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Leslie  \nWorking Paper 32348  \n[http://www.nber.org/papers/w32348](http://www.nber.org/papers/w32348)  \nNATIONAL BUREAU OF ECONOMIC RESEARCH  \n1050 Massachusetts Avenue  \nCambridge, MA 02138  \nApril 2024  \nThis manuscript is in preparation for the Journal of Economic Literature. Programs and data for the empirical case study will be provided online. We are grateful for insightful comments from David Romer and four anonymous referees. In addition, we received constructive feedback from Amanda Agan, Elior Cohen, Rob Collinson, Jason Cook, Gordon Dahl, John Eric Humphries, Lawrence Katz, Magne Mogstad, Samuel Norris, Aurelie Ouss, Roman Rivera, Henrik Sigstad, Kamelia Stavreva, Megan Stevenson, and Winnie van Dijk. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.  \nNBER working papers are circulated for discussion and comment purposes. They have not been peerreviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.  \n© 2024 by Eric Chyn, Brigham Frandsen, and Emily C. Leslie. All rights reserved. Short sections of text, not to exceed two paragraphs, may be quoted without explicit permission provided that full credit, including © notice, is given to the source.  \nExaminer and Judge Designs in Economics: A Practitioner's Guide Eric Chyn, Brigham Frandsen, and Emily C. Leslie  \nNBER Working Paper No. 32348 April 2024  \nJEL No. C21,C26,C31,C54,K14  \nABSTRACT  \nThis article provides empirical researchers with an introduction and guide to research designs based on variation in judge and examiner tendencies to administer treatments or other interventions. Wereview the basic theory behind the research design, outline the assumptions under which the design identifies causal effects, describe empirical tests of those assumptions, and discuss tradeoffs associated with choices researchers must make for estimation. We demonstrate concepts and best practices concretely in an empirical case study that uses an examiner tendency research design to study the effects of pre-trial detention.  \nEric Chyn  \nThe University of Texas at Austin Department of Economics 2225 Speedway, Stop C3100 Austin, TX 78712-1690  \nand NBER  \n[eric.chyn@austin.utexas.edu](eric.chyn@austin.utexas.edu)  \nBrigham Frandsen Department of Economics Brigham Young University Provo, UT 84602 [frandsen@byu.edu](frandsen@byu.edu)  \nEmily C. Leslie Department of Economics Brigham Young University  \n435 Crabtree Technology Building Provo, UT 84602 [emily.leslie@byu.edu](emily.leslie@byu.edu)  \n1 Introduction  \nIn 1932, criminologists in New Jersey documented wide disparities in the sentencing tendencies of trial judges in their state. The most severe judge imprisoned 57.7% of the convicted defendants randomly assigned to their courtroom, and the most lenient only 33.6%(Gaudet et al., 1932) . The same study also described an early experiment in which over a hundred mathematics teachers were asked to grade the same exam, producing scores that ranged from 28 to 92 . While variation indecision-maker tendencies raises issues of fairness, it also provides a convincing empirical strategy.  \nIn the past two decades, researchers have begun using disparities among judges and other decision makers like those documented nearly a century ago to identify causal effects in nonexperimental settings. For example, in pioneering work, Kling (2006) estimated the impact of incarceration length on post-release labor market earnings by leveraging plausibly exogenous variation in sentencing arising from the rules that assign offenders to judges.1 This strategy is sometimes called the “judge fixed effects” design or “judge leniency” design. We will generally refer to this strategy as the “examiner tendency” design, since it has bee","cbCaia2zPPza8ecA","https://ap.wps.com/l/cbCaia2zPPza8ecA","pdf",573129,62,"English","# Introduction\n## Examiner-tendency overview and motivation\n## Core identification ingredients and assumptions\n## Validity conditions: random assignment, exclusion, and monotonicity\n## Purpose and structure of the paper","[{\"question\":\"What research design does the paper focus on, and what drives the identification strategy?\",\"answer\":\"The paper focuses on examiner tendency designs, where differences in examiner tendencies expose comparable individuals to different treatments or interventions. The identification strategy relies on this quasi-random exposure.\"},{\"question\":\"Which assumptions are required for the design to identify causal effects?\",\"answer\":\"Validity rests not only on random (or conditional random) assignment to examiners, but also on exclusion and monotonicity assumptions. Exclusion requires outcomes to be influenced only through the treatment of interest, while monotonicity constrains how treatment responses vary.\"},{\"question\":\"What example case study does the paper use to demonstrate the approach?\",\"answer\":\"The paper uses an empirical case study applying an examiner tendency research design to estimate the effects of pre-trial detention. It uses this example to demonstrate concepts and best practices.\"}]","EXAMINER AND JUDGE DESIGNS IN ECONOMICS - A PRACTITIONER'S GUIDE | PDF",1791051626,156]