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Estimating and Testing Models with Many Treatment Levels and Limited Instruments

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  • Lance Lochner
  • Enrico Moretti

Abstract

Many empirical microeconomic studies estimate econometric models that assume a single finite-valued discrete endogenous regressor (for example: different levels of schooling), exogenous regressors that are additively separable and enter the equation linearly; and coefficients (including per-unit treatment effects) that are homogeneous in the population. Empirical researchers interested in the causal effect of the endogenous regressor often use instrumental variables. When few valid instruments are available, researchers typically estimate restricted specifications that impose uniform per-unit treatment effects, even when these effects are likely to vary depending on the treatment level. In these cases, ordinary least squares (OLS) and instrumental variables (IV) estimators identify different weighted averages of all per-unit effects, so the traditional Hausman test (based on the restricted specification) is uninformative about endogeneity. Addressing this concern, we develop a new exogeneity test that compares the IV estimate from the restricted model with an appropriately weighted average of all per-unit effects estimated from the more general model using OLS. Notably, our test works even when the true model cannot be estimated using IV methods as long as a single valid instrument is available (e.g. a single binary instrument). We re-visit three recent empirical examples that examine the role of educational attainment on various outcomes to demonstrate the practical value of our test.

Suggested Citation

  • Lance Lochner & Enrico Moretti, 2011. "Estimating and Testing Models with Many Treatment Levels and Limited Instruments," NBER Working Papers 17039, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:17039
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    References listed on IDEAS

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    1. Pedro Carneiro & James J. Heckman & Edward Vytlacil, 2010. "Evaluating Marginal Policy Changes and the Average Effect of Treatment for Individuals at the Margin," Econometrica, Econometric Society, vol. 78(1), pages 377-394, January.
    2. James J. Heckman & Lance J. Lochner & Petra E. Todd, 2008. "Earnings Functions and Rates of Return," Journal of Human Capital, University of Chicago Press, vol. 2(1), pages 1-31.
    3. Lance Lochner & Enrico Moretti, 2004. "The Effect of Education on Crime: Evidence from Prison Inmates, Arrests, and Self-Reports," American Economic Review, American Economic Association, vol. 94(1), pages 155-189, March.
    4. Mogstad, Magne & Wiswall, Matthew, 2010. "Linearity in Instrumental Variables Estimation: Problems and Solutions," IZA Discussion Papers 5216, Institute for the Study of Labor (IZA).
    5. Jaeger, David A & Page, Marianne E, 1996. "Degrees Matter: New Evidence on Sheepskin Effects in the Returns to Education," The Review of Economics and Statistics, MIT Press, vol. 78(4), pages 733-740, November.
    6. Yitzhaki, Shlomo, 1996. "On Using Linear Regressions in Welfare Economics," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(4), pages 478-486, October.
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    Cited by:

    1. Lucija Muehlenbachs & Stefan Staubli & Mark A. Cohen, 2016. "The Impact of Team Inspections on Enforcement and Deterrence," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 3(1), pages 159-204.
    2. Firmin Doko Tchatoka & Jean-Marie Dufour, 2016. "Exogeneity tests, weak identification, incomplete models and non-Gaussian distributions: Invariance and finite-sample distributional theory," School of Economics Working Papers 2016-01, University of Adelaide, School of Economics.
    3. Javier Cano-Urbina & Lance Lochner, 2016. "The Effect of Education and School Quality on Female Crime," University of Western Ontario, Centre for Human Capital and Productivity (CHCP) Working Papers 20163, University of Western Ontario, Centre for Human Capital and Productivity (CHCP).
    4. Sergi Jiménez-Martín & Cristina Vilaplana Prieto, 2013. "Informal Care and intergenerational transfers in European Countries," Working Papers 2013-25, FEDEA.
    5. Katrine V. Løken & Magne Mogstad & Matthew Wiswall, 2012. "What Linear Estimators Miss: The Effects of Family Income on Child Outcomes," American Economic Journal: Applied Economics, American Economic Association, vol. 4(2), pages 1-35, April.

    More about this item

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • J0 - Labor and Demographic Economics - - General

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