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.     

No‐Bubble Condition: Model‐Free Tests in Housing Markets

We test for the existence of housing bubbles associated with a failure of the transversality condition that requires the present value of payments occurring infinitely far in the future to be zero. The most prominent such bubble is the classic rational bubble. We study housing markets in the United Kingdom and Singapore, where residential property ownership takes the form of either leaseholds or freeholds. Leaseholds are finite‐maturity, pre‐paid, and tradeable ownership contracts with maturities often exceeding 700 years. Freeholds are infinite‐maturity ownership contracts. The price difference between leaseholds with extremely‐long maturities and freeholds reflects the present value of a claim to the freehold after leasehold expiry, and is thus a direct empirical measure of the transversality condition. We estimate this price difference, and find no evidence of failures of the transversality condition in housing markets in the U.K. and Singapore, even during periods when a sizable bubble was regularly thought to be present.     

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.     

An examination of sexual risk behaviors in adolescent substance users

ABSTRACT. Objectives: This study examined the risk for engaging in high-risk sexual behavior associated with substance use in a sample of U.S. adolescents. Methods: The sample (N = 15,425) was obtained via the Youth Risk Behavior Surveillance System, a survey of public high school students. Results: Substance use was associated with sexual risk behaviors, with odds ratios being greatest for adolescents who used cigarettes and illicit substances, as well as those who engaged in injection drug use. Conclusions: Results highlight the need for prevention and intervention programs targeting sexual risk behaviors in substance-using adolescents as a means to decrease the spread of sexually transmitted infections.     

Governance and strategy within the financial system: tradition and innovation

Italian banks have undergone an evolutionary process and development of corporate, retail and private banking within the Italian banking system as a response to market pressures exerted by business and private customers for a broadening and qualitative expansion of offerings and organization of available competencies. This not only refers to large enterprises, whose relations with the financial system are autonomous, on equal terms and for some time now have opened up internationally, but above all the large number of SMEs found in Italy’s economic system. Interpreting governance and strategy takes place in a broad perspective in which banks and the financial system have to deal with five significant factors today: regulations, customers, knowledge, capital and synergies. Interaction with these five factors is undoubtedly not only guided by a choice made by shareholders and managers but represents the set of decisions that mitigate ideological factors, choices concerning sustainability and social acceptance of these choices. This paper intends to explore this interaction, drawing on and utilizing the most significant studies in the Italian Banking & Finance sector, with a specific focus on relations between the financial system and businesses, that is to say, the corporate banking area.     

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.