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Censored Quantile Instrumental Variable Estimates of the Price Elasticity of Expenditure on Medical Care

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  • Amanda Kowalski

Abstract

Efforts to control medical care costs depend critically on how individuals respond to prices. I estimate the price elasticity of expenditure on medical care using a censored quantile instrumental variable (CQIV) estimator. CQIV allows estimates to vary across the conditional expenditure distribution, relaxes traditional censored model assumptions, and addresses endogeneity with an instrumental variable. My instrumental variable strategy uses a family member’s injury to induce variation in an individual’s own price. Across the conditional deciles of the expenditure distribution, I find elasticities that vary from −0.76 to −1.49, which are an order of magnitude larger than previous estimates. Supplementary materials for this article are available online.

Suggested Citation

  • Amanda Kowalski, 2016. "Censored Quantile Instrumental Variable Estimates of the Price Elasticity of Expenditure on Medical Care," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(1), pages 107-117, January.
  • Handle: RePEc:taf:jnlbes:v:34:y:2016:i:1:p:107-117
    DOI: 10.1080/07350015.2015.1004072
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    1. Hausman, Jerry A, 1985. "The Econometrics of Nonlinear Budget Sets," Econometrica, Econometric Society, vol. 53(6), pages 1255-1282, November.
    2. Manning, Willard G, et al, 1987. "Health Insurance and the Demand for Medical Care: Evidence from a Randomized Experiment," American Economic Review, American Economic Association, vol. 77(3), pages 251-277, June.
    3. Mullahy, John, 1998. "Much ado about two: reconsidering retransformation and the two-part model in health econometrics," Journal of Health Economics, Elsevier, vol. 17(3), pages 247-281, June.
    4. Powell, James L., 1986. "Censored regression quantiles," Journal of Econometrics, Elsevier, vol. 32(1), pages 143-155, June.
    5. Newey, Whitney K., 1987. "Efficient estimation of limited dependent variable models with endogenous explanatory variables," Journal of Econometrics, Elsevier, vol. 36(3), pages 231-250, November.
    6. Newhouse, Joseph P. & Phelps, Charles E. & Marquis, M. Susan, 1980. "On having your cake and eating it too : Econometric problems in estimating the demand for health services," Journal of Econometrics, Elsevier, vol. 13(3), pages 365-390, August.
    7. Willard G. Manning Jr. & Charles E. Phelps, 1979. "The Demand for Dental Care," Bell Journal of Economics, The RAND Corporation, vol. 10(2), pages 503-525, Autumn.
    8. Hausman, Jerry, 2015. "Specification tests in econometrics," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 38(2), pages 112-134.
    9. Duan, Naihua, et al, 1983. "A Comparison of Alternative Models for the Demand for Medical Care," Journal of Business & Economic Statistics, American Statistical Association, vol. 1(2), pages 115-126, April.
    10. Keeler, Emmett B. & Rolph, John E., 1988. "The demand for episodes of treatment in the health insurance experiment," Journal of Health Economics, Elsevier, vol. 7(4), pages 337-367, December.
    11. Chernozhukov, Victor & Hansen, Christian, 2008. "Instrumental variable quantile regression: A robust inference approach," Journal of Econometrics, Elsevier, vol. 142(1), pages 379-398, January.
    12. Buntin, Melinda Beeuwkes & Zaslavsky, Alan M., 2004. "Too much ado about two-part models and transformation?: Comparing methods of modeling Medicare expenditures," Journal of Health Economics, Elsevier, vol. 23(3), pages 525-542, May.
    13. Hong H. & Chernozhukov V., 2002. "Three-Step Censored Quantile Regression and Extramarital Affairs," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 872-882, September.
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    JEL classification:

    • I1 - Health, Education, and Welfare - - Health

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