Institutional Reforms and Educational Attainment in Europe: A Long Run Perspective

In this paper we analyse the effects of changes in the institutional design of the educational system on school attainment. In particular, we test whether alternative reforms have increased the average educational attainment of the population and whether various deciles of the education distribution have been differentially affected. We constructed a dataset of relevant reforms occurred at the national level over the last century, and match individual information to the most likely set-up faced when individual educational choices were undertaken. Thus our identification strategy relies on temporal and geographical variations in the institutional arrangements, controlling for time/country fixed effects, as well as for confounding factors. We also explore who are the individual most likely affected by the reforms. We also group different reforms in order to ascertain the prevailing attitudes of policy makers, showing that reforms can belong to either "inclusive" or "selective" in their nature. Finally we correlate these attitudes to political coalitions prevailing in parliament, finding support to the idea that left wing parties support reforms that are inclusive in nature, while right wing parties prefer selective ones.education, institutions, reform, family background

Evaluating students' evaluations of professors

This paper contrasts measures of teacher effectiveness with the students’ evaluations of the same teachers using administrative data from Bocconi University (Italy). The effectiveness measures are estimated by comparing the subsequent performance in follow-on coursework of students who are randomly assigned to teachers in each of their compulsory courses. We find that, even in a setting where the syllabuses are fixed, teachers still matter substantially. Additionally, we find that our measure of teacher effectiveness is negatively correlated with the students’ evaluations of professors: in other words, teachers who are associated with better subsequent performance receive worse evaluations from their students. We rationalize these results with a simple model where teachers can either engage in real teaching or in teaching-to-the-test, the former requiring greater student effort than the latter. Teaching-to-the-test guarantees high grades in the current course but does not improve future outcomes. Hence, if students are short-sighted and give better evaluations to teachers from whom they derive higher utility in a static framework, the model is capable of predicting our empirical finding that good teachers receive bad evaluations.teacher quality, postsecondary education

Evaluating Students' Evaluations of Professors

This paper contrasts measures of teacher effectiveness with the students' evaluations for the same teachers using administrative data from Bocconi University (Italy). The effectiveness measures are estimated by comparing the subsequent performance in follow-on coursework of students who are randomly assigned to teachers in each of their compulsory courses. We find that, even in a setting where the syllabuses are fixed and all teachers in the same course present exactly the same material, teachers still matter substantially. The average difference in subsequent performance between students who were assigned to the best and worst teacher (on the effectiveness scale) is approximately 43% of a standard deviation in the distribution of exam grades, corresponding to about 5.6% of the average grade. Additionally, we find that our measure of teacher effectiveness is negatively correlated with the students' evaluations: in other words, teachers who are associated with better subsequent performance receive worst evaluations from their students. We rationalize these results with a simple model where teachers can either engage in real teaching or in teaching-to-the-test, the former requiring higher students’ effort than the latter. Teaching-to-the-test guarantees high grades in the current course but does not improve future outcomes. Hence, if students are myopic and evaluate better teachers from which they derive higher utility in a static framework, the model is capable of predicting our empirical finding that good teachers receive bad evaluations, especially when teaching-to-the-test is very effective (for example, with multiple choice tests). Consistently with the predictions of the model, we also find that classes in which high skill students are over-represented produce evaluations that are less at odds with estimated teacher effectiveness.teacher quality, postsecondary education

Surveillance of Summer Mortality and Preparedness to Reduce the Health Impact of Heat Waves in Italy

Since 2004, the Italian Department for Civil Protection and the Ministry of Health have implemented a national program for the prevention of heat-health effects during summer, which to-date includes 34 major cities and 93% of the residents aged 65 years and over. The Italian program represents an important example of an integrated approach to prevent the impact of heat on health, comprising Heat Health Watch Warning Systems, a mortality surveillance system and prevention activities targeted to susceptible subgroups. City-specific warning systems are based on the relationship between temperature and mortality and serve as basis for the modulation of prevention measures. Local prevention activities, based on the guidelines defined by the Ministry of Health, are constructed around the infrastructures and services available. A key component of the prevention program is the identification of susceptible individuals and the active surveillance by General Practitioners, medical personnel and social workers. The mortality surveillance system enables the timely estimation of the impact of heat, and heat waves, on mortality during summer as well as to the evaluation of warning systems and prevention programs. Considering future predictions of climate change, the implementation of effective prevention programs, targeted to high risk subjects, become a priority in the public health agenda

Assessment and prevention of acute health effects of weather conditions in Europe, the PHEWE project: background, objectives, design

<p>Abstract</p> <p>Background</p> <p>The project "Assessment and prevention of acute health effects of weather conditions in Europe" (PHEWE) had the aim of assessing the association between weather conditions and acute health effects, during both warm and cold seasons in 16 European cities with widely differing climatic conditions and to provide information for public health policies.</p> <p>Methods</p> <p>The PHEWE project was a three-year pan-European collaboration between epidemiologists, meteorologists and experts in public health. Meteorological, air pollution and mortality data from 16 cities and hospital admission data from 12 cities were available from 1990 to 2000. The short-term effect on mortality/morbidity was evaluated through city-specific and pooled time series analysis. The interaction between weather and air pollutants was evaluated and health impact assessments were performed to quantify the effect on the different populations. A heat/health watch warning system to predict oppressive weather conditions and alert the population was developed in a subgroup of cities and information on existing prevention policies and of adaptive strategies was gathered.</p> <p>Results</p> <p>Main results were presented in a symposium at the conference of the International Society of Environmental Epidemiology in Paris on September 6^th2006 and will be published as scientific articles. The present article introduces the project and includes a description of the database and the framework of the applied methodology.</p> <p>Conclusion</p> <p>The PHEWE project offers the opportunity to investigate the relationship between temperature and mortality in 16 European cities, representing a wide range of climatic, socio-demographic and cultural characteristics; the use of a standardized methodology allows for direct comparison between cities.</p

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost