# **1 Introduction**

Judges often exhibit bias in decision-making. One particular form of judicial bias documented in recent years is in-group bias, wherein judges are more likely to rule in favor of plaintiffs or defendants that share a certain identity with the judge (Shayo and Zussman 2011; Gazal-Ayal and Sulitzeanu-Kenan 2010; Knepper 2018; Sloan 2020). However, there are still many unknowns regarding the scope and determinants of judicial in-group bias. Indeed, the phenomenon has been studied in relatively few contexts and rarely in the Global South.

Judicial bias in general, and in-group bias in particular, have far-reaching negative consequences. Decisions may be biased against groups that are marginalized, which can exacerbate existing inequalities. This is especially true for in-group bias since privileged groups may be more likely to represent a higher proportion of judges. Moreover, bias could undermine the effectiveness, legitimacy, and inclusivity of courts, which are widely recognized as a key component of a well-functioning economy (Rodrik 2000; Visaria 2009; Ponticelli and Alencar 2016; World Bank 2017). In civil courts–which are the focus of this paper–in-group bias may negatively affect these marginalized groups’ rights over land property, business contracts, and family disputes, which have important effects over their economic rights and well-being.<sup>1</sup> This paper aims to determine the extent and predictors of bias along gender and ethnic lines in judicial decisions in the higher courts in Kenya. The focus is on in-group bias but we also extend the analysis to gender bias more broadly. Kenya provides an ideal setting for studying judicial bias for several reasons. First, political groups in Kenya are sharply divided along ethnic lines (Asingo et al. 2018), which may increase ethnic bias in society. According to the most recent census, Kenya has over 40 ethnic groups, and the largest group (the Kikuyu) accounts for only about 17 percent of the population (KNBS 2019). Economic inequalities and political allegiance are distributed across regions and ethnicity, with political parties and coalitions created along clear ethnic lines (Asingo et al. 2018, Friedrich-Ebert-Stiftung 2012). In this context of diffuse ethnic groups and ethnic-based politics, ethnicity is a highly salient topic. Second, certain ethnic groups are underrepresented in the judiciary (see below), and there is a high degree of gender inequality across a number of dimensions, including representation in the judiciary and a variety of socioeconomic outcomes (IDLO 2020; UNDP 2020).<sup>2</sup> If in-group bias is widespread, it may disproportionately harm these underrepresented groups. Third, there is an ongoing debate regarding the extent to which co-ethnic bias affects decision-making in the context of Africa generally and Kenya specifically (Berge et al. 2015).

We employ several data sources to examine the extent and determinants of judicial bias in Kenya. Our main data source is the Kenyan Judiciary’s publicly available database for Superior Courts cases over the period 1976-2020.<sup>3</sup> Our analysis focuses on cases in which there is both a human defendant and human litigant (i.e., cases for which neither litigant is an organization or representative of the state). As such, since the state is always the prosecutor in criminal cases, criminal cases are not included in our dataset. Most of the cases we examine are civil, land environment, and succession cases. By scraping the metadata associated with each case, we determined key variables, such as case type and names of judges and litigants. We also used additional data sources to determine the gender and ethnicity of participants. Furthermore, we used machine learning techniques to extract other key variables, such as the outcome of the case and the degree to which judges, by associating women with either negative or stereotypical traits, exhibit gender slant against women in their writing, which serve as textual proxies for implicit bias.

To determine the causal effect of an in-group relationship between judges and litigants, we rely on the quasi-random assignment of cases to Kenyan judges. In Kenya, cases filed to a court are assigned to individual judges based on their existing caseload and the date of filing, which is orthogonal to any other characteristics of the case. Random assignment assures us that any relationship between in-group status and case outcomes is driven by bias rather than other factors, such as self-selection of judges to certain cases. To confirm this, we test for random assignment across gender and ethnic lines, and we show that male and female defendants and plaintiffs are equally likely to be assigned to a male or female judge. We also show that, overall, defendants and plaintiffs across all ethnicities are not more likely to be assigned judges from their ethnic group.

> 1Throughout this paper, we use civil courts to refer to non-criminal courts. In our dataset, this includes civil cases, environment and land cases, succession cases, labor cases, and miscellaneous cases.

> 2According to our data, over the past few decades, female judges have been in the majority for only about 37 percent of cases.

> 3See http://kenyalaw.org/caselaw/.

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