Statistical Discrimination in the Pay-setting Process

Faculty Member: 

Proposal Description:

Many public policies aim to reduce employment inequality. They often do so by excluding discriminatory information—such as criminal record, previous unemployment, or salary history—from the hiring process. But suppressing information also creates problems, as the people in charge of hiring may fill the gaps with biased assumptions that penalize women and people of color. This project uses recent public policy changes to explore how the suppression of potentially discriminatory information affects inequality. Using both large-scale survey data and original experiments, we will analyze the effect of suppressing salary history on the gender pay gap.

Requisite Skills and Qualifications:

Coursework in econometrics or statistics, knowledge of statistical software like Stata or R, ability to work independently, and interest in conducting social science research.

Award: 
  • Matthew Lee
  • Alvin Delgado