Cohort Measures of Internal Migration: Understanding Long-Term Trends

Internal migration intensities fluctuate over time, but both migration levels and trends show great diversity. The dynamics underpinning these trends remain poorly understood because they are analyzed almost exclusively by applying period measures to cross-sectional data. This article proposes 10 cohort measures that can be applied to both prospective and retrospective data to systematically examine long-term trends. To demonstrate their benefits, the proposed measures are applied to retrospective survey data for England that provide residential histories from birth to age 50 for cohorts born between 1918 and 1957. The analysis reveals stable lifetime migration for men but increased lifetime migration for women associated with earlier ages at moving in adulthood and a compression of intervals between consecutive moves. The proposed cohort measures provide a more comprehensive picture of migration behavior and should be used to complement period measures in exploring long-term trends. Increasing availability of retrospective and longitudinal survey data means that researchers can now apply the proposed measures to a wide range of countries.     

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.     

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.     

Model averaging inconcentration–QT analyses

This article describes how a frequentist model averaging approach can be used for concentration–QT analyses in the context of thorough QTc studies. Based on simulations, we have concluded that starting from three candidate model families (linear, exponential, and Emax) the model averaging approach leads to treatment effect estimates that are quite robust with respect to the control of the type I error in nearly all simulated scenarios; in particular, with the model averaging approach, the type I error appears less sensitive to model misspecification than the widely used linear model. We noticed also few differences in terms of performance between the model averaging approach and the more classical model selection approach, but we believe that, despite both can be recommended in practice, the model averaging approach can be more appealing because of some deficiencies of model selection approach pointed out in the literature. We think that a model averaging or model selection approach should be systematically considered for conducting concentration–QT analyses. Copyright © 2016 John Wiley & Sons, Ltd.     

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.     

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.     

Disagreement over the immigration of low-income earners in a welfare state

This paper studies natives’ economically motivated preferences over different levels of immigration of low-income earners. Immigration affects natives through both intra- and intergenerational redistribution programmes and in the labour market. Our analysis suggests, in a welfare state that looks after the poor and the aged, economic motivation does not necessarily lead a native to have an extreme opinion on the preferable level of immigration, although it causes disagreement among natives. We find, regardless of parameter values, high-income earners prefer at least as much immigration as low-income earners who, in turn, prefer at least as much immigration as pensioners. The median voter is then likely to be a low-income native.