Objective Measures and Implicit Bias in Evaluating Public Officials

University Of Nevada Las Vegas, Las Vegas NV

Investigators

Abstract

The mechanism for differential evaluation of men and women judges has not been specified. This project hypotheses that implicit bias could shape the evaluation of judges in judicial performance evaluations. Implicit bias mobilizes people's images of who should be holding a position, allowing non-normative people in a position to be evaluated more harshly because they do not fit a stereotype. People may unconsciously penalize judges who do not fit the normative image of a judge. They may frame their evaluations of certain characteristics like courteousness, knowledge, and efficiency based on stereotypes, for example. Unconscious bias would result in lower scores for women and minority judges in judicial performance evaluations, regardless the race and gender of their evaluators. Previous research has not evaluated discrepancies in performance evaluation scores controlling for objective measures of job performance. This project assembles data from judicial performance evaluations across 10 states. It compares the scores of female and minority judges with those of their white male counterparts. It also looks at the relationship between the gender/race gap in scores and the gender and race of those who are evaluating the judges. The results will contribute to understanding whether implicit bias could be a factor in assessing public officials. This project will contribute to improving measures of performance of judges, important in the accountability of public officials. The investigator will make the resulting data base on judicial performance evaluations available to judges, policy makers, researchers and state judicial performance commissions. The investigator will also present the information to the public in an interactive website. The investigator will also share a summary of best practices for performance evaluations with policymakers, allowing them to revise performance evaluations based in empirical data.