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The Econometrics of Antidotal Variables

Author

Listed:
  • Das, Tirthatanmoy

    (Indian Institute of Management Bangalore)

  • Polachek, Solomon

    (Binghamton University, New York)

Abstract

Some interventions or population attributes negate the effects of a treatment. This paper shows that incorporating these, what we call antidotal variables (AV), into a causal treatment effects analysis can with one cross-sectional regression identify the true causal effect, in addition to possible biases from selectivity and SUTVA violations. Whereas we apply the AV technique to analyze the California Paid Family Leave program, it has applications beyond this example.

Suggested Citation

  • Das, Tirthatanmoy & Polachek, Solomon, 2022. "The Econometrics of Antidotal Variables," IZA Discussion Papers 15558, IZA Network @ LISER.
    • Handle: RePEc:iza:izadps:dp15558
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    References listed on IDEAS

    as
    1. Charles F. Manski, 1997. "Monotone Treatment Response," Econometrica, Econometric Society, vol. 65(6), pages 1311-1334, November.
    2. Charles L. Baum & Christopher J. Ruhm, 2016. "The Effects of Paid Family Leave in California on Labor Market Outcomes," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 35(2), pages 333-356, April.
    3. Laura Forastiere & Edoardo M. Airoldi & Fabrizia Mealli, 2021. "Identification and Estimation of Treatment and Interference Effects in Observational Studies on Networks," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(534), pages 901-918, April.
    4. Felix L. Friedt & Jeffrey P. Cohen, 2021. "Valuation of Noise Pollution and Abatement Policy: Evidence from the Minneapolis-St. Paul International Airport," Land Economics, University of Wisconsin Press, vol. 97(1), pages 107-136.
    5. Andreas Madestam & Daniel Shoag & Stan Veuger & David Yanagizawa-Drott, 2013. "Do Political Protests Matter? Evidence from the Tea Party Movement," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 128(4), pages 1633-1685.
    6. James J. Heckman & Sergio Urzua & Edward Vytlacil, 2006. "Understanding Instrumental Variables in Models with Essential Heterogeneity," The Review of Economics and Statistics, MIT Press, vol. 88(3), pages 389-432, August.
    7. Das, Tirthatanmoy & Polachek, Solomon, 2019. "A New Strategy to Identify Causal Relationships: Estimating a Binding Average Treatment Effect," IZA Discussion Papers 12766, IZA Network @ LISER.
    8. L. Liu & M. G. Hudgens & S. Becker-Dreps, 2016. "On inverse probability-weighted estimators in the presence of interference," Biometrika, Biometrika Trust, vol. 103(4), pages 829-842.
    9. Charles F. Manski & John V. Pepper, 2000. "Monotone Instrumental Variables, with an Application to the Returns to Schooling," Econometrica, Econometric Society, vol. 68(4), pages 997-1012, July.
    Full references (including those not matched with items on IDEAS)

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    Keywords

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    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C36 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Instrumental Variables (IV) Estimation
    • I38 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Government Programs; Provision and Effects of Welfare Programs
    • J18 - Labor and Demographic Economics - - Demographic Economics - - - Public Policy
    • J38 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Public Policy

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