IDEAS home Printed from https://ideas.repec.org/a/wly/hlthec/v27y2018i6p937-955.html
   My bibliography  Save this article

2SLS versus 2SRI: Appropriate methods for rare outcomes and/or rare exposures

Author

Listed:
  • Anirban Basu
  • Norma B. Coe
  • Cole G. Chapman

Abstract

This study used Monte Carlo simulations to examine the ability of the two‐stage least squares (2SLS) estimator and two‐stage residual inclusion (2SRI) estimators with varying forms of residuals to estimate the local average and population average treatment effect parameters in models with binary outcome, endogenous binary treatment, and single binary instrument. The rarity of the outcome and the treatment was varied across simulation scenarios. Results showed that 2SLS generated consistent estimates of the local average treatment effects (LATE) and biased estimates of the average treatment effects (ATE) across all scenarios. 2SRI approaches, in general, produced biased estimates of both LATE and ATE under all scenarios. 2SRI using generalized residuals minimized the bias in ATE estimates. Use of 2SLS and 2SRI is illustrated in an empirical application estimating the effects of long‐term care insurance on a variety of binary health care utilization outcomes among the near‐elderly using the Health and Retirement Study.

Suggested Citation

  • Anirban Basu & Norma B. Coe & Cole G. Chapman, 2018. "2SLS versus 2SRI: Appropriate methods for rare outcomes and/or rare exposures," Health Economics, John Wiley & Sons, Ltd., vol. 27(6), pages 937-955, June.
  • Handle: RePEc:wly:hlthec:v:27:y:2018:i:6:p:937-955
    DOI: 10.1002/hec.3647
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/hec.3647
    Download Restriction: no

    References listed on IDEAS

    as
    1. Amanda Kowalski, 2016. "Doing more when you're running LATE: Applying marginal treatment effect methods to examine treatment effect heterogeneity in experiments," Artefactual Field Experiments 00560, The Field Experiments Website.
    2. Richard W. Blundell & James L. Powell, 2004. "Endogeneity in Semiparametric Binary Response Models," Review of Economic Studies, Oxford University Press, vol. 71(3), pages 655-679.
    3. Azeem M. Shaikh & Edward J. Vytlacil, 2011. "Partial Identification in Triangular Systems of Equations With Binary Dependent Variables," Econometrica, Econometric Society, vol. 79(3), pages 949-955, May.
    4. Heckman, James J, 1978. "Dummy Endogenous Variables in a Simultaneous Equation System," Econometrica, Econometric Society, vol. 46(4), pages 931-959, July.
    5. Terza, Joseph V. & Basu, Anirban & Rathouz, Paul J., 2008. "Two-stage residual inclusion estimation: Addressing endogeneity in health econometric modeling," Journal of Health Economics, Elsevier, vol. 27(3), pages 531-543, May.
    6. Chiburis, Richard C. & Das, Jishnu & Lokshin, Michael, 2012. "A practical comparison of the bivariate probit and linear IV estimators," Economics Letters, Elsevier, vol. 117(3), pages 762-766.
    7. Imbens, Guido W & Angrist, Joshua D, 1994. "Identification and Estimation of Local Average Treatment Effects," Econometrica, Econometric Society, vol. 62(2), pages 467-475, March.
    8. James J. Heckman & Sergio Urzua & Edward Vytlacil, 2006. "Understanding Instrumental Variables in Models with Essential Heterogeneity," The Review of Economics and Statistics, MIT Press, vol. 88(3), pages 389-432, August.
    9. Jason Abrevaya & Jerry A. Hausman & Shakeeb Khan, 2010. "Testing for Causal Effects in a Generalized Regression Model With Endogenous Regressors," Econometrica, Econometric Society, vol. 78(6), pages 2043-2061, November.
    10. Chiburis, Richard C., 2010. "Semiparametric bounds on treatment effects," Journal of Econometrics, Elsevier, vol. 159(2), pages 267-275, December.
    11. Horrace, William C. & Oaxaca, Ronald L., 2006. "Results on the bias and inconsistency of ordinary least squares for the linear probability model," Economics Letters, Elsevier, vol. 90(3), pages 321-327, March.
    12. Anirban Basu & James J. Heckman & Salvador Navarro-Lozano & Sergio Urzua, 2007. "Use of instrumental variables in the presence of heterogeneity and self-selection: an application to treatments of breast cancer patients," Health Economics, John Wiley & Sons, Ltd., vol. 16(11), pages 1133-1157.
    13. Gourieroux, Christian & Monfort, Alain & Renault, Eric & Trognon, Alain, 1987. "Generalised residuals," Journal of Econometrics, Elsevier, vol. 34(1-2), pages 5-32.
    14. Amanda E. Kowalski, 2016. "Doing More When You're Running LATE: Applying Marginal Treatment Effect Methods to Examine Treatment Effect Heterogeneity in Experiments for the Young and Privately Insured?," Cowles Foundation Discussion Papers 2045, Cowles Foundation for Research in Economics, Yale University.
    15. Richard W. Blundell & Richard J. Smith, 1989. "Estimation in a Class of Simultaneous Equation Limited Dependent Variable Models," Review of Economic Studies, Oxford University Press, vol. 56(1), pages 37-57.
    16. Anirban Basu & James J. Heckman & Salvador Navarro-Lozano & Sergio Urzua, 2007. "Use of instrumental variables in the presence of heterogeneity and self-selection: An application in breast cancer patients," Health, Econometrics and Data Group (HEDG) Working Papers 07/07, HEDG, c/o Department of Economics, University of York.
    17. Jeffrey M. Wooldridge, 2015. "Control Function Methods in Applied Econometrics," Journal of Human Resources, University of Wisconsin Press, vol. 50(2), pages 420-445.
    18. James Heckman, 1997. "Instrumental Variables: A Study of Implicit Behavioral Assumptions Used in Making Program Evaluations," Journal of Human Resources, University of Wisconsin Press, vol. 32(3), pages 441-462.
    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. Cheny, L.; & Clarke, P.M.; & Petrie, D.J.; & Staub, K.E.;, 2018. "The effects of self-assessed health: Dealing with and understanding misclassification bias," Health, Econometrics and Data Group (HEDG) Working Papers 18/26, HEDG, c/o Department of Economics, University of York.
    2. 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.
    3. Nshakira-Rukundo, Emmanuel & Mussa, Essa Chanie & Gerber, Nicolas & von Braun, Joachim, 2020. "Impact of voluntary community-based health insurance on child stunting: Evidence from rural Uganda," Social Science & Medicine, Elsevier, vol. 245(C).
    4. Erik Ansink & Louise Wijk & Frederiek Zuidmeer, 2019. "No clue about bioplastics," Tinbergen Institute Discussion Papers 19-084/VIII, Tinbergen Institute.
    5. Giuseppe Moscelli & Hugh Gravelle & Luigi Siciliani, 2018. "Effects of Market Structure and Patient Choice on Hospital Quality for Planned Patients," School of Economics Discussion Papers 1118, School of Economics, University of Surrey.

    More about this item

    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:wly:hlthec:v:27:y:2018:i:6:p:937-955. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Wiley Content Delivery). General contact details of provider: http://www3.interscience.wiley.com/cgi-bin/jhome/5749 .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.