Epi-nomics: Applying lessons from epidemiolgy research on misclassifaction to economics policy evaluation

Policy evaluation in economics examines the effect of regulations on aggregate outcomes, but economics research rarely considers misclassification of the outcome of interest. We apply simulation methods that are well established in epidemiological analysis to determine when under- or overreporting leads to bias of causal estimates in policy analysis, with a focus on difference-in-differences methodology. First, we simulate data with perfect sensitivity and imperfect specificity; mean absolute bias in this case is close to zero as long as specificity is similar for exposed and nonexposed units but is sizable when specificity varies by exposure. Next, we show that with perfect specificity and imperfect sensitivity, mean absolute bias is highest when sensitivity is low for untreated units and high for treated units. Finally, we present a new Stata program that presents difference-in-differences estimates corrected for under- and over-counting under a range of plausible assumptions about misclassification.