A multilevel analysis of the determinants of willingness to pay to prevent environmental pollution across countries

This paper discusses the extent to which individual and contextual level factors influence the likelihood of individuals’ willingness to pay (WTP) to prevent environmental pollution. A multilevel probit regression framework was set up to explain WTP to prevent environmental pollution. We use data from the World Values Survey (WVS), which contains socio-economic and socio-demographic information, and merged it with country level covariates. Compared to many previous studies, our dataset encompasses a more indepth set of individual level covariates. We find that rich people, individuals with higher education, as well as those who possess post-materialist values are more likely to be concerned about environmental pollution. This study reveals that in developed countries, 90% of country variation in WTP to prevent environmental pollution can be explained by individual characteristics. This portion reduces to 80% in the case of developing countries. An interesting feature in our study is the ability to investigate the effect of contextual factors on individuals’ willingness to contribute for the environment. We observe that both democracy and government stability reduce individuals’ intention to donate to prevent environmental damage mainly in developed countries.     

Reverse engineering gene regulatory networks using approximate Bayesian computation

Gene regulatory networks are collections of genes that interact with one other and with other substances in the cell. By measuring gene expression over time using high-throughput technologies, it may be possible to reverse engineer, or infer, the structure of the gene network involved in a particular cellular process. These gene expression data typically have a high dimensionality and a limited number of biological replicates and time points. Due to these issues and the complexity of biological systems, the problem of reverse engineering networks from gene expression data demands a specialized suite of statistical tools and methodologies. We propose a non-standard adaptation of a simulation-based approach known as Approximate Bayesian Computing based on Markov chain Monte Carlo sampling. This approach is particularly well suited for the inference of gene regulatory networks from longitudinal data. The performance of this approach is investigated via simulations and using longitudinal expression data from a genetic repair system in Escherichia coli.     

On the voting power of an alliance and the subsequent power of its members

Even, and in fact chiefly, if two or more players in a voting game have on a binary issue independent opinions, they may have interest to form a single voting alliance giving an average gain of influence for all of them. Here, assuming the usual independence of votes, we first study the alliance voting power and obtain new results in the so-called asymptotic limit for which the number of players is large enough and the alliance weight remains a small fraction of the total of the weights. Then, we propose to replace the voting game inside the alliance by a random game which allows new possibilities. The validity of the asymptotic limit and the possibility of new alliances are examined by considering the decision process in the Council of Ministers of the European Union.     

Strategies for international growth in the automobile industry

This article examines alternative strategy approaches in respect of companies seeking growth in the international automobile industry. The author examines these approaches within a developed framework which could also prove helpful to planning in other industries.