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Optimal formula instruments

Author

Listed:
  • Kirill Borusyak
  • Peter Hull

Abstract

When estimating the effects of treatments defined by complex formulas, researchers often use simple functions of exogenous shocks as instruments. A leading example is “simulated instruments†for public policy eligibility, which capture variation in state-level policy generosity. We show how more powerful instruments can be constructed by incorporating heterogeneous shock exposure while using a recentering procedure to avoid bias. We characterize the asymptotically efficient instruments in this class and propose an algorithm for constructing feasible approximations to them. Compared to a simulated instrument approach, our approach yields a 44% smaller standard error on the private insurance crowd-out effect of Medicaid enrollment from the 2014 Affordable Care Act expansions.

Suggested Citation

  • Kirill Borusyak & Peter Hull, 2025. "Optimal formula instruments," CeMMAP working papers 09/25, Institute for Fiscal Studies.
  • Handle: RePEc:azt:cemmap:09/25
    DOI: 10.47004/wp.cem.2025.0925
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    References listed on IDEAS

    as
    1. Hansen, Lars Peter, 1985. "A method for calculating bounds on the asymptotic covariance matrices of generalized method of moments estimators," Journal of Econometrics, Elsevier, vol. 30(1-2), pages 203-238.
    2. Janet Currie & Jonathan Gruber, 1996. "Health Insurance Eligibility, Utilization of Medical Care, and Child Health," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 111(2), pages 431-466.
    3. Atila Abdulkadiroğlu & Joshua D. Angrist & Yusuke Narita & Parag A. Pathak, 2017. "Research Design Meets Market Design: Using Centralized Assignment for Impact Evaluation," Econometrica, Econometric Society, vol. 85, pages 1373-1432, September.
    4. David M. Cutler & Jonathan Gruber, 1996. "Does Public Insurance Crowd out Private Insurance?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 111(2), pages 391-430.
    5. Kirill Borusyak & Peter Hull, 2024. "Negative Weights Are No Concern in Design-Based Specifications," AEA Papers and Proceedings, American Economic Association, vol. 114, pages 597-600, May.
    6. Timothy J. Bartik, 1991. "Who Benefits from State and Local Economic Development Policies?," Books from Upjohn Press, W.E. Upjohn Institute for Employment Research, number wbsle.
    7. Imbens, Guido W & Angrist, Joshua D, 1994. "Identification and Estimation of Local Average Treatment Effects," Econometrica, Econometric Society, vol. 62(2), pages 467-475, March.
    8. Joshua D. Angrist, 1998. "Estimating the Labor Market Impact of Voluntary Military Service Using Social Security Data on Military Applicants," Econometrica, Econometric Society, vol. 66(2), pages 249-288, March.
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    Cited by:

    1. Christopher Carter & Adeline Delavande & Mario Fiorini & Peter Siminski & Patrick Vu, 2025. "Optimal Screening in Experiments with Partial Compliance," Papers 2512.09206, arXiv.org.

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    More about this item

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

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