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.

The article structually estimates an on‐the‐job search model of job separations. Given each employer pays observably equivalent workers the same but wages are dispersed across employers, an employer's separation flow is the sum of an exogenous outflow unrelated to the wage and a job‐to‐job flow that decreases with the employer's wage. Using data from the Danish Integrated Database for Labour Market Research, the empirical results imply, as predicted by theory, that search effort declines with the wage. Furthermore, the estimates explain the employment effect, defined as the horizontal difference between the distribution of wages earned and the wage offer distribution.