Who Buffers Income Losses After Job Displacement? The Role of Alternative Income Sources, the Family, and the State
IWH Discussion Papers,
Using survey data from the German Socio-Economic Panel (SOEP), this paper analyses to what extent alternative income sources, reactions within the household context, and redistribution by the state attenuate earnings losses after job displacement. Applying propensity score matching and fixed effects estimations, we find high individual earnings losses after job displacement and only limited convergence. Income from selfemployment slightly reduces the earnings gap and severance payments buffer losses in the short run. On the household level, we find substantial and rather persistent losses in per capita labour income. We do not find that increased labour supply by other household members contributes to the compensation of the income losses. Most importantly, our results show that redistribution within the tax and transfer system substantially mitigates income losses of displaced workers both in the short and the long run whereas other channels contribute only little.
Is There a Gap in the Gap? Regional Differences in the Gender Pay Gap
Scottish Journal of Political Economy,
In this paper, we investigate regional differences in the gender pay gap both theoretically and empirically. Within a spatial model of monopsonistic competition, we show that more densely populated labour markets are more competitive and constrain employers’ ability to discriminate against women. Utilizing a large administrative data set for western Germany and a flexible semi-parametric propensity score matching approach, we find that the unexplained gender pay gap for young workers is substantially lower in large metropolitan than in rural areas. This regional gap in the gap of roughly 10 percentage points remained surprisingly constant over the entire observation period of 30 years.
Distance Functions for Matching in Small Samples
Computational Statistics & Data Analysis,
The development of ‘standards’ for the application of matching algorithms in empirical evaluation studies is still an outstanding goal. The first step of the matching procedure is the choice of an appropriate distance function. In empirical evaluation situations often the sample sizes are small. Moreover, they consist of variables with different scale levels which have to be considered explicitly in the matching process. A simulation is performed which is directed towards these empirical challenges and supplements former studies in this respect. The choice of the analysed distance functions is determined by the results of former theoretical studies and recommendations in the empirical literature. Thus, two balancing scores (the propensity score and the index score) and the Mahalanobis distance are considered. Additionally, aggregated statistical distance functions not yet used for empirical evaluation are included. The matching outcomes are compared using non-parametric scale-specific tests for identical distributions of the characteristics in the treatment and the control groups. The simulation results show that, in small samples, aggregated statistical distance functions are the better choice for summarising similarities in differently scaled variables compared to the commonly used measures.