Empirical Legal Studies

"T-Testing" Group Differences at Various Percentiles

While the standard t-test compares means, somewhat less clear is what to do if one wants to test, e.g., whether group differences at various percentiles achieve statistical significance. In a recent Statalist discussion (click here), the specific question posed involves an effort to determine whether “Black-White wealth differences at p10 (10^th percentile) and p90 (90^th percentile)” systematically vary.

Possible options, described in the responses, range from using the qreg and ttest commands.

Taxing Autonomy and Budget Volatility

While complaints from public entities about the policy challenges and harms incident to fiscal uncertainty attributable to budget volatility emerge on an almost annual basis, factors that inform budget volatility remain understudied. Moreover, the particular role that a public entity's taxing autonomy might play remains especially obscure in the empirical literature. However, a recent paper, Does Taxing Authority Help School Districts in Managing Revenue Volatility?, by Laiyang Ke (Ark. St. Univ.--Poli Sci), makes a helpful contribution.

The paper defines taxing autonomy in terms of the degree to which a subnational government (here, public school districts) controls their tax revenues. "Budget volatility" is understood as a function of a school district's "fluctuations and uncertainty in revenue streams and expenditure patterns." Notably, both concepts have particular traction in the US public school district context. First, school districts vary in terms of their taxing autonomy. Second, "previous research has shown that state and local government revenue volatility could lead to expenditure volatility, and uncertain spending fluctuations in school districts may impair education outcomes."

One of the paper's key contributions includes its proposed measure of school district taxing autonomy which draws from school district financial data from the 1998/1999 to 2018/2019 school years. The paper's two-pronged empirical execution features a correlated random effects model as well as D-I-D estimations. The paper's findings emphasize that "school districts with a higher degree of tax autonomy—specifically, those able to change tax rates—experience less current expenditure volatility than districts with lower degrees of tax autonomy." The abstract follows.

"This study examines the impact of tax autonomy on school district expenditure volatility in the U.S., where school districts differ in their ability to levy and adjust taxes, with some having full taxing authority and others relying on fixed appropriations or lacking control over tax rates. This research introduces a comprehensive index to measure taxing autonomy across districts and develops an ordinal classification based on this framework. Using finance data from 1998/1999 to 2018/2019 and difference-in-differences, the study investigates whether districts with higher tax autonomy experience lower expenditure volatility compared to those with limited or no tax authority. The findings reveal that districts with greater tax autonomy, particularly those able to adjust tax rates, exhibit less volatility in current expenditures. Additionally, during the Great Recession, districts with higher autonomy faced smaller cuts in current expenditures by increasing tax revenues, which helped protect educational inputs like pupil-teacher ratios."

With Apologies...

Now that some nettlesome technical web domain hosting/forwarding issues have finally been resolved, regularly blog posting will resume.

Serial Correlation Testing for Panel Data

When working with panel data, testing for serial correlation is common—if not expected—diagnostic. To this end, a new user-created command (v.1.0.3) – xtdpdserial – functions as a postestimation command for many (though not all) standard commands (e.g., regress, xtreg, etc.).

For those interested, installation download sites include: ssc install xtdpdserial as well as: net install xtdpdserial, from(http://www.kripfganz.de/stata/)

On a distinct, though related, point, Prof. Kripfganz's (Exeter Business School--Econ. Dept.) website includes a very helpful array of related Stata user-commands (click here).

SCOTUS Database--Updates

The Supreme Court Database, motivated in part by Prof. Harold Spaeth (1930-2017), remains a leading source for Court and judicial decisionmaking scholars. Indeed, "virtually all systematic analyses of the contemporary Supreme Court and its members have relied on it." Given the Database's scholarly prominence, I wanted to pass along a few, brief updates.

First, the Database, which includes information (247 variables) on every SCOTUS decision since the "beginning" (1791), has now been updated to include information on all SCOTUS cases through the 2023 Term. Second, the Database's "home" has migrated from Washington University and, starting with the current (Oct. Term 2024), will be archived at Penn State University (click here).

RSAs & Chapt. 11

While relatively uncommon prior to the mid-2000s, as Figure 1 (below) illustrates, restructuring support agreements ("RSAs") "have become a regular fixture in Chapter 11 proceedings." RSAs typically lay out a pre-agreed reorganization plan and are usually in place prior to a formal Chapter 11 filing.

Figure 1: RSAs in Cases with Over $100M Assets

While RSAs and their rise in Chapter 11 proceedings remain the subject of sustained debate in normative and theoretical literatures, in a recent paper, Restructuring Support Agreements: An Empirical Analysis, the authors, Katherine Waldock (Columbia), Anthony Casey (Chicago) & Frederick Tung (BU), assess RSAs from an empirical lens. In their effort to "study how RSA contracts evolve over time," the authors lever PACER data archives and focus on more than 400 non-financial Chapter 11 filings that involved more than $100M in declared assets during just over the past two decades. While the analyses are entirely descriptive, key trends are identified in the abstract (below).

"Restructuring support agreements (RSAs), or contracts that commit bankruptcy parties to supporting a plan of reorganization that will conform to certain requirements, are now a common feature of Chapter 11. Parties utilize these agreements in nearly half of all large cases. While prior literature has debated the normative value of RSAs, we take an empirical approach to look at what provisions the parties include in these agreements and how those provisions have changed over time.

Our analysis looks at all RSAs associated with large bankruptcies through the end of 2020. We characterize the types of firms with RSAs, the parties involved, and the language contained within their key sections. A significant majority of RSAs are entered into on or before the petition date. Common provisions relate to the bargaining process during the case and often commit the parties in advance to certain elements of the restructuring plan. Provisions related to control such as debtor milestones have become more frequent in the last ten years of the sample."

CELS 2024 -- Registration

Registration is now open for 2024 CELS on November 8–9, 2024, at Emory University School of Law in Atlanta, Georgia. The registration link, hotel information, and program may be found here.

CELS is an interdisciplinary gathering that draws researchers from across the United States and the world, bringing together scholars in law, economics, political science, psychology, and other fields who are interested in the empirical analysis of law and legal institutions. 2024 CELS is co-sponsored by Emory University School of Law and the Society for Empirical Legal Studies.

Data on Homicide Plea Bargaining in Wisconsin

While various criminal law law theories and issues are increasingly subjected to data, critical wrinkles remain largely "hidden in plain sight." One key issue involves how prosecutors engage in--and deploy--prosecutorial discretion. A deeper and more granular understanding of how prosecutorial discretion cashes out when it comes to plea bargaining remains both pressing and enormously consequential.

Unsurprisingly, data limitations pose one critical barrier to studies on how plea bargaining actually works on the ground. One recent and helpful contribution, Plea Bargaining in Homicide Cases: An Empirical Exploration in One State, involves data on one crime type (homicide), from one state (Wisconsin), between 2005-2018.

Under Wisconsin law, defendants alleged to have committed the most serious homicides can be charged with a Class A felony. Homicide defendants charged with a "lesser" crime, a Class B felony, confront a comparatively lighter sentence. As the authors, Michael O’Hear (Marquette) & Darren Wheelock (Marquette—social & cultural sciences), note, "one common plea deal is for the prosecutor to reduce the charge from a Class A to a Class B homicide in return for the defendant’s surrender of his or her right to a trial." And their paper explores "which Class A defendants get this big break [a Class B felony charge] and why." To do so, the authors examine a set of a 293 cases where the defendant was initially charged with a Class A homicide and then pled guilty. In 198 of these cases, the defendant was permitted to plead guilty to a reduced Class B charge. In the remaining 95 cases, the defendant confronted the original Class A charge (despite having plead out).

Their findings include some possible surprises. One includes the apparent lack of statistical salience of defendant or victim race. Factors that did achieve statistical significance, however, include whether a defendant was defended by a court-appointed lawyer. The paper's abstract follows.

“Even the most serious criminal cases are often resolved through plea bargaining, with potentially dramatic consequences for the sentence ultimately imposed. In Wisconsin, the most serious type of homicide is a Class A felony, which results in a mandatory life sentence. However, many individuals who are initially charged with the Class A offense are given the opportunity to plead guilty to a lesser Class B offense, which does not trigger any mandatory minimum sentence at all. In this Article, we compare the characteristics of the cases in which the defendant pleads guilty to a Class A offense (a relatively harsh outcome) with the characteristics of the cases in which the defendant pleads guilty to a Class B offense (a relatively lenient outcome). We find statistically significant differences between these two sets of cases in such areas as county of prosecution, appointed versus private-pay defense lawyer, and gender of defendant, victim, prosecutor, and defense lawyer.”

From PACER to SCALES: Access to Federal Court Data

Even basic research questions on Art.III courts and federal litigation typically confront consequential barriers to needed data (e.g., federal court records). While the federal government's PACER platform and a few third-party operations provide some relief, both possess their own respective shortcomings and limitations (including, but not limited to, cost).

Initial fruits of a new collaborative effort based at Northwestern University, SCALES, have emerged recently. SCALES is an online platform that seeks to "assemble federal court records, systematically organize them and extract key information, and—most importantly—make them freely available to the public." What exists now includes access to information and records for all federal cases initiated in 2016 and 2017. As SCALES' mission includes "to make court records freely accessible to the public," the platform aspires to, in the future, expand coverage to include "all years."

In a recent paper, The SCALES Project: Making Federal Court Records Free, the authors, David Schwartz (Northwestern) et al., explain the SCALES initiative in greater detail. As well, the project also benefits from its own website (click here).

Modeling Panel Data: More Options

While panel data bring with them important benefits, standard modeling challenges necessarily lurk. For example, one can estimate fixed-effects models to control for endogeneity, but at the potential cost of forgoing studying time-invariant variables. Alternatively, one could estimate a random-effects model, but introduce potential bias.

When confronting such a trade-off with panel data, my initial default position is to run two separate models (FE and RE) and assess whether any key results are model-specific. However, other empirical strategies have emerged relatively recently. While I am unsure whether similar coding possibilities exist in R, SAS, or SPSS, Stata's correlated random-effects model (CRE) allows one to estimate both time-varying and time-invariant effects while controlling for endogeneity that arises when variables correlate with unobserved panel-level characteristics. That is, CRE models allow researchers to, in essence, "have it both ways at the same time" by controlling for any endogeneity by including the panel means of time-varying variables as additional controls in the model. For a brief introduction and helpful examples and videos, click here, here, and here.

Initial Data on Trump Judges' Performance

Persistent general public complaints about Trump's Art.III judicial picks include that they are "unqualified" and were drawn from an unduly narrow pool of traditionally plausible candidates. Of course, and as they say, "where data are scant, 'opinions' bloom." In a recent recent paper, How Different Are the Trump Judges, Stephen Choi (NYU) & Mitu Gulati (UVa) bring some initial data to these prevailing conventional wisdoms. The paper sets out to examine how Trump-appointed judges "have done compared to other presidents' appointments." Their findings challenge prevailing and widely-held assumptions.

The paper includes all active circuit court judges between January 1, 2020, through June 30, 2023. While the analyses are largely descriptive, the paper explores judge performance in terms of three key metrics: productivity, influence, and the degree to which a judge is willing to "deviate" from other judges, and particularly from "those closest to them in terms of political ideology." As the paper's abstract (below) implies, and contrary to public predictions of underperformance, the authors find that "Trump judges outperform other judges."

"Donald J. Trump’s presidency broke the mold in many ways, including how to think about judicial appointments. Unlike other recent presidents, Trump was open about how 'his' judges could be depended on to rule in particular ways on key issues important to voters he was courting (e.g., on issues such as guns, religion, and abortion). Other factors such as age and personal loyalty to Trump seemed important criteria. With selection criteria such as these, one might expect that Trump would select from a smaller pool of candidates than other presidents. Given the smaller pool and deviation from traditional norms of picking 'good' judges, we were curious about how the Trump judges performed on a basic set of measures of judging. One prediction is that Trumpian constraints on judicial selection produced a different set of judges that underperform compared to judges appointed by other presidents. Using data on active federal appeals court judges from January 1, 2020 to June 30, 2023, we examine data on judges across three different measures: opinion production, influence (measured by citations), and independence or what we refer to as “maverick” behavior. Contrary to the prediction of underperformance, Trump judges outperform other judges, with the very top rankings of judges predominantly filled by Trump judges."