CANCELLED - WCED Lecture. Ethnic Bias in Judicial Decision-making: Evidence from the Kenyan Appellate Courts

Do judicial outcomes depend on judicial identity? Research on the determinants of judicial behavior have largely focused on the experience of advanced democracies, most notably the U.S. Considerably less attention has been paid to questions of judicial identity and performance in emerging democracies. This paper addresses this gap by turning to Kenya, a multiethnic society that has recently undergone a massive reform of the judiciary aimed at reducing corruption and bias and improving access to justice. We specifically examine whether ethnic bias plays a role in judicial outcomes by focusing on decisions made by the Kenyan High Court. Using an original web-scraped dataset of over 15,000 criminal appeals from 2003-2017 and for offenses ranging from petty theft to murder, we exploit the conditional random assignment of judges to criminal appeals to estimate the effect of judicial ethnicity on appeal outcomes. We find that judges are more likely to favor coethnic appellants or respondents by 3-4% points in comparison to non-coethnics. We also show that the coethnic bias becomes stronger in cases decided after the 2007 Kenyan election violence, during which inter-ethnic violence resulted in more than 1000 fatalities and hundreds of thousands displaced. Our findings contribute to recent debates on the determinants of equitable justice in developing contexts.

Fiona Shen-Bayh is an assistant professor of Government at William & Mary. She was previously a postdoctoral fellow at the Weiser Center for Emerging Democracies and earned her PhD in political science at the University of California, Berkeley. Her research examines the role that law and courts play in strategies of autocratic survival. Her book project examines when and why autocrats use courts to repress and the ramifications of such strategies on the development of rule of law and judicial power. Drawing on cases from sub-Saharan Africa, she utilizes a mixed-methods approach that combines case studies, statistical modeling, and computational text analysis.

If you are a person with a disability who requires an accommodation to attend this event, please reach out to weisercenter@umich.edu at least 2 weeks in advance of this event. Please be aware that advance notice is necessary as some accommodations may require more time for the university to arrange.