Joshua Angrist

Overview

Joshua D. Angrist is Ford Professor of Economics at MIT and a Research Associate at NBER. He is one of the principal architects of the "credibility revolution" in empirical economics — the shift toward quasi-experimental and instrumental-variables methods for causal inference. In 2021, Angrist shared the Nobel Memorial Prize in Economic Sciences with David Card and Guido Imbens for methodological contributions to the analysis of causal relationships. His work spans labor economics, education, immigration, and military service.

Key Contributions / Features

• Local Average Treatment Effect (LATE) — with Imbens (Econometrica 1994), formalized the IV estimand as the average treatment effect for compliers; the foundational result underpinning modern IV interpretation and the basis for the 2021 Nobel
• AIR (1996) framework — with Imbens and Rubin (JASA 1996), embedded LATE in the Rubin Causal Model; introduced the compliance typology (compliers, never-takers, always-takers, defiers); provided sensitivity analysis for exclusion restriction and monotonicity violations; applied to Vietnam draft lottery → military service → civilian mortality
• Quarter-of-birth IV — with Krueger (1991, QJE), used compulsory schooling laws interacted with season of birth as an instrument for years of education; a landmark application of IV to returns to schooling; extensively reviewed in Card (1999): IV estimates approximately equal OLS for most cohorts, but weak-instruments concern (Bound et al. 1995) applies to specifications with many instruments
• Sex ratio and marriage markets — Angrist (2001, QJE), used U.S. immigration quota legislation as an IV for immigrant sex ratios; provided historical causal evidence that sex ratios shift female marriage rates, female LFP, and male wages consistent with the Becker model; 33 year-ethnicity cells from IPUMS 1910/1920/1940 census
• Vietnam draft lottery IV — Angrist (1990, AER), used Vietnam-era draft lottery RSNs (randomly assigned to birthdates) as instrument for veteran status; Social Security CWHS data show white veterans born 1950–52 earned ~15% less than nonveterans in the early 1980s, equivalent to ~2 years of lost civilian labor market experience; introduces Two-Sample IV (TSIV) combining SSA earnings data with SIPP/DMDC veteran-status probabilities; no significant effect for nonwhites due to weak first stage; precursor to the AIR (1996) LATE framework; with Chen (2011, AEJ:AE), uses 2000 Census to show GI Bill added ~0.33 years of schooling for white veterans and that the early earnings penalty had vanished by age 50 — reconciled through a Mincerian framework where lost-experience and GI-Bill schooling effects approximately cancel on a flat age-50 experience profile
• School and class size — with Lavy (1999 QJE), used Israel's Maimonides Rule (class-size cap) as an RD instrument; identified causal effects of class size on achievement
• KIPP Lynn charter school lottery-IV (Angrist et al. 2010): With Dynarski, Kane, Pathak, and Walters, used KIPP Lynn's oversubscription lottery as an instrument for charter attendance; causal estimates of +0.35σ math and +0.12σ reading per year; reading gains concentrated in LEP and special-education students, refuting cream-skimming critique. See Charter School Effectiveness.
• Mostly Harmless Econometrics (2009, with Pischke) — the standard graduate reference for applied causal inference; introduced the "Furious Five" applied micro methods to a generation of economists

Related

• Instrumental Variables
• Local Average Treatment Effect
• Rubin Causal Model
• Charter School Effectiveness
• Marriage Market
• Nonmarital Fertility
• Intergenerational Income Mobility
• Causal Effects of DI Receipt
• Guido Imbens
• Donald Rubin
• Susan Dynarski
• Parag Pathak
• Christopher Walters
• KIPP

Sources

• Angrist et al 2010 — Inputs and Impacts in Charter Schools KIPP Lynn
• Angrist 1990 — Lifetime Earnings and the Vietnam Era Draft Lottery
• Angrist and Chen 2011 — Schooling and the Vietnam-Era GI Bill
• Angrist Imbens and Rubin 1996 — Identification of Causal Effects Using Instrumental Variables
• Angrist Imbens and Rubin 1996 — Identification of Causal Effects Using Instrumental Variables Rejoinder
• Angrist 2001 — How Do Sex Ratios Affect Marriage and Labor Markets Evidence from Americas Second Generation
• Card 1999 — The Causal Effect of Education on Earnings
• Brooks Chapman and Schroeder 2018 — Understanding Treatment Effect Estimates When Treatment Effects Are Heterogeneous for More Than One Outcome
• Heckman 1996 — Identification of Causal Effects Using Instrumental Variables Comment
• Heckman 2008 — Econometric Causality
• Meyer 1995 — Natural and Quasi-Experiments in Economics
• Moffitt 2005 — Remarks on the Analysis of Causal Relationships in Population Research
• Robins and Greenland 1996 — Identification of Causal Effects Using Instrumental Variables Comment