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University of Chicago · Master of Science in Threat and Response Management
Active 1983–2024
Dan A. Black is a professor at the University of Chicago Harris School of Public Policy. He also serves as a senior fellow at the National Opinion Research Center. His research focuses on labor economics and applied econometrics. Black is the project director for the National Longitudinal Survey of Youth and is on the editorial board of the Journal of Labor Economics, Labour Economics, and Journal of Urban Economics. His papers have appeared in top journals in economics, statistics, and demography. He has served on panels for the Census Bureau, the Department of Education, the Environmental Protection Agency, the National Science Foundation, and the National Academy of Science, and has served as a consultant for the governments of New Zealand and Australia. Before joining Chicago Harris, he was on faculty at the University of Kentucky and Syracuse University, and held visiting appointments at the University of Chicago, Australian National University, and Carnegie Mellon University. Black holds a BA and MA in history from the University of Kansas and an MS and PhD in economics from Purdue University.
Relaxing financial constraints with tax credits and migrating out of rural and distressed America
Journal of Public Economics · 2024-04-16 · 2 citations
Criminal Charges, Risk Assessment, and Violent Recidivism in Cases of Domestic Abuse
National Bureau of Economic Research · 2023-01-01 · 2 citations
Collaborative Proposal: TLS: Where are all the Female Engineers?
NSF · $227k · 2009–2013
NIH · $376k · 2014
Lowell J. Taylor
Carnegie Mellon University
Seth Sanders
Cornell University
Natalia Kolesnikova
Academy of Law Management of the Federal Penal Service of Russia
Yu-Chieh Hsu
National Opinion Research Center
Jeffrey A. Smith
Dan A. Black LabPI
B.A., History
University of Kansas
M.A., History
University of Kansas
M.S., Economics
Purdue University
Ph.D., Economics
Purdue University
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Domestic abuse is a pervasive global problem.Here we analyze two approaches to reducing violent DA recidivism.One involves charging the perpetrator with a crime; the other provides protective services to the victim on the basis of a formal risk assessment carried out by the police.We use detailed administrative data to estimate the average effect of treatment on the treated using inverse propensity-score weighting (IPW).We then make use of causal forests to study heterogeneity in the estimated treatment effects.We find that pressing charges substantially reduces the likelihood of violent recidivism.The analysis also reveals substantial heterogeneity in the effect of pressing charges.In contrast, the risk-assessment process has no discernible effect.
Criminal Charges, Risk Assessment, and Violent Recidivism in Cases of Domestic Abuse
SSRN Electronic Journal · 2023-01-01 · 1 citations
Criminal Charges, Risk Assessment, and Violent Recidivism in Cases of Domestic Abuse
SSRN Electronic Journal · 2023-01-01 · 1 citations
Handbook of the economics of education · 2023-01-01 · 2 citations
Criminal Charges, Risk Assessment, and Violent Recidivism in Cases of Domestic Abuse
SSRN Electronic Journal · 2023-01-01
SSRN Electronic Journal · 2023-01-01 · 2 citations
What We Can Learn from Selected, Unmatched Data: Measuring Internet Inequality in Chicago
SSRN Electronic Journal · 2022-01-01 · 1 citations
Simple Tests for Selection: Learning More from Instrumental Variables
SSRN Electronic Journal · 2022-01-01 · 1 citations
Simple Tests for Selection: Learning More from Instrumental Variables
Labour Economics · 2022-08-01 · 6 citations
We provide simple tests for selection on unobserved variables in the Vytlacil-Imbens-Angrist framework for Local Average Treatment Effects (LATEs). Our setup allows researchers not only to test for selection on either or both of the treated and untreated outcomes, but also to assess the magnitude of the selection effect. We show that it applies to the standard binary instrument case, as well as to experiments with imperfect compliance and fuzzy regression discontinuity designs, and we link it to broader discussions regarding instrumental variables. We illustrate the substantive value added by our framework with three empirical applications drawn from the literature.
Mark C. Berger
Lynne Steuerle Schofield
Swarthmore College
John M. Barron
Purdue University West Lafayette