Sensitivity of matching-based program evaluations to the availability of control variables

Based on new, exceptionally informative and large German linked employer–employee administrative data, we investigate the question whether the omission of important control variables in matching estimation leads to biased impact estimates of typical active labor market programs for the unemployed. Such biases would lead to false policy conclusions about the cost-effectiveness of these expensive policies. Using newly developed Empirical Monte Carlo Study methods, we find that besides standard personal characteristics, information about the current unemployment spell, regional information, pre-treatment outcomes, and detailed short-term labor market histories remove most of the selection bias.