Racial discrimination in bail decisions
A new quasi-experimental method to measure racial discrimination reveals that ⅔ of the racial disparity in New York City pretrial release rates is due to racially discriminatory decisions by bail judges.
We develop new quasi-experimental tools to measure disparate impact, regardless of its source, in the context of bail decisions. We show that omitted variables bias in pretrial release rate comparisons can be purged by using the quasi-random assignment of judges to estimate average pretrial misconduct risk by race. We find that two-thirds of the release rate disparity between White and Black defendants in New York City is due to the disparate impact of release decisions. We then develop a hierarchical marginal treatment effect model to study the drivers of disparate impact, finding evidence of both racial bias and statistical discrimination.
The effect of immigration on racial preferences
In a quasi-experimental analysis, Mexican immigration improves whites’ racial attitudes, increases support for pro-Black government policies, and lowers anti-Black hate crimes while simultaneously increasing prejudice against Hispanics.
How do social group boundaries evolve? Does the appearance of a new out-group change the in-group’s perceptions of other out-groups? We introduce a conceptual framework of context-dependent categorization in which exposure to one minority leads to recategorization of other minorities as in- or out-groups depending on perceived distances across groups. We test this framework by studying how Mexican immigration to the United States affected white Americans’ attitudes and behaviors toward Black Americans. We combine survey and crime data with a difference-in-differences design and an instrumental variables strategy. Consistent with the theory, Mexican immigration improves whites’ racial attitudes, increases support for pro-Black government policies, and lowers anti-Black hate crimes while simultaneously increasing prejudice against Hispanics. Results generalize beyond Hispanics and Blacks, and a survey experiment provides direct evidence for recategorization. Our findings imply that changes in the size of one group can affect the entire web of intergroup relations in diverse societies.
The partisan timing of federal judicial retirements
Multiple regression discontinuity analyses reveal that federal judges are more likely to retire under presidents of the same political party as the president who first appointed them.
Long-standing debate over the Politicized Departure Hypothesis (PDH) asserts that federal judges tend to arrange to retire under presidents of the same political party as the president who first appointed them, thereby giving that party the right to nominate their successor. PDH is important for asserting political party agency by judges, who receive no consequent personal benefit, and for explaining the long-term political party orientation of courts. PDH studies inevitably suffer from absence of data on known and unknown determinants of retirement timing. To avoid these and other problems, we apply 11 sharp regression discontinuity (SRD) analyses to voluntary judicial departures before and after five elections that replace Republican presidents with Democrats, and six that replace Democrats with Republicans, 1920 to 2018. Results of difference tests, difference-in-differences tests, and others are as predicted by PDH, for 10 of 11 analyses, for pre-election and post-inauguration observation periods of 270 days. Although unexpected, we find stronger PDH effects for Republican appointees than for Democratic appointees. We offer a novel explanation of PDH based on normative reciprocity rather than ideology.
Confidence Intervals for Empirical Bayes Analysis
A new approach allows for the estimation of robust confidence intervals in nonparametric empirical Bayes analyses.
In an empirical Bayes analysis, we use data from repeated sampling to imitate inferences made by an oracle Bayesian with extensive knowledge of the data-generating distribution. Existing results provide a comprehensive characterization of when and why empirical Bayes point estimates accurately recover oracle Bayes behavior. In this paper, we develop flexible and practical confidence intervals that provide asymptotic frequentist coverage of empirical Bayes estimands, such as the posterior mean or the local false sign rate. The coverage statements hold even when the estimands are only partially identified or when empirical Bayes point estimates converge very slowly. Supplementary materials for this article are available online.
Open science methods for policy-relevant anthropology
Anthropologists can leverage open science methods to contribute to the development of mental health programs sensitive to community and culture.
This commentary asks anthropologists to work within communities to actively address the global mental health impact of COVID-19 and contribute to the pandemic response. Multiple social and physical losses, worsened by numerous factors, have produced syndemic traumatic stress and suffering across populations, highlighting persistent inequalities further amplified by the effects of COVID-19. Specifically, anthropologists can work to contribute to the development of mental health programs; confront the racialization of COVID-19 alongside marginalized communities; support real-time policy making with community responses; and innovate transparent collaborative research methods through open science. This pandemic can serve as an opportunity to prioritize research endeavors, public service, and teaching to better align with societal needs while providing new opportunities for synergy and collaborations between anthropologists in and outside the academy. Anthropologists collaborating directly with mental health clinicians and the public can contribute to knowledge specifically through direct program development and implementation of interventions designed to improve mental well-being. Innovating to find impactful solutions in response to the unprecedented mental health challenges exacerbated by the COVID-19 pandemic has the potential to promote more equitable recovery around the world.
Using text analysis to reveal historical biases
Text analysis of British parliamentary debates reveals that historians have systematically overlooked the political concerns of the less powerful.
Can statistics help historians to identify the events that are most distinctive of a particular era of time? This essay explores the use of a distinctiveness algorithm from library science for measuring the distinctiveness of manuscripts, tf-idf, recast as "tf-ipf" for the study of the terms most distinctive of historical periods. In a case study, tf-ipf is applied to the text of Hansard's Parliamentary Debates, varying the "period" from a 20-year horizon to a 6-month or one-day horizon. It is shown that the algorithm's assessments of what is most distinctive of 20-year and 10-year periods largely matches the consensus of British historians, while debates that held parliament's attention for six months or fewer have largely fallen beneath the threshold of scholarly attention. Attending to concerns that took up parliamentary debate for six months or fewer, the essay argues that tf-ipf thus presents a metric of parliamentary attention that mirrors the exclusions of class, allowing scholars to retrieve a timeline of when the concerns of the distillers, bleachers, dyers, Chartists, crofters, and miners reached a national debate. More generally, tf-ipf represents an important new tool for discovering the distinctive aspects of historical periods based on past experience, not historical bias.
Using brain function measures to predict depression
Levels of whole-brain resting-state functional connectivity measured before the pandemic predict individual variation in the onset of depression during the pandemic.
Stressful life events are significant risk factors for depression, and increases in depressive symptoms have been observed during the COVID-19 pandemic. The aim of this study is to explore the neural makers for individuals’ depression during COVID-19, using connectome-based predictive modeling (CPM). Then we tested whether these neural markers could be used to identify groups at high/low risk for depression with a longitudinal dataset. The results suggested that the high-risk group demonstrated a higher level and increment of depression during the pandemic, as compared to the low-risk group. Furthermore, a support vector machine (SVM) algorithm was used to discriminate major depression disorder patients and healthy controls, using neural features defined by CPM. The results confirmed the CPM’s ability for capturing the depression-related patterns with individuals’ resting-state functional connectivity signature. The exploration for the anatomy of these functional connectivity features emphasized the role of an emotion-regulation circuit and an interoception circuit in the neuropathology of depression. In summary, the present study augments current understanding of potential pathological mechanisms underlying depression during an acute and unpredictable life-threatening event and suggests that resting-state functional connectivity may provide potential effective neural markers for identifying susceptible populations.