A generalized Bayesian nonlinear mixed‐effects regression model for zero‐inflated longitudinal count data in tuberculosis trials

In this paper, we investigate Bayesian generalized nonlinear mixed‐effects (NLME) regression models for zero‐inflated longitudinal count data. The methodology is motivated by and applied to colony forming unit (CFU) counts in extended bactericidal activity tuberculosis (TB) trials. Furthermore, for model comparisons, we present a generalized method for calculating the marginal likelihoods required to determine Bayes factors. A simulation study shows that the proposed zero‐inflated negative binomial regression model has good accuracy, precision, and credibility interval coverage. In contrast, conventional normal NLME regression models applied to log‐transformed count data, which handle zero counts as left censored values, may yield credibility intervals that undercover the true bactericidal activity of anti‐TB drugs. We therefore recommend that zero‐inflated NLME regression models should be fitted to CFU count on the original scale, as an alternative to conventional normal NLME regression models on the logarithmic scale.     

Focused estimation for noisy and small data sets: a Bayesian minimum expected loss estimator approach

Central to many inferential situations is the estimation of rational functions of parameters. The mainstream in statistics and econometrics estimates these quantities based on the plug‐in approach without consideration of the main objective of the inferential situation. We propose the Bayesian Minimum Expected Loss (MELO) approach focusing explicitly on the function of interest, and calculating its frequentist variability. Asymptotic properties of the MELO estimator are similar to the plug‐in approach. Nevertheless, simulation exercises show that our proposal is better in situations characterised by small sample sizes and/or noisy data sets. In addition, we observe in the applications that our approach gives lower standard errors than frequently used alternatives when data sets are not very informative.     

Agency and clientship in public encounters: co‐constructing ‘neediness’ and ‘worthiness’ in shelter placement meetings

This article seeks to develop our understanding of the agency of vulnerable groups who at first sight may not seem to have much agency in their lives. It explores the co‐constructed nature of agency in three Danish homeless shelters. Unlike earlier interview‐based studies, our research is based on naturalistic data drawn from 23 video‐recorded placement meetings. Using concepts from Goffman, we examine how versions of the neediness and worthiness of homeless people are negotiated verbally and bodily between staff and clients. We find that homeless people have to juggle two partly contradictory roles when they are given or take the roles of either a (active) citizen or a (passive) client. Clientship is actively negotiated by both parties and demonstrates the agency of homeless people: they can collaborate with (as clients) or challenge (as citizens) the staff’s attempts to formulate solutions to their troubles. We further examine how the professional ideology of client centredness affects the meeting between the two parties. However, we show that, like any discourse, client centredness has no intrinsic meaning and is played out by actors in very different ways. In work with the homeless, the discourse of client centredness is related to discourses of ‘neediness’, ‘worthiness’ and ‘value for money’ that define agency in different ways and make three different client positions available: the resolute client, the acquiescent client and the passive client.     

Minimum wage violation in central and eastern Europe

This article analyses minimum wage violations over the period 2003–12 in ten central and eastern European countries which all have national statutory minimum wages. Using European Union Statistics on Income and Living Conditions (EU‐SILC) data and the methodology proposed by Bhorat, Kanbur and Mayet (2013), the authors measure the incidence and depth of violation. In addition, they conduct regression analyses on individual, workplace and macro‐level determinants of non‐compliance. While the incidence of violation remains relatively low, the workers that minimum wage policies seek to protect appear to be the most likely to be affected by non‐compliance. Over time, higher minimum to average wage ratios are related to a higher incidence of violation.     

Refugees,not Economic Migrants ‐ Why do Asylum‐Seekers Register in Hungary?

The article analyses why asylum‐seekers choose Hungary as an entry point to the European Union. Among the Central and Eastern European countries Hungary has been by far the most popular choice for asylum‐seekers between 2002 and 2016, yet surprisingly, it has been neglected by the literature. Using a panel dataset and fixed effects regressions, the article finds that beyond being ‘conveniently’ located on the Balkans migration route, variables related to Hungary's immigration policy are the most significant determinants of asylum‐seeker choices. The article finds no evidence to support recent claims by the Hungarian government that arrivals to the country are actually economic migrants and not asylum‐seekers; quite the contrary, the results indicate that on average asylum‐seekers entering Hungary are fleeing violent conflict in their countries of origin.     

Talking about Integration: The Voices of Syrian Refugees Taking Part in Introduction Programmes for Integration into Swedish Society

The significant increase in the number of refugees entering the European Union and the low employment rates among them remain pressing issues across Europe. While previous research has mainly focused on refugee integration prospects from the policymakers’ point of view, we shift the analytical focus to how refugees perceive the introduction programme experience, examining the case of Syrian refugees to Sweden. Drawing upon semi‐structured interviews with Syrian refugees, our results suggest that the quality of language training, the complexity of the validation process for educational qualifications and the lengthy administrative procedures are perceived as important barriers hindering the access of refugees to the Swedish labour market. These barriers are differently perceived, especially by those with differing levels of education. This highlights the utility of an intersectional approach, and the continued need to explore the diversity of migrant experiences when assessing integration policies and programmes.     

EASING THE CONSTRAINTS OF MOTHERHOOD: THE EFFECTS OF ALL‐DAY SCHOOLS ON MOTHERS' LABOR SUPPLY

Low rates of female labor force participation (LFP) have been linked to the absence of childcare policies. This article examines the degree to which extending the school day by 3.5 hours in elementary schools, a large implicit childcare subsidy, affects LFP, the number of weekly hours worked, and the monthly earnings of females with elementary‐school‐age children. To do so, we exploit within‐individual variation in access to full‐time schools and a rotating panel of households that contains 12 years of individual‐level data on labor outcomes and sociodemographic characteristics. Results from long‐difference models show that extending the school day increases mothers' labor supply, increasing LFP by 5.5 percentage points and the number of weekly hours worked by 1.8. Moreover, these increases are accompanied by a raise in monthly earnings. (JEL I25, J13, J22)     

New perspective on the benefits of the gene–environment independence in case–control studies

We study the benefit of exploiting the gene–environment independence (GEI) assumption for inferring the joint effect of genotype and environmental exposure on disease risk in a case–control study. By transforming the problem into a constrained maximum likelihood estimation problem we derive the asymptotic distribution of the maximum likelihood estimator (MLE) under the GEI assumption (MLE‐GEI) in a closed form. Our approach uncovers a transparent explanation of the efficiency gained by exploiting the GEI assumption in more general settings, thus bridging an important gap in the existing literature. Moreover, we propose an easy‐to‐implement numerical algorithm for estimating the model parameters in practice. Finally, we conduct simulation studies to compare the proposed method with the traditional prospective logistic regression method and the case‐only estimator. The Canadian Journal of Statistics 47: 473–486; 2019 © 2019 Statistical Society of Canada     

The Moral Economy of Neoliberalism: How Voters Use Neoliberal Ideology to (De)Legitimate Undocumented Worker Access to Labor Markets

Neoliberalism is prevalent in American life. While researchers have documented the use of neoliberal ideology in institutional and macrolevel policy contexts, they have yet to investigate how voters use neoliberal ideology to legitimate their position on economic policy. I use data from semi‐structured interviews with 85 Tucsonans about why they voted the way they did on Proposition 202 (2008): “Arizona Stop Illegal Hiring”—which sought to reregulate undocumented worker labor market access—to address this gap. I found evidence of two distinct neoliberal ideological legitimations: “fair market competition” and “individual responsibility.” Furthermore, I use these data to shed light on the debate over whether neoliberalism spans partisan affiliation or converges with American conservatism. I found that voters across party lines who supported the measure paired neoliberal legitimations with conservative legitimations. We can interpret this bipartisan use of neoliberal ideology as evidence of a neoliberal “moral economy”—or consensus about the moral principles in which market action is embedded. Evidence of this moral economy indicates that moral principles from neoliberal ideology are simultaneously bipartisan and converge with American conservatism. These findings suggest that there could be a broader moral consensus among voters concerning the legitimacy of anti‐immigration economic policies.