Linking power and inequality in global value chains

There is increasing interest in the study of globalization on whether the emergence and consolidation of global value chains (GVCs) have exacerbated inequalities within and across nations and/or how GVCs may be leveraged to mitigate them. Although power asymmetries have been identified as a central factor shaping (un)successful GVC participation, dominant discourses still disregard the links between power and inequality or use these concepts interchangeably. In this article, we provide an analytical approach to GVC-related inequalities (within, along and through value chains) and examine how they may co-evolve with different types of power (bargaining, demonstrative, institutional and constitutive). We apply this approach to the case study of the hake value chain in South Africa to illustrate how existing inequalities are manifested, challenged, mitigated or exacerbated—and draw an agenda for future research.     

Adaptive cluster sampling with a data driven stopping rule

The adaptive cluster sampling (ACS) is a suitable sampling design for rare and clustered populations. In environmental and ecological applications, biological populations are generally animals or plants with highly patchy spatial distribution. However, ACS would be a less efficient design when the study population is not rare with low aggregation since the final sample size could be easily out of control. In this paper, a new variant of ACS is proposed in order to improve the performance (in term of precision and cost) of ACS versus simple random sampling (SRS). The idea is to detect the optimal sample size by means of a data-driven stopping rule in order to determine when to stop the adaptive procedure. By introducing a stopping rule the theoretical basis of ACS are not respected and the behaviour of the ordinary estimators used in ACS is explored by using Monte Carlo simulations. Results show that the proposed variant of ACS allows to control the effective sample size and to prevent from excessive efficiency loss typical of ACS when the population is less clustered than anticipated. The proposed strategy may be recommended especially when no prior information about the population structure is available as it does not require a prior knowledge of the degree of rarity and clustering of the population of interest.     

Bayesian variable selection for correlated covariates via colored cliques

We propose a Bayesian method to select groups of correlated explanatory variables in a linear regression framework. We do this by introducing in the prior distribution assigned to the regression coefficients a random matrix $G$ that encodes the group structure. The groups can thus be inferred by sampling from the posterior distribution of $G$ . We then give a graph-theoretic interpretation of this random matrix $G$ as the adjacency matrix of cliques. We discuss the extension of the groups from cliques to more general random graphs, so that the proposed approach can be viewed as a method to find networks of correlated covariates that are associated with the response.     

Predicting the Trend of Well-Being in Germany: How Much Do Comparisons, Adaptation and Sociability Matter?

Using longitudinal data from the German Socio-Economic Panel, we estimate the variation of subjective well-being experienced by Germans over the last two decades testing the role of some of the major correlates of people’s well-being. Our results suggest that the variation of Germans’ well-being between 1996 and 2007 is well predicted by changes over time of income, demographics and social capital. The increase in social capital predicts the largest positive change in subjective well-being. Income growth, also predicts a substantial change in subjective well-being, but it is compensated for about three fourths by the joint negative predictions due to income comparison and income adaptation. Finally, we find that aging of the population predicts the largest negative change in subjective well-being. This result appears to hinge on the large loss of satisfaction experienced by individuals in old age.     

A multivariate time series model for the analysis and prediction of carbon monoxide atmospheric concentrations

We use a Bayesian multivariate time series model for the analysis of the dynamics of carbon monoxide atmospheric concentrations. The data are observed at four sites. It is assumed that the logarithm of the observed process can be represented as the sum of unobservable components: a trend, a daily periodicity, a stationary autoregressive signal and an erratic term. Bayesian analysis is performed via Gibbs sampling. In particular, we consider the problem of joint temporal prediction when data are observed at a few sites and it is not possible to fit a complex space–time model. A retrospective analysis of the trend component is also given, which is important in that it explains the evolution of the variability in the observed process.     

Intervention Research as Management Research in Practice: Learning from a Case in the Fashion Design Industry

Research in the field of management and organizational sciences has yielded a deeper understanding of many emerging business issues. However, the relevance of the contributions has been increasingly criticized, in both the academic and public spheres. We propose the intervention research approach – originally developed by the research group at Ecole des Mines de Paris – as a design science approach able to address both the relevance gap issue and the growing complexity of management practice. It is argued that increasing our understanding of management requires research that is more insightful, influential and immediately applicable. This in turn requires closer collaboration between management and researchers during the inquiry process, which is not always easy to achieve. An illustrative case study of an intervention research project focusing on creativity, conducted in Italy in collaboration with a fashion company, demonstrates how intervention research can be rigorous and relevant to practitioners, and how it can advance theoretical knowledge in management science.