Bias in White: A Longitudinal Natural Experiment Measuring Changes in Discrimination

Abstract

Many professions are plagued by disparities in service delivery. Racial disparities in policing, mortgage lending, and healthcare are some notable examples. Because disparities can result from a myriad of mechanisms, crafting effective disparity mitigation policies requires knowing which mechanisms are active and which are not. In this study we can distinguish whether one mechanism-statistical discrimination-is a primary explanation for racial disparities in physicians' treatment of patients. In a longitudinal natural experiment using repeated quasi-audit studies of medical students, we test for within-cohort changes in disparities from medical student behaviors as they interact with white and black patient actors. We find significant increases in medical students' disparate behaviors by patient race between their first and second years of medical school. This finding is inconsistent with statistical discrimination predictions and challenges the idea that statistical discrimination is primarily responsible for racial disparities in patient care. M any professions are plagued by disparities in service delivery. Racial disparities in policing, mortgage lending, and healthcare are some notable examples. Because disparities can result from a myriad of mechanisms, crafting effective disparity mitigation policies requires knowing which mechanisms are active and which are not. In this study we can distinguish whether one mechanism-statistical discrimination-is a primary explanation for racial disparities in physicians' treatment of patients. In a longitudinal natural experiment using repeated quasi-audit studies of medical students, we test for within-cohort changes in disparities from medical student behaviors as they interact with white and black patient actors. We find significant increases in medical students' disparate behaviors by patient race between their first and second years of medical school. This finding is inconsistent with statistical discrimination predictions and challenges the idea that statistical discrimination is primarily responsible for racial disparities in patient care.

Examining the presence, consequences, and reduction of implicit bias in health care: A narrative review

“…Several reviews, including the section above, indicate that implicit prejudice and stereotyping is present when students begin training in health care, and that the level of implicit bias remains constant or increases as students matriculate through their training (see Chapman et al, 2013). Rubineau and Kang (2012) reported significant increases in medical students’ disparate behaviors toward Black standardized patients between their first and second years of medical school. Results from the CHANGES project, a four-year longitudinal study that tracked implicit and explicit bias among 3959 students across 49 medical schools in the United States, revealed similar shortcomings in medical training.…”

What happens before? A field experiment exploring how pay and representation differentially shape bias on the pathway into organizations.

“…Our methodology builds on past field experiments known as "audit studies" (e.g., Fix and Struyk, 1993;Bertrand and Mullainathan, 2004;Correll, Benard, and Paik, 2007;Pager, Western and Bonikowski, 2009;Rubineau and Kang, 2012), designed to measure bias by evaluating whether otherwise identical applicants for a valued outcome receive different treatment when race and/or gender-signaling information (such as the name atop a résumé or the appearance of someone acting out a script) is randomly varied (see Pager, 2007 for a discussion of this methodology). The natural field experiment method simultaneously offers ecological validity and experimental control (Pager, 2007;Quillian, 2006).…”

Digital Discrimination: The Case of Airbnb.com

“…Similarly, Pope and Sydnor (2011) present evidence of statistical discrimination on Prosper.com. Looking at medical students, Rubineau and Kang (2012) show that students exhibit more discriminatory behavior after their first year of medical school, which they present as evidence against statistical discrimination. In our setting, we are unable to fully disentangle these two forms of discrimination.…”

“…Discrimination remains a significant policy concern in settings ranging from the workplace (Bertrand and Mullainathan 2004) to housing markets (Zhao et al 2006, Card et al 2008 to physician treatment of patients (Rubineau and Kang 2012)In principle, the rise of online marketplaces can reduce the scope of discrimination. Face-to-face interactions automatically disclose information about user identity.…”