metadata: authors: 0: author: Chen,Daniel Li
1: author: Graham,Jimmy
2: author: Ramos Maqueda,Manuel
3: author: Singh,Shashank - DIME3
count: Kenya
docna: 0: docna: Do Judges Favor Their Own Ethnicity and
            Gender? : Evidence from Kenya
docty: Policy Research Working Paper
subtopic: Gender and Development,Labor Markets,Inequality,Human Rights,Judicial System Reform
teratopic: Gender,Social Protections and Labor,Poverty Reduction,Law and Development
abstracts: cdata!: Evidence from high-income countries
            suggests that judges often exhibit in-group bias, favoring
            litigants that share an identity with the judge. However,
            there is little evidence on this phenomenon from the Global
            South. Collecting the available universe of High Court
            decisions in Kenya, this paper leverages the random
            assignment of cases to judges to evaluate the existence of
            in-group bias along gender and ethnic lines. It finds that,
            relative to a baseline win rate of 43 percent, defendants
            are 4 percentage points more likely to win if they share the
            judge's gender and 5 percentage points more likely to
            win if they share the judge~^!!^s ethnicity. The paper finds
            that the written judgements are on average shorter and less
            likely to be cited when defendants who are of the same
            gender or ethnicity as the judge win their case. This is
            consistent with in-group biased decisions being of lower
            quality. In addition, the findings show that female
            defendants are less likely to win the case if the judge
            exhibits stereotypical or negative attitudes towards women
            in their writings.
geo_reg_key: 517191
repnme: repnme: Do Judges Favor Their Own Ethnicity and
            Gender? : Evidence from Kenya
display_title: Do Judges Favor Their Own Ethnicity
            and Gender : Evidence from Kenya
listing_relative_url: /research/2022/03/33751509/judges-favor-own-ethnicity-gender-evidence-kenya
topicv3: Inequality under Law m1326002 925,Justice m1326106 840,Gender m1327621 615,Jobs m1503931 2670,Discrimination m1505707 3379
publishtoextweb_dt: 2022-03-07T00:00:00Z
dois: 10.1596/1813-9450-9956
historic_topic: Gender,Social Protections and Labor,Poverty Reduction,Law and Development
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 70
author_keys: 0: author_key: 167904
1: author_key: NA
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model_extractions: Policy Research Working Paper 9956 Do Judges Favor Their Own Ethnicity and Gender?

# Evidence from Kenya

_Daniel Chen Jimmy Graham Manuel Ramos-Maqueda Shashank Singh_ Development Economics Development Impact Evaluation Group March 2022
Policy Research Working Paper 9956

# **Abstract**

Evidence from high-income countries suggests that judges often exhibit in-group bias, favoring litigants that share an identity with the judge. However, there is little evidence on this phenomenon from the Global South. Collecting the available universe of High Court decisions in Kenya, this paper leverages the random assignment of cases to judges to evaluate the existence of in-group bias along gender and ethnic lines. It finds that, relative to a baseline win rate of 43 percent, defendants are 4 percentage points more likely to win if they share the judge’s gender and 5 percentage points more likely to win if they share the judge’s ethnicity. The paper finds that the written judgements are on average shorter and less likely to be cited when defendants who are of the same gender or ethnicity as the judge win their case. This is consistent with in-group biased decisions being of lower quality. In addition, the findings show that female defendants are less likely to win the case if the judge exhibits stereotypical or negative attitudes towards women in their writings.

This paper is a product of the Development Impact Evaluation Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at mramosmaqueda@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Do Judges Favor Their Own Ethnicity and Gender? Evidence from Kenya<sup>∗</sup> Daniel Chen, Jimmy Graham, Manuel Ramos-Maqueda, and Shashank Singh<sup>†</sup>

> ∗The authors are thankful to participants of the World Bank lightning seminar, Arianna Legovini, an anonymous reviewer, and DIME Analytics for their invaluable feedback. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

> †Authors listed in alphabetical order.
# **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/.

2
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 relative to a baseline win rate of 43%.

We also investigate whether and how attitudes toward gender affect decision-making of judges. In particular, we examine whether judges that exhibit stereotypical or negative gender attitudes in their written judgements are more likely to display gender bias in the direction of their decisions. Previous research has shown that attitudes toward social groups are highly predictive of judgments and choices (Bertrand, Chugh, and Mullainathan 2005). Some judicial decisions have raised questions when, for example, a judge closed a sex trafficking case of a female minor because “drinking beer with the clients is not tiring work,” or when magistrates in a sexual assault case ruled out the attack on grounds that the victim’s use of a “daring garment” contradicted her “shy” temperament. A large literature documents that ideological and biographical characteristics of judges matter and suggests that their preferences directly affect their rulings (Boyd and Spriggs. 2009; Glynn and Sen 2015; Kastellec 2013; Sunstein et al. 2007). In addition, judicial decisions are often highly predictable, in the sense that some judges are systematically more or less lenient, which hints at predetermined judgments (Dunn et al. 2017), and/or at the use of heuristics or stereotypes where salient attributes determine decisions (Bordalo et al. 2016; Gainer 2016).

In order to construct our measure of attitudes toward gender, we rely on word embeddings, a language modeling technique from natural language processing that leverages word co-occurrence to create measures that preserve semantic meaning (Mikolovand et al. 2013; Pennington, Socher, and Manning 2014; Kozlowski, Taddy, and Evans 2019). Specifically, we use word embeddings to construct a lexical slant measure that captures the strength of the association between gendered language and either negative langauge or language that embraces gender stereotypes. We measure lexical gender slant by judge, with the majority opinions authored by each judge used as a separate corpus.

We find that slant against women in written judgements is associated with lower win rates for female defendants. We estimate 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.

We also find that when judges are matched with defendants from the in-group, they issue judgements that are shorter and less likely to be cited. The fact that they are cited less often may suggest that they are of a lower quality (Landes, Lessig, and Solimine 1998). The effect is stronger when the defendant wins the case, which is consistent with lower-quality decisions being associated with in-group bias.

These findings have important implications for the Kenyan context. As mentioned, 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 practical terms, keeping in mind the main case types in the dataset, this could imply a range of negative consequences. For civil cases, which often involve disputes over money (among other topics), in-group bias would imply a financial disadvantage for women and underrepresented ethnic groups. For environment and land cases, bias may make these groups more likely to lose disputes over land ownership. Similarly, for succession cases, bias could lead to women being unfairly cut out of family inheritance or property.

This paper contributes to four main literatures. First, it builds on the scant literature related to judicial bias in developing countries and is the first paper, to our knowledge, to focus on civil (non-criminal) cases in the Global South. Judicial bias has been well studied in the United States (Knepper 2018; Depew, Eren, and Mocan 2017) and Israel (Shayo and Zussman 2011; Gazal-Ayal and Sulitzeanu-Kenan 2010), but in few other countries. Even in high-income countries, the literature has rarely evaluated non-criminal cases: To our knowledge, only Shayo and Zussman (2011) and Knepper (2018) examine in-group bias in non-criminal cases, and they focus on a specific case type (small claim courts and sexual discrimination cases, respectively). In this paper, we study in-group bias in the Kenyan context, which builds much-needed evidence for the possibility of judicial bias outside the United States. As such, this paper helps expand our understanding of the scope of judicial bias in the Global South where these data are relatively scarce. Prior research in developing countries has exclusively focused on criminal cases (Ash, Asher, et al. 2021, Choi, Andy Harris, and Shen-Bayh 2021). Instead, we evaluate the entire corpus of non-criminal cases, including contract enforcement, land and property rights, succession cases, and family disputes. We are unaware of any other study in the Global South of cases that ordinarily play a role in economic growth–including those related to contract enforcement, property disputes, and the environment. Relative to the rest of the literature, our 3
findings show moderate in-group bias. Whereas we find that in-group status improves win probability by 4 to 5 percentage points, a recent study from India found null effects (Ash, Asher, et al. 2021), and a study in in Israel found that ethnic in-group status has a 17 to 20 percentage-point impact on win probability (Shayo and Zussman 2011).

Second, to our knowledge, our paper is the first to evaluate how attitudes toward gender in judicial writings and the quality of decisions are associated with the presence of in-group bias in judicial decisions. Ash, Chen, and Ornaghi (2021) show that judges displaying gender bias in their writings vote more conservatively in gender-related cases and their attitudes affect their interactions with their colleagues. We expand this result and find that stereotypical or negative gender attitudes in written judgments are also correlated with bias against female defendants in judicial cases. We complement this finding by showing that low-quality decisions (measured through proxies in the judgment text) are correlated with the presence of in-group bias.

Third, we are one of the first papers to examine judicial bias both towards both defendants and plaintiffs. This is potentially due to the fact that most previous studies on the topic have focused on criminal cases, for which the plaintiff is typically the state.<sup>4</sup> To our knowledge, only Knepper 2018 evaluates biases towards both plaintiffs and defendants, yet focuses on the earlier stages of sexual discrimination cases. Instead, we focus on final decisions in civil cases where both parties are natural persons (i.e., human beings rather than corporations or organizations). We find in-group bias towards defendants, but not towards plaintiffs. A potential explanation for this finding is loss aversion on behalf of the judge’s in-group, which is consistent with social identity theory. The theory predicts that when an individual perceives a “threat” to their in-group (and, by extension, their self-identity), they may be more likely to exhibit bias in favor of their group as a means to defend the group (and their identity) than when no such threat exists (Voci 2010).<sup>5</sup> Nonetheless, we are not able to test the mechanisms that explain this difference in in-group bias across plaintiffs and defendants. Thus, our finding would benefit from further research to better understand the extent to which this finding holds in other contexts, as well as to investigate the mechanisms that might explain it.

Fourth, our findings contribute to the broader literature on ethnic and gender bias. There is a large literature studying the extent to which ethnicity affects decision-making and preferences in sub-Saharan Africa.<sup>6</sup> Considering the high level of ethnic fractionalization on the continent (and in Kenya), ethnic bias has major implications for a range of outcomes, including public service provision (Barkan and Chege 1989), community mobilization (Miguel and Gugerty 2005) and firm productivity (Andrew Harris 2014). Our paper adds to this literature by documenting a high-stakes form of ethnic bias (judicial bias), in Kenya. Relatedly, our study builds on research demonstrating the importance of female representation in public positions for both reducing bias (Beaman et al. 2009) and directly improving outcomes for women (Hessami and Fonseca 2020). It builds evidence for one specific channel (outcomes in court cases) through which female representation in the public sector directly affects women.

The rest of the paper is organized as follows. Section 2 presents background information on the judiciary, gender, and ethnicity in Kenya. Section 3 presents the data used in the analysis. Section 4 outlines the empirical strategy. Section 5 presents the results. Finally, section 6 concludes.

# **2 Background: The Kenyan Judiciary**

The Kenyan judiciary is divided into two main court types: Superior and Subordinate Courts. The vast majority of our data covers the Superior Courts, which include High Courts, which hear both criminal and civil cases and appeals from Subordinate Courts; Environment and Land Courts; Employment and Labour Relations Courts; the Court of Appeal, which hears appeals from the High Courts, Environment and Land

> 4For example, see Ash, Asher, et al. (2021), Gazal-Ayal and Sulitzeanu-Kenan (2010), Abrams, Bertrand, and Mullainathan (2012), Depew, Eren, and Mocan (2017), Lim, Silveira, and Snyder (2016), Yang (2015), Schanzenbach (2014), Mustard (2001), Arnold, Dobbie, and Yang (2018), Grossman et al. (2016), Choi, Andy Harris, and Shen-Bayh (2021), and Alesina and La Ferrara (2014). To our knowledge, only Shayo and Zussman (2011) and Knepper (2018) examine in-group bias in non-criminal cases. However, Shayo and Zussman (2011) focus on small claim courts and only include cases where the defendant and plaintiff are of different religions, so they are not able to examine differences in bias towards each litigant. Knepper (2018) examines workplace sexual discrimination cases, which only have one human litigant, as the defendant is an organization.

> 5As evidence, Dietz-Uhler and Murrell (1998) found that when individuals read negative reviews about their group (i.e., when they were exposed to a threat), they were more likely to make positive affirmations about their own group. For additional evidence see Wann and Grieve (2005) and Voci (2010).

> 6For an overview, see Berge et al. (2015).

4
Courts, and Employment and Labour Relations Courts; and the Supreme Court, which hears appeals from the Court of Appeal and other high-level cases (Kenyan Judiciary 2021).<sup>7</sup> The Kenyan judiciary does not employ a jury system. This means that judges alone are able to decide the outcomes of cases, which implies that bias among judges can have especially serious consequences. For most cases in most courts, there is only one judge. An exception is in Courts of Appeal, where the majority of cases are composed of multi-judge panels.

In August 2010, the judicial system was overhauled by the implementation of a new constitution. The Constitution led to a wide range of reforms, including in the judiciary (Akech 2010). The judicial reforms were designed to reduce executive branch control over judicial outcomes, eliminate the system of bribing judges, increase transparency in judge selection, reduce the large backlog of cases, and increase female participation (Akech 2011; Gainer 2016). The reforms included the appointment of an ombudsperson to address corruption complaints; the creation of a meritocratic judge appointment process, separate from the oversight of the executive; the design of a standardized case management system; a doubling of the judicial budget; and the creation of the requirement (applied across elective and appointive bodies throughout the government) that no more than two-thirds of Kenyan judges be of the same gender. Although not all of these reforms have been fully enacted, progress has been made on many dimensions (Gainer 2015; Mutunga 2011; IDLO 2020). These reforms could have important implications for in-group bias and its effects. For one, some of the reforms, such as the reduction of corruption and increase in meritocratic assignment, could potentially reduce overt bias. Moreover, with more women in the judiciary, the aggregate effects of in-group bias for women would be less severe.

As we show in the summary statistics section, below, there has been substantial progress towards gender parity in the judiciary–though there is still a long way to go to achieve equality. Inequalities in the judiciary are reflective of broader gender inequalities in Kenyan society. According to the 2020 United Nations Development Programme’s Gender Inequality Index, which scores countries based on gender gaps related to representation in government, educational attainment, and labor force participation, Kenya ranks 126th out of 189 countries. Notably, women hold only 23 percent of seats in parliament (UNDP 2020).

Inequalities are also widespread along ethnic lines, a fact which provided an impetus for the adoption of the new constitution and its measures to devolve power (Akech 2010). As we show below, some groups are underrepresented in the judiciary relative to their share of the population, whereas others are overrepresented. This finding showcases relative differences in access to justice and judicial representation across ethnicities.

# **3 Data**

## **3.1 Variable Construction**

The main data source used in our analysis is the Kenyan Judiciary’s publicly available database for court cases.<sup>8</sup> The database includes 159,645 cases, almost exclusively from the Superior Courts, over the period of 1976 to 2020. Kenya Law, an organization within the Kenya Judiciary, began uploading case information in 2006. They upload all cases that are sent to them from the individual courts. Judicial officers in Superior Courts have a mandate to send cases to Kenya Law. For cases prior to 2006, Kenya Law has made (and continues to make) efforts to gather and upload case information.

In order to build our dataset for analysis from this database, we scraped the metadata and full text decision associated with each case. In doing so, we were able to directly extract the following for most cases: the names of plaintiffs, defendants, and judges; the type of case; the court in which the case was heard; whether the case was appealed; and the year the judgement was delivered. We also used the history associated with each case to determine whether it was an appeal.

To determine gender and ethnicity and remove non-human cases (i.e., cases with companies, organizations, or the state as litigants), we used the name information scraped from the database. Cases without gender or

> 7According to our data, the Court of Appeals almost exclusively hears civil cases; the Environment and Land Courts are largely split between civil cases and environment and land cases; the Employment and Labour Relations Courts are largely split between labor cases and civil cases; and the High Courts frequently hear a wide range of cases, including civil cases, land and environment cases, labor cases, criminal cases, and others. We have little data on Supreme Court cases, but it appears to hear mostly civil cases. Despite these general trends, the data appears to show that the courts are generally not restricted in the cases they hear, as they all tend to hear a wide range of case types.

> 8See http://kenyalaw.org/caselaw/.

5
ethnicity information for judges and either plaintiffs or defendants were dropped.<sup>9</sup> Once gender and ethnicity were assigned to each individual, we could determine the majority genders and plurality ethnicities for the judges, defendants, and plaintiffs for each case.<sup>10</sup> We used machine learning to extract several other variables. To determine the winner of each case, we first scraped the case outcome information from the metadata. However, for 58,622 cases, the outcome was not stated. For these cases, we used a Binary Classification Machine Learning Model (described in appendix A) to analyze the text decisions of each case and determine the outcome. In the test set, the model was about 93 percent accurate. To measure the gender bias in judges’ writing, we used a word embedding approach that captures the textual relationship between gendered language and either positive/negative language or careeroriented/family-oriented language, following Ash, Chen, and Ornaghi (2021). This approach allowed us to measure the extent to which judges disproportionately associate women with either negative or stereotypical qualities (i.e., a focus on family rather than career). The two variables resulting from this process are _Median slant, career vs family_ and _Median slant, good vs bad_ . For both measures, positive values indicate greater slant against women.

We also created variables measuring aspects of each written judgement that could signal quality of the judgement, including the number of cases cited in the text, the number of laws and acts cited in the text, the length of the text (measured as the number of words), and the number of times the judgement has been cited by other cases in our dataset. Appendix A describes data cleaning and variable construction in greater detail.

We make available the metadata of all 159,645 cases, but focus the analysis on 29,363 cases with litigants who are individuals and have gender or ethnicity data.<sup>11</sup> This data covers 95 courts and 392 judges. This sample cover the years 1976 to 2020, as figure 1 shows.

Summary statistics of variables in the dataset are presented in Appendix B. they show that the main case types in our dataset are civil cases (46 percent), environment and land cases (32 percent), succession (10 percent), miscellaneous (8 percent), and labor relations (2 percent). All other cases comprise less than 1 percent of the total. Criminal cases are not included because they always have at least one non-human litigant (the state). Table C1 in Appendix C shows the court types that are included in the dataset. It indicates that over 99 percent of cases are from Superior Courts.<sup>12</sup>

> 9The processes for removing non-humans and determining gender and ethnicity (as well as the reasons for missing information) are discussed in appendix A.

> 10By majority, we mean an absolute majority, where one gender comprises more than 50 percent of the total. By plurality, we mean a simple majority, where there is more of one ethnic group than any other. If no majority could be determined for gender, the majority gender was coded as missing. If no plurality could be determined for ethnicity, the plurality ethnicity was coded as “no plurality.” This difference in coding was necessary because the main specification for gender in-group analysis requires binary outcomes, while the main specification for ethnicity in-group analysis does not (see specifications in section 4, below).

> 11Of the initial 159,645 cases, 33,876 had exclusively human litigants for civil cases. An additional 4,513 cases were dropped because we were unable to determine majority gender or ethnicity for the litigants in the case.

> 12In the “other” category, there are a small number of Subordinate Court cases. Most cases are from High Courts, followed by Environment and Land, Court of Appeal, and Employment and Labor. Very few Supreme Court cases are included.

6
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country where crime is more or less severe. If these distributions of crime severity are correlated with the ethnic distributions of defendants and plaintiffs, then we may again falsely perceive in-group bias.

The rest of this section proceeds as follows. Subsection 4.1 presents the qualitative justification that judge assignment is as good as random (at least in the most recent years). Subsections 4.2 and 4.3 present quantitative evidence through balance tests that the random assignment assumption does in fact hold across the full time period for both gender and ethnicity, respectively. The econometric approaches for examining gender and ethnic in-group bias are described in detail in subsections 4.4 and 4.5, respectively. We also aim to examine whether judge gender slant in written opinions is correlated with outcomes for female defendants and plaintiffs. The approaches to these analyses are described in subsection 4.6. Finally, subsection 4.7 presents the specifications for examining the relationship between in-group bias and textual variables other than slant, for both gender and ethnicity.

# **4.1 Random Assignment of Cases to Judges**

To evaluate the existence of in-group bias, we exploit the quasi-random assignment of cases to judges. In Kenyan High Courts, cases filed in a court are categorized by court type and sent to the deputy registrar of the relevant court division (family, commercial and admiralty, labour and employment, constitutional, land and environment, or criminal). The deputy registrar then assigns the case to a judge based on the judge’s caseload and calendar, without considering case characteristics. This exogenous assignment is orthogonal to case characteristics such as the gender or ethnicity of the parties. Thus, this system produces as-good-asrandom assignment of plaintiffs and defendants to judges, conditional on court division. This is consistent with the World Bank Doing Business’ Index, which asserts that cases are in fact randomly assigned to judges in Kenya (World Bank 2021). Nonetheless, it must be noted that this randomized procedure may be a recent phenomenon. Indeed, introducing randomization was allegedly one of the goals of the reform team following the implementation of the 2010 Kenyan Constitution (Gainer 2015). Therefore, it is necessary to provide further evidence that case selection by judges has not been a common feature across our sample. To do so, we present balance tests in the following two subsections. Considering the possibility that assignment has become de jure random after 2010, in addition to conducting the tests for the full sample, we also split the balance tests for de facto randomization before 2011 and after 2010. As a robustness check, we also conduct the main analysis for only after 2010.

# **4.2 Gender Balance Tests**

To confirm that judge assignment to cases is random in terms of gender majority, we use the following balance test for the analysis sample, for case _i_ filed in court _c_ at time _t_ as: where Φ _c,t_ is a court-year fixed effect and _Xi,c,t_ is a vector of additional control variables, which may include: binary variables for judge, defendant, and plaintiff plurality ethnicity; variables for the numbers of judges, plaintiffs, and defendants; a binary variable indicating whether the case is an appeal; and binary variables indicating the case type. Court-year fixed effects are used to ensure that we are comparing defendants and plaintiffs that are in the same court at the same time. Court-year periods with insufficient variation for regression analysis are dropped from the regressions. For this and all other models, we cluster standard errors at the judge level. As a robustness check for the main analyses, we use HC1 robust standard errors.

The results of the balance test are shown in Table D1 in Appendix D. Column (1) does not include additional controls. Column (2) includes controls for ethnicity, and Column (3) adds additional controls (as listed in the table notes). The results indicate that male- and female-majority defendant groups are equally likely to be assigned male- and female-majority judge panels (including single-judge panels). Likewise, maleand female-majority plaintiff groups are equally likely to be assigned male- and female-majority judge panels. In light of the concern that cases have only become randomized after the creation of the new constitution and the accompanying judicial reforms, Tables D2 and D3 present balance tests for pre-2011 and since 2011, respectively. The results are consistent with Table D1.

8
# **4.3 Ethnicity Balance Tests**

To confirm that judge assignment to cases is random in terms of ethnic majority, we use variations of the following balance test: where Φ _c,t_ and _Xi,c,t_ represent the same fixed effects and controls as before, _judge_ plur_ kikuyui,c,t_ is a binary variable indicating whether the judge plurality is the Kikuyu ethnic group, _def_ plur_ kikuyui,c,t_ is a binary variable indicating whether the defendant plurality is the Kikuyu ethnic group, and _pla_ plur_ kikuyui,c,t_ is a binary variable indicating whether the plaintiff plurality is the Kikuyu ethnic group. We run series of 12 tests, with each test using binary variables for different ethnicities.

Tables D4 through D7 in Appendix D report the results of the tests. They show that defendants and plaintiffs across all ethnicities are not more likely to be assigned judges from their ethnic group. One exception is Luhya defendants, as table D5 shows. Balance tests for both pre-2011 and since 2011 are also presented in Appendix D (see Tables D8 through D15). They show that there are significant coefficients for Luhya defendants in the 2011-2020 period and Kamba for the 1976-2010 period. We conduct a robustness check of the main analysis that drops all Luhya and Kamba individuals. Appendix E presents these results. A comparison between these results and the main results below show that the in-group bias we observe is not driven by any possible bias in Luhya or Kamba case assignment.<sup>13</sup>

# **4.4 Main Gender**

To estimate judicial gender bias, we model outcome Y _i,c,t_ (where Y=1 corresponds to the defendant winning the case) for case _i_ filed in court _c_ at time _t_ as: where _judge_ maj_ female_ and _def_ maj_ female_ are binary variables indicating whether judge panels and defendant groups, respectively, are majority female. The main outcome of interest is the interaction term, which indicates in-group bias.

The specification used to test for in-group bias towards plaintiffs is identical to (1), except a binary variable for plaintiff majority gender, _pla_ maj_ female_ substitutes _def_ maj_ female_ . An alternate specification includes both variables, as such:

# **4.5 Main Ethnicity**

For the ethnicity in-group bias analysis, we use a slightly different econometric specification in order to account for the fact that there are many more categories of ethnicity. To estimate judicial ethnic bias, we model outcome Y _i,c,t_ (where Y=1 corresponds to the defendant winning the case) for case _i_ filed in court _c_ at time _t_ as: where _judge_ pla_ samei,c,t_ is a binary variable indicating whether the judge ethnic plurality is the same as the plaintiff ethnic plurality, and _judge_ def_ samei,c,t_ is a binary variable indicating whether the judge ethnic plurality is the same as the defendant ethnic plurality.

> 13Tables D4 through D15 include a full set of controls. To save space, we have not included the results without controls. However, they are qualitatively similar, with no additional significant coefficients for the variables of interest.

9
## **4.6 Gender Slant Analysis**

To examine the conditions under which gender bias can be expected, we examine whether judges’ slant against women in opinions predicts bias against female defendants and plaintiffs. For this analysis, we use a specification that examines bias against women in general, rather than in-group bias. To do so, we build on equation (3) by adding one of the two measures of slant, described above, and an interaction between the slant measure and _def_ maj_ female_ and/or _pla_ maj_ female_ . The main outcomes of interest are the interactions with slant, which indicate whether female defendants and plaintiffs are less likely to win the case if the judge exhibits slant in her/his writing.

## **4.7 Judgement Text**

To study the relationship between in-group bias and characteristics of the judgement text for a given case, we start with variations on the following specification: where Y _i,c,t_ represents one of the four judgement text variables (number of cases cited, number of laws/acts cited outcome, number of times the case has been cited, or length) for case _i_ filed in court _c_ at time _t. judge_ def_ samei,c,t_ is a binary variable indicating whether the judge ethnic plurality is the same as the defendant ethnic plurality. Φ _c,t_ is a court-year fixed effect and _Xi,c,t_ is a vector of additional control variables.

In addition to running this specification on the full sample, we split the sample into two groups–cases where the defendant won and cases where the defendant lost–and run an additional series of regression for each. If in-group bias yields positive outcomes and is associated with different characteristics for judgement texts, then we might see significant coefficients on _judge_ def_ samei,c,t_ and/or _β_ 3 _judge_ maj_ femalei,c,t ⇤ def_ maj_ femalei,c,t_ for the defendant-win sample but not the defendant-lose sample, and the coefficients in the defendant-win sample should be larger than in the full sample. For example, if ethnically biased judgements are associated with the case being cited fewer times, then we should expect to see a negative coefficient on _judge_ def_ samei,c,t_ in the defendant-win sample, a (potentially null) negative coefficient of lesser magnitude in the full sample, and a null coefficient in the defendant-lose sample.

# **5 Results**

## **5.1 Main Gender Results**

The main gender regression results are presented in Table 1. The significantly positive coefficients on the interaction between judge and defendant majority gender provide evidence that there is in-group gender bias from judges towards defendants. This finding is robust to various specifications. The significant results suggest that all else equal, defendants are between 3.6 and 4.0 percentage points more likely to win if they have the same majority gender as the judges. As robustness checks, in Appendix E, we show that the results hold when only cases after 2010 are included, when robust standard errors are used (rather than standard errors clustered at the judge level), and when a probit model is used rather than OLS.

The results do not provide evidence of in-group bias towards plaintiffs. However, the coefficients for plaintiff and defendant in-group bias are not significantly different (p=0.216 for column 5), so we cannot claim that bias is stronger towards defendants. Descriptively, we also see that female judges are in general more likely to rule in favor of defendants, and male plaintiffs are in general more likely to lose.

Figures 2 and 3 visualize these results. Figure 2 displays defendant win proportions by judge and defendant majority gender. With win proportions higher for female majority defendants among female judge panels and win proportions higher for male majority defendants among male judge panels, the figure is suggestive of in-group bias and qualitatively consistent with the findings from Table 1, even though it does not control for the court-year level of randomization. Figure 3 displays plaintiff win proportions by judge and defendant majority ethnicity. It is also consistent with Table 1 as it is not suggestive of in-group bias.

10
Figure 4 visualizes the in-group bias trend for defendants. Based on a series of regressions, one for each individual judge, it plots the distribution of individual judge bias coefficients. Although there are some more extreme judges, in the direction of both in- and out-group bias, the results seem to be largely driven by mildly in-group biased judges.<sup>14</sup>

> 14Figure F1 in appendix F visualizes the results in another way. It helps highlight the fact that some judges exhibit more extreme gender in-group bias. Table G1 in appendix G explores the effects of putting biased judges on panels. It analyzes in-group bias among the 14 judges with significant coefficients for gender in-group bias towards defendants in individual judge regressions. It shows that when these judges make decisions individually, the defendant is 40 percentage points more likely to win if they share the judge’s gender. In contrast, when these judges rule on panels with other judges, the defendant is only 40 percentage points more likely to win if they share the judge’s gender. It is important to note that these results are not causal. Nonetheless, they suggest one potential means for policymakers to reduce bias: put biased judges on panels. There were insufficient observations to conduct this analysis for ethnicity; few of the ethnicity in-group biased judges had ruled on panels.

11
Table 1: Gender main results

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||Def. win|Def. win|Def. win|Def. win|Def. win|
|Judge maj. female|-0.0380<sup>_⇤⇤⇤_</sup>|-0.0397<sup>_⇤⇤_</sup>|-0.0468<sup>_⇤⇤⇤_</sup>|-0.0410<sup>_⇤⇤⇤_</sup>|-0.0411<sup>_⇤⇤⇤_</sup>|
||(0.0122)|(0.0197)|(0.0133)|(0.0132)|(0.0133)|
|Pla. maj. female||-0.0296<sup>_⇤⇤_</sup>|-0.0528<sup>_⇤⇤⇤_</sup>|-0.0426<sup>_⇤⇤⇤_</sup>|-0.0433<sup>_⇤⇤⇤_</sup>|
|||(0.0128)|(0.0109)|(0.0108)|(0.0108)|
|Def. maj. female|-0.00495||0.00144|0.00706|0.00667|
||(0.0106)||(0.0108)|(0.0107)|(0.0107)|
|Judge maj. fem. X pla. maj. fem.||0.0173|0.00690|0.00655|0.00726|
|||(0.0208)|(0.0171)|(0.0169)|(0.0168)|
|Judge maj. fem. X def. maj. fem.|0.0359<sup>_⇤⇤_</sup>||0.0400<sup>_⇤⇤_</sup>|0.0379<sup>_⇤⇤_</sup>|0.0380<sup>_⇤⇤_</sup>|
||(0.0167)||(0.0168)|(0.0168)|(0.0167)|
|DV mean|0.452|0.429|0.454|0.454|0.454|
|Court-year FE|Yes|Yes|Yes|Yes|Yes|
|Ethnicity dummies|No|No|No|No|Yes|
|Other controls|No|No|No|Yes|Yes|
|Observations|22801|25618|20394|20394|20394|

The regressions test whether defendants (plaintiffs) are more likely to win (lose) if they have the same (a different) majority gender as judges. The coefficients of interest are on the interaction terms. Standard errors, in parentheses, are clustered at the judge level. All columns are based on a linear regression model. For specification details, see equations 3 and 4. Ethnicity dummies include binary variables indicating whether a given ethnicity is the plurality, one for each ethnicity, for defendants, plaintiffs, and judges. Other controls include case type dummies, a dummy for an appeal case, and variables for the numbers of defendants, plaintiffs, and judges. To prevent a loss of observations, all categorical controls (such as case type) include a dummy that denotes if data is missing/unknown.Pla. = plaintiff, def. = defendant, maj. = majority.

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Table 2: Gender results with text slant, career vs family measure

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Def. win|Def. win|Def. win|Def. win|
|Judge maj. female|-0.0481<sup>_⇤⇤⇤_</sup>|-0.0479<sup>_⇤⇤⇤_</sup>|-0.0477<sup>_⇤⇤⇤_</sup>|-0.0435<sup>_⇤⇤⇤_</sup>|
||(0.0138)|(0.0138)|(0.0138)|(0.0138)|
|Pla. maj. female|-0.0505<sup>_⇤⇤⇤_</sup>|-0.0486<sup>_⇤⇤⇤_</sup>|-0.0500<sup>_⇤⇤⇤_</sup>|-0.0372<sup>_⇤⇤⇤_</sup>|
||(0.0117)|(0.0118)|(0.0117)|(0.0116)|
|Def. maj. female|-0.00350|-0.00635|-0.00619|-0.00228|
||(0.0117)|(0.0117)|(0.0117)|(0.0116)|
|Judge maj. fem. X pla. maj. fem.|0.00171|0.00211|0.00163|0.000857|
||(0.0180)|(0.0182)|(0.0181)|(0.0177)|
|Judge maj. fem. X def. maj. fem.|0.0460<sup>_⇤⇤⇤_</sup><br>(0.0174)|0.0440<sup>_⇤⇤⇤_</sup><br>(0.0169)|0.0440<sup>_⇤⇤_</sup><br>(0.0169)|0.0430<sup>_⇤⇤_</sup><br>(0.0168)|
|Slant against women, career vs family|0.0110|0.0316|0.0457|-0.0358|
||(0.0823)|(0.0815)|(0.0828)|(0.0840)|
|Pla. maj. fem. X Slant against women|-0.0645||-0.0537|-0.0160|
||(0.0852)||(0.0866)|(0.0884)|
|Def. maj. fem. X Slant against women||-0.156<sup>_⇤_</sup>|-0.152<sup>_⇤_</sup>|-0.146<sup>_⇤_</sup>|
|||(0.0856)|(0.0861)|(0.0828)|
|DV mean|||||
|Court-year FE|Yes|Yes|Yes|Yes|
|Ethnicity dummies|No|No|No|Yes|
|Other controls|No|No|No|Yes|
|Observations|18205|18205|18205|18205|

The regressions test whether defendants/plaintiffs are more likely to lose if they are female and the judge is slanted against females in their writing. The coefficients of interest are on the interaction terms in the last two rows. The measure of slant against women is based on the judges’ stereotypical association of women with family-based qualities rather than career-based qualities. All columns are based on a linear regression model. For specification details, see equation 3. Ethnicity dummies include binary variables indicating whether a given ethnicity is the plurality, one for each ethnicity, for defendants, plaintiffs, and judges. Other controls include case type dummies, a dummy for an appeal case, and variables for the numbers of defendants, plaintiffs, and judges. To prevent a loss of observations, all categorical controls (such as case type) include a dummy that denotes if data is missing/unknown. Pla. = plaintiff, def. = defendant, maj. = majority.

15
Table 3: Gender results with text slant, good vs bad measure

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Def. win|Def. win|Def. win|Def. win|
|Judge maj. female|-0.0434<sup>_⇤⇤⇤_</sup>|-0.0436<sup>_⇤⇤⇤_</sup>|-0.0445<sup>_⇤⇤⇤_</sup>|-0.0392<sup>_⇤⇤_</sup>|
||(0.0156)|(0.0154)|(0.0154)|(0.0158)|
|Pla. maj. female|-0.0322<sup>_⇤_</sup>|-0.0493<sup>_⇤⇤⇤_</sup>|-0.0330<sup>_⇤_</sup>|-0.0223|
||(0.0167)|(0.0128)|(0.0168)|(0.0157)|
|Def. maj. female|-0.00536|0.0168|0.0160|0.0162|
||(0.0117)|(0.0146)|(0.0146)|(0.0142)|
|Judge maj. fem. X pla. maj. fem.|0.00500|0.00159|0.00479|0.00289|
||(0.0203)|(0.0206)|(0.0204)|(0.0201)|
|Judge maj. fem. X def. maj. fem.|0.0464<sup>_⇤⇤_</sup>|0.0505<sup>_⇤⇤⇤_</sup>|0.0504<sup>_⇤⇤⇤_</sup>|0.0502<sup>_⇤⇤⇤_</sup>|
||(0.0185)|(0.0182)|(0.0182)|(0.0181)|
|Slant against women, good vs bad|-0.0882|-0.0765|-0.00212|-0.0307|
||(0.151)|(0.146)|(0.152)|(0.155)|
|Pla. maj. fem. X Slant against women|-0.298<sup>_⇤_</sup>||-0.277|-0.244|
||(0.179)||(0.181)|(0.176)|
|Def. maj. fem. X Slant against women||-0.375<sup>_⇤⇤_</sup>|-0.358<sup>_⇤⇤_</sup>|-0.321<sup>_⇤_</sup>|
|||(0.173)|(0.175)|(0.172)|
|DV mean|||||
|Court-year FE|Yes|Yes|Yes|Yes|
|Ethnicity dummies|No|No|No|Yes|
|Other controls|No|No|No|Yes|
|Observations|15175|15175|15175|15175|

The regressions test whether defendants/plaintiffs are more likely to lose if they are female and the judge is slanted against females in their writing. The coefficients of interest are on the interaction terms in the last two rows. The measure of slant against women is based on the judges’ association of women with negative qualities. All columns are based on a linear regression model. For specification details, see equation 3. Ethnicity dummies include binary variables indicating whether a given ethnicity is the plurality, one for each ethnicity, for defendants, plaintiffs, and judges. Other controls include case type dummies, a dummy for an appeal case, and variables for the numbers of defendants, plaintiffs, and judges. To prevent a loss of observations, all categorical controls (such as case type) include a dummy that denotes if data is missing/unknown. Pla. = plaintiff, def. = defendant, maj. = majority.

16
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# **5.3 Main Ethnicity Results**

The main ethnicity regression results are presented in Table 4. They show that defendants are between 4.4 and 5.8 percentage points more likely to win if they share an ethnicity with the judge. This is evidence of in-group bias among judges towards defendants. The finding is robust to all of the specifications presented. As with gender, in-group bias is not observed for plaintiffs; the coefficients are both positive and negative across specifications and are not significant. Unlike with gender, the coefficients for plaintiff and defendant in-group bias are significantly different (p=0.022 for column 5). As robustness checks, in Appendix E, we show that the results hold when only cases after 2010 are included, when robust standard errors are used rather than standard errors clustered at the judge level, and when a probit model is used rather than OLS.

Figure 7 displays defendant win proportions across various in-group categories relating judges, defendants, and plaintiffs. Figure 7a displays outcomes when the judge and plaintiff have the same ethnicity. Figure 7b displays outcomes when the judge and plaintiff have different ethnicities. Consistent with the regression, they both show that defendants are qualitatively more likely to win when judges and defendants are the same the ethnicity without the inclusion of court-year fixed effects.

Figure 8 visualizes the in-group bias trend for defendants. Based on a series of regressions, one for each individual judge, it plots the distribution of individual judge bias coefficients. Similar to the findings for gender, the results seem to be largely driven by a preponderance of mildly in-group biased judges, despite the presence of some more extreme judges, in the direction of both in- and out-group bias.<sup>16</sup> Table 4: Ethnicity results

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||Def. win|Def. win|Def. win|Def. win|Def. win|
|Judge-pla. same|0.00681||-0.0138|-0.00549|-0.00578|
||(0.0127)||(0.0139)|(0.0153)|(0.0154)|
|Judge-def. same||0.0439<sup>_⇤⇤⇤_</sup>|0.0573<sup>_⇤⇤⇤_</sup>|0.0532<sup>_⇤⇤⇤_</sup>|0.0551<sup>_⇤⇤⇤_</sup>|
|||(0.0122)|(0.0143)|(0.0159)|(0.0158)|
|DV mean|0.450|0.453|0.453|0.453|0.453|
|Court-year FE|Yes|Yes|Yes|Yes|Yes|
|Ethnicity dummies|No|No|No|Yes|Yes|
|Other controls|No|No|No|No|Yes|
|Observations|21842|20971|18952|18952|18952|

The regressions test whether defendants (plaintiffs) are more likely to win (lose) if they have the same (a different) plurality ethnicity as judges. Standard errors, in parentheses, are clustered at the judge level. All columns are based on a linear regression model. For specification details, see equation 5. Judge-pla. same and Judge-def. same refer to similarity in plurality ethnicity. Ethnicity dummies include binary variables indicating whether a given ethnicity is the plurality, one for each ethnicity, for both defendants and plaintifs. Other controls include case type dummies; a dummy for an appeal case; variables for the numbers of defendants, plaintiffs, and judges; and dummies for defendant, plaintiff, and judge majority gender. To prevent a loss of observations, all categorical controls (such as case type) include a dummy that denotes if data is missing/unknown. Pla. = plaintiff, def. = defendant.

> 16Figure F2 in appendix F visualizes the results in another way. It helps highlight the fact that some judges exhibit more extreme gender in-group bias. Appendix I presents the results from a regression with interactions between gender and ethnic in-group status. The results are null.

18
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Table 5: Judgement text regressions, gender, full sample

||(1)<br>Num. citations|(2)<br>Times cited|(3)<br>Num laws cited|(4)<br>Words in judg.|
|---|---|---|---|---|
|Judge maj. female|0.402<sup>_⇤⇤_</sup>|-0.0948<sup>_⇤⇤_</sup>|0.293|82.45|
||(0.195)|(0.0413)|(0.183)|(85.79)|
|Def. maj. female|-0.108<sup>_⇤_</sup>|0.0389|-0.130|-28.85|
||(0.0650)|(0.0613)|(0.0844)|(22.16)|
|Judge maj. fem. X def. maj. fem.|-0.0790|-0.0437|0.228|-25.34|
||(0.123)|(0.0674)|(0.151)|(46.18)|
|DV mean|2.020|0.250|2.200|1484.0|
|Court-year FE|Yes|Yes|Yes|Yes|
|Observations|22801|22801|22801|22801|

The regressions test whether in-group bias is associated with significantly different aspects of judges’ written judgements. If in-group bias is associated with different characteristics for judgement texts, then we should see significant coefficients for the defendant-win sample but not the defendant-lose sample, the coefficients in the defendant-win sample should be larger than in the full sample. This table presents the full sample results. Standard errors, in parentheses, are clustered at the judge level. All columns are based on a linear regression model. For specification details, see equation 6. Num. citations refers to the number of citations in the judgement. Times cited refers to the number of times the case has been cited. Num. laws cited refers to the number of laws and and acts cited in the judgement. Words in judg. refers to the number of words in the written judgement. Def. = defendant, maj. = majority, fem. = female.

Table 6: Judgement text regressions, gender, defendant win

||(1)<br>Num. citations|(2)<br>Times cited|(3)<br>Num laws cited|(4)<br>Words in judg.|
|---|---|---|---|---|
|Judge maj. female|0.326|-0.119<sup>_⇤⇤⇤_</sup>|0.126|85.48|
||(0.205)|(0.0459)|(0.223)|(86.29)|
|Def. maj. female|-0.0684<br>(0.0855)|-0.0653<br>(0.0843)|-0.218<sup>_⇤_</sup><br>(0.129)|56.57<br>(35.86)|
|Judge maj. fem. X def. maj. fem.|-0.176|0.0805|0.186|-140.1<sup>_⇤⇤_</sup>|
||(0.175)|(0.0975)|(0.203)|(67.75)|
|DV mean|2.076|0.257|2.303|1517.9|
|Court-year FE|Yes|Yes|Yes|Yes|
|Observations|10235|10235|10235|10235|

The regressions test whether in-group bias is associated with significantly different aspects of judges’ written judgements. If in-group bias is associated with different characteristics for judgement texts, then we should see significant coefficients for the defendant-win sample but not the defendant-lose sample, the coefficients in the defendant-win sample should be larger than in the full sample. This table presents the defendant-win sample results. Standard errors, in parentheses, are clustered at the judge level. All columns are based on a linear regression model. For specification details, see equation 6. Num. citations refers to the number of citations in the judgement. Times cited refers to the number of times the case has been cited. Num. laws cited refers to the number of laws and and acts cited in the judgement. Words in judg. refers to the number of words in the written judgement. Def. = defendant, maj. = majority, fem. = female.

21
Table 7: Judgement text regressions, gender, defendant lose

||(1)<br>Num. citations|(2)<br>Times cited|(3)<br>Num laws cited|(4)<br>Words in judg.|
|---|---|---|---|---|
|Judge maj. female|0.462<sup>_⇤⇤_</sup>|-0.0939|0.415<sup>_⇤⇤_</sup>|70.47|
||(0.216)|(0.0630)|(0.196)|(96.78)|
|Def. maj. female|-0.140|0.0993|-0.0529|-106.1<sup>_⇤⇤⇤_</sup>|
||(0.103)|(0.0768)|(0.0981)|(35.96)|
|Judge maj. fem. X def. maj. fem.|-0.0416|-0.110|0.221|73.42|
||(0.160)|(0.0818)|(0.183)|(58.10)|
|DV mean|1.963|0.239|2.100|1449.4|
|Court-year FE|Yes|Yes|Yes|Yes|
|Observations|12422|12422|12422|12422|

The regressions test whether in-group bias is associated with significantly different aspects of judges’ written judgements. If in-group bias is associated with different characteristics for judgement texts, then we should see significant coefficients for the defendant-win sample but not the defendant-lose sample, the coefficients in the defendant-win sample should be larger than in the full sample. This table presents the defendant-lost sample results. Standard errors, in parentheses, are clustered at the judge level. All columns are based on a linear regression model. For specification details, see equation 6. Num. citations refers to the number of citations in the judgement. Times cited refers to the number of times the case has been cited. Num. laws cited refers to the number of laws and and acts cited in the judgement. Words in judg. refers to the number of words in the written judgement. Def. = defendant, maj. = majority, fem. = female.

Table 8: Judgement text regressions, ethnicity, full sample

||(1)<br>Num. citations|(2)<br>Times cited|(3)<br>Num laws cited|(4)<br>Words in judg.|
|---|---|---|---|---|
|Judge-defendant same ethnicity=1|-0.134|-0.0828<sup>_⇤⇤⇤_</sup>|-0.146|-67.04|
||(0.110)|(0.0310)|(0.144)|(53.06)|
|DV mean|2.012|0.217|2.199|1480.2|
|Court-year FE|Yes|Yes|Yes|Yes|
|Observations|20971|20971|20971|20971|

The regressions test whether in-group bias is associated with significantly different aspects of judges’ written judgements. If in-group bias is associated with different characteristics for judgement texts, then we should see significant coefficients for the defendant-win sample but not the defendant-lose sample, the coefficients in the defendant-win sample should be larger than in the full sample. This table presents the full sample results. Standard errors, in parentheses, are clustered at the judge level. All columns are based on a linear regression model. For specification details, see equation 6. Num. citations refers to the number of citations in the judgement. Times cited refers to the number of times the case has been cited. Num. laws cited refers to the number of laws and and acts cited in the judgement. Words in judg. refers to the number of words in the written judgement.

22
Table 9: Judgement text regressions, ethnicity, defendant win

||(1)<br>Num. citations|(2)<br>Times cited|(3)<br>Num laws cited|(4)<br>Words in judg.|
|---|---|---|---|---|
|Judge-defendant same ethnicity=1|-0.147|-0.137<sup>_⇤⇤_</sup>|-0.229|-97.65<sup>_⇤_</sup>|
||(0.137)|(0.0583)|(0.177)|(57.53)|
|DV mean|2.054|0.220|2.253|1505.6|
|Court-year FE|Yes|Yes|Yes|Yes|
|Observations|9433|9433|9433|9433|

The regressions test whether in-group bias is associated with significantly different aspects of judges’ written judgements. If in-group bias is associated with different characteristics for judgement texts, then we should see significant coefficients for the defendant-win sample but not the defendant-lose sample, the coefficients in the defendant-win sample should be larger than in the full sample. This table presents the defendant-win sample results. Standard errors, in parentheses, are clustered at the judge level. All columns are based on a linear regression model. For specification details, see equation 6. Num. citations refers to the number of citations in the judgement. Times cited refers to the number of times the case has been cited. Num. laws cited refers to the number of laws and and acts cited in the judgement. Words in judg. refers to the number of words in the written judgement.

Table 10: Judgement text regressions, ethnicity, defendant lose

||(1)<br>Num. citations|(2)<br>Times cited|(3)<br>Num laws cited|(4)<br>Words in judg.|
|---|---|---|---|---|
|Judge-defendant same ethnicity=1|-0.0923|-0.0321|-0.130|-46.70|
||(0.147)|(0.0361)|(0.181)|(61.11)|
|DV mean|1.979|0.211|2.157|1458.6|
|Court-year FE|Yes|Yes|Yes|Yes|
|Observations|11395|11395|11395|11395|

The regressions test whether in-group bias is associated with significantly different aspects of judges’ written judgements. If in-group bias is associated with different characteristics for judgement texts, then we should see significant coefficients for the defendant-win sample but not the defendant-lose sample, the coefficients in the defendant-win sample should be larger than in the full sample. This table presents the defendant-lose sample results. Standard errors, in parentheses, are clustered at the judge level. All columns are based on a linear regression model. For specification details, see equation 6. Num. citations refers to the number of citations in the judgement. Times cited refers to the number of times the case has been cited. Num. laws cited refers to the number of laws and and acts cited in the judgement. Words in judg. refers to the number of words in the written judgement.

23
Table 11: Judgement text regressions, additional controls, defendant win

||(1)<br>Times cited|(2)<br>Times cited|(3)<br>Words in judg.|(4)<br>Words in judg.|
|---|---|---|---|---|
|Judge maj. female|-0.0996<sup>_⇤_</sup>|-0.0748|89.96|100.3|
||(0.0539)|(0.0524)|(93.08)|(91.29)|
|Def. maj. female|-0.0209|0.0236|58.03|87.37<sup>_⇤_</sup>|
||(0.0849)|(0.0960)|(46.72)|(49.78)|
|Judge maj. fem. X def. maj. fem.|0.0407<br>(0.0998)|0.0250<br>(0.105)|-139.0<sup>_⇤_</sup><br>(73.73)|-152.7<sup>_⇤⇤_</sup><br>(73.13)|
|Judge-defendant same ethnicity=1|-0.137<sup>_⇤⇤_</sup>|-0.127<sup>_⇤_</sup>|-97.20|-70.46|
||(0.0612)|(0.0687)|(59.67)|(63.01)|
|DV mean|0.220|0.220|1505.1|1505.1|
|Court-year FE|Yes|Yes|Yes|Yes|
|Other controls|No|Yes|No|Yes|
|Observations|7963|7963|7963|7963|

The regressions test whether in-group bias is associated with significantly different aspects of judges’ written judgements. Standard errors, in parentheses, are clustered at the judge level. Num. citations refers to the number of citations in the judgement. Times cited refers to the number of times the case has been cited. Num. laws cited refers to the number of laws and and acts cited in the judgement. Words in judg. refers to the number of words in the written judgement. Other controls include binary variables indicating whether a given ethnicity is the plurality, one for each ethnicity, for defendants, plaintiffs, and judges; case type dummies; a dummy for an appeal case; and variables for the numbers of defendants, plaintiffs, and judges. To prevent a loss of observations, all categorical controls (such as case type) include a dummy that denotes if data is missing/unknown. Def. = defendant, maj. = majority, fem. = female.

24
# **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.

25
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# **Appendix**

## **Appendix A: Variable Construction**

### **Constructing variables with judge, defendant, and information**

The names of judges, defendants, and plaintiffs were used to remove non-humans and to extract additional information for each case, including gender, ethnicity, and the number of judges and litigants. Cases were identified as non-human and removed if either the plaintiff or defendant name included any of a long list of key words, such as “republic,” “company,” or “medical.” A full list of the keywords can be found in the cleaning scripts in the replication materials posted online.

Afterwards, we could determine the gender of each individual using their first name and the ethnicity of each individual using their last name. To assign gender based on first names we used the genderize.io API and Gender API, both of which use global databases of names and genders to probabilistically assign gender to names.<sup>17</sup> One exception was for the judges, for whom gender was assigned manually.

To assign ethnicity based on last names, we used data available on Harvard Dataverse that links names to ethnicities (Andrew Harris 2014). This data could be used to identify 12 ethnic groups (Meru, Kisii, Kalenjin, Kamba, Luo, Turkana, Mijikenda, Luhya, Kikuyu, Somali, Masai, and Pokot). This includes one ethnic sub-group, the Pokot, which is a sub-group of the Kalenjin. Throughout our analysis, Kalenjin refers to non-Pokot Kalenjin. Together, these groups account for about 91 percent of the population of Kenya. Of the other 29 major ethnic groups (i.e., non-subgroups) identified in the 2019 census, the largest group accounts for only about 0.9 percent of the population.

Gender and ethnicity could not be determined for all individuals in all cases. Gender could not be determined if the first name was either abbreviated (i.e., if only initials were given), it did not clearly match to a single gender, or it was not included in the API datasets. Ethnicity could not be determined if the last name was not included in the ethnicity dataset. For some of the cases included in analysis, information could be extracted for plaintiffs but not defendants (and vice versa) and for gender but not ethnicity (and vice versa).

It is important to note that there is the possibility of a small amount of error resulting from the automated process of removing non-humans and determining gender and ethnicity. For example, although the list of key words for non-humans is long and we have manually scanned the data for non-humans, it is still possible that some non-humans remain. It is also possible that gender and/or ethnicity has been assigned to non-humans with certain key words included in the organization name. Similarly, if names were separated in an unusual way, it is possible that the number of defendants or plaintiffs was incorrectly counted, possibly resulting in an incorrect assignment of majority/plurality gender/ethnicity. However, having thoroughly scanned the data, we are confident that the number of such errors is insignificant.

### **Using the Binary Classification Machine Learning Model to construct the defendant_win outcome variable**

To determine the winner of each case, we created a Binary Classification Machine Learning Model using the Global Vectors for Word Representation (GloVe) algorithm (Pennington, Socher, and Manning 2014). The objective function of GloVe can be written as follows: where _Xij_ denotes the co-occurrence count between words _i_ and _j_ , and _f_ ( _·_ ) is a weighting function that serves to down-weight particularly frequent words. The objective function _J_ ( _·_ ) trains the word vectors to minimize the squared difference between the dot product of the vectors representing two words and their empirical co-occurrence in the corpus. The algorithm requires two hyperparameters, _dimensionality_ of the vectors and the _window_ size for computing co-occurrence statistics. Prior research has found 300 to be the optimum size in many a cases and that increasing dimensionality beyond 300 has negligible improvements for downstream tasks (Pennington, Socher, and Manning 2014; Spirling and Rodriguez 2019). Following that

> 17See the following websites: https://genderize.io/; https://gender-api.com/.

29
Table A1: Model outcomes

|Training set accuracy|92.44%|
|---|---|
|Validation set accuracy|91.92%|
|Test set accuracy (on previously unseen data)|92.83%|
|Accuracy|0.928388|
|Precision|0.896705|
|Recall|0.959647|
|F1 score|0.927109|

literature, we train 300 dimensional vectors. We used a standard 10-word window size, in between a shorter window size (which tends to capture syntactic/functional relations between words) and a longer window size (which tends to capture topical relations between words). To improve accuracy, the classification model was also comprised of a Long Short-Term Memory layer in addition to the fully connected neural network layers and the initial embedding layer (Hochreiter and Schmidhuber 1997).

Applying this model to our data, we used the bottom 500 words of the case judgements, since the outcomes were found to be present towards the bottom of the judgements. As a training dataset, we applied the model to cases for which we could determine the outcome (in favor or against the defendant) directly from the case outcome variable of the metadata. There were 49,706, 6,214, and 6,213 cases in the training, testing, and validation sets, respectively. The results of the model are presented in table A1.

# **Using word embeddings to determine textual slant**

To determine each judge’s textual gender slant (i.e., the degree to which each judge exhibits gender bias in their written judgements), we make use of word embeddings, which model the text present in the judgements in the form of low dimensional Euclidean space vectors (Pennington, Socher, and Manning 2014). In other words, word embeddings are low dimensional vectors which can accommodate large vocabularies and corpora without increasing dimensionality. The representation resulting from them captures relations between the words. In order to catch semantic similarity amongst words, the positions are assigned to word vectors in the Euclidean space, such that the words that appear frequently in the same context have representations close to each other in the space, while words that appear rarely together have representations that are far apart.

To train our word embeddings, we used the GloVe algorithm, described above. The embeddings we trained were then used for identification of cultural dimensions in language (Kozlowski, Taddy, and Evans 2019). That is, we identified a gender dimension by taking the difference between the average normalized vector across a set of male words and the average normalized vector across a set of female words, as such: where _Nmale_ is the number of words used to identify the male dimension. In order to determine the similarity within these dimensions, we used cosine similarity as a measure, defined as follows: where _~x_ and _~y_ are non-zero vectors, _✓_ is the associated angle, and _k · k_ is the 2-norm. Therefore, we can see that words with male (female) _~_ connotations _~_ are going to be positively (negatively) correlated with the gender dimension defined by _male − female_ .

These dimensions were then used to construct the gender slant measures. For the first, we aimed to capture the strength of the association between gender and stereotypical attitudes, which identify men more closely with careers and women with family. Specifically, _~ ~_ we used the cosine similarity between the vector representingdimension, defined bythe gender _career_ dimension, _~ − family~_ defined. For our second measure, we aimed to capture stereotypical attitudesby _male − female_ , and the vector representing the _~_ career-family _~_ that associate _~_ men with “good” and women with “bad” words. For this measure, instead of _career − family_ , we used _good − bad_<sup>_~_</sup> .

30
_~ ~_ fiveFormostthefrequently _male − female_ occurringdimension,in our corpus.we usedWordsvariousforgender-specific _career~ − family~_ wordsandwhich _good~_ were _− bad_<sup>_~_</sup> foundwereoutchosento beinthea similar fashion. Only five words were chosen for each because, given the relatively small size of the corpus, the inclusion of too many words could result in invalid measures of slant. The word used are displayed in table A2.

Table A2: Words used for each vector dimension

|Vector dimension<br>|Words|
|---|---|
|_~_<br>_MaleNames_<br>|john, joseph, peter, james, david|
|_~_<br>_FemaleNames_<br>_~_|faith, mary, rose, jane, margaret|
|_Male_<br>|his, he, him, mr, himself|
|_~_<br>_Female_<br>|her, she, ms, mrs, herself|
|_~_<br>_Good_<br>|competent, strong, power, serious, professional|
|_~_<br>_Bad_<br>|frivolous, vain, incompetent, unreasonable, incapable|
|_~_<br>_Career_<br>|company, service, pay, business, work|
|_~_<br>_Family_|family, wife, mother, father, brother|

Each dimensions includes the five most common relevant words in the corpus. Only five words were chosen for each because, given the relatively small size of the corpus, the inclusion of too many words could results in invalid measures of slant.

To apply this process to the data, we first preprocessed the entire Kenya Law corpus of judgements by removing punctuations (but retaining hyphenated words). To avoid case sensitivity, we transformed all our words to lower case. We then retained only the most common 50,000 words in all judicial opinions. To obtain judge-specific gender slant measures, we took the set of majority opinions authored by each judge as a separate corpus and trained separate GloVe embeddings on each judge’s corpus. To ensure convergence, we trained vectors for 20 iterations with a learning rate of 0.05.

Since each judge might not have a sufficiently large number of tokens, we follow the approach suggested by Antoniak and Mimno (2018) and train embedding models on 25 bootstrap samples of each judge corpus. Specifically, we consider each sentence written by a judge as a document and then create a corpus by sampling with replacement from all sentences. The number of sentences contained in the bootstrapped sample is the same as the total number of sentences in the original judge corpus. We then calculate our slant measure for all bootstrap samples and assign to each judge the median value of the measure across the samples. Given that embeddings trained on small corpora tend to be sensitive to the inclusion of specific documents, the bootstrap procedure produces more stable results. In addition, bootstrapping ensures stability with respect to the initialization of the word vectors–a potential concern given that GloVe presents a non-convex objective function (Spirling and Rodriguez 2019). The two variables resulting from this process are _Median slant, career vs. family_ and _Median slant, good vs. bad_ . For both measures, positive values indicate greater slant against women.

To validate that the embeddings capture meaningful information about gender, after following the bootstrapping procedure, we compute the cosine similarity between the gender dimension and each of the vectors representing the five most common male and female names for each judge and bootstrap sample. We then regress a dummy for whether the name is male on the median cosine similarity between the vector representing the name and the gender dimension across bootstrap samples, separately for each judge. Figure A1 shows the cumulative distribution of the t-statistics resulting from these regressions for sets of judges with different numbers of tokens. It shows that most t-statistics are significant (and they are never lower than zero). This shows that the gender dimension identified in the embeddings does indeed contain meaningful gender information.

# **Constructing other textual variables**

To create the measure of the number of cases cited in the text, we extracted a window of 10 words (5 on each side) around the words v, vs, and ndashvs (because sometimes HTML elements from the website are included in the text), which were found to be a common way of citing other judgements. This window of 10 words was then cleaned to produce the final cited judgements. A similar process was used to cite the 31
<!-- Start of picture text --> Number of Tokens a<br>s < 500K foe<br>--------- 2500K and <1M a y<br>SN4——-— 21Mand<1,5M ay<br>g if<br>ic=] To) fogel i!<br>§ of /<br>o WA<br>ro aaa<br>xs)=p eZJ<br>a no<br>a a<br>Hed<br>,)<br>oe)<br>-5 -2.5 0 2.5 5 7.5 10<br>T-statistic<br>*Vertical line denotes T-Statistic = 1.96, for p< 0.05<br><!-- End of picture text -->
# **Appendix B: Variable Summaries**

Table B1: Summary of main variables

||count|mean|sd|min|max|
|---|---|---|---|---|---|
|Def. win|29373|.4290675|.4949514|0|1|
|Judge maj. female|28627|.3650051|.48144|0|1|
|Pla. maj. female|26418|.2499432|.4329881|0|1|
|Def. maj. female|23555|.2379962|.4258658|0|1|
|Judge-plaintif same ethnicity|21958|.1321159|.3386244|0|1|
|Judge-defendant same ethnicity|21094|.1257704|.3315982|0|1|
|This is an appeal case|29373|.2739591|.4459958|0|1|
|This case is appealed|29373|.0176352|.1316238|0|1|
|Decision is reversed in the appeal|518|.507722|.5004236|0|1|
|Number of defendants|29373|1.584619|1.440699|1|68|
|Number of plaintifs|29373|1.315528|1.134249|1|65|
|Number of judges|29373|1.108944|.4580071|1|9|
|Median slant, career v family|26113|-.0283425|.0988901|-.2812362|.30826|
|Median slant, good v bad|22023|.0615131|.0548388|-.0875989|.2815675|
|Case type: civil|28512|.4645062|.4987473|0|1|
|Case type: tax|28512|.0023148|.0480576|0|1|
|Case type: human rights|28512|.0012626|.0355116|0|1|
|Case type: judicial review|28512|.000947|.0307588|0|1|
|Case type: divorce|28512|.0019992|.044668|0|1|
|Case type: election|28512|.0018589|.0430752|0|1|
|Case type: labor relations|28512|.0166947|.1281272|0|1|
|Case type: environment and land|28512|.3231622|.4676923|0|1|
|Case type: family|28512|.0068042|.0822076|0|1|
|Case type: industrial|28512|.0033319|.0576276|0|1|
|Case type: miscellaneous|28512|.0818603|.2741565|0|1|
|Case type: succession|28512|.0952581|.2935763|0|1|
|Number of cases cited in judgement|29373|1.926089|3.534747|0|87|
|Times judgement cited|29373|.23365|1.938323|0|109|
|Laws cited in judgement|29373|2.203793|4.106973|0|146|
|Words in judgement|29373|1451.541|1337.457|0|42980|

33
Table B2: Summary of main variables, count only

||count|
|---|---|
|Court ID|29373|
|Year of delivery|29373|
|Court-year FE<br>|29373|
|Plurality ethnicity of plaintifs|24356|
|Plurality ethnicity of defendants|23415|
|Plurality ethnicity of judges|26425|

34
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# **Appendix D: Balance Tests, Before and After 2011**

Table D1: Gender randomization checks

||(1)<br>Judge maj. female|(2)<br>Judge maj. female|(3)<br>Judge maj. female|
|---|---|---|---|
|Pla. maj. female|0.0114|0.0116|0.00739|
||(0.00863)|(0.00860)|(0.00679)|
|Def. maj. female|0.00351<br>(0.00764)|0.00335<br>(0.00755)|-0.000446<br>(0.00651)|
|DV mean|0.363|0.363|0.363|
|Court-year FE|Yes|Yes|Yes|
|Ethnicity dummies|No|Yes|Yes|
|Other controls|No|No|Yes|
|Observations|20394|20394|20394|

The regressions test whether female plaintiffs/defendants are more likely to be matched with female judges than male judges. Standard errors, in parentheses, are clustered at the judge level. All columns are based on a linear regression model. For specification details, see equation 1. Ethnicity dummies include binary variables indicating whether a given ethnicity is the plurality, one for each ethnicity, for defendants, plaintiffs, and judges. Other controls include case type dummies, a dummy for an appeal case, and variables for the numbers of defendants, plaintiffs, and judges. To prevent a loss of observations, all categorical controls (such as case type) include a dummy that denotes if data is missing/unknown. Pla. = plaintiffs, def. = defendants, maj. = majority.

38
Table D2: Gender randomization checks, before 2011

||(1)<br>Judge maj. female|(2)<br>Judge maj. female|(3)<br>Judge maj. female|
|---|---|---|---|
|Pla. maj. female|0.0222|0.0210|0.0115|
||(0.0190)|(0.0189)|(0.0154)|
|Def. maj. female|-0.00690|-0.00613|-0.0115|
||(0.0137)|(0.0133)|(0.0111)|
|DV mean|0.298|0.298|0.298|
|Court-year FE|Yes|Yes|Yes|
|Ethnicity dummies|No|Yes|Yes|
|Other controls|No|No|Yes|
|Observations|4717|4717|4717|

The regressions test whether female plaintiffs/defendants are more likely to be matched with female judges than male judges. Standard errors, in parentheses, are clustered at the judge level. All columns are based on a linear regression model. For specification details, see equation 1. Sample is restricted to the years 1976-2010. Ethnicity dummies include binary variables indicating whether a given ethnicity is the plurality, one for each ethnicity, for defendants, plaintiffs, and judges. Other controls include case type dummies, a dummy for an appeal case, and variables for the numbers of defendants, plaintiffs, and judges. To prevent a loss of observations, all categorical controls (such as case type) include a dummy that denotes if data is missing/unknown. Pla. = Plaintiffs, Def. = defendants, maj. = majority.

Table D3: Gender randomization checks, 2011 and after

||(1)<br>Judge maj. female|(2)<br>Judge maj. female|(3)<br>Judge maj. female|
|---|---|---|---|
|Pla. maj. female|0.00827|0.00850|0.00514|
||(0.00968)|(0.00951)|(0.00735)|
|Def. maj. female|0.00674|0.00617|0.00325|
||(0.00845)|(0.00832)|(0.00719)|
|DV mean|0.382|0.382|0.382|
|Court-year FE|Yes|Yes|Yes|
|Ethnicity dummies|No|Yes|Yes|
|Other controls|No|No|Yes|
|Observations|15677|15677|15677|

The regressions test whether female plaintiffs/defendants are more likely to be matched with female judges than male judges. Standard errors, in parentheses, are clustered at the judge level. All columns are based on a linear regression model. For specification details, see equation 1. Sample is restricted to the years 2011-2020. Ethnicity dummies include binary variables indicating whether a given ethnicity is the plurality, one for each ethnicity, for defendants, plaintiffs, and judges. Other controls include case type dummies, a dummy for an appeal case, and variables for the numbers of defendants, plaintiffs, and judges. To prevent a loss of observations, all categorical controls (such as case type) include a dummy that denotes if data is missing/unknown. Pla. = Plaintiffs, Def. = defendants, maj. = majority.

39
Table D4: Ethnicity randomization checks 1

||(1)|(2)|(3)|
|---|---|---|---|
||Judge plur. Kalenjin|Judge plur. Kamba|Judge plur. Kikuyu|
|Pla. plur. Kalenjin|0.00699<br>(0.00894)|||
|Def. plur. Kalenjin|-0.00933<br>(0.0103)|||
|Pla. plur. Kamba||-0.00785<br>(0.00651)||
|Def. plur. Kamba||0.00337<br>(0.00771)||
|Pla. plur. Kikuyu|||0.00640<br>(0.00760)|
|Def. plur. Kikuyu|||0.00167<br>(0.00759)|
|DV mean|0.0895|0.125|0.275|
|Court-year FE|Yes|Yes|Yes|
|Ethnicity dummies||||
|Other controls|Yes|Yes|Yes|
|Observations|14594|14594|14944|

The regressions test whether female plaintiffs/defendants are more likely to be matched with judges of their own ethnicity than judges of other ethnicities. Standard errors, in parentheses, are clustered at the judge level. All columns are based on a linear regression model. For specification details, see equation 2. Other controls include case type dummies, a dummy for an appeal case, and variables for the numbers of defendants, plaintiffs, and judges. To prevent a loss of observations, all categorical controls (such as case type) include a dummy that denotes if data is missing/unknown. Pla. = plaintiffs, def. = defendants, plur. = plurality, maj. = majority.

40
Table D5: Ethnicity randomization checks 2

||(1)<br>Judge plur. Kisii|(2)<br>Judge plur. Luhya|(3)<br>Judge plur. Luo|
|---|---|---|---|
|Pla. plur.|Kisii<br>-0.0111<br>(0.00753)|||
|Def. plur.|Kisii<br>0.00201<br>(0.00795)|||
|Pla. plur.|Luhya|0.00489<br>(0.00674)||
|Def. plur.|Luhya|0.0112<sup>_⇤_</sup><br>(0.00589)||
|Pla. plur.|Luo||0.000715<br>(0.00907)|
|Def. plur.|Luo||0.00195<br>(0.0104)|
|DV mean|0.0765|0.174|0.173|
|Court-yea|r FE<br>Yes|Yes|Yes|
|Ethnicity|dummies|||
|Other con|trols<br>Yes|Yes|Yes|
|Observatio|ns<br>14944|14944|14944|

The regressions test whether female plaintiffs/defendants are more likely to be matched with judges of their own ethnicity than judges of other ethnicities. Standard errors, in parentheses, are clustered at the judge level. All columns are based on a linear regression model. For specification details, see equation 2. Other controls include case type dummies, a dummy for an appeal case, and variables for the numbers of defendants, plaintiffs, and judges. To prevent a loss of observations, all categorical controls (such as case type) include a dummy that denotes if data is missing/unknown. Pla. = plaintiffs, def. = defendants, plur. = plurality, maj. = majority.

41
Table D6: Ethnicity randomization checks 3

||(1)|(2)|(3)|
|---|---|---|---|
||Judge plur. Masai|Judge plur. Meru|Judge plur. Mijikenda|
|Pla. plur. Masai|-0.000333<br>(0.00218)|||
|Def. plur. Masai|0.000336<br>(0.000782)|||
|Pla. plur. Meru||0.00203<br>(0.00313)||
|Def. plur. Meru||-0.000889<br>(0.00268)||
|Pla. plur. Mijikenda|||-0.000673<br>(0.00299)|
|Def. maj. Mijikenda|||0.00369<br>(0.00408)|
|DV mean|0.00589|0.0260|0.0110|
|Court-year FE|Yes|Yes|Yes|
|Ethnicity dummies||||
|Other controls|Yes|Yes|Yes|
|Observations|14944|14944|14944|

The regressions test whether female plaintiffs/defendants are more likely to be matched with judges of their own ethnicity than judges of other ethnicities. Standard errors, in parentheses, are clustered at the judge level. All columns are based on a linear regression model. For specification details, see equation 2. Other controls include case type dummies, a dummy for an appeal case, and variables for the numbers of defendants, plaintiffs, and judges. To prevent a loss of observations, all categorical controls (such as case type) include a dummy that denotes if data is missing/unknown. Pla. = plaintiffs, def. = defendants, plur. = plurality, maj. = majority.

42
Table D7: Ethnicity randomization checks 4

||(1)|(2)|(3)|
|---|---|---|---|
||Judge plur. Pokot|Judge plur. Somali|Judge plur. Turkana|
|Pla. plur. Pokot|-0.00444<br>(0.00458)|||
|Def. plur. Pokot|0.00327<br>(0.00832)|||
|Pla. plur. Somali||0.00474<br>(0.00516)||
|Def. plur. Somali||-0.00715<br>(0.00518)||
|Pla. plur. Turkana|||0.0000974<br>(0.000118)|
|Def. plur. Turkana|||-0.000940<br>(0.00102)|
|DV mean|0.00482|0.00964|0.000468|
|Court-year FE|Yes|Yes|Yes|
|Ethnicity dummies||||
|Other controls|Yes|Yes|Yes|
|Observations|14944|14944|14944|

The regressions test whether female plaintiffs/defendants are more likely to be matched with judges of their own ethnicity than judges of other ethnicities. Standard errors, in parentheses, are clustered at the judge level. All columns are based on a linear regression model. For specification details, see equation 2. Other controls include case type dummies, a dummy for an appeal case, and variables for the numbers of defendants, plaintiffs, and judges. To prevent a loss of observations, all categorical controls (such as case type) include a dummy that denotes if data is missing/unknown. Pla. = plaintiffs, def. = defendants, plur. = plurality, maj. = majority.

43
Table D8: Ethnicity randomization checks, before 2011, 1

||(1)|(2)|(3)|
|---|---|---|---|
||Judge plur. Kalenjin|Judge plur. Kamba|Judge plur. Kikuyu|
|Pla. plur. Kalenjin|0.000868<br>(0.00136)|||
|Def. plur. Kalenjin|-0.00107<br>(0.00132)|||
|Pla. plur. Kamba||-0.0483<sup>_⇤⇤⇤_</sup><br>(0.0181)||
|Def. plur. Kamba||0.0687<sup>_⇤⇤⇤_</sup><br>(0.0233)||
|Pla. plur. Kikuyu|||0.00681<br>(0.0109)|
|Def. plur. Kikuyu|||-0.00961<br>(0.0101)|
|DV mean|0.0438|0.128|0.169|
|Court-year FE|Yes|Yes|Yes|
|Ethnicity dummies||||
|Other controls|Yes|Yes|Yes|
|Observations|3106|3106|3106|

The regressions test whether female plaintiffs/defendants are more likely to be matched with judges of their own ethnicity than judges of other ethnicities.Standard errors, in parentheses, are clustered at the judge level. All columns are based on a linear regression model. For specification details, see equation 2. Sample is restricted to the years 1976-2010. Other controls include case type dummies, a dummy for an appeal case, and variables for the numbers of defendants, plaintiffs, and judges. To prevent a loss of observations, all categorical controls (such as case type) include a dummy that denotes if data is missing/unknown. Pla. = plaintiffs, def. = defendants, plur. = plurality, maj. = majority.

44
Table D9: Ethnicity randomization checks, before 2011, 2

||(1)|(2)|(3)|
|---|---|---|---|
||Judge plur. Kisii|Judge plur. Luhya|Judge plur. Luo|
|Pla. plur. Kisii|-0.0345<sup>_⇤⇤_</sup><br>(0.0164)|||
|Def. plur. Kisii|-0.0140<br>(0.0159)|||
|Pla. plur. Luhya||0.0141<br>(0.0285)||
|Def. plur. Luhya||-0.00399<br>(0.0188)||
|Pla. plur. Luo|||0.0197<br>(0.0268)|
|Def. plur. Luo|||-0.00110<br>(0.0267)|
|DV mean|0.0802|0.216|0.221|
|Court-year FE|Yes|Yes|Yes|
|Ethnicity dummies||||
|Other controls|Yes|Yes|Yes|
|Observations|3106|3106|3106|

The regressions test whether female plaintiffs/defendants are more likely to be matched with judges of their own ethnicity than judges of other ethnicities.Standard errors, in parentheses, are clustered at the judge level. All columns are based on a linear regression model. For specification details, see equation 2. Sample is restricted to the years 1976-2010. Other controls include case type dummies, a dummy for an appeal case, and variables for the numbers of defendants, plaintiffs, and judges. To prevent a loss of observations, all categorical controls (such as case type) include a dummy that denotes if data is missing/unknown. Pla. = plaintiffs, def. = defendants, plur. = plurality, maj. = majority.

45
Table D10: Ethnicity randomization checks, before 2011, 3

||(1)|(2)|(3)|
|---|---|---|---|
||Judge plur. Masai|Judge plur. Meru|Judge plur. Mijikenda|
|Pla. plur. Masai|0.000167<br>(0.000473)|||
|Def. plur. Masai|0.000227<br>(0.000414)|||
|Pla. plur. Meru||0.00370<br>(0.00939)||
|Def. plur. Meru||-0.0123<br>(0.00906)||
|Pla. plur. Mijikenda|||-0.0129<br>(0.0129)|
|Def. maj. Mijikenda|||0.000787<br>(0.00325)|
|DV mean|0.000966|0.0599|0.0167|
|Court-year FE|Yes|Yes|Yes|
|Ethnicity dummies||||
|Other controls|Yes|Yes|Yes|
|Observations|3106|3106|3106|

The regressions test whether female plaintiffs/defendants are more likely to be matched with judges of their own ethnicity than judges of other ethnicities.Standard errors, in parentheses, are clustered at the judge level. All columns are based on a linear regression model. For specification details, see equation 2. Sample is restricted to the years 1976-2010. Other controls include case type dummies, a dummy for an appeal case, and variables for the numbers of defendants, plaintiffs, and judges. To prevent a loss of observations, all categorical controls (such as case type) include a dummy that denotes if data is missing/unknown. Pla. = plaintiffs, def. = defendants, plur. = plurality, maj. = majority.

46
Table D11: Ethnicity randomization checks, before 2011, 4

||(1)|(2)|(3)|
|---|---|---|---|
||Judge plur. Pokot|Judge plur. Somali|Judge plur. Turkana|
|Pla. plur. Pokot|-0.0102<br>(0.00724)|||
|Def. plur. Pokot|0.0236<br>(0.0701)|||
|Pla. plur. Somali||0.0142<br>(0.0211)||
|Def. plur. Somali||-0.0308<br>(0.0226)||
|Pla. plur. Turkana|||0<br>(.)|
|Def. plur. Turkana|||0<br>(.)|
|DV mean|0.0109|0.0174|0|
|Court-year FE|Yes|Yes|Yes|
|Ethnicity dummies||||
|Other controls|Yes|Yes|Yes|
|Observations|3106|3106|3106|

The regressions test whether female plaintiffs/defendants are more likely to be matched with judges of their own ethnicity than judges of other ethnicities.Standard errors, in parentheses, are clustered at the judge level. All columns are based on a linear regression model. For specification details, see equation 2. Sample is restricted to the years 1976-2010. Other controls include case type dummies, a dummy for an appeal case, and variables for the numbers of defendants, plaintiffs, and judges. To prevent a loss of observations, all categorical controls (such as case type) include a dummy that denotes if data is missing/unknown. Pla. = plaintiffs, def. = defendants, plur. = plurality, maj. = majority.

47
Table D12: Ethnicity randomization checks, 2011 and after, 1

||(1)|(2)|(3)|
|---|---|---|---|
||Judge plur. Kalenjin|Judge plur. Kamba|Judge plur. Kikuyu|
|Pla. plur. Kalenjin|0.00864<br>(0.0108)|||
|Def. plur. Kalenjin|-0.0113<br>(0.0123)|||
|Pla. plur. Kamba||-0.000521<br>(0.00806)||
|Def. plur. Kamba||-0.00935<br>(0.00840)||
|Pla. plur. Kikuyu|||0.00681<br>(0.00987)|
|Def. plur. Kikuyu|||0.00469<br>(0.00970)|
|DV mean|0.100|0.125|0.303|
|Court-year FE|Yes|Yes|Yes|
|Ethnicity dummies||||
|Other controls|Yes|Yes|Yes|
|Observations|11838|11838|11838|

The regressions test whether female plaintiffs/defendants are more likely to be matched with judges of their own ethnicity than judges of other ethnicities.Standard errors, in parentheses, are clustered at the judge level. All columns are based on a linear regression model. For specification details, see equation 2. Sample is restricted to the years 2011-2020. Other controls include case type dummies, a dummy for an appeal case, and variables for the numbers of defendants, plaintiffs, and judges. To prevent a loss of observations, all categorical controls (such as case type) include a dummy that denotes if data is missing/unknown. Pla. = plaintiffs, def. = defendants, plur. = plurality, maj. = majority.

48
Table D13: Ethnicity randomization checks, 2011 and after, 2

||(1)|(2)|(3)|
|---|---|---|---|
||Judge plur. Kisii|Judge plur. Luhya|Judge plur. Luo|
|Pla. plur. Kisii|-0.00434<br>(0.00743)|||
|Def. plur. Kisii|0.00863<br>(0.00983)|||
|Pla. plur. Luhya||0.00416<br>(0.00644)||
|Def. plur. Luhya||0.0143<sup>_⇤⇤_</sup><br>(0.00607)||
|Pla. plur. Luo|||-0.00583<br>(0.00963)|
|Def. plur. Luo|||0.00269<br>(0.0106)|
|DV mean|0.0755|0.163|0.160|
|Court-year FE|Yes|Yes|Yes|
|Ethnicity dummies||||
|Other controls|Yes|Yes|Yes|
|Observations|11838|11838|11838|

The regressions test whether female plaintiffs/defendants are more likely to be matched with judges of their own ethnicity than judges of other ethnicities.Standard errors, in parentheses, are clustered at the judge level. All columns are based on a linear regression model. For specification details, see equation 2. Sample is restricted to the years 2011-2020. Other controls include case type dummies, a dummy for an appeal case, and variables for the numbers of defendants, plaintiffs, and judges. To prevent a loss of observations, all categorical controls (such as case type) include a dummy that denotes if data is missing/unknown. Pla. = plaintiffs, def. = defendants, plur. = plurality, maj. = majority.

49
Table D14: Ethnicity randomization checks, 2011 and after, 3

||(1)|(2)|(3)|
|---|---|---|---|
||Judge plur. Masai|Judge plur. Meru|Judge plur. Mijikenda|
|Pla. plur. Masai|-0.000695<br>(0.00261)|||
|Def. plur. Masai|0.000584<br>(0.000898)|||
|Pla. plur. Meru||0.00183<br>(0.00265)||
|Def. plur. Meru||0.00195<br>(0.00287)||
|Pla. plur. Mijikenda|||0.00181<br>(0.00255)|
|Def. maj. Mijikenda|||0.00498<br>(0.00480)|
|DV mean|0.00718|0.0171|0.00946|
|Court-year FE|Yes|Yes|Yes|
|Ethnicity dummies||||
|Other controls|Yes|Yes|Yes|
|Observations|11838|11838|11838|

The regressions test whether female plaintiffs/defendants are more likely to be matched with judges of their own ethnicity than judges of other ethnicities.Standard errors, in parentheses, are clustered at the judge level. All columns are based on a linear regression model. For specification details, see equation 2. Sample is restricted to the years 2011-2020. Other controls include case type dummies, a dummy for an appeal case, and variables for the numbers of defendants, plaintiffs, and judges. To prevent a loss of observations, all categorical controls (such as case type) include a dummy that denotes if data is missing/unknown. Pla. = plaintiffs, def. = defendants, plur. = plurality, maj. = majority.

50
Table D15: Ethnicity randomization checks, 2011 and after, 4

||(1)|(2)|(3)|
|---|---|---|---|
||Judge plur. Pokot|Judge plur. Somali|Judge plur. Turkana|
|Pla. plur. Pokot|-0.00227<br>(0.00281)|||
|Def. plur. Pokot|-0.000560<br>(0.00171)|||
|Pla. plur. Somali||0.00220<br>(0.00364)||
|Def. plur. Somali||-0.00161<br>(0.00244)||
|Pla. plur. Turkana|||0.000157<br>(0.000191)|
|Def. plur. Turkana|||-0.00111<br>(0.00120)|
|DV mean|0.00321|0.00760|0.000591|
|Court-year FE|Yes|Yes|Yes|
|Ethnicity dummies||||
|Other controls|Yes|Yes|Yes|
|Observations|11838|11838|11838|

The regressions test whether female plaintiffs/defendants are more likely to be matched with judges of their own ethnicity than judges of other ethnicities.Standard errors, in parentheses, are clustered at the judge level. All columns are based on a linear regression model. For specification details, see equation 2. Sample is restricted to the years 2011-2020. Other controls include case type dummies, a dummy for an appeal case, and variables for the numbers of defendants, plaintiffs, and judges. To prevent a loss of observations, all categorical controls (such as case type) include a dummy that denotes if data is missing/unknown. Pla. = plaintiffs, def. = defendants, plur. = plurality, maj. = majority.

51
# **Appendix E: Robustness Checks**

Table E1: Gender results, 2010 and after

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||Def. win|Def. win|Def. win|Def. win|Def. win|
|Judge maj. female|-0.0380<sup>_⇤⇤⇤_</sup>|-0.0397<sup>_⇤⇤_</sup>|-0.0468<sup>_⇤⇤⇤_</sup>|-0.0410<sup>_⇤⇤⇤_</sup>|-0.0411<sup>_⇤⇤⇤_</sup>|
||(0.0122)|(0.0197)|(0.0133)|(0.0132)|(0.0133)|
|Pla. maj. female||-0.0296<sup>_⇤⇤_</sup><br>(0.0128)|-0.0528<sup>_⇤⇤⇤_</sup><br>(0.0109)|-0.0426<sup>_⇤⇤⇤_</sup><br>(0.0108)|-0.0433<sup>_⇤⇤⇤_</sup><br>(0.0108)|
|Def. maj. female|-0.00495||0.00144|0.00706|0.00667|
||(0.0106)||(0.0108)|(0.0107)|(0.0107)|
|Judge maj. fem. X pla. maj. fem.||0.0173|0.00690|0.00655|0.00726|
|||(0.0208)|(0.0171)|(0.0169)|(0.0168)|
|Judge maj. fem. X def. maj. fem.|0.0359<sup>_⇤⇤_</sup>||0.0400<sup>_⇤⇤_</sup>|0.0379<sup>_⇤⇤_</sup>|0.0380<sup>_⇤⇤_</sup>|
||(0.0167)||(0.0168)|(0.0168)|(0.0167)|
|DV mean|0.452|0.429|0.454|0.454|0.454|
|Court-year FE|Yes|Yes|Yes|Yes|Yes|
|Ethnicity dummies|No|No|No|No|Yes|
|Other controls|No|No|No|Yes|Yes|
|Observations|22801|25618|20394|20394|20394|

The regressions test whether defendants (plaintiffs) are more likely to win (lose) if they have the same (a different) majority gender as judges. The coefficients of interest are on the interaction terms. Years before 2010 are dropped. Standard errors, in parentheses, are clustered at the judge level. All columns are based on a linear regression model. For specification details, see equations 3 and 4. Ethnicity dummies include binary variables indicating whether a given ethnicity is the plurality, one for each ethnicity, for defendants, plaintiffs, and judges. Other controls include case type dummies, a dummy for an appeal case, and variables for the numbers of defendants, plaintiffs, and judges. To prevent a loss of observations, all categorical controls (such as case type) include a dummy that denotes if data is missing/unknown. Pla. = plaintiff, def. = defendant, maj. = majority.

52
Table E2: Gender results, robust standard errors

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||Def. win|Def. win|Def. win|Def. win|Def. win|
|Judge maj. female|-0.0380<sup>_⇤⇤⇤_</sup>|-0.0397<sup>_⇤⇤⇤_</sup>|-0.0468<sup>_⇤⇤⇤_</sup>|-0.0410<sup>_⇤⇤⇤_</sup>|-0.0411<sup>_⇤⇤⇤_</sup>|
||(0.0106)|(0.00956)|(0.0122)|(0.0122)|(0.0122)|
|Pla. maj. female||-0.0296<sup>_⇤⇤⇤_</sup><br>(0.00918)|-0.0528<sup>_⇤⇤⇤_</sup><br>(0.0102)|-0.0426<sup>_⇤⇤⇤_</sup><br>(0.0102)|-0.0433<sup>_⇤⇤⇤_</sup><br>(0.0102)|
|Def. maj. female|-0.00495||0.00144|0.00706|0.00667|
||(0.00986)||(0.0105)|(0.0105)|(0.0105)|
|Judge maj. fem. X pla. maj. fem.||0.0173|0.00690|0.00655|0.00726|
|||(0.0148)|(0.0164)|(0.0164)|(0.0164)|
|Judge maj. fem. X def. maj. fem.|0.0359<sup>_⇤⇤_</sup>||0.0400<sup>_⇤⇤_</sup>|0.0379<sup>_⇤⇤_</sup>|0.0380<sup>_⇤⇤_</sup>|
||(0.0163)||(0.0172)|(0.0171)|(0.0171)|
|DV mean|0.452|0.429|0.454|0.454|0.454|
|Court-year FE|Yes|Yes|Yes|Yes|Yes|
|Ethnicity dummies|No|No|No|No|Yes|
|Other controls|No|No|No|Yes|Yes|
|Observations|22801|25618|20394|20394|20394|

The regressions test whether defendants (plaintiffs) are more likely to win (lose) if they have the same (a different) majority gender as judges. The coefficients of interest are on the interaction terms. Includes robust standard errors. All columns are based on a linear regression model. For specification details, see equations 3 and 4. Ethnicity dummies include binary variables indicating whether a given ethnicity is the plurality, one for each ethnicity, for defendants, plaintiffs, and judges. Other controls include case type dummies, a dummy for an appeal case, and variables for the numbers of defendants, plaintiffs, and judges. To prevent a loss of observations, all categorical controls (such as case type) include a dummy that denotes if data is missing/unknown.Pla. = plaintiff, def. = defendant, maj. = majority.

53
Table E3: Gender results, probit

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||Def. win|Def. win|Def. win|Def. win|Def. win|
|Judge maj. female|-0.0990<sup>_⇤⇤⇤_</sup>|-0.105<sup>_⇤_</sup>|-0.123<sup>_⇤⇤⇤_</sup>|-0.108<sup>_⇤⇤⇤_</sup>|-0.108<sup>_⇤⇤⇤_</sup>|
||(0.0315)|(0.0539)|(0.0344)|(0.0344)|(0.0346)|
|Pla. maj. female||-0.0779<sup>_⇤⇤_</sup>|-0.139<sup>_⇤⇤⇤_</sup>|-0.113<sup>_⇤⇤⇤_</sup>|-0.115<sup>_⇤⇤⇤_</sup>|
|||(0.0344)|(0.0289)|(0.0287)|(0.0287)|
|Def. maj. female|-0.0135||0.00311|0.0187|0.0177|
||(0.0275)||(0.0282)|(0.0282)|(0.0282)|
|Judge maj. fem. X pla. maj. fem.||0.0469|0.0186|0.0170|0.0193|
|||(0.0562)|(0.0450)|(0.0446)|(0.0445)|
|Judge maj. fem. X def. maj. fem.|0.0944<sup>_⇤⇤_</sup>||0.106<sup>_⇤⇤_</sup>|0.100<sup>_⇤⇤_</sup>|0.100<sup>_⇤⇤_</sup>|
||(0.0431)||(0.0436)|(0.0438)|(0.0437)|
|DV mean|0.452|0.429|0.454|0.454|0.454|
|Court-year FE|Yes|Yes|Yes|Yes|Yes|
|Ethnicity dummies|No|No|No|No|Yes|
|Other controls|No|No|No|Yes|Yes|
|Observations|22595|25397|20162|20162|20162|

The regressions test whether defendants (plaintiffs) are more likely to win (lose) if they have the same (a different) majority gender as judges. The coefficients of interest are on the interaction terms. Standard errors, in parentheses, are clustered at the judge level. All columns are based on a probit regression model. Ethnicity dummies include binary variables indicating whether a given ethnicity is the plurality, one for each ethnicity, for defendants, plaintiffs, and judges. Other controls include case type dummies, a dummy for an appeal case, and variables for the numbers of defendants, plaintiffs, and judges. To prevent a loss of observations, all categorical controls (such as case type) include a dummy that denotes if data is missing/unknown.Pla. = plaintiff, def. = defendant, maj. = majority.

Table E4: Ethnicity results, no Kamba or Luhya

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||Def. win|Def. win|Def. win|Def. win|Def. win|
|Judge-pla. same|0.0103||-0.00517|0.00163|0.00224|
||(0.0153)||(0.0170)|(0.0206)|(0.0207)|
|Judge-def. same||0.0446<sup>_⇤⇤⇤_</sup>|0.0530<sup>_⇤⇤⇤_</sup>|0.0481<sup>_⇤⇤_</sup>|0.0502<sup>_⇤⇤_</sup>|
|||(0.0145)|(0.0179)|(0.0235)|(0.0235)|
|DV mean|0.450|0.451|0.452|0.452|0.452|
|Court-year FE|Yes|Yes|Yes|Yes|Yes|
|Ethnicity dummies|No|No|No|Yes|Yes|
|Other controls|No|No|No|No|Yes|
|Observations|11368|10987|9741|9741|9741|

The regressions test whether defendants (plaintiffs) are more likely to win (lose) if they have the same (a different) plurality ethnicity as judges. Standard errors, in parentheses, are clustered at the judge level. All columns are based on a linear regression model. For specification details, see equation 5. Judge-pla. same and Judge-def. same refer to similarity in plurality ethnicity. Ethnicity dummies include binary variables indicating whether a given ethnicity is the plurality, one for each ethnicity, for both defendants and plaintifs. Other controls include case type dummies; a dummy for an appeal case; variables for the numbers of defendants, plaintiffs, and judges; and dummies for defendant, plaintiff, and judge majority gender. To prevent a loss of observations, all categorical controls (such as case type) include a dummy that denotes if data is missing/unknown. Pla. = plaintiff, def. = defendant.

54
Table E5: Ethnicity results, 2010 and after

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||Def. win|Def. win|Def. win|Def. win|Def. win|
|Judge-pla. same|0.00582||-0.0143|-0.00466|-0.00424|
||(0.0142)||(0.0153)|(0.0169)|(0.0170)|
|Judge-def. same||0.0456<sup>_⇤⇤⇤_</sup>|0.0589<sup>_⇤⇤⇤_</sup>|0.0533<sup>_⇤⇤⇤_</sup>|0.0561<sup>_⇤⇤⇤_</sup>|
|||(0.0137)|(0.0157)|(0.0178)|(0.0176)|
|DV mean|0.447|0.452|0.451|0.451|0.451|
|Court-year FE|Yes|Yes|Yes|Yes|Yes|
|Ethnicity dummies|No|No|No|Yes|Yes|
|Other controls|No|No|No|No|Yes|
|Observations|17878|17194|15555|15555|15555|

The regressions test whether defendants (plaintiffs) are more likely to win (lose) if they have the same (a different) plurality ethnicity as judges. Standard errors, in parentheses, are clustered at the judge level. All columns are based on a linear regression model. For specification details, see equation 5. Judge-pla. same and Judge-def. same refer to similarity in plurality ethnicity. Ethnicity dummies include binary variables indicating whether a given ethnicity is the plurality, one for each ethnicity, for both defendants and plaintifs. Other controls include case type dummies; a dummy for an appeal case; variables for the numbers of defendants, plaintiffs, and judges; and dummies for defendant, plaintiff, and judge majority gender. To prevent a loss of observations, all categorical controls (such as case type) include a dummy that denotes if data is missing/unknown. Pla. = plaintiff, def. = defendant. Years before 2010 are dropped Table E6: Ethnicity results, robust standard errors

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||Def. win|Def. win|Def. win|Def. win|Def. win|
|Judge-pla. same|0.00681||-0.0138|-0.00549|-0.00578|
||(0.0115)||(0.0142)|(0.0151)|(0.0151)|
|Judge-def. same||0.0439<sup>_⇤⇤⇤_</sup>|0.0573<sup>_⇤⇤⇤_</sup>|0.0532<sup>_⇤⇤⇤_</sup>|0.0551<sup>_⇤⇤⇤_</sup>|
|||(0.0120)|(0.0145)|(0.0155)|(0.0155)|
|DV mean|0.450|0.453|0.453|0.453|0.453|
|Court-year FE|Yes|Yes|Yes|Yes|Yes|
|Ethnicity dummies|No|No|No|Yes|Yes|
|Other controls|No|No|No|No|Yes|
|Observations|21842|20971|18952|18952|18952|

The regressions test whether defendants (plaintiffs) are more likely to win (lose) if they have the same (a different) plurality ethnicity as judges. Includes robust standard errors. All columns are based on a linear regression model. For specification details, see equation 5. Judge-pla. same and Judge-def. same refer to similarity in plurality ethnicity. Ethnicity dummies include binary variables indicating whether a given ethnicity is the plurality, one for each ethnicity, for both defendants and plaintifs. Other controls include case type dummies; a dummy for an appeal case; variables for the numbers of defendants, plaintiffs, and judges; and dummies for defendant, plaintiff, and judge majority gender. To prevent a loss of observations, all categorical controls (such as case type) include a dummy that denotes if data is missing/unknown. Pla. = plaintiff, def. = defendant. Years before 2010 are dropped 55
Table E7: Ethnicity results, probit

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||Def. win|Def. win|Def. win|Def. win|Def. win|
|Judge-pla. same|0.0166||-0.0391|-0.0173|-0.0183|
||(0.0330)||(0.0366)|(0.0401)|(0.0406)|
|Judge-def. same||0.115<sup>_⇤⇤⇤_</sup>|0.153<sup>_⇤⇤⇤_</sup>|0.142<sup>_⇤⇤⇤_</sup>|0.148<sup>_⇤⇤⇤_</sup>|
|||(0.0314)|(0.0376)|(0.0415)|(0.0415)|
|DV mean|0.451|0.455|0.455|0.455|0.455|
|Court-year FE|Yes|Yes|Yes|Yes|Yes|
|Ethnicity dummies|No|No|No|Yes|Yes|
|Other controls|No|No|No|No|Yes|
|Observations|21617|20759|18726|18726|18726|

The regressions test whether defendants (plaintiffs) are more likely to win (lose) if they have the same (a different) plurality ethnicity as judges. Standard errors, in parentheses, are clustered at the judge level. All columns are based on a probit regression model. Judge-pla. same and Judge-def. same refer to similarity in plurality ethnicity. Ethnicity dummies include binary variables indicating whether a given ethnicity is the plurality, one for each ethnicity, for both defendants and plaintifs. Other controls include case type dummies; a dummy for an appeal case; variables for the numbers of defendants, plaintiffs, and judges; and dummies for defendant, plaintiff, and judge majority gender. To prevent a loss of observations, all categorical controls (such as case type) include a dummy that denotes if data is missing/unknown. Pla. = plaintiff, def. = defendant.

56
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# **Appendix G: of Putting Biased Judges on Panels**

Table G1: Results for in-group gender biased judges, and on panels

||(1)<br>Def. win|(2)<br>Def. win|
|---|---|---|
|Judge maj. female|-0.189<sup>_⇤⇤⇤_</sup><br>(0.0447)|0.130<br>(0.161)|
|Def. maj. female|-0.223<sup>_⇤⇤⇤_</sup><br>(0.0523)|-0.0501<br>(0.0804)|
|Judge maj. fem. X def. maj.|fem.<br>0.396<sup>_⇤⇤⇤_</sup><br>(0.0605)|0.0797<br>(0.167)|
|DV mean|0.430|0.665|
|Court-year FE|Yes|Yes|
|Individual decisions|Yes|No|
|Panel decisions|No|Yes|
|Observations|1787|200|

Sample is restricted to the 14 judges with significant gender in-group bias coefficients for defendants in individual regression Column 1 includes only cases where the judges ruled individually. Column 2 includes only cases where they ruled on panels. Standard errors, in parentheses, are clustered at the judge level. All columns are based on a linear regression model. For specification details, see equations 3 and 4. Pla. = plaintiff, def. = defendant, maj. = majority.

59
# **Appendix H: Relationship Between Slant and Appeals, and Slant and Reverals**

Table H1: Appeals and slant, family vs career

||(1)<br>appealed|(2)<br>appealed|(3)<br>appealed|(4)<br>appealed|(5)<br>appealed|
|---|---|---|---|---|---|
|Slant against women, career vs family|-0.00499|-0.0211|0.0190|0.00413|-0.0665<sup>_⇤⇤_</sup>|
||(0.0155)|(0.0182)|(0.0250)|(0.0160)|(0.0268)|
|Def. maj. female||0.00186<br>(0.00278)|0.000931<br>(0.00431)|||
|Pla. maj. female||||0.00311<br>(0.00279)|0.00338<br>(0.00448)|
|Def. maj. fem. X Slant against women||0.0300<br>(0.0246)|-0.00766<br>(0.0387)|||
|Pla. maj. fem. X Slant against women||||-0.00585<br>(0.0221)|-0.0113<br>(0.0408)|
|DVmean|0.0184|0.0191|0.0187|0.0183|0.0197|
|Court-year FE|Yes|Yes|Yes|Yes|Yes|
|Restricted sample|No|No|Yes|No|Yes|
|Observations|26019|20751|11530|23480|9734|

The regressions test whether slanted judges are more likely to have case decisions appealed.The coefficients of interest are ’Slant against women, family vs career’ and the interactions. Column 3 (5) restricts the sample to cases where the defendant (plaintiff) loses, and the interaction tests if reverals are more likely if the judges is more slanted and the defendant (plaintiff) is female. These cases have the most potentail for gender bias. Standard errors, in parentheses, are clustered at the judge level. All columns are based on a linear regression model.

60
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Table H2: Appeals and slant, good vs bad

||(1)<br>appealed|(2)<br>appealed|(3)<br>appealed|(4)<br>appealed|(5)<br>appealed|
|---|---|---|---|---|---|
|Slant against women, good vs bad|0.0421<sup>_⇤_</sup>|0.0394|0.0199|0.0474|0.0267|
||(0.0253)|(0.0349)|(0.0420)|(0.0333)|(0.0594)|
|Def. maj. female||0.00503<br>(0.00431)|0.00472<br>(0.00715)|||
|Pla. maj. female||||0.00448<br>(0.00537)|0.00645<br>(0.00840)|
|Def. maj. fem. X Slant against women||-0.0376<br>(0.0515)|-0.0341<br>(0.0710)|||
|Pla. maj. fem. X Slant against women||||-0.0175<br>(0.0629)|-0.0457<br>(0.0902)|
|DVmean|0.0199|0.0206|0.0203|0.0198|0.0217|
|Court-year FE|Yes|Yes|Yes|Yes|Yes|
|Restricted sample|No|No|Yes|No|Yes|
|Observations|21951|17316|9664|19785|8098|

The regressions test whether slanted judges are more likely to have case decisions appealed.The coefficients of interest are ’Slant against women, good vs bad’ and the interactions. Column 3 (5) restricts the sample to cases where the defendant (plaintiff) loses, and the interaction tests if reverals are more likely if the judges is more slanted and the defendant (plaintiff) is female. These cases have the most potentail for gender bias. Standard errors, in parentheses, are clustered at the judge level. All columns are based on a linear regression model.

62
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Table H3: Reversals and slant, family vs career

||(1)<br>reversed|(2)<br>reversed|(3)<br>reversed|(4)<br>reversed|(5)<br>reversed|
|---|---|---|---|---|---|
|Slant against women, career vs family|0.0103|0.00565|0.0369<sup>_⇤⇤_</sup>|0.0146|-0.0380<sup>_⇤⇤_</sup>|
||(0.00966)|(0.0120)|(0.0167)|(0.0109)|(0.0170)|
|Def. maj. female||0.000515<br>(0.00212)|0.000841<br>(0.00342)|||
|Pla. maj. female||||0.000890<br>(0.00185)|0.00246<br>(0.00253)|
|Def. maj. fem. X Slant against women||0.0213<br>(0.0199)|0.00771<br>(0.0315)|||
|Pla. maj. fem. X Slant against women||||-0.00453<br>(0.0143)|0.00322<br>(0.0214)|
|DVmean|0.00949|0.00983|0.0116|0.00920|0.00760|
|Court-year FE|Yes|Yes|Yes|Yes|Yes|
|Restricted sample|No|No|Yes|No|Yes|
|Observations|26019|20751|11530|23480|9734|

The regressions test whether slanted judges are more likely to have case decisions reversed. The coefficients of interest are ’Slant against women, career vs family’ and the interactions. Column 3 (5) restricts the sample to cases where the defendant (plaintiff) loses, and the interaction tests if reverals are more likely if the judges is more slanted and the defendant (plaintiff) is female. These cases have the most potentail for gender bias. Standard errors, in parentheses, are clustered at the judge level. All columns are based on a linear regression model. Pla. = plaintiffs, def. = defendants, plur. = plurality, maj. = majority.

64
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Table H4: Reversals and slant, good vs bad

||(1)<br>reversed|(2)<br>reversed|(3)<br>reversed|(4)<br>reversed|(5)<br>reversed|
|---|---|---|---|---|---|
|Slant against women, good vs bad|0.0568<sup>_⇤⇤⇤_</sup>|0.0505<sup>_⇤⇤_</sup>|0.0461|0.0644<sup>_⇤⇤⇤_</sup>|0.0420|
||(0.0169)|(0.0241)|(0.0355)|(0.0212)|(0.0264)|
|Def. maj. female||0.00127<br>(0.00240)|0.00144<br>(0.00427)|||
|Pla. maj. female||||0.00312<br>(0.00366)|0.00488<br>(0.00556)|
|Def. maj. fem. X Slant against women||-0.00465<br>(0.0305)|-0.00295<br>(0.0446)|||
|Pla. maj. fem. X Slant against women||||-0.0317<br>(0.0418)|-0.0395<br>(0.0623)|
|DVmean|0.0102|0.0104|0.0125|0.0100|0.00852|
|Court-year FE|Yes|Yes|Yes|Yes|Yes|
|Restricted sample|No|No|Yes|No|Yes|
|Observations|21951|17316|9664|19785|8098|

The regressions test whether slanted judges are more likely to have case decisions reversed. The coefficients of interest are ’Slant against women, good vs bad’ and the interactions. Column 3 (5) restricts the sample to cases where the defendant (plaintiff) loses, and the interaction tests if reverals are more likely if the judges is more slanted and the defendant (plaintiff) is female. These cases have the most potentail for gender bias. Standard errors, in parentheses, are clustered at the judge level. All columns are based on a linear regression model. Pla. = plaintiffs, def. = defendants, plur. = plurality, maj. = majority.

66
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# **Appendix I: Interaction Between Gender and Ethnicity In-groups**

Table I1: Ethnicity and gender interaction results

||(1)|(2)|(3)|
|---|---|---|---|
||Def. win|Def. win|Def. win|
|Judge-def. same gender|0.0183<sup>_⇤_</sup>||0.0160|
||(0.00996)||(0.0109)|
|Judge-def. same ethnicity|0.0440<sup>_⇤⇤_</sup><br>(0.0176)||0.0622<sup>_⇤⇤⇤_</sup><br>(0.0189)|
|Judge-def. same gender X Judge-def. same ethnicity|-0.0131<br>(0.0225)||-0.0224<br>(0.0237)|
|Judge-pla. same gender||0.0194<sup>_⇤⇤_</sup>|0.0143|
|||(0.00912)|(0.0119)|
|Judge-pla. same ethnicity||0.0268|0.00874|
|||(0.0174)|(0.0176)|
|Judge-pla. same gender X Judge-pla. same ethnicity||-0.0260<br>(0.0240)|-0.0261<br>(0.0288)|
|DV mean|0.455|0.453|0.458|
|Court-year FE|Yes|Yes|Yes|
|Observations|17675|19172|14580|

Standard errors, in parentheses, are clustered at the judge level. All columns are based on a linear regression model. For specification details, see equation 5. Pla. = plaintiff, def. = defendant, maj. = majority.

68