Empirical Legal Studies

Pairwise Mean Comparison Command (pwmc)

An updated version of a command -- pwmc -- is now available for Stata users via SSC and is discussed (and described more fully) on Statalist (here). The command performs pairwise comparisons of means with unequal variances.

As the discussion makes clear, the pwmc command differs from other, related commands with the mcompare() option, such as margins with the pwcompare option, as it (pwmc) does not assume equal variances where the alternatives typically do.

Experimental Designs with Unequal Variance Across Treated and Control Data

While I generally do not dabble all that much in experiments, I am familiar enough with the literature to know that, aside from general statistical power concerns, ex ante questions about how many subjects should be in the treated and control groups elude easy answers. One variant to this question considers when a researcher should have unequal numbers of units in the treatment and control groups. To be sure, such practical factors as a treatment's cost and/or disruption surely play some role. A recent discussion on Andrew Gelman's blog (here) takes up more complicated issues relating to how best to maximize the statistical efficiency of treated and control subjects' distribution and describes how simulations might assist.

Northwestern Univ. L. Rev.'s 7th Annual "Empirical Issue": Call For Papers

If evidence of empirical legal studies’ growth was not already obvious enough in the peer-reviewed world, some movement in traditional student law reviews also evidences a turn toward ELS as well. And no more so than perhaps the Northwestern University Law Review which is pleased to announce its seventh annual issue dedicated to empirical legal scholarship, to be published in Spring 2026. The student editors "seek to bring cutting-edge, interdisciplinary, empirical work to our legal audience, and enrich our understanding of the law, legal actors, and legal doctrine through robust and reliable examination of qualitative, quantitative, and mixed-method data."

The student editors note that the "exclusive submission window for the Vol. 120 Empirical Issue of the Northwestern University Law Review will open on April 1, 2025, and run until April 28, 2025. A subset of submissions will be selected to move forward to peer review. The Law Review promises make every effort to notify authors of rejection or of advancement to peer review by June 30, 2025. Final publication decisions will be issued by August 10, 2025." Submissions must land in the 15,000-30,000 word count range.

For those interested, additional details are found here.

CELS 2025--Save the Date

At the request of the CELS 2025 conference co-organizers, David Hyman, Neel Sukhatme, and Josh Teitelbaum, I am delighted to pass along the following information:

Conference on Empirical Legal Studies

24-25 October 2025

 

The 19th Annual Conference on Empirical Legal Studies (CELS) will be held on 24-25 October 2025, at Georgetown University Law Center in Washington, D.C.

 

More conference information here.

 

CELS 2025 is co-sponsored by Georgetown Law and the SELS.

Public Input Data on SEC Rulemaking

Incident to its rulemaking activities, the SEC, similar to other agencies, solicits public input on proposed rules and reduces to formal memoranda SEC meetings with industry advocates (and lobbyists) seeking to influence final rules. The emergence of large language models (LLMs) enables scholars to assess possible relations between both forms of public input (comments and memoranda) and final rules. And this is precisely what Adam Pritchard (Michigan) et al., set out to do in their recent paper, SEC Rulemaking: An Empirical Analysis of Comments and Memoranda.

The paper draws from 482 rules, 1018 releases, 84,828 comments, and 5,700 memoranda, which are then linked (through Westlaw and Lexis) to any subsequent trial and appellate court activity. These data were then analyzed for "sentiment" with leading LLMs, including OpenAI, specifically GPT-3.5. A focus on rulemaking activity, at least with respect to SEC's proposed rules, is warranted owing to, at the very least, a detectable trend towards increased public commentary over time as the figure (below) implies.

Among this paper's key findings include that while comment volume and sentiment systematically inform final SEC rules, unsurprisingly, this influence varies across different types of commentators. Similarly, direct advocacy engagement with SEC officials--indicated by SEC memoranda--"is an important marker of proposed rules’ adoption, and if adopted, judicial challenge and survival." An excerpted abstract follows.

"... We find that public inputs contain valuable information regarding the probability that a rule is adopted, challenged, and survives judicial scrutiny. Longer average comments and greater disagreement among commenters correlate with a lower likelihood of rule survival, whereas an increase in the number of suggestions correlates with a greater survival likelihood. Rules affecting issuers are less likely to be adopted and more likely to face a court challenge, while rules affecting broker-dealers are more likely to survive judicial scrutiny. As for specific issues, comments emphasizing the burden on competition correlate with a lower likelihood of rule survival, comments raising constitutional questions reduce both the likelihood of rule adoption and survival conditional on a judicial challenge. Comments by academics have no statistically significant effect at any stage of the process...."

14th Annual Workshop on Research Design for Causal Inference

Once again hosted at Northwestern's Pritzker School of Law, Chicago, IL, the 14th annual summer Workshop on Research Design for Causal Inference will include distinct--though related--"Main" and "Advanced" workshop sessions.

Main Workshop:  Monday – Friday, July 28-August 1, 2025; Advanced Workshop:  Sunday – Wednesday, August 3-6, 2025.

Main Workshop Topics/Faculty: Introduction to Modern Methods for Causal Inference:  Donald B. Rubin (Harvard), Matching and Reweighting Designs for "Pure" Observational Studies: Jens Hainmueller (Stanford), Panel Data and Difference-in-Differences: Jens Hainmueller (Stanford), Regression Discontinuity: Heather Royer (UCSB), and Instrumental Variable Methods: Tymon Sloczynski (Brandeis).

Advanced Workshop Topics/Faculty: Primer on machine learning approaches to prediction: Christian Hansen (Chicago), Applications of machine learning to causal inference:  Christian Hansen (Chicago), Advanced Difference-in Differences: Andrew Goodman-Bacon (Fed. Reserve Bd.), and Advanced Instrumental Variables: Peter Hull (Brown).

In person-registration is limited to 125 participants for each workshop (there will also be a Zoom option as well).

For further information and registration: (click here).

Binary Choice Models: Logit/Probit/LPM/LDM?

In the "old days," debates about binary choice models (where the dependent variable= [0,1]) typically centered on probit v. logit. More recent discussions of binary choice models, however, have broadened to include various linear regression possibilities, including LPM and LDM models (click here for one recent example). As discussions of binary choice model options continue to evolve--and some of these discussions evidence (or reflect) variation in norms across fields (e.g., poli sci, econ, epi, etc.)--so to do resources explaining the competing costs and benefits of various empirical strategies. For examples of a few helpful resources, click here and here.

2025 ICPSR Summer Quant. Methods Programs/Workshops

While best known as a leading social science data archive, one of the ICPSR's under-appreciated assets includes its suite of summer programs/workshops. For more than 50 years, the ICPSR has conducted a range (90+) of courses/seminars/workshops, principally based at the University of Michigan-Ann Arbor campus, that spans the arc of quantitative methods as well as a diverse array of social scientific fields. While I recommend in-person programs where possible, online options also exist. Workshop formats vary as well, ranging from 3-5 day courses to 4-week offerings. These offerings can be invaluable for graduate (and undergraduate) students as well as faculty. For those interested, links follow regarding additional registration (click here) and scholarship (click here) information.

Selection Effects & Prof. George Priest (1947-2024)

A prior post noting Prof. George Priest's passing predictably emphasizes his enduring influence on the topic of selection effects in litigation. Among the many papers scheduled for publication in 42:3 Yale J. on Reg. (2025) and presented at a symposium (or 'Festschrift') just months before Priest's recent passing includes 'The Selection of Disputes' at Forty, by Daniel Klerman (USC) & Yoon-Ho Alex Lee (Northwestern). The Klerman & Lee paper (abstract below) complements, contributes to, and reflects Priest's enormous scholarly legacy.

“Priest and Klein's ‘Selection of Disputes for Litigation’ has been one of the most influential law review articles of all time. This Article reviews its contribution to legal scholarship. Priest and Klein's central and enduring contribution is the recognition that some cases are more likely to settle than others. It follows that litigated disputes are not a random sample of all disputes. This basic insight is true under nearly all litigation models and is also confirmed by a large body of empirical evidence. Priest and Klein's article is also famous for its prediction that, under certain conditions, the plaintiff trial win rate will tend to approach fifty percent. That prediction, however, is not supported by most other litigation models and has received only modest support from the empirical literature. Our citation analysis also suggests that 'The Selection of Disputes for Litigation' is one of the rare articles whose importance was both recognized almost immediately and whose influence has continued to grow over several decades.”

Contingent Fees & Access to Justice: Data From NYC

While "contingent fees are certainly not without their problems," recent analyses of NYC data imply that they contribute to increased equity in terms of access to justice "between rich and poor and between whites and racial minorities."

It turns out that since 1957, New York courts require contingent fee lawyers to file a "closing statement" for all cases involving "personal injury, property damage, or wrongful death." While typically confidential, these closing statements also include settlement amounts.

In Contingent Fees and Access to Justice, the authors, Eric Helland (Claremont McKenna--econ) and Dan Klerman (USC), reports results from their analyses of closing data "from 2003–2013 from the First Appellate Division, which has supervisory authority over lawyers in Manhattan and the Bronx."

To be sure, even these rich data (especially the settlement data) are not without limitations. After all, the data only involve eleven years, two NYC boroughs, and certain tort claims. Any data limitations aside, key findings include that "the poor make claims and recover at higher rates than the rich, and African Americans and Hispanics make claims at higher rates than whites." The paper's abstract follows.

"Using a unique dataset from New York City, this article shows that contingent fees seem to have, at least partly, solved the access-to-justice problem in tort litigation. People in poorer zip codes, in fact, make legal claims at a higher rate than those in wealthier areas. This greater propensity to seek and achieve legal redress is partly explained by the fact that poor people are more likely to be injured. In addition, the disparity between rich and poor manifests itself solely in smaller claims. For larger claims, rich and poor make claims at roughly the same rate. African Americans and Hispanics also make claims at a higher rate than whites, and this difference also attenuates when one controls for accident rates or focuses on larger claims."

Transforming "Vertical" Regression Output Into a "Horizontal" Table Format

A recent Statalist discussion (here) walks readers through the coding necessary to transform the following (traditionally "vertical") regression output:

-----------------------------------------
                               mpg       
-----------------------------------------
Weight (lbs.)                      -0.006
                       [-0.009    -0.003]
                                     0.00
Displacement (cu. in.)              0.006
                       [-0.014     0.025]
                                     0.57
Turn circle (ft.)                  -0.139
                       [-0.496     0.219]
                                     0.44
Intercept                          43.588
                       [33.688    53.489]
                                     0.00
Number of observations                 74
-----------------------------------------

into a "horizontal" output format:

-------------------------------------------------------------
                       Coefficient       95% CI       p-value
-------------------------------------------------------------
Weight (lbs.)               -0.006 [-0.009    -0.003]    0.00
Displacement (cu. in.)       0.006 [-0.014     0.025]    0.57
Turn circle (ft.)           -0.139 [-0.496     0.219]    0.44
Intercept                   43.588 [33.688    53.489]    0.00
-------------------------------------------------------------

Anticipated and Unanticipated Costs and Benefits of Mandatory Insider Trading "Blackout" Requirements--Evidence From the EU

Many countries, including the U.S., impose closed periods (a.k.a. "blackouts") on corporate insider trading for a period of time preceding earnings announcements. Such regulations on insiders' trading activity are typically justified due to insiders' unique role and position in capital markets. Whether these regulations achieve their stated goals remains relatively understudied.

In July 2016, all E.U.-listed firms were required to impose a 30-day blackout period on corporate insider trading prior to interim or fiscal year-end earnings announcements. The variation across firms prior to the July 2016 rule created a quasi-natural experiment to assess the new (2016) rule's impact on "insider trading activity, profitability, and market consequences."

In The Impact of Mandatory Closed Periods on Corporate Insider Trading, Francois Brochet (BU—Accounting) et al. report results from a difference-in-differences strategy that compares 30 compare countries that adopted mandated closed periods after July 2016 (treatment group) with companies that already had such policies (control group) in place prior to 2016. Notably, what the authors report is that their results highlight that uniform closed periods "deter informed trading but may unintentionally harm market liquidity and information quality." The abstract follows.

"The Market Abuse Regulation (MAR), implemented in July 2016 across the EU, mandates uniform closed periods, prohibiting insider trading 30 days before earnings announcements. We find a significant reduction in insider trading during these periods in treated countries without prior mandates. Although some trades still occur, they do not appear to be driven by private information. However, treated insiders experience an increase in compensation. Furthermore, information asymmetry rises before earnings announcements, and treated firms experience a decline in institutional ownership. Overall, the study suggests that mandated closed periods do not lead to a wealth transfer from insiders to outsiders, nor do they enhance the information environment. These results underscore the limitations of regulatory one-size-fits-all measures in curbing corporate insider trading."

Empirical Evidence, Courts, and the 4th Amendment

Over time, and since Katz (1954), Justice Harlan's two-part approach has largely guided how courts construe privacy protections in the 4th Amendment context. As the second part of Harlan's approach involves what many consider to be amenable to empirical social science, a growing body of scholarship sets out to mark the boundaries of where, as Christine Scott-Hayward (CSU-Long Beach--criminology) et al., note, "society [reasonably] expects privacy." Increasingly, defense attorneys increasing turn to such research findings in their briefs and pleadings to courts.

While entirely descriptive, The Role of Empirical Scholarship in Fourth Amendment Privacy Jurisprudence levers 13 leading empirical papers in the 4th Amendment space and then identifies and dives deeply into the relatively (and absolutely) small number of judicial opinions that cite to the leading empirical work. The authors' main takeaway is one of frustration with the courts' reluctance to engage with empirical legal scholarship relevant to the question of societal expectations of privacy in the search and seizure context. (To be sure, any judicial reluctance to engage with empirical legal research is merely a sub-set of, for better or worse, courts' collective benign neglect of legal scholarship generally.)

In the end, the paper's findings (and the authors' frustration) both bump into and provide indirect support for a larger point that my Cornell colleague (and friend) Jeff Rachlinski described years ago (2011): “Empirical legal scholarship . . . is not the same as evidence-based law. Empirical legal scholarship resides primarily in the academy.” That relevant empirical legal scholarship will migrate from the academy and into judicial opinions is not a given. Moreover, any such migration will likely continue to vary across legal doctrines and case types.

Options for Small and Unbalanced ("Rare Events") Data Sets

While relogit is a standard "go-to" Stata command when a data set includes "rare events," a threat of bias is enhanced if rare events derive from a "small sample." While bootstraping may be an option for some, a recent Stata listserv discussion (here) emphasizes a pair of commands -- firthlogit and exlogit -- that might make sense for others. Relatedly, Prof. Williams (ND-sociology) helpfully delves into the challenge a bit more comprehensively (here).

George L. Priest (1947-2024)

When my former mentor, Ted Eisenberg (1947-2014), periodically reminded me that "it is difficult to overestimate the influence of selection effects in litigation," his point of reference was a seminal 1984 article, The Selection of Disputes for Litigation. Sadly, one-half of the "Priest-Klein" hypothesis, George L. Priest, the Edward J. Phelps Professor of Law and Economics at Yale Law School, passed away recently.

Yale Law School's memorial notice echoes what Ted emphasized for decades, "[Priest's] 1984 article (with UCLA economist Benjamin Klein) “The Selection of Disputes for Litigation,” revolutionized theories of the common law and engendered decades of inquiry regarding the probability of litigation outcomes. The article presented what became known as the ‘Priest-Klein hypothesis’ and introduced the concept of selection bias into legal literature.” Empirical scholars, and many others, mourn Prof. Priest's passing.

Job Opportunity of Note (& ELS' Expanding Footprint)

While my proverbial "gaze" typically fixes on scholarship, "ELS-adjacent" issues periodically catch my eye as well. In this spirit, I recently came across a "real-world" (that is, non-academic) job opportunity that might interest statistics-savvy post-docs (and others). The job posting also implies the possibility that ELS is expanding beyond its traditional academic "silo."

What I learned from the job posting includes that: 1) an "Quantitative Research Unit" operates within (and services) the New Jersey judiciary, and 2) this Unit is actively hiring a "Research Analyst Manager" (click here for a direct link to the job posting). According to the [NJ] Judiciary Research Manager, the Unit is looking to hire "an individual to lead the team doing research across the Civil Court and Family Court practice areas. This is a great opportunity to lead applied work in the Law and Courts arena that has near-immediate impact."

"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."

Judicial Decisionmaking "In the Time of COVID-19"

As the scope and reach of the COVID-19 pandemic's impact remain difficult to over-state, it will surprise few to learn that the full reach of public health officials' legal/statutory claims did not escape judicial scrutiny. What is not yet well understood, however, is a more granular understanding of how legal decisions informed particular legal doctrines, including those involving religious liberty and statutory interpretation in the public health silo. To this end, a recent paper by Michelle Mello et al., Judicial Decisions Constraining Public Health Powers During COVID-19: Implications for Public Health Policy Making, takes an initial empirical cut.

Deploying "legal epidemiology," the paper analyzes 112 federal and state courts decisions between March 2020 and March 2023 that involve successful plaintiff challenges to federal, state, or local pandemic-motivated "mitigation" orders. The authors argue that the 112 cases reveal "areas of profound instability in how courts analyze challenges to exercises of public health legal powers." The disruptions were particularly acute in the religious liberty and statutory authority areas. The findings make clear that going forward assertions of public health authority will unlikely benefit from "broad [judicial] deference." The paper's abstract follows.

"Public health legal powers are increasingly under pressure from the courts in the United States. During the COVID-19 pandemic, individuals and organizations successfully challenged many community mitigation orders (for example, mask mandates, vaccination mandates, and restrictions on gatherings), demonstrating the legal vulnerability of disease control measures. Analyzing 112 judicial decisions in which the plaintiff prevailed from March 2020 through March 2023, we examined the ways in which courts constrained public health powers during the COVID-19 pandemic. We found that in these 112 decisions, courts shifted how they analyze religious liberty claims and reviewed challenges to the exercise of statutory powers by health officials in novel ways. We discuss implications for public health policy going forward, and we recommend ways in which legislatures and health officials can design policies to maximize their prospects of surviving legal challenges."

A Brief Introductory Primer on DiD Models

While treatment effect research designs continue to grow in popularity in various empirical literatures, too many researchers (as well as non-specialists) remain unclear about their objectives. To this end, a recent addition to Stata's instructional video archive includes a brief primer (here) on Stata's DiD commands as well as an easy-to-understand visual explication of what treatment effects models set out to accomplish. This particular video (one in a series) focuses on Stata's two core DiD commands--didregress and xtdidregress;the former command is appropriate for repeated cross-sectional data and the latter for panel data.

Municipal Revenue Generation and Enforcement Incentives

While an emerging literature explores possible links between government revenue generation and enforcement incentives, much of the literature is theoretical. Even though conflicts of interest are rife in this context, empirical testing is comparatively scarce.

In a recent paper, Agency Incentives and Disparate Revenue Collection: Evidence from Chicago Parking Tickets, Elizabeth Luh (SEC) et al. explore whether a recent increase in motor vehicle registration fees as well as related fines for non-compliance in Chicago informed the Chicago Police Department's ("CPD") parking enforcement patterns. By increasing both the cost of motor vehicle registration and the fine for non-compliance in 2012, the City of Chicago simultaneously made vehicle registration compliance more expensive and enforcement more profitable relative to other parking fines.

The paper executes a difference-in-differences (DiD) strategy to assess the relative change in sticker enforcement across various types of neighborhoods, "focusing on Black versus non-Black areas due to the purported claims of disparate impacts from the general public." The paper reports that CPD vehicle registration enforcement increased by nearly 2,500 tickets in Black relative to non-Black neighborhoods per year. It also finds that "non-sticker ticket enforcement significantly increased by nearly 12,000 tickets, despite the non-sticker fines remaining largely the same." The authors interpret these patterns as evidence consistent with a broader revenue collection effort. The paper's abstract follows.

"We leverage a sharp 2012 parking fine increase for failing to purchase vehicle registration to examine disparate ticketing patterns across enforcement agencies in Chicago. Using an event-study framework, we find that Chicago police increased their enforcement of car registration non-compliance in Black relative to non-Black neighborhoods, with no observed disparate response for non-police enforcement agencies. This disparity is unexplained by differences in non-compliance and is instead driven by departmental revenue incentives and lower marginal search costs in Black neighborhoods. Disparate enforcement also exacerbated existing gaps in financial instability, including increased rates of ticket non-payment and bankruptcy filings in Black neighborhoods."

Farmland Property Transfers Across Generations in the Mid-20th Century

Farmland property succession is a central issue in agricultural policy, and succession in the agricultural sector is quickly becoming even more salient owing to the number of farmland transfers expected in the coming years. Nevertheless, and owing to a paucity of data, the scarcity of research on farmland property transfers across generations is, according to Adrian Haws (Cornell-Econ.) et al., “jarring.”

In their recent paper, Who (Actually) Gets the Farm? Intergenerational Farm Succession in the United States, the authors link Census microdata for census records for 1.3 million farmers in 1900 or 1910 to their children’s succession outcomes up to 40 years later. Such a data set provides glimpses into which farmers’ children, if any, took title to farmland property.

A within-family identification strategy yielded some notable findings, including that daughters were rarely successors and evidence of a small and monotonic birth order effect among sons. An excerpted abstract follows.

“Farm succession is a central issue in agricultural policy. Yet while many studies explore succession planning, little is known about how farms are actually transferred. … Using a within-family identification strategy, we find that first-born sons are slightly more likely than their younger brothers to be successors while their parents are working aged. However, birth order is not predictive of who receives the farm when parents are older or deceased. For later farm transfers, sons who were previously tenant farmers are much more likely than their brothers to be successors, possibly because they are better prepared. Fewer than one-fifth of farmers transfer their farm to any son….”

Birth Order Effects in Developing Countries

While birth order effect studies' popularity has waned a bit over time, most focus on developed countries. The consistently negative results in developed countries imply that "… the later a child is born within a family, the worse their adult economic outcomes relative to their earlier- born siblings” incident to potential intra-familial (small- or capital D) discrimination. By contrast, the comparatively smaller number of studies that use data from developing countries yield unstable results.

A recent paper, Birth Order Effects on Education: Insights from Low- and Middle-Income Countries, sets out to fill a scholarly void involving involving emerging nations. In it, Silvia Mendolia (Univ. Turin) et al., estimate mothers’ fixed effects models assessing the impact of birth order on schooling and school attendance. The models exploit "almost 1.8 million observations gathered from the Demographic and Health Survey (DHS) to measure birth order effects on children’s educational outcomes in 35 developing countries, between the mid-1980s and 2020."

Overall, the paper finds evidence of negative birth order effects on educational attainment in 32 out of 35 developing countries. Notably, the paper finds that in most developing countries, “overall birth order effects do not vary by gender, family wealth, location or over time.” The paper's (excerpted) abstract follows.

"... To the best of our knowledge, this is the first study analyzing birth order effects in a comprehensive set of developing countries. In developing countries, families tend to be relatively large and within-family resources scarce. The DHS contains harmonized data and variables for all countries, providing a picture of birth order effects that is consistent across the developing world. Using mothers’ fixed effects models, we estimate the impact of birth order on standardized years of schooling and school attendance, exploring non-linearities in birth order effects, as well as heterogeneous effects by gender, socio-economic characteristics and over time. Overall, we find negative birth order effects on educational attainment in 32 out of 35 countries. Consistent with this, we find that the probability of not being in school increases with birth order. We find that in most countries, overall birth order effects do not vary by gender, family wealth, location or over time...."

Improving Stata Performance When Processessing "Large" Amounts of Data

"Big Data" projects that involve processessing larger and larger data sets impose increased loads onto soft/hardware performance. When it comes to moving large amounts of data through various Stata commands, a recent discussion (here) identifies a few quite easy methods for improving computational performance.

Bonferroni Correction & T-Tests

While typically under-appreciated, multiple comparisons typically warrant consideration of whether a Bonferroni correction is necessary to reduce the risk of generating "false-positive" results. A recent discussion in the multiple t-tests context (here) includes suggested Stata coding on how one can incorporate a Bonferroni correction in a command for a series of t-tests.

Data on Police Separations

While national news and policymakers focused on a surge in police separations in many large city departments and identified social unrest incident to the George Floyd murder as a likely contributor, much of the public discussion unfolded amid a paucity of data. By contrast, a recent paper by Ben Grunwald (Duke), A Large-Scale Study of the Police Retention Crisis, sets out to submit prevailing conventional wisdoms to data.

To do so, the paper exploits data drawn from 6,800 local law enforcement agencies across 15 states that, taken together, serve one-half of the U.S. population. As the Figure (below) descriptively implies, the acceleration in police officer separations beginning in the summer of 2020 was not off-set by any increasing in police hiring.

While what to infer from these two trend lines is not obvious, the paper lands on two main descriptive findings. First, "the increase in separations in IPED agencies after the summer of 2020 was smaller, later, less sudden, and possibly less pervasive than the retention-crisis narrative suggests. All told, the cumulative impact on the labor force by the end of 2021 was just 1%." Second, these "[a]ggregate figures, however, mask variation at the agency level. As I show, a substantial minority of large agencies meaningfully shrank by the end of 2021.”

CELS 2024 -- Call For Papers Deadline Extended

EXTENDED Call For Papers Submission Deadline: Wednesday, July 31, 2024 (11:59 PM Eastern Daylight Time).

To submit a paper, please click here. There is no charge for submissions.

Additional CELS 2024 information is found here.

JLEA

Further evidence of a healthy, robust, and growing scholarly field is provided by the emergence of an important new online journal in the ELS space. Co-edited by Christoph Engel (Max Plank Institute), Lee Epstein (USC), Daniel Klerman (USC), and Eyal Zamir (Hebrew) and published by Sage, the Journal of Law & Empirical Analysis (JLEA), a peer-reviewed, open access journal, "aspires to spearhead the empirical turn in law by publishing cutting-edge research that is accessible and of interest to the wider legal community. JLEA aims to attract papers written by social scientists and legal academics alike that have clear legal relevance, that appeal to an international audience, and that combine theory and empirics, whether to draw inferences or to offer rich description."

Information on its inaugural issue can be found here.

CELS 2024 -- Call For Papers

2024 Conference on Empirical Legal Studies

NOVEMBER 8-9, 2024

Call for Papers

Emory University School of Law

The 2024 Conference on Empirical Legal Studies (CELS) is now accepting submissions. CELS will consider empirical papers spanning all areas of empirical legal studies. Authors are encouraged to submit works-in-progress; however, submissions should be completed drafts that include principal results. Submitted papers must be unpublished (and expected to be unpublished at the time of the conference).

The [Extended] Paper Submission Deadline is Wednesday, July 31, 2024 (11:59 PM Eastern Daylight Time). There is no charge for submissions.

To submit a paper, please visit the CELS 2024 paper submission website. We encourage preregistration of all experiments, and we encourage all authors to make their data, scripts, and code publicly available upon publication to the extent your methodology permits. We welcome replication studies and papers that develop and demonstrate empirical methods.

Papers are selected through a peer review process. If accepted, authors will have an opportunity to submit a revised draft prior to the conference for presentation and discussion. Discussion at the conference includes assigned commentators and audience questions. Accepted papers will be made available to all conference participants.

About: Emory University School of Law and the Society for Empirical Legal Studies (SELS) are pleased to host the 18th Annual Conference on Empirical Legal Studies (CELS) on November 8–9, 2024, in-person, at Emory University School of Law in Atlanta, Georgia, U.S.A. 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, statistics, and other fields who are interested in the empirical analysis of law and legal institutions.

High Court Data From 'Down Under'

Thanks to Zoe Robinson (ANU) and Patrick Leslie (ANU), a database of all cases decided by the High Court of Australia between 1995 and 2020, is now publicly-accessible (click here). The database was consciously designed to supply data on cases heard by the High Court of Australia as well as the justices that hear the cases and facilitate empirical analyses.

To this end, the database contains "over 315 variables for every case argued before a panel of at least two justices on the Australian High Court between 1995 and 2020, as well as variables on key identity information about the justices hearing the cases. Examples include justice votes, justice coalitions, legal issues in the case, the parties to the case, justices who granted special leave to appeal, justice gender, and justice ideology, amongst many others." Those intrigued might want to skim the dataset's codebook to get a more granular feel for what's included in the data.

Finally, and notably, the researchers strategically constructed the High Court database with an eye towards facilitating comparative work with the U.S. Supreme Court database in terms of variable categorization and naming.

Federal Court Judicial Opinons and Judge Gender

For years (decades, even), legal and political science literatures have considered whether judge gender informs judicial outcomes. While the standard interpretation emphasizes that judicial attributes (including judge gender) play a causal role, an alternative explanation considers the possibility that case characteristics (and therefore potentially outcome) become associated with judicial attributes via some other mechanism, such as case assignment, or decisions about what cases to publish or report.

Results in a recent paper, Gendered Judicial Opinions, by Michael Livermore (UVa) et al., substantially support an assignment/selection pathway explanation. To be sure, the paper is appropriately cautious cautious and notes that while its findings support the assignment/selection pathway, the possibility of a gender causation pathway cannot be properly excluded as the two are not mutually exclusive.

The paper levers a rich data set that includes all federal circuit court opinions, published and unpublished, from 2001 through 2017, except for decisions of the Court of Appeals for the Federal Circuit, en banc opinions, and opinions that include fewer than 900 characters. The authors then merged textual data from judicial opinions with judge-level demographic information collected by the Federal Judicial Center (FJC). The paper's abstract follows.

“In this paper we investigate whether gender is associated with the content of judicial opinions in the U.S. courts of appeals. Using a topic model analysis, we find that gender is a significant predictor of the content of judicial opinions. Two causal pathways could explain this result: (1) men and women judges write differently about the same cases; (2) men and women judges write about different cases, either due to assignment or selection effects. To untangle these two pathways, we carry out three additional analyses. First, we examine whether the United States as a party is associated with judge gender. We next examine whether case codes are associated with judge gender. Finally, we examine the relationship between topic prevalence and gender, controlling for case codes. Our findings lend greater support to the second pathway than the first. This result raises the prospect that prior work on gender-based differences in judicial behavior may be confounded by assignment or selection effects. Our results also raise normative concerns about gender disparities in voice and influence in the U.S. courts.”

Regulation & 3rd-Party Litigation Financing

As the world of 3rd-party litigation financing ("LTPF") grows, state efforts to regulate this industry sector endeavor to keep pace. While much of this activity takes place outside of public view, scholarly attention is increasing.

One example includes a recent paper by Ronen Avraham (Tel Aviv, Texas), Anthony Sebok (Cardozo) & Joanna Shepherd (Emory), The Whac-A-Mole Game: An Empirical Analysis of the Regulation of Litigant Third Party Financing. What the paper finds, unsurprisingly, is that as states ramp up regulation on LTPF, funding activity declines; where loosening of state regulations corresponds with increased funding activity.

Another core finding, and inspiration for the paper's title, is that funders continually adjust contract terms in an effort to circumvent regulations. To illustrate, "when legislation prohibits compounding or limits how long funders can charge interest, the funder responds by increasing the posted monthly interest rate. Although these interest rate increases are not enough to completely offset the impact of the other restrictions on the funder’s returns, the funder nevertheless has higher returns than it would have without the attempted circumvention." Thus, akin to the "classic Whac-A-Mole game, funders try to maintain their pre-regulation per-funding-return no matter how states’ regulatory activity tries to get rid of it."

Customizing Stata Tables

While delving into Stata graphics and output (e.g., tables) manipulation is not for the faint-of-heart, as it relates to the latter things recently became a bit easier with Stata's tables command. A three-part video series (click here for Part 1, Part 2; and Part 3) provides some helpful instruction and context. Additional resources also exist (e.g., here and here).

Power Testing

Too often I read papers that consider various explanations for a null result save for an obvious one: sample size. That is, a null result may simply be an artifact of a too small sample. And in some research settings, notably many experimental settings, an ex ante power calculation can inform researchers on the necessary sample size before experiments are executed. A few resources (e.g., here, here, and here) provide some general introductory context and examples.

The Empirics of Rental Evictions in Kentucky

As statutory eviction pauses implemented during the COVID-19 pandemic expire, eviction rates are beginning to rebound to pre-pandemic levels. Though this issue is fraught with an array of legal and policy implications, the topic is also noted for a paucity of empirical research. A recent paper, Rural Renting: An Empirical Portrait of Eviction, by Cassie Chambers Armstrong (Louisville) & Christopher J. Ryan, Jr. (IU-Bloomington), makes a helpful contribution by focusing on rental eviction outcomes in Kentucky. Specifically, the paper analyzes factors "influencing the eviction process in the rental market—and the likelihood that it will result in a judgment against a renter— once a property owner initiates legal proceedings."

To this end, the paper levers data gathered by the Kentucky Administrative Office of the Courts (“KOC”) from all 120 Kentucky counties from 2018-2022, yielding usable data on 202,572 renter evictions. One data set limitation is that it excludes information on pre-filing interactions and negotiations between renters and property owners.

Key findings include that those living in rural areas were more than 55 percent more likely to experience a judgment of eviction just by virtue of where they lived and those living in jurisdictions that had adopted renter-friendly policies (URLTA) were more likely to avoid a judgment of eviction—even though these laws do not impact the eviction process itself. Interestingly, renters who lived in the same ZIP code and/or state as their landlord were significantly more likely to resolve their case prior to judgment. An excerpted abstract follows.

"... Collectively, these findings, and others, have implications for eviction policy. They suggest that we must pay more attention to rural courts and the factors that lead to disparate outcomes in them. They call for programs that encourage collaboration between property owners and home renters, such as mediation and other types of eviction diversion. These results also must lead to further study of the time period between when an eviction case is filed and when it is concluded to more fully understand how the various parties approach decision-making with this legal process...."

Initial Data on Data Breach Litigation

News reports about massive data breaches are quickly becoming all-too common. While states have adopted data breach notification statutes and an increasing number of privacy statutes, litigation in this space remains largely by common law doctrines. Despite steady increases in litigation over time, quite little is know, systematically, about how such litigation has unfolded.

At the risk of stacking "turtles all the way down," a recent paper, Unto the (Data) Breach, sets out to gather initial data on data breach litigation. Drawing from WestLaw and PACER documents, the paper identifies 225 cases (between 2005 and 2022). While the analyses are exclusively descriptive, this is representative of helpful granular foundational work from which more technically sophisticated analyses can flow in the future. One take-away from the paper is that a comparative absence of a "cohesive data breach common law" likely exerts upward pressure on settlement activity. An excerpted abstract follows.

“Since the early 2000s, U.S. courts have begun hearing “data breach” liability cases, the inevitable result of a growing internet-connected technology infrastructure. The relatively recent development of case law signals a body of law in development, stunted by significant limiting factors that prevent the coalescence of legal principles. … This descriptive empirical study analyzes, in detail, 225 data breach cases from 2005-2022, reviewing these cases over a three-year period to descriptively identify key trends and changes within a case’s life on the docket.

This study identifies the type of plaintiff, settlement amounts, type and disposition of information compromised, claims, common motions, and key strategies most likely to result in a favorable plaintiff outcome. It also explores broad trends in data breach litigation, including acceptance of claims by courts, the status of future harms in 12(b)(1) and 12(b)(6) standing challenges, and the degree to which courts are willing to let cases proceed beyond preliminary motions….”

Periodic General Resources Update-Statistical Analyses

This is another periodic--if too infrequent--reminder to readers about the array of on-line resources available that provide increasingly sophisticated information on a wide variety of questions that span multiple software platforms (e.g., Stata, SPSS, R, MPlus, etc.). One of the major "first-movers" in this space is UCLA's OARC site (click here) which has been a "go-to" of mine for years, especially early on. Resources exist (here) even for those who simply want to more effectively "consume" or interpret various standard statistical output.

Where "vce(cluster var)" and "vce(robust)" Differ and Where They Do Not

A recent discussion on the StataList (here) identifies something of an anomaly when it comes to how "vce()" operates for the xtreg,fe command versus other commands.

As the unfolding discussion notes, when using the xtreg,fe command, vce(cluster var) and vce(robust) operate similarly. With other commands, by contrast, they generally operate differently. The difference emerges because where vce(robust) requires that observations are independent in order for it to be valid, vce(cluster var), by contrast, accommodates and adjusts for correlation of observations within values of groupvar (or "cluster var").

CELS 2024--Save the Date (8-9 Nov. 2024)

Co-organizers Tonja Jacobi, Jonathan Nash & Joanna Shepherd are delighted to announce that CELS 2024, hosted by Emory University School of Law, will take place on Fri.-Sat., November 8-9, 2024. A call for papers will follow later this spring.

Data on U.S. Solicitor General's "Flips"

While many criticize the Supreme Court for "unsettling" Court precedent, exceptionally few focus on instances where the Solicitor General's Office ("SG") "advances legal arguments that differ from those advanced by government lawyers in previous cases." Notably, '“[e]ven those observers who defend the SG, including veterans of the office, caution that inconsistency in legal argument poses a threat to the SG’s credibility with the Court." A recent paper by Margaret Lemos (Duke) & Deborah Widiss (Indiana), The Solicitor General, Consistency, and Credibility, sets out to empirically better understand the circumstances that lead the SG to "change its position on the meaning of the law, and to unpack the connections between consistency and credibility.”

To do so, the paper leverages an original dataset that includes 131 cases drawn from 1892 to the Court's 2022 term. The authors make clear that the sample is under-inclusive in that it contains only those SG "flips" that are "evident": that is, where "the government itself, other parties or amici, or the justices flagged that the government had changed its legal position." In about 80% of the cases, the SG itself acknowledged the change in its briefing. Of course, that also means that in 20% of the cases the government "either did not discuss, or denied, the changed position."

While the paper's analyses are strictly descriptive, main findings include that changes in the SG's litigating positions have increased over time. The authors also emphasize that SG reversals (or "flips") happen for an array of (often overlapping) reasons, "many of which stem from the SG’s unique role in coordinating litigation across a vast and constantly changing federal government." An excerpted abstract follows.

“This Article offers the first comprehensive look at cases in which the Solicitor General (SG) rejects a legal argument offered on behalf of the United States in prior litigation. Such reversals have received considerable attention in recent years, as shifts in presidential administrations have produced multiple high-profile “flip-flops”—as the justices sometimes call them—by the SG. Even those observers who defend the SG, including veterans of the office, caution that inconsistency in legal argument poses a threat to the SG’s credibility with the Court. Our goal is to better understand the circumstances that lead the SG to change its position on the meaning of the law, and to unpack the connections between consistency and credibility.

… Indeed, our study calls into question the idea that ideological swings associated with changes of presidential administrations can be isolated, either in theory or in practice, from other sorts of legal, social, and technological changes that shape the government’s understanding of the law. It also shows that the connection between consistency and credibility, while intuitive at first blush, rests on a formalist understanding of law and an unpersuasive equation of the judiciary and the executive.”

COVID-19 Relief Funding: Following the Money Across States

In response to the global COVID-19 pandemic, the U.S. federal government, through the CARES Act, FFCRA, RRA, and ARPA, provided as much as "$6 trillion in income support to households, a combination of loans, grants, and tax relief to firms and non-profits, funding for (public) health efforts, and intragovernmental grants to sub-national governments." These federal funds included "roughly $900 billion in aid to state and local government entities." And a recent paper, What Do State Do With Fiscal Windfalls? Evidence from the Pandemic, by Jeffrey Clemens (UCSD--econ.) et al., focuses on how state governments deployed this federal aid.

To do so, the paper benefits from a unique dataset constructed from a rich array of sources and an estimation strategy that levers variations across states with "low vs. high levels of per resident representation in the U.S. Congress." More specifically, owing to the possibility that federal aid to states may be endogenous along many dimensions, the paper deploys "an instrumental-variable approach to estimate the effect of federal relief to state and local governments on the revenue and expenditure decisions of state governments."

Among the findings include that "for every $1000 of committed federal aid per capita, $645 appears as increased intergovernmental grant revenue in states over the years 2020-2022." When it came to deploying the federal funds, state governments emphasized "General Government Expenditures ($379 per $1000 of federal aid) and state and local pension contributions ($72 per $1000 of federal aid)." An excerpted abstract follows.

“Using variation in federal pandemic-era fiscal aid to states driven by the strength of political representation, we find that incremental pandemic-era fiscal aid to states was most likely to end up in the categories of general administrative service spending and employee pension benefit funding. Spending on categories that motivated the aid in the first place, such as healthcare, education, and infrastructure, may also have responded but does not show robust patterns. Total state government revenues and expenditures had increased by around 70 cents per incremental windfall dollar of committed federal funds by 2022. Of this, the statistically significant categorical spending effects are 38 cents to general government expenditures (the residual that in principle excludes healthcare, education, infrastructure, and other functional categories) and 7 cents to pension funding, even though the latter use was inconsistent with the objectives of the legislation.”