We investigate patterns of racial bias in small business loans denial rates in the U.S. across different credit risk scores. We motivate this inquiry with a simple and generalizable statistical discrimination model where banks observe noisy signals of creditworthiness and hold prior beliefs of repayment probability based on the applicant’s group. Our model predicts that differences in approval rating across groups are more pronounced at middle range values and disappear at very high and very low credit scores. Using data constructed from the 1998 Survey of Small Business Finances and the restricted access Kauffman Firm Survey we find disparities in loan approval ratings between Black and White entrepreneurs in intermediate risk categories but not for the best and worst categories.
{"title":"Why are there Racial Disparities in the Small Business Loan Market","authors":"A. Rakshit, J. Peterson","doi":"10.2139/ssrn.3889590","DOIUrl":"https://doi.org/10.2139/ssrn.3889590","url":null,"abstract":"We investigate patterns of racial bias in small business loans denial rates in the U.S. across different credit risk scores. We motivate this inquiry with a simple and generalizable statistical discrimination model where banks observe noisy signals of creditworthiness and hold prior beliefs of repayment probability based on the applicant’s group. Our model predicts that differences in approval rating across groups are more pronounced at middle range values and disappear at very high and very low credit scores. Using data constructed from the 1998 Survey of Small Business Finances and the restricted access Kauffman Firm Survey we find disparities in loan approval ratings between Black and White entrepreneurs in intermediate risk categories but not for the best and worst categories.","PeriodicalId":166384,"journal":{"name":"PSN: Politics of Race (Topic)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127612632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Algorithmic decision-making can lead to discrimination against legally protected groups, but measuring such discrimination is often hampered by a fundamental selection challenge. We develop new quasi-experimental tools to overcome this challenge and measure algorithmic discrimination in pretrial bail decisions. We show that the selection challenge reduces to the challenge of measuring four moments, which can be estimated by extrapolating quasi-experimental variation across as-good-as-randomly assigned decision-makers. Estimates from New York City show that both a sophisticated machine learning algorithm and a simpler regression model discriminate against Black defendants even though defendant race and ethnicity are not included in the training data.
{"title":"Measuring Racial Discrimination in Algorithms","authors":"David Arnold, Will Dobbie, Peter Hull","doi":"10.2139/ssrn.3753043","DOIUrl":"https://doi.org/10.2139/ssrn.3753043","url":null,"abstract":"Algorithmic decision-making can lead to discrimination against legally protected groups, but measuring such discrimination is often hampered by a fundamental selection challenge. We develop new quasi-experimental tools to overcome this challenge and measure algorithmic discrimination in pretrial bail decisions. We show that the selection challenge reduces to the challenge of measuring four moments, which can be estimated by extrapolating quasi-experimental variation across as-good-as-randomly assigned decision-makers. Estimates from New York City show that both a sophisticated machine learning algorithm and a simpler regression model discriminate against Black defendants even though defendant race and ethnicity are not included in the training data.","PeriodicalId":166384,"journal":{"name":"PSN: Politics of Race (Topic)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128125130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Alternative data, or information such as cell phone payments or utility bills, is increasingly used in underwriting by the financial services industry, especially financial technology or “fintech” companies. Some financial companies have begun to use information about borrowers’ education history, including the identity or sector of the college or university a borrower attended, when determining access to and the cost of credit. For years, policymakers have weighed the use of alternative data to help expand access to credit for marginalized or underserved communities. Although helping consumers trapped outside of the credit market is an important policy goal, regulators have made clear that certain data can also pose serious fair lending and discrimination risks by introducing unfair biases and perpetuating existing disparities.
The use of education data in credit decisions is particularly troublesome given the continuing pattern of disparate access to education in America and the historical inequality perpetuated by the use of this information. Widespread use of this data by lenders could reinforce systemic barriers to financial inclusion for black and Latinx consumers. For example, African American and Latinx students are especially underrepresented at the nation’s most selective colleges and universities, with nine percent and 12 percent, respectively, represented at the most prestigious public universities.
The SBPC examined a private loan product at a large bank and a private loan refinance product offered by a fintech lender. Using lenders’ publicly available online rate check tools, the SBPC tested loan applications from fictional borrowers from different schools while maintaining all other borrower characteristics constant (e.g., income, savings, occupation, loan amount). The sample credit estimates generated by the big bank indicated higher loan costs charged to borrowers for attending a community college. In the case of the fintech lender, higher costs were charged to a borrower who attended certain Minority-Serving Institutions (MSIs).
The companies used in the analysis are Wells Fargo and Upstart Network, Inc. Wells Fargo is one of the nation’s largest banks and the second-largest lender of new private student loans to college students. Upstart Network is a fintech company that uses machine learning and alternative data, including degree attainment, school attended, and area of study, in its underwriting processes.
Specific takeaways from the consumer case studies included in this report: 1) A private student loan borrower may pay a penalty for attending a community college. Wells Fargo charges a hypothetical community college borrower $1,134 more on a $10,000 loan, when compared to a similarly situated borrower enrolled at a four-year college. 2) A borrower who refinances student loans may pay a penalty for attending an HBCU. When refinancing with Upstart, a hypothetical graduate of Howard University, an H
金融服务行业，尤其是金融科技或“金融科技”公司，越来越多地在承保中使用替代数据或信息，如手机支付或水电费。一些金融公司已经开始使用借款人的教育历史信息，包括借款人的身份或就读的学院或大学的部门，来确定获得信贷的途径和成本。多年来，政策制定者一直在权衡使用替代数据来帮助边缘化或服务不足的社区扩大获得信贷的机会。尽管帮助被困在信贷市场之外的消费者是一项重要的政策目标，但监管机构已明确表示，某些数据也可能带来严重的公平贷款和歧视风险，因为它们会引入不公平的偏见，并使现有的差距长期存在。在信贷决策中使用教育数据尤其麻烦，因为美国受教育机会的差异一直存在，而且这种信息的使用使历史上的不平等得以延续。贷款机构对这些数据的广泛使用可能会加强对黑人和拉丁裔消费者的金融包容性的系统性障碍。例如，非裔美国人和拉丁裔学生在美国最顶尖的大学中所占比例尤其不足，在最著名的公立大学中所占比例分别为9%和12%。SBPC检查了大型银行的民间贷款产品和金融科技贷款公司的民间贷款再融资产品。利用贷款人公开的在线利率检查工具，SBPC测试了来自不同学校的虚构借款人的贷款申请，同时保持所有其他借款人特征不变(例如，收入、储蓄、职业、贷款金额)。这家大银行提供的样本信贷估计显示，就读社区大学的借款人需要支付更高的贷款成本。在金融科技贷款机构的案例中，参加某些少数民族服务机构(msi)的借款人要承担更高的成本。分析中使用的公司是富国银行和Upstart Network, Inc.。富国银行是美国最大的银行之一，也是向大学生发放新私人学生贷款的第二大银行。Upstart Network是一家金融科技公司，在其承保流程中使用机器学习和替代数据，包括学历、就读学校和学习领域。本报告中包含的消费者案例研究的具体要点:1)私人学生贷款借款人可能会因就读社区大学而支付罚款。富国银行(Wells Fargo)向一名假设的社区大学借款人收取1万美元贷款的费用，比向一名就读于四年制大学的借款人多收取1134美元。2)为学生贷款再融资的借款人可能会因就读HBCU而支付罚款。在Upstart进行再融资时，假设霍华德大学(HBCU)的毕业生在五年期贷款期限内要比纽约大学(NYU)的毕业生多支付3499美元。3)为学生贷款再融资的借款人可能会因就读于西班牙裔服务机构(HSI)而支付罚款。在Upstart进行再融资时，一个假设从新墨西哥州立大学拉斯克鲁塞斯分校(New Mexico State University-Las Cruces，简称HSI)获得学士学位的毕业生，在五年期贷款期限内要比同样情况的纽约大学毕业生多支付至少1,724美元。
{"title":"Educational Redlining","authors":"Student Borrower Protection Center","doi":"10.2139/ssrn.3759925","DOIUrl":"https://doi.org/10.2139/ssrn.3759925","url":null,"abstract":"Alternative data, or information such as cell phone payments or utility bills, is increasingly used in underwriting by the financial services industry, especially financial technology or “fintech” companies. Some financial companies have begun to use information about borrowers’ education history, including the identity or sector of the college or university a borrower attended, when determining access to and the cost of credit. For years, policymakers have weighed the use of alternative data to help expand access to credit for marginalized or underserved communities. Although helping consumers trapped outside of the credit market is an important policy goal, regulators have made clear that certain data can also pose serious fair lending and discrimination risks by introducing unfair biases and perpetuating existing disparities.<br><br>The use of education data in credit decisions is particularly troublesome given the continuing pattern of disparate access to education in America and the historical inequality perpetuated by the use of this information. Widespread use of this data by lenders could reinforce systemic barriers to financial inclusion for black and Latinx consumers. For example, African American and Latinx students are especially underrepresented at the nation’s most selective colleges and universities, with nine percent and 12 percent, respectively, represented at the most prestigious public universities.<br><br>The SBPC examined a private loan product at a large bank and a private loan refinance product offered by a fintech lender. Using lenders’ publicly available online rate check tools, the SBPC tested loan applications from fictional borrowers from different schools while maintaining all other borrower characteristics constant (e.g., income, savings, occupation, loan amount). The sample credit estimates generated by the big bank indicated higher loan costs charged to borrowers for attending a community college. In the case of the fintech lender, higher costs were charged to a borrower who attended certain Minority-Serving Institutions (MSIs).<br><br>The companies used in the analysis are Wells Fargo and Upstart Network, Inc. Wells Fargo is one of the nation’s largest banks and the second-largest lender of new private student loans to college students. Upstart Network is a fintech company that uses machine learning and alternative data, including degree attainment, school attended, and area of study, in its underwriting processes.<br><br>Specific takeaways from the consumer case studies included in this report: 1) A private student loan borrower may pay a penalty for attending a community college. Wells Fargo charges a hypothetical community college borrower $1,134 more on a $10,000 loan, when compared to a similarly situated borrower enrolled at a four-year college. 2) A borrower who refinances student loans may pay a penalty for attending an HBCU. When refinancing with Upstart, a hypothetical graduate of Howard University, an H","PeriodicalId":166384,"journal":{"name":"PSN: Politics of Race (Topic)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122478856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper examines race and police use of force using data on 1.6 million 911 calls in two cities, neither of which allows for discretion in officer dispatch. Results indicate White officers increase force much more than minority officers when dispatched to more minority neighborhoods. Estimates indicate Black (Hispanic) civilians are 55 (75) percent more likely to experience any force, and five times as likely to experience a police shooting, compared to if White officers scaled up force similarly to minority officers. Additionally, 14 percent of White officers use excess force in Black neighborhoods relative to our statistical benchmark. (JEL H76, J15, K42, R23)
{"title":"Does Race Matter for Police Use of Force? Evidence from 911 Calls","authors":"Mark Hoekstra, CarlyWill Sloan","doi":"10.3386/w26774","DOIUrl":"https://doi.org/10.3386/w26774","url":null,"abstract":"This paper examines race and police use of force using data on 1.6 million 911 calls in two cities, neither of which allows for discretion in officer dispatch. Results indicate White officers increase force much more than minority officers when dispatched to more minority neighborhoods. Estimates indicate Black (Hispanic) civilians are 55 (75) percent more likely to experience any force, and five times as likely to experience a police shooting, compared to if White officers scaled up force similarly to minority officers. Additionally, 14 percent of White officers use excess force in Black neighborhoods relative to our statistical benchmark. (JEL H76, J15, K42, R23)","PeriodicalId":166384,"journal":{"name":"PSN: Politics of Race (Topic)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131645614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper records the path by which African Americans were transformed from enslaved persons in the American economy to partial participants in the progress of the economy. The path was not monotonic, and we organize our tale by periods in which inclusiveness rose and fell. The history we recount demonstrates the staying power of the myth of black inferiority held by a changing white majority as the economy expanded dramatically. Slavery was outlawed after the Civil War, and blacks began to participate in American politics en masse for the first time during Reconstruction. This process met with white resistance, and black inclusion in the growing economy fell as the Gilded Age followed and white political will for black political participation faded. The Second World War also was followed by prosperity in which blacks were included more fully into the white economy, but still not completely. The Civil Rights Movement proved no more durable than Reconstruction, and blacks lost ground as the 20th century ended in the growth of a New Gilded Age. Resources that could be used to improve the welfare of whites and blacks continue to be spent on the continued repressions of blacks.
{"title":"Inclusive American Economic History: Containing Slaves, Freedmen, Jim Crow Laws, and the Great Migration","authors":"Trevon Logan, P. Temin","doi":"10.36687/inetwp110","DOIUrl":"https://doi.org/10.36687/inetwp110","url":null,"abstract":"This paper records the path by which African Americans were transformed from enslaved persons in the American economy to partial participants in the progress of the economy. The path was not monotonic, and we organize our tale by periods in which inclusiveness rose and fell. The history we recount demonstrates the staying power of the myth of black inferiority held by a changing white majority as the economy expanded dramatically. Slavery was outlawed after the Civil War, and blacks began to participate in American politics en masse for the first time during Reconstruction. This process met with white resistance, and black inclusion in the growing economy fell as the Gilded Age followed and white political will for black political participation faded. The Second World War also was followed by prosperity in which blacks were included more fully into the white economy, but still not completely. The Civil Rights Movement proved no more durable than Reconstruction, and blacks lost ground as the 20th century ended in the growth of a New Gilded Age. Resources that could be used to improve the welfare of whites and blacks continue to be spent on the continued repressions of blacks.","PeriodicalId":166384,"journal":{"name":"PSN: Politics of Race (Topic)","volume":"190 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117334985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We investigate the relationship between segregation and public spending from the viewpoint of theory on social identification by developing a model wherein ethnic minority assimilation and public goods provision are both endogenous. We first show the possibility of multiple equilibria with respect to assimilation: in one equilibrium, individuals belonging to minorities choose to assimilate into the majority society whereas in the other, they reject assimilation, resulting in segregation. We then show that the government's public spending is smaller in the latter equilibrium than in the former one, which is consistent with the empirical finding that segregation decreases public spending. We further examine how changes in the government's objective affect the possibility of multiple equilibria.
{"title":"Segregation and Public Spending Under Social Identification","authors":"Mariko Nakagawa, Yasuhiro Sato, K. Yamamoto","doi":"10.2139/ssrn.3490953","DOIUrl":"https://doi.org/10.2139/ssrn.3490953","url":null,"abstract":"We investigate the relationship between segregation and public spending from the viewpoint of theory on social identification by developing a model wherein ethnic minority assimilation and public goods provision are both endogenous. We first show the possibility of multiple equilibria with respect to assimilation: in one equilibrium, individuals belonging to minorities choose to assimilate into the majority society whereas in the other, they reject assimilation, resulting in segregation. We then show that the government's public spending is smaller in the latter equilibrium than in the former one, which is consistent with the empirical finding that segregation decreases public spending. We further examine how changes in the government's objective affect the possibility of multiple equilibria.","PeriodicalId":166384,"journal":{"name":"PSN: Politics of Race (Topic)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121931300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We use panel data covering 118 million homes in the United States, merged with geolocation detail for 75,000 taxing entities, to document a nationwide "assessment gap" which leads local governments to place a disproportionate fiscal burden on racial and ethnic minorities. We show that holding jurisdictions and property tax rates fixed, black and Hispanic residents nonetheless face a 10-13% higher tax burden for the same bundle of public services. This assessment gap arises through two channels. First, property assessments are less sensitive to neighborhood attributes than market prices are. This generates racially correlated spatial variation in tax burden within jurisdiction. Second, appeals behavior and appeals outcomes differ by race. This results in higher assessment growth rates for minority residents. We propose an alternate approach for constructing assessments based on small-geography home price indexes, and show that this reduces inequality by at least 55-70%.
{"title":"The Assessment Gap: Racial Inequalities in Property Taxation","authors":"Carlos F. Avenancio-León, Troup Howard","doi":"10.2139/ssrn.3465010","DOIUrl":"https://doi.org/10.2139/ssrn.3465010","url":null,"abstract":"We use panel data covering 118 million homes in the United States, merged with geolocation detail for 75,000 taxing entities, to document a nationwide \"assessment gap\" which leads local governments to place a disproportionate fiscal burden on racial and ethnic minorities. We show that holding jurisdictions and property tax rates fixed, black and Hispanic residents nonetheless face a 10-13% higher tax burden for the same bundle of public services. This assessment gap arises through two channels. First, property assessments are less sensitive to neighborhood attributes than market prices are. This generates racially correlated spatial variation in tax burden within jurisdiction. Second, appeals behavior and appeals outcomes differ by race. This results in higher assessment growth rates for minority residents. We propose an alternate approach for constructing assessments based on small-geography home price indexes, and show that this reduces inequality by at least 55-70%.","PeriodicalId":166384,"journal":{"name":"PSN: Politics of Race (Topic)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125895902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A distinct set of disadvantages experienced by black Americans increases their likelihood of experiencing negative financial shocks, decreases their ability to mitigate the impact of such shocks, and ultimately results in debt collection cases being far more common in black neighborhoods than in non-black neighborhoods. In this paper, we create a novel dataset that links debt collection court cases with information from credit reports to document the disparity in debt collection judgments across black and non-black neighborhoods and to explore potential mechanisms that could be driving this judgment gap. We find that majority black neighborhoods experience approximately 40% more judgments than non-black neighborhoods, even after controlling for differences in median incomes, median credit scores, and default rates. The racial disparity in judgments cannot be explained by differences in debt characteristics across black and non-black neighborhoods, nor can it be explained by differences in attorney representation, the share of contested judgments, or differences in neighborhood lending institutions.
{"title":"Racial Disparities in Debt Collection","authors":"Jessica LaVoice, Domonkos F. Vamossy","doi":"10.2139/ssrn.3465203","DOIUrl":"https://doi.org/10.2139/ssrn.3465203","url":null,"abstract":"A distinct set of disadvantages experienced by black Americans increases their likelihood of experiencing negative financial shocks, decreases their ability to mitigate the impact of such shocks, and ultimately results in debt collection cases being far more common in black neighborhoods than in non-black neighborhoods. In this paper, we create a novel dataset that links debt collection court cases with information from credit reports to document the disparity in debt collection judgments across black and non-black neighborhoods and to explore potential mechanisms that could be driving this judgment gap. We find that majority black neighborhoods experience approximately 40% more judgments than non-black neighborhoods, even after controlling for differences in median incomes, median credit scores, and default rates. The racial disparity in judgments cannot be explained by differences in debt characteristics across black and non-black neighborhoods, nor can it be explained by differences in attorney representation, the share of contested judgments, or differences in neighborhood lending institutions.","PeriodicalId":166384,"journal":{"name":"PSN: Politics of Race (Topic)","volume":"23 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114118587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The US criminal justice system is exceptionally punitive. We test whether racial heterogeneity is one cause, exploiting cross-jurisdiction variation in punishment severity in four Southern states. We estimate the causal effect of jurisdiction on arrest outcomes using a fixed effects model that incorporates extensive charge and defendant controls. We validate our estimates using defendants charged in multiple jurisdictions. Consistent with a model of ingroup bias in electorate preferences, the relationship between local severity and Black population share follows an inverted U-shape. Within states, defendants are 27–54 percent more likely to be incarcerated in “peak” heterogeneous jurisdictions than in homogeneous jurisdictions. We estimate that confinement rates and race-based confinement rate gaps would fall by 15 percent if all jurisdictions adopted the severity of homogeneous jurisdictions within their state. (JEL H76, J15, K42)
{"title":"Racial Divisions and Criminal Justice: Evidence from Southern State Courts","authors":"B. Feigenberg, Conrad Miller","doi":"10.3386/w24726","DOIUrl":"https://doi.org/10.3386/w24726","url":null,"abstract":"The US criminal justice system is exceptionally punitive. We test whether racial heterogeneity is one cause, exploiting cross-jurisdiction variation in punishment severity in four Southern states. We estimate the causal effect of jurisdiction on arrest outcomes using a fixed effects model that incorporates extensive charge and defendant controls. We validate our estimates using defendants charged in multiple jurisdictions. Consistent with a model of ingroup bias in electorate preferences, the relationship between local severity and Black population share follows an inverted U-shape. Within states, defendants are 27–54 percent more likely to be incarcerated in “peak” heterogeneous jurisdictions than in homogeneous jurisdictions. We estimate that confinement rates and race-based confinement rate gaps would fall by 15 percent if all jurisdictions adopted the severity of homogeneous jurisdictions within their state. (JEL H76, J15, K42)","PeriodicalId":166384,"journal":{"name":"PSN: Politics of Race (Topic)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123788213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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