Elliott Ash

We present the first large-scale field experiment evaluating the integration of generative AI into a national justice system. In partnership with Pakistan’s judiciary, we built JudgeGPT, a custom generative AI assistant designed for Pakistan’s trial courts, and randomized 1,559 judges serving across 118 courts into one of three arms: (i) access to the assistant with targeted training tailored to its use; (ii) access to AI with generic training on technology and law; and (iii) generic training without access to AI. Judges who received AI access together with targeted training on the use of the tool were more likely to adopt it, use it more intensively, and continue using it over time. Their attitudes toward AI also shift: they expect the tool and the targeted training to increase their productivity. Administrative records move in the same direction: districts with greater exposure to treated judges resolve more cases. At median-district exposure, introducing AI with targeted training corresponds to 1,848 additional cases resolved per year, a 6.3 percent increase over the mean. This increase does not appear to come at the expense of reduced quality, as measured by case appeals (which slightly decline) or text-based measures of judicial writing (which slightly improve). Chat-log-based usage patterns suggest that judges use AI mainly to clarify legal concepts and support drafting. Targeted training appears to shift use toward tasks where language models are likely to be more useful, such as text improvement, and away from more open-ended legal queries where responses are more costly to verify. Overall, the results suggest that generative AI can raise public-sector productivity, but that these gains may depend on targeted training that directs use toward tasks for which the tool is better suited.

Recent work has used LLM agents to reproduce empirical social science results with access to both the data and code. We broaden this scope by asking: Can they reproduce results given only a paper's methods description and original data? We develop an agentic reproduction system that extracts structured methods descriptions from papers, runs reimplementations under strict information isolation—agents never see the original code, results, or paper—and enables deterministic, cell-level comparison of reproduced outputs to the original results. An error attribution step traces discrepancies through

Online creators have become a central source of political information for young adults in recent years, and over the same period, young men have shifted toward the political right relative to young women. We examine whether these two major developments are connected by focusing on “The Joe Rogan Experience”, the leading long-form podcast, which has a disproportionately male audience. Our identification leverages the timing and location of Ultimate Fighting Championship (UFC) events, for which Rogan has long served as the lead commentator. In a difference-in-differences design, we find that local UFC events trigger a significant increase in Google searches and in time spent consuming Joe Rogan episodes among young men. Applying text analysis to podcast transcripts, we document a pronounced shift in Rogan’s political rhetoric: until 2020, Rogan frequently expressed support for Sanders with left-populist rhetoric; starting in 2021, his commentary became supportive of Trump with a right-populist slant. Using administrative voter files, we show that exposure to UFC events until 2020 increased Democratic voting in primaries with Sanders, while after that, exposure to UFC events increased Republican voting in primaries with Trump with all effects concentrated among young men.

Interviews allow employers to learn about workers, but do they also enable workers to learn about firms? Studying 500,000 interview reports from Glassdoor, we find candidates for high-paying jobs are more likely to reject a job offer if they believe the interview was easy. Easy interviews appear to convey poor "fit" as those who accept offers after easy interviews are two-fifths of a standard deviation less satisfied with their jobs and 10 percent less likely to remain with their employer for at least one year. Analysis of interview narratives using large language models reveals difficult inte

We study the effect of televised broadcasts of floor debates on the rhetoric and behavior of U.S. Congress Members. First, we show in a differences-in-differences analysis that the introduction of C-SPAN broadcasts in 1979 increased the use of emotional appeals in the House relative to the Senate, where televised floor debates were not introduced until later. Second, we use exogenous variation in C-SPAN channel positioning as an instrument for C-SPAN viewership by Congressional district and show that House Members from districts with exogenously higher C-SPAN viewership are more emotive in flo

We study the effect of televised broadcasts of floor debates on the rhetoric and behavior of U.S. Congress Members. First, we show in a differences-in-differences analysis that the introduction of C-SPAN broadcasts in 1979 increased the use of emotional appeals in the House relative to the Senate, where televised floor debates were not introduced until later. Second, we use exogenous variation in C-SPAN channel positioning as an instrument for C-SPAN viewership by Congressional district and show that House Members from districts with exogenously higher C-SPAN viewership are more emotive in flo

We present Apertus, a fully open suite of large language models (LLMs) designed to address two systemic shortcomings in today's open model ecosystem: data compliance and multilingual representation. Unlike many prior models that release weights without reproducible data pipelines or regard for content-owner rights, Apertus models are pretrained exclusively on openly available data, retroactively respecting robots.txt exclusions and filtering for non-permissive, toxic, and personally identifiable content. To mitigate risks of memorization, we adopt the Goldfish objective during pretraining, str

This paper empirically studies the effects of the early law-and-economics movement on the U.S. judiciary. We focus on the Manne Economics Institute for Federal Judges, an intensive economics course that trained almost half of federal judges between 1976 and 1999. Using the universe of published opinions in U.S. Circuit Courts and 1 million District Court criminal sentencing decisions, we estimate the within-judge effect of Manne program attendance. Selection into attendance was limited, as the program was popular among judges of all backgrounds, frequently oversubscribed, and admitted particip

This paper empirically studies the effects of the early law-and-economics movement on the U.S. judiciary. We focus on the Manne Economics Institute for Federal Judges, an intensive economics course that trained almost half of federal judges between 1976 and 1999. Using the universe of published opinions in U.S. Circuit Courts and 1 million District Court criminal sentencing decisions, we estimate the within-judge effect of Manne program attendance. Selection into attendance was limited, as the program was popular among judges of all backgrounds, frequently oversubscribed, and admitted particip

This paper analyzes the conditions under which more legislation contributes to economic growth. In the context of U.S. states, we apply natural language processing tools to measure legislative flows for the years 1965-2012. We implement a novel shift-share design for text data, where the instrument for legislation is leave-one-out legal-topic flows interacted with pre-treatment legal-topic shares. We find that at the margin, higher legislative output causes more economic growth. Consistent with more complete laws reducing ex-post hold-up, we find that the effect is driven by the use of conting

We study judicial in-group bias in Indian criminal courts using a newly collected dataset on over 5 million criminal case records from 2010-2018. After detecting gender and religious identity using a neural-net classifier applied to judge and defendant names, we exploit quasi-random assignment of cases to judges to examine whether defendant outcomes are affected by assignment to a judge with a similar identity. In the aggregate, we estimate tight zero effects of in-group bias based on shared gender, religion, and last name (a proxy for caste). We do find limited in-group bias in some (but not

We study judicial in-group bias in Indian criminal courts using a newly collected dataset on over 5 million criminal case records from 2010-2018. After detecting gender and religious identity using a neural-net classifier applied to judge and defendant names, we exploit quasi-random assignment of cases to judges to examine whether defendant outcomes are affected by assignment to a judge with a similar identity. In the aggregate, we estimate tight zero effects of in-group bias based on shared gender, religion, and last name (a proxy for caste). We do find limited in-group bias in some (but not

Anecdotal evidence often points to aging as a cause for reduced work performance. This paper provides empirical evidence on this issue in a context where performance is measurable and there is variation in mandatory retirement policies: U.S. state supreme courts. We find that introducing mandatory retirement reduces the average age of working judges and improves court performance, as measured by output (number of published opinions) and legal impact (number of forward citations to those opinions). Consistent with aging effects as a contributing factor, we find that older judges do about the sa

Anecdotal evidence often points to aging as a cause for reduced work performance. This paper provides empirical evidence on this issue in a context where performance is measurable and there is variation in mandatory retirement policies: U.S. state supreme courts. We find that introducing mandatory retirement reduces the average age of working judges and improves court performance, as measured by output (number of published opinions) and legal impact (number of forward citations to those opinions). Consistent with aging effects as a contributing factor, we find that older judges do about the sa

Local exposure to conservative news causes judges to impose harsher criminal sentences. Our evidence comes from an instrumental variables analysis, where randomness in television channel positioning across localities induces exogenous variation in exposure to Fox News Channel. These treatment data on news viewership are taken to outcomes data on almost 7 million criminal sentencing decisions in the United States for the years 2005-2017. Higher Fox News viewership increases incarceration length, and the effect is stronger for black defendants and for drug-related crimes. We can rule out changes

Local exposure to conservative news causes judges to impose harsher criminal sentences. Our evidence comes from an instrumental variables analysis, where randomness in television channel positioning across localities induces exogenous variation in exposure to Fox News Channel. These treatment data on news viewership are taken to outcomes data on almost 7 million criminal sentencing decisions in the United States for the years 2005-2017. Higher Fox News viewership increases incarceration length, and the effect is stronger for black defendants and for drug-related crimes. We can rule out changes

We use computational linguistics techniques to study the use of emotion and reason in political discourse. Our new measure of emotionality in language combines lists of emotive and cognitive words, as well as word embeddings, to construct a text-based scale between emotion and reason. After validating the method against human annotations, we apply it to scale 6 million speeches in the U.S. Congressional Record for the years 1858 through 2014. Intuitively, emotionality spikes during times of war and is highest for patriotism-related topics. In the time series, emotionality was relatively low an

Are non-verbal reactions during parliamentary debate gendered? Do male and female Members of Parliament (MPs) experience applause or jeering differently? In short, yes, and the gendered nature of a speech matters. Using an original corpus of over 544,000 speeches given in German state parliaments, we first estimate the gendered nature of parliamentary speeches, then examine how reactions to speeches given by male and female MPs differ. Female and male MPs receive similarly positive and negative reactions to their speeches on average, but they receive different reactions depending on the gender

Social scientists have become increasingly interested in how narratives — the stories in fiction, politics, and life — shape beliefs, behavior, and government policies. This paper provides an unsupervised method to quantify latent narrative structures in text documents. Our pipeline identifies coherent entity groups and maps explicit relations between them in the text. We provide an application to the United States Congressional Record to analyze political and economic narratives in recent decades. Our analysis highlights the dynamics, sentiment, polarization, and interconnectedness of narrati

Social scientists have become increasingly interested in how narratives — the stories in fiction, politics, and life — shape beliefs, behavior, and government policies. This paper provides an unsupervised method to quantify latent narrative structures in text documents. Our pipeline identifies coherent entity groups and maps explicit relations between them in the text. We provide an application to the United States Congressional Record to analyze political and economic narratives in recent decades. Our analysis highlights the dynamics, sentiment, polarization, and interconnectedness of narratives in political discourse.

Social scientists have become increasingly interested in how narratives — the stories in fiction, politics, and life — shape beliefs, behavior, and government policies. This paper provides an unsupervised method to quantify latent narrative structures in text documents. Our pipeline identifies coherent entity groups and maps explicit relations between them in the text. We provide an application to the United States Congressional Record to analyze political and economic narratives in recent decades. Our analysis highlights the dynamics, sentiment, polarization, and interconnectedness of narrati

Best-of-N (BoN) is a popular and effective algorithm for aligning language models to human preferences. The algorithm works as follows: at inference time, N samples are drawn from the language model, and the sample with the highest reward, as judged by a reward model, is returned as the output. Despite its effectiveness, BoN is computationally expensive; it reduces sampling throughput by a factor of N. To make BoN more efficient at inference time, one strategy is to fine-tune the language model to mimic what BoN does during inference. To achieve this, we derive the distribution induced by the

Large language models such as ChatGPT exhibit striking political biases. If users query them about political information, they often take a normative stance. To overcome this, we align LLMs with diverse political viewpoints from 100,000 comments written by candidates running for national parliament in Switzerland. Models aligned with this data can generate more accurate political viewpoints from Swiss parties, compared to commercial models such as ChatGPT. We also propose a procedure to generate balanced overviews summarizing multiple viewpoints using such models. The replication package conta

We present the Legal Passage Retrieval Dataset, LePaRD. LePaRD contains millions of examples of U.S. federal judges citing precedent in context. The dataset aims to facilitate work on legal passage retrieval, a challenging practice-oriented legal retrieval and reasoning task. Legal passage retrieval seeks to predict relevant passages from precedential court decisions given the context of a legal argument. We extensively evaluate various approaches on LePaRD, and find that classification-based retrieval appears to work best. Our best models only achieve a recall of 59% when trained on data corr

Story detection in online communities is a challenging task as stories are scattered across communities and interwoven with non-storytelling spans within a single text. We address this challenge by building and releasing the StorySeeker toolkit, including a richly annotated dataset of 502 Reddit posts and comments, a detailed codebook adapted to the social media context, and models to predict storytelling at the document and span levels. Our dataset is sampled from hundreds of popular English-language Reddit communities ranging across 33 topic categories, and it contains fine-grained expert an

Story detection in online communities is a challenging task as stories are scattered across communities and interwoven with non-storytelling spans within a single text. We address this challenge by building and releasing the StorySeeker toolkit, including a richly annotated dataset of 502 Reddit posts and comments, a detailed codebook adapted to the social media context, and models to predict storytelling at the document and span levels. Our dataset is sampled from hundreds of popular English-language Reddit communities ranging across 33 topic categories, and it contains fine-grained expert an

Topic models are used to make sense of large text collections. However, automatically evaluating topic model output and determining the optimal number of topics both have been longstanding challenges, with no effective automated solutions to date. This paper proposes using large language models to evaluate such output. We find that large language models appropriately assess the resulting topics, correlating more strongly with human judgments than existing automated metrics. We then investigate whether we can use large language models to automatically determine the optimal number of topics. We

Text classifiers have promising applications in high-stake tasks such as resume screening and content moderation. These classifiers must be fair and avoid discriminatory decisions by being invariant to perturbations of sensitive attributes such as gender or ethnicity. However, there is a gap between human intuition about these perturbations and the formal similarity specifications capturing them. While existing research has started to address this gap, current methods are based on hardcoded word replacements, resulting in specifications with limited expressivity or ones that fail to fully alig

Topic models are used to make sense of large text collections. However, automatically evaluating topic model output and determining the optimal number of topics both have been longstanding challenges, with no effective automated solutions to date. This paper proposes using large language models to evaluate such output. We find that large language models appropriately assess the resulting topics, correlating more strongly with human judgments than existing automated metrics. We then investigate whether we can use large language models to automatically determine the optimal number of topics. We

Trademark law protects marks in order to enable firms to signal their products’ qualities to consumers. To qualify for protection, a mark must be able to identify and distinguish goods. U.S. courts typically locate a mark on a “spectrum of distinctiveness” – known as the Abercrombie spectrum – that categorizes marks as fanciful, arbitrary, or suggestive, and thus as “inherently distinctive,” or as descriptive or generic, and thus as not inherently distinctive. This paper explores whether locating trademarks on the Abercrombie spectrum can be automated using current natural-language processing

This chapter explores the links between legal reasoning and the post-training regimes of aligned large language models (LLMs). Reinforcement learning from human feedback (RLHF), which optimizes model outputs to match graded human preference rankings, leads to LLMs that perform value-weighted interpolations across task-response pairs. Reinforcement learning with verifiable rewards (RLVR), which rewards outputs that pass objective external checks, produces LLMs that faithfully follow structured reasoning paths. The RLHF approach parallels the analogical, precedent-based reasoning characteristic of common-law systems, while RLVR mirrors the deterministic code-based reasoning associated with civil law. These analogies inform the design of multi-agent legal AI systems, which should deploy RLVR-based LLMs for compliance with bright-line rules, while deploying RLHF-based systems to apply more subtle standards.

Trademark law protects marks in order to enable firms to signal their products’ qualities to consumers. To qualify for protection, a mark must be able to identify and distinguish goods. U.S. courts typically locate a mark on a “spectrum of distinctiveness” – known as the Abercrombie spectrum – that categorizes marks as fanciful, arbitrary, or suggestive, and thus as “inherently distinctive,” or as descriptive or generic, and thus as not inherently distinctive. This paper explores whether locating trademarks on the Abercrombie spectrum can be automated using current natural-language processing

Law sets out the rules for society and the economy, particularly important for interactions between strangers. Legal code is a form of non-rival infrastructure, a public good important for investment and innovation. This paper investigates whether legal code complexity scales with population size in US localities. We analyse a corpus of municipal codes from 3259 cities and measure legal complexity using various metrics, including number of words, bytes, and compressed bytes. We find that legal complexity scales geometrically with jurisdiction population, with a scaling parameter of approximate

A widely held view for why the Supreme Court would be right to revive the nondelegation doctrine is that Congress has perverse incentives to abdicate its legislative role and evade accountability through the use of delegations, either expressly delineated or implied through statutory imprecision, and that enforcement of the nondelegation doctrine would correct for those incentives. We call this the Field of Dreams Theory—if we build the nondelegation doctrine, Congress will legislate. Unlike originalist arguments for the revival of the nondelegation doctrine, this theory has widespread appeal

Judicial opinions are written to be persuasive and could build public trust in court decisions, yet they can be difficult for non-experts to understand. We present a pipeline for using an AI assistant to generate simplified summaries of judicial opinions. Compared to existing expert-written summaries, these AI-generated simple summaries are more accessible to the public and more easily understood by non-experts. We show in a survey experiment that the AI summaries help respondents understand the key features of a ruling, and have higher perceived quality, especially for respondents with less f

A widely held view for why the Supreme Court would be right to revive the nondelegation doctrine is that Congress has perverse incentives to abdicate its legislative role and evade accountability through the use of delegations, either expressly delineated or implied through statutory imprecision, and that enforcement of the nondelegation doctrine would correct for those incentives. We call this the Field of Dreams Theory—if we build the nondelegation doctrine, Congress will legislate. Unlike originalist arguments for the revival of the nondelegation doctrine, this theory has widespread appeal

Issues of racial justice and economic inequalities between racial and ethnic groups have risen to the top of public debate. Economists' ability to contribute to these debates is based on the body of race-related research. We study the volume and content of race-related research in economics. We base our analysis on a corpus of 225,000 economics publications from 1960 to 2020 to which we apply an algorithmic approach to classify race-related work. We present three new facts. First, since 1960 less than 2% of economics publications have been race-related. There is an uptick in such work in the m

This paper studies racial in-group disparities in Wisconsin, which has one of the highest Black-to-White incarceration rates among all U.S. states. The analysis is motivated by a model in which a judge may want to incarcerate more due to three factors: (1) taste-based preferences about the defendant's group identity; (2) higher recidivism risk where the defendant is more likely to commit future crimes; and (3) image motives stemming from the defendant being in the same group as the judge. Further, a judge may have better information on recidivism risk due to two factors: (4) becoming more expe

We distinguish between ideational and interest-based appeals to voters on the supply side of politics, integrating the Keynes-Hayek perspective on the importance of ideas with the Stigler-Becker approach emphasizing vested interests. In our model, political entrepreneurs discover identity and worldview “memes” (narratives, cues, frames) that invoke voters' identity concerns or shift their views of how the world works. We identify a complementarity between worldview politics and identity politics and illustrate how they may reinforce each other. Furthermore, we show how adverse economic shocks

Empirical work on political communication has so far left out a potentially pivotal dimension – the unspoken emotional responses indicated by facial expressions. This paper shows how to measure these responses using deep-learning-based computer-vision algorithms in the context of U.S. cable news video. Using machine-generated metrics for expressed emotion, combined with mentions of politically divisive entities, we estimate the difference in emotion when cable-channel speakers hear mentions of entities that are from the opposing side of the political divide. We find that the most responsive em

Empirical work on political communication has so far left out a potentially pivotal dimension – the unspoken emotional responses indicated by facial expressions. This paper shows how to measure these responses using deep-learning-based computer-vision algorithms in the context of U.S. cable news video. Using machine-generated metrics for expressed emotion, combined with mentions of politically divisive entities, we estimate the difference in emotion when cable-channel speakers hear mentions of entities that are from the opposing side of the political divide. We find that the most responsive em

This paper presents novel causal evidence on the relationship between various communication channels employed by central banks and households' expectations about future inflation. In a pre-registered randomized survey experiment administered in 2022, we examine adjustment of inflation expectations when confronted with a press conference statement by the president of the European Central Bank (ECB) articulating the bank's commitment to a 2% inflation target. First, we replicate previous literature showing that respondents update toward the inflation target. Second, we show that the medium of co

This paper presents novel causal evidence on the relationship between various communication channels employed by central banks and households' expectations about future inflation. In a pre-registered randomized survey experiment administered in 2022, we examine adjustment of inflation expectations when confronted with a press conference statement by the president of the European Central Bank (ECB) articulating the bank's commitment to a 2% inflation target. First, we replicate previous literature showing that respondents update toward the inflation target. Second, we show that the medium of co

An important philosophical tradition identifies persons as those entities that have minds, such that mind perception is a window into person perception. Psychological research has found that human perceptions of mind consist of at least two distinct dimensions: agency (e.g. planning, deciding) and experience (e.g. feeling, hungering). Taking this insight into the semantic space of natural language, we develop a generalizable, scalable computational-linguistics method for measuring variation in perceived agency and experience in large archives of plain-text documents. The resulting text-based r

An important philosophical tradition identifies persons as those entities that have minds, such that mind perception is a window into person perception. Psychological research has found that human perceptions of mind consist of at least two distinct dimensions: agency (e.g. planning, deciding) and experience (e.g. feeling, hungering). Taking this insight into the semantic space of natural language, we develop a generalizable, scalable computational-linguistics method for measuring variation in perceived agency and experience in large archives of plain-text documents. The resulting text-based r

Socialist courts are supposed to apply the law, not make it, and socialist legality denies judicial decisions any precedential status. In 2011, however, the Chinese Supreme People’s Court designated selected decisions as Guiding Cases to be referred to by all judges when adjudicating similar disputes. One decade on, the paucity of citations to Guiding Cases has been taken as demonstrating the incongruity of case-based adjudication and socialist legality.

Should technocratic public officials be selected through politics or by merit? This paper explores how selection procedures influence the quality of selected officials in the context of U.S. state supreme courts for the years 1947-1994. In a unique set of natural experiments, state governments enacted a variety of reforms making judicial elections less partisan and establishing merit-based procedures that delegate selection to experts. We compare post-reform judges to pre-reform judges in their work quality, measured by forward citations to their opinions. In this setting we can hold constant

Bureaucratic discretion and executive delegation are central topics in political economy and political science. The previous empirical literature has measured discretion and delegation by manually coding large bodies of legislation. Drawing from computational linguistics, we provide an automated procedure for measuring discretion and delegation in legal texts to facilitate large-scale empirical analysis. The method uses information in syntactic parse trees to identify legally relevant provisions, as well as agents and delegated actions. We undertake two applications. First, we produce a measur

This paper provides a theoretical and empirical analysis of the intrinsic preferences of state appellate court judges. We construct a panel data set using published decisions from state supreme court cases merged with institutional and biographical information on all (1,636) state supreme court judges for the 50 states of the United States from 1947 to 1994. We estimate the effects of changes in judge employment conditions on a number of measures of judicial performance. The results are consistent with the hypothesis that judges are intrinsically motivated to provide high-quality decisions, an

Should technocratic public officials be selected through politics or by merit? This paper explores how selection procedures influence the quality of selected officials in the context of U.S. state supreme courts for the years 1947-1994. In a unique set of natural experiments, state governments enacted a variety of reforms making judicial elections less partisan and establishing merit-based procedures that delegate selection to experts. We compare post-reform judges to pre-reform judges in their work quality, measured by forward citations to their opinions. In this setting we can hold constant

This paper provides evidence of racial variation in traffic enforcement responses to local government budget stress using data from policing agencies in the state of Missouri from 2001 through 2012. Like previous studies, we find that local budget stress is associated with higher citation rates; we also find an increase in traffic-stop arrest rates. However, we find that these effects are concentrated among white (rather than black or Latino) drivers. The results are robust to the inclusion of a range of covariates and a variety of model specifications, including a a regression-discontinuity examining bare budget shortfalls. Considering potential mechanisms, we find that targeting of white drivers is higher where the white-to-black income ratio is higher, consistent with the targeting of drivers who are better able to pay fines. Further, the relative effect on white drivers is higher in areas with statistical over-policing of black drivers: when black drivers are already getting too many fines, police cite white drivers from whom they are presumably more likely to be able to raise the needed extra revenue. These results highlight the relationship between policing-as-taxation and racial inequality in policing outcomes.

Bureaucratic discretion and executive delegation are central topics in political economy and political science. The previous empirical literature has measured discretion and delegation by manually coding large bodies of legislation. Drawing from computational linguistics, we provide an automated procedure for measuring discretion and delegation in legal texts to facilitate large-scale empirical analysis. The method uses information in syntactic parse trees to identify legally relevant provisions, as well as agents and delegated actions. We undertake two applications. First, we produce a measur

This paper presents rankings of U.S. public policy schools based on their research publication output. In 2016 we collected the names of about 5,000 faculty members at 44 such schools. We use bibliographic databases to gather measures of the quality and quantity of these individuals' academic publications. These measures include the number of articles and books written, the quality of the journals the articles have appeared in, and the number of citations all have garnered. We aggregate these data to the school level to produce a set of rankings. The results differ significantly from existing

This paper draws on machine learning methods for text classification to predict the ideological direction of decisions from the associated text. Using a 5% hand-coded sample of cases from U.S. Circuit Courts, we explore and evaluate a variety of machine classifiers to predict “conservative decision” or “liberal decision” in held-out data. Our best classifier is highly predictive ( F1=.65 ) and allows us to extrapolate ideological direction to the full sample. We then use these predictions to replicate and extend Landes and Posner’s (2009) analysis of how the party of the nominating president influences circuit judge’s votes.

In sequential decision-making experiments, participants often conform to the decisions of others rather than reveal private information — resulting in less information produced and potentially lower payoffs for the group. This paper asks whether experimentally induced group identity affects players' decisions to conform, even when payoffs are only a function of individual actions. As motivation for the experiment, we show that U.S. Supreme Court Justices in preliminary hearings are more likely to conform to their same-party predecessors when the share of predecessors from their party is high.

In sequential decision-making experiments, participants often conform to the decisions of others rather than reveal private information — resulting in less information produced and potentially lower payoffs for the group. This paper asks whether experimentally induced group identity affects players’ decisions to conform, even when payoffs are only a function of individual actions. As motivation for the experiment, we show that U.S. Supreme Court Justices in preliminary hearings are more likely to conform to their same-party predecessors when the share of predecessors from their party is high. Lab players, in turn, are more likely to conform to the decisions of in-group members when their share of in-group predecessors is high. We find that exposure to information from in-group members increases the probability of reverse information cascades (herding on the wrong choice), reducing average payoffs. Therefore, alternating decision-making across members of different groups may improve welfare in sequential decision-making contexts.

This paper provides a theoretical and empirical analysis of the intrinsic preferences of state appellate court judges. We construct a panel data set using published decisions from state supreme court cases merged with institutional and biographical information on all (1,636) state supreme court judges for the 50 states of the United States from 1947 to 1994. We estimate the effects of changes in judge employment conditions on a number of measures of judicial performance. The results are consistent with the hypothesis that judges are intrinsically motivated to provide high-quality decisions, and that at the margin they prefer quality over quantity. When judges face less time pressure, they write more well-researched opinions that are cited more often by later judges. When judges are up for election then performance falls, suggesting that election politics take time away from judging work – rather than providing an incentive for good performance. These effects are strongest when judges have more discretion to select their case portfolio, consistent with psychological theories that posit a negative effect of contingency on motivation.

This paper examines the implications of the brain sciences’ mechanistic model of human behavior for our understanding of crime. The standard rational-choice crime model is refined by a behavioral approach, which proposes a decision model comprising cognitive and emotional decision systems. According to the behavioral approach, a criminal is not irrational but rather ‘ecologically rational,’ outfitted with evolutionarily conserved decision modules adapted for survival in the human ancestral environment. Several important cognitive as well as emotional factors for criminal behavior are discussed and formalized, using tax evasion as a running example. The behavioral crime model leads to new perspectives on criminal policy-making.

This paper tackles the task of legal extractive summarization using a dataset of 430K U.S. court opinions with key passages annotated. According to automated summary quality metrics, the reinforcement-learning-based MemSum model is best and even out-performs transformer-based models. In turn, expert human evaluation shows that MemSum summaries effectively capture the key points of lengthy court opinions. Motivated by these results, we open-source our models to the general public. This represents progress towards democratizing law and making U.S. court opinions more accessible to the general pu

This paper describes an unsupervised legal document parser which performs a decomposition of labor union contracts into discrete assignments of rights and duties among agents of interest. We use insights from deontic logic applied to modal categories and other linguistic patterns to generate topic-specific measures of relative legal authority. We illustrate the consistency and efficiency of the pipeline by applying it to a large corpus of 35K contracts and validating the resulting outputs.

Notwithstanding the widely held view that data generation and data curation processes are prominent sources of bias in machine learning algorithms, there is little empirical research seeking to document and understand the specific data dimensions affecting algorithmic unfairness. Contra the previous work, which has focused on modeling using simple, small-scale benchmark datasets, we hold the model constant and methodically intervene on relevant dimensions of a much larger, more diverse dataset. For this purpose, we introduce a new dataset on recidivism in 1.5 million criminal cases from courts

This paper describes an unsupervised legal document parser which performs a decomposition of labor union contracts into discrete assignments of rights and duties among agents of interest. We use insights from deontic logic applied to modal categories and other linguistic patterns to generate topic-specific measures of relative legal authority. We illustrate the consistency and efficiency of the pipeline by applying it to a large corpus of 35K contracts and validating the resulting outputs.

How does economics compare to other social sciences in its study of issues related to race and ethnicity? We assess this using a corpus of 500,000 academic publications in economics, political science, and sociology. Using an algorithmic approach to classify race-related publications, we document that economics lags far behind the other disciplines in the volume and share of race-related research, despite having higher absolute volumes of research output. Since 1960, there have been 13,000 race-related publications in sociology, 4,000 in political science, and 3,000 in economics. Since around

We offer the first attempt at empirically testing the level of transnational consensus on the legal language controlling international tax matters. We also investigate the institutional framework of such consensus-building. We build a dataset of 4,052 bilateral income tax treaties, as well as 16 model tax treaties published by the United Nations (UN), Organisation for Economic Co-operation and Development (OECD) and the United States. We use natural language processing to perform pair-wise comparison of all treaties in effect at any given year. We identify clear trends of convergence of legal

We offer the first attempt at empirically testing the level of transnational consensus on the legal language controlling international tax matters. We also investigate the institutional framework of such consensus-building. We build a dataset of 4,052 bilateral income tax treaties, as well as 16 model tax treaties published by the United Nations (UN), Organisation for Economic Co-operation and Development (OECD) and the United States. We use natural language processing to perform pair-wise comparison of all treaties in effect at any given year. We identify clear trends of convergence of legal language in bilateral tax treaties since the 1960s, particularly on the taxation of cross-border business income. To explore the institutional source of such consensus, we compare all treaties in effect at any given year to the model treaties in effect during that year. We also explore whether newly concluded treaties converge towards legal language in newly introduced models. We find the OECD Model Tax Convention (OECD Model) to have a significant influence. In the years following the adoption of a new OECD Model there is a clear trend of convergence in newly adopted bilateral tax treaties towards the language of the new OECD Model. We also find that model treaties published by the UN (UN Model) have little immediate observable effect, though UN treaty policies seem to have a delayed, yet lasting effect. We conclude that such findings support the argument that a trend towards international legal consensus on certain tax matters exists, and that the OECD is the institutional source of the consensus building process.