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Nonparametric estimation of a primary care production function in urban Brazil

Author

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  • Bruno Wichmann

    (University of Alberta)

  • Roberta Wichmann

    (World Bank
    Department of Public Health University of Brasília)

Abstract

Background The Brazilian public health system is one of the largest health systems in the world, with a mandate to deliver medical care to more than 200 million Brazilians. The objective of this study is to estimate a production function for primary care in urban Brazil. Our goal is to use flexible estimates to identify heterogeneous returns and complementarities between medical capital and labor. Methods We use a large dataset from 2012 to 2016 (with more than 400 million consultations, 270 thousand physicians, and 11 thousand clinics) to nonparametrically estimate a primary care production function and calculate the elasticity of doctors’ visits (output) to two inputs: capital stock (number of clinics) and labor (number of physicians). We benchmark our nonparametric estimates against estimates of a Cobb-Douglas (CD) production function. The CD model was chosen as a baseline because it is arguably the most popular parametric production function model. By comparing our nonparametric results with those from the CD model, our paper shed some light on the limitations of the parametric approach, and on the novelty of nonparametric insights. Results The nonparametric results show significantly heterogeneity of returns to both capital and labor, depending on the scale of operation. We find that diseconomies of scale, diminishing returns to scale, and increasing returns to scale are possible, depending on the input range. Conclusions The nonparametric model identifies complementarities between capital and labor, which is essential in designing efficient policy interventions. For example, we find that the response of primary care consultations to labor is steeper when capital level is high. This means that, if the goal is to allocate labor to maximize increases in consultations, adding physicians in cities with a high number of clinics is preferred to allocating physicians to low medical infrastructure municipalities. The results highlight how the CD model hides useful policy information by not accounting for the heterogeneity in the data.

Suggested Citation

  • Bruno Wichmann & Roberta Wichmann, 2020. "Nonparametric estimation of a primary care production function in urban Brazil," Health Economics Review, Springer, vol. 10(1), pages 1-10, December.
    • Handle: RePEc:spr:hecrev:v:10:y:2020:i:1:d:10.1186_s13561-020-00294-9
    DOI: 10.1186/s13561-020-00294-9
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    References listed on IDEAS

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    1. Henderson,Daniel J. & Parmeter,Christopher F., 2015. "Applied Nonparametric Econometrics," Cambridge Books, Cambridge University Press, number 9781107010253, January.
    2. Sisira Sarma & Rose Anne Devlin & William Hogg, 2010. "Physician's production of primary care in Ontario, Canada," Health Economics, John Wiley & Sons, Ltd., vol. 19(1), pages 14-30, January.
    3. Aragon, Y. & Daouia, A. & Thomas-Agnan, C., 2005. "Nonparametric Frontier Estimation: A Conditional Quantile-Based Approach," Econometric Theory, Cambridge University Press, vol. 21(2), pages 358-389, April.
    4. Niek Stadhouders & Xander Koolman & Christel van Dijk & Patrick Jeurissen & Eddy Adang, 2019. "The marginal benefits of healthcare spending in the Netherlands: Estimating cost‐effectiveness thresholds using a translog production function," Health Economics, John Wiley & Sons, Ltd., vol. 28(11), pages 1331-1344, November.
    5. Helmut Herwartz & Christoph Strumann, 2014. "Hospital efficiency under prospective reimbursement schemes: an empirical assessment for the case of Germany," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 15(2), pages 175-186, March.
    6. Douglas, Paul H, 1976. "The Cobb-Douglas Production Function Once Again: Its History, Its Testing, and Some New Empirical Values," Journal of Political Economy, University of Chicago Press, vol. 84(5), pages 903-915, October.
    7. Timothy J. Coelli & D.S. Prasada Rao & Christopher J. O’Donnell & George E. Battese, 2005. "An Introduction to Efficiency and Productivity Analysis," Springer Books, Springer, edition 0, number 978-0-387-25895-9, March.
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    Cited by:

    1. Roland Pongou & Guy Tchuente & Jean-Baptiste Tondji, 2023. "Optimal interventions in networks during a pandemic," Journal of Population Economics, Springer;European Society for Population Economics, vol. 36(2), pages 847-883, April.
    2. Pongou, Roland & Tchuente, Guy & Tondji, Jean-Baptiste, 2021. "Optimally Targeting Interventions in Networks during a Pandemic: Theory and Evidence from the Networks of Nursing Homes in the United States," GLO Discussion Paper Series 957, Global Labor Organization (GLO).
    3. Roland Pongou & Guy Tchuente & Jean-Baptiste Tondji, 2021. "Optimally Targeting Interventions in Networks during a Pandemic: Theory and Evidence from the Networks of Nursing Homes in the United States," Papers 2110.10230, arXiv.org.

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