Migration Policy and Industrial Structure: The Case of Switzerland

Structural change in OECD countries, emphasizing knowledge‐based sectors, has led to an increasing demand for highly skilled labour. One means of meeting this demand has been to implement a selective immigration policy. Such policies, however, have been criticized for channelling labour into low‐producing sectors and occupations, hampering structural change. Proponents of such criticism point to Switzerland's former policy of channelling immigrants into so‐called seasonal sectors, a practice abandoned in the early 1990s, as having contributed to Switzerland's low growth rates. To assess this, we here analyse the amended migration policy's effects on skill structure and sectoral distribution of immigration flows using data from the Swiss Census of 1990 and 2000 to determine whether the new policy has led to an immigrant inflow more adapted to the processes of structural change. We find that the share of highly skilled immigrants has increased notably under the new migration policy. Our analysis also shows an important change in the sectoral focus of the new arrival inflow. Not only have fewer immigrants been entering declining sectors, but the majority of migrants arriving under the new policy regime have been absorbed into growing and knowledge‐based sectors, meaning they are employed primarily in service and knowledge‐intensive sectors. Overall, the analysis provides ample evidence that the current admission policy as ositively contributed to tructural change in Switzerland.     

Bayesian fractional polynomials

This paper sets out to implement the Bayesian paradigm for fractional polynomial models under the assumption of normally distributed error terms. Fractional polynomials widen the class of ordinary polynomials and offer an additive and transportable modelling approach. The methodology is based on a Bayesian linear model with a quasi-default hyper-g prior and combines variable selection with parametric modelling of additive effects. A Markov chain Monte Carlo algorithm for the exploration of the model space is presented. This theoretically well-founded stochastic search constitutes a substantial improvement to ad hoc stepwise procedures for the fitting of fractional polynomial models. The method is applied to a data set on the relationship between ozone levels and meteorological parameters, previously analysed in the literature.     

Dynamic Discrete-time Duration Models: Estimation via Markov Chain Monte Carlo

Discrete-time or grouped duration data, with one or multiple types of terminating events, are often observed in social sciences or economics. In this paper we suggest and discuss dynamic models for flexible Bayesian nonparametric analysis of such data. These models allow simultaneous incorporation and estimation of baseline hazards and time-varying covariate effects, without imposing particular parametric forms. Methods for exploring the possibility of time-varying effects, as for example the impact of nationality or unemployment insurance benefits on the probability of reemployment, have recently gained increasing interest. Our modeling and estimation approach is fully Bayesian and makes use of Markov Chain Monte Carlo (MCMC) simulation techniques. A detailed analysis of unemployment duration data, with full-time job, part-time job and other causes as terminating events, illustrates our methods and shows how they can be used to obtain refined results and interpretations.     

A hierarchical model for space–time surveillance data on meningococcal disease incidence

Summary. We describe a model-based approach to analyse space–time surveillance data on meningococcal disease. Such data typically comprise a number of time series of disease counts, each representing a specific geographical area. We propose a hierarchical formulation, where latent parameters capture temporal, seasonal and spatial trends in disease incidence. We then add—for each area—a hidden Markov model to describe potential additional (autoregressive) effects of the number of cases at the previous time point. Different specifications for the functional form of this autoregressive term are compared which involve the number of cases in the same or in neighbouring areas. The two states of the Markov chain can be interpreted as representing an 'endemic' and a 'hyperendemic' state. The methodology is applied to a data set of monthly counts of the incidence of meningococcal disease in the 94 départements of France from 1985 to 1997. Inference is carried out by using Markov chain Monte Carlo simulation techniques in a fully Bayesian framework. We emphasize that a central feature of our model is the possibility of calculating—for each region and each time point—the posterior probability of being in a hyperendemic state, adjusted for global spatial and temporal trends, which we believe is of particular public health interest.     

Dienstleister der Finanzialisierung: Fragmentierte Organisation und kalkulierte Profite in der Immobilienwirtschaft

The financial crisis of 2007 has brought questions about the social order in capitalist societies back into the limelight. Economic sociology in particular emphasizes the growing influence of financial markets?? actors and logics. Most research focuses on macro phenomena or intra-organizational and individual perspectives. In contrast, concepts of financialization that explicitly take an inter-organizational perspective are in their infancy. Therefore, this article investigates the business and financing practices of a Europe-wide real estate firm. The historic case study shows the importance of service firms in the real estate sector and analyzes cooperation between different financial service firms (banks, estate agents, notaries, auditing firms, customers). Furthermore, it investigates how they jointly organize border-crossing and the relocation of legal liabilities and economic accountability. The article thus contributes to the current financialization debate in two ways: for one, it analyzes and explains inter-organizational cooperation between different service firms as being constitutive for financialization dynamics. For another, it points to the importance of calculative practices for establishing financialized corporations.     

Adaptive power priors with empirical Bayes for clinical trials

Incorporating historical information into the design and analysis of a new clinical trial has been the subject of much discussion as a way to increase the feasibility of trials in situations where patients are difficult to recruit. The best method to include this data is not yet clear, especially in the case when few historical studies are available. This paper looks at the power prior technique afresh in a binomial setting and examines some previously unexamined properties, such as Box P values, bias, and coverage. Additionally, it proposes an empirical Bayes‐type approach to estimating the prior weight parameter by marginal likelihood. This estimate has advantages over previously criticised methods in that it varies commensurably with differences in the historical and current data and can choose weights near 1 when the data are similar enough. Fully Bayesian approaches are also considered. An analysis of the operating characteristics shows that the adaptive methods work well and that the various approaches have different strengths and weaknesses.     

Conditional Prior Proposals in Dynamic Models

ABSTRACT. Dynamic models extend state space models to non-normal observations. This paper suggests a specific hybrid Metropolis–Hastings algorithm as a simple device for Bayesian inference via Markov chain Monte Carlo in dynamic models. Hastings proposals from the (conditional) prior distribution of the unknown, time-varying parameters are used to update the corresponding full conditional distributions. It is shown through simulated examples that the methodology has optimal performance in situations where the prior is relatively strong compared to the likelihood. Typical examples include smoothing priors for categorical data. A specific blocking strategy is proposed to ensure good mixing and convergence properties of the simulated Markov chain. It is also shown that the methodology is easily extended to robust transition models using mixtures of normals. The applicability is illustrated with an analysis of a binomial and a binary time series, known in the literature.     

Improved auxiliary mixture sampling for hierarchical models of non-Gaussian data

The article considers Bayesian analysis of hierarchical models for count, binomial and multinomial data using efficient MCMC sampling procedures. To this end, an improved method of auxiliary mixture sampling is proposed. In contrast to previously proposed samplers the method uses a bounded number of latent variables per observation, independent of the intensity of the underlying Poisson process in the case of count data, or of the number of experiments in the case of binomial and multinomial data. The bounded number of latent variables results in a more general error distribution, which is a negative log-Gamma distribution with arbitrary integer shape parameter. The required approximations of these distributions by Gaussian mixtures have been computed. Overall, the improvement leads to a substantial increase in efficiency of auxiliary mixture sampling for highly structured models. The method is illustrated for finite mixtures of generalized linear models and an epidemiological case study.     

Why do we still need participatory technology assessment?

The paper contributes to the current discussion on the role of participatory methods in the context of technology assessment (TA) and science and technology (S&T) governance. It is argued that TA has to be understood as a form of democratic policy consulting in the sense of the Habermasian model of a ??pragmatist?? relation of science and politics. This notion implies that public participation is an indispensable element of TA in the context of policy advice. Against this background, participatory TA (pTA) is defended against recent criticism of procedures of lay participation which states that pTA is lacking impact on S&T decision making, that pTA instead of opening S&T policies to new perspectives is used as a means to support mainstream S&T policy and that in pTA procedure the authentic lay perspective is systematically contorted by dominant expert knowledge.