Pub Date : 2020-11-19DOI: 10.1093/oso/9780197504000.003.0002
B. Wiggins
Chapter 1 focuses on the early history of race-based insurance. When the Newark-based Prudential Insurance Company of America incorporated in 1875, it revolutionized the American insurance industry by offering policies to the working class for an affordable three cents per week. What made the Prudential doubly unique was that the company insured not simply industrial laborers, but also African American laborers. The company was not in the progressive vanguard, though. Rather, the Northern upstart, in contrast to its Southern competitors, simply had not thought to craft a company policy to explicitly ban African Americans from purchasing life insurance. Just five years after becoming the first insurer to cover black lives, the Prudential began to charge differential, race-based premiums and commenced a public relations effort to defend its discriminatory practices. This foundational chapter traces how the theoretical work of scientific racism became embedded in the business practices of American insurers.
第一章主要介绍种族保险的早期历史。1875年，总部位于纽瓦克的美国保诚保险公司(Prudential Insurance Company of America)成立，通过向工薪阶层提供每周3美分的保险，彻底改变了美国保险业。保诚保险的独特之处在于，它不仅为工业工人提供保险，还为非裔美国人提供保险。不过，这家公司并不是进步的先锋。相反，与南方竞争对手相比，这家北方新贵根本没有想过制定一项明确禁止非裔美国人购买人寿保险的公司政策。在成为第一家覆盖黑人生命的保险公司仅仅五年后，保诚就开始收取基于种族的差别保费，并开始进行公关努力，为其歧视性做法辩护。这一基础章节追溯了科学种族主义的理论工作如何嵌入到美国保险公司的商业实践中。
{"title":"Life","authors":"B. Wiggins","doi":"10.1093/oso/9780197504000.003.0002","DOIUrl":"https://doi.org/10.1093/oso/9780197504000.003.0002","url":null,"abstract":"Chapter 1 focuses on the early history of race-based insurance. When the Newark-based Prudential Insurance Company of America incorporated in 1875, it revolutionized the American insurance industry by offering policies to the working class for an affordable three cents per week. What made the Prudential doubly unique was that the company insured not simply industrial laborers, but also African American laborers. The company was not in the progressive vanguard, though. Rather, the Northern upstart, in contrast to its Southern competitors, simply had not thought to craft a company policy to explicitly ban African Americans from purchasing life insurance. Just five years after becoming the first insurer to cover black lives, the Prudential began to charge differential, race-based premiums and commenced a public relations effort to defend its discriminatory practices. This foundational chapter traces how the theoretical work of scientific racism became embedded in the business practices of American insurers.","PeriodicalId":350640,"journal":{"name":"Calculating Race","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129117358","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}
Pub Date : 2020-11-19DOI: 10.1093/oso/9780197504000.003.0005
B. Wiggins
Calculating Race’s fourth chapter demonstrates that race has become so highly correlated with other social statistics that actuarial science in general has developed a baked-in racial bias. Racial discrimination by proxy (e.g., zip code standing in for race) can be glimpsed in the disparate impact of data-driven decision-making in housing, healthcare, policing, sentencing, and more. Simply leaving out racial data in statistically aided decision-making distances institutions from claims of intentional discrimination, but a disparate, discriminatory impact lingers when other factors correlated with race power actuarial analyses. Chapter 4 considers how insurance law in the United States has defined the limits of acceptable discrimination. By surveying the progression of state-by-state regulations that prohibit or accept the use of race, gender, sex, sexuality, ability, age, and genetics in an industry that revolves around the ability to discriminate risk, it uncovers who the United States has historically chosen to protect.
{"title":"Proxies","authors":"B. Wiggins","doi":"10.1093/oso/9780197504000.003.0005","DOIUrl":"https://doi.org/10.1093/oso/9780197504000.003.0005","url":null,"abstract":"\u0000 Calculating Race’s fourth chapter demonstrates that race has become so highly correlated with other social statistics that actuarial science in general has developed a baked-in racial bias. Racial discrimination by proxy (e.g., zip code standing in for race) can be glimpsed in the disparate impact of data-driven decision-making in housing, healthcare, policing, sentencing, and more. Simply leaving out racial data in statistically aided decision-making distances institutions from claims of intentional discrimination, but a disparate, discriminatory impact lingers when other factors correlated with race power actuarial analyses. Chapter 4 considers how insurance law in the United States has defined the limits of acceptable discrimination. By surveying the progression of state-by-state regulations that prohibit or accept the use of race, gender, sex, sexuality, ability, age, and genetics in an industry that revolves around the ability to discriminate risk, it uncovers who the United States has historically chosen to protect.","PeriodicalId":350640,"journal":{"name":"Calculating Race","volume":"259 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122675737","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}
Pub Date : 2020-11-19DOI: 10.1093/oso/9780197504000.003.0003
B. Wiggins
From the mid-to-late nineteenth century, in the period after the use of branding and before the use of fingerprinting, penal institutions faced the problem of how to identify repeat offenders. In this interim, Alphonse Bertillon, a clerk in the Paris Prefecture of Police, developed an anthropometric system that measured the bodies of criminals at their intake and catalogued these measurements in order to identify them should they offend again. Calculating Race’s second chapter traces the importation of the Bertillon System of Classification to the United States, where its data collection practices were racialized. It then investigates University of Chicago sociologist Ernest Burgess’s 1920s work on this data set to build a formula for sentencing and parole decisions. The resulting algorithm from Burgess’s work relied heavily on race-based Bertillon data and factored race into its recommendations for length of sentence and supervised release, installing racial statistics as a key variable in matters of criminal justice.
{"title":"Crime","authors":"B. Wiggins","doi":"10.1093/oso/9780197504000.003.0003","DOIUrl":"https://doi.org/10.1093/oso/9780197504000.003.0003","url":null,"abstract":"From the mid-to-late nineteenth century, in the period after the use of branding and before the use of fingerprinting, penal institutions faced the problem of how to identify repeat offenders. In this interim, Alphonse Bertillon, a clerk in the Paris Prefecture of Police, developed an anthropometric system that measured the bodies of criminals at their intake and catalogued these measurements in order to identify them should they offend again. Calculating Race’s second chapter traces the importation of the Bertillon System of Classification to the United States, where its data collection practices were racialized. It then investigates University of Chicago sociologist Ernest Burgess’s 1920s work on this data set to build a formula for sentencing and parole decisions. The resulting algorithm from Burgess’s work relied heavily on race-based Bertillon data and factored race into its recommendations for length of sentence and supervised release, installing racial statistics as a key variable in matters of criminal justice.","PeriodicalId":350640,"journal":{"name":"Calculating Race","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132162953","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}
Pub Date : 2020-11-19DOI: 10.1093/oso/9780197504000.003.0004
B. Wiggins
The economic collapse that set into motion the Great Depression of the 1930s was portended by mass mortgage defaults in the mid-1920s. To address this unprecedented housing crisis, New Deal legislation created the Federal Housing Administration (FHA) to insure mortgage loans. Without predecessors or peers and faced with a national emergency, the FHA turned to risk-rating experts in real estate valuation to craft underwriting policies that would shape the geography of the country and cement racial segregation in the United States for generations to come. Chapter 3 details how FHA officials utilized risk-rating standards that disqualified people of color from obtaining federally subsidized mortgage insurance. This institutional discrimination had the deleterious effect of essentially precluding people of color from obtaining middle-class America’s most important wealth-generating asset: the single-family home. Though others have written about the agency’s policies before, my analysis is notably the first to locate each version of the FHA’s underwriting manual, to take stock of each facet of race-based risk rating until the conclusion of the practice in 1947, and to analyze the agency’s effect on the lending industry thereafter.
{"title":"Home","authors":"B. Wiggins","doi":"10.1093/oso/9780197504000.003.0004","DOIUrl":"https://doi.org/10.1093/oso/9780197504000.003.0004","url":null,"abstract":"The economic collapse that set into motion the Great Depression of the 1930s was portended by mass mortgage defaults in the mid-1920s. To address this unprecedented housing crisis, New Deal legislation created the Federal Housing Administration (FHA) to insure mortgage loans. Without predecessors or peers and faced with a national emergency, the FHA turned to risk-rating experts in real estate valuation to craft underwriting policies that would shape the geography of the country and cement racial segregation in the United States for generations to come. Chapter 3 details how FHA officials utilized risk-rating standards that disqualified people of color from obtaining federally subsidized mortgage insurance. This institutional discrimination had the deleterious effect of essentially precluding people of color from obtaining middle-class America’s most important wealth-generating asset: the single-family home. Though others have written about the agency’s policies before, my analysis is notably the first to locate each version of the FHA’s underwriting manual, to take stock of each facet of race-based risk rating until the conclusion of the practice in 1947, and to analyze the agency’s effect on the lending industry thereafter.","PeriodicalId":350640,"journal":{"name":"Calculating Race","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126596684","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：