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Does Obamacare Care? A Fuzzy Difference-in-Discontinuities Approach

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  • Galindo-Silva, Hector
  • Somé, Nibene Habib
  • Tchuente, Guy

Abstract

This paper explores the use of a fuzzy regression discontinuity design where multiple treatments are applied at the threshold. The identification results show that, under the very strong assumption that the change in the probability of treatment at the cutoff is equal across treatments, a differencein- discontinuities estimator identifies the treatment effect of interest. The point estimates of the treatment effect using a simple fuzzy difference-in-discontinuities design are biased if the change in the probability of a treatment applying at the cutoff differs across treatments. Modifications of the fuzzy difference-in-discontinuities approach that rely on milder assumptions are also proposed. Our results suggest caution is needed when applying before-and-after methods in the presence of fuzzy discontinuities. Using data from the National Health Interview Survey, we apply this new identification strategy to evaluate the causal effect of the Affordable Care Act (ACA) on older Americans' health care access and utilization. Our results suggest that the ACA has (1) led to a 5% increase in the hospitalization rate of elderly Americans, (2) increased the probability of delaying care for cost reasons by 3.6%, and (3) exacerbated cost-related barriers to follow-up care and continuity of care: 7.0% more elderly individuals could not afford prescriptions, 7.2% more could not see a specialist, and 5.5% more could not afford a follow-up visit. Our results can be explained by an increase in the demand for health services without a corresponding adjustment in supply following the implementation of the ACA.

Suggested Citation

  • Galindo-Silva, Hector & Somé, Nibene Habib & Tchuente, Guy, 2020. "Does Obamacare Care? A Fuzzy Difference-in-Discontinuities Approach," GLO Discussion Paper Series 666, Global Labor Organization (GLO).
  • Handle: RePEc:zbw:glodps:666
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    1. Otsu, Taisuke & Xu, Ke-Li & Matsushita, Yukitoshi, 2015. "Empirical likelihood for regression discontinuity design," Journal of Econometrics, Elsevier, vol. 186(1), pages 94-112.
    2. David S. Lee & Thomas Lemieux, 2010. "Regression Discontinuity Designs in Economics," Journal of Economic Literature, American Economic Association, vol. 48(2), pages 281-355, June.
    3. Sebastian Calonico & Matias D. Cattaneo & Rocio Titiunik, 2014. "Robust Nonparametric Confidence Intervals for Regression‐Discontinuity Designs," Econometrica, Econometric Society, vol. 82, pages 2295-2326, November.
    4. Patrick Kline & Christopher R. Walters, 2016. "Evaluating Public Programs with Close Substitutes: The Case of HeadStart," The Quarterly Journal of Economics, Oxford University Press, vol. 131(4), pages 1795-1848.
    5. Courtemanche, Charles & Friedson, Andrew & Koller, Andrew P. & Rees, Daniel I., 2019. "The affordable care act and ambulance response times," Journal of Health Economics, Elsevier, vol. 67(C).
    6. David Card & Carlos Dobkin & Nicole Maestas, 2008. "The Impact of Nearly Universal Insurance Coverage on Health Care Utilization: Evidence from Medicare," American Economic Review, American Economic Association, vol. 98(5), pages 2242-2258, December.
    7. Guido Imbens & Karthik Kalyanaraman, 2012. "Optimal Bandwidth Choice for the Regression Discontinuity Estimator," Review of Economic Studies, Oxford University Press, vol. 79(3), pages 933-959.
    8. Veronica Grembi & Tommaso Nannicini & Ugo Troiano, 2016. "Do Fiscal Rules Matter?," American Economic Journal: Applied Economics, American Economic Association, vol. 8(3), pages 1-30, July.
    9. Imbens, Guido W. & Lemieux, Thomas, 2008. "Regression discontinuity designs: A guide to practice," Journal of Econometrics, Elsevier, vol. 142(2), pages 615-635, February.
    10. Alonso, J. & Orfila, F. & Ruigómez, A. & Ferrer, M. & Antó, J.M., 1997. "Unmet health care needs and mortality among Spanish elderly," American Journal of Public Health, American Public Health Association, vol. 87(3), pages 365-370.
    11. Scott, Anthony, 2000. "Economics of general practice," Handbook of Health Economics, in: A. J. Culyer & J. P. Newhouse (ed.), Handbook of Health Economics, edition 1, volume 1, chapter 22, pages 1175-1200, Elsevier.
    12. Andrew C. Eggers & Ronny Freier & Veronica Grembi & Tommaso Nannicini, 2018. "Regression Discontinuity Designs Based on Population Thresholds: Pitfalls and Solutions," American Journal of Political Science, John Wiley & Sons, vol. 62(1), pages 210-229, January.
    13. Hahn, Jinyong & Todd, Petra & Van der Klaauw, Wilbert, 2001. "Identification and Estimation of Treatment Effects with a Regression-Discontinuity Design," Econometrica, Econometric Society, vol. 69(1), pages 201-209, January.
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    1. Ellegård, Lina Maria & Kjellsson, Gustav & Mattisson, Linn, 2021. "An App Call a Day Keeps the Patient Away? Substitution of Online and In-Person Doctor Consultations Among Young Adults," Working Papers in Economics 808, University of Gothenburg, Department of Economics.

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    More about this item

    Keywords

    Fuzzy Difference-in-Discontinuities; Identification; Regression Discontinuity Design; Affordable Care Act;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior
    • I13 - Health, Education, and Welfare - - Health - - - Health Insurance, Public and Private
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

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