Law, Economics, and Data Science Group
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Law and Economics, Political Economy, Public Finance, Computational Linguistics, Machine Learning
Recent Working Papers
“A Machine Learning Approach to Analyzing Corruption in Local Public Finances” (with Sergio Galletta and Tommaso Giommoni) Abstract
This paper applies machine learning tools to detect local-government corruption using budget accounts data. In the context of Brazilian municipalities, we have gold-truth labels for corruption from a set of federally mandated audits (assigned randomly by lottery) for the years 2003 to 2010. Our tree-based gradient-boosted classifier can predict the presence of corruption in held-out test data, consistent with an expected link between corruption and budget composition. The trained model, when applied to new data, provides a synthetic measure of corruption which can be used for new empirical analysis. We confirm the empirical usefulness of this measure by replicating, and extending, some previous empirical evidence on corruption issues in Brazil. In particular, we exploit the longitudinal nature of our data to produce new evidence on the dynamic effects of audits on corruption, including spillover effects on neighboring municipalities.
“Ideas Have Consequences: The Impact of Law and Economics on American Justice” (with Daniel L. Chen and Suresh Naidu). Abstract
This paper provides a quantitative analysis of the effects of the early law-and-economics movement on the U.S. judiciary. Using the universe of published opinions in U.S. Circuit Courts and 1 million District Court criminal sentencing decisions linked to judge identity, we estimate the effect of attendance in the controversial Manne economics training program, an intensive course attended by almost half of federal judges between 1976 and 1999. After attending economics training, participating judges use more economics language in their opinions, issue more conservative decisions in economics-related cases, rule against regulatory agencies more often, vote in a pro-merger direction in antitrust cases, and impose more/longer criminal sentences. We identify these effect of the Manne program in a difference-in-differences framework, controlling for judge fixed effects, exploiting random assignment of judges to cases, and adjusting for machine-learning-selected covariates predicting the timing of attendance. The law -and -economics movement had policy consequences via its influence in U.S. courts, showing that theoretical legal ideas can directly influence economic policies by persuading federal judges.
“Stereotypes in High-Stakes Decisions: Evidence from U.S. Circuit Courts” (with Arianna Ornaghi and Daniel L. Chen) Abstract
Stereotypes are thought to be an important determinant of decision making, but they are hard to systematically measure, especially for individuals in policy-making roles. In this paper, we propose and implement a novel language-based measure of gender stereotypes for the high-stakes context of U.S. Appellate Courts. We construct a judge-specific measure of gender-stereotyped language use – gender slant – by looking at the linguistic association of words identifying gender (male versus female) and words identifying gender stereotypes (career versus family) in the judge’s authored opinions. Exploiting quasi-random assignment of judges to cases and conditioning on detailed biographical characteristics of judges, we study how gender stereotypes influence judicial behavior. We find that judges with higher slant vote more conservatively on women’s rights’ issues (e.g. reproductive rights, sexual harassment, and gender discrimination). These more slanted judges also influence workplace outcomes for female colleagues: they are less likely to assign opinions to female judges, they are more likely to reverse lower-court decisions if the lower-court judge is a woman, and they cite fewer female-authored opinions.
“How Cable News Reshaped Local Government” (with Sergio Galletta). Abstract
Partisan cable news broadcasts have a causal effect on the size and composition of budgets in U.S. localities. Utilizing channel positioning as an instrument for viewership, we show that exposure to the conservative Fox News Channel shrinks local government budgets, while liberal MSNBC enlarges them. Revenue changes are driven by shifts in property taxes, a key tool for local redistributive policy. Expenditure changes are driven by public hospital expenditures, an important discretionary public good provided by local governments. We also find evidence that Fox exposure increased privatization (while MSNBC decreased it). An analysis of mechanisms suggests that the results are driven by changes in voter preferences, but not by changes in partisan control of city governments.
“Measuring Topics Using Cross-Domain Supervised Learning: Methods and Application to New Zealand Parliament” (with Massimo Morelli and Moritz Osnabruegge). Abstract
This paper studies and assesses a novel method for assigning topics to political texts: cross-domain supervised learning. A machine learning algorithm is trained to classify topics in a labeled source corpus and then applied to extrapolate topics in an unlabeled target corpus. An advantage of the method is that, unlike standard (unsupervised) topic models, the set of assigned topics are interpretable and scientifically meaningful by construction. We demonstrate the method in the case of labeled party manifestos (source corpus) and unlabeled parliamentary speeches (target corpus). Besides the standard cross-validated within-domain error metrics, we further validate the cross-domain performance by labeling a subset of target corpus documents. We find that the classifier assigns topics accurately in the parliamentary speeches, although accuracy varies substantially by topic. To assess the construct validity, we analyze the impact on parliamentary speech topics of New Zealand’s 1996 electoral reform, which replaced a first-past-the-post system with proportional representation.
“Measuring Discretion and Delegation in Legislative Texts: Methods and Application to U.S. States” (with Massimo Morelli and Matia Vannoni), Political Analysis (forthcoming). Abstract
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 measure of bureaucratic discretion by looking at the level of legislative detail for U.S. states and find that this measure increases after reforms giving agencies more independence. This effect is consistent with an agency cost model where a more independent bureaucracy requires more specific instructions (less discretion) to avoid bureaucratic drift. Second, we construct measures of delegation to governors in state legislation. Consistent with previous estimates using non-text metrics, we find that executive delegation increases under unified government.
“Fiscal pressures and discriminatory policing: Evidence from traffic stops in Missouri” (with Allison Harris and Jeffrey Fagan), Journal of Race, Ethnicity, and Politics (forthcoming). Abstract
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.
“Elections and divisiveness: Theory and evidence” (with Massimo Morelli and Richard Van Weelden), Journal of Politics (2017). Abstract
This paper provides a theoretical and empirical analysis of how politicians allocate their time across issues. When voters are uncertain about an incumbent’s preferences, there is a pervasive incentive to “posture” by spending too much time on divisive issues (which are more informative about a politician’s preferences) at the expense of time spent on common-values issues (which provide greater benefit to voters). Higher transparency over the politicians’ choices can exacerbate the distortions. These theoretical results motivate an empirical study of how Members of the U.S. Congress allocate time across issues in their floor speeches. We find that U.S. Senators spend more time on divisive issues when they are up for election, consistent with electorally induced posturing. In addition, we find that U.S. House Members spend more time on divisive issues in response to higher news transparency.
“Intrinsic motivation in public service: Theory and evidence from state supreme courts” (with Bentley MacLeod), Journal of Law and Economics (2015).Abstract
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.
More Working Papers
“Reducing Partisanship in Judicial Elections Can Improve Judge Quality: Evidence from U.S. State Appellate Courts” (with W. Bentley MacLeod). Abstract
This paper analyzes how selection procedures for technocrats affect the quality of the selected officials. A representative voter model predicts that better technocrats are selected when there is lower partisan bias and when the process has better information regarding candidate ability. Along these lines, U.S. state governments have historically chosen to make judicial elections less partisan and to introduce merit-based procedures that delegate selection to experts. Using data for all state supreme court judges, 1947-1994, we identify the effect of these reforms on quality by comparing performance of judges selected before and after the rule change. We find, consistent with the representative voter model, that judges selected by nonpartisan elections, or by merit commissions, produce higher-quality work than judges selected by partisan elections.
“Conservative News Media and Criminal Justice: Evidence from Exposure to Fox News Channel” (with Michael Poyker). Abstract
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. The effect is observed for elected, and not appointed, judges, consistent with voter attitudes as a potential mechanism. The effect becomes weaker as judges get closer to election, suggesting a diminishing marginal effect for judges who are already politically engaged.
Media Coverage: New Statesman.
“What Drives Partisan Tax Policy? The Effective Tax Code” Abstract
This paper contributes to recent work in political economy and public finance that focuses on how details of the tax code, rather than tax rates, are used to implement redistributive fiscal policies. I use tools from natural language processing to construct a high-dimensional representation of tax code changes from the text of 1.6 million statutes enacted by state legislatures for the years 1963 through 2010. A data-driven approach is taken to recover the effective tax code – the language in tax law that has the largest impact on revenues, holding major tax rates constant. I then show that the effective tax code drives partisan tax policy: relative to Republicans, Democrats use revenue-increasing language for income taxes but use revenue-decreasing language for sales taxes (consistent with a more redistributive fiscal policy) despite making no changes on average to statutory tax rates. These results are consistent with the view that due to their relative salience, changing tax rates is politically more difficult than changing the tax code.
“Slanted Frames: Predicting Partisanship from Video Data” (with Dominik Borer). Abstract
We use machine learning to predict partisanship based on video data. Using a dataset of video frames from over 2,000 televised political ads, we train a convolutional neural network to predict the associated partisan label (whether the associated candidate is Democrat or Republican). In the best model we achieved a prediction accuracy of 77% (F1=.79) on the held-out test dataset. In an empirical application, we show that video frames from a cable news channel with a conservative reputation (Fox News) tend to be predicted as Republican, while those from the more liberal networks (CNN, MSNBC) tend to be predicted as Democrat.
Causal effects of judicial sentiment: Methods and application to U.S. Circuit Courts (with Sergio Galletta and Daniel L. Chen). Abstract
This paper provides a general method for analyzing the causal effects of sentiments expressed in the language of judicial rulings, with an application to the effect on social attitudes. We apply natural language processing tools to the text of U.S. appellate court opinions to extrapolate judges’ sentiments toward a number of specific target groups. Exogenous variation in those sentiments comes from an instrumental variables approach, which exploits the random assignment of judges to cases (and the fact that judge characteristics provide good cross-validated predictors of expressed sentiments). Our estimates are consistent with a backlash effect from judge sentiments to social attitudes. This effect does not persist over time and is heterogeneous depending on the target group considered.
“Polarization and Political Selection” (with Tinghua Yu). Abstract
Does political polarization among voters affect the quality of elected officials? We examine the question both theoretically and empirically in the context where expertise and intrinsic motivation are crucial determinants of quality. In our model, high-quality candidates prefer to spend time on their current careers over electoral campaigning. In a polarized electorate, however, voters cast their votes mainly based on candidates’ party affiliations, reducing electoral campaign effort in equilibrium. Hence under higher polarization among voters, higher-quality candidates are more likely to run for high office and to get elected. Our testable prediction is that electorates with higher polarization select candidates who perform better. We take the predictions to data on judges’ performance constructed from the opinions of all state supreme court judges working between 1965 and 1994. We find that judges who joined the court when polarization was high write higher-quality decisions (receiving more citations from other judges) than judges who joined when polarization was low.
“Divided Government, Delegation, and Civil Service Reform” (with Massimo Morelli and Matia Vannoni). Abstract
This paper sheds new light on the drivers of civil service reform in U.S. states. We first demonstrate theoretically that divided government is a key trigger of civil service reform, providing nuanced predictions for specific configurations of divided government. We then show empirical evidence for these predictions using data from the second half of the 20th century: States tended to introduce these reforms under divided government, and in particular when legislative chambers (rather than legislature and governor) were divided.
“A research-based ranking of public policy schools” (with Miguel Urquiola). Abstract
This paper presents research-based rankings of public policy schools in the United States. 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’ publication output. 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 rankings, and in addition display substantial across-field variation.
“Text classification of political ideology labels in judicial opinions” (with Carina Hausladen and Marcel Schubert), International Review of Law and Economics (2020). Abstract
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.
“Automated Fact-Value Distinction in Court Opinions” (with Yu Cao and Daniel L. Chen), European Journal of Law and Economics (forthcoming). Abstract
This paper studies the problem of automated classification of fact statements and value statements in written judicial decisions. We compare a range of methods and demonstrate that the linguistic features of sentences and paragraphs can be used to successfully classify them along this dimension. The Wordscores method by Laver et al. (2003) performs best in held out data. In an application, we show that the value segments of opinions are more informative than fact segments of the ideological direction of U.S. Circuit Court opinions.”
“The Making of International Tax Law: Evidence from Treaty Text” (with Omri Marian), Florida Tax Review (forthcoming). Abstract
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.
“Automated Classification of Modes of Moral Reasoning in Judicial Decisions” (with Nischal Mainali, Liam Meier, and Daniel L. Chen), in: Computational legal studies: The promise and challenge of data-driven research, Edward Elgar (forthcoming). Abstract
What modes of moral reasoning do judges employ? We attempt to automatically classify moral reasoning with a linear SVM trained on applied ethics articles. The model classifies paragraphs of text in hold out data with over 90 percent accuracy. We then apply the classifier to a corpus of circuit court opinions and find a significant increase in consequentialist reasoning over time. We report rankings of relative use of reasoning modes by legal topic, by judge, and by judge law school. Though statistical techniques inherently face significant limitations in this task, we show some of the promise of machine learning for understanding human moral reasoning.
“Case vectors: Spatial representations of the law using document embeddings” (with Daniel L. Chen), in: Law as Data, Santa Fe Institute Press (2019). Abstract
Recent work in natural language processing represents language objects (words and documents) as dense vectors that encode the relations between those objects. This paper explores the application of these methods to legal language, with the goal of understanding judicial reasoning and the relations between judges. In an application to federal appellate courts, we show that these vectors encode information that distinguishes courts, time, and legal topics. The vectors do not reveal spatial distinctions in terms of political party or law school attended, but they do highlight generational differences across judges. We conclude the paper by outlining a range of promising future applications of these methods.
“Sequential decision-making with group identity” (with Jessica Van Parys), Journal of Economic Psychology (2018). Abstract
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.
“Hickle: A HDF5-based python pickle replacement” (with Danny C. Price, Ellert van der Velden, Sebastien Celles, Peter T. Eendebak, Michael M. McKerns, Eben M. Olson, Colin Raffel, and Bairen Yi), Journal of Open Source Software (2018).
“Analysis of Vocal Implicit Bias in SCOTUS Decisions Through Predictive Modelling” (with Ramya Vunikili, Hitesh Ochani, Divisha Jaiswal, Richa Deshmukh, and Daniel L. Chen), Proceedings of Experimental Linguistics (2018). Abstract
Several existing pen-and-paper tests to measure implicit bias have been found to have discrepancies. This could be largely due to the fact that the subjects are aware of the implicit bias tests and they consciously choose to change their answers. Hence, we’ve leveraged machine learning techniques to detect bias in the judicial context by examining the oral arguments. The adverse implications due to the presence of implicit bias in judiciary decisions could have far-reaching consequences. This study aims to check if the vocal intonations of the Justices and lawyers at the Supreme Court of the United States could act as an indicator for predicting the case outcome.
“What kind of judge is Brett Kavanaugh? A quantitative analysis” (with Daniel L. Chen), Cardozo Law Review Online (2018). Abstract
This article reports the results of a series of data analyses of how recent Supreme Court nominee Brett Kavanaugh compares to other potential Supreme Court nominees and current Supreme Court Justices in his judging style. The analyses reveal a number of ways in which Judge Kavanaugh differs systematically from his colleagues. First, Kavanaugh dissents and is dissented against along partisan lines. More than other Judges and Justices, Kavanaugh dissents at a higher rate during the lead-up to elections, suggesting that he feels personally invested in national politics. Far more often than his colleagues, he justifies his decisions with conservative doctrines, including politicized precedents that tend to be favored by Republican-appointed judges, the original Articles of the Constitution, and the language of economics and free markets. These findings demonstrate the usefulness of quantitative analysis in the evaluation of judicial nominees.
“Judge, Jury, and EXEcute File: The brave new world of legal automation,” Social Market Foundation (2018).
“Emerging tools for a ‘driverless’ legal system: Comment,” Journal of Institutional and Theoretical Economics (2018).
“New Policing, New Segregation: From Ferguson to New York” (with Jeffrey Fagan), Georgetown Law Journal Online (2017).Abstract
Modern policing emphasizes advanced statistical metrics, new forms of organizational accountability, and aggressive tactical enforcement of minor crimes as the core of its institutional design. Recent policing research has shown how this policing regime has been woven into the social, political and legal systems in urban areas, but there has been little attention to these policing regimes in smaller areas. In these places, where relationships between citizens, courts and police are more intimate and granular, and local boundaries are closely spaced with considerable flow of persons through spaces, the “new policing” has reached deeply into the everyday lives of predominantly non-white citizens through multiple contacts that lead to an array of legal financial obligations including a wide array of fines and fees. Failure to pay these fees often leads to criminal liability. We examine two faces of modern policing, comparing the Ferguson, Missouri and New York City. We analyze rich and detailed panel data from both places on police stops, citations, warrants, arrests, court dispositions, and penalties, to show the web of social control and legal burdens that these practices create. The data paint a detailed picture of racially discriminatory outcomes at all stages of the process that are common to these two very different social contexts. We link the evidence on the spatial concentration of the racial skew in these policing regimes to patterns of social and spatial segregation, and in turn, to the social, economic and health implications for mobility. We conclude with a discussion of the implications of the “new policing” for constitutional regulation and political reform.
“On the behavioral economics of crime” (with Frans van Winden), Review of Law & Economics (2012).Abstract
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.
Works in Progress
“Media slant is contagious” (with Philine Widmer and Sergio Galletta).
“More laws, more growth? Theory and evidence from U.S. states” (with Massimo Morelli and Matia Vannoni).
“Mining narratives from large text corpora” (with Germain Gauthier and Philine Widmer).
“Emotion and reason in political language” (with Gloria Gennaro)
“Property taxes and local labor markets: Evidence from staggered property reassessments.”
“Mandatory Retirement for Judges Improved Performance on State Supreme Courts” (with Bentley MacLeod).
“The language of contract: Promises and power in union collective bargaining agreements” (with Bentley MacLeod and Suresh Naidu).
“Algorithms as prosecutors: Lowering rearrest rates without disparate impacts” (with Daniel L. Chen).
“Judicial retention incentives and political polarization” (with Tinghua Yu and W. Bentley MacLeod).