Life-Cycle Incidence of Family Policy Measures in Germany: Evidence from a Dynamic Microsimulation Model

Abstract

Die Dis cus si on Pape rs die nen einer mög lichst schnel len Ver brei tung von neue ren For schungs arbei ten des ZEW. Die Bei trä ge lie gen in allei ni ger Ver ant wor tung der Auto ren und stel len nicht not wen di ger wei se die Mei nung des ZEW dar.Dis cus si on Papers are inten ded to make results of ZEW research prompt ly avai la ble to other eco no mists in order to encou ra ge dis cus si on and sug gesti ons for revi si ons. The aut hors are sole ly respon si ble for the con tents which do not neces sa ri ly repre sent the opi ni on of the ZEW. AbstractThis paper quantifies the life-cycle incidence of key family policy measures in Germany. The analysis is based on a novel dynamic microsimulation model that combines simulated family life-cycles for a base population from the 2009 wave of the German Socio-Economic Panel (SOEP) with a comprehensive tax-benefit model. The results indicate that households in Germany benefit considerably from family-and marriage-related transfers, yet also reveal substantial variation behind the population average. Moreover, it is shown that some measures, such as income tax splitting, may make individuals in fact worse off, in financial terms, over the long course, as a result of negative labour supply incentives which are reinforced through detrimental effects on human capital accumulation. IntroductionStudies of earnings inequality increasingly compare outcomes not in a cross-section, but over the life-cycle (e.g., Bowlus and Robin 2012). Adopting such a life-cycle perspective is also important for assessing the effects of tax-benefit systems (e.g., Nelissen 1998). However, the analysis is made difficult by the fact that available data sets rarely span the entire lifecycle of individuals or households. Researchers have dealt with this challenge in two ways:one possibility is to adopt a "slicing" approach which stiches agents observed at different ages together, with the extreme case of converting a single cross-section into a synthetic cohort, as in the computation of total fertility rates. The second possibility is to rely on simulation meth- In the following, we will present the different components of the model in greater detail.We will also show results from a baseline simulation that constitutes the point of departure for the policy analysis in the next section. Base populationOur base population is drawn from the SOEP wave of 2009. The SOEP is a longitudinal survey of private households, with a rich set of information on personal and household characteristics. 1 We keep only those agents for whom all information required for simulating taxes and transfers (cf. Section 3) is available.ZEWDMM is able to simulate trajectories for the entire cross-section. However, for the present purpose, we focus on households in which at least one individual is between 25 and 29 years old in 2009. The members of this group are (1) old enough so that most individuals have completed their education and at the same time (2) young enough so that we can follow them over the major part ...

The Gender Gap in Lifetime Earnings: The Role of Parenthood

“…Other studies close to ours are the ones byBonin et al (2015) andHänisch and Klos (2016) which also simulate employment biographies using SOEP data.…”