College Education and Social Trust: An Evidence-Based Study on the Causal Mechanisms

This paper examines the influence of college education on social trust at the individual level. Based on the literature of trust and social trust, we hypothesize that life experience/development since adulthood and perceptions of cultural/social structures are two primary channels in the causal linkage between college education and social trust. In the first part of the empirical study econometric techniques are employed to tackle the omitted-variable problem and substantial evidence is found to confirm the positive effect of college education. In the second part contemporary information is used to examine the hypothetical mechanisms in the causal inference. That life experience is a primary channel via which college education promotes social trust fails to find support in our examination, while individual perceptions of cultural and social structures explain up to 77% of the causal effect.     

Gender,Culture and the Living Group Climate

Child and Adolescent Social Work Journal - This study focused on the cultural and gender differences in the perception of living group climate quality between native Dutch youth and Dutch youth...     

British terrorism preemption: Subjectivity and disjuncture in Channel “de-radicalization” interventions

This article examines Channel “de-radicalization” interventions, which take place on individuals suspected of having the potential to commit terrorist crimes. Situated within critical security studies, the article explores the British Prevent programme by utilizing primary interviews with hard-to-reach Channel mentors and senior Prevent officials. Following the work of anticipatory risk-governance scholarship, this research illuminates the three processes of risk-visibilization (how an individual becomes sufficiently “seen” as harbouring risk that they are offered Channel mentorship), risk-calculation (how practitioners negotiate supposed riskiness), and risk-knowing (how practitioners “know” risks they observe). It demonstrates how the practice of preemptive counter-terrorism is subsumed inherently by—even relies upon—subjectivity and human prejudice, and fundamental disagreements between practitioners. Through substantial empirical contribution on the phenomenon of Channel interventions, the discussion highlights ultimately that the algorithmic rationale of preemptive risk-spotting normalizes the suspicion of banal and everyday behaviors, precisely because such interventions are ultimately deployed through worst-case imaginations.     

Perceptions of Migration and Diversity by Local Public Administrators

This article contributes to the recent debate on the perception of migration and diversity; it empirically examines whether public decision-makers and public administrators perceive migration and diversity as related or if they distinguish between them. It also seeks to identify the most important characteristics of respondents with positive attitudes to migration and diversity. The sample of the respondents covers Turkey, Greece, Hungary and Slovakia; those located on the “Balkan migration route” into Europe. Due to different experiences, respondents from countries with large-scale migration perceive migration and diversity differently from those in countries directly threatened by migration or countries without any direct impact of migration. Gender appears to be a statistically significant predictor in the need for high managerial skills to manage migration and diversity. Some size categories of municipalities where a respondent works appear to be statistically significant when ranking the local impacts and local economic benefits of migration.     

The decline of substitute pathways into retirement: Empirical evidence from the Dutch health care sector

Early retirement schemes and disability insurance in the Netherlands have undergone several reforms in recent decades. The reforms have increased incentives for older workers to continue working and have decreased the roles of “substitute pathways” into retirement. This article gives an overview of the reforms and, using administrative data for workers in the health care sector, tests a number of hypotheses about the labour market participation of older workers. The results offer two main findings: i) that the Dutch reforms have indeed been effective, as the labour force participation rate of older workers has increased; and ii) the concept of “substitute pathways” has become less relevant as the use of disability insurance has been closed off as an exit route to early retirement. Nevertheless, caution is required before generalizing the implications of these Dutch findings to other OECD countries.     

Welfarism and the multidimensionality of welfare state legitimacy: Evidence from The Netherlands, 2006

Oorschot W van, Meuleman B. Welfarism and the multidimensionality of welfare state legitimacy: evidence from The Netherlands, 2006 Is it possible that citizens who support a substantial role for government in the provision of welfare are, at the same time, critical about specific aspects of such provision? Based on confirmatory factor analyses, and using a 2006 Dutch survey, this study shows that welfare state legitimacy is indeed multidimensional, i.e. that opinions tend to cluster together in several dimensions referring to various aspects of the welfare state. There is partial evidence for the existence of a single, underlying welfarism dimension which consists basically of views regarding the range of governmental responsibility, as well as of the idea that these governmental provisions do not have unfavourable repercussions in economic or moral spheres. However, the separate dimensions cannot be reduced entirely to this overall welfarism dimension. This is illustrated by the finding that the various attitude dimensions are affected differently by socio‐structural position and ideological dispositions.     

Small area estimation under random regression coefficient models

Statistical agencies are interested to report precise estimates of linear parameters from small areas. This goal can be achieved by using model-based inference. In this sense, random regression coefficient models provide a flexible way of modelling the relationship between the target and the auxiliary variables. Because of this, empirical best linear unbiased predictor (EBLUP) estimates based on these models are introduced. A closed-formula procedure to estimate the mean-squared error of the EBLUP estimators is also given and empirically studied. Results of several simulation studies are reported as well as an application to the estimation of household normalized net annual incomes in the Spanish Living Conditions Survey.     

Bayesian Recovery of the Initial Condition for the Heat Equation

We study a Bayesian approach to recovering the initial condition for the heat equation from noisy observations of the solution at a later time. We consider a class of prior distributions indexed by a parameter quantifying “smoothness” and show that the corresponding posterior distributions contract around the true parameter at a rate that depends on the smoothness of the true initial condition and the smoothness and scale of the prior. Correct combinations of these characteristics lead to the optimal minimax rate. One type of priors leads to a rate-adaptive Bayesian procedure. The frequentist coverage of credible sets is shown to depend on the combination of the prior and true parameter as well, with smoother priors leading to zero coverage and rougher priors to (extremely) conservative results. In the latter case, credible sets are much larger than frequentist confidence sets, in that the ratio of diameters diverges to infinity. The results are numerically illustrated by a simulated data example.     

Subsemble: an ensemble method for combining subset-specific algorithm fits

Ensemble methods using the same underlying algorithm trained on different subsets of observations have recently received increased attention as practical prediction tools for massive data sets. We propose Subsemble: a general subset ensemble prediction method, which can be used for small, moderate, or large data sets. Subsemble partitions the full data set into subsets of observations, fits a specified underlying algorithm on each subset, and uses a clever form of V-fold cross-validation to output a prediction function that combines the subset-specific fits. We give an oracle result that provides a theoretical performance guarantee for Subsemble. Through simulations, we demonstrate that Subsemble can be a beneficial tool for small- to moderate-sized data sets, and often has better prediction performance than the underlying algorithm fit just once on the full data set. We also describe how to include Subsemble as a candidate in a SuperLearner library, providing a practical way to evaluate the performance of Subsemble relative to the underlying algorithm fit just once on the full data set.