Replication data for: Implementing Anti-discrimination Policies in Statistical Profiling Models
Principal Investigator(s): View help for Principal Investigator(s) Devin G. Pope; Justin R. Sydnor
Version: View help for Version V1
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Project Citation:
Pope, Devin G., and Sydnor, Justin R. Replication data for: Implementing Anti-discrimination Policies in Statistical Profiling Models. Nashville, TN: American Economic Association [publisher], 2011. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2019-10-13. https://doi.org/10.3886/E114766V1
Project Description
Summary:
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How should statistical models used for assigning prices or eligibility be implemented when there is concern about discrimination? In many settings, factors such as race, gender, and age are prohibited. However, the use of variables that correlate with these omitted characteristics (e.g., zip codes, credit scores) is often contentious. We provide a framework to address these issues and propose a method that can eliminate proxy effects while maintaining predictive accuracy
relative to an approach that restricts the use of contentious variables outright. We illustrate the value of our proposed method using data from the Worker Profiling and Reemployment Services system. (JEL C53, J15, J65, J71)
Scope of Project
JEL Classification:
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C53 Forecasting Models; Simulation Methods
J15 Economics of Minorities, Races, Indigenous Peoples, and Immigrants; Non-labor Discrimination
J65 Unemployment Insurance; Severance Pay; Plant Closings
J71 Labor Discrimination
C53 Forecasting Models; Simulation Methods
J15 Economics of Minorities, Races, Indigenous Peoples, and Immigrants; Non-labor Discrimination
J65 Unemployment Insurance; Severance Pay; Plant Closings
J71 Labor Discrimination
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