Introvision in der Arbeitsvermittlung für Fallmanager und Arbeitslose

In the context of a pilot study the effects of the educational-psychological intervention Introvision were tested with older long-term unemployed people. The project was accomplished in the time by 1.4.2010 to 31.12.2010 in co-operation between ARGE Paderborn and the research group Introvision. In the context of the project 16 employees of ARGE Paderborn participated in an advanced training to Introvision, 31 customers of ARGE Paderborn began a coaching to get introduction in Introvision and 20 have also finished. Nine customers decided to participate five individual coachings. The results show, that the offers have a high individual use for the participants. To the central changes belong improved handling of stress, decrease of social isolation, improved (self-) perception as well as a higher self-confidence and a more open co-operation between customers and employees of ARGE Paderborn. Besides four participation began a job on the first labour market and two could be connected to a honorary activity.     

(Un)Gleichheit durch soziale Schlie?ung

The concept of social closure is frequently used in recent debates about wage inequality. The following article shows that social closure generates not only inequality but equality, as well. Economic, segmentational and closure theoretical arguments shed light on the different relations of an employee to his employer in different labour market structures. Properties of the employee like vocational training, seniority or gender lose their impact on wages the more closed a labour market is. These hypotheses are tested with the 2006 BIBB/BAuA-Employment-Survey. Results support a deep effect of social closure in the German labour market.     

The effects of state-dependent human behavior on the design of a serial line

Most research on line design assumes that human operators perform independently from the status of the line. Recent empirical evidence is contradictory. Humans are likely to change their working speed if they might otherwise cause idle time (Schultz et al., Manage Sci 44(12):1595–1607, 1998). This peculiarity of worker behavior is observed in a variety of settings but little is still known about optimal line design that accounts for this more realistic modeling of worker behavior. Therefore, we analyze work allocation in a serial line with limited buffer capacity and adaptive human behavior. An extensive simulation study reveals that optimal work allocation in state-dependent models is different from classical state-independent models. A bowl-shaped work allocation might be suboptimal and design guidelines are more complicated. Depending on the extent of human reactions, a bowl-shaped, balanced, or reversed-bowl work allocation can be preferable.     

Sensitivity Analysis Using Risk Measures

In a quantitative model with uncertain inputs, the uncertainty of the output can be summarized by a risk measure. We propose a sensitivity analysis method based on derivatives of the output risk measure, in the direction of model inputs. This produces a global sensitivity measure, explicitly linking sensitivity and uncertainty analyses. We focus on the case of distortion risk measures, defined as weighted averages of output percentiles, and prove a representation of the sensitivity measure that can be evaluated on a Monte Carlo sample, as a weighted average of gradients over the input space. When the analytical model is unknown or hard to work with, nonparametric techniques are used for gradient estimation. This process is demonstrated through the example of a nonlinear insurance loss model. Furthermore, the proposed framework is extended in order to measure sensitivity to constant model parameters, uncertain statistical parameters, and random factors driving dependence between model inputs.     

Public Support for Sickle-Cell Disease Funding: Does Race Matter?

Despite the fact that sickle-cell disease (SCD) is perhaps the most “racialized” medical condition in the USA, very little is known about how “race” impacts public support for health policies related to the condition. We embedded an experiment within the 2011 Cooperative Congressional Election Study in order to assess perceptions about SCD among 1250 participants from diverse backgrounds and evaluate the extent to which these perceptions were associated with support for government spending on SCD-related benefits. We manipulated the racial phenotype of SCD advocates who requested additional government funding and asked participants to indicate how much the government should provide. Overall, participants expressed moderately positive attitudes about SCD, and there were no differences in funding support based on the race of the advocate. However, white participants supported less funding compared to nonwhite participants, even after adjusting for a number of demographic and attitudinal covariates. These findings suggest that a complex relationship between racial identification and implicit racism may shape public perceptions about SCD that negatively influences perceivers’ support for SCD-related policy.     

Reciprocal Family,Friendship and Church Support Networks of African Americans: Findings from the National Survey of American Life

This study examined reciprocal support networks involving extended family, friends and church members among African Americans. Our analysis examined specific patterns of reciprocal support (i.e., received only, gave only, both gave and received, neither gave or received), as well as network characteristics (i.e., contact and subjective closeness) as correlates of reciprocal support. The analysis is based on the African American subsample of the National Survey of American Life. Overall, our findings indicate that African Americans are very involved in reciprocal support networks with their extended family, friends and church members. Respondents were most extensively involved in reciprocal supports with extended family members, followed closely by friends and church networks. Network characteristics (i.e., contact and subjective closeness) were significantly and consistently associated with involvement with reciprocal support exchanges for all three networks. These and other findings are discussed in detail. This study complements previous work on the complementary roles of family, friend and congregational support networks, as well as studies of racial differences in informal support networks.     

On the robustness of the generalized fused lasso to prior specifications

Using networks as prior knowledge to guide model selection is a way to reach structured sparsity. In particular, the fused lasso that was originally designed to penalize differences of coefficients corresponding to successive features has been generalized to handle features whose effects are structured according to a given network. As any prior information, the network provided in the penalty may contain misleading edges that connect coefficients whose difference is not zero, and the extent to which the performance of the method depend on the suitability of the graph has never been clearly assessed. In this work we investigate the theoretical and empirical properties of the adaptive generalized fused lasso in the context of generalized linear models. In the fixed \(p\) setting, we show that, asymptotically, adding misleading edges in the graph does not prevent the adaptive generalized fused lasso from enjoying asymptotic oracle properties, while forgetting suitable edges can be more problematic. These theoretical results are complemented by an extensive simulation study that assesses the robustness of the adaptive generalized fused lasso against misspecification of the network as well as its applicability when theoretical coefficients are not exactly equal. Our contribution is also to evaluate the applicability of the generalized fused lasso for the joint modeling of multiple sparse regression functions. Illustrations are provided on two real data examples.     

Choosing summary statistics by least angle regression for approximate Bayesian computation

Bayesian statistical inference relies on the posterior distribution. Depending on the model, the posterior can be more or less difficult to derive. In recent years, there has been a lot of interest in complex settings where the likelihood is analytically intractable. In such situations, approximate Bayesian computation (ABC) provides an attractive way of carrying out Bayesian inference. For obtaining reliable posterior estimates however, it is important to keep the approximation errors small in ABC. The choice of an appropriate set of summary statistics plays a crucial role in this effort. Here, we report the development of a new algorithm that is based on least angle regression for choosing summary statistics. In two population genetic examples, the performance of the new algorithm is better than a previously proposed approach that uses partial least squares.     

The West,Russia, and China in Central Asia: What Kind of Game Is Being Played in the Region?

The article discusses the nature of the tangled interaction between the ex-Soviet Central Asian republics and the outside great powers – Russia, the United States and China. Although the so-called Great Game model is often used to explain the geopolitical rivalry in the region, the current pattern of relations between various international actors is much more complex than a traditional tug-of-war between the old colonial empires. While Russia's strategic role in Central Asia appears to be on the rise, the capacity of the West to influence political developments in the region has diminished. However, the overall situation in Central Asia remains volatile and its geopolitical landscape is far from being settled.     

Modeling Demand For Unionization With Nontraditional Data Analysis Methods

Upon reviewing the extant literature on determinants of unionism, it becomes clear that many areas that have had a plethora of research attention do not converge upon singularly directional findings. This study explores a potential cause of such an apparent anomaly: nonlinearity of data. An exploratory examination of correlation coefficients among typical union determinant variables seems to show different patterns of relationships at different levels of union demand. Thus, a break from traditional linear data analysis techniques is explored in the interest of explaining more variance with typical, theoretically derived variables by using neural network analysis. Results of analyses on industry level data reveal that using neural network analysis to model union demand explained over four times as much variance as multiple regression analysis.