Göra illa för att hjälpa eller hjälpa till att göra illa? : Arbetslösas reservationslöner, jobbchanser och återanställningsinkomster

Hurting to help or helping to hurt? The reservation wages of unemployed, job chances and reemployment incomes in Sweden Economic incentives and their impact on the job search behaviour of the unemployed have been a central focus in the academic and political debate in Sweden. A key concept has been the reservation wages of the unemployed, the lowest income at which an unemployed person would be willing to accept a job offer. Unemployment benefit systems have been argued to raise and maintain reservation wages at high levels that lower job chances. This has been supported by a large number of international studies. From this perspective lower reservation wages would function as protection against long term unemployment and the scarring effects associated with it. High reservation wages might however, based on the same behavioural assumptions, have a human capital preserving effect. The possibility to hold out for the right job should reduce human capital losses compared to accepting the first available job offer. In this article we use Swedish longitudinal micro data combining interview and register data in order to investigate three central aspects reservation wages in a Swedish context: factors influencing the setting of reservation wages, the effect of reservation wage on job chances and the impact of reservation wages on reemployment incomes. Our findings show that benefit level and pre-unemployment position in the wage structure are central factors for setting the reservation wage. The effects of reservation wages were however not the expected. No effects were found on job chances, while a strong positive effect was found on reemployment income. This together indicates that high reservation wages have a human capital preserving effect in Sweden.

Parameter estimation of Poisson generalized linear mixed models based on three different statistical principles : a simulation study

Generalized linear mixed models are flexible tools for modeling non-normal data and are useful for accommodating overdispersion in Poisson regression models with random effects. Their main difficulty resides in the parameter estimation because there is no analytic solution for the maximization of the marginal likelihood. Many methods have been proposed for this purpose and many of them are implemented in software packages. The purpose of this study is to compare the performance of three different statistical principles - marginal likelihood, extended likelihood, Bayesian analysis-via simulation studies. Real data on contact wrestling are used for illustration.

A modest start, but a steady rise in research use : A longitudinal study of nurses during the first five years in professional life

Background: Newly graduated nurses are faced with a challenging work environment that may impede theirability to provide evidence-based practice. However, little is known about the trajectory of registered nurses’ use ofresearch during the first years of professional life. Thus, the aim of the current study was to prospectively examinethe extent of nurses’ use of research during the first five years after undergraduate education and specifically assesschanges over time.Method: Survey data from a prospective cohort of 1,501 Swedish newly graduated nurses within the nationalLANE study (Longitudinal Analyses of Nursing Education and Entry in Worklife) were used to investigate perceiveduse of research over the first five years as a nurse. The dependent variables consisted of three single itemsassessing instrumental, conceptual, and persuasive research use, where the nurses rated their use on a five-pointscale, from ‘never’ (1) to ‘on almost every shift’ (5). These data were collected annually and analyzed bothdescriptively and by longitudinal growth curve analysis.Results: Instrumental use of research was most frequently reported, closely followed by conceptual use, withpersuasive use occurring to a considerably lower extent. The development over time showed a substantial generalupward trend, which was most apparent for conceptual use, increasing from a mean of 2.6 at year one to 3.6 atyear five (unstandardized slope +0.25). However, the descriptive findings indicated that the increase started onlyafter the second year. Instrumental use had a year one mean of 2.8 and a year five mean of 3.5 (unstandardizedslope +0.19), and persuasive use showed a year one mean of 1.7 and a year five mean of 2.0 (unstandardized slope+0.09).Conclusion: There was a clear trend of increasing research use by nurses during their first five years of practice.The level of the initial ratings also indicated the level of research use in subsequent years. However, it took morethan two years of professional development before this increase ‘kicked in.’ These findings support previousresearch claiming that newly graduated nurses go through a ‘transition shock,’ reducing their ability to useresearch findings in clinical work.

Spatial modeling of data with excessive zeros applied to reindeer pellet-group counts

We analyze a real data set pertaining to reindeer fecal pellet-group counts obtained from a survey conducted in a forest area in northern Sweden. In the data set, over 70% of counts are zeros, and there is high spatial correlation. We use conditionally autoregressive random effects for modeling of spatial correlation in a Poisson generalized linear mixed model (GLMM), quasi-Poisson hierarchical generalized linear model (HGLM), zero-inflated Poisson (ZIP), and hurdle models. The quasi-Poisson HGLM allows for both under- and overdispersion with excessive zeros, while the ZIP and hurdle models allow only for overdispersion. In analyzing the real data set, we see that the quasi-Poisson HGLMs can perform better than the other commonly used models, for example, ordinary Poisson HGLMs, spatial ZIP, and spatial hurdle models, and that the underdispersed Poisson HGLMs with spatial correlation fit the reindeer data best. We develop R codes for fitting these models using a unified algorithm for the HGLMs. Spatial count response with an extremely high proportion of zeros, and underdispersion can be successfully modeled using the quasi-Poisson HGLM with spatial random effects.