover.We focus on the demographic composition of municipalities, and in particular thepopulation shares of Hispanic or Latino, Asian or Pacific Islanders (not of Hispanic73origins), Black or African American (not of Hispanic origins), and White (not ofHispanic origin). The shares are computed relative to the voting age population of 18years and older.Voter registration data is based on the proprietary Voter Lists Online (VLO) databaseof Aristotle (2021), a nonpartisan technology and voter information firm based inWashington DC. Aristotle maintains data on the live universe of registered voters inthe U.S.Using information on the registration date we can recover, for each municipality, acount of registered voters at any point in time, conditional on being registered today.We collect data on registered voters by the end of each year from 2007 to 2020. Wecan also distinguish the counts by race or ethnicity, either provided on the officialvoters lists or inferred by Aristotle. The sample covers the universe of municipalitiesin the contiguous U.S. and with population 2,500 and over based on the 2010 and2020 U.S. Census.3Table 4.1 reports the summary statistics at the municipality level for a set of 2020registration variables.4 The top panel describes the composition of registered voters interms of race or ethnicity. Voters to which Aristotle does not assign an inferred raceor ethnicity are imputed to one using the observed distributions. The second panelreports the breakdown of voting age populations by race or ethnicity, while the thirdpanel summarizes underregistration for each group, given by the difference betweenthe voting age population share of a Minority and its share of registered voters.The fourth panel shows the registration rates for each race or ethnicity, as well as forthe total population. The registration rate is defined simply as the fraction of voterswho register relative to their corresponding voting age populations, that is how manyvoting age individuals of a given race or ethnicity are also registered to vote. Thebottom panel matches Table 3.1 and includes a set of sociodemographic variables usedas controls, namely the total and voting age population, the fractions of over 65, rural,and foreign populations, as well as the median household income.53The sample also excludes North Dakota municipalities, given the high number of registered votersnot included in any of the explicit races or ethnicities.4A description of the variables is available in Table C.1.5A description of the common variables shared with Chapter 3 is available in Table B.1.74Table 4.1: Summary Statistics - Registration (2020)Mean Std. Dev. Min. Max. Obs.Shares of Registered Voters (frac.):Latino 0.087 0.162 0.000 0.979 6,436Asian 0.021 0.046 0.000 0.672 6,436African American 0.079 0.173 0.000 0.987 6,436Minority 0.187 0.231 0.005 0.989 6,436White 0.813 0.231 0.011 0.995 6,436Voting Age Population (frac.):Latino 0.130 0.171 0.001 0.985 6,436Asian 0.036 0.064 0.000 0.719 6,436African American 0.104 0.162 0.000 0.971 6,436Minority 0.313 0.232 0.024 0.995 6,436White 0.687 0.232 0.005 0.976 6,436Underregistration:Latino 0.044 0.048 -0.293 0.465 6,436Asian 0.015 0.025 -0.155 0.221 6,436African American 0.025 0.063 -0.453 0.508 6,436Minority 0.126 0.089 -0.235 0.635 6,436Registration Rates:Latino 0.390 0.192 0.000 1.000 6,436Asian 0.485 0.237 0.000 1.000 6,436African American 0.475 0.330 0.000 1.000 6,436Minority 0.347 0.197 0.008 1.000 6,436White 0.873 0.133 0.024 1.000 6,436Total 0.751 0.130 0.285 1.000 6,436Sociodemographics:Population 30,086.356 147,209.615 2,502 8,804,190 6,436Voting Age Population 23,413.266 117,290.619 1,733 7,064,048 6,436Over 65* (frac.) 0.145 0.055 0.010 0.795 6,436Rural* (frac.) 0.055 0.155 0.000 1.000 6,436Foreign* (frac.) 0.087 0.099 0.000 0.970 6,436Median Household Income* 42,416.786 20,508.293 11,578.660 197,427.141 6,436All summary statistics based on 2020 registration and U.S. Census data. Variables denoted by * are based on 2010U.S. Census data. Note that these are municipality-level summary statistics, and therefore reflect the characteristicsof the average municipality in our sample. This should be taken into consideration when comparing these measureswith nation-wide statistics, especially for sociodemographic variables. Census race/ethnicity measures (excludingLatinos) are based on people not of Hispanic or latino origin.754.2.1 ValidationThe voter registration data collected by Aristotle (2021) is based on the official voterlists of each state and county. While the source is official administrative data, the dataprocessed and provided by Aristotle (2021) is proprietary. In particular, the race orethnicity of registered voters is either based on the official voters lists or inferred. Inthe empirical analysis, registered voters with unknown race or ethnicity are imputedto Minority or White based on the distributions among voters with observed race orethnicity. This decision has no effect on the results presented in this chapter. Resultsusing only measures based on known Latino, Asian, and Black registered voters areavailable and consistent with the one presented here.We present below a number of validation exercises we conducted, to show the reliabilityand consistency of the registration data used in the rest of the chapter. We compare2020 registered voters data with state-level citizen voting age population data fromthe 2019 American Community Survey (ACS).Table 4.2 shows the composition of registered voters by race or ethnicity based on ourdata. White voters make up 67% of registered voters. Latinos edge Black voters by9.2% to 8.6%. Finally, Asian voters account for 2.7% of the registered population.This is remarkably similar to survey results from Pew Research from 2019, reporting69% White registered voters, and 11% each Black and Latino registered voters.6Table 4.2: Registered Voters and CVAP Shares(1) (2) (3) (4)Registered CVAP Registered(known only)CVAP(no other)Latino 0.092 0.131 0.105 0.134African American 0.086 0.129 0.099 0.132Asian 0.027 0.045 0.031 0.046White 0.669 0.672 0.765 0.688Unknown/Other 0.126 0.033Registered voters count at December 31, 2020. Citizen Voting Age Population (CVAP) from ACS-1 2019.For registered voters, unknown means with no inferred race/ethnicity information. For CVAP, other means arace/ethnicity other than the ones explicitly reported in the table.6https://www.pewresearch.org/short-reads/2020/10/26/what-the-2020-electorate-looks-like-by-party-race-and-ethnicity-age-education-and-religion/76Table 4.3 reports the registration rates of the main racial and ethnic groups of interests,measured over the entire universe of registered voters covered by Aristotle (2021). Forcomparison, we look at the 2020 CPS Voting and Registration supplement. Our ownregistration rates are higher for White voters and lower for Minority voters. Differencesbetween the two sources are expected, the CPS being a sample-based survey whichlikely overestimates participation, while the VLO data is based on the administrativeregistered voter lists.Table 4.3: Average Registration Rates(1) (2) (3) (4)Latino AfricanAmericanAsian WhiteNational level 0.571 0.546 0.493 0.813CPS survey 0.611 0.690 0.638 0.765Registration Rate as ratio of number of registered voters to citizen voting age population for a given race/ethnicity.Registered voters count at December 31, 2020. Citizen Voting Age Population (CVAP) from ACS-1 2019. CPSsurvey data from the 2020 Voting and Registration Supplement, available at https://www.census.gov/newsroom/press-releases/2021/2020-presidential-election-voting-and-registration-tables-now-available.html.Finally, Figure C.1 shows state-level scatter plots of the relationship between registeredvoters and citizen voting age population (CVAP) for the total population and forMinority groups. All counts of registered voters correlate strongly with the underlyingcitizen voting age population. This is reassuring and suggests that the attribution ofrace and ethnicity to registered voters by Aristotle (2021) is reliable.4.3 UnderregistrationThis section discusses differential voter registration levels by ethnic group as a potentialdriver of Minority underrepresentation. The goal is to empirically test whether theregistration patterns of Minority voters are consistent with the underrepresentationobserved in Chapter 3. Indeed, we find evidence of underregistration to an extent inline with that of underrepresentation, and with a similar non-linear relationship withthe share of the Minority population. These results strongly suggest that underregis-tration is potentially a key mechanism behind the underrepresentation established inChapter 3.77Using information on the registration date, we recover a count of registered voters atany point in time, conditional on being registered in June 2021, at the time of collection.For each municipality m, we count the number of voters who were registered at time t,and define Rmt as the number of voters in municipality m whose registration date isbefore or equal to time t. In general, t will be the end of a given calendar year, namelyDecember 31, from 2007 to 2020, except for the count at the time of Shelby Countyv. Holder (2013) on June 25, 2013. We distinguish counts of registered voters byethnicity, either provided on the voters lists or inferred by Aristotle (2021), dependingon the state or county. Overall, the final sample covers the universe of municipalitiesin the contiguous U.S. and population above or equal to 2,500 based on the U.S. Census.We focus the analysis to the 2020 cross-section and drop the t subscript for easeof notation, as the most accurate and recent data on population comes from 2020.We construct three main dependent variables of interest. First, we define Minorityunderregistration as:urm ≡ pMm − rMm , (4.1)where pMm is the Minority voting age population share in municipality m, and rMm isthe Minority share of registered voters.The second main dependent variable measures the registration rate of Minority voters,that is the share of Minority voters who are registered. For each municipality m,let RMm be the absolute number of Minority voters who are registered, and PMm theMinority voting age population, then the Minority registration rate is:rrMm ≡RMmPMm. (4.2)The third variable of interest is then the registration rate gap between White andMinority voters, measured by:rgm ≡ RWmPWm− RMmPMm, (4.3)where RWm and PWm are the number of registered White voters and the White popula-tion in municipality m.78Figure 4.1 presents a map of the geographic dispersion of underregistration measuredas Equation 4.1. Somewhat differently from the case of underrepresentation in Fig-ure 3.1, underregistration appears more intense in areas of the West North Centraland West South Central.Figure 4.1: Minority UnderregistrationCounty-level average Minority underregistration in 2020. Color breaks by quintiles.Figure 4.2 shows that a non-linear relationship between Minority share of the votingage population and underregistration of Minority voters of the city holds similarlyto what observed for Minority underrepresentation in Figure 3.2, suggesting the twoassociations may be related. Minority voters are underregistered throughout theentire spectrum of population shares, that is they consistently make up for a smallershare of registered voters than they do of the underlying voting age population. Thisunderregistration appears to reach a maximum when the share of Minority populationis approximately 40% to 50%.We can confirm this intuition using a parametric approach by estimating the quadraticspecification:ym = β0 + β1pm + β2p2m + δs(m) + x′mα + εm, (4.4)79Figure 4.2: Minority Underregistration0.2.4.6.81Minority share of Registered Voters0 .2 .4 .6 .8 1Minority VAP shareRegistered voters with unknown race/ethnicity are imputed to Minority or White based onthe observed distributions. The fitted line is a LOWESS based on the underlying data.Minority Underregistration (2020)Solid line represents a LOWESS smoothing of the underlying municipality-level data. Markersrepresent population bins averages.where ym is one of the dependent variables of interest in Equations 4.1-4.3, δs(m)is a state fixed effect for state s(m) of municipality m, xm is a vector of controls,including the log-population, the log-median household income, and the fractions ofrural, foreign, and over 65 population.Similar to Chapter 3, Given the quadratic specification in Equation 4.4 and the set of es-timates βˆ0, βˆ1, and βˆ2, the estimated max and argmax of Minority underrepresentationwith respect to the Minority population fraction p are given by:max = βˆ0 − βˆ214βˆ2; argmax = − βˆ12βˆ2. (4.5)Table 4.4 reports the estimates of Equation 4.4 for the Minority underregistrationmeasure (Equation 4.1). The results show a strong, significant concave relationshipbetween Minority population share and Minority share of registered voters, much inline with our results for underrepresentation in Section 3.3. The estimated maximumunderregistration gap is about 20 percentage points. The location of the argmaxis consistently estimated between 46-49% Minority population shares, close to theestimated argmax for underrepresentation. This indicates that minorities exhibit80Table 4.4: Minority Underregistration(1) (2) (3)MinorityUnderreg.MinorityUnderreg.MinorityUnderreg.Regression estimates:Minority 0.831∗∗∗ 0.957∗∗∗ 0.901∗∗∗(0.014) (0.015) (0.019)Minority2 -0.870∗∗∗ -0.971∗∗∗ -0.963∗∗∗(0.015) (0.015) (0.017)Max/Argmax estimates:max 0.196∗∗∗ 0.209∗∗∗ 0.201∗∗∗(0.002) (0.002) (0.002)argmax 0.477∗∗∗ 0.493∗∗∗ 0.468∗∗∗(0.003) (0.003) (0.005)Population VAP VAP VAPYear 2020 2020 2020State FE - X XControls - - XObs. 6,436 6,435 6,435Adjusted R2 0.359 0.586 0.597The top panel reports regression estimates. Robust standard errors in paren-theses. Significance levels: *** p < 0.01, ** p < 0.05, * p < 0.1. Registeredvoters with unknown race/ethnicity are imputed to Minority or White basedon the distributions among voters with observed race/ethnicity. The depen-dent variable in all specifications is Minority underregistration, computed asthe difference between the fraction of Minority population and the Minorityfraction of registered voters. The main independent variables are the fractionof Minority population and its squared value. Population measures based onvoting age population. When included, the controls are the log-population,the log-median household income, and the fractions of rural, foreign, and over65 population. The sample is restricted to the year 2020. The second panel re-ports Max/Argmax estimates based on the regression estimates. Standard er-rors in parentheses are asymptotic. Given a quadratic formula y = ax2+bx+c,the max is computed as c− b24a , while the argmax is computed as − b2a .the widest gap between their share of registered voters and their share of the votingage population right at the point where they could shift the control of the citycouncil. Relative to underrepresentation, however, the argmax appears about 4%lower for underregistration than for underrepresentation. This difference is statisticallysignificant at the 1% level, indicating that underregistration is a stronger driver ofunderrepresentation in cities at lower levels of Minority shares of the population andother voting procedures reduce Minority representation as Minority shares increase.This is further confirmed looking at Minority voter registration rates. Figure 4.3reports a non-linear relationship of Minority share of the population with Minority81Figure 4.3: Minority Registration Rate.2.3.4.5.6.7Share of Minority Registered0 .2 .4 .6 .8 1Minority VAP shareRegistered voters with unknown race/ethnicity are imputed to Minority or White based onthe observed distributions. The fitted line is a LOWESS based on the underlying data.Minority Registration Rate (2020)Solid line represents a LOWESS smoothing of the underlying municipality-level data. Markers represent population bins averages. Basedon data from 2020.Figure 4.4: Minority Registration Gap.1.2.3.4.5.6.7Registration Rate Gap0 .2 .4 .6 .8 1Minority VAP shareRegistered voters with unknown race/ethnicity are imputed to Minority or White based onthe observed distributions. The fitted line is a LOWESS based on the underlying data.White-Minority Registration Rate Gap (2020)Solid line represents a LOWESS smoothing of the underlying municipality-level data. Markers represent population bins averages. Basedon data from 2020.82registration rates, as defined in Equation 4.2. The lowest registration rates forminorities occur where minorities constitute 15-20% of the voting age populationin the city. The White-Minority registration gap has also a similar shape, with aWhite-minority voter registration gap, as defined in Equation 4.3, being maximumwhere minorities constitute 15-25% of the voting age population in the city. This isreported in Figure 4.4.Figure 4.5 presents the cross-sectional relationship between underrepresentation (in2018) and underregistration (in 2020), our most current and accurate estimates forboth of these measures. Variation in underregistration across U.S. cities explainsabout 17 percent of the variation in underrepresentation in city governments. Basedon the size of the linear coefficient (about 0.7, which is statistically significantlysmaller than 1), we can infer that, as Minority underregistration increases, Minorityunderrepresentation in the council increases as well, but by less. There are thereforeinstitutional features that somewhat blunt the effect of underregistration of minoritiesand make representation an issue not exclusively driven by registration rates. Anexample could be a SMD electoral rule. In a SMD city, consider Minority votersthat happen to be geographically packed in a district 100% Minority. Then MinorityFigure 4.5: Minority Underrepresentation and Underregistration83representation would remain the same, even when Minority voter registration rates godown. Overall, Minority voter registration appears to be a sizable, but far from theonly driver of underrepresentation of ethnic minorities.Finally, as for the case of underrepresentation, Figures C.2, C.3, and C.4, and Table C.2in Appendix C.2 confirm that the underregistration results are not driven by eitherRepublican or Democratic leaning municipalities.4.4 Shelby Effects on UnderregistrationWe conclude by investigating the effects of Shelby County v. Holder (2013) on Minorityunderregistration. Registration mirrors the patterns observed for representation inSection 3.5.2: Once the VRA Section 5 protections are removed in 2013, we reporta differential decrease in Minority registration relative to the White population incovered municipalities relative to non-covered ones.The institutional background at the time of Shelby County v. Holder (2013) isdiscussed in Chapter 3. We refer readers to Section 3.5.1 for an in-depth discussion.Here we summarize the key features of the Voting Rights Act of 1965 and what theShelby County v. Holder (2013) meant for it. The VRA provided that a numberof jurisdictions with a history of racial discrimination and disenfranchisement wereprohibited from enacting any change pertaining to election without federal approval.These are the jurisdictions we refer to as covered by the VRA.7In 2013 the Supreme Court effectively removed this restriction in previously coveredjurisdictions, in the landmark Shelby County v. Holder (2013) ruling. Effectively,all jurisdictions were now free to implement any change to their election procedures,including registration. These include for instance ID requirements for a valid registra-tion, but also finer details like the timing of when registration can take place. Thepotential for disenfranchisement of Minority voters is extremely high.87A complete list of jurisdictions covered by the VRA at the time of Shelby County v. Holder (2013)can be found here: https://www.justice.gov/crt/jurisdictions-previously-covered-section-5. Lastaccessed November 2021.8See https://www.brennancenter.org/our-work/research-reports/effects-shelby-county-v-holder-voting-rights-act.844.4.1 Modelling Registration DynamicsThe methodology that we adopt in this subsection requires a special discussion, becauseit is nonstandard and driven by the data available for the analysis. VLO registrationdata from Aristotle (2021) covers a cross-section of voters that had registered atdifferent time periods (cohorts) prior (and survived to) 2021 (a "live" sample), ratherthan a longitudinal, panel data sample. The use of a live sample implies standarddifference-in-differences approaches cannot be used, as differential survival of all cohortsand entry profiles of cohorts registered after 2013 may be differentially affected byShelby.We develop a novel identification approach making explicit the underlying populationdynamics in terms of arrival of newly registered voters and exits of older registeredvoters using a Leslie (1945) matrix approach.9 Appendix C.1 presents the detailsof the model of population dynamics used to derive the estimator. More generally,our modelling strategy may be useful for any data environment where live samplesor repeated cross-sections are used (e.g. Demographic and Health Surveys) and amethodological tool of broader interest.Taking the set of registered voters as an evolving population composed by a finiteset of voters registered at different times between 2008 and 2020 (cohorts of vintageτ ∈ 1, . . . , T = 13), the effect of Shelby County v. Holder (2013) is modeled as astructural break in fecundity parameters (f , how many newly registered voters enterthe system at each period before and after Shelby) and survival parameters (s, thelikelihood of remaining registered for each cohort of voters). These fecundity andsurvival parameters form the Leslie (1945) matrix governing the dynamics of thesystem. Breaks in the parameters of the Leslie matrix are detectable from a single"live" sample because the population dynamics parameters estimated for cohorts bornin periods before Shelby County v. Holder (2013) in each municipality allow one topredict the size of each surviving counterfactual cohort born in the period after Shelbythat should be observed absent a break in that municipality. The difference betweenactual and counterfactual registrations produces an estimate of the effect of ShelbyCounty v. Holder (2013) that accounts for entry and for observing only surviving9This is an approach well suited to model the evolution of a population composed by a finite anddiscrete number of cohorts. It is frequently employed in mathematical demography, evolutionarybiology, etc. See Cushing (1998) for a general overview and Robson and Samuelson (2011); Robsonet al. (2012) for applications in economics.85registered voters in the live sample.Given the data limitations of our source, define R(τ) as the ratio between the cohortof vintage τ still registered in 2020 and the oldest cohort still registered in 2020. Weshow in Appendix C.1 that for cohorts before Shelby County v. Holder (2013) wehave:R(τ) =(λs)T−τ. (4.6)For cohorts after Shelby County v. Holder (2013) we have:R(τ) = TEτ +(λs)T−τ, (4.7)where we define TEτ the treatment effect of Shelby County v. Holder (2013) forcohort τ as the difference in the ratio relative to the counterfactual with no structuralbreaks. To estimate the treatment effect, we estimate first Equation 4.6 using cohortsentering before the treatment and recover the estimated ratio λs. With that and usingcohorts after the event period, it is possible to estimate the treatment effect fromEquation 4.7. In practice, we employ a Generalized Method of Moments estimator toestimate all parameters and effects simultaneously.4.4.2 ResultsOur approach shows how one recovers for each cohort τ of registered voters thetreatment effect TEτ of Shelby County v. Holder (2013). We obtain treatment effectsfor Minority voters in covered and in non-covered municipalities and for White votersin covered and in non-covered municipalities. We are interested in the differential orrelative effect of Shelby County v. Holder (2013) on the registration of minorities,while potentially allowing for an impact on all groups of voters.10 The method thenallows to derive a (pseudo) difference-in-differences estimator given by the differencein treatment effects for Minority voter registrations in covered versus non-covered mu-nicipalities relative to the difference in treatment effects for White voter registrationsin covered versus non-covered municipalities.10The targeted disenfranchisement of Minority voters can have spillover effects on the registration ofvoters of any background.86Table 4.5 presents the estimates of the treatment effects for each cohort of voters,splitting by Minority/White voters and covered/non-covered municipalities (columns(1), (2), (4), and (5) respectively).11 For instance, each entry of column (4) reportsthe treatment effect and the county-level clustered standard error for the Minorityvoter group in non-covered municipalities who entered in 2009 to 2020. Column (5)repeats the exercise for covered municipalities and column (6) presents the difference forMinority voters in covered versus non-covered municipalities, showing that registrationsof Minority voters became relatively lower in covered municipalities after Shelby (allTable 4.5: Effects of Shelby (2013) on Minority and White Registration(1) (2) (3) (4) (5) (6) (7)White(non-covered)White(covered)White(difference)Minority(non-covered)Minority(covered)Minority(difference)DiD(pseudo)2009 (pre) -0.494∗∗∗ -0.507∗∗∗ -0.014∗∗ -0.510∗∗∗ -0.520∗∗∗ -0.010 0.003(0.004) (0.005) (0.006) (0.006) (0.006) (0.008) (0.007)2010 (pre) -0.147∗∗∗ -0.156∗∗∗ -0.009 -0.182∗∗∗ -0.211∗∗∗ -0.028∗∗∗ -0.019∗∗(0.004) (0.006) (0.007) (0.006) (0.007) (0.009) (0.008)2011 (pre) -0.106∗∗∗ -0.093∗∗∗ 0.013∗ -0.123∗∗∗ -0.097∗∗∗ 0.026∗∗∗ 0.013∗(0.004) (0.006) (0.007) (0.007) (0.007) (0.010) (0.008)2012 (pre) 0.595∗∗∗ 0.627∗∗∗ 0.032∗∗∗ 0.667∗∗∗ 0.666∗∗∗ -0.001 -0.033∗∗(0.009) (0.008) (0.012) (0.012) (0.012) (0.017) (0.013)2013 (pre) 0.152∗∗∗ 0.129∗∗∗ -0.023∗∗ 0.148∗∗∗ 0.161∗∗∗ 0.014 0.036∗∗∗(0.007) (0.006) (0.009) (0.009) (0.007) (0.012) (0.008)2014 0.467∗∗∗ 0.437∗∗∗ -0.030∗∗ 0.480∗∗∗ 0.454∗∗∗ -0.026 0.004(0.011) (0.009) (0.014) (0.015) (0.013) (0.020) (0.014)2015 0.569∗∗∗ 0.492∗∗∗ -0.077∗∗∗ 0.570∗∗∗ 0.473∗∗∗ -0.097∗∗∗ -0.021(0.020) (0.008) (0.022) (0.020) (0.013) (0.024) (0.015)2016 1.934∗∗∗ 1.581∗∗∗ -0.352∗∗∗ 2.242∗∗∗ 1.469∗∗∗ -0.773∗∗∗ -0.421∗∗∗(0.046) (0.024) (0.052) (0.067) (0.033) (0.075) (0.044)2017 0.842∗∗∗ 0.816∗∗∗ -0.026 0.862∗∗∗ 0.767∗∗∗ -0.094∗∗ -0.069∗∗∗(0.028) (0.025) (0.037) (0.036) (0.024) (0.043) (0.023)2018 1.992∗∗∗ 1.501∗∗∗ -0.491∗∗∗ 2.061∗∗∗ 1.400∗∗∗ -0.661∗∗∗ -0.170∗∗∗(0.076) (0.038) (0.085) (0.089) (0.044) (0.099) (0.039)2019 1.893∗∗∗ 1.357∗∗∗ -0.536∗∗∗ 1.922∗∗∗ 1.282∗∗∗ -0.640∗∗∗ -0.103∗∗(0.101) (0.048) (0.112) (0.118) (0.047) (0.127) (0.044)2020 3.466∗∗∗ 2.287∗∗∗ -1.179∗∗∗ 3.387∗∗∗ 1.926∗∗∗ -1.461∗∗∗ -0.282∗∗∗(0.121) (0.060) (0.135) (0.144) (0.058) (0.155) (0.062)Obs. (per year) 5,032 1,359 . 4,847 1,361 . .Cluster level County County County County County County CountyStandard errors in parentheses are clustered in columns (1), (2), (4), (5), and asymptotic based on delta method in columns (3), (6), (7).Significance levels: *** p < 0.01, ** p < 0.05, * p < 0.1. Registered voters with unknown race/ethnicity are imputed to Minority orWhite based on the distributions among voters with observed race/ethnicity. Estimates based on the steps described in Appendix C.1.11For the ratios defined in Appendix C.1, specifically in Equation C.2, Equation C.3, and Equation C.4,we exclude from the analysis the observations of any municipality for which the ratio is above the99.9th percentile, for any of the sample years.87treatment effects differences are negative and significant after 2014). Notice thatthe same exercise is performed for White voters (columns (1)-(3)) and the (pseudo)difference-in-differences estimates comparing Minority voter registrations in coveredversus non-covered municipalities relative to White voter registrations in covered versusnon-covered municipalities are in column (7). Column (7) shows that for Minorityvoters in previously VRA covered municipalities registration is substantially lowerthan for non-covered municipalities and significantly lower than for White voters.The results from column (7) are presented graphically in Figure 4.6 that shows aclear differential gap in the registration rates of Minority voters compared to Whitevoters, in previously VRA covered jurisdictions after Shelby County v. Holder (2013).Magnitudes indicate an up to 40 percentage points lower registration of Minoritiescompared to Whites in covered municipalities relative to non-covered ones. The effectexhibits a lag, possibly due to the time necessary to implement changes to votingprocedures, and it is particularly strong after the 2016 presidential election, the firstafter Shelby County v. Holder (2013). Overall, we see larger magnitudes in ourestimates in presidential and, to a lesser extent, midterm election years, when mostFigure 4.6: Effects of Shelby (2013) on Minority v. White Registration-.6-.4-.20.2(pseudo) DiD2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021YearC.I. 95%, standard errors clustered at County level.Effects of Shelby (2013) on Minority Registration v. WhiteRegistered voters with unknown race/ethnicity are imputed to Minority or White based on the distributions among voters with observedrace/ethnicity. The estimates use data on registrations as of January 1 of the year indicated on the horizontal axis. Estimates based onthe steps described in Appendix C.1 and column (7) of Tables 4.5. The starting point of the event study is limited by the data at ourdisposal.88voters register. We cannot entirely rule out a 2016-election specific effect, but such aneffect would need to be concentrated in previously covered municipalities, and moreso in high-Minority localities, as we discuss next. While the first significant effect isobserved in 2016, the pattern in the following election cycles suggest that this was notjust a one-off event. Nonetheless, we acknowledge that these effects might be drivenby a broader realignment of U.S. politics, in which case our results would suggest thatthe Shelby County v. Holder (2013) ruling has intensified the effects in jurisdictionspreviously covered by the VRA.In Figure 4.7 we perform a sample split between above and below median Minorityvoting age population shares calculated in 2010 using the same methodology as for theupper panel. By confining to these subsamples, we are able to see that the differentiallower registration of Minority voters relative to White voters is clearly driven bypreviously VRA covered areas with a high percentage of Minority voters before ShelbyCounty v. Holder (2013). These are exactly those localities where the incentives torepress Minority voters are higher due to their likely electoral importance. This is oneFigure 4.7: Effects of Shelby (2013) on Minority v. White Registration (Above/BelowMedian Minority)-.6-.4-.20.2.4.6(pseudo) DiD2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021YearBelow (<) Above (>=)C.I. 95%, standard errors clustered at County level.Above/Below Median Minority VAPS (= 0.179)Effects of Shelby (2013) on Minority Registration v. WhiteRegistered voters with unknown race/ethnicity are imputed to Minority or White based on the distributions among voters with observedrace/ethnicity. The estimates use data on registrations as of January 1 of the year indicated on the horizontal axis. Estimates based onthe steps described in Appendix C.1 and column (7) of Tables C.3 and C.4. Above/below median based on Minority VAPS in 2010.89more piece of evidence supporting the strategic nature of the disenfranchisement efforts,in line with what we have shown in terms of underrepresentation and electoral rules inSection 3.4.1. Magnitudes indicate an up to 48 percentage point lower registration ofMinorities compared to Whites for covered municipalities above the median in termsof share of Minority population. Instead, for municipalities below the median in termsof share of Minority population, there is a positive, albeit less precise, difference interms of Minority compared to White registration between covered and non-coveredmunicipalities after VRA coverage is removed. If anything, in the subsample of citieswith low Minority presence previously covered municipalities seem to perform better.As in the case of underrepresentation in Section 3.5.2, the results are robust to usingsamples restricted to 200 and 100 miles within the VRA border. We report resultsrestricted to 200 miles with Table C.5 and Figure C.5. Table C.8 and Figure C.6report results for the 100 miles sample. Similarly, the results are robust to Democraticor Republican partisanship, reported in Figure C.7. Perhaps surprisingly, the negativeeffect of Shelby County v. Holder (2013) on Minority registration appears somewhatstronger in municipalities in Democratic leaning counties.4.5 ConclusionThis chapter investigates the role of voter registration as possible mechanism behindthe underrepresentation of Minority voters in city councils, discussed in Chapter 3.Using proprietary data on registered voters from Aristotle (2021), based on officialadministrative registered voters lists, we find that Minority voters are underregisteredrelative to their share of the voting age population. This is the result of lowerregistration rates for Minority voters compared to White voters. The pattern ofunderregistration matches what we observe for underrepresentation, with the largestgap of up to 20 percentage points occurring when Minority voters make up between46% and 49% of the voting age population. The results suggest that underregistrationis a key channel generating the underrepresentation of Minority voters in city councils,in addition to the institutional features highlighted in Section 3.4.Next, we turn out attention to the effects of Shelby County v. Holder (2013). Mountinganecdotal evidence shows an increase in restrictive voting legislation passing rightafter the Supreme Court ruling, with potential for the disenfranchisement of Minority90voters. Evidence on its effects remains mixed. We develop a new methodology basedon the study of population dynamics that allows us to estimate the effects of ShelbyCounty v. Holder (2013) from a "live" dataset of registered voters. We find evidenceof significantly lower registration of Minority relative to White voters in jurisdictionspreviously protected by the VRA. This is particularly true for areas with above-medianshares of Minority population, stressing the strategic nature of disenfranchisement.Our results add an important piece to the puzzle of Minority voter registration. Theyhelp us make sense of the extent and nature of the underrepresentation of Minorityvoters we observe in city councils. Future research should strive to reassess theseresults using better voter registration data, ideally using panels of eligible voters.Our results suggest negative effects of Shelby County v. Holder (2013) on Whitevoter registration as well. Making it harder to vote impacts everyone, but it impactsMinority voters more. Investigating the spillover effects of disenfranchisement ongroups not directly targeted could be a fruitful avenue for future studies. Finally,studying a longer time period will allow us to provide statistical evidence on the actualmeasures being implemented to make registration and voting harder, going beyond theexisting anecdotal evidence. Mobilization efforts to counter Shelby County v. Holder(2013) may also be underway and should be studied further, along with attempts topass the John R. Lewis Voting Rights Advancement Act currently stalled in Congress,further contributing to the uncertainty of voting rights in the post-Shelby era.1212https://www.brennancenter.org/our-work/analysis-opinion/restoring-voting-rights-act.91Chapter 5ConclusionsThis thesis is composed of three chapters analyzing the understudied relationshipbetween race, politics, and finances of U.S. cities. Chapter 2 studies how the racialand ethnic demographic composition of U.S. municipalities is priced in the municipalbonds market. I show that more diverse cities—cities with higher shares of non-Whitepopulation—pay significantly higher interests on their debt.The chapter contributes to the literature on diversity and bonds, with a comprehensivestudy focused on municipal issuers and robust causal evidence of a diversity premiumthat cities pay to finance their investments. The results are not driven by the creditrating agencies or other financial institutions involved in the issuance process. I arguethat this premium is consistent with the discrimination of minorities in the primarymarket for municipal bonds.Chapter 3 contributes to the always relevant literature on voting rights and represen-tation, with several novel results. We show that non-White minorities are significantlyunderrepresented in U.S. city councils relative to their underlying population shares.The result is strongest when Minority are electorally pivotal. We find that electoralrules are used strategically to minimize the representation of minorities. This confirmsthe results of Trebbi et al. (2008) around the passage of the VRA and extends themto today.We then estimate the effects of the Shelby County v. Holder (2013) ruling on under-representation. We compare municipalities that required federal preclearance beforethe ruling to those never covered by the VRA. We find that removing voting rightsprotection increased the underrepresentation of Minority voters in city councils.92In Chapter 4, we expand on our underrepresentation results by exploring the votingregistration channel. Minority voters display lower registration levels than Whitevoters, in a nonlinear pattern consistent with underrepresentation. Minority votersare again most underregistered when electorally pivotal, and have lower registrationrates throughout the population spectrum.Shelby County v. Holder (2013) made things worse. We show that registration islower in jurisdictions no longer covered by the VRA. The effect is more pronouncedfor Minority voters relative to White, and driven by municipalities with above-medianMinority population shares. This is consistent with the strategic disenfranchisement ofMinority voters in U.S. cities. In the literature, the debate on the disenfranchisementeffects of Shelby County v. Holder (2013) remains far from settled. Our results addto the growing research on the post-Shelby era of voting rights.93BibliographyAbott, C., and A. Magazinnik. 2020. “At-Large Elections and Minority Represen-tation in Local Government.” American Journal of Political Science, 64(3): 717–733.Abott, C., A. Singla, et al. 2021. “Service Solvency and Quality of Life AfterMunicipal Bankruptcy.” Journal of Political Institutions and Political Economy,2(2): 249–280.Acemoglu, D., and J. A. Robinson. 2006. Economic Origins of Dictatorship andDemocracy. Cambridge University Press.Adelino, M., S. C. Cheong, J. Choi, and J. Y. J. Oh. 2023. “Mutual FundFlows and the Supply of Capital in Municipal Financing.” NBER Working PaperNo. w30980.Aghion, P., A. Alesina, and F. Trebbi. 2004. “Endogenous Political Institutions.”The Quarterly Journal of Economics, 119(2): 565–611.Alesina, A., and A. Passalacqua. 2016. “The Political Economy of GovernmentDebt.” Handbook of Macroeconomics, 2: 2599–2651.Alesina, A., and E. Glaeser. 2004. Fighting Poverty in the US and Europe: AWorld of Difference. Oxford University Press.Alesina, A., R. Baqir, and W. Easterly. 1999. “Public Goods and EthnicDivisions.” The Quarterly Journal of Economics, 114(4): 1243–1284.Amy, D. J. 2002. Real Choices/New Voices: How Proportional RepresentationElections Could Revitalize American Democracy. Columbia University Press.Aneja, A. P., and C. F. Avenancio-León. 2019. “Disenfranchisement and Eco-94nomic Inequality: Downstream Effects of Shelby County v. Holder.” AEA Papersand Proceedings, 109: 161–65.Ang, D. 2019. “Do 40-Year-Old Facts Still Matter? Long-Run Effects of FederalOversight Under the Voting Rights Act.” American Economic Journal: AppliedEconomics, 11(3): 1–53.Angrist, J., and M. Kolesár. 2024. “One Instrument to Rule Them All: The Biasand Coverage of Just-ID IV.” Journal of Econometrics, 240(2): 105398.AristotleAristotle. 2021. Voter Lists Online, subscription service.ASPP. 2024. Annual Survey of Public Pensions. US Census Bureau.Atkeson, L. R., Y. P. Kerevel, R. M. Alvarez, and T. E. Hall. 2014. “WhoAsks for Voter Identification? Explaining Poll-Worker Discretion.” The Journal ofPolitics, 76(4): 944–957.Azzimonti, M., M. Battaglini, and S. Coate. 2016. “The Costs and Benefits ofBalanced Budget Rules: Lessons from a Political Economy Model of Fiscal Policy.”Journal of Public Economics, 136: 45–61.Babina, T., C. Jotikasthira, C. Lundblad, and T. Ramadorai. 2021. “Hetero-geneous Taxes and Limited Risk Sharing: Evidence from Municipal Bonds.” TheReview of Financial Studies, 34(1): 509–568.Bartlett, R., A. Morse, R. Stanton, and N. Wallace. 2022. “Consumer-LendingDiscrimination in the FinTech Era.” Journal of Financial Economics, 143(1): 30–56.Bassetto, M. 2006. “Politics and Efficiency of Separating Capital and OrdinaryGovernment Budgets.” The Quarterly Journal of Economics, 121(4): 1167–1210.Battaglini, M., and S. Coate. 2007. “Inefficiency in Legislative Policymaking: aDynamic Analysis.” American Economic Review, 97(1): 118–149.Battaglini, M., and S. Coate. 2008. “A Dynamic Theory of Public Spending,Taxation, and Debt.” American Economic Review, 98(1): 201–36.95Beach, B., and D. B. Jones. 2017. “Gridlock: Ethnic Diversity in Governmentand the Provision of Public Goods.” American Economic Journal: Economic Policy,9(1): 112–36.Beach, B., D. B. Jones, T. Twinam, and R. Walsh. 2024. “Racial and EthnicRepresentation in Local Government.” American Economic Journal: EconomicPolicy, 16(2): 1–36.Bergstresser, D., and P. Orr. 2014. “Direct Bank Investment in Municipal Debt.”Municipal Finance Journal, 35(1).Bergstresser, D., R. Cohen, and S. Shenai. 2013. “Demographic Fractionalizationand the Municipal Bond Market.” Municipal Finance Journal, 34(3).Berkovec, J. A., G. B. Canner, S. A. Gabriel, and T. H. Hannan. 1998.“Discrimination, Competition, and Loan Performance in FHA Mortgage Lending.”Review of Economics and Statistics, 80(2): 241–250.Bernini, A., G. Facchini, and C. Testa. 2023a. “Race, Representation, and LocalGovernments in the US South: the Effect of the Voting Rights Act.” Journal ofPolitical Economy, 131(4): 994–1056.Bernini, A., G. Facchini, M. Tabellini, and C. Testa. 2023b. “Black Empower-ment and White Mobilization: the Effects of the Voting Rights Act.”Bernini, A., G. Facchini, M. Tabellini, and C. Testa. 2024. “Sixty Years ofthe Voting Rights Act: Progress and Pitfalls.” Oxford Review of Economic Policy,40(3): 486–497.Besley, T., T. Persson, and D. M. Sturm. 2010. “Political Competition, Policyand Growth: Theory and Evidence from the US.” The Review of Economic Studies,77(4): 1329–1352.Billings, S. B., N. Braun, D. B. Jones, and Y. Shi. 2024. “Disparate RacialImpacts of Shelby County v. Holder on Voter Turnout.” Journal of Public Economics,230: 105047.Borusyak, K., and P. Hull. 2023. “Nonrandom Exposure to Exogenous Shocks.”96Econometrica, 91(6): 2155–2185.Borusyak, K., P. Hull, and X. Jaravel. 2022. “Quasi-Experimental Shift-ShareResearch Designs.” The Review of Economic Studies, 89(1): 181–213.Boustan, L. P. 2010. “Was Postwar Suburbanization "White Flight"? Evidencefrom the Black Migration.” The Quarterly Journal of Economics, 125(1): 417–443.Bouton, L., A. Lizzeri, and N. Persico. 2020. “The Political Economy of Debtand Entitlements.” The Review of Economic Studies, 87(6): 2568–2599.Bowles, G., J. D. Tarver, C. L. Beale, and E. S. Lee. 2016. Net Migration ofthe Population by Age, Sex, and Race, 1950-1970. Inter-university Consortium forPolitical and Social Research.Braconnier, C., J.-Y. Dormagen, and V. Pons. 2017. “Voter Registration Costsand Disenfranchisement: Experimental Evidence from France.” American PoliticalScience Review, 111(3): 584–604.Brancaccio, G., D. Li, and N. Schürhoff. 2020. “Learning by Trading: The Caseof the US Market for Municipal Bonds.” Working Paper.Broockman, D. E. 2013. “Black Politicians Are More Intrinsically Motivated toAdvance Blacks’ Interests: A Field Experiment Manipulating Political Incentives.”American Journal of Political Science, 57(3): 521–536.Calderon, A., V. Fouka, and M. Tabellini. 2023. “Racial Diversity and RacialPolicy Preferences: the Great Migration and Civil Rights.” The Review of EconomicStudies, 90(1): 165–200.Cantoni, E., and V. Pons. 2021. “Strict ID Laws Don’t Stop Voters: Evidencefrom a US Nationwide Panel, 2008-2018.” The Quarterly Journal of Economics,136(4): 2615–2660.Cantoni, E., and V. Pons. 2022. “Does Context Outweigh Individual Character-istics in Driving Voting Behavior? Evidence from Relocations within the UnitedStates.” American Economic Review, 112(4): 1226–72.Carlee, R. 2008. “Budgeting for Local Governments in the United States: Deciding97Who Gets What, How Much, and Who Pays?” Business and Public AdministrationStudies, 3(3): 106–106.Carlson, M., R. Giammarino, and R. L Heinkel. 2022. “Municipal CapitalStructure.” Working Paper, available at SSRN 4032097.Cascio, E. U., and E. Washington. 2014. “Valuing the Vote: The Redistributionof Voting Rights and State Funds Following the Voting Rights Act of 1965.” TheQuarterly Journal of Economics, 129(1): 379–433.Cestau, D., B. Hollifield, D. Li, and N. Schürhoff. 2019. “Municipal BondMarkets.” Annual Review of Financial Economics, 11: 65–84.Chen, M. K., K. Haggag, D. G. Pope, and R. Rohla. 2022. “Racial Disparitiesin Voting Wait Times: Evidence from Smartphone Data.” Review of Economicsand Statistics, 104(6): 1341–1350.Coate, S., and B. Knight. 2011. “Government Form and Public Spending: Theoryand Evidence from US Municipalities.” American Economic Journal: EconomicPolicy, 3(3): 82–112.Cushing, J. M. 1998. An Introduction to Structured Population Dynamics. SIAM.Dagostino, R. 2022. “The Impact of Bank Financing on Municipalities’ BondIssuance and the Real Economy.” Working Paper.Dagostino, R., and A. Nakhmurina. 2023. “Partisan Cities.” Working Paper,available at SSRN 4480061.Davidson, C. 1994. Quiet Revolution in the South: The Impact of the Voting RightsAct, 1965-1990. Princeton University Press.Davidson, C., and G. Korbel. 1981. “At-Large Elections and Minority-GroupRepresentation: A Re-Examination of Historical and Contemporary Evidence.” TheJournal of Politics, 43(4): 982–1005.Derenoncourt, E. 2022. “Can You Move to Opportunity? Evidence from the GreatMigration.” American Economic Review, 112(2): 369–408.98Dippel, C. 2022. “Political Parties Do Matter in US Cities... For Their UnfundedPensions.” American Economic Journal: Economic Policy, 14(3): 33–54.Dougal, C., P. Gao, W. J. Mayew, and C. A. Parsons. 2019. “What’s in a(School) Name? Racial Discrimination in Higher Education Bond Markets.” Journalof Financial Economics, 134(3): 570–590.Eldemire-Poindexter, A., K. F. Luchtenberg, and M. Wynter. 2022. “BlackTax: Evidence of Racial Discrimination in Municipal Borrowing Costs.” WorkingPaper, available at SSRN 4064013.Facchini, G., B. G. Knight, and C. Testa. 2020. “The Franchise, Policing,and Race: Evidence from Arrests Data and the Voting Rights Act.” ConditionallyAccepted at the American Economic Journal: Applied Economics.Feder, C., and M. G. Miller. 2020. “Voter Purges After Shelby: Part of SpecialSymposium on Election Science.” American Politics Research, 48(6): 687–692.Ferreira, F., and J. Gyourko. 2009. “Do Political Parties Matter? Evidence fromUS Cities.” The Quarterly Journal of Economics, 124(1): 399–422.Fraga, B. L. 2016. “Candidates or Districts? Reevaluating the Role of Race in VoterTurnout.” American Journal of Political Science, 60(1): 97–122.Fraga, B. L. 2018. The Turnout Gap: Race, Ethnicity, and Political Inequality in aDiversifying America. Cambridge University Press.Francois, P., I. Rainer, and F. Trebbi. 2015. “How is Power Shared in Africa?”Econometrica, 83(2): 465–503.Frendreis, J. P., J. L. Gibson, and L. L. Vertz. 1990. “The Electoral Relevanceof Local Party Organizations.” American Political Science Review, 84(1): 225–235.Gao, P., C. Lee, and D. Murphy. 2019a. “Municipal Borrowing Costs and StatePolicies for Distressed Municipalities.” Journal of Financial Economics, 132(2): 404–426.Gao, P., D. Murphy, and Y. Qi. 2019b. “Political Uncertainty and PublicFinancing Costs: Evidence from US Gubernatorial Elections and Municipal Bond99Markets.” Working Paper, available at SSRN 1992200.Gardner, J., and W. Cohen. 1992. Demographic Characteristics of the Populationof the United States, 1930-1950: County-Level. Inter-university Consortium forPolitical and Social Research.Garrett, D., A. Ordin, J. W. Roberts, and J. C. Suárez Serrato. 2023. “TaxAdvantages and Imperfect Competition in Auctions for Municipal Bonds.” TheReview of Economic Studies, 90(2): 815–851.Geys, B. 2006. “Explaining Voter Turnout: A Review of Aggregate-Level Research.”Electoral Studies, 25(4): 637–663.Giesecke, O., H. Mateen, and M. Jardim Sena. 2023. “Local Government DebtValuation.” Working Paper, available at SSRN 4160225.Gisselquist, R. M. 2014. “Ethnic Divisions and Public Goods Provision, Revisited.”Ethnic and Racial Studies, 37(9): 1605–1627.Goldsmith-Pinkham, P., M. T. Gustafson, R. C. Lewis, and M. Schw-ert. 2023. “Sea-level Rise Exposure and Municipal Bond Yields.” The Review ofFinancial Studies, 36(11): 4588–4635.Gordon, G., and P. Guerron-Quintana. 2024. “On Regional Borrowing, Default,and Migration.” Journal of International Economics, 103916.Griffin, J. D. 2014. “When and Why Minority Legislators Matter.” Annual Reviewof Political Science, 17: 327–336.Griffin, J. D., and B. Newman. 2007. “The Unequal Representation of Latinosand Whites.” The Journal of Politics, 69(4): 1032–1046.Habyarimana, J., M. Humphreys, D. N. Posner, and J. M. Weinstein.2007. “Why Does Ethnic Diversity Undermine Public Goods Provision?” AmericanPolitical Science Review, 101(4): 709–725.Hajnal, Z. L. 2009. America’s Uneven Democracy: Race, Turnout, and Representa-tion in City Politics. Cambridge University Press.100Hajnal, Z., N. Lajevardi, and L. Nielson. 2017. “Voter Identification Laws andthe Suppression of Minority Votes.” The Journal of Politics, 79(2): 363–379.Highton, B. 2017. “Voter Identification Laws and Turnout in the United States.”Annual Review of Political Science, 20: 149–167.Hopkins, D. J. 2009. “The Diversity Discount: When Increasing Ethnic and RacialDiversity Prevents Tax Increases.” The Journal of Politics, 71(1): 160–177.Husted, T. A., and L. W. Kenny. 1997. “The Effect of the Expansion of the VotingFranchise on the Size of Government.” Journal of Political Economy, 105(1): 54–82.ICMA. 2020. International City/County Management Association. https://icma.org/.Ivanov, I. T., and T. Zimmermann. 2024. “The "Privatization" of MunicipalDebt.” Journal of Public Economics, 237: 105156.Janas, P. 2023. “Public Goods Under Financial Distress.” Working Paper.Jenkins, D. 2021. The Bonds of Inequality: Debt and the Making of the AmericanCity. University of Chicago Press.Jones, D. B., W. Troesken, and R. Walsh. 2012. “A Poll Tax by Any OtherName: The Political Economy of Disenfranchisement.” NBER Working Paper No.w18612.Knowles, J., N. Persico, and P. Todd. 2001. “Racial Bias in Motor VehicleSearches: Theory and Evidence.” Journal of Political Economy, 109(1): 203–229.Komisarchik, M., and A. White. 2024. “Throwing Away the Umbrella: MinorityVoting after the Supreme Court’s Shelby Decision.” (forthcoming) Quarterly Journalof Political Science.Leslie, P. H. 1945. “On the Use of Matrices in Certain Population Mathematics.”Biometrika, 33(3): 183–212.Lewis, J. 2005. “The Voting Rights Act-Ensuring Dignity and Democracy.” HumanRights, 32: 2.Longstaff, F. A., S. Mithal, and E. Neis. 2005. “Corporate Yield Spreads:101Default Risk or Liquidity? New Evidence from the Credit Default Swap Market.”The Journal of Finance, 60(5): 2213–2253.Lowande, K., M. Ritchie, and E. Lauterbach. 2019. “Descriptive and Substan-tive Representation in Congress: Evidence from 80,000 Congressional Inquiries.”American Journal of Political Science, 63(3): 644–659.Lublin, D. 2021. The Paradox of Representation. Princeton University Press.Manson, S., J. Schroeder, D. Van Riper, T. Kugler, and S. Ruggles.2022. IPUMS National Historical Geographic Information System: Version 17.0.Minneapolis, MN: IPUMS. http://doi.org/10.18128/D050.V17.0.Marschall, M. J., A. V. Ruhil, and P. R. Shah. 2010. “The New Racial Calculus:Electoral Institutions and Black Representation in Local Legislatures.” AmericanJournal of Political Science, 54(1): 107–124.Mayda, A. M., G. Peri, and W. Steingress. 2022. “The Political Impact ofImmigration: Evidence from the United States.” American Economic Journal:Applied Economics, 14(1): 358–389.Mayda, A. M., M. Z. Senses, and W. Steingress. 2023. “Immigration andProvision of Public Goods: Evidence at the Local Level in the US.” Bank of CanadaWorking Paper.Moody’s. 2022. “U.S. Municipal Bond Defaults and Recoveries, 1970-2020.” Moody’sInvestors Service.MSRB. 2023. Municipal Securities Rulemaking Board. https://www.msrb.org/.NCSL. 2021. “State Legislatures at a Glance.” National Conference of State Legisla-tures.NIBRS. 2024. National Incident-Based Reporting System (NIBRS). https://bjs.ojp.gov/national-incident-based-reporting-system-nibrs.Nickerson, D. W. 2015. “Do Voter Registration Drives Increase Participation? ForWhom and When?” The Journal of Politics, 77(1): 88–101.102NLC. 2023. National League of Cities. https://www.nlc.org/.Pinardon-Touati, N. 2023. “The Crowding Out Effect of Local Government Debt:Micro- and Macro-Estimates.” Working Paper.Poterba, J. M. 1995. “Balanced Budget Rules and Fiscal Policy: Evidence fromthe States.” National Tax Journal, 48(3): 329–336.Raze, K. 2022. “Voting Rights and the Resilience of Black Turnout.” EconomicInquiry, 60(3): 1127–1141.Ren, P., and W. Zhao. 2023. “Taking Sides: Political Alignment and MunicipalBond Yield.” Working Paper, available at SSRN 3949992.Ricca, F., and F. Trebbi. 2022. “Minority Underrepresentation in US Cities.”NBER Working Paper No. w29738.Robson, A. J., and L. Samuelson. 2011. “The Evolutionary Foundations ofPreferences.” Handbook of Social Economics, 1: 221–310.Robson, A. J., B. Szentes, and E. Iantchev. 2012. “The Evolutionary Basisof Time Preference: Intergenerational Transfers and Sex.” American EconomicJournal: Microeconomics, 4(4): 172–201.Ruggles, S., M. A. Nelson, M. Sobek, C. A. Fitch, R. Goeken, J. D. Hacker,E. Roberts, and J. R. Warren. 2023. IPUMS Ancestry Full Count Data:Version 4.0. Minneapolis, MN: IPUMS. .Rugh, J. S., and J. Trounstine. 2011. “The Provision of Local Public Goodsin Diverse Communities: Analyzing Municipal Bond Elections.” The Journal ofPolitics, 73(4): 1038–1050.Sass, T. R., and B. J. Pittman. 2000. “The Changing Impact of Electoral Structureon Black Representation in the South, 1970–1996.” Public Choice, 104(3): 369–388.Schuit, S., and J. C. Rogowski. 2017. “Race, Representation, and the VotingRights Act.” American Journal of Political Science, 61(3): 513–526.Schwert, M. 2017. “Municipal Bond Liquidity and Default Risk.” The Journal of103Finance, 72(4): 1683–1722.SDC. 2021. Refinitiv SDC Platinum Global Public Finance. Subscription based.Shah, P. 2014. “It Takes a Black Candidate: A Supply-Side Theory of MinorityRepresentation.” Political Research Quarterly, 67(2): 266–279.Shah, P. R., M. J. Marschall, and A. V. Ruhil. 2013. “Are we There Yet? TheVoting Rights Act and Black Representation on City Councils, 1981–2006.” TheJournal of Politics, 75(4): 993–1008.Shelby County v. Holder. 2013. 570 U.S. 529.Smull, E., E. Kodra, A. Stern, A. Teras, M. Bonanno, and M. Doyle. 2023.“Climate, Race, and the Cost of Capital in the Municipal Bond Market.” Plos One,18(8): e0288979.Trebbi, F., P. Aghion, and A. Alesina. 2008. “Electoral Rules and MinorityRepresentation in US Cities.” The Quarterly Journal of Economics, 123(1): 325–357.Trounstine, J. 2010. “Representation and Accountability in Cities.” Annual Reviewof Political Science, 13: 407–423.Trounstine, J. 2016. “Segregation and Inequality in Public Goods.” AmericanJournal of Political Science, 60(3): 709–725.Trounstine, J. 2020. “Local Political Economy: The State of the Field: Past, Present,and Future.” Journal of Political Institutions and Political Economy, 1(3): 319–340.Trounstine, J., and M. E. Valdini. 2008. “The Context Matters: The Effectsof Single-Member Versus At-Large Districts on City Council Diversity.” AmericanJournal of Political Science, 52(3): 554–569.U.S. Census of Governments. 2022. US Census Bureau.Wallace, S. J. 2014. “Representing Latinos: Examining Descriptive and SubstantiveRepresentation in Congress.” Political Research Quarterly, 67(4): 917–929.Warshaw, C. 2019. “Local Elections and Representation in the United States.”Annual Review of Political Science, 22: 461–479.104Washington, E. 2006. “How Black Candidates Affect Voter Turnout.” The QuarterlyJournal of Economics, 121(3): 973–998.Washington, E. 2012. “Do Majority-Black Districts Limit Blacks’ Representation?The Case of the 1990 Redistricting.” The Journal of Law and Economics, 55(2): 251–274.White, A. R., N. L. Nathan, and J. K. Faller. 2015. “What Do I Need to Vote?Bureaucratic Discretion and Discrimination by Local Election Officials.” AmericanPolitical Science Review, 109(1): 129–142.Yi, H. L. 2021. “Financing Public Goods.” Working Paper, available at SSRN3907391.Zillow. 2023. Zillow Home Value Index (ZHVI). Zillow.105Appendix AAppendix to Chapter 2A.1 Effective Tax RateI follow Schwert (2017) and Garrett et al. (2023) to compute state-specific and time-varying tax advantages and the corresponding tax-adjusted yields. State taxes arealways deductible from federal taxes. Deductibility of federal taxes from state taxesvaries by state. Let Tf and Ts be the amount of tax paid on an income I, and τf andτs be the federal and state income tax rates. In states without deduction of federaltaxes:Tf = τf (I − Ts)= τf (I − τsI)= τf (1− τs)I,(A.1)while simply Ts = τsI. It follows that the effective tax rate is:τ = τf (1− τs) + τs. (A.2)In states with deduction of federal taxes from state taxes:Ts = τs [I − τf (I − Ts)]= τs(1− τf )I + τsτfTs= τs(1− τf )1− τsτf I.(A.3)106Using Equation A.3:Tf = τf [I − Ts]= τf[I − τs(1− τf )1− τsτf I]= τf (1− τs)1− τsτf I,(A.4)and the effective tax rate is:τ = τf (1− τs) + τs(1− τf )1− τfτs . (A.5)107A.2 Tables and FiguresFigure A.1: Yield Curves by Maturity0200400600800Tax-adjusted Yield (bp)2004 2007 2010 2013 2016 2019Year of Issue1y Bond0200400600800Tax-adjusted Yield (bp)2004 2007 2010 2013 2016 2019Year of Issue3y Bond0200400600800Tax-adjusted Yield (bp)2004 2007 2010 2013 2016 2019Year of Issue5y Bond0200400600800Tax-adjusted Yield (bp)2004 2007 2010 2013 2016 2019Year of Issue7y Bond0200400600800Tax-adjusted Yield (bp)2004 2007 2010 2013 2016 2019Year of Issue10y Bond0200400600800Tax-adjusted Yield (bp)2004 2007 2010 2013 2016 2019Year of Issue15y BondYield-to-maturity (at issuance) curves estimated on the bond level data used in the chapter, covering the universe of municipal bondsissued by cities between 2004 and 2019. Local means regressions by maturity of the bonds.108Figure A.2: Spread by Maturity050100150200250300350Spread (bp)2004 2007 2010 2013 2016 2019Year of Issue1y Bond050100150200250300350Spread (bp)2004 2007 2010 2013 2016 2019Year of Issue3y Bond050100150200250300350Spread (bp)2004 2007 2010 2013 2016 2019Year of Issue5y Bond050100150200250300350Spread (bp)2004 2007 2010 2013 2016 2019Year of Issue7y Bond050100150200250300350Spread (bp)2004 2007 2010 2013 2016 2019Year of Issue10y Bond050100150200250300350Spread (bp)2004 2007 2010 2013 2016 2019Year of Issue15y BondSpread (yield-spread in Equation 2.4, measured in basis points (0.01%)) curves estimated on the bond level data used in the chapter,covering the universe of municipal bonds issued by cities between 2004 and 2019. Local means regressions by maturity of the bonds.109Table A.1: Bonds Summary StatisticsMean Std. Dev. Min. Max. Obs.Yields and SpreadYield to Maturity (bp) 261.747 109.452 15.000 613.000 256,185Price at Issue ($ per $100 of face value) 104.935 6.729 95.427 141.763 256,185Tax-adjusted Yield (bp) 429.526 175.594 26.000 978.700 256,185Yield-spread (bp) 172.871 86.054 9.700 519.300 256,185Bond AmountsPar Amount ($1mm) 1.139 3.306 0.003 284.003 256,185Per capita Par Amount ($ per capita) 24.392 46.018 0.003 4,494.763 256,185Maturity (years) 8.495 5.143 1.000 20.000 256,185Issue AmountsIssue Amount ($1mm) 17.178 52.989 0.047 1,325.495 19,932Per capita Issue Amount ($ per capita) 351.987 447.237 0.438 9,406.164 19,932Outstanding Debt at Issue ($1mm) 754.803 6,322.408 0.005 127,659.766 19,932FeaturesCallable (indic.) 0.428 0.495 0.000 1.000 256,185GO (indic.) 0.722 0.448 0.000 1.000 256,185Competitive (indic.) 0.493 0.500 0.000 1.000 256,185Rated (indic.) 0.856 0.351 0.000 1.000 256,185Credit Enhanced (indic.) 0.291 0.454 0.000 1.000 256,185New Money (indic.) 0.585 0.493 0.000 1.000 256,185Bank Qualified (indic.) 0.466 0.499 0.000 1.000 256,185Sinking Fund (indic.) 0.355 0.479 0.000 1.000 256,185Values deflated to 2010 dollars. The construction of yields and spreads is discussed in Section 2.3. Callable bondscan be repaid by the issuer before maturity, usually after a minimum period. GO bonds are discussed in Section 2.2,as well as the competitive or negotiated issuance processes. Credit enhanced bonds contain additional insuranceclauses for the timely payments of interests and principal. New Money bonds are used to raise new capital, asopposed to refinance existing debt at better conditions. When bonds are Bank Qualified, banks may deduct 80% ofthe cost of purchasing and holding a bond, under the condition that the issuer intends to sell no more than $10million in bonds annually. Issuers can create Sinking Funds into which they make periodic deposits to ensure theirability to service the debt.110Table A.2: Means Balance(1) (2) (3)Non-Issuers Issuers Diff.Demographic CompositionMinority (frac.) 0.258 0.242 -0.017∗∗∗(0.004)Black (frac.) 0.102 0.080 -0.022∗∗∗(0.003)Latino (frac.) 0.105 0.107 0.002(0.003)SociodemographicsPopulation 15672.248 52041.447 36369.200∗∗∗(5216.402)Median Household Income 50451.160 56328.447 5877.287∗∗∗(476.997)Over 65 (frac.) 0.156 0.140 -0.016∗∗∗(0.001)Unemployment (rate) 0.077 0.070 -0.007∗∗∗(0.001)College Educ. (frac.) 0.232 0.268 0.036∗∗∗(0.002)Graduate Educ. (frac.) 0.091 0.108 0.017∗∗∗(0.002)Pop. Growth (10y) 0.131 0.186 0.054∗∗∗(0.009)AccountsRevenues (per capita) 1569.188 1966.897 397.708∗∗∗(27.727)Expenditures (per capita) 1531.692 1956.071 424.378∗∗∗(28.021)Outstanding Debt (per capita) 1137.312 2033.746 896.434∗∗∗(34.726)Obs. 21,929 21,929 21,929Municipalities 6,032 6,032 6,032Cluster level Municipality Municipality MunicipalityStandard errors clustered at the municipality level in parentheses. ∗ Significant at 10%, ∗∗significant at 5%, ∗∗∗ significant at 1%. Values deflated to 2010 dollars. Simple means balancetest between municipalities issuing and not issuing bonds during my sample period 2004-2019.Having removed outliers, the universe of potential issuers is 6,106 municipalities. Almost60% of those, or 3,514 are issuers in the data used throughout the paper. This is a resultof an extensive data cleaning, including probabilistic name matching (95% match-rate) andtrimming of outliers. This is therefore a lower-bound number of issuers, with some not beingincluded most likely due to imperfections of the raw data.111Figure A.3: Ratings0510152025PercentNot rated A+ or below AA- AA AA+ AAARatingShare of bonds in the sample assigned to each rating category group.112Table A.3: Blacks, Latinos, and Yield-Spread(1) (2) (3) (4) (5) (6)Spread (bp) Spread (bp) Spread (bp) Spread (bp) Spread (bp) Spread (bp)Black (sd) 4.555∗∗∗ 1.370∗∗ 1.528∗∗∗ 2.717∗∗∗ 1.166∗∗ 1.009∗(0.564) (0.600) (0.533) (0.527) (0.582) (0.572)Latino (sd) 3.994∗∗∗ 1.243∗ 1.542∗∗∗ 1.995∗∗∗ 2.190∗∗∗ 1.555∗∗∗(0.623) (0.687) (0.598) (0.489) (0.672) (0.584)Other Minorities (sd) 1.292∗∗ 0.149 0.633 0.549 0.616 1.369∗∗∗(0.609) (0.615) (0.587) (0.530) (0.576) (0.510)Census Controls - X - - X XAccounts Controls - - X - X XFinancial Controls - - - X - XMean Dep. Var. 172.871 172.871 172.871 172.871 172.871 172.871SD Dep. Var. 86.054 86.054 86.054 86.054 86.054 86.054Obs. 256,185 256,185 256,185 256,185 256,185 256,185Municipalities 3,514 3,514 3,514 3,514 3,514 3,514Reporting only the coefficients on Census controls. Standard errors clustered at the municipality level in parentheses. ∗ Significantat 10%, ∗∗ significant at 5%, ∗∗∗ significant at 1%. OLS estimates. The observations are at the bond level. Spread (yield-spread inEquation 2.4) is measured in basis points (0.01%). The Black and Latino fractions are shares of non-White voting age population,and are standardized to mean 0 and standard deviation 1. Other minorities are the residual non-White, non-Black and non-Latinominorities. All columns include state, year×maturity, and rating fixed effects. All columns include census and accounts controls.The census controls are city-level and include: (log) population, (log) median household income, share of over-65 population,shares of college and graduate educated, unemployment rate, population growth rate over 10 years. The accounts controls arecity-level and include: (log) revenues, (log) expenditures, (log) outstanding debt at the time of issuance, (log) mid-tier housevalues, mid-tier house values growth rate over 1 year. All columns include state, year×maturity, and rating fixed effects. Thefinancial controls are bond level and include: (log) amount of the bond, (log) amount of the issue, and indicators for callable, GO,competitively issued, new money, credit enhanced, bank qualified, and sink fund bonds.113Table A.4: Property Crime Robustness(1) (2) (3) (4) (5) (6)Spread (bp) Spread (bp) Spread (bp) Spread (bp) Spread (bp) Spread (bp)Minority (sd) 2.844∗∗ 2.837∗∗(1.126) (1.134)Black (sd) 1.405 1.505 4.685 3.943(1.059) (1.089) (6.366) (6.101)Latino (sd) 2.378∗∗ 2.187∗∗ 3.837 3.197(0.934) (0.957) (3.083) (3.022)Crime control - X - X - XMethod OLS OLS OLS OLS 2SLS 2SLSF-stat 5.082 / 12.909 5.139 / 13.591Obs. 82,072 82,072 82,072 82,072 82,072 82,072Cluster Level Municipality Municipality Municipality Municipality County CountyClusters 1,161 1,161 1,161 1,161 606 606Clustered standard errors in parentheses. ∗ Significant at 10%, ∗∗ significant at 5%, ∗∗∗ significant at 1%. Data at the bond level.The dependent variable is Spread (yield-spread in Equation 2.4), measured in basis points (0.01%). The Minority, Black, andLatino fractions are share of non-White voting age population, and it are standardized to mean 0 and standard deviation 1. Allcolumns include census and accounts controls. The census controls are city-level and include: (log) population, (log) medianhousehold income, share of over-65 population, shares of college and graduate educated, unemployment rate, population growthrate over 10 years. The accounts controls are city-level and include: (log) revenues, (log) expenditures, (log) outstanding debtat the time of issuance, (log) mid-tier house values, mid-tier house values growth rate over 1 year. All columns include state,year×maturity, and rating fixed effects. The crime control is property crimes per 1000 people. Crime data from NIBRS (2024).The shift share instruments are constructed according to Equation 2.6 and standardized to mean 0 and standard deviation 1.Shifts are defined in Section 2.5. The 2SLS specifications are recentered adding the sum of shares∑j sjc/pc as control (Borusyakand Hull, 2023).114Table A.5: Pension Liabilities Robustness(1) (2) (3) (4) (5) (6)Spread (bp) Spread (bp) Spread (bp) Spread (bp) Spread (bp) Spread (bp)Minority (sd) 5.049∗∗∗ 5.043∗∗∗(1.457) (1.452)Black (sd) 2.239∗ 2.240∗ 15.785 15.594(1.221) (1.221) (18.498) (18.440)Latino (sd) 4.496∗∗∗ 4.490∗∗∗ 11.085 11.019(1.283) (1.282) (7.575) (7.539)Pensions control - X - X - XMethod OLS OLS OLS OLS 2SLS 2SLSF-stat 4.939 / 4.956 5.026 / 5.037Obs. 67,031 67,031 67,031 67,031 67,031 67,031Cluster Level Municipality Municipality Municipality Municipality County CountyClusters 862 862 862 862 416 416Clustered standard errors in parentheses. ∗ Significant at 10%, ∗∗ significant at 5%, ∗∗∗ significant at 1%. Data at the bond level.The dependent variable is Spread (yield-spread in Equation 2.4), measured in basis points (0.01%). The Minority, Black, andLatino fractions are share of non-White voting age population, and it are standardized to mean 0 and standard deviation 1. Allcolumns include census and accounts controls. The census controls are city-level and include: (log) population, (log) medianhousehold income, share of over-65 population, shares of college and graduate educated, unemployment rate, population growthrate over 10 years. The accounts controls are city-level and include: (log) revenues, (log) expenditures, (log) outstanding debtat the time of issuance, (log) mid-tier house values, mid-tier house values growth rate over 1 year. All columns include state,year×maturity, and rating fixed effects. The pension control is total benefits/total gains and contributions. Pensions data fromASPP (2024). The shift share instruments are constructed according to Equation 2.6 and standardized to mean 0 and standarddeviation 1. Shifts are defined in Section 2.5. The 2SLS specifications are recentered adding the sum of shares∑j sjc/pc as control(Borusyak and Hull, 2023).115Table A.6: Alternative Mechanisms Robustness(1) (2) (3)Spread (bp) Spread (bp) Spread (bp)Minority (sd) 13.549∗∗∗ 2.511∗∗∗ 2.842∗∗(4.209) (0.727) (1.320)Council Minority (sd) -0.024(0.838)Municipality FE X - -State × Year FE - X -Mean Dep. Var. 172.871 172.870 169.846SD Dep. Var. 86.054 86.053 83.396Obs. 256,185 256,183 102,699Municipalities 3,514 3,514 2,006Standard errors clustered at the municipality level in parentheses. ∗ Significantat 10%, ∗∗ significant at 5%, ∗∗∗ significant at 1%. OLS estimates. The observa-tions are at the bond level. Spread (yield-spread in Equation 2.4) is measuredin basis points (0.01%). The Minority fraction is the share of non-White vot-ing age population, and it is standardized to mean 0 and standard deviation 1.All columns include state, year×maturity, and rating fixed effects. All columnsinclude census and accounts controls. The census controls are city-level andinclude: (log) population, (log) median household income, share of over-65 popu-lation, shares of college and graduate educated, unemployment rate, populationgrowth rate over 10 years. The accounts controls are city-level and include: (log)revenues, (log) expenditures, (log) outstanding debt at the time of issuance, (log)mid-tier house values, mid-tier house values growth rate over 1 year. All columnsinclude year×maturity and rating fixed effects. Column (3) includes state fixedeffects. Council minority is measured as the share of council members belongingto a non-White Minority, and standardized to mean 0 and standard deviation 1.116Table A.7: Black and Latino Shares, Alternative Mechanisms Robustness(1) (2) (3) (4) (5)Spread (bp) Spread (bp) Spread (bp) Spread (bp) Spread (bp)Black (sd) 6.214 1.473∗∗ 10.335∗∗ 1.050 27.797∗∗(4.568) (0.572) (4.153) (1.157) (12.367)Latino (sd) 17.482∗∗∗ 1.848∗∗∗ 5.205∗∗ 1.956 6.474(4.873) (0.659) (2.418) (1.427) (7.230)Municipality FE X - - - -State × Year FE - X X - -Council shares - - - X XMethod OLS OLS 2SLS OLS 2SLSF-stat 20.644 / 42.039 15.828 / 33.333Obs. 256,185 256,183 256,183 102,699 102,699Cluster Level Municipality Municipality County Municipality CountyClusters 3,514 3,514 1,324 2,006 908Clustered standard errors in parentheses. ∗ Significant at 10%, ∗∗ significant at 5%, ∗∗∗ significant at 1%. The observa-tions are at the bond level. Spread (yield-spread in Equation 2.4) is measured in basis points (0.01%). The Black andLatino fractions are shares of non-White voting age population, and are standardized to mean 0 and standard deviation1. All columns include census and accounts controls. The census controls are city-level and include: (log) population,(log) median household income, share of over-65 population, shares of college and graduate educated, unemploymentrate, population growth rate over 10 years. The accounts controls are city-level and include: (log) revenues, (log) expen-ditures, (log) outstanding debt at the time of issuance, (log) mid-tier house values, mid-tier house values growth rate over1 year. All columns include year×maturity and rating fixed effects. Columns (4) and (5) include state fixed effects. Theshift share instruments are constructed according to Equation 2.6 and standardized to mean 0 and standard deviation1. Shifts are defined in Section 2.5. Columns (3) and (5) are recentered adding the sum of shares∑j sjc/pc as control(Borusyak and Hull, 2023). The reported F-stats are for the Black and Latino IVs first-stages respectively. Council sharesare measured as the shares of Black and Latino council members, and standardized to mean 0 and standard deviation 1.117Figure A.4: Rating Probabilities (OLS)-.04-.020.02.04Minority (sd) coeff. on Rating Group (indic.)Not Rated A+ or below AA- AA AA+ AAARating GroupsC.I. 95%, standard errors clustered at the municipality level. Linear Probability Model estimates, plotting the coefficients of Minoritypopulation shares. The observations are at the bond level. The dependent variables are indicators for each bond being assigned a specificrating category. The Minority fraction is the share of non-White voting age population, standardized to mean 0 and standard deviation1. The census controls are city-level and include: (log) population, (log) median household income, share of over-65 population, sharesof college and graduate educated, unemployment rate, population growth rate over 10 years. The accounts controls are city-level andinclude: (log) revenues, (log) expenditures, (log) outstanding debt at the time of issuance, (log) mid-tier house values, mid-tier housevalues growth rate over 1 year. Including state and year fixed effects.118Figure A.5: Rating Probabilities (OLS), Black, Latino, and Other Shares-.04-.020.02.04Minorities (sd) coeff. on Rating Group (indic.)Not rated A+ or below AA- AA AA+ AAARating GroupsBlack Latino Other minoritiesC.I. 95%, standard errors clustered at the municipality level. Linear Probability Model estimates, plotting the coefficients of Black andLatino population shares. The observations are at the bond level. The dependent variables are indicators for each bond being assigned aspecific rating category. The Black and Latino fractions are shares of non-White voting age population, and are standardized to mean 0and standard deviation 1. Other minorities are the residual non-White, non-Black and non-Latino minorities. The census controls arecity-level and include: (log) population, (log) median household income, share of over-65 population, shares of college and graduateeducated, unemployment rate, population growth rate over 10 years. The accounts controls are city-level and include: (log) revenues,(log) expenditures, (log) outstanding debt at the time of issuance, (log) mid-tier house values, mid-tier house values growth rate over 1year. Including state and year fixed effects.119Table A.8: Issuance(1) (2) (3) (4) (5) (6)Issued Issued # of Issues # of Issues (log) Amount (log) AmountMinority (sd) -0.016∗∗∗ -0.015 -0.082∗∗∗ -0.180∗∗ -0.005 0.063(0.005) (0.029) (0.020) (0.082) (0.021) (0.111)State FE X - X - X -Municipality FE - X - X - XYear FE X X X X X XObs. 18,881 18,881 18,881 18,881 6,123 6,123Municipalities 5,663 5,663 5,663 5,663 2,191 2,191Cluster level Municipality Municipality Municipality Municipality Municipality MunicipalityClustered standard errors in parentheses. ∗ Significant at 10%, ∗∗ significant at 5%, ∗∗∗ significant at 1%. The observations areat the municipality level collapsed to CoG-year level (2002, 2007, 2012, 2017). It includes municipalities that never issue bondsthroughout my sample period. The dependent variable in columns (1) and (2) is an indicator for whether a municipality issued anybonds in the corresponding CoG cycle (e.g. between 2002 and 2007). The average probability of issuing is 38%. The dependentvariable in columns (3) and (4) is the number of issues in the corresponding CoG cycle. The average is 1 issue per cycle. Thedependent variable in columns (5) and (6) is the log of the amount issued in the corresponding CoG cycle. The Minority fraction isthe share of non-White voting age population, and it is standardized to mean 0 and standard deviation 1. The controls are: (log)population, (log) median household income, share of over-65 population, shares of college and graduate educated, unemploymentrate, (log) city revenues, (log) city expenditures, (log) mid-tier house values.120Appendix BAppendix to Chapter 3B.1 Measures of underrepresentationThis Appendix aims at clarifying what measure of underrepresentation one shoulduse in the analysis, and some of the properties of this measure. In Section 3.3 of thechapter it is stated that, for a given municipality m at time t, underrepresentation isdefined as the absolute difference:umt = pmt − cmtwhere p is the Minority share of the voting age population and c is the Minority shareof the council members. A higher value of underrepresentation u corresponds to alarger disproportionality between population size and council representation. Moreprecisely, in the case u is positive, the measure u indicates the percentage points ofMinority population that are left without political representation.Stated plainly, for the calculus of political power on the part of a White population,"units of u" measure what matters. They measure votes necessary for victory. Eachpercentage point of the Minority population that is not represented means more votesfor the Majority relative to the Minority. To be more specific, for given p in a city,the optimum, u∗ = p− c(p) measures the Minority disenfranchisement necessary toreach/maintain control of the council by the White group, calculated as the u wherethe marginal benefit of reducing the representation of a Minority voter and increasingthe probability of electoral success for the White group is equalized to the marginalcost of disenfranchising that additional Minority voter.121We notice here that alternatives to u are possible, but are not as informative. Forexample, one could focus on the relative difference:vmt = 1− cmt/pmtthat is on the percent of the Minority population that is left without representation.This measure is obviously not what a White majority would target and it is not ameasure useful for our empirical analysis. To see this clearly, consider a city withonly Black and White citizens and zero Black council members elected. Accordingto the definition of v, this city would present the same level of underrepresentation,v = 1, for the case where the Black Minority corresponds to 2 percent of the votingage population and for the case where the Black Minority corresponds to 48 percent ofthe voting age population. In the first case 2 percent of the voters see no descriptiverepresentation, in the second case it is 48 percent. These are obviously two dramaticallydifferent electoral scenarios, massively confounding the informativeness of v, and wherethe amounts of disenfranchisement and electoral distortions are very different.B.1.1 Some properties of uIt is worthwhile focusing on some immediate properties of u. First of all, properties of uare essentially properties of the relationship between c and p, and, as both c ∈ [0, 1] andp ∈ [0, 1], it follows that u ∈ [−1, 1]. This implies that u is not necessarily only definedover positive values and Minority overrepresentation (u < 0) cannot be excluded apriori. In fact, overrepresentation is precisely what we observe systematically for othergroups (Whites) or for the case of American Liberians in pre-1980 Liberia (Francoiset al., 2015).A second important property of u is its possibility of being convex, concave, or neither.That is, it is neither given nor a mechanical result that c may be a convex functionof p. Simply because p is close to 0 does not mean that c must be small as well,and simply because p is close to 1 does not mean that c must be equal to 1 as well(bar integer constraints of no interest - as in the case that there are no five/six adultcitizens that are Minority or White living in the municipality). Valid examples ofc(p) include the case of constant underrepresentation c = {p− θ if p > θ; 0 if p ≤ θ} ;the case of pure majority control c = {1 if p ≥ .5; 0 if p < .5} ; the case of nonconvexcontrol c ={p if p > θ; p− θ if θ ≥ p > θ; 0 if p ≤ θ}, etc.122Finally, in our analysis we estimate a specific relation c = αp+ βp2. For this specificinstance, we note that it must be v = 1 − α − βp, which implies in turn that thedisappearance of any convexity from comparing measure u to measure v and a linearrelationship between v and p is a mathematical restatement of the same finding.123B.2 Tables and FiguresTable B.1: Variables DefinitionsVariable Definition SourceCouncil Composition:Latino Share of Latino council members ICMAAsian Share of Asian council members ICMAAfrican American Share of African American council members ICMAMinority Share of Minority (non-White) council members ICMAWhite Share of White council members ICMAVoting Age Population:Latino Share of Latino voting age population CensusAsian Share of Asian voting age population CensusAfrican American Share of African American voting age population CensusMinority Share of Minority (non-White) voting age population CensusWhite Share of White voting age population CensusUnderrepresentation:Latino Difference between Latino voting age population share ICMA and Censusand Latino council shareAsian Difference between Asian voting age population share ICMA and Censusand Asian council shareAfrican American Difference between African American voting age population share ICMA and Censusand African American council shareMinority Difference between Minority (non-White) voting age population share ICMA and Censusand Minority (non-White) council shareMain Institutional Features:Mayor-Council Indicator for Mayor-Council municipalities with a directly ICMAelected mayor, as opposed to Council-Manager municipalitieswith only an elected council and a manager nominatedby the councilCouncil Size Number of seats in the city council ICMAAt-Large (frac.) Share of council seats elected At-Large (city-wide multi-member elections) ICMAPaid Council Members Indicator for municipalities with paid council members ICMAVRA covered Indicator for municipalities covered by the VRA Dept. of JusticeAdditional Institutional Features:Partisanship Indicator for municipalities with partisan affiliation indicated ICMAon the ballotRunning Fees Indicator for municipalities with running fee for candidates ICMAVoters per Councilor (thousands) Number of voters per councilor (defined as voting age population ICMAover council size)Staggered Terms Indicator for municipalities with staggered terms (not all ICMAcouncil elected at once)Term Length Length of term for council members ICMATerm Limits Indicator for municipalities with term limits for council members ICMASociodemographics:Population Total population CensusVoting Age Population Population over 18 years of age CensusOver 65 (frac.) Share of population over 65 years of age CensusRural (frac.) Share of rural population CensusForeign (frac.) Share of foreign born population CensusMedian Household Income Median household income (deflated to $ in year 2000) CensusCensus race/ethnicity measures (excluding Latinos) are based on people not of Hispanic or latino origin.124Figure B.1: Underrepresentation by Race/EthnicitySolid lines represent LOWESS smoothing of the underlying municipality-level data. Markers represent population bins averages.125Table B.2: Latino Underrepresentation(1) (2) (3) (4) (5)LatinoUnderrep.LatinoUnderrep.LatinoUnderrep.LatinoUnderrep.LatinoUnderrep.Regression estimates:Latino 1.001∗∗∗ 0.997∗∗∗ 1.127∗∗∗ 1.236∗∗∗ 1.262∗∗∗(0.020) (0.020) (0.035) (0.047) (0.056)Latino2 -1.057∗∗∗ -1.053∗∗∗ -1.049∗∗∗ -1.137∗∗∗ -1.141∗∗∗(0.036) (0.036) (0.085) (0.093) (0.093)Max/Argmax estimates:max 0.235∗∗∗ 0.234∗∗∗ 0.291∗∗∗ 0.319∗∗∗ 0.330∗∗∗(0.006) (0.006) (0.011) (0.011) (0.017)argmax 0.473∗∗∗ 0.474∗∗∗ 0.537∗∗∗ 0.544∗∗∗ 0.553∗∗∗(0.011) (0.011) (0.030) (0.028) (0.029)Population VAP VAP VAP VAP VAPYear FE - X - X XMunicipality FE - - X X XControls - - - - XObs. 27,656 27,656 26,282 26,282 26,142Municipalities 7,479 7,479 6,105 6,105 6,088Cluster level Municipality Municipality Municipality Municipality MunicipalityAdjusted R2 0.476 0.477 0.639 0.641 0.641The top panel reports regression estimates. Cluster robust standard errors in parentheses. Significance levels: *** p <0.01, ** p < 0.05, * p < 0.1. The dependent variable in all specifications is Latino underrepresentation, computed as thedifference between the fraction of Latino population and the fraction of Latino council members. The main independentvariables are the fraction of Latino population and its squared value. Population measures based on voting age population.The controls include the log-population, the log-median household income, and the fractions of rural, foreign, and over65 population. The second panel reports Max/Argmax estimates based on the regression estimates. Standard errors inparentheses are asymptotic. Given a quadratic formula y = ax2 + bx+ c, the max is computed as c− b24a , while the argmaxis computed as − b2a .126Table B.3: African American Underrepresentation(1) (2) (3) (4) (5)African AmericanUnderrep.African AmericanUnderrep.African AmericanUnderrep.African AmericanUnderrep.African AmericanUnderrep.Regression estimates:African American 0.395∗∗∗ 0.397∗∗∗ 1.071∗∗∗ 1.239∗∗∗ 1.283∗∗∗(0.021) (0.021) (0.052) (0.056) (0.057)African American2 -0.393∗∗∗ -0.393∗∗∗ -1.407∗∗∗ -1.535∗∗∗ -1.567∗∗∗(0.041) (0.041) (0.095) (0.094) (0.095)Max/Argmax estimates:max 0.100∗∗∗ 0.100∗∗∗ 0.178∗∗∗ 0.214∗∗∗ 0.225∗∗∗(0.005) (0.005) (0.010) (0.011) (0.012)argmax 0.503∗∗∗ 0.505∗∗∗ 0.381∗∗∗ 0.404∗∗∗ 0.409∗∗∗(0.031) (0.031) (0.015) (0.015) (0.015)Population VAP VAP VAP VAP VAPYear FE - X - X XMunicipality FE - - X X XControls - - - - XObs. 27,569 27,569 26,182 26,182 26,042Municipalities 7,479 7,479 6,092 6,092 6,075Cluster level Municipality Municipality Municipality Municipality MunicipalityAdjusted R2 0.120 0.124 0.418 0.429 0.432The top panel reports regression estimates. Cluster robust standard errors in parentheses. Significance levels: *** p < 0.01, ** p < 0.05, * p < 0.1. Thedependent variable in all specifications is African American underrepresentation, computed as the difference between the fraction of African Americanpopulation and the fraction of African American council members. The main independent variables are the fraction of African American population andits squared value. Population measures based on voting age population. The controls include the log-population, the log-median household income, andthe fractions of rural, foreign, and over 65 population. The second panel reports Max/Argmax estimates based on the regression estimates. Standarderrors in parentheses are asymptotic. Given a quadratic formula y = ax2 + bx+ c, the max is computed as c− b24a , while the argmax is computed as − b2a .127Table B.4: Asian Underrepresentation(1) (2) (3) (4) (5)AsianUnderrep.AsianUnderrep.AsianUnderrep.AsianUnderrep.AsianUnderrep.Regression estimates:Asian 1.014∗∗∗ 1.010∗∗∗ 1.079∗∗∗ 1.108∗∗∗ 1.096∗∗∗(0.028) (0.030) (0.033) (0.041) (0.045)Asian2 -1.140∗∗∗ -1.133∗∗∗ -1.205∗∗∗ -1.249∗∗∗ -1.243∗∗∗(0.092) (0.094) (0.099) (0.106) (0.110)Max/Argmax estimates:max 0.224∗∗∗ 0.224∗∗∗ 0.239∗∗∗ 0.243∗∗∗ 0.239∗∗∗(0.013) (0.013) (0.014) (0.014) (0.014)argmax 0.445∗∗∗ 0.446∗∗∗ 0.448∗∗∗ 0.443∗∗∗ 0.441∗∗∗(0.029) (0.029) (0.029) (0.028) (0.028)Population VAP VAP VAP VAP VAPYear FE - X - X XMunicipality FE - - X X XControls - - - - XObs. 25,688 25,688 24,160 24,160 24,013Municipalities 7,396 7,396 5,868 5,868 5,843Cluster level Municipality Municipality Municipality Municipality MunicipalityAdjusted R2 0.540 0.540 0.620 0.620 0.619The top panel reports regression estimates. Cluster robust standard errors in parentheses. Significance levels: *** p < 0.01,** p < 0.05, * p < 0.1. The dependent variable in all specifications is Asian underrepresentation, computed as the differencebetween the fraction of Asian population and the fraction of Asian council members. The main independent variables arethe fraction of Asian population and its squared value. Population measures based on voting age population. The controlsinclude the log-population, the log-median household income, and the fractions of rural, foreign, and over 65 population.The second panel reports Max/Argmax estimates based on the regression estimates. Standard errors in parentheses areasymptotic. Given a quadratic formula y = ax2 + bx+ c, the max is computed as c− b24a , while the argmax is computed as− b2a .128We report results splitting observation in Republican and Democrat leaning. Namely,for each municipality we look at the corresponding county-level vote shares in themost recent previous Presidential election1, and tag municipalities as Republican orDemocrat when the vote share of the Republican candidate was higher or lower thanthe Democrat ones respectively.Figure B.2: Minority Underrepresentation by Rep/Dem LeaningSolid line represents a LOWESS smoothing of the underlying municipality-level data. Markers represent population bins averages.1Data based on Dave Leip’s Atlas of U.S. Presidential Elections. http://uselectionatlas.org (2020).129Table B.5: Minority Underrepresentation by Rep/Dem Leaning(1) (2) (3) (4) (5)MinorityUnderrep.MinorityUnderrep.MinorityUnderrep.MinorityUnderrep.MinorityUnderrep.Regression estimates:Minority 0.913∗∗∗ 0.885∗∗∗ 1.296∗∗∗ 1.492∗∗∗ 1.376∗∗∗(0.026) (0.026) (0.036) (0.044) (0.049)Minority2 -0.873∗∗∗ -0.848∗∗∗ -1.147∗∗∗ -1.244∗∗∗ -1.213∗∗∗(0.038) (0.037) (0.058) (0.061) (0.061)Rep. advantage 0.006∗∗∗ 0.007∗∗∗ 0.006∗∗ -0.006∗∗ -0.006∗∗(0.002) (0.002) (0.003) (0.003) (0.003)Rep. advantage × Minority -0.202∗∗∗ -0.197∗∗∗ -0.157∗∗∗ -0.063∗ -0.056(0.034) (0.033) (0.036) (0.036) (0.036)Rep. advantage × Minority2 0.281∗∗∗ 0.280∗∗∗ 0.230∗∗∗ 0.116∗ 0.110∗(0.056) (0.056) (0.060) (0.060) (0.060)Max/Argmax estimates:max (Dem. leaning) 0.231∗∗∗ 0.225∗∗∗ 0.307∗∗∗ 0.364∗∗∗ 0.324∗∗∗(0.005) (0.005) (0.006) (0.009) (0.012)max (Rep. leaning) 0.212∗∗∗ 0.209∗∗∗ 0.301∗∗∗ 0.363∗∗∗ 0.322∗∗∗(0.007) (0.007) (0.010) (0.011) (0.014)argmax (Dem. leaning) 0.523∗∗∗ 0.522∗∗∗ 0.565∗∗∗ 0.600∗∗∗ 0.567∗∗∗(0.011) (0.011) (0.016) (0.016) (0.017)argmax (Rep. leaning) 0.601∗∗∗ 0.606∗∗∗ 0.621∗∗∗ 0.634∗∗∗ 0.598∗∗∗(0.030) (0.032) (0.025) (0.021) (0.022)Test max (p-value) 0.019 0.054 0.470 0.935 0.818Test argmax (p-value) 0.010 0.009 0.010 0.067 0.074Population VAP VAP VAP VAP VAPYear FE - X - X XMunicipality FE - - X X XControls - - - - XObs. 27,372 27,372 26,004 26,004 25,866Municipalities 7,422 7,422 6,054 6,054 6,037Cluster level Municipality Municipality Municipality Municipality MunicipalityAdjusted R2 0.265 0.270 0.496 0.503 0.503The top panel reports regression estimates. Cluster robust standard errors in parentheses. Significance levels: *** p < 0.01,** p < 0.05, * p < 0.1. The dependent variable in all specifications is Minority underrepresentation, computed as thedifference between the fraction of Minority population and the fraction of Minority council members. The main independentvariables are the fraction of Minority population and its squared value. Population measures based on voting age population.Rep. advantage is an indicator for municipalities in counties in which the Republican vote share at the most recent previousPresidential election was higher than the Democrat. The controls include the log-population, the log-median householdincome, and the fractions of rural, foreign, and over 65 population. The second panel reports Max/Argmax estimates basedon the regression estimates. Standard errors in parentheses are asymptotic. Given a quadratic formula y = ax2 + bx+ c,the max is computed as c− b24a , while the argmax is computed as − b2a . We report the p-values of the tests for whether themax and argmax are different between groups.130Figure B.3: Minority Underrepresentation in State Legislatures (2020)Solid lines represent LOWESS smoothing of the underlying state-level data. Markers represent state-specific underrepresentation.ALAKAZARCACOCTDEFLGAHIIDILINIAKSKYLAMEMDMAMIMNMSMOMTNENVNHNJNMNYNCNDOHOKORPARISCSDTNTXUTVTVAWAWVWIWY0.2.4.6.8Minority Representatives (frac.)0 .2 .4 .6 .8Minority Population (frac.)Underrepresentation in State Legislatures (2020)131Table B.6: Latino Underrepresentation by Electoral Rule(1) (2) (3) (4) (5)LatinoUnderrep.LatinoUnderrep.LatinoUnderrep.LatinoUnderrep.LatinoUnderrep.Regression estimates:Latino 0.907∗∗∗ 0.902∗∗∗ 1.002∗∗∗ 1.107∗∗∗ 1.145∗∗∗(0.035) (0.036) (0.043) (0.053) (0.061)Latino2 -0.973∗∗∗ -0.966∗∗∗ -0.876∗∗∗ -0.962∗∗∗ -0.978∗∗∗(0.071) (0.071) (0.098) (0.103) (0.105)At-Large -0.004∗∗∗ -0.004∗∗∗ -0.002 -0.005∗∗∗ -0.005∗∗∗(0.001) (0.001) (0.002) (0.002) (0.002)At-Large × Latino 0.141∗∗∗ 0.143∗∗∗ 0.191∗∗∗ 0.193∗∗∗ 0.187∗∗∗(0.042) (0.042) (0.047) (0.047) (0.048)At-Large × Latino2 -0.130 -0.132∗ -0.217∗∗ -0.218∗∗ -0.206∗∗(0.080) (0.080) (0.097) (0.096) (0.096)Max/Argmax estimates:max (SMD) 0.212∗∗∗ 0.211∗∗∗ 0.275∗∗∗ 0.304∗∗∗ 0.318∗∗∗(0.009) (0.009) (0.016) (0.016) (0.021)max (At-Large) 0.245∗∗∗ 0.244∗∗∗ 0.312∗∗∗ 0.339∗∗∗ 0.353∗∗∗(0.007) (0.007) (0.014) (0.014) (0.019)argmax (SMD) 0.466∗∗∗ 0.466∗∗∗ 0.572∗∗∗ 0.576∗∗∗ 0.586∗∗∗(0.022) (0.022) (0.045) (0.040) (0.042)argmax (At-Large) 0.475∗∗∗ 0.475∗∗∗ 0.546∗∗∗ 0.551∗∗∗ 0.563∗∗∗(0.012) (0.012) (0.034) (0.032) (0.034)Test max (p-value) 0.002 0.002 0.024 0.033 0.040Test argmax (p-value) 0.716 0.711 0.501 0.486 0.523Population VAP VAP VAP VAP VAPYear FE - X - X XMunicipality FE - - X X XControls - - - - XObs. 26,498 26,498 25,100 25,100 24,963Municipalities 7,407 7,407 6,009 6,009 5,992Cluster level Municipality Municipality Municipality Municipality MunicipalityAdjusted R2 0.483 0.484 0.643 0.645 0.645The top panel reports regression estimates. Cluster robust standard errors in parentheses. Significance levels: *** p <0.01, ** p < 0.05, * p < 0.1. The dependent variable in all specifications is Latino underrepresentation, computed as thedifference between the fraction of Latino population and the fraction of Latino council members. The main independentvariables are the fraction of Latino population and its squared value. Population measures based on voting age population.At-Large is an indicator for municipalities in which at least 50% of council seats are elected using an At-Large electoralrule. The controls include the log-population, the log-median household income, and the fractions of rural, foreign, andover 65 population. The second panel reports Max/Argmax estimates based on the regression estimates. Standard errors inparentheses are asymptotic. Given a quadratic formula y = ax2+bx+c, the max is computed as c− b24a , while the argmax iscomputed as − b2a . We report the p-values of the tests for whether the max and argmax are different between electoral rules.132Table B.7: African American Underrepresentation by Electoral Rule(1) (2) (3) (4) (5)African AmericanUnderrep.African AmericanUnderrep.African AmericanUnderrep.African AmericanUnderrep.African AmericanUnderrep.Regression estimates:African American 0.215∗∗∗ 0.220∗∗∗ 0.707∗∗∗ 0.840∗∗∗ 0.885∗∗∗(0.029) (0.029) (0.064) (0.067) (0.067)African American2 -0.198∗∗∗ -0.203∗∗∗ -0.938∗∗∗ -1.043∗∗∗ -1.085∗∗∗(0.054) (0.054) (0.112) (0.112) (0.113)At-Large -0.001 -0.001 -0.004∗ -0.007∗∗∗ -0.008∗∗∗(0.001) (0.001) (0.002) (0.002) (0.002)At-Large × African American 0.340∗∗∗ 0.334∗∗∗ 0.561∗∗∗ 0.551∗∗∗ 0.549∗∗∗(0.040) (0.040) (0.053) (0.052) (0.053)At-Large × African American2 -0.341∗∗∗ -0.334∗∗∗ -0.569∗∗∗ -0.572∗∗∗ -0.562∗∗∗(0.076) (0.076) (0.110) (0.107) (0.109)Max/Argmax estimates:max (SMD) 0.058∗∗∗ 0.059∗∗∗ 0.110∗∗∗ 0.141∗∗∗ 0.150∗∗∗(0.007) (0.007) (0.010) (0.012) (0.012)max (At-Large) 0.142∗∗∗ 0.141∗∗∗ 0.239∗∗∗ 0.264∗∗∗ 0.274∗∗∗(0.008) (0.008) (0.011) (0.012) (0.012)argmax (SMD) 0.541∗∗∗ 0.541∗∗∗ 0.377∗∗∗ 0.403∗∗∗ 0.408∗∗∗(0.087) (0.085) (0.023) (0.023) (0.023)argmax (At-Large) 0.515∗∗∗ 0.516∗∗∗ 0.421∗∗∗ 0.431∗∗∗ 0.435∗∗∗(0.035) (0.036) (0.019) (0.018) (0.018)Test max (p-value) 0.000 0.000 0.000 0.000 0.000Test argmax (p-value) 0.773 0.779 0.030 0.149 0.165Population VAP VAP VAP VAP VAPYear FE - X - X XMunicipality FE - - X X XControls - - - - XObs. 26,413 26,413 25,001 25,001 24,864Municipalities 7,406 7,406 5,994 5,994 5,977Cluster level Municipality Municipality Municipality Municipality MunicipalityAdjusted R2 0.150 0.152 0.454 0.460 0.463The top panel reports regression estimates. Cluster robust standard errors in parentheses. Significance levels: *** p < 0.01, ** p < 0.05, * p < 0.1. Thedependent variable in all specifications is African American underrepresentation, computed as the difference between the fraction of African Americanpopulation and the fraction of African American council members. The main independent variables are the fraction of African American population and itssquared value. Population measures based on voting age population. At-Large is an indicator for municipalities in which at least 50% of council seats areelected using an At-Large electoral rule. The controls include the log-population, the log-median household income, and the fractions of rural, foreign, andover 65 population. The second panel reports Max/Argmax estimates based on the regression estimates. Standard errors in parentheses are asymptotic.Given a quadratic formula y = ax2 + bx+ c, the max is computed as c− b24a , while the argmax is computed as − b2a . We report the p-values of the tests forwhether the max and argmax are different between electoral rules.133Table B.8: Asian Underrepresentation by Electoral Rule(1) (2) (3) (4) (5)AsianUnderrep.AsianUnderrep.AsianUnderrep.AsianUnderrep.AsianUnderrep.Regression estimates:Asian 0.950∗∗∗ 0.941∗∗∗ 1.009∗∗∗ 1.054∗∗∗ 1.022∗∗∗(0.041) (0.043) (0.057) (0.064) (0.077)Asian2 -0.728∗∗∗ -0.699∗∗∗ -0.918∗∗∗ -1.052∗∗∗ -0.898∗(0.262) (0.267) (0.348) (0.353) (0.474)At-Large -0.001 -0.001 -0.001 -0.001 -0.002∗(0.001) (0.001) (0.001) (0.001) (0.001)At-Large × Asian 0.070 0.075 0.084 0.065 0.082(0.053) (0.053) (0.066) (0.065) (0.075)At-Large × Asian2 -0.434 -0.456 -0.323 -0.228 -0.374(0.282) (0.284) (0.362) (0.358) (0.473)Max/Argmax estimates:max (SMD) 0.310∗∗∗ 0.316∗∗∗ 0.275∗∗∗ 0.262∗∗∗ 0.289∗∗(0.094) (0.101) (0.081) (0.065) (0.118)max (At-Large) 0.222∗∗∗ 0.222∗∗∗ 0.238∗∗∗ 0.241∗∗∗ 0.237∗∗∗(0.014) (0.014) (0.014) (0.014) (0.015)argmax (SMD) 0.653∗∗∗ 0.673∗∗∗ 0.550∗∗∗ 0.501∗∗∗ 0.569∗∗(0.215) (0.235) (0.183) (0.144) (0.264)argmax (At-Large) 0.439∗∗∗ 0.440∗∗∗ 0.440∗∗∗ 0.437∗∗∗ 0.434∗∗∗(0.031) (0.031) (0.030) (0.029) (0.029)Test max (p-value) 0.357 0.359 0.645 0.751 0.656Test argmax (p-value) 0.325 0.324 0.554 0.659 0.608Population VAP VAP VAP VAP VAPYear FE - X - X XMunicipality FE - - X X XControls - - - - XObs. 24,587 24,587 23,011 23,011 22,873Municipalities 7,313 7,313 5,737 5,737 5,716Cluster level Municipality Municipality Municipality Municipality MunicipalityAdjusted R2 0.539 0.540 0.617 0.618 0.616The top panel reports regression estimates. Cluster robust standard errors in parentheses. Significance levels: *** p < 0.01,** p < 0.05, * p < 0.1. The dependent variable in all specifications is Asian underrepresentation, computed as the differencebetween the fraction of Asian population and the fraction of Asian council members. The main independent variables arethe fraction of Asian population and its squared value. Population measures based on voting age population. At-Large is anindicator for municipalities in which at least 50% of council seats are elected using an At-Large electoral rule. The controlsinclude the log-population, the log-median household income, and the fractions of rural, foreign, and over 65 population.The second panel reports Max/Argmax estimates based on the regression estimates. Standard errors in parentheses areasymptotic. Given a quadratic formula y = ax2 + bx+ c, the max is computed as c− b24a , while the argmax is computed as− b2a . We report the p-values of the tests for whether the max and argmax are different between electoral rules.134Table B.9: Mayor-Council v. Council-Manager(1) (2) (3) (4) (5)MinorityUnderrep.MinorityUnderrep.MinorityUnderrep.MinorityUnderrep.MinorityUnderrep.Regression estimates:Minority 0.835∗∗∗ 0.811∗∗∗ 1.249∗∗∗ 1.493∗∗∗ 1.385∗∗∗(0.026) (0.026) (0.037) (0.047) (0.053)Minority2 -0.720∗∗∗ -0.701∗∗∗ -1.054∗∗∗ -1.200∗∗∗ -1.180∗∗∗(0.041) (0.041) (0.059) (0.063) (0.064)Mayor-Council 0.005∗∗ 0.006∗∗ 0.003 -0.004 -0.004(0.002) (0.002) (0.004) (0.004) (0.004)Mayor-Council × Minority -0.114∗∗∗ -0.118∗∗∗ -0.012 0.022 0.022(0.035) (0.035) (0.041) (0.040) (0.040)Mayor-Council × Minority2 0.015 0.021 -0.042 -0.074 -0.067(0.054) (0.054) (0.060) (0.059) (0.059)Max/Argmax estimates:max (Council-Manager) 0.235∗∗∗ 0.230∗∗∗ 0.306∗∗∗ 0.368∗∗∗ 0.328∗∗∗(0.006) (0.006) (0.007) (0.010) (0.012)max (Mayor-Council) 0.182∗∗∗ 0.178∗∗∗ 0.289∗∗∗ 0.351∗∗∗ 0.315∗∗∗(0.006) (0.006) (0.008) (0.010) (0.013)argmax (Council-Manager) 0.580∗∗∗ 0.579∗∗∗ 0.593∗∗∗ 0.622∗∗∗ 0.587∗∗∗(0.019) (0.019) (0.019) (0.018) (0.018)argmax (Mayor-Council) 0.511∗∗∗ 0.510∗∗∗ 0.565∗∗∗ 0.595∗∗∗ 0.565∗∗∗(0.015) (0.015) (0.018) (0.016) (0.018)Test max (p-value) 0.000 0.000 0.039 0.044 0.110Test argmax (p-value) 0.002 0.003 0.096 0.080 0.120Population VAP VAP VAP VAP VAPYear FE - X - X XMunicipality FE - - X X XControls - - - - XObs. 24,855 24,855 23,658 23,658 23,553Municipalities 6,783 6,783 5,586 5,586 5,574Cluster level Municipality Municipality Municipality Municipality MunicipalityAdjusted R2 0.261 0.267 0.493 0.499 0.500The top panel reports regression estimates. Cluster robust standard errors in parentheses. Significance levels: *** p < 0.01,** p < 0.05, * p < 0.1. The dependent variable in all specifications is Minority underrepresentation, computed as thedifference between the fraction of Minority population and the fraction of Minority council members. Mayor-council is anindicator for municipalities adopting that systems as opposed to a council-manager form. The main independent variablesare the fraction of Minority population and its squared value, and their interactions. Population measures based on votingage population. The controls are the log-population, the log-median household income, and the fractions of rural, foreign,and over 65 population. The second panel reports Max/Argmax estimates based on the regression estimates. Standarderrors in parentheses are asymptotic. Given a quadratic formula y = ax2 + bx+ c, the max is computed as c− b24a , whilethe argmax is computed as − b2a . We report the p-values of the tests for whether the max and argmax are different betweengroups.135Table B.10: Council Size(1) (2) (3) (4) (5)MinorityUnderrep.MinorityUnderrep.MinorityUnderrep.MinorityUnderrep.MinorityUnderrep.Regression estimates:Minority 0.887∗∗∗ 0.870∗∗∗ 1.309∗∗∗ 1.543∗∗∗ 1.437∗∗∗(0.027) (0.027) (0.046) (0.055) (0.059)Minority2 -0.782∗∗∗ -0.767∗∗∗ -1.134∗∗∗ -1.272∗∗∗ -1.258∗∗∗(0.042) (0.042) (0.071) (0.075) (0.076)Council size (CS) 0.005∗∗ 0.007∗∗∗ -0.002 -0.002 -0.004(0.002) (0.002) (0.006) (0.006) (0.006)CS × Minority -0.131∗∗∗ -0.145∗∗∗ -0.121∗∗ -0.093∗ -0.105∗∗(0.036) (0.035) (0.053) (0.052) (0.052)CS × Minority2 0.041 0.056 0.139 0.099 0.128(0.057) (0.057) (0.085) (0.084) (0.084)Max/Argmax estimates:max (CS below median) 0.244∗∗∗ 0.240∗∗∗ 0.320∗∗∗ 0.378∗∗∗ 0.338∗∗∗(0.006) (0.006) (0.009) (0.011) (0.014)max (CS above median) 0.190∗∗∗ 0.185∗∗∗ 0.294∗∗∗ 0.355∗∗∗ 0.316∗∗∗(0.005) (0.005) (0.009) (0.011) (0.013)argmax (CS below median) 0.567∗∗∗ 0.567∗∗∗ 0.578∗∗∗ 0.607∗∗∗ 0.571∗∗∗(0.017) (0.017) (0.020) (0.019) (0.020)argmax (CS above median) 0.511∗∗∗ 0.509∗∗∗ 0.597∗∗∗ 0.618∗∗∗ 0.589∗∗∗(0.015) (0.015) (0.022) (0.020) (0.020)Test max (p-value) 0.000 0.000 0.043 0.092 0.069Test argmax (p-value) 0.011 0.010 0.462 0.650 0.428Population VAP VAP VAP VAP VAPYear FE - X - X XMunicipality FE - - X X XControls - - - - XObs. 27,576 27,576 26,205 26,205 26,065Municipalities 7,472 7,472 6,101 6,101 6,084Cluster level Municipality Municipality Municipality Municipality MunicipalityAdjusted R2 0.268 0.275 0.494 0.500 0.501The top panel reports regression estimates. Cluster robust standard errors in parentheses. Significance levels: *** p < 0.01,** p < 0.05, * p < 0.1. The dependent variable in all specifications is Minority underrepresentation, computed as thedifference between the fraction of Minority population and the fraction of Minority council members. Council size isan indicator for municipalities with council size above or equal the median. The main independent variables are thefraction of Minority population and its squared value, and their interactions. Population measures based on voting agepopulation. The controls are the log-population, the log-median household income, and the fractions of rural, foreign, andover 65 population. The second panel reports Max/Argmax estimates based on the regression estimates. Standard errors inparentheses are asymptotic. Given a quadratic formula y = ax2 + bx+ c, the max is computed as c− b24a , while the argmaxis computed as − b2a . We report the p-values of the tests for whether the max and argmax are different between groups.136Table B.11: Paid Council Members(1) (2) (3) (4) (5)MinorityUnderrep.MinorityUnderrep.MinorityUnderrep.MinorityUnderrep.MinorityUnderrep.Regression estimates:Minority 0.914∗∗∗ 0.883∗∗∗ 1.210∗∗∗ 1.467∗∗∗ 1.355∗∗∗(0.050) (0.050) (0.055) (0.063) (0.066)Minority2 -0.843∗∗∗ -0.817∗∗∗ -0.939∗∗∗ -1.106∗∗∗ -1.077∗∗∗(0.079) (0.078) (0.081) (0.085) (0.084)Paid council members 0.009∗∗ 0.009∗∗ 0.004 0.002 0.001(0.004) (0.004) (0.005) (0.005) (0.005)Paid × Minority -0.126∗∗ -0.118∗∗ 0.037 0.032 0.030(0.052) (0.052) (0.055) (0.055) (0.055)Paid × Minority2 0.129 0.123 -0.147∗ -0.140∗ -0.143∗(0.083) (0.082) (0.079) (0.080) (0.079)Max/Argmax estimates:max (not Paid) 0.234∗∗∗ 0.228∗∗∗ 0.327∗∗∗ 0.394∗∗∗ 0.352∗∗∗(0.011) (0.011) (0.015) (0.017) (0.018)max (Paid) 0.213∗∗∗ 0.209∗∗∗ 0.300∗∗∗ 0.359∗∗∗ 0.320∗∗∗(0.004) (0.004) (0.006) (0.009) (0.012)argmax (not Paid) 0.542∗∗∗ 0.541∗∗∗ 0.645∗∗∗ 0.663∗∗∗ 0.629∗∗∗(0.028) (0.029) (0.033) (0.029) (0.029)argmax (Paid) 0.552∗∗∗ 0.551∗∗∗ 0.574∗∗∗ 0.602∗∗∗ 0.568∗∗∗(0.014) (0.014) (0.016) (0.015) (0.016)Test max (p-value) 0.059 0.091 0.047 0.021 0.018Test argmax (p-value) 0.743 0.729 0.017 0.022 0.014Population VAP VAP VAP VAP VAPYear FE - X - X XMunicipality FE - - X X XControls - - - - XObs. 27,077 27,077 25,704 25,704 25,564Municipalities 7,436 7,436 6,063 6,063 6,046Cluster level Municipality Municipality Municipality Municipality MunicipalityAdjusted R2 0.261 0.267 0.494 0.500 0.501The top panel reports regression estimates. Cluster robust standard errors in parentheses. Significance levels: *** p < 0.01,** p < 0.05, * p < 0.1. The dependent variable in all specifications is Minority underrepresentation, computed as thedifference between the fraction of Minority population and the fraction of Minority council members. Paid is an indicatorfor municipalities with paid council members. The main independent variables are the fraction of Minority populationand its squared value, and their interactions. Population measures based on voting age population. The controls are thelog-population, the log-median household income, and the fractions of rural, foreign, and over 65 population. The secondpanel reports Max/Argmax estimates based on the regression estimates. Standard errors in parentheses are asymptotic.Given a quadratic formula y = ax2 + bx+ c, the max is computed as c− b24a , while the argmax is computed as − b2a . Wereport the p-values of the tests for whether the max and argmax are different between groups.137Figure B.4: VRA Coverage at the time of Shelby (2013)138Table B.12: Minority Underrepresentation Pre/Post Shelby (2013) - Parameters(1) (2) (3)MinorityUnderrep.MinorityUnderrep.MinorityUnderrep.Pre, VRA non-coveredconstant -0.007∗∗∗ -0.044 -0.030(0.001) (.) (0.082)Minority 0.841∗∗∗ 1.236∗∗∗ 1.152∗∗∗(0.023) (0.036) (0.043)Minority2 -0.696∗∗∗ -1.029∗∗∗ -1.052∗∗∗(0.039) (0.064) (0.066)Pre, VRA coveredconstant -0.009 -0.141 -0.114(0.006) (.) (0.082)Minority 0.708∗∗∗ 1.324∗∗∗ 1.209∗∗∗(0.042) (0.077) (0.081)Minority2 -0.711∗∗∗ -1.164∗∗∗ -1.137∗∗∗(0.056) (0.112) (0.113)Post, VRA non-coveredconstant -0.010∗∗ -0.064∗∗∗ -0.046(0.004) (0.006) (0.082)Minority 1.024∗∗∗ 1.341∗∗∗ 1.237∗∗∗(0.048) (0.064) (0.070)Minority2 -0.990∗∗∗ -1.235∗∗∗ -1.245∗∗∗(0.063) (0.090) (0.092)Post, VRA coveredconstant -0.020 -0.185∗∗∗ -0.151∗(0.019) (0.025) (0.086)Minority 0.973∗∗∗ 1.556∗∗∗ 1.407∗∗∗(0.109) (0.147) (0.152)Minority2 -0.915∗∗∗ -1.323∗∗∗ -1.264∗∗∗(0.123) (0.166) (0.168)Population VAP VAP VAPSample Full Full FullMunicipality FE - X XControls - - XObs. 27,562 26,190 26,050Clusters 7,470 6,098 6,081Cluster level Municipality Municipality MunicipalityCluster robust standard errors in parentheses. Significance levels: *** p < 0.01,** p < 0.05, * p < 0.1. The dependent variable in all specifications is Minorityunderrepresentation, computed as the difference between the fraction of Minoritypopulation and the fraction of Minority council members. We report the estimatesfor municipalities covered/non-covered by the VRA at the time of Shelby, and forpre/post Shelby periods. See Table B.13 and Table B.14 for the underlying esti-mates and argmax estimates. The controls are the log-population, the log-medianhousehold income, and the fractions of rural, foreign, and over 65 population.139Table B.13: Minority Underrepresentation Pre/Post Shelby (2013) - Estimates(1) (2) (3)MinorityUnderrep.MinorityUnderrep.MinorityUnderrep.Minority 0.841∗∗∗ 1.236∗∗∗ 1.152∗∗∗(0.023) (0.036) (0.043)Minority2 -0.696∗∗∗ -1.029∗∗∗ -1.052∗∗∗(0.039) (0.064) (0.066)Covered -0.002(0.006)Covered × Minority -0.134∗∗∗ 0.088 0.057(0.048) (0.085) (0.085)Covered × Minority2 -0.015 -0.134 -0.085(0.069) (0.129) (0.128)Post -0.003 -0.020∗∗∗ -0.016∗∗(0.004) (0.006) (0.006)Post × Minority 0.183∗∗∗ 0.105∗ 0.084(0.048) (0.054) (0.056)Post × Minority2 -0.293∗∗∗ -0.205∗∗∗ -0.193∗∗∗(0.065) (0.071) (0.074)Post × Covered -0.008 -0.024 -0.021(0.019) (0.026) (0.026)Post × Covered × Minority 0.082 0.127 0.114(0.119) (0.134) (0.134)Post × Covered × Minority2 0.089 0.046 0.066(0.142) (0.149) (0.148)Population VAP VAP VAPSample Full Full FullMunicipality FE - X XControls - - XObs. 27,562 26,190 26,050Clusters 7,470 6,098 6,081Cluster level Municipality Municipality MunicipalityAdjusted R2 0.279 0.495 0.497Cluster robust standard errors in parentheses. Significance levels: *** p < 0.01, **p < 0.05, * p < 0.1. The dependent variable in all specifications is Minority underrepre-sentation, computed as the difference between the fraction of Minority population andthe fraction of Minority council members. The main independent variables are the frac-tion of Minority population and its squared value, and the interaction terms. Populationmeasures based on voting age population. Covered is an indicator for municipalitiescovered by the VRA at the time of Shelby. Post is an indicator for post-Shelby periods.The controls are the log-population, the log-median household income, and the fractionsof rural, foreign, and over 65 population.140Table B.14: Minority Underrepresentation Pre/Post Shelby (2013) - Argmax(1) (2) (3)MinorityUnderrep.MinorityUnderrep.MinorityUnderrep.Argmax (Pre, VRA non-covered) 0.604∗∗∗ 0.600∗∗∗ 0.548∗∗∗(0.021) (0.023) (0.021)Argmax (Pre, VRA covered) 0.498∗∗∗ 0.569∗∗∗ 0.532∗∗∗(0.017) (0.030) (0.028)Argmax (Post, VRA non-covered) 0.517∗∗∗ 0.543∗∗∗ 0.497∗∗∗(0.015) (0.018) (0.017)Argmax (Post, VRA covered) 0.531∗∗∗ 0.588∗∗∗ 0.557∗∗∗(0.023) (0.028) (0.027)Population VAP VAP VAPSample Full Full FullMunicipality FE - X XControls - - XObs. 27,562 26,190 26,050Clusters 7,470 6,098 6,081Cluster level Municipality Municipality MunicipalityAsymptotic standard errors in parentheses. Significance levels: *** p < 0.01, ** p < 0.05, *p < 0.1. The dependent variable in all specifications is Minority underrepresentation, com-puted as the difference between the fraction of Minority population and the fraction of Minoritycouncil members. We report the estimates for municipalities covered/non-covered by the VRAat the time of Shelby, and for pre/post Shelby periods. Estimates based on the parameters inTable B.12. The controls are the log-population, the log-median household income, and thefractions of rural, foreign, and over 65 population. Given a quadratic formula y = ax2+ bx+ c,the argmax is computed as − b2a .141Table B.15: Minority Underrepresentation Pre/Post Shelby (2013) - Restricted (200miles)(1) (2) (3)MinorityUnderrep.MinorityUnderrep.MinorityUnderrep.Max (Pre, VRA non-covered) 0.246∗∗∗ 0.323∗∗∗ 0.277∗∗∗(0.007) (0.012) (0.098)Max (Pre, VRA covered) 0.169∗∗∗ 0.235∗∗∗ 0.205∗∗(0.009) (0.022) (0.100)Max (Post, VRA non-covered) 0.254∗∗∗ 0.301∗∗∗ 0.258∗∗∗(0.010) (0.012) (0.100)Max (Post, VRA covered) 0.229∗∗∗ 0.263∗∗∗ 0.229∗∗(0.013) (0.022) (0.101)Shelby 0.052∗∗∗ 0.050∗∗∗ 0.043∗∗(0.018) (0.018) (0.017)Population VAP VAP VAPSample 200 miles 200 miles 200 milesMunicipality FE - X XControls - - XObs. 20,198 19,139 19,051Clusters 5,571 4,512 4,499Cluster level Municipality Municipality MunicipalityAsymptotic standard errors in parentheses. Significance levels: *** p < 0.01, ** p < 0.05,* p < 0.1. Estimates based on a sample restricted to municipalities within 200 milesfrom the VRA coverage border at the time of Shelby (2013). The controls are the log-population, the log-median household income, and the fractions of rural, foreign, and over65 population, averaged over the entire sample. When split, the average controls are takenseparately for covered and non-covered municipalities, pre and post Shelby (2013). Givena quadratic formula y = ax2 + bx+ c, the max is computed as c− b24a , while the shelby isa diff-in-diffs estimate based on the max.142Table B.16: Minority Underrepresentation Pre/Post Shelby (2013) - Restricted (100miles)(1) (2) (3)MinorityUnderrep.MinorityUnderrep.MinorityUnderrep.Max (Pre, VRA non-covered) 0.234∗∗∗ 0.311∗∗∗ 0.260∗∗(0.008) (0.015) (0.130)Max (Pre, VRA covered) 0.192∗∗∗ 0.249∗∗∗ 0.216(0.014) (0.024) (0.133)Max (Post, VRA non-covered) 0.242∗∗∗ 0.291∗∗∗ 0.244∗(0.012) (0.015) (0.132)Max (Post, VRA covered) 0.251∗∗∗ 0.286∗∗∗ 0.245∗(0.016) (0.024) (0.133)Shelby 0.052∗∗ 0.058∗∗∗ 0.044∗∗(0.022) (0.021) (0.020)Population VAP VAP VAPSample 100 miles 100 miles 100 milesMunicipality FE - X XControls - - XObs. 11,717 11,131 11,076Clusters 3,174 2,588 2,581Cluster level Municipality Municipality MunicipalityAsymptotic standard errors in parentheses. Significance levels: *** p < 0.01, ** p < 0.05,* p < 0.1. Estimates based on a sample restricted to municipalities within 100 milesfrom the VRA coverage border at the time of Shelby (2013). The controls are the log-population, the log-median household income, and the fractions of rural, foreign, and over65 population, averaged over the entire sample. When split, the average controls are takenseparately for covered and non-covered municipalities, pre and post Shelby (2013). Givena quadratic formula y = ax2 + bx+ c, the max is computed as c− b24a , while the shelby isa diff-in-diffs estimate based on the max.143Table B.17: Minority Underrepresentation Pre/Post Shelby (2013) - Effects, byRep/Dem Leaning(1) (2) (3) (4) (5) (6)MinorityUnderrep.MinorityUnderrep.MinorityUnderrep.MinorityUnderrep.MinorityUnderrep.MinorityUnderrep.Argmax (Pre, VRA non-covered) 0.560∗∗∗ 0.730∗∗∗ 0.559∗∗∗ 0.717∗∗∗ 0.516∗∗∗ 0.623∗∗∗(0.019) (0.069) (0.028) (0.066) (0.026) (0.057)Argmax (Pre, VRA covered) 0.490∗∗∗ 0.515∗∗∗ 0.514∗∗∗ 0.587∗∗∗ 0.475∗∗∗ 0.545∗∗∗(0.016) (0.032) (0.051) (0.044) (0.045) (0.042)Argmax (Post, VRA non-covered) 0.506∗∗∗ 0.561∗∗∗ 0.519∗∗∗ 0.526∗∗∗ 0.478∗∗∗ 0.471∗∗∗(0.014) (0.061) (0.020) (0.034) (0.020) (0.032)Argmax (Post, VRA covered) 0.538∗∗∗ 0.497∗∗∗ 0.545∗∗∗ 0.607∗∗∗ 0.519∗∗∗ 0.569∗∗∗(0.024) (0.031) (0.043) (0.053) (0.043) (0.050)Max (Pre, VRA non-covered) 0.246∗∗∗ 0.269∗∗∗ 0.309∗∗∗ 0.364∗∗∗ 0.273 0.299∗∗∗(0.007) (0.019) (0.016) (0.028) (0.197) (0.103)Max (Pre, VRA covered) 0.182∗∗∗ 0.163∗∗∗ 0.196∗∗∗ 0.242∗∗∗ 0.181 0.210∗∗(0.009) (0.007) (0.063) (0.023) (0.199) (0.105)Max (Post, VRA non-covered) 0.270∗∗∗ 0.244∗∗∗ 0.299∗∗∗ 0.282∗∗∗ 0.260 0.241∗∗(0.011) (0.019) (0.015) (0.018) (0.199) (0.102)Max (Post, VRA covered) 0.293∗∗∗ 0.223∗∗∗ 0.251∗∗∗ 0.272∗∗∗ 0.231 0.238∗∗(0.023) (0.012) (0.060) (0.024) (0.202) (0.106)Shelby 0.086∗∗∗ 0.085∗∗∗ 0.065∗∗ 0.112∗∗∗ 0.063∗∗∗ 0.087∗∗∗(0.025) (0.029) (0.026) (0.031) (0.024) (0.026)Population VAP VAP VAP VAP VAP VAPSample Dem. advantage Rep. advantage Dem. advantage Rep. advantage Dem. advantage Rep. advantageMunicipality FE - - X X X XControls - - - - X XObs. 9,913 17,459 8,275 15,734 8,219 15,662Clusters 4,189 6,044 2,551 4,319 2,541 4,307Cluster level Municipality Municipality Municipality Municipality Municipality MunicipalityAsymptotic standard errors in parentheses. Significance levels: *** p < 0.01, ** p < 0.05, * p < 0.1. The dependent variable in all specifications is Minority underrepresen-tation, computed as the difference between the fraction of Minority population and the fraction of Minority council members. We report the estimates for municipalitiescovered/non-covered by the VRA at the time of Shelby, and for pre/post Shelby periods. The controls are the log-population, the log-median household income, and thefractions of rural, foreign, and over 65 population. Given a quadratic formula y = ax2 + bx+ c, the max is computed as c− b24a , while the shelby effect is a diff-in-diffsestimate based on the max, while the argmax is computed as − b2a .144Appendix CAppendix to Chapter 4C.1 Estimating Changes in Population Dynamicsfrom a Cross-Section of SurvivorsC.1.1 Representing Population DynamicsTime is discrete, given by t = 0, 1, . . .. We consider a set of agents evolving dynamicallyover time and call it a "system". At each t the system’s population is composed of Tcohorts of agents of vintage τ ∈ 1, 2, . . . , T , where τ indicates the number of periodsthat an agent has lived in the system. Let Nτ (t) be the size of the cohort alive for τperiods at t, where τ = 1 indicates newly entering agents, τ = 2 agents who enteredthe previous period, etc. No agent lives longer than T periods.Each cohort produces the entry of some new agents, may that be actual fertilityor simply facilitating the access to the system of some outsider. We indicate with"fecundity", fτ ≥ 0, the expected number of new agents that a vintage-τ agent producesfor the following period. In the context of voter registration, for instance, fecunditycould mean a registered senior citizen convincing her adult grandson to register tovote as well. The new entrants at t+ 1 are:N1(t+ 1) =T∑τ=1fτNτ (t).Every period each agent of each vintage τ ∈ 1, . . . , T either exits or survives, withsτ the probability of an agent for vintage τ remaining from one period to the next.145Agents of vintage T exit from the pool (e.g. because of old age).The system can be represented by a Leslie (1945) matrix that expresses the populationdynamics, relating the population at t with that at t+ 1:N1(t+ 1)N2(t+ 1)...NT (t+ 1) =f1 f2 · · · fT−1 fTs1 0 · · · 0 00 s2 · · · 0 0... ... . . . ... ...0 0 · · · sT−1 0N1(t)N2(t)...NT (t) ,or N(t+ 1) = LN(t), where L is the Leslie matrix (note the implicit assumption ofsT = 0). Then, we have total population:N(t) =T∑τ=1Nτ (t)= N1(t) + s1N1(t− 1) + s2N2(t− 1) + ...+ sT−1NT−1(t− 1)= N1(t) + s1N1(t− 1) + s2s1N1(t− 2) + ...+ sT−1 · · · s1N1(t− T + 1).C.1.2 Detecting Changes in the Leslie Matrix from Cross-Sectional DataSuppose now that between t and t+1 there is a change to L˜ and indicate with "~" themodified elements of the system. Highlighting in color the elements directly affectedby the change, we have at t+ 1:N˜(t+ 1) = N˜1(t+ 1) + s˜1N1(t) + s˜2N2(t) + s˜3N3(t) + ...+ s˜T−1NT−1(t)= N˜1(t+ 1) + s˜1N1(t) + s˜2s1N1(t− 1) + s˜3s2s1N1(t− 2) + ...++s˜T−1sT−2 · · · s1N1(t− T + 2),and as we move one period forward in time (and so on for further periods):N˜(t+ 2) = N˜1(t+ 2) + s˜1N˜1(t+ 1) + s˜2N˜2(t+ 1) + s˜3N˜3(t+ 1) + ...+ s˜T−1N˜T−1(t+ 1)= N˜1(t+ 2) + s˜1N˜1(t+ 1) + s˜2s˜1N1(t) + s˜3s˜2s1N1(t− 1) + ...++s˜T−1s˜T−2sT−3 · · · s1N1(t− T + 3).146C.1.3 Estimating the Treatment EffectSuppose only cross-sectional data N(t) be available (this is the case for the Aristotle,2021 data). The goal is to show whether changes from L to L˜ at any point at or beforet may be detectable empirically. It can be shown that a population vector with stableage distribution evolves according to the dominant eigenvalue of the Leslie matrix.Define λ the dominant eigenvalue of L, then N(t+ 1) = λN(t).A change in L˜ takes place at time t − g, with g < T and g is known. The oldestcohort to survive to t is composed of those who entered at t − T + 1 given by:s˜T−1s˜T−2 · · · s˜T−gsT−g−1 · · · s1N1(t− T + 1).Consider the ratio between the size of the cohort born at t − g and still living at trelative to the cohort born at t− T + 1 and still living at t, in the treated population:s˜g · · · s˜1N1(t− g)s˜T−1s˜T−2 · · · s˜T−gsT−g−1 · · · s1N1(t− T + 1) .From N1(t− g) = λT−g−1N1(t− T + 1), we can simplifys˜g · · · s˜1s˜T−1s˜T−2 · · · s˜T−gsT−g−1 · · · s1λT−g−1.Assuming constant survival rates s across cohorts, while allowing for a break at t− g,we can simplify further as: (λs)T−g−1.More in general, letting s the constant rate before the change, and s˜ the constant oneafter, for any vintage τ = g+1, ..., T , the numerator simplifies to s˜gsτ−g−1λT−τN1(t−T + 1), and the ratio overall simplifies to(λs)T−τ.For any vintage τ = 1, ..., g born after the event and surviving at t, we have:s˜τ−1 · · · s˜1N˜1(t− τ + 1)s˜T−1s˜T−2 · · · s˜T−gsT−g−1 · · · s1N1(t− T + 1) =N˜1(t− τ + 1)s˜g−τ+1sT−g−1N1(t− T + 1) .The size of the treatment effect for any vintage τ = 1, ..., g born after the event relativeto the counterfactual with no change in the treated population is given by:TEτ =N˜1(t− τ + 1)s˜g−τ+1sT−g−1N1(t− T + 1) −(λs)T−τ. (C.1)147Define R(τ) as the ratio of the vintage τ (the cohort entering in t− τ + 1) and stillliving in t relative to the oldest cohort still living in t, that is the one entering int− T + 1. This is the data that is actually available to us. Then, for τ = g + 1, ..., Twe have:R(τ) =(λs)T−τ. (C.2)Given Equation C.1, for τ = 1, ..., g we have:R(τ) = N˜1(t− τ + 1)s˜g−τ+1sT−g−1N1(t− T + 1) , (C.3)and:R(τ) = TEτ +(λs)T−τ. (C.4)To estimate the treatment effect, we estimate first Equation C.2 using cohorts enteringbefore the treatment and recover the estimated ratio λs. With that and using cohortsafter the event period, it is possible to estimate the treatment effect from Equation C.4.In practice, we employ a Generalized Method of Moments estimator to estimate allparameters simultaneously.In the implementation, we have new-entrant cohorts in Aristotle (2021) VLO from2008 to 2020, or alternatively for periods 1 to 13. The Shelby County v. Holder(2013) treatment takes place between 2013 and 2014 (cohorts between periods 6 and7). Translating to the notation used here, we set t = 13, T = 13 and g = 7.148C.2 Tables and FiguresTable C.1: Variables Definitions - RegistrationVariable Definition SourceShare of Registered Voters:Latino Share of registered voters who are Latino VLO AristotleAsian Share of registered voters who are Asian VLO AristotleAfrican American Share of registered voters who are African American VLO AristotleMinority Share of registered voters who are Minority (non-White) VLO AristotleWhite Share of registered voters who are White VLO AristotleUnderregistration:Latino Difference between Latino voting age population share VLO Aristotle and Censusand Latino share of registered votersAsian Difference between Asian voting age population share VLO Aristotle and Censusand Asian share of registered votersAfrican American Difference between African American voting age population share VLO Aristotle and Censusand African American share of registered votersMinority Difference between Minority (non-White) voting age population share VLO Aristotle and Censusand Minority (non-White) share of registered votersRegistration Rates:Latino Share of Latino voters who are registered VLO Aristotle and CensusAsian Share of Asian voters who are registered VLO Aristotle and CensusAfrican American Share of African American voters who are registered VLO Aristotle and CensusMinority Share of Minority (non-White) voters who are registered VLO Aristotle and CensusWhite Share of White voters who are registered VLO Aristotle and CensusCensus race/ethnicity measures (excluding Latinos) are based on people not of Hispanic or latino origin.149Figure C.1: Registered Voters and Citizen Voting Age PopulationFigure 1.1: Total05101520Total Registered Voters (million)0 5 10 15 20 25Citizen Voting Age Population (million)Correlation: 0.993. Registered voters count at December 31, 2020. Citizen Voting AgePopulation from ACS-1 2019.Total Registered Voters and CVAPFigure 1.2: Latino012345Latino Registered Voters (million)0 2 4 6 8Latino Citizen Voting Age Population (million)Correlation: 0.989. Registered voters count at December 31, 2020. Citizen Voting AgePopulation from ACS-1 2019.Latino Registered Voters and CVAPFigure 1.3: African American0.511.52African American Registered Voters (million)0 .5 1 1.5 2 2.5African American Citizen Voting Age Population (million)Correlation: 0.896. Registered voters count at December 31, 2020. Citizen Voting AgePopulation from ACS-1 2019.African American Registered Voters and CVAPFigure 1.4: Asian0.2.4.6Asian Registered Voters (million)0 .2 .4 .6 .8 1Asian Citizen Voting Age Population (million)Correlation: 0.983. Registered voters count at December 31, 2020. Citizen Voting AgePopulation from ACS-1 2019. Excluding CA for illustration purposes.Asian Registered Voters and CVAP150In the following tables and figures, we report results splitting observation in Republicanand Democrat leaning. Namely, for each municipality we look at the correspondingcounty-level vote shares in the most recent previous Presidential election1, and tagmunicipalities as Republican or Democrat when the vote share of the Republicancandidate was higher or lower than the Democrat ones respectively.Figure C.2: Minority Underregistration by Rep/Dem LeaningSolid lines represent LOWESS smoothings of the underlying municipality-level data. Markers represent population bins averages.1Data based on Dave Leip’s Atlas of U.S. Presidential Elections. http://uselectionatlas.org (2020).151Table C.2: Minority Underregistration by Rep/Dem Leaning(1) (2) (3)MinorityUnderreg.MinorityUnderreg.MinorityUnderreg.Regression estimates:Minority 0.880∗∗∗ 0.968∗∗∗ 0.905∗∗∗(0.022) (0.023) (0.027)Minority2 -0.904∗∗∗ -0.965∗∗∗ -0.951∗∗∗(0.022) (0.021) (0.022)Rep. advantage 0.007∗ 0.006 0.004(0.004) (0.004) (0.004)Rep. advantage × Minority -0.011 0.047∗ 0.052∗(0.031) (0.028) (0.028)Rep. advantage × Minority2 -0.062 -0.103∗∗∗ -0.096∗∗∗(0.040) (0.037) (0.037)Max/Argmax estimates:max (Dem. leaning) 0.203∗∗∗ 0.207∗∗∗ 0.198∗∗∗(0.003) (0.003) (0.004)max (Rep. leaning) 0.191∗∗∗ 0.212∗∗∗ 0.205∗∗∗(0.003) (0.002) (0.003)argmax (Dem. leaning) 0.487∗∗∗ 0.501∗∗∗ 0.476∗∗∗(0.003) (0.004) (0.006)argmax (Rep. leaning) 0.450∗∗∗ 0.475∗∗∗ 0.457∗∗∗(0.007) (0.006) (0.007)Test max (p-value) 0.003 0.241 0.062Test argmax (p-value) 0.000 0.000 0.004Population VAP VAP VAPYear 2020 2020 2020State FE - X XControls - - XObs. 6,436 6,435 6,435Adjusted R2 0.364 0.589 0.599The top panel reports regression estimates. Robust standard errors in paren-theses. Significance levels: *** p < 0.01, ** p < 0.05, * p < 0.1. Registeredvoters with unknown race/ethnicity are imputed to Minority or White basedon the observed distributions. The dependent variable in all specifications isMinority underregistration, computed as the difference between the fractionof Minority population and the Minority fraction of registered voters. Themain independent variables are the fraction of Minority population and itssquared value. Population measures based on voting age population. Rep.advantage is an indicator for municipalities in counties in which the Republi-can vote share at the most recent previous Presidential election was higherthan the Democrat. The controls include the log-population, the log-medianhousehold income, and the fractions of rural, foreign, and over 65 population.The second panel reports Max/Argmax estimates based on the regression es-timates. Standard errors in parentheses are asymptotic. Given a quadraticformula y = ax2 + bx+ c, the max is computed as c− b24a , while the argmax iscomputed as − b2a . We report the p-values of the tests for whether the maxand argmax are different between groups.152Figure C.3: Minority Registration Rate by Rep/Dem LeaningSolid lines represent LOWESS smoothings of the underlying municipality-level data. Markers represent population bins averages.Figure C.4: Minority Registration Gap by Rep/Dem LeaningSolid lines represent LOWESS smoothings of the underlying municipality-level data. Markers represent population bins averages.153Table C.3: Effects of Shelby (2013) on Minority and White Registration (Below MedianMinority VAPS)(1) (2) (3) (4) (5) (6) (7)White(non-covered)White(covered)White(difference)Minority(non-covered)Minority(covered)Minority(difference)DiD(pseudo)2009 (pre) -0.480∗∗∗ -0.495∗∗∗ -0.016 -0.491∗∗∗ -0.516∗∗∗ -0.024 -0.009(0.005) (0.009) (0.010) (0.008) (0.018) (0.020) (0.017)2010 (pre) -0.147∗∗∗ -0.164∗∗∗ -0.017∗ -0.171∗∗∗ -0.215∗∗∗ -0.044∗ -0.026(0.004) (0.009) (0.010) (0.008) (0.021) (0.023) (0.023)2011 (pre) -0.102∗∗∗ -0.098∗∗∗ 0.005 -0.123∗∗∗ -0.121∗∗∗ 0.002 -0.003(0.005) (0.009) (0.010) (0.009) (0.020) (0.022) (0.021)2012 (pre) 0.574∗∗∗ 0.626∗∗∗ 0.052∗∗∗ 0.647∗∗∗ 0.706∗∗∗ 0.059 0.008(0.009) (0.015) (0.017) (0.016) (0.039) (0.042) (0.038)2013 (pre) 0.155∗∗∗ 0.132∗∗∗ -0.023∗∗ 0.138∗∗∗ 0.145∗∗∗ 0.007 0.030(0.005) (0.010) (0.011) (0.010) (0.019) (0.021) (0.019)2014 0.486∗∗∗ 0.435∗∗∗ -0.051∗∗∗ 0.498∗∗∗ 0.518∗∗∗ 0.019 0.070∗(0.011) (0.014) (0.018) (0.018) (0.041) (0.044) (0.038)2015 0.589∗∗∗ 0.500∗∗∗ -0.090∗∗∗ 0.592∗∗∗ 0.549∗∗∗ -0.043 0.047(0.018) (0.016) (0.024) (0.022) (0.031) (0.038) (0.033)2016 1.901∗∗∗ 1.636∗∗∗ -0.265∗∗∗ 2.231∗∗∗ 1.968∗∗∗ -0.263∗∗ 0.002(0.045) (0.038) (0.059) (0.070) (0.107) (0.128) (0.104)2017 0.832∗∗∗ 0.807∗∗∗ -0.024 0.841∗∗∗ 0.901∗∗∗ 0.060 0.084(0.026) (0.034) (0.043) (0.035) (0.063) (0.072) (0.059)2018 1.782∗∗∗ 1.408∗∗∗ -0.374∗∗∗ 1.832∗∗∗ 1.655∗∗∗ -0.177 0.197∗∗(0.051) (0.057) (0.077) (0.061) (0.118) (0.133) (0.092)2019 1.553∗∗∗ 1.244∗∗∗ -0.308∗∗∗ 1.541∗∗∗ 1.552∗∗∗ 0.011 0.320∗∗∗(0.053) (0.051) (0.074) (0.063) (0.119) (0.135) (0.100)2020 3.296∗∗∗ 2.167∗∗∗ -1.129∗∗∗ 3.258∗∗∗ 2.412∗∗∗ -0.846∗∗∗ 0.283∗∗(0.101) (0.074) (0.125) (0.127) (0.139) (0.188) (0.123)Obs. (per year) 3,029 263 . 2,847 255 . .Median Minority VAPS 0.179 0.179 0.179 0.179 0.179 0.179 0.179Above/Below Median Below Below Below Below Below Below BelowCluster level County County County County County County CountyStandard errors in parentheses are clustered in columns (1), (2), (4), (5), and asymptotic based on delta method in columns (3), (6), (7). Signifi-cance levels: *** p < 0.01, ** p < 0.05, * p < 0.1. Registered voters with unknown race/ethnicity are imputed to Minority or White based on theobserved distributions. Estimates based on the steps described in Appendix C.1. Sample restricted to municipalities with Minority VAPS in 2010below the median.154Table C.4: Effects of Shelby (2013) on Minority and White Registration (AboveMedian Minority VAPS)(1) (2) (3) (4) (5) (6) (7)White(non-covered)White(covered)White(difference)Minority(non-covered)Minority(covered)Minority(difference)DiD(pseudo)2009 (pre) -0.514∗∗∗ -0.510∗∗∗ 0.003 -0.533∗∗∗ -0.521∗∗∗ 0.012 0.008(0.005) (0.005) (0.007) (0.006) (0.005) (0.008) (0.007)2010 (pre) -0.146∗∗∗ -0.154∗∗∗ -0.008 -0.196∗∗∗ -0.210∗∗∗ -0.014 -0.006(0.008) (0.007) (0.010) (0.007) (0.007) (0.010) (0.008)2011 (pre) -0.111∗∗∗ -0.092∗∗∗ 0.019∗∗ -0.121∗∗∗ -0.091∗∗∗ 0.030∗∗∗ 0.011(0.006) (0.006) (0.008) (0.007) (0.007) (0.010) (0.007)2012 (pre) 0.625∗∗∗ 0.627∗∗∗ 0.002 0.693∗∗∗ 0.657∗∗∗ -0.036∗∗ -0.039∗∗∗(0.012) (0.009) (0.015) (0.014) (0.010) (0.017) (0.013)2013 (pre) 0.145∗∗∗ 0.129∗∗∗ -0.017 0.156∗∗∗ 0.165∗∗∗ 0.009 0.025∗∗∗(0.013) (0.006) (0.014) (0.013) (0.008) (0.015) (0.007)2014 0.435∗∗∗ 0.437∗∗∗ 0.002 0.445∗∗∗ 0.438∗∗∗ -0.006 -0.008(0.017) (0.010) (0.020) (0.018) (0.012) (0.022) (0.013)2015 0.536∗∗∗ 0.491∗∗∗ -0.045 0.528∗∗∗ 0.455∗∗∗ -0.073∗∗ -0.028∗(0.031) (0.009) (0.033) (0.027) (0.013) (0.030) (0.016)2016 1.979∗∗∗ 1.568∗∗∗ -0.411∗∗∗ 2.244∗∗∗ 1.353∗∗∗ -0.891∗∗∗ -0.480∗∗∗(0.069) (0.026) (0.074) (0.101) (0.030) (0.105) (0.051)2017 0.853∗∗∗ 0.819∗∗∗ -0.034 0.877∗∗∗ 0.736∗∗∗ -0.141∗∗ -0.106∗∗∗(0.041) (0.027) (0.050) (0.053) (0.025) (0.058) (0.025)2018 2.306∗∗∗ 1.524∗∗∗ -0.782∗∗∗ 2.372∗∗∗ 1.341∗∗∗ -1.031∗∗∗ -0.249∗∗∗(0.143) (0.043) (0.149) (0.163) (0.044) (0.169) (0.053)2019 2.402∗∗∗ 1.383∗∗∗ -1.019∗∗∗ 2.447∗∗∗ 1.219∗∗∗ -1.228∗∗∗ -0.209∗∗∗(0.196) (0.056) (0.204) (0.225) (0.050) (0.230) (0.059)2020 3.719∗∗∗ 2.316∗∗∗ -1.403∗∗∗ 3.553∗∗∗ 1.813∗∗∗ -1.740∗∗∗ -0.337∗∗∗(0.209) (0.068) (0.220) (0.240) (0.060) (0.247) (0.078)Obs. (per year) 2,003 1,096 . 2,000 1,106 . .Median Minority VAPS 0.179 0.179 0.179 0.179 0.179 0.179 0.179Above/Below Median Above Above Above Above Above Above AboveCluster level County County County County County County CountyStandard errors in parentheses are clustered in columns (1), (2), (4), (5), and asymptotic based on delta method in columns (3), (6), (7). Signifi-cance levels: *** p < 0.01, ** p < 0.05, * p < 0.1. Registered voters with unknown race/ethnicity are imputed to Minority or White based on theobserved distributions. Estimates based on the steps described in Appendix C.1. Sample restricted to municipalities with Minority VAPS in 2010above or equal to the median.155Table C.5: Effects of Shelby (2013) on Minority and White Registration (Restrictedto 200 Miles)(1) (2) (3) (4) (5) (6) (7)White(non-covered)White(covered)White(difference)Minority(non-covered)Minority(covered)Minority(difference)DiD(pseudo)2009 (pre) -0.484∗∗∗ -0.504∗∗∗ -0.020∗∗∗ -0.505∗∗∗ -0.523∗∗∗ -0.018∗∗ 0.002(0.005) (0.005) (0.007) (0.006) (0.006) (0.009) (0.007)2010 (pre) -0.151∗∗∗ -0.156∗∗∗ -0.006 -0.186∗∗∗ -0.216∗∗∗ -0.030∗∗∗ -0.025∗∗∗(0.006) (0.007) (0.009) (0.007) (0.008) (0.011) (0.009)2011 (pre) -0.095∗∗∗ -0.079∗∗∗ 0.016∗∗ -0.106∗∗∗ -0.081∗∗∗ 0.025∗∗ 0.009(0.005) (0.006) (0.008) (0.007) (0.008) (0.010) (0.008)2012 (pre) 0.563∗∗∗ 0.615∗∗∗ 0.052∗∗∗ 0.624∗∗∗ 0.670∗∗∗ 0.046∗∗∗ -0.006(0.009) (0.009) (0.012) (0.011) (0.013) (0.017) (0.014)2013 (pre) 0.167∗∗∗ 0.124∗∗∗ -0.043∗∗∗ 0.173∗∗∗ 0.150∗∗∗ -0.023∗ 0.020∗∗(0.009) (0.006) (0.011) (0.011) (0.008) (0.013) (0.008)2014 0.431∗∗∗ 0.421∗∗∗ -0.010 0.445∗∗∗ 0.432∗∗∗ -0.013 -0.004(0.013) (0.010) (0.016) (0.016) (0.015) (0.022) (0.015)2015 0.568∗∗∗ 0.492∗∗∗ -0.076∗∗∗ 0.562∗∗∗ 0.460∗∗∗ -0.101∗∗∗ -0.025(0.028) (0.009) (0.029) (0.026) (0.015) (0.030) (0.017)2016 1.750∗∗∗ 1.603∗∗∗ -0.147∗∗∗ 1.991∗∗∗ 1.441∗∗∗ -0.549∗∗∗ -0.403∗∗∗(0.050) (0.027) (0.057) (0.070) (0.037) (0.079) (0.045)2017 0.775∗∗∗ 0.850∗∗∗ 0.075∗ 0.781∗∗∗ 0.761∗∗∗ -0.020 -0.095∗∗∗(0.034) (0.028) (0.044) (0.042) (0.027) (0.050) (0.024)2018 1.881∗∗∗ 1.526∗∗∗ -0.356∗∗∗ 1.929∗∗∗ 1.384∗∗∗ -0.545∗∗∗ -0.190∗∗∗(0.100) (0.043) (0.109) (0.114) (0.051) (0.125) (0.044)2019 1.900∗∗∗ 1.420∗∗∗ -0.480∗∗∗ 1.907∗∗∗ 1.298∗∗∗ -0.609∗∗∗ -0.129∗∗(0.135) (0.055) (0.146) (0.155) (0.056) (0.165) (0.050)2020 3.195∗∗∗ 2.408∗∗∗ -0.787∗∗∗ 3.007∗∗∗ 1.999∗∗∗ -1.008∗∗∗ -0.222∗∗∗(0.143) (0.068) (0.158) (0.166) (0.067) (0.179) (0.068)Obs. (per year) 3,622 1,145 . 3,511 1,144 . .Sample 200 miles 200 miles 200 miles 200 miles 200 miles 200 miles 200 milesCluster level County County County County County County CountyStandard errors in parentheses are clustered in columns (1), (2), (4), (5), and asymptotic based on delta method in columns (3), (6), (7).Significance levels: *** p < 0.01, ** p < 0.05, * p < 0.1. Registered voters with unknown race/ethnicity are imputed to Minority orWhite based on the distributions among voters with observed race/ethnicity. Estimates based on the steps described in Appendix C.1.Sample restricted to municipalities within 200 miles on either side of the VRA coverage border at the time of Shelby (2013).156Figure C.5: Effects of Shelby (2013) on Minority v. White Registration (Restricted to200 Miles)-.6-.4-.20.2(pseudo) DiD2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021YearC.I. 95%, standard errors clustered at County level. Sample restricted to municipalitieswithin 200 miles of the VRA coverage border at the time of Shelby (2013).(restricted to 200 miles)Effects of Shelby (2013) on Minority Registration v. White-.6-.4-.20.2.4.6(pseudo) DiD2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021YearBelow (<) Above (>=)C.I. 95%, standard errors clustered at County level. Sample restricted to municipalitieswithin 200 miles of the VRA coverage border at the time of Shelby (2013).Above/Below Median Minority VAPS (= 0.212), restricted to 200 milesEffects of Shelby (2013) on Minority Registration v. WhiteRegistered voters with unknown race/ethnicity are imputed to Minority or White based on the distributions among voters with observedrace/ethnicity. The estimates use data on registrations as of January 1 of the year indicated on the horizontal axis. Estimates based onthe steps described in Appendix C.1 and column (7) of Tables C.5, C.6, and C.7. Above/below median based on Minority VAPS in 2010.Sample restricted to municipalities within 200 miles on either side of the VRA coverage border at the time of Shelby (2013).157Table C.6: Effects of Shelby (2013) on Minority and White Registration(Below Median Minority VAPS, Restricted to 200 Miles)(1) (2) (3) (4) (5) (6) (7)White(non-covered)White(covered)White(difference)Minority(non-covered)Minority(covered)Minority(difference)DiD(pseudo)2009 (pre) -0.463∗∗∗ -0.499∗∗∗ -0.036∗∗∗ -0.483∗∗∗ -0.515∗∗∗ -0.033∗ 0.003(0.005) (0.008) (0.010) (0.009) (0.017) (0.019) (0.017)2010 (pre) -0.156∗∗∗ -0.161∗∗∗ -0.006 -0.182∗∗∗ -0.218∗∗∗ -0.037∗ -0.031(0.005) (0.010) (0.011) (0.009) (0.019) (0.021) (0.022)2011 (pre) -0.087∗∗∗ -0.090∗∗∗ -0.003 -0.097∗∗∗ -0.097∗∗∗ -0.000 0.003(0.005) (0.010) (0.011) (0.009) (0.019) (0.021) (0.020)2012 (pre) 0.527∗∗∗ 0.617∗∗∗ 0.089∗∗∗ 0.587∗∗∗ 0.685∗∗∗ 0.098∗∗ 0.009(0.008) (0.015) (0.017) (0.015) (0.036) (0.039) (0.034)2013 (pre) 0.179∗∗∗ 0.135∗∗∗ -0.045∗∗∗ 0.174∗∗∗ 0.146∗∗∗ -0.028 0.016(0.007) (0.010) (0.012) (0.011) (0.018) (0.021) (0.018)2014 0.445∗∗∗ 0.428∗∗∗ -0.017 0.459∗∗∗ 0.493∗∗∗ 0.035 0.051(0.012) (0.015) (0.019) (0.019) (0.037) (0.042) (0.033)2015 0.603∗∗∗ 0.498∗∗∗ -0.105∗∗∗ 0.598∗∗∗ 0.554∗∗∗ -0.044 0.061∗(0.026) (0.017) (0.031) (0.029) (0.032) (0.043) (0.034)2016 1.650∗∗∗ 1.663∗∗∗ 0.013 1.892∗∗∗ 1.844∗∗∗ -0.049 -0.061(0.036) (0.039) (0.053) (0.054) (0.088) (0.103) (0.087)2017 0.745∗∗∗ 0.824∗∗∗ 0.078∗ 0.743∗∗∗ 0.853∗∗∗ 0.110∗ 0.032(0.030) (0.034) (0.045) (0.037) (0.047) (0.059) (0.044)2018 1.573∗∗∗ 1.429∗∗∗ -0.144∗ 1.589∗∗∗ 1.605∗∗∗ 0.016 0.160∗(0.056) (0.056) (0.079) (0.062) (0.109) (0.125) (0.086)2019 1.444∗∗∗ 1.298∗∗∗ -0.145∗ 1.375∗∗∗ 1.516∗∗∗ 0.141 0.286∗∗∗(0.065) (0.053) (0.084) (0.067) (0.109) (0.128) (0.093)2020 2.890∗∗∗ 2.257∗∗∗ -0.633∗∗∗ 2.707∗∗∗ 2.410∗∗∗ -0.297∗ 0.337∗∗∗(0.097) (0.076) (0.123) (0.111) (0.129) (0.171) (0.115)Obs. (per year) 2,151 294 . 2,038 286 . .Median Minority VAPS 0.212 0.212 0.212 0.212 0.212 0.212 0.212Above/Below Median Below Below Below Below Below Below BelowSample 200 miles 200 miles 200 miles 200 miles 200 miles 200 miles 200 milesCluster level County County County County County County CountyStandard errors in parentheses are clustered in columns (1), (2), (4), (5), and asymptotic based on delta method in columns (3), (6), (7). Signifi-cance levels: *** p < 0.01, ** p < 0.05, * p < 0.1. Registered voters with unknown race/ethnicity are imputed to Minority or White based on theobserved distributions. Estimates based on the steps described in Appendix C.1. Sample restricted to municipalities with Minority VAPS in 2010below the median, and within 200 miles on either side of the VRA coverage border at the time of Shelby (2013).158Table C.7: Effects of Shelby (2013) on Minority and White Registration(Above Median Minority VAPS, Restricted to 200 Miles)(1) (2) (3) (4) (5) (6) (7)White(non-covered)White(covered)White(difference)Minority(non-covered)Minority(covered)Minority(difference)DiD(pseudo)2009 (pre) -0.514∗∗∗ -0.506∗∗∗ 0.007 -0.533∗∗∗ -0.526∗∗∗ 0.007 -0.000(0.006) (0.005) (0.008) (0.006) (0.006) (0.009) (0.008)2010 (pre) -0.142∗∗∗ -0.154∗∗∗ -0.013 -0.189∗∗∗ -0.215∗∗∗ -0.026∗∗ -0.014∗(0.010) (0.008) (0.013) (0.009) (0.008) (0.011) (0.008)2011 (pre) -0.106∗∗∗ -0.075∗∗∗ 0.031∗∗∗ -0.118∗∗∗ -0.075∗∗∗ 0.043∗∗∗ 0.012(0.006) (0.007) (0.009) (0.008) (0.008) (0.011) (0.008)2012 (pre) 0.614∗∗∗ 0.614∗∗∗ -0.000 0.674∗∗∗ 0.665∗∗∗ -0.009 -0.009(0.014) (0.010) (0.017) (0.014) (0.011) (0.018) (0.013)2013 (pre) 0.147∗∗∗ 0.121∗∗∗ -0.026 0.166∗∗∗ 0.151∗∗∗ -0.015 0.011(0.016) (0.007) (0.017) (0.016) (0.008) (0.018) (0.008)2014 0.407∗∗∗ 0.419∗∗∗ 0.011 0.419∗∗∗ 0.411∗∗∗ -0.008 -0.019(0.020) (0.010) (0.022) (0.019) (0.014) (0.024) (0.014)2015 0.514∗∗∗ 0.490∗∗∗ -0.024 0.502∗∗∗ 0.429∗∗∗ -0.073∗∗ -0.050∗∗∗(0.038) (0.010) (0.040) (0.033) (0.014) (0.036) (0.018)2016 1.891∗∗∗ 1.582∗∗∗ -0.308∗∗∗ 2.115∗∗∗ 1.307∗∗∗ -0.808∗∗∗ -0.500∗∗∗(0.082) (0.030) (0.088) (0.118) (0.036) (0.124) (0.056)2017 0.813∗∗∗ 0.859∗∗∗ 0.045 0.822∗∗∗ 0.730∗∗∗ -0.092 -0.137∗∗∗(0.048) (0.033) (0.058) (0.060) (0.031) (0.067) (0.026)2018 2.326∗∗∗ 1.559∗∗∗ -0.767∗∗∗ 2.386∗∗∗ 1.310∗∗∗ -1.076∗∗∗ -0.309∗∗∗(0.177) (0.049) (0.183) (0.202) (0.053) (0.209) (0.062)2019 2.562∗∗∗ 1.462∗∗∗ -1.100∗∗∗ 2.630∗∗∗ 1.225∗∗∗ -1.404∗∗∗ -0.304∗∗∗(0.240) (0.065) (0.249) (0.276) (0.060) (0.283) (0.072)2020 3.635∗∗∗ 2.461∗∗∗ -1.175∗∗∗ 3.409∗∗∗ 1.861∗∗∗ -1.548∗∗∗ -0.373∗∗∗(0.252) (0.078) (0.264) (0.289) (0.072) (0.297) (0.086)Obs. (per year) 1,471 851 . 1,473 858 . .Median Minority VAPS 0.212 0.212 0.212 0.212 0.212 0.212 0.212Above/Below Median Above Above Above Above Above Above AboveSample 200 miles 200 miles 200 miles 200 miles 200 miles 200 miles 200 milesCluster level County County County County County County CountyStandard errors in parentheses are clustered in columns (1), (2), (4), (5), and asymptotic based on delta method in columns (3), (6), (7). Signifi-cance levels: *** p < 0.01, ** p < 0.05, * p < 0.1. Registered voters with unknown race/ethnicity are imputed to Minority or White based on theobserved distributions. Estimates based on the steps described in Appendix C.1. Sample restricted to municipalities with Minority VAPS in 2010above or equal to the median, and within 200 miles on either side of the VRA coverage border at the time of Shelby (2013).159Table C.8: Effects of Shelby (2013) on Minority and White Registration (Restrictedto 100 Miles)(1) (2) (3) (4) (5) (6) (7)White(non-covered)White(covered)White(difference)Minority(non-covered)Minority(covered)Minority(difference)DiD(pseudo)2009 (pre) -0.482∗∗∗ -0.510∗∗∗ -0.029∗∗∗ -0.499∗∗∗ -0.531∗∗∗ -0.031∗∗∗ -0.002(0.005) (0.005) (0.008) (0.007) (0.007) (0.010) (0.009)2010 (pre) -0.152∗∗∗ -0.165∗∗∗ -0.014 -0.187∗∗∗ -0.220∗∗∗ -0.033∗∗∗ -0.020∗(0.007) (0.008) (0.010) (0.008) (0.009) (0.012) (0.011)2011 (pre) -0.096∗∗∗ -0.083∗∗∗ 0.013 -0.102∗∗∗ -0.088∗∗∗ 0.014 0.001(0.005) (0.007) (0.009) (0.008) (0.009) (0.012) (0.010)2012 (pre) 0.557∗∗∗ 0.620∗∗∗ 0.063∗∗∗ 0.619∗∗∗ 0.680∗∗∗ 0.062∗∗∗ -0.001(0.010) (0.009) (0.013) (0.013) (0.015) (0.019) (0.016)2013 (pre) 0.172∗∗∗ 0.138∗∗∗ -0.034∗∗∗ 0.170∗∗∗ 0.159∗∗∗ -0.011 0.022∗∗(0.010) (0.007) (0.012) (0.011) (0.010) (0.015) (0.010)2014 0.416∗∗∗ 0.420∗∗∗ 0.004 0.424∗∗∗ 0.435∗∗∗ 0.011 0.007(0.014) (0.012) (0.019) (0.018) (0.019) (0.026) (0.017)2015 0.503∗∗∗ 0.500∗∗∗ -0.003 0.483∗∗∗ 0.479∗∗∗ -0.004 -0.001(0.024) (0.011) (0.026) (0.023) (0.018) (0.030) (0.020)2016 1.627∗∗∗ 1.620∗∗∗ -0.007 1.763∗∗∗ 1.506∗∗∗ -0.257∗∗∗ -0.250∗∗∗(0.051) (0.033) (0.061) (0.072) (0.046) (0.086) (0.053)2017 0.738∗∗∗ 0.868∗∗∗ 0.129∗∗∗ 0.724∗∗∗ 0.804∗∗∗ 0.080 -0.049∗(0.034) (0.033) (0.048) (0.044) (0.034) (0.055) (0.028)2018 1.766∗∗∗ 1.528∗∗∗ -0.239∗∗ 1.741∗∗∗ 1.444∗∗∗ -0.297∗∗ -0.058(0.105) (0.054) (0.118) (0.115) (0.065) (0.132) (0.051)2019 1.844∗∗∗ 1.437∗∗∗ -0.406∗∗ 1.790∗∗∗ 1.362∗∗∗ -0.427∗∗ -0.021(0.143) (0.070) (0.159) (0.155) (0.071) (0.171) (0.059)2020 2.987∗∗∗ 2.426∗∗∗ -0.561∗∗∗ 2.664∗∗∗ 2.063∗∗∗ -0.601∗∗∗ -0.040(0.153) (0.085) (0.175) (0.165) (0.086) (0.186) (0.077)Obs. (per year) 2,013 833 . 1,966 829 . .Sample 100 miles 100 miles 100 miles 100 miles 100 miles 100 miles 100 milesCluster level County County County County County County CountyStandard errors in parentheses are clustered in columns (1), (2), (4), (5), and asymptotic based on delta method in columns (3), (6), (7).Significance levels: *** p < 0.01, ** p < 0.05, * p < 0.1. Registered voters with unknown race/ethnicity are imputed to Minority orWhite based on the observed distributions. Estimates based on the steps described in Appendix C.1. Sample restricted to municipalitieswithin 100 miles on either side of the VRA coverage border at the time of Shelby (2013).160Figure C.6: Effects of Shelby (2013) on Minority v. White Registration (Restricted to100 Miles)-.6-.4-.20.2(pseudo) DiD2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021YearC.I. 95%, standard errors clustered at County level. Sample restricted to municipalitieswithin 100 miles of the VRA coverage border at the time of Shelby (2013).(restricted to 100 miles)Effects of Shelby (2013) on Minority Registration v. White-.6-.4-.20.2.4.6(pseudo) DiD2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021YearBelow (<) Above (>=)C.I. 95%, standard errors clustered at County level. Sample restricted to municipalitieswithin 100 miles of the VRA coverage border at the time of Shelby (2013).Above/Below Median Minority VAPS (= 0.254), restricted to 100 milesEffects of Shelby (2013) on Minority Registration v. WhiteRegistered voters with unknown race/ethnicity are imputed to Minority or White based on the observed distributions. The estimates usedata on registrations as of January 1 of the year indicated on the horizontal axis. Estimates based on the steps described in Appendix C.1and column (7) of Tables C.8, C.9, and C.10. Above/below median based on Minority VAPS in 2010. Sample restricted to municipalitieswithin 100 miles on either side of the VRA coverage border at the time of Shelby (2013).161Table C.9: Effects of Shelby (2013) on Minority and White Registration(Below Median Minority VAPS, Restricted to 100 Miles)(1) (2) (3) (4) (5) (6) (7)White(non-covered)White(covered)White(difference)Minority(non-covered)Minority(covered)Minority(difference)DiD(pseudo)2009 (pre) -0.459∗∗∗ -0.507∗∗∗ -0.049∗∗∗ -0.476∗∗∗ -0.538∗∗∗ -0.062∗∗∗ -0.014(0.006) (0.008) (0.010) (0.009) (0.014) (0.017) (0.016)2010 (pre) -0.156∗∗∗ -0.169∗∗∗ -0.012 -0.180∗∗∗ -0.216∗∗∗ -0.036∗ -0.024(0.006) (0.010) (0.011) (0.010) (0.018) (0.020) (0.022)2011 (pre) -0.092∗∗∗ -0.094∗∗∗ -0.002 -0.099∗∗∗ -0.114∗∗∗ -0.015 -0.014(0.005) (0.011) (0.012) (0.011) (0.019) (0.022) (0.020)2012 (pre) 0.525∗∗∗ 0.630∗∗∗ 0.106∗∗∗ 0.577∗∗∗ 0.711∗∗∗ 0.134∗∗∗ 0.028(0.010) (0.014) (0.017) (0.017) (0.034) (0.038) (0.035)2013 (pre) 0.182∗∗∗ 0.139∗∗∗ -0.044∗∗∗ 0.178∗∗∗ 0.157∗∗∗ -0.020 0.023(0.007) (0.010) (0.013) (0.012) (0.017) (0.021) (0.019)2014 0.424∗∗∗ 0.423∗∗∗ -0.000 0.436∗∗∗ 0.480∗∗∗ 0.044 0.044(0.013) (0.017) (0.022) (0.022) (0.035) (0.042) (0.033)2015 0.522∗∗∗ 0.501∗∗∗ -0.021 0.505∗∗∗ 0.537∗∗∗ 0.031 0.052(0.016) (0.017) (0.023) (0.022) (0.032) (0.039) (0.034)2016 1.569∗∗∗ 1.712∗∗∗ 0.143∗∗ 1.700∗∗∗ 1.829∗∗∗ 0.129 -0.014(0.038) (0.041) (0.056) (0.062) (0.089) (0.108) (0.093)2017 0.729∗∗∗ 0.878∗∗∗ 0.149∗∗∗ 0.721∗∗∗ 0.889∗∗∗ 0.169∗∗∗ 0.020(0.028) (0.039) (0.048) (0.040) (0.048) (0.063) (0.048)2018 1.546∗∗∗ 1.470∗∗∗ -0.076 1.503∗∗∗ 1.604∗∗∗ 0.101 0.177∗(0.072) (0.060) (0.094) (0.080) (0.109) (0.135) (0.091)2019 1.504∗∗∗ 1.358∗∗∗ -0.146 1.416∗∗∗ 1.505∗∗∗ 0.090 0.235∗∗(0.093) (0.058) (0.109) (0.098) (0.108) (0.146) (0.097)2020 2.790∗∗∗ 2.347∗∗∗ -0.443∗∗∗ 2.477∗∗∗ 2.395∗∗∗ -0.082 0.360∗∗∗(0.127) (0.083) (0.152) (0.150) (0.134) (0.201) (0.131)Obs. (per year) 1,153 299 . 1,103 291 . .Median Minority VAPS 0.254 0.254 0.254 0.254 0.254 0.254 0.254Above/Below Median Below Below Below Below Below Below BelowSample 100 miles 100 miles 100 miles 100 miles 100 miles 100 miles 100 milesCluster level County County County County County County CountyStandard errors in parentheses are clustered in columns (1), (2), (4), (5), and asymptotic based on delta method in columns (3), (6), (7). Signifi-cance levels: *** p < 0.01, ** p < 0.05, * p < 0.1. Registered voters with unknown race/ethnicity are imputed to Minority or White based on theobserved distributions. Estimates based on the steps described in Appendix C.1. Sample restricted to municipalities with Minority VAPS in 2010below the median, and within 100 miles on either side of the VRA coverage border at the time of Shelby (2013).162Table C.10: Effects of Shelby (2013) on Minority and White Registration(Above Median Minority VAPS, Restricted to 100 Miles)(1) (2) (3) (4) (5) (6) (7)White(non-covered)White(covered)White(difference)Minority(non-covered)Minority(covered)Minority(difference)DiD(pseudo)2009 (pre) -0.511∗∗∗ -0.512∗∗∗ -0.002 -0.527∗∗∗ -0.526∗∗∗ 0.001 0.003(0.007) (0.006) (0.009) (0.007) (0.007) (0.010) (0.010)2010 (pre) -0.144∗∗∗ -0.164∗∗∗ -0.020 -0.195∗∗∗ -0.223∗∗∗ -0.028∗∗ -0.008(0.011) (0.011) (0.015) (0.009) (0.009) (0.013) (0.009)2011 (pre) -0.101∗∗∗ -0.076∗∗∗ 0.024∗∗ -0.106∗∗∗ -0.074∗∗∗ 0.032∗∗∗ 0.008(0.007) (0.009) (0.011) (0.008) (0.009) (0.012) (0.008)2012 (pre) 0.600∗∗∗ 0.615∗∗∗ 0.014 0.671∗∗∗ 0.664∗∗∗ -0.007 -0.021(0.014) (0.010) (0.018) (0.016) (0.012) (0.020) (0.015)2013 (pre) 0.155∗∗∗ 0.138∗∗∗ -0.017 0.157∗∗∗ 0.160∗∗∗ 0.002 0.019∗∗(0.016) (0.008) (0.018) (0.016) (0.011) (0.019) (0.009)2014 0.401∗∗∗ 0.418∗∗∗ 0.017 0.403∗∗∗ 0.410∗∗∗ 0.007 -0.010(0.021) (0.013) (0.025) (0.022) (0.018) (0.028) (0.017)2015 0.471∗∗∗ 0.499∗∗∗ 0.028 0.448∗∗∗ 0.447∗∗∗ -0.001 -0.029(0.038) (0.013) (0.040) (0.031) (0.019) (0.037) (0.022)2016 1.698∗∗∗ 1.568∗∗∗ -0.129 1.838∗∗∗ 1.331∗∗∗ -0.507∗∗∗ -0.378∗∗∗(0.085) (0.041) (0.094) (0.115) (0.049) (0.125) (0.061)2017 0.743∗∗∗ 0.862∗∗∗ 0.118∗ 0.722∗∗∗ 0.758∗∗∗ 0.036 -0.082∗∗∗(0.052) (0.042) (0.067) (0.063) (0.042) (0.075) (0.030)2018 2.053∗∗∗ 1.560∗∗∗ -0.493∗∗ 2.038∗∗∗ 1.358∗∗∗ -0.680∗∗∗ -0.187∗∗∗(0.179) (0.069) (0.192) (0.193) (0.075) (0.207) (0.063)2019 2.291∗∗∗ 1.482∗∗∗ -0.809∗∗∗ 2.261∗∗∗ 1.285∗∗∗ -0.976∗∗∗ -0.167∗∗(0.246) (0.094) (0.264) (0.264) (0.087) (0.278) (0.074)2020 3.243∗∗∗ 2.470∗∗∗ -0.772∗∗∗ 2.895∗∗∗ 1.883∗∗∗ -1.012∗∗∗ -0.240∗∗∗(0.245) (0.111) (0.269) (0.258) (0.101) (0.277) (0.090)Obs. (per year) 860 534 . 863 538 . .Median Minority VAPS 0.254 0.254 0.254 0.254 0.254 0.254 0.254Above/Below Median Above Above Above Above Above Above AboveSample 100 miles 100 miles 100 miles 100 miles 100 miles 100 miles 100 milesCluster level County County County County County County CountyStandard errors in parentheses are clustered in columns (1), (2), (4), (5), and asymptotic based on delta method in columns (3), (6), (7). Signifi-cance levels: *** p < 0.01, ** p < 0.05, * p < 0.1. Registered voters with unknown race/ethnicity are imputed to Minority or White based on theobserved distributions. Estimates based on the steps described in Appendix C.1. Sample restricted to municipalities with Minority VAPS in 2010above or equal to the median, and within 100 miles on either side of the VRA coverage border at the time of Shelby (2013).163In the following tables and figures, we report results splitting observation in Republicanand Democrat leaning. Namely, for each municipality we look at the correspondingcounty-level vote shares in the most recent previous Presidential election2, and tagmunicipalities as Republican or Democrat when the vote share of the Republicancandidate was higher or lower than the Democrat ones respectively.Figure C.7: Effects of Shelby (2013) on Minority v. White Registration (by Rep./Dem.Leaning)-.6-.4-.20.2(pseudo) DiD2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021YearDem. leaning Rep. leaningC.I. 95%, standard errors clustered at County level.By Rep./Dem. LeaningEffects of Shelby (2013) on Minority Registration v. WhiteRegistered voters with unknown race/ethnicity are imputed to Minority or White based on the distributions among voters with observedrace/ethnicity. The estimates use data on registrations as of January 1 of the year indicated on the horizontal axis. Estimates basedon the steps described in Appendix C.1 and column (7) of Tables C.11 and C.12. For each municipality, the Republican or Democratleaning is based on the corresponding county-level vote shares in the most recent previous Presidential election.2Data based on Dave Leip’s Atlas of U.S. Presidential Elections. http://uselectionatlas.org (2020).164Table C.11: Effects of Shelby (2013) on Minority and White Registration (Dem.Leaning)(1) (2) (3) (4) (5) (6) (7)White(non-covered)White(covered)White(difference)Minority(non-covered)Minority(covered)Minority(difference)DiD(pseudo)2009 (pre) -0.481∗∗∗ -0.522∗∗∗ -0.040∗∗∗ -0.497∗∗∗ -0.533∗∗∗ -0.036∗∗∗ 0.005(0.007) (0.008) (0.011) (0.008) (0.011) (0.013) (0.012)2010 (pre) -0.132∗∗∗ -0.148∗∗∗ -0.016 -0.164∗∗∗ -0.191∗∗∗ -0.027∗ -0.011(0.007) (0.014) (0.016) (0.008) (0.013) (0.015) (0.012)2011 (pre) -0.070∗∗∗ -0.093∗∗∗ -0.023 -0.087∗∗∗ -0.095∗∗∗ -0.008 0.015(0.009) (0.013) (0.016) (0.009) (0.013) (0.016) (0.011)2012 (pre) 0.615∗∗∗ 0.632∗∗∗ 0.017 0.686∗∗∗ 0.668∗∗∗ -0.018 -0.035(0.015) (0.017) (0.023) (0.018) (0.019) (0.027) (0.024)2013 (pre) 0.177∗∗∗ 0.122∗∗∗ -0.055∗∗∗ 0.175∗∗∗ 0.142∗∗∗ -0.033 0.021∗(0.015) (0.011) (0.018) (0.016) (0.015) (0.022) (0.012)2014 0.460∗∗∗ 0.406∗∗∗ -0.054∗ 0.470∗∗∗ 0.427∗∗∗ -0.043 0.011(0.023) (0.019) (0.030) (0.025) (0.026) (0.036) (0.021)2015 0.583∗∗∗ 0.430∗∗∗ -0.152∗∗∗ 0.580∗∗∗ 0.410∗∗∗ -0.170∗∗∗ -0.018(0.041) (0.019) (0.045) (0.037) (0.026) (0.046) (0.027)2016 2.052∗∗∗ 1.579∗∗∗ -0.473∗∗∗ 2.354∗∗∗ 1.463∗∗∗ -0.890∗∗∗ -0.417∗∗∗(0.093) (0.067) (0.115) (0.125) (0.063) (0.140) (0.079)2017 0.913∗∗∗ 0.850∗∗∗ -0.063 0.952∗∗∗ 0.762∗∗∗ -0.190∗∗ -0.127∗∗∗(0.060) (0.074) (0.095) (0.072) (0.061) (0.094) (0.047)2018 2.332∗∗∗ 1.504∗∗∗ -0.828∗∗∗ 2.417∗∗∗ 1.414∗∗∗ -1.003∗∗∗ -0.175∗∗(0.167) (0.114) (0.202) (0.185) (0.111) (0.216) (0.072)2019 2.390∗∗∗ 1.382∗∗∗ -1.009∗∗∗ 2.492∗∗∗ 1.253∗∗∗ -1.239∗∗∗ -0.230∗∗∗(0.222) (0.154) (0.270) (0.247) (0.131) (0.280) (0.080)2020 3.657∗∗∗ 2.213∗∗∗ -1.444∗∗∗ 3.618∗∗∗ 1.800∗∗∗ -1.818∗∗∗ -0.373∗∗∗(0.260) (0.176) (0.314) (0.295) (0.154) (0.332) (0.102)Obs. (total) 28,383 4,376 . 28,081 4,467 . .Rep./Dem. Leaning Dem. Dem. Dem. Dem. Dem. Dem. Dem.Cluster level County County County County County County CountyStandard errors in parentheses are clustered in columns (1), (2), (4), (5), and asymptotic based on delta method in columns (3), (6), (7).Significance levels: *** p < 0.01, ** p < 0.05, * p < 0.1. Registered voters with unknown race/ethnicity are imputed to Minority or Whitebased on the observed distributions. Estimates based on the steps described in Appendix C.1. Sample restricted to municipalities leaningDemocrat based on the corresponding county-level vote shares in the most recent previous Presidential election.165Table C.12: Effects of Shelby (2013) on Minority and White Registration (Rep.Leaning)(1) (2) (3) (4) (5) (6) (7)White(non-covered)White(covered)White(difference)Minority(non-covered)Minority(covered)Minority(difference)DiD(pseudo)2009 (pre) -0.502∗∗∗ -0.502∗∗∗ 0.000 -0.519∗∗∗ -0.515∗∗∗ 0.004 0.004(0.006) (0.005) (0.008) (0.010) (0.007) (0.012) (0.010)2010 (pre) -0.156∗∗∗ -0.159∗∗∗ -0.003 -0.197∗∗∗ -0.218∗∗∗ -0.021 -0.019(0.006) (0.007) (0.009) (0.010) (0.009) (0.014) (0.012)2011 (pre) -0.141∗∗∗ -0.093∗∗∗ 0.047∗∗∗ -0.158∗∗∗ -0.097∗∗∗ 0.061∗∗∗ 0.013(0.007) (0.006) (0.010) (0.011) (0.009) (0.014) (0.012)2012 (pre) 0.565∗∗∗ 0.625∗∗∗ 0.060∗∗∗ 0.638∗∗∗ 0.666∗∗∗ 0.028 -0.032∗(0.011) (0.010) (0.015) (0.018) (0.014) (0.023) (0.017)2013 (pre) 0.116∗∗∗ 0.132∗∗∗ 0.016 0.108∗∗∗ 0.168∗∗∗ 0.061∗∗∗ 0.045∗∗∗(0.007) (0.007) (0.010) (0.012) (0.009) (0.015) (0.011)2014 0.457∗∗∗ 0.448∗∗∗ -0.010 0.473∗∗∗ 0.463∗∗∗ -0.010 -0.001(0.011) (0.010) (0.015) (0.018) (0.016) (0.024) (0.019)2015 0.541∗∗∗ 0.515∗∗∗ -0.026 0.543∗∗∗ 0.496∗∗∗ -0.047∗ -0.021(0.014) (0.009) (0.017) (0.019) (0.015) (0.024) (0.020)2016 1.823∗∗∗ 1.581∗∗∗ -0.241∗∗∗ 2.128∗∗∗ 1.469∗∗∗ -0.659∗∗∗ -0.418∗∗∗(0.045) (0.023) (0.050) (0.072) (0.039) (0.082) (0.054)2017 0.764∗∗∗ 0.803∗∗∗ 0.039 0.765∗∗∗ 0.768∗∗∗ 0.003 -0.036(0.025) (0.021) (0.033) (0.033) (0.025) (0.041) (0.028)2018 1.727∗∗∗ 1.499∗∗∗ -0.228∗∗∗ 1.769∗∗∗ 1.394∗∗∗ -0.375∗∗∗ -0.147∗∗∗(0.050) (0.033) (0.060) (0.062) (0.044) (0.076) (0.046)2019 1.519∗∗∗ 1.346∗∗∗ -0.173∗∗∗ 1.471∗∗∗ 1.291∗∗∗ -0.180∗∗ -0.007(0.056) (0.034) (0.065) (0.065) (0.043) (0.078) (0.048)2020 3.308∗∗∗ 2.313∗∗∗ -0.995∗∗∗ 3.188∗∗∗ 1.971∗∗∗ -1.217∗∗∗ -0.222∗∗∗(0.099) (0.052) (0.111) (0.124) (0.055) (0.136) (0.077)Obs. (total) 32,001 11,932 . 30,083 11,865 . .Rep./Dem. leaning Rep. Rep. Rep. Rep. Rep. Rep. Rep.Cluster level County County County County County County CountyStandard errors in parentheses are clustered in columns (1), (2), (4), (5), and asymptotic based on delta method in columns (3), (6), (7).Significance levels: *** p < 0.01, ** p < 0.05, * p < 0.1. Registered voters with unknown race/ethnicity are imputed to Minority or Whitebased on the observed distributions. Estimates based on the steps described in Appendix C.1. Sample restricted to municipalities leaningRepublican based on the corresponding county-level vote shares in the most recent previous Presidential election.166
Thesis/Dissertation
eng
Economics
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Milligan, Kevin
Trebbi, Francesco
Essays on local political economy
Text