Volume 239, Issue 2 -

Recently, the homeownership rate of immigrants in Germany has increased by more than 20 percentage points. To shed light on this sharp rise, this paper investigates the driving forces of the trend in the homeownership rate of immigrant households in Germany between 1996 to 2005 and 2001 to 2011 using a probit-based non-linear decomposition method. Empirical findings suggest that 50 % of the change in immigrants’ homeownership rate within the first time period can be explained by characteristics such as age and educational attainment. In the second time period, the explanatory power of characteristics is almost zero, indicating that it is rather the favorable economic and institutional environment as well as changes in immigrants’ tenure choice process that contributed to the substantial increase in immigrants’ homeownership rate in Germany. We additionally find that housing quality of immigrant homeowners has slightly improved as well, but that there is still a substantial nativity gap in housing quality among tenants as well as among owners.

We report results of a survey among active forecasters of the German business cycle. Using data for 82 respondents from 37 different institutions, we investigate what models and theories forecasters subscribe to and find that they are pronounced conservative in the sense that they overwhelmingly rely on methods and theories that have been well-established for a long time, while more recent approaches are relatively unimportant for the practice of business cycle forecasting. DSGE models are mostly used in public institutions. In line with findings in the literature there are tendencies of “leaning towards consensus” (especially for public institutions) and “sticky adjustment of forecasts” with regard to new information. A stable relationship between preferred theories and methods and forecast accuracy cannot be established.

The statutory minimum wage in Germany was set as an hourly wage. Thus, valid information on gross hourly wages must be calculated from monthly wages and weekly working hours. This paper compares the German Socio-Economic Panel (GSOEP) and the (Structure of) Earnings Survey (SES/ES). The sampling and collection of data on employees in the household survey GSOEP, and on jobs in the administrative surveys SES/ES exhibit fundamental conceptual differences. Accordingly, there is variation in the definition of types of employment and in the distribution of the observed units regarding central characteristics. Monthly wages, weekly working hours and gross hourly wages differ especially in the lower range of the respective distribution. Against this backdrop specific implications can be derived for minimum wage research.

Using data from the German Structure of Earnings Survey (GSES), this paper studies the role of changes in working hours for the increase in male and female earnings inequality between 2001 and 2010. We provide both classic decompositions of the variance of log earnings into the variances of hours, wage rates and their covariance, and decompositions based on reweighting the conditional hours distribution. Depending on the inequality measure considered, our results suggest that between 10 and 30% of the increase in male earnings inequality and 37 to 47% of the increase in female earnings inequality can be explained by changes in working hours. In addition, a large part of the inequality increase can be accounted for by changes in the composition of person and firm characteristics.