Iatrogenic Specification Error: A Cautionary Tale of Cleaning Data

24 Pages Posted: 10 Apr 2004

See all articles by Christopher R. Bollinger

Christopher R. Bollinger

University of Kentucky - Department of Economics

Amitabh Chandra

Harvard University - Harvard Kennedy School (HKS); National Bureau of Economic Research (NBER); IZA Institute of Labor Economics

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Date Written: March 2004

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.

Keywords: measurement error models, trimming, winsorizing

JEL Classification: C1, J1

Suggested Citation

Bollinger, Christopher R. and Chandra, Amitabh, Iatrogenic Specification Error: A Cautionary Tale of Cleaning Data (March 2004). Available at SSRN: https://ssrn.com/abstract=527007

Christopher R. Bollinger

University of Kentucky - Department of Economics ( email )

Lexington, KY 40506
United States

Amitabh Chandra (Contact Author)

Harvard University - Harvard Kennedy School (HKS) ( email )

79 John F. Kennedy Street
Cambridge, MA 02138
United States

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

IZA Institute of Labor Economics

P.O. Box 7240
Bonn, D-53072
Germany

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