Suchergebnisse

SummaryThis paper presents new evidence on the differences in quality and achievement of public, private, and charter schools by using the educational outcomes for the more than 1,200 schools of the Madrid region over the period 2005–2009. By applying an external test including three different areas, mathematics, writing, and language, the evolution of the achievement of the pupils in the three different types of schools, public, charter, and private, are analyzed.Our results show that charter and, especially, private schools attain better results than public schools and are more responsive to its academic evolution, both at a lower cost. Private schools do their best to converge to the leading schools in their district in the previous year, whereas public schools do not seem to do so. This result holds even after controlling for the number of immigrants in the school, the age of the school, and its size. Also, the results seem quite robust, since we tested the relevance of different variables such as immigration, socioeconomic status, and foreign students and we obtained results that support our main hypothesis.

AbstractThe growing number of immigrants in the Madrid region raises several questions concerning the welfare of future native generations. The debates shift from increasing concern about the congestion of public services like education or healthcare, to how immigration helps to ease tension in relation to financing those services and other benefits to the region's general welfare. In order to evaluate the global effect, our analysis uses a generational accounting method which is applied to different productivity, interest rate and growth scenarios. The results show that the impact of immigrants is positive, with intergenerational distribution towards the currently most active taxpayers.

The impact of companies? adoption of robotics is increasingly interesting. This study aims to elucidate how the adoption of these technologies will affect companies and society. Companies that use these technologies expect to gain a competitive advantage, but robotization implies risks that must be managed by companies and governments. This research focuses on one of the most sensitive elements of this transformation process?the workforce. First, we analyze the characteristics of the workforce and the degree of adoption of robotics using a sample of 4,640 firms with 26 years of observation. We develop a predictive model using a supervised artificial neural network multilayer perceptron (ANN-MLP) to evaluate a company?s readiness to make this transformation according to its workforce?s characteristics. Second, we focus on the characterization and segmentation of the companies for which the ANN-MLP is unable to correctly predict the degree of adoption of robotics. This classification failure means that there are unidentified factors that determine why a company has a workforce composition and structure that do not correspond to its expected degree of robotization. For this analysis, we investigate the main business indicators of these companies and cluster them using an unsupervised artificial neural network, specifically the Kohonen self-organizing map. Our findings will enable companies to understand the importance of transforming to robotics at the right moment, considering factors such as the optimum structure and composition of the workforce. The combination of technology and human capital is the key to boosting the efficiency of the transformation process toward robotics. At this point, a recommendation model to determine whether the company has sufficient maturity to make the transition is crucial for decision makers.

In the weeks around March 8, 2020, Spanish political authorities moved from denying and minimizing COVID-19 (veiling international recommendations) to establishing a State of Alarm. This uncertainty scenario is a natural experiment for exploring how concealment and diffusion of critical messages in official discourse affected public and published media, information transmission, and collective risk assessment. This study explores, through Natural Language Processing (NLP) and network theory, press, and Twitter agendas those days when (after international warnings, chaos on data, and the authorization of large demonstrations) Spain made the "alarming discovery" of COVID-19. Results show a swift change in the climate of opinion, from the week before to the week after Women's Day (March 8). Noninformation influenced agendas in terms of themes, feelings, and behaviors. The way different societies made COVID-19's "discovery" became essential on the framing of the crisis and on the subsequent trust in authorities during the pandemic. The suppression of information in the first moments remains a key study question.

In the weeks around March 8, 2020, Spanish political authorities moved from denying and minimizing COVID-19 (veiling international recommendations) to establishing a State of Alarm. This uncertainty scenario is a natural experiment for exploring how concealment and diffusion of critical messages in official discourse affected public and published media, information transmission, and collective risk assessment. This study explores, through Natural Language Processing (NLP) and network theory, press, and Twitter agendas those days when (after international warnings, chaos on data, and the authorization of large demonstrations) Spain made the "alarming discovery" of COVID-19. Results show a swift change in the climate of opinion, from the week before to the week after Women's Day (March 8). Noninformation influenced agendas in terms of themes, feelings, and behaviors. The way different societies made COVID-19's "discovery" became essential on the framing of the crisis and on the subsequent trust in authorities during the pandemic. The suppression of information in the first moments remains a key study question.