Outsiders and Intrapreneurs: The Institutional Embeddedness of Social Entrepreneurship in Germany

The social entrepreneurship discourse in Germany has become more prominent at a time when the deeply rooted corporatist traditions of social provision have come under pressure for marketization. This article examines the potential role of “social entrepreneurs” in the institutionally established German welfare state. The article analyzes the opportunities and constraints that new players face. Drawing on survey data and case studies in the areas of elderly care and advancement of children with immigrant background, the analysis retraces the structure and diffusion of social entrepreneurial projects. It concludes that the simple transfer of the social entrepreneurship model is unlikely. The analysis suggests that successful social ventures in Germany adapt the notion of social entrepreneurship to prevalent institutional realities. In the context of more encompassing social services, dense decentralized networks, and different cultures of philanthropism, new players have a complementary role that stimulates rather than dominates the process of social innovation.     

Competing risks quantile regression at work: in-depth exploration of the role of public child support for the duration of maternity leave

Despite its emergence as a frequently used method for the empirical analysis of multivariate data, quantile regression is yet to become a mainstream tool for the analysis of duration data. We present a pioneering empirical study on the grounds of a competing risks quantile regression model. We use large-scale maternity duration data with multiple competing risks derived from German linked social security records to analyse how public policies are related to the length of economic inactivity of young mothers after giving birth. Our results show that the model delivers detailed insights into the distribution of transitions out of maternity leave. It is found that cumulative incidences implied by the quantile regression model differ from those implied by a proportional hazards model. To foster the use of the model, we make an R-package (cmprskQR) available.     

Sampling and empirical risk minimization

ABSTRACT

In certain situations that shall be undoubtedly more and more common in the Big Data era, the datasets available are so massive that computing statistics over the full samples is hardly feasible, if not unfeasible. A natural approach in this context consists in using survey schemes and substituting the ‘full data’ statistics with their counterparts based on the resulting random samples, of manageable size. It is the main purpose of this paper to investigate the impact of survey sampling on statistical learning methods based on empirical risk minimization through the standard binary classification problem, considered here as a ‘case in point’. Precisely, we prove that, in presence of auxiliary information, appropriate use of optimally coupled Poisson survey plans may not affect much the learning rates, while possibly reducing significantly the number of terms that must be averaged to compute the empirical risk functional with overwhelming probability. These striking results are next shown to extend to more general sampling schemes by means of a coupling technique, originally introduced by Hajek [Asymptotic theory of rejective sampling with varying probabilities from a finite population. Ann Math Stat. 1964;35(4):1491–1523].     

Comparing the transpirational and shading effects of two contrasting urban tree species

Urban Ecosystems - Urban trees are getting increasing attention as a tool to mitigate urban heat island effects. A more functional and quantitative view of transpirational and shading effect,...     

Exceptional contributions to US science by the foreign-born and foreign-educated

This paper contributes to the debate on high-skilled migration byexamining whether the foreign-born and foreign-educated are disproportionatelyrepresented among individuals making exceptional contributions to science and engineering (S & E) in the U.S. Six indicators of scientific achievement areused: individuals elected to the National Academy of Sciences and/or National Academy of Engineering, authors of citation classics, authors of hot papers, the 250 most-cited authors, authors of highly cited patents, and scientistswho have played a key role in launching biotechnology firms. We do not claim that this list is exhaustive, merely illustrative. Using a variety of sources, we are able to determine the birth andeducational origin of 89.3% of the study group of over 4,500 scientists and engineers. For each indicator of scientific achievement, we test to see if the observed frequency by birth (or educational) origin is significantly differentfrom the frequency one would expect given the composition of the scientific labor force in the U.S. We find that although there is some variation by discipline, individuals making exceptional contributions to S & E in the U.S. aredisproportionately drawn from the foreign born. Only in the instance of hot papers in the life sciences were we unable to reject the null hypothesis that the proportion is the same as that in the underlying population. The most frequent country of origin in the life sciences is Great Britain followed by Germany. In the physical sciences the reverse is true. We also find that individuals making exceptional contributions are, in many instances, disproportionately foreign educated, both at the undergraduate and at the graduate level. We conclude that immigrants have been a source of strength and vitalityfor U.S. science and, on balance, the U.S. appears to have benefited from the educational investments made by other countries. We do not investigate, however, whether U.S. scientists and engineers have borne part of thecost of the inflow of foreign talent by being displaced from jobs and/or earning lower wages. Nor do we investigate the cost to the countries of origin.     

Kernel Estimation of the Spectral Density of Stationary Random Closed Sets

A non‐parametric kernel estimator of the spectral density of stationary random closed sets is studied. Conditions are derived under which this estimator is asymptotically unbiased and mean‐square consistent. For the planar Boolean model with isotropic compact and convex grains, an averaged version of the kernel estimator is compared with the theoretical spectral density.     

University student enrollment forecasts by analysis structural ratios using ARIMA-methods

Summary Forecasts for the number of students in Germany are conducted by the Kultusministerkonferenz. They use a transition model which does not allow for prediction intervals and therefore lack a measure of uncertainty of the forecast. Since the uncertainty is high for such forecasts, this lack is of importance. In this paper, structural ratios, relating the number of university students to the population of the same age, are analyzed and forescasted using ARIMA-models with outliers. Multiplying these ratios with official population forecasts for Germany provides the future number of students, additionally giving prediction intervals. This number will increase from 1.94 million in 2002 to 2.35 million in 2015. The uncertainty of the forecast is high; the forecast interval in 2015 will range between 1.72 and 2.98 million at a 95% confidence level. Supported by the German Research Foundation (DFG). We are grateful to an anonymous referee for some helpful comments.     

Impact of Price Postponement on Capacity and Flexibility Investment Decisions

Investments in dedicated and flexible capacity have traditionally been based on demand forecasts obtained under the assumption of a predetermined product price. However, the impact on revenue of poor capacity and flexibility decisions can be mitigated by appropriately changing prices. While investment decisions need to be made years before demand is realized, pricing decisions can easily be postponed until product launch, when more accurate demand information is available. We study the effect of this price decision delay on the optimal investments on dedicated and flexible capacity. Computational experiments show that considering price postponement at the planning stage leads to a large reduction in capacity investments, especially in the more expensive flexible capacity, and a significant increase in profits. Its impact depends on demand correlation, elasticity and diversion, ratio of fixed to variable capacity costs, and uncertainty remaining at the times the pricing and production decisions are made.