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ExtrapoLATE-ing: External Validity and Overidentification in the LATE Framework

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  • Joshua Angrist
  • Ivan Fernandez-Val

Abstract

This paper develops a covariate-based approach to the external validity of instrumental variables (IV) estimates. Assuming that differences in observed complier characteristics are what make IV estimates differ from one another and from parameters like the effect of treatment on the treated, we show how to construct estimates for new subpopulations from a given set of covariate-specific LATEs. We also develop a reweighting procedure that uses the traditional overidentification test statistic to define a population for which a given pair of IV estimates has external validity. These ideas are illustrated through a comparison of twins and sex-composition IV estimates of the effects childbearing on labor supply.

Suggested Citation

  • Joshua Angrist & Ivan Fernandez-Val, 2010. "ExtrapoLATE-ing: External Validity and Overidentification in the LATE Framework," NBER Working Papers 16566, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:16566
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    References listed on IDEAS

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    1. Richard K. Crump & V. Joseph Hotz & Guido W. Imbens & Oscar A. Mitnik, 2009. "Dealing with limited overlap in estimation of average treatment effects," Biometrika, Biometrika Trust, vol. 96(1), pages 187-199.
    2. Frolich, Markus, 2007. "Nonparametric IV estimation of local average treatment effects with covariates," Journal of Econometrics, Elsevier, vol. 139(1), pages 35-75, July.
    3. Hahn, Jinyong, 1996. "A Note on Bootstrapping Generalized Method of Moments Estimators," Econometric Theory, Cambridge University Press, vol. 12(01), pages 187-197, March.
    4. Abadie, Alberto, 2003. "Semiparametric instrumental variable estimation of treatment response models," Journal of Econometrics, Elsevier, vol. 113(2), pages 231-263, April.
    5. Hall, Peter & Horowitz, Joel L, 1996. "Bootstrap Critical Values for Tests Based on Generalized-Method-of-Moments Estimators," Econometrica, Econometric Society, vol. 64(4), pages 891-916, July.
    6. Joshua Angrist & Victor Lavy & Analia Schlosser, 2010. "Multiple Experiments for the Causal Link between the Quantity and Quality of Children," Journal of Labor Economics, University of Chicago Press, vol. 28(4), pages 773-824, October.
    7. Avraham Ebenstein, 2009. "When is the Local Average Treatment Close to the Average?: Evidence from Fertility and Labor Supply," Journal of Human Resources, University of Wisconsin Press, vol. 44(4).
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    Citations

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    Cited by:

    1. Rajeev Dehejia & Cristian Pop-Eleches & Cyrus Samii, 2015. "From Local to Global: External Validity in a Fertility Natural Experiment," NBER Working Papers 21459, National Bureau of Economic Research, Inc.
    2. James Bisbee & Rajeev Dehejia & Cristian Pop-Eleches & Cyrus Samii, 2017. "Local Instruments, Global Extrapolation: External Validity of the Labor Supply-Fertility Local Average Treatment Effect," Journal of Labor Economics, University of Chicago Press, vol. 35(S1), pages 99-147.
    3. Pritchett, Lant & Samji, Salimah & Hammer, Jeffrey S., 2012. "It's All about MeE: Using Structured Experiential Learning ('e') to Crawl the Design Space," WIDER Working Paper Series 104, World Institute for Development Economic Research (UNU-WIDER).
    4. Michael Woolcock, 2013. "Using Case Studies to Explore the External Validity of ‘Complex’ Development Interventions," CID Working Papers 270, Center for International Development at Harvard University.
    5. Sara Cools & Jon H. Fiva & Lars J. Kirkebøen, 2015. "Causal Effects of Paternity Leave on Children and Parents," Scandinavian Journal of Economics, Wiley Blackwell, vol. 117(3), pages 801-828, July.
    6. Aaronson, Daniel & Dehejia, Rajeev & Jordon, Andrew & Pop-Eleches, Cristian & Samii, Cyrus & Schultze, Karl, 2017. "The Effect of Fertility on Mothers’ Labor Supply over the Last Two Centuries," MPRA Paper 76768, University Library of Munich, Germany.
    7. Joshua Angrist & Miikka Rokkanen, 2012. "Wanna Get Away? RD Identification Away from the Cutoff," NBER Working Papers 18662, National Bureau of Economic Research, Inc.
    8. de Luna, Xavier & Johansson, Per, 2012. "Testing for nonparametric identification of causal effects in the presence of a quasi-instrument," Working Paper Series 2012:14, IFAU - Institute for Evaluation of Labour Market and Education Policy.
    9. Hunt Allcott, 2012. "Site Selection Bias in Program Evaluation," NBER Working Papers 18373, National Bureau of Economic Research, Inc.
    10. Dionissi Aliprantis, 2013. "Covariates and causal effects: the problem of context," Working Paper 1310, Federal Reserve Bank of Cleveland.
    11. repec:aea:jecper:v:31:y:2017:i:2:p:3-32 is not listed on IDEAS
    12. Markus Frölich & Blaise Melly, 2013. "Identification of Treatment Effects on the Treated with One-Sided Non-Compliance," Econometric Reviews, Taylor & Francis Journals, vol. 32(3), pages 384-414, November.
    13. Brutscher, P-B., 2012. "Making Sense of Oil Stamp Saving Schemes," Cambridge Working Papers in Economics 1203, Faculty of Economics, University of Cambridge.
    14. Christian N. Brinch & Magne Mogstad & Matthew Wiswall, 2012. "Beyond LATE with a discrete instrument. Heterogeneity in the quantity-quality interaction of children," Discussion Papers 703, Statistics Norway, Research Department.
    15. Lant Pritchett & Salimah Samji & Jeffrey Hammer, 2013. "It‘s All About MeE: Using Structured Experiential Learning (“e”) to Crawl the Design Space," Working Papers 322, Center for Global Development.

    More about this item

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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