Providing transparency into operational processes can change consumer and worker behavior. However, it is unclear whether operational transparency is beneficial with potentially biased service providers. We explore this in the context of ridesharing platforms where early evidence documents bias similar to what has been observed in traditional transportation systems. Platforms responded by reducing operational transparency through removing information about riders’ gender and race from the ride request presented to drivers. However, following this change, bias may still manifest through driver cancelation after a request is accepted, at which point the rider’s picture is displayed. Our primary research question is to what extent a rider’s gender, race, and perception of support for lesbian, gay, bisexual, and transgender (LGBT) rights impact cancelation rates. We investigate this through a large field experiment on a major ridesharing platform in Washington, DC. By manipulating rider names and profile pictures, we observe drivers’ behavior patterns in accepting and canceling rides. Our results confirm that bias at the ride request stage has been eliminated. However, after acceptance, racial and LGBT biases are persistent, while we find no evidence of gender biases. We also explore whether peak times moderate (through increased pay to drivers) or exacerbate (by signaling that there are many riders, allowing drivers to be more selective) these biases. We find a moderating effect of peak timing, with lower cancelation rates for non-Caucasian riders. We do not find a similar moderating effect for riders that signal support for the LGBT community. This paper was accepted by Vishal Gaur, operations management.
{"title":"When Transparency Fails: Bias and Financial Incentives in Ridesharing Platforms","authors":"Jorge Mejia, Chris Parker","doi":"10.2139/ssrn.3209274","DOIUrl":"https://doi.org/10.2139/ssrn.3209274","url":null,"abstract":"Providing transparency into operational processes can change consumer and worker behavior. However, it is unclear whether operational transparency is beneficial with potentially biased service providers. We explore this in the context of ridesharing platforms where early evidence documents bias similar to what has been observed in traditional transportation systems. Platforms responded by reducing operational transparency through removing information about riders’ gender and race from the ride request presented to drivers. However, following this change, bias may still manifest through driver cancelation after a request is accepted, at which point the rider’s picture is displayed. Our primary research question is to what extent a rider’s gender, race, and perception of support for lesbian, gay, bisexual, and transgender (LGBT) rights impact cancelation rates. We investigate this through a large field experiment on a major ridesharing platform in Washington, DC. By manipulating rider names and profile pictures, we observe drivers’ behavior patterns in accepting and canceling rides. Our results confirm that bias at the ride request stage has been eliminated. However, after acceptance, racial and LGBT biases are persistent, while we find no evidence of gender biases. We also explore whether peak times moderate (through increased pay to drivers) or exacerbate (by signaling that there are many riders, allowing drivers to be more selective) these biases. We find a moderating effect of peak timing, with lower cancelation rates for non-Caucasian riders. We do not find a similar moderating effect for riders that signal support for the LGBT community. This paper was accepted by Vishal Gaur, operations management.","PeriodicalId":443237,"journal":{"name":"WGSRN: Discrimination","volume":"198 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123349778","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}
Recent research demonstrates that land use choices, which date back to a century ago still powerfully shape the current development patterns. With advances in digitization, scholars have started studying the long-term effects of historic federal placed-based policies. One such area that has drawn attention from the housing and real estate community is “redlining,” designated by the Home Owners’ Loan Corporation in 1930s. Scholarly evidence suggest that neighborhoods graded poorly decades ago still face challenges in access to mortgage credit, homeownership rate, and housing value. However, the literature is not clear about the mechanisms through which the placed based policy in the past affects the current neighborhood performance. In this paper, we unravel one of these mechanisms. We argue that redlining has shaped the geography of housing segregation, not just that of racial segregation, by delineating the predominant location of single family and multi-family stock. We then hypothesize that these built environment effects persist even while redlining-related racial segregation diminished over time. Specifically, we explore how and why poorly-graded multi-family neighborhoods became locked in a continued cycle of impoverishment, with continued under-resourced and underinvested housing stock. We test this idea for ten U.S. cities for which digitized maps and 1930-2010 census data are available. By instrumenting the geography of housing segregation through the redlining policy, we find the connection between redlining policy and contemporary neighborhood outcomes in local housing markets with a mechanism identified.
{"title":"The Physical Legacy of Racism: How Redlining Cemented the Modern Built Environment","authors":"Brian Y. An, Anthony W. Orlando, Seva Rodnyansky","doi":"10.2139/ssrn.3500612","DOIUrl":"https://doi.org/10.2139/ssrn.3500612","url":null,"abstract":"Recent research demonstrates that land use choices, which date back to a century ago still powerfully shape the current development patterns. With advances in digitization, scholars have started studying the long-term effects of historic federal placed-based policies. One such area that has drawn attention from the housing and real estate community is “redlining,” designated by the Home Owners’ Loan Corporation in 1930s. Scholarly evidence suggest that neighborhoods graded poorly decades ago still face challenges in access to mortgage credit, homeownership rate, and housing value. However, the literature is not clear about the mechanisms through which the placed based policy in the past affects the current neighborhood performance. In this paper, we unravel one of these mechanisms. We argue that redlining has shaped the geography of housing segregation, not just that of racial segregation, by delineating the predominant location of single family and multi-family stock. We then hypothesize that these built environment effects persist even while redlining-related racial segregation diminished over time. Specifically, we explore how and why poorly-graded multi-family neighborhoods became locked in a continued cycle of impoverishment, with continued under-resourced and underinvested housing stock. We test this idea for ten U.S. cities for which digitized maps and 1930-2010 census data are available. By instrumenting the geography of housing segregation through the redlining policy, we find the connection between redlining policy and contemporary neighborhood outcomes in local housing markets with a mechanism identified.","PeriodicalId":443237,"journal":{"name":"WGSRN: Discrimination","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129484248","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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