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.     

Individual adoption of convergent mobile phone in Italy

The present study integrates the technology acceptance and convergence streams of research to develop and test a model of individual adoption of convergent mobile technologies. Adopting structural equation modelling, we hypothesize that relative advantage, effort expectancy, social influence and facilitating conditions affect directly individual attitude and, indirectly the intention to use convergent mobile technologies. The model explains a highly significant 53.2% of the variance for individual attitude, while individual attitude accounts for 33.9% of the variance in behavioral intention. Authors are listed alphabetically.     

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.     

Maximum Likelihood Estimation of Input Demand Models with Fixed Costs of Adjustment

Traditional models of input demand rely upon convex and symmetric adjustment costs. However, the fortune of this highly restrictive approach is due more to analytical convenience than to empirical relevance. In this note we examine the model under more realistic hypothesis of fixed costs, show that it can be cast in the form of a Double Censored Random Effect Tobit Model, derive its likelihood function, and finally evaluate the performance of the ML estimators through a Monte Carlo experiment. The performances, although strongly dependent on the degree of censoring, appear to be promising.     

Evaluating positive leadership: pilot study on the psychometric properties of a reduced version of the Positive Leadership Assessment Scale / Evaluando el liderazgo positivo: estudio piloto de las propiedades psicométricas de una versión reducida del Positive Leadership Assessment Scale

Positive leadership is considered a fundamental factor which contributes significantly to the development of healthy organizations. Positive leadership has been address via other leadership models, primarily transformational and authentic leadership, with which some affinities have been established. Although there is a large body of literature on positive leadership, especially related to its practical aspects, the construct is not properly delimited and there are only a few relevant contributions on how to measure it. The purpose of this paper is to conduct a pilot study to examine the psychometric properties of a reduced version of the PLAS (Positive Leadership Assessment Scale). Results from a confirmatory factorial analysis show that a five-correlated factors model achieves a good fit with the empirical data (on a sample of Spanish students). Likewise, this study also offers a range of evidence of validity, showing a relationship with the constructs of both transformational and authentic leadership and engagement.     

Some new results on univariate and multivariate permutation tests for ordinal categorical variables under restricted alternatives

In several sciences, especially when dealing with performance evaluation, complex testing problems may arise due in particular to the presence of multidimensional categorical data. In such cases the application of nonparametric methods can represent a reasonable approach. In this paper, we consider the problem of testing whether a “treatment” is stochastically larger than a “control” when univariate and multivariate ordinal categorical data are present. We propose a solution based on the nonparametric combination of dependent permutation tests (Pesarin in Multivariate permutation test with application to biostatistics. Wiley, Chichester, 2001), on variable transformation, and on tests on moments. The solution requires the transformation of categorical response variables into numeric variables and the breaking up of the original problem’s hypotheses into partial sub-hypotheses regarding the moments of the transformed variables. This type of problem is considered to be almost impossible to analyze within likelihood ratio tests, especially in the multivariate case (Wang in J Am Stat Assoc 91:1676–1683, 1996). A comparative simulation study is also presented along with an application example.     

Integrating Poverty and Environmental Concerns into Value‐Chain Analysis: A Strategic Framework and Practical Guide

This article aims to guide the design and implementation of action‐research projects in value‐chain analysis by presenting a strategic framework focused on small producers and trading and processing firms in developing countries. Its stepwise approach – building on the conceptual framework set out in a companion article – covers in detail what to do, questions to be asked and issues to be considered, and integrates poverty, gender, labour and environmental concerns.‘Upgrading’ strategies potentially available for improving value‐chain participation for small producers are identified, with the ultimate purpose of increasing the rewards and/or reducing the risks.