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Why Do Previous Choices Matter for Hospital Demand? Decomposing Switching Costs from Unobserved Preferences

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  • Devesh Raval

    (Federal Trade Commission)

  • Ted Rosenbaum

    (Federal Trade Commission)

Abstract

Using data on women’s choice of hospital for childbirth in Florida, we find that women return to the same hospital approximately 70% of the time. We separate explanations of switching costs and unobserved preference heterogeneity using a panel data fixed effects estimator and find that switching costs account for approximately 40% of the demand effects of a lagged dependent variable. The welfare effects of excluding a hospital from a payer’s network are smaller in the short run but higher in the long run, given our estimates of switching costs, and the dynamic effects of entry on competition are significantly smaller.

Suggested Citation

  • Devesh Raval & Ted Rosenbaum, 2018. "Why Do Previous Choices Matter for Hospital Demand? Decomposing Switching Costs from Unobserved Preferences," The Review of Economics and Statistics, MIT Press, vol. 100(5), pages 906-915, December.
    • Handle: RePEc:tpr:restat:v:100:y:2018:i:5:p:906-915
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    Citations

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    Cited by:

    1. Hill, Elaine L. & Slusky, David J.G. & Ginther, Donna K., 2019. "Reproductive health care in Catholic-owned hospitals," Journal of Health Economics, Elsevier, vol. 65(C), pages 48-62.
    2. Bisceglia, Michele & Cellini, Roberto & Siciliani, Luigi & Straume, Odd Rune, 2020. "Optimal dynamic volume-based price regulation," International Journal of Industrial Organization, Elsevier, vol. 73(C).
    3. Shakeeb Khan & Fu Ouyang & Elie Tamer, 2020. "Inference on Semiparametric Multinomial Response Models," Discussion Papers Series 627, School of Economics, University of Queensland, Australia.
    4. Sá, Luís & Straume, Odd Rune, 2021. "Quality provision in hospital markets with demand inertia: The role of patient expectations," Journal of Health Economics, Elsevier, vol. 80(C).
    5. Shakeeb Khan & Fu Ouyang & Elie Tamer, 2019. "Inference on Semiparametric Multinomial Response Models," Boston College Working Papers in Economics 980, Boston College Department of Economics.
    6. Abigail Ferguson & Nellie Lew & Michael Lipsitz & Devesh Raval, 2023. "Economics at the FTC: Spatial Demand, Veterinary Hospital Mergers, Rulemaking, and Noncompete Agreements," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 63(4), pages 435-465, December.
    7. Khan, S. & Ponomareva, M. & Tamer, E., 2023. "Identification of dynamic binary response models," Journal of Econometrics, Elsevier, vol. 237(1).
    8. Dahl, Gordon B. & Forbes, Silke J., 2023. "Doctor switching costs," Journal of Public Economics, Elsevier, vol. 221(C).
    9. Raval, Devesh & Rosenbaum, Ted & Wilson, Nathan E., 2021. "How do machine learning algorithms perform in predicting hospital choices? evidence from changing environments," Journal of Health Economics, Elsevier, vol. 78(C).
    10. Devesh Raval & Ted Rosenbaum & Nathan E. Wilson, 2022. "Using disaster‐induced closures to evaluate discrete choice models of hospital demand," RAND Journal of Economics, RAND Corporation, vol. 53(3), pages 561-589, September.
    11. Devesh Raval & Ted Rosenbaum, 2021. "Why is Distance Important for Hospital Choice? Separating Home Bias From Transport Costs," Journal of Industrial Economics, Wiley Blackwell, vol. 69(2), pages 338-368, June.
    12. Shakeeb Khan & Maria Ponomareva & Elie Tamer, 2019. "Identification of Dynamic Panel Binary Response Models," Boston College Working Papers in Economics 979, Boston College Department of Economics.

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