Suchergebnisse

Les sites qui proposent à leurs utilisateurs de reconstituer en ligne leur arbre généalogique fleurissent sur Internet. Cet article analyse le travail de collecte et de saisie effectué par ces utilisateurs et comment il pourrait être utilisé en démographie historique, afin de compléter la connaissance des générations du passé. Pour cela, les résultats obtenus à partir de la base Geneanet sont confrontés à ceux connus de la littérature, et concernent les enregistrements de 2 457 450 individus français ou d'origine française ayant vécu au xix e siècle. Est ainsi mis en évidence un biais important du rapport de masculinité (sous-représentation des femmes). La fécondité est elle aussi fortement sous-estimée. Quant à la mortalité, (par comparaison aux valeurs historiques), ces données sous-estiment la mortalité des hommes jusqu'à 40 ans environ et celle des femmes jusqu'à 25 ans, puis elles la surestiment. Enfin, la richesse des caractéristiques spatiales contenues dans les arbres généalogiques est également exploitée pour produire de nouvelles données sur les migrations internes au xix e siècle.

National audience ; This study explores the possibility to retrieve information on partisan polarization from data generated by online social media users. The specific application that we pursue consists in placing a sample of over 1,000 French politicians on a unidimensional left-right scale by using their followers on Twitter as a proxy for their relative ideological positions. The methodology that we use to that end closely replicates that of Barberá (2015), who developed a Bayesian Spatial Following model to retrieve such ideal point estimates in the United States and in five European countries. Our results concur with existing measures of the French party space, and yield additional insights into the behaviour of ideologically extreme social media users.

National audience ; This study explores the possibility to retrieve information on partisan polarization from data generated by online social media users. The specific application that we pursue consists in placing a sample of over 1,000 French politicians on a unidimensional left-right scale by using their followers on Twitter as a proxy for their relative ideological positions. The methodology that we use to that end closely replicates that of Barberá (2015), who developed a Bayesian Spatial Following model to retrieve such ideal point estimates in the United States and in five European countries. Our results concur with existing measures of the French party space, and yield additional insights into the behaviour of ideologically extreme social media users.

National audience ; This study explores the possibility to retrieve information on partisan polarization from data generated by online social media users. The specific application that we pursue consists in placing a sample of over 1,000 French politicians on a unidimensional left-right scale by using their followers on Twitter as a proxy for their relative ideological positions. The methodology that we use to that end closely replicates that of Barberá (2015), who developed a Bayesian Spatial Following model to retrieve such ideal point estimates in the United States and in five European countries. Our results concur with existing measures of the French party space, and yield additional insights into the behaviour of ideologically extreme social media users.

National audience ; This study explores the possibility to retrieve information on partisan polarization from data generated by online social media users. The specific application that we pursue consists in placing a sample of over 1,000 French politicians on a unidimensional left-right scale by using their followers on Twitter as a proxy for their relative ideological positions. The methodology that we use to that end closely replicates that of Barberá (2015), who developed a Bayesian Spatial Following model to retrieve such ideal point estimates in the United States and in five European countries. Our results concur with existing measures of the French party space, and yield additional insights into the behaviour of ideologically extreme social media users.

National audience ; This study explores the possibility to retrieve information on partisan polarization from data generated by online social media users. The specific application that we pursue consists in placing a sample of over 1,000 French politicians on a unidimensional left-right scale by using their followers on Twitter as a proxy for their relative ideological positions. The methodology that we use to that end closely replicates that of Barberá (2015), who developed a Bayesian Spatial Following model to retrieve such ideal point estimates in the United States and in five European countries. Our results concur with existing measures of the French party space, and yield additional insights into the behaviour of ideologically extreme social media users.

Uprising in China, the global COVID-19 epidemic soon started to spread out in Europe. As no medical treatment was available, it became urgent to design optimal non-pharmaceutical policies. With the help of a SIR model, we contrast two policies, one based on herd immunity (adopted by Sweden and the Netherlands), the other based on ICU capacity shortage. Both policies led to the danger of a second wave. Policy efficiency corresponds to the absence or limitation of a second wave. The aim of the paper is to measure the efficiency of these policies using statistical models and data. As a measure of efficiency, we propose the ratio of the size of two observed waves using a double sigmoid model coming from the biological growth literature. The Oxford data set provides a policy severity index together with observed number of cases and deaths. This severity index is used to illustrate the key features of national policies for ten European countries and to help for statistical inference. We estimate basic reproduction numbers, identify key moments of the epidemic and provide an instrument for comparing the two reported waves between January and October 2020. We reached the following conclusions. With a soft but long lasting policy, Sweden managed to master the first wave for cases thanks to a low R 0 , but at the cost of a large number of deaths compared to other Nordic countries and Denmark is taken as an example. We predict the failure of herd immunity policy for the Netherlands. We could not identify a clear sanitary policy for large European countries. What we observed was a lack of control for observed cases, but not for deaths.

Uprising in China, the global COVID-19 epidemic soon started to spread out in Europe. As no medical treatment was available, it became urgent to design optimal non-pharmaceutical policies. With the help of a SIR model, we contrast two policies, one based on herd immunity (adopted by Sweden and the Netherlands), the other based on ICU capacity shortage. Both policies led to the danger of a second wave. Policy efficiency corresponds to the absence or limitation of a second wave. The aim of the paper is to measure the efficiency of these policies using statistical models and data. As a measure of efficiency, we propose the ratio of the size of two observed waves using a double sigmoid model coming from the biological growth literature. The Oxford data set provides a policy severity index together with observed number of cases and deaths. This severity index is used to illustrate the key features of national policies for ten European countries and to help for statistical inference. We estimate basic reproduction numbers, identify key moments of the epidemic and provide an instrument for comparing the two reported waves between January and October 2020. We reached the following conclusions. With a soft but long lasting policy, Sweden managed to master the first wave for cases thanks to a low R 0 , but at the cost of a large number of deaths compared to other Nordic countries and Denmark is taken as an example. We predict the failure of herd immunity policy for the Netherlands. We could not identify a clear sanitary policy for large European countries. What we observed was a lack of control for observed cases, but not for deaths.

International audience ; Two main nonpharmaceutical policy strategies have been used in Europe in response to the COVID-19 epidemic: one aimed at natural herd immunity and the other at avoiding saturation of hospital capacity by crushing the curve. The two strategies lead to different results in terms of the number of lives saved on the one hand and production loss on the other hand. Using a susceptible–infected–recovered–dead model, we investigate and compare these two strategies. As the results are sensitive to the initial reproduction number, we estimate the latter for 10 European countries for each wave from January 2020 till March 2021 using a double sigmoid statistical model and the Oxford COVID-19 Government Response Tracker data set. Our results show that Denmark, which opted for crushing the curve, managed to minimize both economic and human losses. Natural herd immunity, sought by Sweden and the Netherlands does not appear to have been a particularly effective strategy, especially for Sweden, both in economic terms and in terms of lives saved. The results are more mixed for other countries, but with no evident trade-off between deaths and production losses.

Uprising in China, the global COVID-19 epidemic soon started to spread out in Europe. As no medical treatment was available, it became urgent to design optimal non-pharmaceutical policies. With the help of a SIR model, we contrast two policies, one based on herd immunity (adopted by Sweden and the Netherlands), the other based on ICU capacity shortage. Both policies led to the danger of a second wave. Policy efficiency corresponds to the absence or limitation of a second wave. The aim of the paper is to measure the efficiency of these policies using statistical models and data. As a measure of efficiency, we propose the ratio of the size of two observed waves using a double sigmoid model coming from the biological growth literature. The Oxford data set provides a policy severity index together with observed number of cases and deaths. This severity index is used to illustrate the key features of national policies for ten European countries and to help for statistical inference. We estimate basic reproduction numbers, identify key moments of the epidemic and provide an instrument for comparing the two reported waves between January and October 2020. We reached the following conclusions. With a soft but long lasting policy, Sweden managed to master the first wave for cases thanks to a low R 0 , but at the cost of a large number of deaths compared to other Nordic countries and Denmark is taken as an example. We predict the failure of herd immunity policy for the Netherlands. We could not identify a clear sanitary policy for large European countries. What we observed was a lack of control for observed cases, but not for deaths.

International audience ; Two main nonpharmaceutical policy strategies have been used in Europe in response to the COVID-19 epidemic: one aimed at natural herd immunity and the other at avoiding saturation of hospital capacity by crushing the curve. The two strategies lead to different results in terms of the number of lives saved on the one hand and production loss on the other hand. Using a susceptible–infected–recovered–dead model, we investigate and compare these two strategies. As the results are sensitive to the initial reproduction number, we estimate the latter for 10 European countries for each wave from January 2020 till March 2021 using a double sigmoid statistical model and the Oxford COVID-19 Government Response Tracker data set. Our results show that Denmark, which opted for crushing the curve, managed to minimize both economic and human losses. Natural herd immunity, sought by Sweden and the Netherlands does not appear to have been a particularly effective strategy, especially for Sweden, both in economic terms and in terms of lives saved. The results are more mixed for other countries, but with no evident trade-off between deaths and production losses.

Cet article évalue dans quelle mesure la crise financière de 2007 a affecté la mise en œuvre d'une politique monétaire conventionnelle dans la zone euro. Cette question est abordée dans un cadre théorique reprenant le modèle de synthèse de la nouvelle économie keynésienne qui prévalait avant la crise de 2007. On observe que la crise a fortement réduit la performance de la politique conventionnelle après la détérioration de l'arbitrage entre la variance de l'inflation et celle de l'activité (telle que définie par la courbe de Taylor), et après la dégradation de son efficacité (telle que mesurée à partir de l'écart à la courbe de Taylor provenant d'une forte augmentation de la contribution de l' output gap ). Les valeurs de taux d'intérêt simulées par notre modèle montrent que la BCE aurait dû fixer des taux d'intérêt inférieurs à ceux observés, qui plus est négatifs en fin de période. De nouveaux instruments non conventionnels s'avèrent de fait nécessaires afin de suppléer à une pratique de la politique monétaire qui était centrée prioritairement sur la stabilité des prix, dans un environnement macroéconomique calme.