We estimate the effect of rising health insurance premiums on wages, employment, and the distribution of part-time and full-time work using variation in medical malpractice payments driven by the recent "medical malpractice crisis." We estimate that a 10% increase in health insurance premiums reduces the aggregate probability of being employed by 1.2 percentage points, reduces hours worked by 2.4%, and increases the likelihood that a worker is employed only part time by 1.9 percentage points. For workers covered by employer provided health insurance, this increase in premiums results in an offsetting decrease in wages of 2.3%.

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.