The aim of this paper is to compare estimates of the adjusted wage gap from different methods and sets of conditioning variables. We apply available parametric and non-parametric methods to LFS data from Poland for 2012. While the raw gap amounts to nearly 10 percent of the female wage; the adjusted wage gap estimates range between 15 percent and as much as 23 percent depending on the method and the choice of conditional variables. The differences across conditioning variables within the same method do not exceed 3pp, but including more variables almost universally results in larger estimates of the adjusted wage gaps. Methods that account for common support and selection into employment yielded higher estimates of the adjusted wage gap. While the actual point estimates of adjusted wage gap are slightly different, all of them are roughly twice as high as the raw gap, which corroborates the policy relevance of this methodological study.JEL Codes: C24, J31, J71

In this paper we link the estimates of the gender wage gap with the gender sensitivity of the language spoken in a given country. We find that nations with more gender neutral languages tend to be characterized by lower estimates of GWG. The results are robust to a number of sensitivity checks.

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte.

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. We explore data from all transition economies over nearly two decades, providing insights on the mechanisms behind labor force reallocation. We show that worker ows between jobs in dierent industries are rare relative to the demographic ows of youth entry and elderly exit. The same applies to the ows between state-owned enterprises and private rms. In fact, evidence suggest that changes in the demand for labor were accommodated mostly through demographic ows, with a smaller role left for job transitions. We also show that the speed of changing the ownership structure in the economy has driven exits to retirement, in particular the early exits. Terms of use: Documents in EconStor mayJEL Codes: P2, P5, D2, J6

We investigate the reliability of data from the Wage Indicator (WI), the largest online survey on earnings and working conditions. Comparing WI to nationally representative data sources for 17 countries reveals that participants of WI are not likely to have been representatively drawn from the respective populations. Previous literature has proposed to utilize weights based on inverse propensity scores, but this procedure was shown to leave reweighted WI samples different from the benchmark nationally representative data. We propose a novel procedure, building on covariate balancing propensity score, which achieves complete reweighting of the WI data, making it able to replicate the structure of nationally representative samples on observable characteristics. While rebalancing assures the match between WI and representative benchmark data sources, we show that the wage schedules remain different for a large group of countries. Using the example of a Mincerian wage regression, we find that in more than a third of the cases, our proposed novel reweighting assures that estimates obtained on WI data are not biased relative to nationally representative data. However, in the remaining 60 percent of the analyzed 95 data sets, systematic differences in the estimated coefficients of the Mincerian wage regression between WI and nationally representative data persist even after reweighting. We provide some intuition about the reasons behind these biases. Notably, objective factors such as access to the Internet or richness appear to matter, but self-selection (on unobservable characteristics) among WI participants appears to constitute an important source of bias.