IDEAS home Printed from https://ideas.repec.org/p/nbr/nberwo/24493.html
   My bibliography  Save this paper

The Health Effects of Cesarean Delivery for Low-Risk First Births

Author

Listed:
  • David Card
  • Alessandra Fenizia
  • David Silver

Abstract

Cesarean delivery for low-risk pregnancies is generally associated with worse health outcomes for infants and mothers. The interpretation of this correlation, however, is confounded by potential selectivity in the choice of birth mode. We use birth records from California, merged with hospital and emergency department (ED) visits for infants and mothers in the year after birth, to study the causal health effects of cesarean delivery for low-risk first births. Building on McClellan, McNeil, and Newhouse (1994), we use the relative distance from a mother’s home to hospitals with high and low c-section rates as an instrument for c-section. We show that relative distance is a strong predictor of c-section but is orthogonal to many observed risk factors, including birth weight and indicators of prenatal care. Our IV estimates imply that cesarean delivery causes a relatively large increase in ED visits of the infant, mainly due to acute respiratory conditions. We find no significant effects on mothers’ hospitalizations or ED use after birth, or on subsequent fertility, but we find a ripple effect on second birth outcomes arising from the high likelihood of repeat c-section. Offsetting these morbidity effects, we find that delivery at a high c-section hospital leads to a significant reduction in infant mortality, driven by lower death rates for newborns with high rates of pre-determined risk factors.

Suggested Citation

  • David Card & Alessandra Fenizia & David Silver, 2018. "The Health Effects of Cesarean Delivery for Low-Risk First Births," NBER Working Papers 24493, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:24493
    Note: CH EH LS
    as

    Download full text from publisher

    File URL: http://www.nber.org/papers/w24493.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Joseph G. Altonji & Todd E. Elder & Christopher R. Taber, 2005. "Selection on Observed and Unobserved Variables: Assessing the Effectiveness of Catholic Schools," Journal of Political Economy, University of Chicago Press, vol. 113(1), pages 151-184, February.
    2. Abadie, Alberto, 2003. "Semiparametric instrumental variable estimation of treatment response models," Journal of Econometrics, Elsevier, vol. 113(2), pages 231-263, April.
    3. Nicole Maestas & Kathleen J. Mullen & Alexander Strand, 2013. "Does Disability Insurance Receipt Discourage Work? Using Examiner Assignment to Estimate Causal Effects of SSDI Receipt," American Economic Review, American Economic Association, vol. 103(5), pages 1797-1829, August.
    4. Halla, Martin & Mayr, Harald & Pruckner, Gerald J. & García-Gómez, Pilar, 2020. "Cutting fertility? Effects of cesarean deliveries on subsequent fertility and maternal labor supply," Journal of Health Economics, Elsevier, vol. 72(C).
    5. Liran Einav & Amy Finkelstein & Heidi Williams, 2016. "Paying on the Margin for Medical Care: Evidence from Breast Cancer Treatments," American Economic Journal: Economic Policy, American Economic Association, vol. 8(1), pages 52-79, February.
    6. Janet Currie & W. Bentley MacLeod, 2017. "Diagnosing Expertise: Human Capital, Decision Making, and Performance among Physicians," Journal of Labor Economics, University of Chicago Press, vol. 35(1), pages 1-43.
    7. Walter Beckert & Mette Christensen & Kate Collyer, 2012. "Choice of NHS‐funded Hospital Services in England," Economic Journal, Royal Economic Society, vol. 122(560), pages 400-417, May.
    8. Victor Chernozhukov & Christian Hansen, 2005. "An IV Model of Quantile Treatment Effects," Econometrica, Econometric Society, vol. 73(1), pages 245-261, January.
    9. Grytten, Jostein & Skau, Irene & Sørensen, Rune, 2011. "Do expert patients get better treatment than others? Agency discrimination and statistical discrimination in obstetrics," Journal of Health Economics, Elsevier, vol. 30(1), pages 163-180, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Domenico Depalo, 2020. "Explaining the causal effect of adherence to medication on cholesterol through the marginal patient," Health Economics, John Wiley & Sons, Ltd., vol. 29(S1), pages 110-126, October.
    2. Halla, Martin & Mayr, Harald & Pruckner, Gerald J. & García-Gómez, Pilar, 2020. "Cutting fertility? Effects of cesarean deliveries on subsequent fertility and maternal labor supply," Journal of Health Economics, Elsevier, vol. 72(C).
    3. Sofia Amaral-Garcia & Mattia Nardotto & Carol Propper & Tommaso Valletti, 2022. "Mums Go Online: Is the Internet Changing the Demand for Health Care?," The Review of Economics and Statistics, MIT Press, vol. 104(6), pages 1157-1173, November.
    4. Mireille Jacobson & Maria Kogelnik & Heather Royer, 2021. "Holiday, Just One Day out of Life: Birth Timing and Postnatal Outcomes," Journal of Labor Economics, University of Chicago Press, vol. 39(S2), pages 651-702.
    5. de Elejalde, Ramiro & Giolito, Eugenio, 2019. "More Hospital Choices, More C-Sections: Evidence from Chile," IZA Discussion Papers 12297, Institute of Labor Economics (IZA).
    6. Zachary Bleemer, 2022. "Affirmative Action, Mismatch, and Economic Mobility after California’s Proposition 209," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 137(1), pages 115-160.
    7. Gabriel A. Facchini Palma, 2020. "Low Staffing in the Maternity Ward: Keep Calm and Call the Surgeon," Working Papers wpdea2009, Department of Applied Economics at Universitat Autonoma of Barcelona.
    8. Ding, Yu & Liu, Chenyuan, 2021. "Alternative payment models and physician treatment decisions: Evidence from lower back pain," Journal of Health Economics, Elsevier, vol. 80(C).
    9. Tonei, Valentina, 2019. "Mother’s mental health after childbirth: Does the delivery method matter?," Journal of Health Economics, Elsevier, vol. 63(C), pages 182-196.
    10. Facchini, Gabriel, 2022. "Low staffing in the maternity ward: Keep calm and call the surgeon," Journal of Economic Behavior & Organization, Elsevier, vol. 197(C), pages 370-394.
    11. Bertoli, Paola & Grembi, Veronica & Llaneza Hesse, Catalina & Vall Castelló, Judit, 2020. "The effect of budget cuts on C-section rates and birth outcomes: Evidence from Spain," Social Science & Medicine, Elsevier, vol. 265(C).
    12. Hanna Mühlrad, 2022. "Cesarean sections for high‐risk births: health, fertility, and labor market outcomes," Scandinavian Journal of Economics, Wiley Blackwell, vol. 124(4), pages 1056-1086, October.
    13. de Elejalde, Ramiro & Giolito, Eugenio, 2021. "A demand-smoothing incentive for cesarean deliveries," Journal of Health Economics, Elsevier, vol. 75(C).
    14. Surana, Mitul & Dongre, Ambrish, 2018. "Too much care? Private health care sector and surgical interventions during childbirth in India," IIMA Working Papers WP 2018-11-01, Indian Institute of Management Ahmedabad, Research and Publication Department.
    15. Pilvar, Hanifa & Yousefi, Kowsar, 2021. "Changing physicians’ incentives to control the C-section rate: Evidence from a major health care reform in Iran," Journal of Health Economics, Elsevier, vol. 79(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. David Card & Alessandra Fenizia & David Silver, 2019. "The Health Impacts of Hospital Delivery Practices," NBER Working Papers 25986, National Bureau of Economic Research, Inc.
    2. Huber, Martin & Wüthrich, Kaspar, 2017. "Evaluating local average and quantile treatment effects under endogeneity based on instruments: a review," FSES Working Papers 479, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    3. Manuel Arellano & Stéphane Bonhomme, 2017. "Quantile Selection Models With an Application to Understanding Changes in Wage Inequality," Econometrica, Econometric Society, vol. 85, pages 1-28, January.
    4. Callaway, Brantly & Li, Tong & Oka, Tatsushi, 2018. "Quantile treatment effects in difference in differences models under dependence restrictions and with only two time periods," Journal of Econometrics, Elsevier, vol. 206(2), pages 395-413.
    5. Gabriel A. Facchini Palma, 2020. "Low Staffing in the Maternity Ward: Keep Calm and Call the Surgeon," Working Papers wpdea2009, Department of Applied Economics at Universitat Autonoma of Barcelona.
    6. Joshua D. Angrist & Parag A. Pathak & Christopher R. Walters, 2013. "Explaining Charter School Effectiveness," American Economic Journal: Applied Economics, American Economic Association, vol. 5(4), pages 1-27, October.
    7. Gracious M. Diiro & Abdoul G. Sam & David Kraybill, 2017. "Heterogeneous Effects of Maternal Labor Market Participation on the Nutritional Status of Children: Empirical Evidence from Rural India," Child Indicators Research, Springer;The International Society of Child Indicators (ISCI), vol. 10(3), pages 609-632, September.
    8. Huber Martin & Wüthrich Kaspar, 2019. "Local Average and Quantile Treatment Effects Under Endogeneity: A Review," Journal of Econometric Methods, De Gruyter, vol. 8(1), pages 1-27, January.
    9. Sloczynski, Tymon, 2018. "A General Weighted Average Representation of the Ordinary and Two-Stage Least Squares Estimands," IZA Discussion Papers 11866, Institute of Labor Economics (IZA).
    10. Anthony Bald & Eric Chyn & Justine Hastings & Margarita Machelett, 2022. "The Causal Impact of Removing Children from Abusive and Neglectful Homes," Journal of Political Economy, University of Chicago Press, vol. 130(7), pages 1919-1962.
    11. Guido W. Imbens & Jeffrey M. Wooldridge, 2009. "Recent Developments in the Econometrics of Program Evaluation," Journal of Economic Literature, American Economic Association, vol. 47(1), pages 5-86, March.
    12. Pereda-Fernández, Santiago, 2023. "Identification and estimation of triangular models with a binary treatment," Journal of Econometrics, Elsevier, vol. 234(2), pages 585-623.
    13. Kaspar W thrich, 2015. "Semiparametric estimation of quantile treatment effects with endogeneity," Diskussionsschriften dp1509, Universitaet Bern, Departement Volkswirtschaft.
    14. Pinghui Wu, 2022. "Wage Inequality and the Rise in Labor Force Exit: The Case of US Prime-Age Men," Working Papers 22-16, Federal Reserve Bank of Boston.
    15. Andreas Haller & Stefan Staubli & Josef Zweimüller, 2024. "Designing Disability Insurance Reforms: Tightening Eligibility Rules or Reducing Benefits?," Econometrica, Econometric Society, vol. 92(1), pages 79-110, January.
    16. Eric French & Jae Song, 2014. "The Effect of Disability Insurance Receipt on Labor Supply," American Economic Journal: Economic Policy, American Economic Association, vol. 6(2), pages 291-337, May.
    17. Frolich, Markus, 2007. "Nonparametric IV estimation of local average treatment effects with covariates," Journal of Econometrics, Elsevier, vol. 139(1), pages 35-75, July.
    18. Nicolás Grau & Damián Vergara, "undated". "A Simple Test for Prejudice in Decision Processes: The Prediction-Based Outcome Test," Working Papers wp493, University of Chile, Department of Economics.
    19. John Eric Humphries & Nicholas Mader & Daniel Tannenbaum & Winnie van Dijk, 2019. "Does Eviction Cause Poverty? Quasi-Experimental Evidence from Cook County, IL," CESifo Working Paper Series 7800, CESifo.
    20. Ma, Jun & Marmer, Vadim & Yu, Zhengfei, 2023. "Inference on individual treatment effects in nonseparable triangular models," Journal of Econometrics, Elsevier, vol. 235(2), pages 2096-2124.

    More about this item

    JEL classification:

    • I11 - Health, Education, and Welfare - - Health - - - Analysis of Health Care Markets
    • J13 - Labor and Demographic Economics - - Demographic Economics - - - Fertility; Family Planning; Child Care; Children; Youth

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nbr:nberwo:24493. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/nberrus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.