Contributions published at Empirical Research in Business, Industrial Relations and Human Resource Management (Uschi Backes-Gellner)

This study estimates the earning losses of workers experiencing an involuntary job separation. We employ, for the first time in the earning losses literature, a Poisson pseudo-maximum-likelihood estimator with fixed effects that has several advantages with respect to conventional fixed effects models. The Poisson estimator allows considering the full set of involuntary separations, including those with zero labor market earnings because of unemployment. By including individuals with zero earnings and by using our new method, the loss in the year of separation becomes larger than in previous studies. The loss starts with roughly 30 percent and, although it quickly shrinks, it remains at around 15 percent in the following years. In addition, we find that compared to other reasons for separation, the earning loss pattern is unique for involuntary separations, because no other type of separation implies such permanent scarring. This latter finding makes us confident that the self-reported involuntariness of a separation is a reliable source of information.

This study analyzes the relationship between discriminatory social attitudes and the variation of within-firm pay gaps by combining data on regional votes on gender equality laws with a data set of multi-establishments firms and their workers. The data set allows us for the first time to study gender pay gaps within the same firm across establishments located in regions with varying discriminatory social attitudes. Our results show that firms have larger pay gaps in regions with stronger discriminatory social attitudes. This result remains robust when we account for detailed worker and job characteristics and prevails for different subsamples. Thus we show that a relationship between discriminatory social attitudes and gender pay gaps prevails even after accounting for the sorting of women and men into different firms and occupations.

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Using unique data from an internet-based foreign-exchange trading platform, we show that the black market efficiently incorporated public information on the state of the Belarusian economy during the Balance of Payments crisis of 2011. Between May and October 2011, the government repeatedly devalued the Belarusian ruble and eventually abandoned its fixed exchange rate regime. Measures derived from black market transaction data have significant predictive power for these devaluations. The significance of these black market measures survives even when we include standard macroeconomic indicators in our forecasting model. In line with standard economic theory, activity in the black market has dried up subsequently.

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