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Instrumental Variables and the Search for Identification: From Supply and Demand to Natural Experiments
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Instrumental variables was first used in the 1920s to estimate supply and demand elasticities and later to correct for measurement error in single equation models. Recently, instrumental variables have been widely used to reduce bias from omitted variables in estimates of causal relationships. Intuitively, instrumental variables methods use only a portion of the variability in key variables to estimate the relationships of interest; if the instruments are valid, that portion is unrelated to the omitted variables. We discuss the mechanics of instrumental variables and the qualities that make for a good instrument, devoting particular attention to instruments derived from “natural experiments.”
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“Observational studies using carefully chosen IVs are sometimes regarded as being equivalent to quasi experiments regarding their power to support causal claims (Angrist and Krueger 2001).”
From Financial literacy, financial advice, and financial behavior (2017) · cites this paper as Angrist and Krueger 2001
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“IV analyses can be applied to any potential source of randomization, including intentionally designed RCTs or quasi-randomization in natural experiments.15,16”
From Mendelian randomization (2022) · cites this paper as [15]
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“IV analyses are an alternative estimation method that can be used to derive an estimate of the causal effect of the treatment (here, CRP) on the outcome (SBP) by accounting for the size of the association between randomization and CRP.3,4,12–15”
From Mendelian randomization (2022) · cites this paper as [15]
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Flagged first, then by citations
Research works printed but not linked: 6
- Angrist Joshua D, 1990, American Economic Review, 80, 313
- Bronars Stephen G, 1994, American Economic Review., 84, 1141
- Haavelmo Trygve, 1944, Econometrica, 1
- Levitt Steven D, 1997, American Economic Review, 87, 270
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Cited by
Sorted by citations
Do free trade agreements actually increase members' international trade?
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Miguel, E., Satyanath, S., & Sergenti, E. (2004) · Journal of Political Economy · doi:10.1086/421174 · Cited by 2,460
On making causal claims: A review and recommendations
Antonakis, J., Bendahan, S., Jacquart, P., & Lalive, R. (2010) · The Leadership Quarterly · doi:10.1016/j.leaqua.2010.10.010 · Cited by 2,441
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Metadata
- Authors
- Angrist, J. D., & Krueger, A. B.
- Affiliations
- Massachusetts Institute of Technology (US)
- Princeton University (US)
- ORCID
- Angrist, Joshua D
- Venue
- Journal of Economic Perspectives
- Publisher
- American Economic Association
- Published
- 2001-11-01
- Volume
- 15
- Issue
- 4
- Pages
- 69-85
- ISSN
- 0895-3309 (ISSN-L)
- OpenAlex
- W3123059484
- Cited by
- 2,355
- References
- 98
- Citations by year
- 2026: 58 · 2025: 115 · 2024: 105 · 2023: 124 · 2022: 107 · 2021: 119 · 2020: 144 · 2019: 116 · 2018: 92 · 2017: 103 · 2016: 115 · 2015: 124 · 2014: 121 · 2013: 113 · 2012: 131
- FWCI
- 28.80
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- other-oa
- Locations
- The Journal of Economic Perspectives (publishedVersion, other-oa)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Social Sciences › Economics, Econometrics and Finance › General Economics, Econometrics and Finance › Monetary Policy and Economic Impact
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Spatial and Panel Data Analysis
- Keywords
- instrumental variables, identification, natural experiment, omitted variable bias, causal inference, supply and demand elasticities, measurement error, assessment validity, econometrics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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