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Iatrogenic Specification Error: A Cautionary Tale of Cleaning Data

  • Christopher R. Bollinger

    (University of Kentucky)

  • Amitabh Chandra

    (Dartmouth College, Institute for the Study of Labor (Bonn), and National Bureau of Economic 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 where the dependent variable has values that 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 solutions for a narrow class of error processes. Indeed such procedures can induce or exacerbate bias. Monte Carlo simulations and empirical results demonstrate the fragility of cleaning. Even on root mean square error criteria, we cannot find generalizable justifications for these procedures.

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Article provided by University of Chicago Press in its journal Journal of Labor Economics.

Volume (Year): 23 (2005)
Issue (Month): 2 (April)
Pages: 235-258

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Handle: RePEc:ucp:jlabec:v:23:y:2005:i:2:p:235-258
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