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IV Quantile Regression for Group-level Treatments, with an Application to the Distributional Effects of Trade

Author

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  • Denis Chetverikov
  • Bradley Larsen
  • Christopher Palmer
Abstract
We present a methodology for estimating the distributional effects of an endogenous treatment that varies at the group level when there are group-level unobservables, a quantile extension of Hausman and Taylor (1981). Because of the presence of group-level unobservables, standard quantile regression techniques are inconsistent in our setting even if the treatment is independent of unobservables. In contrast, our estimation technique is consistent as well as computationally simple, consisting of group-by-group quantile regression followed by two-stage least squares. Using the Bahadur representation of quantile estimators, we derive weak conditions on the growth of the number of observations per group that are sufficient for consistency and asymptotic zero-mean normality of our estimator. As in Hausman and Taylor (1981), micro-level covariates can be used as internal instruments for the endogenous group-level treatment if they satisfy relevance and exogeneity conditions. An empirical application indicates that low-wage earners in the US from 1990--2007 were significantly more affected by increased Chinese import competition than high-wage earners. Our approach applies to a broad range of settings in labor, industrial organization, trade, public finance, and other applied fields.

Suggested Citation

  • Denis Chetverikov & Bradley Larsen & Christopher Palmer, 2015. "IV Quantile Regression for Group-level Treatments, with an Application to the Distributional Effects of Trade," NBER Working Papers 21033, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:21033
    Note: DEV IO ITI LS PE TWP
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    1. Robert C. Feenstra & Gordon H. Hanson, 1999. "The Impact of Outsourcing and High-Technology Capital on Wages: Estimates For the United States, 1979–1990," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 114(3), pages 907-940.
    2. Alberto Abadie & Joshua Angrist & Guido Imbens, 2002. "Instrumental Variables Estimates of the Effect of Subsidized Training on the Quantiles of Trainee Earnings," Econometrica, Econometric Society, vol. 70(1), pages 91-117, January.
    3. Hausman, Jerry A & Taylor, William E, 1981. "Panel Data and Unobservable Individual Effects," Econometrica, Econometric Society, vol. 49(6), pages 1377-1398, November.
    4. Victor Chernozhukov & Iván Fernández-Val, 2011. "Inference for Extremal Conditional Quantile Models, with an Application to Market and Birthweight Risks," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 78(2), pages 559-589.
    5. Katz, Lawrence F. & Autor, David H., 1999. "Changes in the wage structure and earnings inequality," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 3, chapter 26, pages 1463-1555, Elsevier.
    6. Ivan A. Canay, 2011. "A simple approach to quantile regression for panel data," Econometrics Journal, Royal Economic Society, vol. 14(3), pages 368-386, October.
    7. Joshua D. Angrist & Kevin Lang, 2004. "Does School Integration Generate Peer Effects? Evidence from Boston's Metco Program," American Economic Review, American Economic Association, vol. 94(5), pages 1613-1634, December.
    8. Koenker, Roger, 2004. "Quantile regression for longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 91(1), pages 74-89, October.
    9. Abrevaya, Jason & Dahl, Christian M, 2008. "The Effects of Birth Inputs on Birthweight," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 379-397.
    10. Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521845731.
    11. Hahn, Jinyong & Meinecke, Juergen, 2005. "Time-Invariant Regressor In Nonlinear Panel Model With Fixed Effects," Econometric Theory, Cambridge University Press, vol. 21(2), pages 455-469, April.
    12. Tolbert, Charles M. & Sizer, Molly, 1996. "U.S. Commuting Zones and Labor Market Areas: A 1990 Update," Staff Reports 278812, United States Department of Agriculture, Economic Research Service.
    13. 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.
    14. Victor Chernozhukov, 2005. "Extremal quantile regression," Papers math/0505639, arXiv.org.
    15. Powell, James L., 1984. "Least absolute deviations estimation for the censored regression model," Journal of Econometrics, Elsevier, vol. 25(3), pages 303-325, July.
    16. Bryan S. Graham & James L. Powell, 2012. "Identification and Estimation of Average Partial Effects in “Irregular” Correlated Random Coefficient Panel Data Models," Econometrica, Econometric Society, vol. 80(5), pages 2105-2152, September.
    17. David H. Autor & David Dorn & Gordon H. Hanson, 2013. "The China Syndrome: Local Labor Market Effects of Import Competition in the United States," American Economic Review, American Economic Association, vol. 103(6), pages 2121-2168, October.
    18. James Heckman & Edward Vytlacil, 1998. "Instrumental Variables Methods for the Correlated Random Coefficient Model: Estimating the Average Rate of Return to Schooling When the Return is Correlated with Schooling," Journal of Human Resources, University of Wisconsin Press, vol. 33(4), pages 974-987.
    19. Kato, Kengo & F. Galvao, Antonio & Montes-Rojas, Gabriel V., 2012. "Asymptotics for panel quantile regression models with individual effects," Journal of Econometrics, Elsevier, vol. 170(1), pages 76-91.
    20. Edward E. Leamer, 1994. "Trade, Wages and Revolving Door Ideas," NBER Working Papers 4716, National Bureau of Economic Research, Inc.
    21. Joshua Angrist & Victor Chernozhukov & Iván Fernández-Val, 2006. "Quantile Regression under Misspecification, with an Application to the U.S. Wage Structure," Econometrica, Econometric Society, vol. 74(2), pages 539-563, March.
    22. Lee, Sokbae, 2007. "Endogeneity in quantile regression models: A control function approach," Journal of Econometrics, Elsevier, vol. 141(2), pages 1131-1158, December.
    23. Matthew Masten & Alexander Torgovitsky, 2014. "Instrumental variables estimation of a generalized correlated random coefficients model," CeMMAP working papers 02/14, Institute for Fiscal Studies.
    24. Chernozhukov, Victor & Hansen, Christian, 2008. "Instrumental variable quantile regression: A robust inference approach," Journal of Econometrics, Elsevier, vol. 142(1), pages 379-398, January.
    25. Lamarche, Carlos, 2010. "Robust penalized quantile regression estimation for panel data," Journal of Econometrics, Elsevier, vol. 157(2), pages 396-408, August.
    26. Rosen, Adam M., 2012. "Set identification via quantile restrictions in short panels," Journal of Econometrics, Elsevier, vol. 166(1), pages 127-137.
    27. Chernozhukov, Victor & Hansen, Christian, 2006. "Instrumental quantile regression inference for structural and treatment effect models," Journal of Econometrics, Elsevier, vol. 132(2), pages 491-525, June.
    28. J. P. Florens & J. J. Heckman & C. Meghir & E. Vytlacil, 2008. "Identification of Treatment Effects Using Control Functions in Models With Continuous, Endogenous Treatment and Heterogeneous Effects," Econometrica, Econometric Society, vol. 76(5), pages 1191-1206, September.
    29. Guido W. Imbens & Whitney K. Newey, 2009. "Identification and Estimation of Triangular Simultaneous Equations Models Without Additivity," Econometrica, Econometric Society, vol. 77(5), pages 1481-1512, September.
    30. Jerry Hausman, 2001. "Mismeasured Variables in Econometric Analysis: Problems from the Right and Problems from the Left," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 57-67, Fall.
    31. Robert C. Feenstra, 2010. "Offshoring in the Global Economy: Microeconomic Structure and Macroeconomic Implications," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262013835, April.
    32. Galvao Jr., Antonio F., 2011. "Quantile regression for dynamic panel data with fixed effects," Journal of Econometrics, Elsevier, vol. 164(1), pages 142-157, September.
    33. Joseph G. Altonji & Rosa L. Matzkin, 2005. "Cross Section and Panel Data Estimators for Nonseparable Models with Endogenous Regressors," Econometrica, Econometric Society, vol. 73(4), pages 1053-1102, July.
    34. Krugman, Paul R., 2000. "Technology, trade and factor prices," Journal of International Economics, Elsevier, vol. 50(1), pages 51-71, February.
    35. Matthew Backus, 2020. "Why Is Productivity Correlated With Competition?," Econometrica, Econometric Society, vol. 88(6), pages 2415-2444, November.
    36. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2012. "Gaussian approximations and multiplier bootstrap for maxima of sums of high-dimensional random vectors," Papers 1212.6906, arXiv.org, revised Jan 2018.
    37. Andrew Chesher, 2003. "Identification in Nonseparable Models," Econometrica, Econometric Society, vol. 71(5), pages 1405-1441, September.
    38. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    39. David H. Autor & Lawrence F. Katz & Melissa S. Kearney, 2008. "Trends in U.S. Wage Inequality: Revising the Revisionists," The Review of Economics and Statistics, MIT Press, vol. 90(2), pages 300-323, May.
    40. Victor Chernozhukov & Christian Hansen, 2005. "An IV Model of Quantile Treatment Effects," Econometrica, Econometric Society, vol. 73(1), pages 245-261, January.
    41. Nathaniel Baum-Snow, 2007. "Did Highways Cause Suburbanization?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 122(2), pages 775-805.
    42. Jonathan Haskel & Robert Z. Lawrence & Edward E. Leamer & Matthew J. Slaughter, 2012. "Globalization and U.S. Wages: Modifying Classic Theory to Explain Recent Facts," Journal of Economic Perspectives, American Economic Association, vol. 26(2), pages 119-140, Spring.
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    More about this item

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • 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
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C36 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Instrumental Variables (IV) Estimation
    • F16 - International Economics - - Trade - - - Trade and Labor Market Interactions
    • J30 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - General

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