IDEAS home Printed from https://ideas.repec.org/a/wly/japmet/v28y2013i6p982-1017.html
   My bibliography  Save this article

Estimation Of Treatment Effects Without An Exclusion Restriction: With An Application To The Analysis Of The School Breakfast Program

Author

Listed:
  • Daniel L. Millimet
  • Rusty Tchernis

Abstract

While the rise in childhood obesity is clear, the policy ramifications are not. School nutrition programs such as the School Breakfast Program (SBP) have come under much scrutiny. However, the lack of experimental evidence, combined with non-random selection into these programs, makes identification of the causal effects of such programs difficult. In the case of the SBP, this difficulty is exacerbated by the apparent lack of exclusion restrictions. Here, we compare via Monte Carlo study several existing estimators that do not rely on exclusion restrictions for identification. In addition, we propose two new estimation strategies. Simulations illustrate the usefulness of our new estimators, as well as provide applied researchers several practical guidelines when analyzing the causal effects of binary treatments. More importantly, we find consistent evidence of a beneficial causal effect of SBP participation on childhood obesity when applying estimators designed to circumvent selection on unobservables.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Daniel L. Millimet & Rusty Tchernis, 2013. "Estimation Of Treatment Effects Without An Exclusion Restriction: With An Application To The Analysis Of The School Breakfast Program," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(6), pages 982-1017, September.
    • Handle: RePEc:wly:japmet:v:28:y:2013:i:6:p:982-1017
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1002/jae.2286
    Download Restriction: no
    ---><---

    Other versions of this item:

    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. David S. Lee & Thomas Lemieux, 2009. "Regression Discontinuity Designs In Economics," Working Papers 1118, Princeton University, Department of Economics, Industrial Relations Section..
    3. James J. Heckman & Edward Vytlacil, 2005. "Structural Equations, Treatment Effects, and Econometric Policy Evaluation," Econometrica, Econometric Society, vol. 73(3), pages 669-738, May.
    4. Alberto Abadie & Guido W. Imbens, 2016. "Matching on the Estimated Propensity Score," Econometrica, Econometric Society, vol. 84, pages 781-807, March.
    5. Daniel L. Millimet & Rusty Tchernis & Muna Husain, 2010. "School Nutrition Programs and the Incidence of Childhood Obesity," Journal of Human Resources, University of Wisconsin Press, vol. 45(3).
    6. Millimet, Daniel L. & Tchernis, Rusty, 2009. "On the Specification of Propensity Scores, With Applications to the Analysis of Trade Policies," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(3), pages 397-415.
    7. Stock, James H & Wright, Jonathan H & Yogo, Motohiro, 2002. "A Survey of Weak Instruments and Weak Identification in Generalized Method of Moments," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(4), pages 518-529, October.
    8. 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.
    9. Keisuke Hirano & Guido W. Imbens & Geert Ridder, 2003. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," Econometrica, Econometric Society, vol. 71(4), pages 1161-1189, July.
    10. James Heckman & Salvador Navarro-Lozano, 2004. "Using Matching, Instrumental Variables, and Control Functions to Estimate Economic Choice Models," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 30-57, February.
    11. Sara Bleich & David Cutler & Christopher Murray & Alyce Adams, 2007. "Why Is The Developed World Obese?," NBER Working Papers 12954, National Bureau of Economic Research, Inc.
    12. Jayanta Bhattacharya & Janet Currie & Steven J. Haider, 2006. "Breakfast of Champions?: The School Breakfast Program and the Nutrition of Children and Families," Journal of Human Resources, University of Wisconsin Press, vol. 41(3).
    13. Millimet, Daniel L. & Tchernis, Rusty, 2009. "On the Specification of Propensity Scores, With Applications to the Analysis of Trade Policies," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(3), pages 397-415.
    14. Roger Klein & Francis Vella, 2009. "A semiparametric model for binary response and continuous outcomes under index heteroscedasticity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(5), pages 735-762.
    15. A. Smith, Jeffrey & E. Todd, Petra, 2005. "Does matching overcome LaLonde's critique of nonexperimental estimators?," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 305-353.
    16. Odelia Rosin, 2008. "The Economic Causes Of Obesity: A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 22(4), pages 617-647, September.
    17. Guthrie, Joanne F. & Newman, Constance & Ralston, Katherine L., 2009. "USDA School Meal Programs Face New Challenges," Choices: The Magazine of Food, Farm, and Resource Issues, Agricultural and Applied Economics Association, vol. 24(3), pages 1-9.
    18. 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.
    19. David S. Lee & Thomas Lemieux, 2010. "Regression Discontinuity Designs in Economics," Journal of Economic Literature, American Economic Association, vol. 48(2), pages 281-355, June.
    20. Black, Dan A. & Smith, J.A.Jeffrey A., 2004. "How robust is the evidence on the effects of college quality? Evidence from matching," Journal of Econometrics, Elsevier, vol. 121(1-2), pages 99-124.
    21. Todd Headrick & Shlomo Sawilowsky, 1999. "Simulating correlated multivariate nonnormal distributions: Extending the fleishman power method," Psychometrika, Springer;The Psychometric Society, vol. 64(1), pages 25-35, March.
    22. Guido W. Imbens, 2004. "Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 4-29, February.
    Full references (including those not matched with items on IDEAS)

    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. Jeffrey Smith & Arthur Sweetman, 2016. "Viewpoint: Estimating the causal effects of policies and programs," Canadian Journal of Economics, Canadian Economics Association, vol. 49(3), pages 871-905, August.
    2. Daniel Millimet & Rusty Tchernis, 2008. "Minimizing Bias in Selection on Observables Estimators When Unconfoundness Fails," CAEPR Working Papers 2008-008, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    3. Black, Dan A. & Joo, Joonhwi & LaLonde, Robert & Smith, Jeffrey A. & Taylor, Evan J., 2022. "Simple Tests for Selection: Learning More from Instrumental Variables," Labour Economics, Elsevier, vol. 79(C).
    4. Flores, Carlos A. & Mitnik, Oscar A., 2009. "Evaluating Nonexperimental Estimators for Multiple Treatments: Evidence from Experimental Data," IZA Discussion Papers 4451, Institute of Labor Economics (IZA).
    5. Chad D. Meyerhoefer & Muzhe Yang, 2011. "The Relationship between Food Assistance and Health: A Review of the Literature and Empirical Strategies for Identifying Program Effects," Applied Economic Perspectives and Policy, Agricultural and Applied Economics Association, vol. 33(3), pages 304-344.
    6. Huber, Martin, 2019. "An introduction to flexible methods for policy evaluation," FSES Working Papers 504, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    7. Millimet, Daniel L. & Tchernis, Rusty, 2008. "Minimizing Bias in Selection on Observables Estimators When Unconfoundness Fails," IZA Discussion Papers 3632, Institute for the Study of Labor (IZA).
    8. 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.
    9. Daniel L. Millimet & Rusty Tchernis & Muna Husain, 2010. "School Nutrition Programs and the Incidence of Childhood Obesity," Journal of Human Resources, University of Wisconsin Press, vol. 45(3).
    10. Sokbae Lee & Yoon-Jae Whang, 2009. "Nonparametric Tests of Conditional Treatment Effects," Cowles Foundation Discussion Papers 1740, Cowles Foundation for Research in Economics, Yale University.
    11. Flores-Lagunes, Alfonso & Gonzalez, Arturo & Neumann, Todd C., 2007. "Estimating the Effects of Length of Exposure to a Training Program: The Case of Job Corps," IZA Discussion Papers 2846, Institute of Labor Economics (IZA).
    12. Huber, Martin & Lechner, Michael & Wunsch, Conny, 2013. "The performance of estimators based on the propensity score," Journal of Econometrics, Elsevier, vol. 175(1), pages 1-21.
    13. Huber, Martin & Lechner, Michael & Wunsch, Conny, 2010. "How to Control for Many Covariates? Reliable Estimators Based on the Propensity Score," IZA Discussion Papers 5268, Institute of Labor Economics (IZA).
    14. Froehlich, Anderson G. & Melo, Andrea S.S.A. & Sampaio, Breno, 2018. "Comparing the Profitability of Organic and Conventional Production in Family Farming: Empirical Evidence From Brazil," Ecological Economics, Elsevier, vol. 150(C), pages 307-314.
    15. 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.
    16. Halbert White & Karim Chalak, 2013. "Identification and Identification Failure for Treatment Effects Using Structural Systems," Econometric Reviews, Taylor & Francis Journals, vol. 32(3), pages 273-317, November.
    17. Banerjee, Soumendra Nath & Roy, Jayjit & Yasar, Mahmut, 2021. "Exporting and pollution abatement expenditure: Evidence from firm-level data," Journal of Environmental Economics and Management, Elsevier, vol. 105(C).
    18. Frölich, Markus & Huber, Martin & Wiesenfarth, Manuel, 2017. "The finite sample performance of semi- and non-parametric estimators for treatment effects and policy evaluation," Computational Statistics & Data Analysis, Elsevier, vol. 115(C), pages 91-102.
    19. Flores, Carlos A. & Flores-Lagunes, Alfonso, 2009. "Identification and Estimation of Causal Mechanisms and Net Effects of a Treatment under Unconfoundedness," IZA Discussion Papers 4237, Institute of Labor Economics (IZA).
    20. Arun Advani & Toru Kitagawa & Tymon Słoczyński, 2019. "Mostly harmless simulations? Using Monte Carlo studies for estimator selection," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(6), pages 893-910, September.

    More about this item

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • J13 - Labor and Demographic Economics - - Demographic Economics - - - Fertility; Family Planning; Child Care; Children; Youth

    Lists

    This item is featured on the following reading lists, Wikipedia, or ReplicationWiki pages:
    1. ESTIMATION OF TREATMENT EFFECTS WITHOUT AN EXCLUSION RESTRICTION: WITH AN APPLICATION TO THE ANALYSIS OF THE SCHOOL BREAKFAST PROGRAM (Journal of Applied Econometrics 2013) in ReplicationWiki

    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:japmet:v:28:y:2013:i:6:p:982-1017. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.interscience.wiley.com/jpages/0883-7252/ .

    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.