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This paper takes a closer look at the consequences of using a market index as a proxy for the latent market return in the capital asset pricing model. In particular, the consequences of two major sources of misspecification are analyzed: (i) the use of inaccurate weights and (ii) the use of only a subset of the asset universe to construct the index. The consequences resulting from the use of a badly chosen market proxy reach from inconsistent parameter estimates to misinterpretation of test outcomes indicating the existence of abnormal returns.

A minimum distance approach of estimating the CAPM under measurement error is presented, which identifies the CAPM parameters by exploiting the cross-equation cross-sectional restrictions resulting from a common measurement error. The new approach allows for quantifying the impact of measurement error and for testing the presence of spurious abnormal returns. Practical guidelines are presented to mitigate potential biases in the estimated CAPM parameters.


We investigate sources of educational differences in smoking. Using a large German data set containing retrospective information on the age at smoking onset, we compare age-specific hazard rates of starting smoking between (future) low and high educated individuals. We find that up to 90 % of the educational differences in smoking develop before the age of 16, i. e. before compulsory schooling is completed. This education gap persists into adulthood. Further, we examine the role of health-related knowledge (proxied by working in health-related occupations) and find it hardly explains smoking decisions. Our findings suggest that (unobserved) factors determining both the selection into smoking and education are almost exclusively responsible for educational differences in smoking. Only small parts of the education gap seem to be caused by general or health-specific education. The effectiveness of education policy to combat smoking is thus likely limited.



The following article informs about the Micro Data Linking-Panel (hereafter MDL-Panel), a new firm 1 dataset, available since May 2018 via the Research Data Centre of the Federal Statistical Office and the Research Data Centre of the Statistical Offices of the Federal States (RDC) (Forschungsdatenzentren der Statistischen Ämter des Bundes und der Länder (2018a, 2018b). The dataset originates from two projects funded by Eurostat over the period 2014 to 2016 with the title “Micro data linking of structural business statistics and other business statistics” (MDL). The projects combined data of various business statistics for the reference years 2008 to 2013 and were aimed at offering new perspectives on business statistics topics, which are not addressed in the ordinary publication program. Furthermore, MDL served to gain insights about the involved methodological challenges of linked data in Official Statistics. The first chapter provides background information on the projects funded by Eurostat. Chapter 2 contrasts the contents of the MDL-Panel to other products available via the RDC. The third chapter describes the scope of the dataset and its content in more detail. The article closes with future prospects for the MDL-Panel.



In German business statistics the statistical unit, or firm, is currently referred to as the smallest legally independent unit which keeps accounts for commercial and/or tax purposes.


Economic theory predicts a positive effect of an increase in income inequality on the prevalence of crime, but the international empirical evidence is mixed. For Germany, research on this topic is virtually non-existent. Therefore, I used fixed effect regressions to estimate the effect of a market income inequality proxy on property damages, thefts from motor vehicles, domestic burglaries and assaults in Germany. The models without spatial lags suggest economically small to moderate own-district elasticities between 0.13 and 0.95. The models with spatial lags generally show insignificant own-district estimates, but significant spatial spillovers.


The authors analyze the nexus between temporary agency work (TAW) and firm performance. Compared to Hirsch and Mueller (2012, The Productivity Effect of Temporary Agency Work: Evidence from German Panel Data. Economic Journal 122 (562): F216-F235) and Nielen and Schiersch (2014, Temporary Agency Work and Firm Competitiveness: Evidence from German Manufacturing Firms. Industrial Relations 53: 365–393), the authors support a concave relationship between the TAW share and productivity, but find a convex relationship between the TAW share and unit labor costs, instead of a u-shape. Moreover, a new finding is that the correlation between the TAW share and profitability is only moderate.


This paper analyses the effect of the economic crisis in the years 2008 and 2009 on individual training activities of different employee groups within establishments. We use a unique German linked employer–employee panel data set with detailed information on individual training history (WeLL-ADIAB). The so-called Great Recession can be seen as an exogenous, unexpected, and time-limited shock. Although our results cannot be interpreted in a strictly causal manner, our Diff-in-Diff analyses suggest a direct negative effect of the crisis on individual training activities in 2009 and 2010. The negative effect therefore sets in with a time lag and lasts until after the recession. Furthermore, the recession has a stronger effect for employees in unskilled jobs than for employees in skilled jobs.