I am a PhD student at the Walter A. Haas School of Business at UC Berkeley.

Under what conditions do judges favor their own group? Collecting the available universe of Superior Court decisions in Kenya, we leverage the random assignment of cases to judges to evaluate the extent of judicial in-group bias along gender and ethnic lines. We find that defendants are 4 or 6 percentage points more likely to win if they share the judge’s gender or ethnicity, respectively, and that judges are significantly more biased in favor of defendants than plaintiffs. Our findings highlight the need to re-examine the emerging consensus that judges uniformly favor their own group and continue investigating the mechanisms driving judicial bias. We propose that the uneven application of bias can be explained by a framework of social identity and loss aversion, and we support this claim with data on the amount of damages in each case, which we extract using a large language model. Finally, we argue that our framework could serve as a more complete lens through which to decipher the cognitive underpinnings of judicial biases.

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We conduct a survey of economists and a representative sample of Americans to infer the reduction in the perceived value of a paper when its authors have conflicts of interest, meaning they have financial, professional, or ideological stakes in the results. On average, a conflict of interest decreases trust in the conclusions of an economics paper by 30 percent. We introduce the conflict-of-interest discount, which measures the reduction in the value of a conflicted paper relative to a non-conflicted one. We show that conflicted papers are worth less than half of non-conflicted ones on average, though this effect varies significantly with the nature of the conflict.

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Can judges enforce environmental justice? Though citizens increasingly rely on the judiciary to enforce environmental regulations, there is little empirical evidence on the effectiveness of judicial policies in improving environmental outcomes. We report the first estimates of the causal effects of judicial orders on water pollution and infant mortality in India. We construct a comprehensive dataset spanning four decades that includes court cases, judicial decisions, pollution indices, and infant mortality rates. We find that green cases are temporally associated with reductions in peak toxicity levels, but have almost no impact on infant mortality rates in subsequent months. Several years after decisions, pollution and mortality rates exceed pre-decision levels.

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Collecting the available universe of High Court decisions in Kenya, we leverage the random assignment of cases to judges to evaluate the extent and drivers of judicial bias along gender and ethnic lines. We find that defendants are 4 or 5 percentage points more likely to win if they share the judge’s gender or ethnicity, respectively, but there is no in-group bias toward plaintiffs. We show that this effect is driven by mild bias among a large group of judges. Judges displaying stereotypical or negative gender attitudes in their written judgments are also more likely to display gender bias in their decisions.

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AI-assisted methods can analyze large-scale legal datasets with approximately 70 percent accuracy, enabling more effective monitoring of environmental litigation outcomes. Large language models can assess whether court rulings have a positive environmental impact with accuracy that approaches human expert analysis. Analysis of 12,615 environmental court cases in India reveals that approximately 35 percent of rulings are intended to be favorable to the environment, with significant variation across courts and case types.

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