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Interpretation of Regressions with Multiple Proxies

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  • Darren Lubotsky
  • Martin Wittenberg

Abstract

Multiple proxy variables are typically available for an unobserved explanatory variable in a regression. We provide a procedure by which the coefficient of interest can be estimated from a regression in which all the proxies are included simultaneously. This estimator is superior in large samples to the common practice of creating a summary measure of the proxy variables. We examine the relationship between parents' income and children's reading test scores in the United States, and between parents' assets and children's school enrollment in India, and demonstrate that the reduction in attenuation bias from a better use of proxy variables can be significant. Copyright by the President and Fellows of Harvard College and the Massachusetts Institute of Technology.

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Bibliographic Info

Paper provided by Princeton University, Department of Economics, Industrial Relations Section. in its series Working Papers with number 836.

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Date of creation: Sep 2001
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Handle: RePEc:pri:indrel:dsp01fq977t77z

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Keywords: Proxy variables; measurement error; index construction;

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  1. Goldberger, Arthur S, 1972. "Structural Equation Methods in the Social Sciences," Econometrica, Econometric Society, vol. 40(6), pages 979-1001, November.
  2. Aigner, Dennis J. & Hsiao, Cheng & Kapteyn, Arie & Wansbeek, Tom, 1984. "Latent variable models in econometrics," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 23, pages 1321-1393 Elsevier.
  3. David M. Blau, 1999. "The Effect Of Income On Child Development," The Review of Economics and Statistics, MIT Press, vol. 81(2), pages 261-276, May.
  4. Mauro, Paolo, 1995. "Corruption and Growth," The Quarterly Journal of Economics, MIT Press, vol. 110(3), pages 681-712, August.
  5. Scheinkman, Jose A. & Soutter, Christine L. & Glaeser, Edward Ludwig & Laibson, David I., 2000. "Measuring Trust," Scholarly Articles 4481497, Harvard University Department of Economics.
  6. Griliches, Zvi, 1986. "Economic data issues," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 3, chapter 25, pages 1465-1514 Elsevier.
  7. Anne Case & Darren Lubotsky & Christina Paxson, 2002. "Economic status and health in childhood: the origins of the gradient," Working Papers 262, Princeton University, Woodrow Wilson School of Public and International Affairs, Center for Health and Wellbeing..
  8. Leamer, Edward E., 1983. "Model choice and specification analysis," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 1, chapter 5, pages 285-330 Elsevier.
  9. Solon, Gary, 1992. "Intergenerational Income Mobility in the United States," American Economic Review, American Economic Association, vol. 82(3), pages 393-408, June.
  10. Filmer, Deon & Pritchett, Lant, 1998. "Estimating wealth effects without expenditure data - or tears : with an application to educational enrollments in states of India," Policy Research Working Paper Series 1994, The World Bank.
  11. Sendhil Mullainathan & Marianne Bertrand, 2001. "Do People Mean What They Say? Implications for Subjective Survey Data," American Economic Review, American Economic Association, vol. 91(2), pages 67-72, May.
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