# **6 Conclusion**

In this paper, we examine the extent and determinants of judicial bias in Kenya, with a focus on gender and ethnic in-group bias. Our data cover Kenyan Superior Court cases spanning 1976-2020 (with a focus on civil, land and environment, and succession cases) and our identification strategy relies on the random assignment of judges to cases. Our analysis also looks at the relationship between bias in judge decisions and measures of slant against women in judges’ written decisions, which we derive through machine learning.

Our main finding is that judges in Kenya display both gender and ethnic in-group bias towards defendants. Our results suggest that defendants are about 4 percentage points more likely to win if they share the judge’s gender and about 5 percentage points more likely to win if they share the judge’s ethnicity.

We also find evidence that slant against women in written judgements is associated with lower win-rates for female defendants. The results show that a one standard deviation change in the measure of gender slant is associated with about a 2 percentage point decrease in win probability for female defendants. Finally, we show that potentially biased judgements are associated with shorter written judgements (for gender and ethnic bias) that are less likely to be cited (for ethnic bias), which suggests that biased decisions are linked to poorer quality written judgements.

These findings have important implications for the Kenyan context. Women and certain ethnic groups are underrepresented in the judiciary. As such, they are more likely to be negatively affected by in-group bias. In concrete terms–since the main cases in the dataset are civil cases, environment and land cases, and succession cases–in-group bias might imply a financial disadvantage, greater likelihood of losing disputes over land ownership, or being cut out of family inheritance or property.

Several approaches could be taken to reduce bias. Primarily, greater efforts could be made to achieve equal representation of female judges and representation of ethnic groups relative to their proportion of the total population. Second, implicit bias trainings, which have been proven effective in some settings (Jackson, Hillard, and Schneider 2014), could be implemented for judges. Third, judges could simply be provided with data on the extent of their bias in decision-making. Some research has shown that the provision of information on biases can lead to more action in favor of out-groups (Hillard, Ryan, and Gervais 2013). Importantly, the application of these approaches to the Kenyan context should be rigorously tested.

The findings also make important contributions to the literature on judicial bias. They expand the study of in-group judicial bias outside the most heavily studied contexts and provide further evidence that such bias may be prevalent across many contexts. The results are also the first to show that judges may exhibit greater bias towards defendants than towards plaintiffs, a phenomenon which is consistent with social identity theory. Furthermore, they contribute to the broader literature on ethnic bias in Kenya and sub-Saharan Africa more broadly, showing that ethnic preferences influence decision-making in courts. They also build on the literatures related to gender discrimination and the importance of female representation in the public sector. Finally, the paper presents a novel application of machine learning techniques to help understand the determinants of bias. Future research should focus on further unveiling the determinants and scope of bias in the judiciary, as well as on how to reduce the presence of bias in the judiciary.

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