Using randomized field experiments to evaluate support programs for older and low-skilled workers with combined survey-register data

In this project, we use randomized experiments to evaluate labor market programs for two important groups of disadvantaged workers in the labor force: older unemployed workers as well as low-skilled and older employed workers. Despite the resilience of the German labor market in the face of the worldwide financial crisis, these two groups face labor market difficulties that may intensify in the future. German labor market policy contains a number of unique programs that do not exist in other countries and hence call for a careful evaluation.
First, we investigate to what extent knowledge about subsidies for in-company training of low-skilled and older employed workers improves their labor market outcomes. Second, we analyze to what extent knowledge about a targeted wage support program for older unemployed workers affects their labor market outcomes. In each case, we examine effects on the employment probability, wages, and other job characteristics. Furthermore, we analyze to what extent these programs produce deadweight losses. The analysis is based on combined survey and register data.
Both of our experiments are based on information treatments, which are low-threshold interventions. We send out information to a randomly selected treatment group among the eligible, informing this group about access possibilities to a support program. The other eligibles in the population, who did not receive extra information, serve as the control group. Such information treatment experiments allow for a two-stage evaluation strategy. In the first stage, we assess the effect of the information provision on the average outcomes of interest. In the second stage, we use the information provision as an instrumental variable to assess average effects of knowing about the program on the outcomes of interest. Differences in outcome variables between both groups may then be related to changes in these shares. Specifically, this allows for the identification of local average treatment effects (LATE) and for the partial identification of average treatment effects (ATE) of program participation. A methodological challenge is to examine effects on post-unemployment outcomes in the presence of right-censored unemployment durations in the data.
As we propose an innovative approach for the evaluation of German labor market programs, our results will not only add new insights to the literature and policy results, but also explore the potential and limitations of future information treatments and field experiments. Moreover, we will provide novel theoretical analysis and apply recently developed non-parametric econometric methods based on partial identification.

Prof. Gerard van den Berg, PhD (University of Bristol)
Prof. Dr. Gesine Stephan (IAB)

Pia Homrighausen (BAMF)
Prof. Lena Janys, PhD (University of Bonn)
Christine Singer (IAB)