Empirical Legal Studies

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."