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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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    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.

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    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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