Clash of Career and Family: Fertility Decisions after Job Displacement

In this paper we investigate how fertility decisions respond to unexpected career interruptions which occur as a consequence of job displacement. Using an event study approach we compare the birth rates of displaced women with those of women unaffected by job loss after establishing the pre-displacement comparability of these groups. Our results reveal that job displacement reduces average fertility by 5 to 10% in both the short and medium term (3 and 6 years) and that these effects are largely explained by the response of white collar women. Using an instrumental variable approach we provide evidence that the reduction in fertility is not due to the income loss generated by unemployment but arises because displaced workers undergo a career interruption. These results are interpreted in the light of a model in which the rate of human capital accumulation slows down after the birth of a child and all specific human capital is destroyed upon job loss.fertility, unemployment, plant closings, human capital

Fertility and Economic Instability: The Role of Unemployment and Job Displacement

We study the effect of job displacement on fertility in a sample of white collar women in Austria. Using instrumental variables methods we show that unemploy- ment incidence as such has no negative effect on fertility decisions, but the very fact of being displaced from a career-oriented job has; fertility rates for women affected by a plant closure are signiffcantly below those of a control group, even after six years.fertility, unemployment, plant closings, human capital

A Multi-Component Model for the Observed Astrophysical Neutrinos

We propose a multi-component model for the observed diffuse neutrino flux, including the residual atmospheric backgrounds, a Galactic contribution (such as from cosmic ray interactions with gas), an extra-galactic contribution from pp interactions (such as from starburst galaxies) and a hard extragalatic contribution from photo-hadronic interactions at the highest energies (such as from Tidal Disruption Events or Active Galactic Nuclei). We demonstrate that this model can address the key problems of astrophysical neutrino data, such as the different observed spectral indices in the high-energy starting and through-going muon samples, a possible anisotropy due to Galactic events, the non-observation of point sources, and the constraint from the extragalatic diffuse gamma-ray background. Furthermore, the recently observed muon track with a deposited energy of 4.5 PeV might be interpreted as evidence for the extragalactic photo-hadronic contribution. We perform the analysis based on the observed events instead of the unfolded fluxes by computing the probability distributions for the event type and reconstructed neutrino energy. As a consequence, we give the probability to belong to each of these astrophysical components on an event-to-event basis.Comment: 20 pages, 12 figures. Accepted for publication in A&

Labor Market Entry and Earnings Dynamics: Bayesian Inference Using Mixtures-of-Experts Markov Chain Clustering

This paper analyzes patterns in the earnings development of young labor market entrants over their life cycle. We identify four distinctly different types of transition patterns between discrete earnings states in a large administrative data set. Further, we investigate the effects of labor market conditions at the time of entry on the probability of belonging to each transition type. To estimate our statistical model we use a model-based clustering approach. The statistical challenge in our application comes from the di±culty in extending distance-based clustering approaches to the problem of identify groups of similar time series in a panel of discrete-valued time series. We use Markov chain clustering, proposed by Pamminger and Frühwirth-Schnatter (2010), which is an approach for clustering discrete-valued time series obtained by observing a categorical variable with several states. This method is based on finite mixtures of first-order time-homogeneous Markov chain models. In order to analyze group membership we present an extension to this approach by formulating a probabilistic model for the latent group indicators within the Bayesian classification rule using a multinomial logit model.Labor Market Entry Conditions, Transition Data, Markov Chain Monte Carlo, Multinomial Logit, Panel Data, Auxiliary Mixture Sampler, Bayesian Statistics