Iatrogenic specification error: A cautionary tale of cleaning data

Christopher R. Bollinger, Amitabh Chandra

Research output: Contribution to journalArticlepeer-review

43 Scopus citations


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.

Original languageEnglish
Pages (from-to)235-257
Number of pages23
JournalJournal of Labor Economics
Issue number2
StatePublished - Apr 2005

ASJC Scopus subject areas

  • Industrial relations
  • Economics and Econometrics


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