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Regression Coefficient Identification Decay in the Presence of Infrequent Classification Errors

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  • Kreider, Brent

Abstract

Recent evidence from Bound et al. (2001) and Black et al. (2003) suggests that reporting errors in survey data routinely violate all of the classical measurement error assumptions. The econometrics literature has not considered the consequences of arbitrary measurement error for identification of regression coefficients. This paper highlights the severity of the identification problem given the presence of even infrequent arbitrary errors in a binary regressor. In the empirical component, health insurance misclassification rates of less than 1.3 percent generate double-digit percentage point ranges of uncertainty about the variable's true marginal effect on the use of health services.

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Bibliographic Info

Paper provided by Iowa State University, Department of Economics in its series Staff General Research Papers with number 12822.

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Date of creation: 11 Jun 2007
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Publication status: Published in Review of Economics and Statistics, November 2010, vol. 92 no. 4, pp. 1017-1023
Handle: RePEc:isu:genres:12822

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Postal: Iowa State University, Dept. of Economics, 260 Heady Hall, Ames, IA 50011-1070
Phone: +1 515.294.6741
Fax: +1 515.294.0221
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Web page: http://www.econ.iastate.edu
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Related research

Keywords: nonclassical measurement error; classification error; health insurance; corrupt sampling; binary regressor;

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Cited by:
  1. Gundersen, Craig & Kreider, Brent, 2009. "Bounding the effects of food insecurity on children's health outcomes," Journal of Health Economics, Elsevier, vol. 28(5), pages 971-983, September.
  2. Gundersen, Craig & Kreider, Brent & Pepper, John, 2012. "The impact of the National School Lunch Program on child health: A nonparametric bounds analysis," Journal of Econometrics, Elsevier, vol. 166(1), pages 79-91.
  3. Hullegie, Patrick & Klein, Tobias J., 2010. "The Effect of Private Health Insurance on Medical Care Utilization and Self-Assessed Health in Germany," IZA Discussion Papers 5004, Institute for the Study of Labor (IZA).

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