The state intervenes in the battle of the sexes: Causal effects of paternity leave

Do family policies influence attitudes and behavior or are they merely reflections of pre-existing attitudes? We consider the implementation of the Norwegian daddy quota, 4 weeks of parental leave reserved for the father, as a natural experiment, and examine the long-run causal effects on attitudes toward gender equality, on conflicts and sharing of household labor, and on support for public childcare. We find that respondents who had their last born child just after the reform report an 11% lower level of conflicts over household division of labor and that they are 50% more likely to equally divide the task of washing clothes than respondents who had their last child just before the reform.     

Trend effects on perceived avalanche hazard

Hazard-level forecasts constitute an important risk mitigation tool to reduce loss of economic values and human life. Avalanche forecasts represent an example of this. As for many other domains, avalanche risk is communicated using a color-coded, categorical risk scale aimed at informing the public about past, current, and future risk. We report the results from three experiments in which we tested if an irrelevant past trend in forecasted avalanche danger affects perceptions of current and future avalanche risk. Our sample consisted of individuals from three different populations targeted by national avalanche warning services. All three experiments showed that the perception of avalanche risk is influenced by the trend, but that the effect is opposite for perceptions of current and expectations of future avalanche risk. While future avalanche risk is extrapolated in the same direction as the change from the previous day, we found that perceived current risk appears to be based on an average of past and current risk. These effects diminish when we provide participants with a scale indicating the exact level of avalanche danger. For most of our measurement instruments, however, the effects remain significant. These results imply that targeted populations may consider historic information more than was intended by the sender. As such, our results have implications for both avalanche warning services and risk communication in general.     

When what you have is not enough—Acquiring Australian qualifications to overcome non-recognition of overseas skills

Skilled migration is an important strategy in developed economies seeking to address skills shortages and population ageing. Research on the labour market outcomes of skilled migrants tends to focus on employers' devaluation of skills without considering the role of immigration policy in the migration process. Moreover, there is little understanding of whether efforts to meet employer demands for local qualifications improve labour market outcomes. Drawing on a study on skilled migrants sponsored under the State-Specific and Regional Migration Scheme in the regional state of South Australia, we explore the shaping of skills and skills recognition in the migration journey, particularly migrants' strategy of reskilling in response to employer demands for local qualifications. Our logistic regressions on the association between the acquisition of Australian qualifications and labour market outcomes reveal only marginal returns to these efforts. We argue that Australia should consider developing a more coherent skilled migration process to better harness the human capital of skilled migrants.     

The geometry of Gaussian double Markovian distributions

Gaussian double Markovian models consist of covariance matrices constrained by a pair of graphs specifying zeros simultaneously in the matrix and its inverse. We study the semi-algebraic geometry of these models, in particular their dimension, smoothness, and connectedness as well as algebraic and combinatorial properties.     

Robust variable selection with application to quality of life research

A large database containing socioeconomic data from 60 communities in Austria and Germany has been built, stemming from 18,000 citizens’ responses to a survey, together with data from official statistical institutes about these communities. This paper describes a procedure for extracting a small set of explanatory variables to explain response variables such as the cognition of quality of life. For better interpretability, the set of explanatory variables needs to be very small and the dependencies among the selected variables need to be low. Due to possible inhomogeneities within the data set, it is further required that the solution is robust to outliers and deviating points. In order to achieve these goals, a robust model selection method, combined with a strategy to reduce the number of selected predictor variables to a necessary minimum, is developed. In addition, this context-sensitive method is applied to obtain responsible factors describing quality of life in communities.     

Variable selection for generalized linear mixed models by L 1-penalized estimation

Generalized linear mixed models are a widely used tool for modeling longitudinal data. However, their use is typically restricted to few covariates, because the presence of many predictors yields unstable estimates. The presented approach to the fitting of generalized linear mixed models includes an L ₁-penalty term that enforces variable selection and shrinkage simultaneously. A gradient ascent algorithm is proposed that allows to maximize the penalized log-likelihood yielding models with reduced complexity. In contrast to common procedures it can be used in high-dimensional settings where a large number of potentially influential explanatory variables is available. The method is investigated in simulation studies and illustrated by use of real data sets.     

Insurance demand and social comparison: An experimental analysis

This paper analyzes whether social comparison can explain the low take-up of disaster insurance usually reported in field studies. We argue that risks in the case of disasters are highly correlated between subjects whereas risks for which high insurance take-up can be observed (e.g. extended warranties or cell phone insurance) are typically idiosyncratic. We set up a simple model with social reference points and show that in the presence of inequality aversion social comparison makes insurance indeed less attractive if risks are correlated. In addition we conducted a simple experiment which confirms these theoretical results. The average willingness to pay for insurance is significantly higher for idiosyncratic than for correlated risks.     

Standardisierungspotentiale lebenszyklusbasierter Modelle des strategischen Kostenmanagements

Tremendous increases in indirect costs can be found in many industries. Consequently, life cycle cost analysis models are getting more important in strategic cost management. However, existing models differ significantly from each other as they have been developed mainly for specific applications. In order to enable a common use of those models, a standardization of these models is required. We examine the potentials of standardizing models for life cycle cost analysis. In a first step, requirements regarding a standard model are derived from literature. As a second step, existing approaches found in literature are compared to the afore derived demands and it is analyzed to what extent they match the standardization requirements. Based on this comparison, we identify and discuss avenues for further research.     

The Dynamics of Research Alliances: Examining the Effect of Alliance Experience and Partner Characteristics on the Speed of Alliance Entry in the Biotech Industry

Few studies have moved beyond the dyadic level of an ongoing alliance and examined factors contributing to the success of entering a series of alliances. In this paper we expect biotechnology firms over time to learn from their alliance experience and to develop general alliance capabilities. Specifically, we expect the speed with which they enter into new research alliances, e.g. their alliance formation rate, to be affected by capabilities built up in prior alliances as well as by characteristics of their partners. We use longitudinal event history data for the complete population of US biotechnology firms for 1973–1999 to test four hypotheses about factors affecting the rate of new alliance formation. Our analysis suggests that the speed of entering research alliances is affected by prior experience of the focal firm, but not by partner characteristics. Our findings provide evidence that biotech firms learn how to learn more effectively from multiple research alliances; however, this effect is generalized and not tied to specific characteristics of the alliance partner.     

Analysis of the importance of on-field covariates in the German Bundesliga

In modern football, various variables as, for example, the distance a team runs or its percentage of ball possession, are collected throughout a match. However, there is a lack of methods to make use of these on-field variables simultaneously and to connect them with the final result of the match. This paper considers data from the German Bundesliga season 2015/2016. The objective is to identify the on-field variables that are connected to the sportive success or failure of the single teams. An extended Bradley–Terry model for football matches is proposed that is able to take into account on-field covariates. Penalty terms are used to reduce the complexity of the model and to find clusters of teams with equal covariate effects. The model identifies the running distance to be the on-field covariate that is most strongly connected to the match outcome.