Pathways into Irregular Status Among Senegalese Migrants in Europe

This study examines how immigration policies construct pathways into irregular legal statuses and models three pathways: no‐visa entry, overstaying, and befallen irregularity. Drawing on literature on the sociolegal production of migrant irregularity, this study hypothesizes that variation in contexts of reception and migrants’ access to forms of capital and institutional connections will produce different pathways. Retrospective MAFE‐Senegal data provide legal status histories. Results show pathways that occur early in a migrant's trip‐no‐visa entry and overstaying‐are more sensitive to both contextual variables and access to forms of capital. In contrast, befallen irregularity is less related to contextual variation.     

Regulatory fit effects on perceived fiscal exchange and tax compliance

Paying taxes can be considered a contribution to the welfare of a society. But even though tax payments are redistributed to citizens in the form of public goods and services, taxpayers often do not perceive many benefits from paying taxes. Information campaigns about the use of taxes for financing public goods and services could increase taxpayers’ understanding of the importance of taxes, strengthen their perception of fiscal exchange and consequently also increase tax compliance. Two studies examined how fit between framing of information and taxpayers’ regulatory focus affects perceived fiscal exchange and tax compliance. Taxpayers should perceive the exchange between tax payments and provision of public goods and services as higher if information framing suits their regulatory focus. Study 1 supported this hypothesis for induced regulatory focus. Study 2 replicated the findings for chronic regulatory focus and further demonstrated that regulatory fit also affects tax compliance. The results provide further evidence for findings from previous studies concerning regulatory fit effects on tax attitudes and extend these findings to a context with low tax morale.     

Whether and How Much to Give: Uncovering the Contrasting Determinants of the Decisions of Whether and How Much to Give to Charity with Two-Stage Alternatives to the Prevailing Tobit Model

Charitable giving involves two seemingly distinct decisions: whether to give and how much to give. However, many researchers methodologically assume that these decisions are one and the same. The present study supports the argument that this is an incorrect assumption that is likely to generate misleading conclusions, in part, since the second decision is much more financial in nature than the first. The argument that charitable giving entails two distinct decisions is validated by empirically dismissing the prevailing Tobit model, which assumes a single decision, in favor of less restrictive two-stage approaches: Cragg’s model and the Heckman model. Most importantly, it is shown that only by adopting a two-stage approach may it be uncovered that common determinants of charitable giving such as income and gender affect the two decisions at hand very differently. Data comes from a high-quality 2012 Danish survey and administrative registers.     

Putting measures of individual well-being to use for ex-ante policy evaluation

Most studies using microsimulation techniques have considered the effect of potential reforms on the income distribution. However, it has become increasingly recognized, both at the academic and political level, that focusing purely on income provides a limited picture of social progress. We illustrate how ex-ante policy evaluation can be performed in terms of richer concepts of individual well-being, such as subjective life satisfaction and equivalent incomes. Our analysis makes use of EUROMOD, the EU-wide tax-benefit microsimulation model, along with 2013 EU-SILC data for Sweden, which for the first time provides information on life satisfaction. Our results show that the effect of potential reforms varies widely depending on the well-being concept used in the evaluation. We discuss the normative questions that are raised by this finding.     

"I'LL DIE WITH THE HAMMER IN MY HAND": JOHN HENRYISM AS A PREDICTOR OF HAPPINESS

This paper examines the association between John Henryism - a behavioral predisposition to cope actively with psycho-social environmental stressors - and happiness. On the basis of previous research on aspiration and goal regulation, we predicted that John Henryism would be negatively associated with happiness when controlling for demographic factors and attainment in various domains of life. We tested the prediction in a sample of hypertensive participants (n = 758) drawn from an inner-city, mainly African-American, safety-net hospital in Jefferson County, Alabama. Bivariate analysis revealed no association between John Henryism and attainment in six domains of life: marriage, children, education, employment, income, and health. However, a significant negative association between John Henryism and happiness was found both in bivariate analysis (Spearman’s ρ = -0.335; p < .001) and when controlling for demographic factors and attainment using ordinal logistic regression analysis. There was a significant interaction effect between John Henryism and gender: being male was positively associated with happiness among participants with low John Henryism, but negatively associated with happiness among participants with high John Henryism. While further study would be required in order to establish the extent to which these findings can be generalized as well as their causal underpinnings, the results support the hypothesis that John Henryism is negatively associated with happiness, especially among men, and underscore the limitations of using self-report measures of happiness as proxies for well-being for purposes of public policy.     

Improved Sampling-Importance Resampling and Reduced Bias Importance Sampling

Abstract.  The sampling-importance resampling (SIR) algorithm aims at drawing a random sample from a target distribution π. First, a sample is drawn from a proposal distribution q , and then from this a smaller sample is drawn with sample probabilities proportional to the importance ratios π/ q . We propose here a simple adjustment of the sample probabilities and show that this gives faster convergence. The results indicate that our version converges better also for small sample sizes. The SIR algorithms are compared with the Metropolis–Hastings (MH) algorithm with independent proposals. Although MH converges asymptotically faster, the results indicate that our improved SIR version is better than MH for small sample sizes. We also establish a connection between the SIR algorithms and importance sampling with normalized weights. We show that the use of adjusted SIR sample probabilities as importance weights reduces the bias of the importance sampling estimate.     

Gene Pathways Discovery in Asbestos-Related Diseases using Local Causal Discovery Algorithm

To learn about the progression of a complex disease, it is necessary to understand the physiology and function of many genes operating together in distinct interactions as a system. In order to significantly advance our understanding of the function of a system, we need to learn the causal relationships among its modeled genes. To this end, it is desirable to compare experiments of the system under complete interventions of some genes, e.g., knock-out of some genes, with experiments of the system without interventions. However, it is expensive and difficult (if not impossible) to conduct wet lab experiments of complete interventions of genes in animal models, e.g., a mouse model. Thus, it will be helpful if we can discover promising causal relationships among genes with observational data alone in order to identify promising genes to perturb in the system that can later be verified in wet laboratories. While causal Bayesian networks have been actively used in discovering gene pathways, most of the algorithms that discover pairwise causal relationships from observational data alone identify only a small number of significant pairwise causal relationships, even with a large dataset. In this article, we introduce new causal discovery algorithms—the Equivalence Local Implicit latent variable scoring Method (EquLIM) and EquLIM with Markov chain Monte Carlo search algorithm (EquLIM-MCMC)—that identify promising causal relationships even with a small observational dataset.     

The Evolution of Food Safety Policy–making Institutions in the UK, EU and Codex Alimentarius

After the British government announced in March 1996 that a novel fatal human disease (now called variant Creutzfeldt–Jakob disease) had emerged and was almost certainly caused by consuming BSE–contaminated food, national and international authorities have been struggling to deal with the consequences of a serious loss of public confidence in the safety of foods and in food safety policy–making institutions. One of the main ways in which governments and officials have responded to those challenges has been by initiating a broad range of structural and procedural reforms to the ways in which public policies are decided, legitimated and communicated. This paper outlines some of the more important respects in which national and international authorities have changed the ways in which they assess and manage the risks to human consumers of food–borne hazards. The focus is on developments in the UK, the EU and, at the global level, the Codex Alimentarius Commission; the period covered runs from the late 1960s until summer 2002. The discussion focuses on the case for separating the responsibilities for regulating and sponsoring the agricultural and food industries, for conducting risk appraisals and decision–making in open and democratically accountable ways and for drawing on experts representing a wide range of interests and expertise rather than on a narrow industry–based group. The paper concludes by indicating some key structural and procedural conditions for effectively differentiating the scientific from the political aspects of risk appraisal and decision–making, and then for coupling them together in ways that would provide both scientific and democratic legitimacy.     

An Approach to Asset Pricing Under Incomplete and Diverse Perceptions

We model a dynamic, competitive market, where in every period, risk‐neutral traders trade a one‐period bond against an infinitely lived asset, with limited short‐selling of the long‐term asset. Traders lack structural knowledge and use different “incomplete theories,” all of which give statistically correct beliefs about next period's market price of the long‐term asset. The more theories there are in the market, the higher is the equilibrium price of the long‐term asset. Investors with more complete theories do not necessarily earn higher returns than those with less complete ones, who can earn above the risk‐free rate. We provide two necessary conditions for a trader to earn above the risk‐free rate.