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Regression Coefficient Identification Decay in The Presence of Infrequent Classification Errors

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  • Brent Kreider

    (Iowa State University)

Abstract

Recent evidence from Bound, Brown, and Mathiowetz (2001) and Black, Sanders, and Taylor (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 fully 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% generate double-digit percentage point ranges of uncertainty about the variable's true marginal effect on the use of health services. (c) 2010 The President and Fellows of Harvard College and the Massachusetts Institute of Technology.

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File URL: http://www.mitpressjournals.org/doi/pdf/10.1162/REST_a_00044
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Bibliographic Info

Article provided by MIT Press in its journal The Review of Economics and Statistics.

Volume (Year): 92 (2010)
Issue (Month): 4 (November)
Pages: 1017-1023

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Handle: RePEc:tpr:restat:v:92:y:2010:i:4:p:1017-1023

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Web page: http://mitpress.mit.edu/journals/

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Cited by:
  1. Patrick Hullegie & Tobias J. Klein, 2010. "The effect of private health insurance on medical care utilization and self‐assessed health in Germany," Health Economics, John Wiley & Sons, Ltd., vol. 19(9), pages 1048-1062, 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. 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.

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