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The Credibility Revolution in Empirical Economics: How Better Research Design is Taking the Con out of Econometrics

Angrist, Joshua D, Pischke, Jörn-Steffen·2010·Journal of Economic Perspectives
DOI 10.1257/jep.24.2.3
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Abstract

Since Edward Leamer's memorable 1983 paper, “Let's Take the Con out of Econometrics,” empirical microeconomics has experienced a credibility revolution. While Leamer's suggested remedy, sensitivity analysis, has played a role in this, we argue that the primary engine driving improvement has been a focus on the quality of empirical research designs. The advantages of a good research design are perhaps most easily apparent in research using random assignment. We begin with an overview of Leamer's 1983 critique and his proposed remedies. We then turn to the key factors we see contributing to improved empirical work, including the availability of more and better data, along with advances in theoretical econometric understanding, but especially the fact that research design has moved front and center in much of empirical micro. We offer a brief digression into macroeconomics and industrial organization, where progress—by our lights—is less dramatic, although there is work in both fields that we find encouraging. Finally, we discuss the view that the design pendulum has swung too far. Critics of design-driven studies argue that in pursuit of clean and credible research designs, researchers seek good answers instead of good questions. We briefly respond to this concern, which worries us little.

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  • Under certain conditions, it is possible to rule out non-causal explanations for associations even in observational studies without random assignment.

    “However, under certain conditions, it is possible to rule out non-causal explanations for associations, even in studies without random assignment that are based on observational data (see refs. 56–58).”

    From A systematic review of worldwide causal and correlational evidence on digital media and democracy (2022) · cites this paper as [58]

  • The absence of randomized treatment assignment in observational data precludes identification of causal effects because individuals differ systematically on variables other than the treatment variable.

    “Usually, the absence of randomized treatment assignment, an inescapable feature of observational data (for example, survey data), precludes the identification of causal effects because individuals differ systematically on variables other than the treatment (or independent) variable.”

    From A systematic review of worldwide causal and correlational evidence on digital media and democracy (2022) · cites this paper as [58]

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Authors
Angrist, J. D., & Pischke, J.-S.
Affiliations
  • Massachusetts Institute of Technology (US)
  • London School of Economics and Political Science (GB)
ORCID
Venue
Journal of Economic Perspectives
Publisher
American Economic Association
Published
2010-05-01
Volume
24
Issue
2
Pages
3-30
ISSN
0895-3309 (ISSN-L)
DOI
10.1257/jep.24.2.3
OpenAlex
W3123293195
Cited by
1,566
References
3
Citations by year
2026: 94 · 2025: 142 · 2024: 113 · 2023: 132 · 2022: 119 · 2021: 113 · 2020: 150 · 2019: 107 · 2018: 103 · 2017: 85 · 2016: 92 · 2015: 75 · 2014: 67 · 2013: 57 · 2012: 42
FWCI
46.84
Type
journal article
Language
en
Open access
closed
Locations
  • The Journal of Economic Perspectives (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
Funders
Economic and Social Research Council (ES/H02123X/1)
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 › Decision Sciences › Management Science and Operations Research › Business Strategies and Management Research
Keywords
credibility revolution, research design, sensitivity analysis, random assignment, econometric methodology, data quality, industrial organization, macroeconomics

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