Inequality and Income Distribution in Global Value Chains

Global value chains (GVCs), led by transnational corporations (TNCs), have reshaped the world division of labor over the past two decades. GVCs are pervasive in low technology manufacturing, such as textile and apparel, as well as in more advanced industries like automobiles, electronics, and machines. This hierarchical division of labor generates wild competition at the lower value-added stages of production, where low wages and low profit margins prevail for workers and contract manufacturers in developing countries. At the top of the hierarchy another kind of competition prevails, centered on the ability to monitor and control intellectual property rights related to innovation, finance, and marketing. We argue that GVCs have had crucial effects on income inequality and the appropriation of rents in modern capitalism.     

Gender and ethnic differences in factors that influence food intake

Abstract The objective of this research was to ascertain the gender-and ethnic-related factors that influence what one eats. This survey was conducted using a validated, pilot-tested questionnaire in 11 states and the District of Columbia, which were participating in the W-182 Western Regional Research project. The results indicated that the factors that influence what one eats differ by gender and ethnicity. Men were very much influenced by likes and dislikes, and by their spouse or significant other. Women were influenced by likes and dislikes, and health. Food likes/dislikes and concerns about health and weight were among the leading four factors for all ethnic groups. Food likes/dislikes were the top factor influencing what is eaten by 51% or more of Asians, Pacific Islanders, Caucasians and Hispanics. Concern about health was the top factor for 62% and 58%, respectively, of African Americans and Native Americans.     

Moment-based estimation of smooth transition regression models with endogenous variables

Nonlinear regression models have been widely used in practice for a variety of time series and cross-section datasets. For purposes of analyzing univariate and multivariate time series data, in particular, smooth transition regression (STR) models have been shown to be very useful for representing and capturing asymmetric behavior. Most STR models have been applied to univariate processes, and have made a variety of assumptions, including stationary or cointegrated processes, uncorrelated, homoskedastic or conditionally heteroskedastic errors, and weakly exogenous regressors. Under the assumption of exogeneity, the standard method of estimation is nonlinear least squares. The primary purpose of this paper is to relax the assumption of weakly exogenous regressors and to discuss moment-based methods for estimating STR models. The paper analyzes the properties of the STR model with endogenous variables by providing a diagnostic test of linearity of the underlying process under endogeneity, developing an estimation procedure and a misspecification test for the STR model, presenting the results of Monte Carlo simulations to show the usefulness of the model and estimation method, and providing an empirical application for inflation rate targeting in Brazil. We show that STR models with endogenous variables can be specified and estimated by a straightforward application of existing results in the literature.     

A proposed method to evaluate the quality of services using Fuzzy sets theory

This paper seeks to provide the services sector with a focus on the assessment of quality and for this purpose, a technique that may able a quantitative approach to evaluating quality is proposed. The use of the fuzzy sets theory to process data was used, thus allowing a more flexible and suitable insight into the characteristics of the service sector. An extension of the technique for order performance by similarity to the ideal solution was used. This informs managers of the distance from the company $\prime $ s current level of quality, if compared to a company of perfect quality by means of an overall evaluation. The same technique was used to detect changes in the level of quality during the period surveyed by using a stratified assessment. Finally, a practical application of the approach proposed is presented.     

Brazilian Consumer Preferences for Processed Tomato Packaging Using Conjoint Analysis

Brazil is the largest producer and consumer of processed tomato products in Latin America. However, the consumption preferences related to this product are poorly studied. The aim of this study was to evaluate the influence of certain characteristics of processed tomato product packaging on Brazilian consumer preferences. The revised Personal Involvement Inventory (PII) and conjoint analysis (CA) were applied to 206 consumers in Brazil. Four packaging attributes were evaluated: colour (green, red, and yellow); material (sachets, tin, and brick carton packaging); the presence of additional information; and the presence of “easy-open” devices. The CA technique showed that in general, the Brazilian participants were influenced positively by red brick carton and sachet packaging and by the presence of additional information and a device that facilitated opening the package. The results presented in this study are useful for the food industry to develop packaging and marketing strategies using consumer profiles.     

The post-war American technological development as a military enterprise

This paper argues that the basic industrial innovations thatshaped the modern American technology was conceptualised anddeveloped as a military enterprise. The ‘military–industrial–academic’complex created a scientific-driven process of innovation aimedsimultaneously to win the cold war against the Soviet Union,to push the science frontier and to consolidate the Americanleadership in the world. After the collapse of the Soviet Unionnew initiatives and conceptions about future wars were formulated.The terrorist attack of September 11 gave a special momentumto new military questions that seem to be pushing again theAmerican science for a new wave of industrial innovations     

A multiple regime smooth transition Heterogeneous Autoregressive model for long memory and asymmetries

In this paper we propose a flexible model to describe nonlinearities and long-range dependence in time series dynamics. The new model is a multiple regime smooth transition extension of the Heterogeneous Autoregressive (HAR) model, which is specifically designed to model the behavior of the volatility inherent in financial time series. The model is able to simultaneously approximate long memory behavior, as well as describe sign and size asymmetries. A sequence of tests is developed to determine the number of regimes, and an estimation and testing procedure is presented. Monte Carlo simulations evaluate the finite-sample properties of the proposed tests and estimation procedures. We apply the model to several Dow Jones Industrial Average index stocks using transaction level data from the Trades and Quotes database that covers ten years of data. We find strong support for long memory and both sign and size asymmetries. Furthermore, the new model, when combined with the linear HAR model, is viable and flexible for purposes of forecasting volatility.     

An alternative approach to estimating demand: Neural network regression with conditional volatility for high frequency air passenger arrivals

In this paper we provide an alternative approach to analyze the demand for international tourism in the Balearic Islands, Spain, by using a neural network model that incorporates time-varying conditional volatility. We consider daily air passenger arrivals to Palma de Mallorca, Ibiza and Mahon, which are located in the islands of Mallorca, Ibiza and Menorca, respectively, as a proxy for international tourism demand for the Balearic Islands. Spain is a world leader in terms of total international tourist arrivals and receipts, and Mallorca is one of the most popular destinations in Spain. For tourism management and marketing, it is essential to forecast high frequency international tourist demand accurately. As it is important to provide sensible international tourism demand forecast intervals, it is also necessary to model their variances accurately. Moreover, time-varying variances provide useful information regarding the risks associated with variations in international tourist arrivals.     

Conditions for policy implementation via co-production: the control of dengue fever in Brazil

In this paper, we identify the conditions that must be present for effective policy implementation using co-production. To this end, we investigated five cases in which co-production was used to combat the vector of dengue, the Aedes Aegypti mosquito, in Brazil. Data were analysed using content analysis and crisp-set QCA methods to identify the combination of conditions that explain successful outcomes. Five conditions were found to be necessary for effective implementation via co-production: technical, economic, normative, cognitive and structural. The contribution of this study is to provide empirical evidence with respect to the conditions that effectively lead to successful outcomes.