Fixed-Term Contracts and Labor Market Duality in France

The French labor market is segmented between permanent and temporary workers. The second category has difficulty in getting an open-ended contract. This paper aims at depicting workers on short-term contracts and shows the consequences on their professional career are negative and significant. A large part of the wage gap between permanent and temporary workers remains unexplained by observable characteristics. They receive less on-the-job training and their likelihood of obtaining a stable job is lower than 30% after 1 year and a half. They have also a higher probability of being the adjustment variable in case of an economic negative shock. These different findings show the importance of using public policies to encourage transitions from fixed-term to permanent employment and reducing labor-market duality.     

Inflation,real economic growth and unemployment expectations: an empirical analysis based on the ECB survey of professional forecasters

Expectations are at the centre of modern macroeconomic theory and policymakers. In this article, we examine the predictive ability and the consistency properties of macroeconomic expectations using data of the European Central Bank (ECB) Survey of Professional Forecasters (SPF). In particular, we provide evidence on the properties of forecasts for three key macroeconomic variables: the inflation rate, the growth rate of real gross domestic product and the unemployment rate.     

A New Regulatory Paradigm for the Digital Sector in Europe

The new regulatory framework in the digital sector in Europe is likely to fall short of expectations. A fragmented digital market with insuffi cient incentives for investment is the probable outcome. European regulators should abandon their approach in favour of a new and more flexible set of ideas discussed in this paper.     

Integrated bio-economic models as tools to support land-use decision making: a review of potential and limitations

Bio-economic modelling has become a useful tool for anticipating the outcomes of policies and technologies before their implementation. Advances in mathematical programming have made it possible to build more comprehensive models. In an overview of recent studies about bio-economic models applied to land-use problems in agriculture and forestry, we evaluated how aspects such as uncertainty, multiple objective functions, system dynamics and time have been incorporated into models. We found that single objective models were more frequently applied at the farm level, while multiple objective modelling has been applied to meet concerns at the landscape level. Among the objectives, social aspects are seldom represented in all models, when being compared to economic and environmental aspects. The integration of uncertainty is occasionally a topic, while stochastic approaches are more frequently applied than non-stochastic robust methods. Most multiple-objective models do not integrate uncertainty or sequential decision making. Static approaches continue to be more recurrent than truly dynamic models. Even though integrating multiple aspects may enhance our understanding of a system; it involves a tradeoff between complexity and robustness of the results obtained. Land-use models have to address this balance between complexity and robustness in order to evolve towards robust multiple-objective spatial optimization as a prerequisite to achieve sustainability goals.     

Information and Knowledge as Antecedents of Consumer Attitudes and Intentions to Buy and Recommend Fair-Trade Products

The authors propose and empirically test a causal model to understand how the availability of fair-trade information and consumer knowledge about this issue affect consumers’ attitudes and intentions toward fair-trade products. The model is built upon the attitude-behavior paradigm and the premises of agency theory. It is tested through structural equation modeling with a sample of 292 Spanish consumers. The findings are that consumers do not have good knowledge about fair trade and that this is significantly determined by the lack of information about this in the market. It is also observed that consumers’ perceptions about the availability of fair-trade information have negative effects on their concern about this issue and that such information as is available is not effective in reducing consumer skepticism. The research represents an extension of previous fair-trade literature because the role of information and communication in improving consumer attitudes and buying intentions has rarely been explored in the case of ethical products.     

Stock market volatility forecasting: Do we need high-frequency data?

The general consensus in the volatility forecasting literature is that high-frequency volatility models outperform low-frequency volatility models. However, such a conclusion is reached when low-frequency volatility models are estimated from daily returns. Instead, we study this question considering daily, low-frequency volatility estimators based on open, high, low, and close daily prices. Our data sample consists of 18 stock market indices. We find that high-frequency volatility models tend to outperform low-frequency volatility models only for short-term forecasts. As the forecast horizon increases (up to one month), the difference in forecast accuracy becomes statistically indistinguishable for most market indices. To evaluate the practical implications of our results, we study a simple asset allocation problem. The results reveal that asset allocation based on high-frequency volatility model forecasts does not outperform asset allocation based on low-frequency volatility model forecasts.     

A multivariate distance nonlinear causality test based on partial distance correlation: a machine learning application to energy futures

This paper proposes a multivariate distance nonlinear causality test (MDNC) using the partial distance correlation in a time series framework. Partial distance correlation as an extension of the Brownian distance correlation calculates the distance correlation between random vectors X and Y controlling for a random vector Z. Our test can detect nonlinear lagged relationships between time series, and when integrated with machine learning methods it can improve the forecasting power. We apply our method as a feature selection procedure and combine it with the support vector machine and random forests algorithms to study the forecast of the main energy financial time series (oil, coal, and natural gas futures). It shows substantial improvement in forecasting the fuel energy time series in comparison to the classical Granger causality method in time series.     

Ownership structure and hospital service costs and fees: A decomposition approach

This paper analyzes hospital characteristics that are associated with higher average costs and charges for venipuncture, computed tomography procedures (computerized axial tomography [CAT] scans), and electrocardiograms (EKG). Using data from a Medicare database, our results indicate that higher wages, larger hospital sizes, and greater service quality are associated with higher procedure costs, whereas system membership is generally associated with lower procedure costs. Blinder‐type decompositions, which are the main focus of this study, suggest (a) that venipuncture costs are about 17% to 19% lower at proprietary hospitals than at nonprofit or government hospitals, (b) CAT scan costs are about 6% to 12% lower at nonprofit hospitals than at proprietary and government hospitals, and (c) that EKG costs are about 3% lower at proprietary hospitals than at nonprofit or government hospitals. Lastly, large portions of each of these differences are found to be due to both differences in mean values of the hospitals' characteristics by ownership type and differences in the mechanism by which the hospitals' characteristics are transmitted to procedure costs.     

“Going natural”: Black women’s identity project shifts in hair care practices

Hair care, a seemingly mundane consumption practice, is packed with sociocultural meanings and constitutes an important realm of identity work for consumers. Drawing upon existing literature on consumer identity projects, this paper investigates the experiences of urban, Black women in Kenya as they disengage from the normative practice of altering their natural hair texture using chemical straighteners (a practice that conforms to Eurocentric beauty ideals of straight, flowing hair), and adopt the non-normative hair care practice of “going natural,” in which they embrace their natural hair texture and hairstyles. This paper traces the historical, sociocultural, and political events that underpin the normative ideology associated with the identity marker of natural Black hair, and how this ideology influences the women’s “going natural” experience. Findings reveal tensions that emerge in the women’s identity project disengagement and reconstruction process, and how the women navigate their position as they reimagine their embodied identity quest.