Information and Investor Behavior Surrounding Earnings Announcements

The goal of this paper is to analyze the impact of annual earnings announcements on the market through the order flow data in addition to the usual transaction data. In this respect, examining order flow data can potentially reveal valuable information that is not available from transaction data. In fact, the data allow us to test hypotheses about asymmetric information and investor behavior and to test if the behavior varies with investor sophistication. In addition, the paper tries to identify the determinants of the impact on a firm's value using assumptions about investor behavior.     

Is Supply's Actual Contribution to Sustainable Development Strategic and Operational?

This paper addresses the issue of how sustainable supply practices are actually used as a leverage for sustainable development (SD). In order to assess the level of sustainable supply management within an organization, the authors have reviewed the literature extensively and then developed a five‐step maturity model around five management dimensions. A qualitative exploratory approach based on two detailed case studies of organizations whose reputation for SD is recognized internationally has been used. This methodology allowed us to show (1) how sustainable supply practices could be used as a leverage for an organization's sustainable development approach and (2) that sustainable supply practices still have quite a distance to go with regards to the maturity model for sustainable supply, even in organizations that are often mentioned as leaders in the SD area. In these organizations, managers still emphasize environmental considerations, while neglecting practices that would make it possible to reach the three SD objectives simultaneously. Copyright © 2017 John Wiley & Sons, Ltd and ERP Environment     

Competition and collaboration between public and private sectors: the historical construction of the Spanish hospital system, 1942–86

Throughout history, healthcare, along with diet, has been an essential component of life and a country's welfare. In particular, a country's hospital system is a key indicator for analysing the level of welfare achieved by health coverage. From an economic history perspective, the study of hospital systems is relevant since they stem from public and private investment and produce positive externalities by creating employment and stimulating other economic sectors such as construction and health. Spain provides a significant case study for determining the factors of backwardness in the construction of a modern hospital system in a country on the European periphery. Moreover, it also helps us understand how, despite initial obstacles, this system had attained a significant degree of quality by the end of the twentieth century, as confirmed by its current international hospital rankings and even by the phenomenon of health tourism. The study analyses the creation of the Spanish hospital system during Franco's dictatorship and the transition to democracy. It reveals how the maintenance of a regressive tax system, the use of health policy as political propaganda, and disputes within the political elite of the dictatorship led to an inadequate and fragmented public hospital system, which had to collaborate with the private hospital system, was full of financial holes and tainted by corruption, and remained at the service of privileged groups.     

Political uncertainty,risk of Frexit and European sovereign spreads

Using data from a prediction market (crowd-based forecasts), we build a daily measure capturing the risk of Frexit related to the 2017 French presidential elections. We study how unexpected changes in this new measure of political uncertainty in France affect European sovereign spreads vis-à-vis Germany. We show that our uncertainty proxy drives not only the French sovereign spread but also the spreads of those EU countries deemed the most vulnerable to the risk of desegregation of the Euro Zone. These results suggest that specific political uncertainty affects short-term investor’s expectations and may outweigh other economic determinants of sovereign spreads shortly prior to high stake elections     

How many educated workers for your economy? European targets,optimal public spending,and labor market impact

This paper studies optimal taxation schemes for education in a search-matching model where the labor market is divided between a high-skill and a low-skill sector. Two public policy targets - maximizing the total employment level and optimizing the social surplus - are studied according to three different public taxation strategies. We calibrate our model using evidence from thirteen European countries, and compare our results with the target from the Europe 2020 Agenda for achievement in higher education. We show that, with current labor market characteristics, the target set by governments seems compatible with the social surplus maximization objective for some countries, while being too high for other countries. For all countries, maximizing employment would imply higher educational spending than that required for the social surplus to reach its maximum.     

Sparse Partial Least Squares in Time Series for Macroeconomic Forecasting

Factor models have been applied extensively for forecasting when high‐dimensional datasets are available. In this case, the number of variables can be very large. For instance, usual dynamic factor models in central banks handle over 100 variables. However, there is a growing body of literature indicating that more variables do not necessarily lead to estimated factors with lower uncertainty or better forecasting results. This paper investigates the usefulness of partial least squares techniques that take into account the variable to be forecast when reducing the dimension of the problem from a large number of variables to a smaller number of factors. We propose different approaches of dynamic sparse partial least squares as a means of improving forecast efficiency by simultaneously taking into account the variable forecast while forming an informative subset of predictors, instead of using all the available ones to extract the factors. We use the well‐known Stock and Watson database to check the forecasting performance of our approach. The proposed dynamic sparse models show good performance in improving efficiency compared to widely used factor methods in macroeconomic forecasting. Copyright © 2014 John Wiley & Sons, Ltd.