Empirical Legal Studies

Data on Insider "Giving"

When corporate insiders learn about material, non-public bad news at their company that devalues stock prices, they can avoid losses by selling their stock before the public finds out. This form of “insider trading,” is, of course, illegal and risky. But selling stock is not the only way for corporate insiders to cash out. One alternative strategy, which is almost as financially effective and benefits from comparatively less onerous reporting requirements and legal restrictions, involves donating the stock to a charity and taking a tax deduction at the stock's inflated price. While a financial "second-best" strategy, "insider giving" nonetheless endures as a plausibly potent substitute for insider trading.

In Insider Giving, Sureyya B. Avci (Sabanci Univ.--Business) et al., submit their "insider giving" hypothesis to data. As the paper describes, the dataset includes "all reported gifts of common stock and contains over 9,000 observations between 1986 and 2020. The total volume of gifts contained in our dataset is approximately 2.1 billion shares, with a dollar value of approximately $50 billion." And what they find is that "large shareholders’ gifts are suspiciously well timed. Stock prices rise abnormally about 6% during the one-year period before the gift date and they fall abnormally by about 4% during the one year after the gift date, meaning that large shareholders tend to find the perfect day on which to give."

Rather than ascribe these finding to insider-donors' incredible good luck, the paper instead develops two possible alternative explanations: "information leakage" and "backdating." Specifically, "executives seem to provide large shareholders with material non-public information, who then use it to time gifts. We also find a second explanation to be supported, though its magnitude is smaller – backdating. The telltale sign of backdating is that the givers’ extraordinary luck tends to grow alongside the delay they take in reporting the gift. A donor who waits a few weeks to report a gift can cherry pick the very best date to retroactively claim their gift was consummated. That is precisely what we find." An excerpted abstract follows.

"Corporate insiders can avoid losses if they dispose of their stock while in possession of material, non-public information. One means of disposal, selling the stock, is illegal and subject to prompt mandatory reporting. A second strategy is almost as effective and it faces lax reporting requirements and legal restrictions. That second method is to donate the stock to a charity and take a charitable tax deduction at the inflated stock price. “Insider giving” is a potent substitute for insider trading. We show that insider giving is far more widespread than previously believed. In particular, we show that it is not limited to officers and directors. Large investors appear to regularly receive material non-public information and use it to avoid losses. Using a vast dataset of essentially all transactions in public company stock since 1986, we find consistent and economically significant evidence that these shareholders’ impeccable timing likely reflects information leakage. We also document substantial evidence of backdating – investors falsifying the date of their gift to capture a larger tax break….”

6th Annual TAU Workshop for Junior Scholars--Call For Papers (and Optimism)

With the shared hope that some semblance of "healthy normalcy" will return to all sometime soon, I pass along the following Call For Papers.

The Tel Aviv University Buchmann Faculty of Law is pleased to invite submissions to its sixth junior legal scholar workshop. The workshop provides junior scholars with the opportunity to present and discuss their work, receive meaningful feedback from faculty members and peers, and actively participate in an international community of junior legal scholars.

"The upcoming 2021 TAU workshop for junior scholars in law, which will be held in Tel-Aviv University Law School on November 14-16, 2021, will be dedicated to 'Legal Change in Revolutionary Times.' We are seeking cutting-edge works in progress, in varied legal fields and written from a variety of methodological and theoretical perspectives, that investigate the role of law in revolutionary times, and we welcome works that broadly relate to the core themes of the workshop."

Conference organizers expressly welcomes proposals from: "doctoral candidates, and recent graduates of doctoral programs submissions. Participants from the global South and under-represented communities are particularly encouraged to apply." Paper proposals are due to conference organizers by June 1, 2021. More conference specific information can be found (here); those wishing to contact conference organizers directly can send an email to: TAU.junior.scholars@gmail.com

Finally, conference organizers have adopted an "optimistic" assumption and, at present, anticipate a traditional, "in-person" conference. They also note, however, that if "Covid-19 is till ruling our lives by November 2021," the conference workshop format will transform into a "hybrid-virtual."

What Would Happen if Plea Bargaining Was Banned?

For an array of reasons, plea bargaining endures as a concern for many criminal law scholars. Proponents of plea bargaining "point to its efficiency−since trials are costly if both sides form reasonably similar beliefs about the outcome to arise at trial they can just agree to that expected sanction beforehand and save the costs." Opponents, by contrast, fear that excessive plea bargaining rates--combined with various structural differences that distinguish many defendants (especially indigent ones) and prosecutors' offices--may help coerce some factually innocent defendants into pleading guilty rather than defending their innocence at trail.

Amid growing calls to "regulate the plea bargaining process in an attempt to reduce its prevalence," one state--Alaska--acted. Specifically, Alaska Attorney General Michael Geraghty announced a new policy limiting plea bargaining in July 2013. The limitation prohibits prosecutors from "negotiating over the length of the jail/prison sentence for those accused of committing violent crimes."

In a recent paper, Alaska's Plea Bargaining Ban, Bryan McCannon (Univ. West Vir.--Economics) sets out to assess the efficacy of Alaska's experiment. The paper exploits a data set that includes annual (2007-2019) court-year data on how all of Alaska's Superior Courts disposed of their criminal and civil dockets (N=874). That Alaska's plea bargaining ban can affect a variety of margins raises important identification concerns. One thing the ban cannot affect, however, are pretrial plea negotiations in civil cases. Thus, the paper compares changes in the civil and criminal contexts.

The primary variable of interest is the rate at which cases settled pretrial and the author estimates a diff-in-diff specification. The critical take-away from the findings is that Alaska's plea bargaining ban for defendants charged with violent criminal crimes "does not have a meaningful effect" (emphasis added). One explanation for the absence of any detected affect involves the (often-underappreciated) importance of prosecutors' charge-bargaining policies and practices (which fall outside of the Alaska's ban on negotiating jail/prison sentences).

To be sure, important study limitations exist. Alaska may be an anomalous state. As well, as the study draws from an aggregated panel data set it cannot account for various case-specific factors. Finally, aggregating the case outcome data to the court level invites ignoring various heterogeneous treatment effects. Research design limitations aside, however, while this study will certainly not be the final word on this topic it is a helpful first step. The paper's abstract follows.

“Plea bargaining dominates the U.S. criminal justice system and has garnered calls to reduce its prevalence. In 2013 Alaska’s Attorney General acted banning a practice known as sentencing bargaining where the prosecutor negotiates with the defense over the length of the incarceration. I provide the first causal identification of this policy’s impact on the plea bargaining rate. I show that the policy, by leaving open the charging discretion (in effect allowing charge bargaining), was ineffective at changing plea bargaining’s prevalence across the state. Policymakers should look elsewhere for tools if they want to mitigate its rate.”

"Count" Variables & Dispersion

In many ELS contexts, count data are noted over-dispersion. In many legal settings this issue presents in the form of a "zeros-problem." When such a "skewed, count" dependent variable arises, various approaches include: negative binomial, Poisson, and zero-inflated regression estimations. A recent discussion of this general topic on the StataList (here) includes a robust back-and-forth about the relative strengths and weaknesses of various approaches. The claim that Poisson regression is not inappropriate when there is overdispersion in the dependent variable is forcefully defended. (For an earlier discussion of this general topic click here.)

A New and Interesting Take on Measuring Case Complexity

Most judicial decisionmaking papers can be criticized for inadequately controlling for variation in "case complexity." Lingering disputes over what "complexity" even means in terms of litigated cases only increase the degree of difficulty in constructing such a control. And even if a consensus on what "case complexity" means existed, constructing a reliable, consistent, and stable measure introduces additional challenges.

Difficulties, conceptual and mechanical, aside, a recent paper, Measuring Supreme Court Case Complexity, makes a helpful contribution. In it, Greg Goelzhauser (Utah State--Poli Sci) et al., both critique an array of earlier efforts and advance their own Bayesian measurement model to construct a latent (Supreme Court) case complexity measure that exploits issue and provision counts from the cases' merits briefs. By measuring case complexity from pre-decision data (merits briefs) the paper argues that its ex poste approach "diminishes endogeneity and post-treatment bias concerns when studying merits stage outcomes." To develop its measure, the authors draw from issue and provision (hand-coded) counts extracted from merits briefs filed in the 1954 through 2017 Court Terms, incident to Supreme Court Rules 24(1)(a) and 24(1)(f).

While perhaps limited to Supreme Court cases, new approaches to a difficult challenge can make important methodological contributions. The paper's abstract follows.

"Case complexity is central to the study of judicial politics. The dominant measures of Supreme Court case complexity use information on legal issues and provisions observed post-decision. As a result, scholars using these measures to study merits stage outcomes such as bargaining, voting, separate opinion production, and opinion content introduce post-treatment bias and exacerbate endogeneity concerns. Furthermore, existing issue measures are not valid proxies for complexity. Leveraging information on issues and provisions extracted from merits briefs, we develop a new latent measure of Supreme Court case complexity. This measure maps with the prevailing understanding of the underlying concept while mitigating inferential threats that hamper empirical evaluations. Our brief-based measurement strategy is generalizable to other contexts where it is important to generate exogenous and pre-treatment indicators for use in explaining merits decisions."

Multi-Level (Mixed) Modeling v. Regress & Cluster

When it comes to decisions between, e.g., a "-mixed- or a -regress and cluster- approach," what is increasingly clear (to me, anyway--and reflected in a helpful StataList discussion here) is that no firm, hard rules exists. What is clear, however, is that model specification decisions need to pivot on a granular understanding of the data and their underlying structure.

One classic context that frequently gives rise to such a decision involves efforts to assess student-level educational outcomes given hypothesized varied influences at the classroom- and school-levels into which the individual students nest. While a regression and clustering specification may yield results similar to a multi-level specification under certain circumstances (e.g., "if intraclass correlations at the school and class levels are very close to zero"), as the comments note: "The -regress- approach, even with -vce(cluster ...)- does not adjust for potential confounding due to systematic differences among classes or schools. The -mixed- model does so."

Panel Data Sets & Serial Correlation and Heteroskedasticity Challenges

For those working with a panel data set noted for its "large N, small T," a recent discussion here (from the StataList) might help save some time when it comes to working through challenges posed by serialcorrelation and heteroskedasticity. While these issues are especially common in corp finance and securities papers, as the latter comments in the discussion imply the magnitude of the problem (for 'short panel' data sets) remains somewhat of a contested point.

Theodore Eisenberg Memorial Article Prize -- Update

Each year, the Conference on Empirical Legal Studies (CELS) awards the Theodore Eisenberg Memorial Prize for the best poster presented at the Conference. Inasmuch as last year’s Conference was postponed due to the pandemic, this year the Journal of Empirical Legal Studies will award the Theodore Eisenberg Memorial Article Prize to the best paper submitted to JELS in 2021 and accepted for publication. The prize will be judged by the JELS editors. To honor our late colleague’s strong and enduring interest in mentoring and developing new empirical scholars, we will heavily favor submissions by early-career scholars in making this assessment. We will automatically consider any paper submitted between January 1, 2021, and December 31, 2021, as eligible for consideration. The award will be noted in the volume in which the article is published and an announcement sent to the Society for Empirical Legal Studies.

Data Familiarization/Visualization

A recent post by Jessica Hullman (here) served as my most recent periodic reminder about the enduring need for folks (myself included) to take a few, basic, simple steps to become more familiar with the underlying structure of and relationships among critical data points. Tukey's elegant tables and figures notwithstanding, what is perhaps more important is what follows from Tukey's general impulse: Sometimes simple data plots can unlock key analytic insights. And even if such insights aren't necessary for the researcher, readers' (and journal editors') interests should not be taken for granted.

In trying to explain why researchers might ignore--or bypass--simple initial data visualization/familiarization steps, Hullman points to "latent beliefs that plotting data is somehow inferior to anything that feels more like math. And because making plots for the sake of checking what the data look like or discovering things you didn’t expect seems disconnected and even distracting from the ultimate goal of fitting some model." That such beliefs may be entirely understandable, however, does not detract from their potential harm. It remains awfully difficult to overestimate the value of simple, Tukey-inspired data familiarization/visualization efforts.

Another Neat Stata Output "Trick"

A long-time ELS Blog reader, Jeff Yates (SUNY-Binghamton), alerted me to another under-appreciated command that can make life a bit easier for those needing/desiring to convert Stata results into Word docs.  asdoc is a Stata program that makes it easy to send output from Stata to MS Word by creating publication-ready tables from such Stata commands as summarize, correlate, tabstat, cross-tabs, regressions, t-tests, flexible table, as well as others. More information can be found here. To access (or update) the command:

ssc install asdoc, update

JELS' Research Notes and Datasets Section

If memory serves, a suggestion in 2016 (or so) from Bernie Black (Northwestern) and Marty Wells (Cornell-Statistics) to the JELS editors set into motion a chain of events that culminated in 2017 with the introduction of the "Research Notes and Datasets" section in JELS. The core motivation behind introducing a new paper "genre" for JELS was a fundamental belief that dataset construction, data sharing, and methods testing are a vital part of ELS' growth. We felt that a new section in JELS would help contribute to the realization of such beliefs. What we did not fully appreciate or anticipate, however, was the response. Since the Research Notes and Datasets section's launch in 2017 JELS has already published a number of important papers, including:

Patent Similarity Data and Innovation Metrics, by Ryan Whalen Alina Lungeanu Leslie DeChurch Noshir Contractor JELS 2020 (September). https://onlinelibrary.wiley.com/doi/10.1111/jels.12261

Developing High‐Quality Data Infrastructure for Legal Analytics: Introducing the Israeli Supreme Court Database, by Keren Weinshall Lee Epstein JELS 2020 (June). https://onlinelibrary.wiley.com/doi/10.1111/jels.12250

The German Federal Courts Dataset 1950–2019: From Paper Archives to Linked Open Data, by Hanjo Hamann Sokol JELS 2019 (September). https://onlinelibrary.wiley.com/doi/10.1111/jels.12230

Competition Law Gone Global: Introducing the Comparative Competition Law and Enforcement Datasets, by Anu Bradford Adam S. Chilton Christopher Megaw Nathaniel Sokol JELS 2019 (June). https://onlinelibrary.wiley.com/doi/10.1111/jels.12215

Accuracy and Fairness for Juvenile Justice Risk Assessments, by Richard Berk JELS 2019 (March). https://onlinelibrary.wiley.com/doi/10.1111/jels.12206

Text of Trade Agreements (ToTA)—A Structured Corpus for the Text‐as‐Data Analysis of Preferential Trade Agreements, by Wolfgang Alschner Julia Seiermann Dmitriy Skougarevskiy  JELS 2018 (September). https://onlinelibrary.wiley.com/doi/10.1111/jels.12189

Medical Liability Insurance Premia: 1990–2016 Dataset, with Literature Review and Summary Information, by Bernard Black Jeanette W. Chung Jeffrey Traczynski Victoria Udalova Sonal Vats, JELS 2017 (March). https://onlinelibrary.wiley.com/doi/10.1111/jels.12146

A New Tool for Detecting Various Characters in Coding

While a new user-written command, chartab, (briefly described and discussed here) may be criticized as a "command looking for a problem to solve," but for anyone who has ever tried to "de-bug" code a quick tabulation of various "characters" can help identify, and thus solve, problems. These problems include, e.g., the letter l for the number 1; the letter O for the number 0 (zero). These problems typically arise in the context of importing data, especially if it involves string data transited via Microsoft Word or Excel. And anyone merging data based on string variables should probably invest some time in some degree of "Unicode due diligence" before hand to help get in front of any potential problems. To install this command:

ssc install chartab

Empirical Conjectures About the Effects of Raising the Jurisdictional Amount

As my Civ Pro colleagues periodically remind me, we're likely overdue for an increase to jurisdictional thresholds. Even if they're correct, however, what might be the expected (and unexpected) consequences of various increases to jurisdictional thresholds? A recent paper by Steven Gensler (Oklahoma) and Roger Michalski (Oklahoma), The Million Dollar Diversity Docket, puts these counter-factual questions to data. And what they find should interest more than procedeuralists.

The authors hand-coded data from five (non-randomly selected) federal districts (Minnesota, Georgia Northern, New Jersey, Texas Northern, and Utah). From each of these districts they selected a random draw of diversity jurisdiction cases based on Federal Judicial Center (“FJC”) data on cases completed in 2018, resulting in 2,896 usable cases. The paper's methodological move involves substituting the temporal component in survival analysis with a measure of jurisdictional amount. That is, instead of asking on which day a machine fails, we ask at which jurisdictional amount a case fails. To do so, the authors estimated modified KaplanMeier survival functions and Cox Proportional Hazard Regression models.

The paper's main takeaway is that jurisdictional amounts "are not neutral throttles. They do not modulate all cases evenhandedly. Instead, different areas of law, different parts of the country, and different litigants are more affected by changes in the jurisdictional amount than others." The abstract follows.

"What would happen if Congress raised the jurisdictional amount in the diversity jurisdiction statute? Given that it has been almost 25 years since the last increase, we are probably overdue for another one. But to what amount? And with what effect? What would happen if Congress raised the jurisdictional amount from the current $75,000 to $250,000 or, say, $1 million?

Using modified Kaplan-Meier survival functions and Cox Proportional Hazard Regression models to analyze a novel hand-coded data set of pleadings in 2900 cases, we show that the jurisdictional amount is not a neutral throttle. It does not modulate all cases evenhandedly. Instead, different areas of law, different parts of the country, and different litigants are more affected by changes in the jurisdictional amount than others. As such, setting the amount in controversy at any level entails normative choices about which cases to bless and curse with a federal forum.

Our findings implicate not only policy discussions about diversity jurisdiction in general, but also debates currently underway in Congress to utilize modifications of the jurisdictional amount requirement to channel COVID-19 related litigation into federal courts. We hope our empirical work will inform these debates and enable a new wave of scholarship on the basic functions and functioning of the federal diversity docket. We outline some beginnings of this type of work by exploring radically different avenues of thought that develop and re-imagine the diversity docket anew. They range from incremental inflation-adjustments, to carve-outs, experiments with lotteries, and the radical replacement of an amount in controversy minimum with a maximum."

Some Stats Humor

(Thanks to: https://xkcd.com/2048/)

State Supreme Court Elections Data Set

Judicial decisionmaking scholars comparing outcomes from "elected" and "non-elected" judges invariably turn to state court systems (if the analytic focus is domestic) for the necessary variation. Those turning to the array of state court systems--and the range of state supreme court election types--invariably confront data gathering challenges that materially differ from challenges associated with, e.g., Art. III judges. One of the leading state court scholars, Bert Kritzer (Minn.), continues to make state court election data gathering efforts a bit easier for the rest of us. Not only has Bert engaged in the often under-appreciated yet Herculean effort of original data set building, but he shares his data sets (and associated documentation, codebooks, etc.) with other interested scholars. Bert's most recent updating effort, curated on Harvard's Dataverse, includes a collection of state supreme court election results that now range from 1946 through 2020.

"The archive includes three core datasets plus state-level Excel spreadsheets for all states that employed elections at least part of the time since 1946 to select or retain members of the state supreme court (known New York state as the Court of Appeals) including for Texas and Oklahoma both the state supreme court and the Court of Criminal Appeals which are the final courts for criminal cases in those states."

Those interested in the data sets can click here.