Asymmetric social protection systems with migration

We study the consequences of the coexistence of different social protection systems on contribution rate levels and migration in a two-country model. Before any migration takes place, the levels of contribution rates are chosen by a representative elected in each country. The migration of each agent depends on her anticipation of other agents’ migrations. We show that the richest agents are attracted to the Bismarckian country. The poorest agents tend to migrate toward one country or the other depending on the Beveridgean country contribution rate. The Beveridgean country can set a higher contribution rate to limit the departures of rich agents.

On the use of extreme value tail modeling for generalized pairwise comparisons with censored outcomes

In randomized clinical trials, methods of pairwise comparisons such as the ‘Net Benefit’ or the ‘win ratio’ have recently gained much attention when interests lies in assessing the effect of a treatment as compared to a standard of care. Among other advantages, these methods are usually praised for delivering a treatment measure that can easily handle multiple outcomes of different nature, while keeping a meaningful interpretation for patients and clinicians. For time-to-event outcomes, a recent suggestion emerged in the literature for estimating these treatment measures by providing a natural handling of censored outcomes. However, this estimation procedure may lead to biased estimates when tails of survival functions cannot be reliably estimated using Kaplan–Meier estimators. The problem then extrapolates to the other outcomes incorporated in the pairwise comparison construction. In this work, we suggest to extend the procedure by the consideration of a hybrid survival function estimator that relies on an extreme value tail model through the Generalized Pareto distribution. We provide an estimator of treatment effect measures that notably improves on bias and remains easily apprehended for practical implementation. This is illustrated in an extensive simulation study as well as in an actual trial of a new cancer immunotherapy.     

Implementing the right project structure to achieve coopetitive innovation projects

This research focuses on the project structure used by coopetitors to achieve common innovation projects. Scholars have recently identified an original but complex project structure that they call the Coopetitive Project Team (CPT). However, other project structures can also be implemented by coopetitors to achieve innovation. Therefore, we address the following question: for which types of innovation projects is CPT appropriate? We argue that coopetitors need to use CPT for high-risk and high-cost projects when the aim is to develop radical innovation. CPT allows coopetitors not only to develop innovation capabilities through close resource and knowledge sharing but also to manage the risk of opportunism. Conversely, coopetitors should use another project structure, Separated Project Teams (SPTs), for low-cost and low-risk projects when the aim is to develop incremental innovation. The SPT design allows coopetitors both to achieve the goal of the project and to minimize the risk of opportunism. To confirm our assumptions, we studied the project portfolios of Airbus and Thales, two firms in the space satellite industry. Our findings confirm that coopetitors should implement CPTs to handle innovation projects that are costly, risky and highly innovative. CPTs permit the sharing of knowledge and the management of high opportunism risk, both of which are necessary to achieve radical innovation. Conversely, coopetitors rely on SPTs for low-cost projects that require a low degree of knowledge sharing, thus avoiding the risk of opportunism in achieving their incremental innovation objectives.     

On the most imbalanced orientation of a graph

We study the problem of orienting the edges of a graph such that the minimum over all the vertices of the absolute difference between the outdegree and the indegree of a vertex is maximized. We call this minimum the imbalance of the orientation, i.e. the higher it gets, the more imbalanced the orientation is. The studied problem is denoted by \({{\mathrm{\textsc {MaxIm}}}}\). We first characterize graphs for which the optimal objective value of \({{\mathrm{\textsc {MaxIm}}}}\) is zero. Next we show that \({{\mathrm{\textsc {MaxIm}}}}\) is generally NP-hard and cannot be approximated within a ratio of \(\frac{1}{2}+\varepsilon \) for any constant \(\varepsilon >0\) in polynomial time unless \(\texttt {P}=\texttt {NP}\) even if the minimum degree of the graph \(\delta \) equals 2. Then we describe a polynomial-time approximation algorithm whose ratio is almost equal to \(\frac{1}{2}\). An exact polynomial-time algorithm is also derived for cacti. Finally, two mixed integer linear programming formulations are presented. Several valid inequalities are exhibited with the related separation algorithms. The performance of the strengthened formulations is assessed through several numerical experiments.     

Testing the Comparability of Different Types of Social Indicators Across Groups

The social indicator movement has realized a considerable increase in importance during the last 50 years. This happened not the least by the publications in the journal Social Indicators Research. Social indicators have been developed for many aspects of life. The scores on these indicators often are compared through time and across groups. An essential requirement for such research is that the measures used are comparable through time and across groups. The comparability through time is not such a big problem but the comparability of measures across groups is not so obvious. This requires harmonization of indicators based on objective variables and invariance testing for indicators based on subjective and objective variables. In this paper we discuss how the comparability of different types of social indicators based on subjective and objective variables can be tested using invariance testing. This is a relevant issue because the existing testing procedure is designed for only one type of social indicators and this test is not directly applicable on the other types of social indicators.     

Predicting Public Corruption with Neural Networks: An Analysis of Spanish Provinces

We contend that corruption must be detected as soon as possible so that corrective and preventive measures may be taken. Thus, we develop an early warning system based on a neural network approach, specifically self-organizing maps, to predict public corruption based on economic and political factors. Unlike previous research, which is based on the perception of corruption, we use data on actual cases of corruption. We apply the model to Spanish provinces in which actual cases of corruption were reported by the media or went to court between 2000 and 2012. We find that the taxation of real estate, economic growth, the increase in real estate prices, the growing number of deposit institutions and non-financial firms, and the same political party remaining in power for long periods seem to induce public corruption. Our model provides different profiles of corruption risk depending on the economic conditions of a region conditional on the timing of the prediction. Our model also provides different time frameworks to predict corruption up to 3 years before cases are detected.     

The uneven geography of research on “environmental migration”

Climate change and environmental hazards affect the entire world, but their interactions with—and consequences on—human migration are unevenly distributed geographically. Research on climate and migration have their own geographies which do not necessarily coincide. This paper critically confronts these two geographies by presenting the first detailed mapping of research in the field of environmentally induced migration. After a brief review of the geography of research on climate change, the paper presents an overview of nearly 50 years of case studies on the basis of CliMig, a bibliographic database of 1193 scientific papers and books on climate/environmental change and migration, among them 463 empirical case studies. We analyze the locations of these case studies, the academic affiliations of their researchers, and the origin of their funding. Mapping the locations of case studies worldwide points toward blind spots in the research and identifies “overstudied” areas. We describe the methodologies used in the studies and present a typology of environmental hazards. Our results show that research on environmental migration is mainly done in countries of the Global South, whereas climate science research in general is focused on countries of the Global North. We contend that the peculiar geography of environmental migration cannot be explained solely by the uneven vulnerability of southern populations to the environment. It must also be understood through the lens of post-colonial and securitization studies as the result of a framing of “environmental refugees” (and refugees in general) as an intrinsically “southern problem” and as a security risk for the North. This paper is an original contribution to the literature on the North-South divide in scientific research and will help to outline future directions of investigation.     

Measuring Human,Physical and Structural Capital Efficiency Performance of Insurance Companies

Based on the strategy perspective of intellectual capital, this paper proposes an integrated framework that is practical to estimate the human, physical, and structural capitals (intellectual capital) efficiency performance, at firm level. This paper uses a dynamic network data envelopment analysis model to estimate the intellectual capital efficiency at three levels in the insurance industry over the period of 2005–2012. Within the insurance industry, deficiencies occurred in the human and structural capital stages as opposed to the physical capital stage. A further investigation indicates that total investment is the major concern for the deficiencies. Moreover, the cluster analysis highlights the strengths and weaknesses of the insurers based on their inherently similar efficiencies. Malaysian insurance industry need to translate and promote the existing knowledge-based economy agenda lauded by the government to improve the intellectual capital efficiency, particularly at the human capital level.     

Resistance training is linked to heightened positive motivational state and lower negative affect among healthy women aged 65–70

Resistance training (RT) improves overall health, but the psychological effects of RT in healthy old adults have not been tested. The aim of this study was to investigate a sample of 65–70-year-old healthy and physically active women to assess their sense of coherence, health-related quality of life, hope, and affect, before and after taking part in a 24-week RT intervention (N = 14), compared to controls (N = 18). Findings showed a significant increase in hope (p = 0.013) and a significant decrease in negative affect (p = 0.002). Starting RT after age 65 does not appear to negatively impact on women’s psychological health but seems to be associated with important psychological health benefits.     

The male immigrant–native employment gap in Sweden: migrant admission categories and human capital

Journal of Population Research - Despite having a celebrated labor market integration policy, the immigrant–native employment gap in Sweden is one of the largest in the OECD. From a...