Research

Abstract: Most U.S. criminal defendants are represented by public defenders (PDs), who consistently face higher caseloads than recommended by professional guidelines. I study the effect of caseloads using novel data from three U.S. counties and quasi-random variation in case assignment timing. Higher caseloads do not change conviction rates but lengthen sentences significantly: shifting a PD from the 25th to 75th percentile of their caseload increases an average sentence by 50-70%. PDs facing high caseloads maintain time spent on high-severity felonies at the expense of lower severity cases. These results suggest counties may realize substantial cost-savings on incarcerations by hiring additional PDs.

Abstract: Right to Counsel policies, which provide free lawyers to tenants facing eviction, have expanded rapidly despite limited and conflicting evidence on whether and how lawyers help tenants. In a randomized controlled trial in Memphis (N = 1,140), lawyers reduce court eviction judgments by 25 percentage points (37%) on average. However, these impacts fall by 86% and become indistinguishable from zero when a concurrent rental assistance program ends. The decisiveness of rental assistance for lawyers’ impacts points to a broader lesson: a meta-analysis of prior work shows that lawyers’ integration with out-of-court rental assistance programs largely reconciles earlier studies’ mixed findings. Yet, lawyers create value for tenants even when their courtroom effects are limited. Incentivized surveys find that tenants value lawyers at twice the cost of provision — and especially for what lawyers do outside court, rather than their in-court impacts. Together, these findings that eviction lawyers’ value lies primarily out of court — both in their interaction with rental assistance programs and what tenants actually demand — clarify the rationales that would justify Right to Counsel programs and underscore how access-to-justice policies reach beyond the courtroom.  [Survey Insturments] 

Abstract: Tax authorities use audits to detect and deter tax evasion. In practice, they commonly rely on quantitative predictions about taxpayers’ noncompliance to inform their decisions about which taxpayers to audit. We study the problem of optimal audit selection in this context. Specifically, we investigate how variation in the distribution of predicted noncompliance across taxpayers, including the uncertainty in quantitative predictions, shapes optimal audit policy. Our results highlight the contribution of these factors through a sufficient statistics characterization of the optimal audit selection rule. We leverage this characterization to quantify the social welfare benefit of varying the information available to the tax authority, for example by expanding third-party information reporting.

Abstract: Political debates often invoke “rights” to justify public transfers (e.g., the right to health care), whereas economists use welfarist frameworks which evaluate transfers’ impacts based on how they affect people’s utility. We conduct real-stakes online experiments that isolate non-welfarist from welfarist motives, and find sizable non-welfarist preferences to provide health care and legal aid to the indigent. 73% of participants make choices which are incompatible with welfarism. Non-welfarist concerns are weaker but still pervasive with neutral comparison goods. Additional experiments highlight drivers of non-welfarist motives and a key policy implication: non-welfarist concerns make Social Welfare Functions less progressive. [Survey Insturments] 

Abstract: Many institutions depend on reasoned discourse to reach decisions, but the degree to which debates are publicly observable varies. We examine reasoned discourse in the U.S. Senate, and study how increasing transparency through the introduction of C-SPAN changed legislative discourse. We find that the introduction of C-SPAN encouraged members to herd with co-partisans and to anti-herd with cross-partisans; it also appears to have led to the restructuring of Senate time to facilitate performative speech. Suggesting the information problems and career incentives at play, these effects are strongest for those closest to an election and for those with less sophisticated constituencies.

Abstract: Scholars increasingly use machine learning techniques such as Latent Dirichlet Allocation (LDA) to reduce the dimensionality of textual data and to study discourse in collective bodies. However, measures of discourse based on algorithmic results typically have no intuitive meaning or obvious relationship to humanly observed discourse. Such measures of discourse must be carefully validated before relied on and interpreted. We examine several common measures of discourse based on algorithmic results, and propose a number of ways to validate them in the setting of Federal Open Market Committee meetings. We also suggest that validation techniques may be used as a principled approach to model selection and parameterization.

Abstract: Videoconferencing has recently become ubiquitous due to the COVID-19 pandemic but has been growing in importance for decades. Despite this growth, we have limited understanding of the costs associated with adopting this technology. In this paper I leverage a novel dataset tracking 1.7 million individuals attending 1.2 million videoconference meetings over 6 months to evaluate individual punctuality in the remote workplace. I find that participants spend a significant amount of time waiting for others to arrive. An average meeting causes 6 minutes of per participant waiting time and even small meetings (≤ 5 participants) waste 14 minutes of total participant time. I investigate the predictors of these coordination failures and find that punctuality is best (and waiting time is minimized) for smaller, shorter meetings scheduled on the hour and half hour. I find some evidence for the development of norms that lead to relatively lower coordination failures than the distributions of arrival times might suggest and discuss the implications of these findings to a time of many new users of this technology.