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Iatrogenic Specification Error: A Cautionary Tale of Cleaning Data

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Author Info
Bollinger, Christopher R. (University of Kentucky)
Chandra, Amitabh () (Dartmouth College, NBER and IZA Bonn)
Abstract

In empirical research it is common practice to use sensible rules of thumb for cleaning data. Measurement error is often the justification for removing (trimming) or recoding (winsorizing) observations whose values lie outside a specified range. We consider a general measurement error process that nests many plausible models. Analytic results demonstrate that winsorizing and trimming are only solutions for a narrow class of measurement error processes. Indeed, for the measurement error processes found in most social-science data, such procedures can induce or exacerbate bias, and even inflate the variance estimates. We term this source of bias “Iatrogenic” (or econometrician induced) error. Monte Carlo simulations and empirical results from the Census PUMS data and 2001 CPS data demonstrate the fragility of trimming and winsorizing as solutions to measurement error in the dependent variable. Even on asymptotic variance and RMSE criteria, we are unable to find generalizable justifications for commonly used cleaning procedures.

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Paper provided by Institute for the Study of Labor (IZA) in its series IZA Discussion Papers with number 1093.

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Length: 24 pages
Date of creation: Mar 2004
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Handle: RePEc:iza:izadps:dp1093

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Related research
Keywords: measurement error models; trimming; winsorizing;

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Find related papers by JEL classification:
C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General
J1 - Labor and Demographic Economics - - Demographic Economics

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  1. Bollinger, Christopher R, 1998. "Measurement Error in the Current Population Survey: A Nonparametric Look," Journal of Labor Economics, University of Chicago Press, vol. 16(3), pages 576-94, July. [Downloadable!] (restricted)
  2. Bound, John & Krueger, Alan B, 1991. "The Extent of Measurement Error in Longitudinal Earnings Data: Do Two Wrongs Make a Right?," Journal of Labor Economics, University of Chicago Press, vol. 9(1), pages 1-24, January. [Downloadable!] (restricted)
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  3. Hyslop, Dean R & Imbens, Guido W, 2001. "Bias from Classical and Other Forms of Measurement Error," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(4), pages 475-81, October.
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  4. Hirsch, Barry T. & Schumacher, Edward J., 2003. "Match Bias in Wage Gap Estimates Due to Earnings Imputation," IZA Discussion Papers 783, Institute for the Study of Labor (IZA). [Downloadable!]
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  5. Bound, John, et al, 1994. "Evidence on the Validity of Cross-Sectional and Longitudinal Labor Market Data," Journal of Labor Economics, University of Chicago Press, vol. 12(3), pages 345-68, July. [Downloadable!] (restricted)
  6. Mellow, Wesley & Sider, Hal, 1983. "Accuracy of Response in Labor Market Surveys: Evidence and Implications," Journal of Labor Economics, University of Chicago Press, vol. 1(4), pages 331-44, October. [Downloadable!] (restricted)
  7. Goldberger, Arthur S., 1981. "Linear regression after selection," Journal of Econometrics, Elsevier, vol. 15(3), pages 357-366, April. [Downloadable!] (restricted)
  8. Card, David & Krueger, Alan B, 1992. "School Quality and Black-White Relative Earnings: A Direct Assessment," The Quarterly Journal of Economics, MIT Press, vol. 107(1), pages 151-200, February. [Downloadable!] (restricted)
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  9. MacDonald, Glenn M & Robinson, Chris, 1985. "Cautionary Tails about Arbitrary Deletion of Observations; or, Throwing the Variance Out with the Bathwater," Journal of Labor Economics, University of Chicago Press, vol. 3(2), pages 124-52, April. [Downloadable!] (restricted)
  10. Juhn, Chinhui & Murphy, Kevin M & Pierce, Brooks, 1993. "Wage Inequality and the Rise in Returns to Skill," Journal of Political Economy, University of Chicago Press, vol. 101(3), pages 410-42, June. [Downloadable!] (restricted)
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