IDEAS home Printed from https://ideas.repec.org/p/nbr/nberwo/16566.html
   My bibliography  Save this paper

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

Author

Listed:
  • 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
    Note: CH ED HE LS PE
    as

    Download full text from publisher

    File URL: http://www.nber.org/papers/w16566.pdf
    Download Restriction: no

    References listed on IDEAS

    as
    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(1), 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. 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.
    6. 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.
    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).
    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. Hao Bo & Sebastian Galiani, 2019. "Assessing External Validity," NBER Working Papers 26422, National Bureau of Economic Research, Inc.
    2. Rajeev Dehejia & Cristian Pop-Eleches & Cyrus Samii, 2021. "From Local to Global: External Validity in a Fertility Natural Experiment," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(1), pages 217-243, January.
    3. Martin Huber, 2019. "An introduction to flexible methods for policy evaluation," Papers 1910.00641, arXiv.org.
    4. Joshua Angrist & Miikka Rokkanen, 2012. "Wanna Get Away? RD Identification Away from the Cutoff," NBER Working Papers 18662, National Bureau of Economic Research, Inc.
    5. Aaronson, Daniel & Dehejia, Rajeev & Jordan, Andrew & Pop-Eleches, Cristian & Samii, Cyrus & Schulze, Karl, 2017. "The Effect of Fertility on Mothers' Labor Supply over the Last Two Centuries," IZA Discussion Papers 10559, Institute of Labor Economics (IZA).
    6. Strobl, Renate & Wunsch, Conny, 2018. "Identification of causal mechanisms based on between-subject double randomization designs," CEPR Discussion Papers 13028, C.E.P.R. Discussion Papers.
    7. Dionissi Aliprantis, 2013. "Covariates and causal effects: the problem of context," Working Papers (Old Series) 1310, Federal Reserve Bank of Cleveland.
    8. Heng Chen & Geoffrey Dunbar & Q. Rallye Shen, 2020. "The Mode is the Message: Using Predata as Exclusion Restrictions to Evaluate Survey Design," Advances in Econometrics, in: Tong Li & M. Hashem Pesaran & Dek Terrell (ed.), Essays in Honor of Cheng Hsiao, volume 41, pages 341-357, Emerald Publishing Ltd.
    9. de Luna, Xavier & Johansson, Per, 2012. "Testing for Nonparametric Identification of Causal Effects in the Presence of a Quasi-Instrument," IZA Discussion Papers 6692, Institute of Labor Economics (IZA).
    10. Lant Pritchett & Salimah Samji & Jeffrey Hammer, 2012. "It’s All About MeE: Using Structured Experiential Learning (‘e’) to Crawl the Design Space," CID Working Papers 249, Center for International Development at Harvard University.
    11. Ethan Kaplan & Fernando Saltiel & Sergio S. Urzúa, 2019. "Voting for Democracy: Chile's Plebiscito and the Electoral Participation of a Generation," NBER Working Papers 26440, National Bureau of Economic Research, Inc.
    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. Christian N. Brinch & Magne Mogstad & Matthew Wiswall, 2017. "Beyond LATE with a Discrete Instrument," Journal of Political Economy, University of Chicago Press, vol. 125(4), pages 985-1039.
    14. Peter Hull, 2018. "Estimating Treatment Effects in Mover Designs," Papers 1804.06721, arXiv.org.
    15. 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.
    16. Lant Pritchett & Salimah Samji & Jeffrey Hammer, 2012. "It’s All About MeE: Using Structured Experiential Learning (‘e’) to Crawl the Design Space," CID Working Papers 249, Center for International Development at Harvard University.
    17. Williams, Martin J., 2020. "Beyond ‘context matters’: Context and external validity in impact evaluation," World Development, Elsevier, vol. 127(C).
    18. 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.
    19. Hunt Allcott, 2012. "Site Selection Bias in Program Evaluation," NBER Working Papers 18373, National Bureau of Economic Research, Inc.
    20. Woolcock, Michael, 2013. "Using Case Studies to Explore the External Validity of 'Complex' Development Interventions," Working Paper Series rwp13-048, Harvard University, John F. Kennedy School of Government.
    21. Fujiwara, Daniel, 2013. "A general method for valuing non-market goods using wellbeing data: three-stage wellbeing valuation," LSE Research Online Documents on Economics 51577, London School of Economics and Political Science, LSE Library.
    22. Susan Athey & Guido W. Imbens, 2017. "The State of Applied Econometrics: Causality and Policy Evaluation," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 3-32, Spring.
    23. Brutscher, P-B., 2012. "Making Sense of Oil Stamp Saving Schemes," Cambridge Working Papers in Economics 1203, Faculty of Economics, University of Cambridge.
    24. Daniel Fujiwara, 2013. "A General Method for Valuing Non-Market Goods Using Wellbeing Data: Three-Stage Wellbeing Valuation," CEP Discussion Papers dp1233, Centre for Economic Performance, LSE.

    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. Seojeong Lee, 2018. "A Consistent Variance Estimator for 2SLS When Instruments Identify Different LATEs," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(3), pages 400-410, July.
    2. Donald, Stephen G. & Hsu, Yu-Chin & Lieli, Robert P., 2014. "Inverse probability weighted estimation of local average treatment effects: A higher order MSE expansion," Statistics & Probability Letters, Elsevier, vol. 95(C), pages 132-138.
    3. Jason Abrevaya & Yu-Chin Hsu & Robert P. Lieli, 2015. "Estimating Conditional Average Treatment Effects," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(4), pages 485-505, October.
    4. Luc Behaghel & Clément de Chaisemartin & Marc Gurgand, 2017. "Ready for Boarding? The Effects of a Boarding School for Disadvantaged Students," American Economic Journal: Applied Economics, American Economic Association, vol. 9(1), pages 140-164, January.
    5. Joachim Inkmann, 2000. "Finite Sample Properties of One-Step, Two-Step and Bootstrap Empirical Likelihood Approaches to Efficient GMM Estimation," Econometric Society World Congress 2000 Contributed Papers 0332, Econometric Society.
    6. Joshua D. Angrist, 2004. "Treatment effect heterogeneity in theory and practice," Economic Journal, Royal Economic Society, vol. 114(494), pages 52-83, March.
    7. de Luna, Xavier & Johansson, Per, 2012. "Testing for Nonparametric Identification of Causal Effects in the Presence of a Quasi-Instrument," IZA Discussion Papers 6692, Institute of Labor Economics (IZA).
    8. Aassve, Arnstein & Arpino, Bruno, 2008. "Estimation of causal effects of fertility on economic wellbeing: evidence from rural Vietnam," ISER Working Paper Series 2007-27, Institute for Social and Economic Research.
    9. Markus Frölich & Martin Huber, 2017. "Direct and indirect treatment effects–causal chains and mediation analysis with instrumental variables," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(5), pages 1645-1666, November.
    10. 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.
    11. Markus Frölich & Martin Huber, 2014. "Treatment Evaluation With Multiple Outcome Periods Under Endogeneity and Attrition," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(508), pages 1697-1711, December.
    12. Joshua Angrist & Miikka Rokkanen, 2012. "Wanna Get Away? RD Identification Away from the Cutoff," NBER Working Papers 18662, National Bureau of Economic Research, Inc.
    13. Bravo, Francesco & Crudu, Federico, 2012. "Efficient bootstrap with weakly dependent processes," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3444-3458.
    14. Goncalves, Silvia & White, Halbert, 2004. "Maximum likelihood and the bootstrap for nonlinear dynamic models," Journal of Econometrics, Elsevier, vol. 119(1), pages 199-219, March.
    15. Allen, Jason & Gregory, Allan W. & Shimotsu, Katsumi, 2011. "Empirical likelihood block bootstrapping," Journal of Econometrics, Elsevier, vol. 161(2), pages 110-121, April.
    16. Matias D. Cattaneo & Michael Jansson, 2014. "Bootstrapping Kernel-Based Semiparametric Estimators," CREATES Research Papers 2014-25, Department of Economics and Business Economics, Aarhus University.
    17. Zhang, Junchao, 2017. "A dilemma of fertility and female labor supply: Identification using Taiwanese twins," China Economic Review, Elsevier, vol. 43(C), pages 47-63.
    18. Lee, Seojeong, 2014. "Asymptotic refinements of a misspecification-robust bootstrap for generalized method of moments estimators," Journal of Econometrics, Elsevier, vol. 178(P3), pages 398-413.
    19. Priebe, Jan, 2011. "Child Costs and the Causal Effect of Fertility on Female Labor Supply: An investigation for Indonesia 1993-2008," Proceedings of the German Development Economics Conference, Berlin 2011 67, Verein für Socialpolitik, Research Committee Development Economics.
    20. 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.

    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

    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:16566. 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: (). General contact details of provider: http://edirc.repec.org/data/nberrus.html .

    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.