Benchmarking regions: estimating the counterfactual distribution of labor market outcomes

This paper develops and implements a new benchmarking approach for labor market regions. Based on panel data for regions, we use nonparametric matching techniques to account for observed labor market characteristics and for spatial proximity. As the benchmark, we estimate the counterfactual distribution of labor market outcomes for a region based on outcomes of similar regions. This allows to measure both the rank (relative performance) and the absolute performance based on the actual outcome for a region. Our outcome variable of interest is the hiring rate among the unemployed. We implement different similarity measures to account for differences in labor market conditions and spatial proximity, and we choose the tuning parameters in our matching approach based on a cross-validation procedure. The results show that both observed labor market characteristics and spatial proximity are important features to successfully match regions. Specifically, the modified Zhao (2004) distance measure and geographic distance in logs work best in our applications. Our estimated performance measures remain quite stable over time.