Which control variables do we really need for matching based evaluations of labour market programmes?

Based on a new, exceptionally informative and large German linked employer-employee administrative dataset, we investigate the question whether the omission of important control variables in matching estimation leads to biased impact estimates of typical active labour market programmes for the unemployed. Such biases would lead to false policy conclusions about the effectiveness of these expensive policies. Based on our preliminary findings, it seems that controlling for standard demographic variables removes already a substantial part of the potential selection bias.