GALLIC Ewen

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.

Sites that offer their users to reconstruct their family tree online are flourishing on the Internet. This article analyzes the work of collection and data entry carried out by these users and how it could be used in historical demography, in order to complete the knowledge of past generations. To do so, the results obtained from the Geneanet database are compared with those known from the literature, and concern the records of 2,457,450 French individuals or individuals of French origin who lived in the 19th century. A significant bias in the sex ratio (under-representation of women) is thus highlighted. Fertility is also strongly underestimated. As for mortality (in comparison with historical values), these data underestimate male mortality up to about 40 years of age and female mortality up to 25 years of age, and then overestimate it. Finally, the wealth of spatial characteristics contained in the family trees is also exploited to produce new data on internal migration in the nineteenth century.

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Family history is usually seen as a significant factor insurance companies look at when applying for a life insurance policy. Where it is used, family history of cardiovascular diseases, death by cancer, or family history of high blood pressure and diabetes could result in higher premiums or no coverage at all. In this article, we use massive (historical) data to study dependencies between life length within families. If joint life contracts (between a husband and a wife) have been long studied in actuarial literature, little is known about child and parents dependencies. We illustrate those dependencies using 19th century family trees in France, and quantify implications in annuities computations. For parents and children, we observe a modest but significant positive association between life lengths. It yields different estimates for remaining life expectancy, present values of annuities, or whole life insurance guarantee, given information about the parents (such as the number of parents alive). A similar but weaker pattern is observed when using information on grandparents.

How much do weather shocks matter? The literature addresses this question in two isolated ways: either by looking at long-term effects through the prism of calibrated theoretical models, or by focusing on both short and long terms through the lens of empirical models. We propose a framework that reconciles these two approaches by taking the theory to the data in two complementary ways. We first document the propagation mechanism of a weather shock using a Vector Auto-Regressive model on New Zealand Data. To explain the mechanism, we build and estimate a general equilibrium model with a weather-dependent agricultural sector to investigate the weather’s business cycle implications. We find that weather shocks: (i) explain about 35% of GDP and agricultural output fluctuations in New Zealand. (ii) entail a welfare cost of 0.30% of permanent consumption. (iii) critically increases the macroeconomic volatility under climate change, resulting in a higher welfare cost peaking to 0.46% in the worst case scenario of climate change.

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The digital age allows data collection to be done on a large scale and at low cost. This is the case of genealogy trees, which flourish on numerous digital platforms thanks to the collaboration of a mass of individuals wishing to trace their origins and share them with other users. The family trees constituted in this way contain information on the links between individuals and their ancestors, which can be used in historical demography, and more particularly to study migration phenomena. The case of 19th century France is taken as an example, using data from the family trees of 238,009 users of the Geneanet website, or 2.

The digital age allows data collection to be done on a large scale and at low cost. This is the case of genealogy trees, which flourish on numerous digital platforms thanks to the collaboration of a mass of individuals wishing to trace their origins and share them with other users. The family trees constituted in this way contain information on the links between individuals and their ancestors, which can be used in historical demography, and more particularly to study migration phenomena. The case of 19th century France is taken as an example, using data from the family trees of 238,009 users of the Geneanet website, or 2.

In the digital age, data can be collected massively, in a collaborative way and at a lower cost. Genealogy websites are flourishing on the Internet, offering their users the opportunity to reconstruct their family tree online. The work of collection and data entry done by these users can potentially be reused in historical demography to complete the knowledge of our ancestors' past. In our study, we use records concerning 2,457,450 French individuals or individuals of French origin who lived in the 19th century. First, we study the quality of these data. We highlight the presence of important biases, notably concerning the gender of individuals. Women are under-represented in the data compared to men. Biases related to fertility are also observed. Despite these limitations of collaborative genealogy data, we show in a second step that it is possible to find results known in the literature in historical demography. In particular, we exploit the dates of birth and death to examine the mortality of individuals in the database. We also exploit the richness of the spatial characteristics contained in the family trees to analyze the internal migrations in France.

How much do weather shocks matter? The literature addresses this question in two isolated ways: either by looking at long-term effects through the prism of theoretical models, or by focusing on short-term effects using empirical analysis. We propose a framework to bring together both the short and long-term effects through the lens of an estimated DSGE model with a weather-dependent agricultural sector. The model is estimated using Bayesian methods and quarterly data for New Zealand using the weather as an observable variable. In the short-run, our analysis underlines the key role of weather as a driver of business cycles over the sample period. An adverse weather shock generates a recession, boosts the non-agricultural sector and entails a domestic currency depreciation. Taking a long-term perspective, a welfare analysis reveals that weather shocks are not a free lunch: the welfare cost of weather is currently estimated at 0.19% of permanent consumption. Climate change critically increases the variability of key macroeconomic variables (such as GDP, agricultural output or the real exchange rate) resulting in a higher welfare cost peaking to 0.29% in the worst case scenario.

In many cities around the world, short-term accommodation platforms have become an alternative for tourists. These new players, led by Airbnb, are disrupting the market, raising concerns from the hotel industry and public authorities. Using data from hotels and Airbnb in Paris, this paper proposes a new methodology to measure the competitive pressure Airbnb is exerting on the hotel industry. The results indicate that an increase in the number of Airbnb bidders near a hotel leads the hotel to lower its price. This effect is amplified on weekend nights.

Global climate is warming, and the effects of climate change are associated with a lot of uncertainty. Not only average temperatures are expected to rise, but also the occurrence of extreme events such as floods or droughts. Agriculture is particularly at risk, due to the importance of weather conditions in production. This thesis therefore aims at investigating the relationship between weather variations and agricultural production, to better assess the potential effects of climate change on agriculture, relying on both theoretical and empirical methods. The first two chapters focus on developing countries and provide two empirical studies based on Indian data at the individual farm level that link climate to agricultural production and profits and to consumption decisions. We find contrasted results, with an overall damaging effect of climate change scenarios on Indian agricultural production and profits, especially for farmers in southern India. Irrigation may however help mitigating the losses, as well as crop mixing, particularly for small farms. The last two chapters consider developed countries. The first step focuses on crop yields in Europe. Under the tested climate scenarios, wheat yields are projected to slightly increase by the end of the 21$^\textrm{st}$ century relative to the observed yields from the past 25 years. These small gains are however accompanied by a lot of regional heterogeneity. For European corn yields, the projections highlight small gains in by the middle of the 21$^\textrm{st}$ century, followed by relatively higher losses in the long run. The second step relies on a general equilibrium approach, and aims at investigating the short-run impacts of weather shocks on business cycles, through their damaging effects on agriculture. Increasing the variance of climate shocks in accordance with forthcoming climate change leads to a sizeable increase in the volatility of key macroeconomic variables, such as production and inflation.

The world's climate is warming and its effects are fraught with uncertainty. An increase in temperature and in the frequency of extreme events such as floods or droughts is predicted. The strong dependence of agriculture on climatic conditions makes it de facto a privileged field of application. This thesis aims to study the relationship between climate and agriculture, in order to assess the potential consequences of climate change, by mixing empirical and theoretical work. The first two chapters focus on developing countries through two studies examining agricultural production and profits as well as consumption decisions of Indian agricultural households. The various climate scenarios considered show an overall negative effect on production and profits, particularly for farm households in the south of the country. Irrigation as well as crop mixing, however, can reduce the damage, especially for smallholders. The next two chapters consider economically developed countries, starting with a study of European cereal yields. Projections under different climate scenarios indicate small growth in wheat yields by the end of the 21st century, compared to observations over the past 25 years. However, these small gains are accompanied by strong regional heterogeneity. For maize, small gains by the middle of the 21st century fade behind larger losses in the long term. The partial approach is then abandoned in favour of a general equilibrium analysis that studies the short-term effects of climate shocks on economic cycles, through their impact on agriculture. An increase in the variance of climate shocks in line with that predicted by climate scenarios leads to a substantial increase in macroeconomic variables such as output and inflation.

This article assesses the extent to which the 2007 financial crisis has affected the implementation of conventional monetary policy in the euro area. This question is addressed within a theoretical framework based on the New Keynesian synthesis model that prevailed before the 2007 crisis. We observe that the crisis has strongly reduced the performance of conventional policy after the deterioration of the trade-off between the variance of inflation and that of activity (as defined by the Taylor curve), and after the deterioration of its effectiveness (as measured from the deviation from the Taylor curve resulting from a strong increase in the contribution of the output gap). The interest rate values simulated by our model show that the ECB should have set interest rates lower than those observed, and negative at the end of the period. New unconventional instruments are in fact necessary in order to make up for a monetary policy practice that was focused primarily on price stability, in a calm macroeconomic environment.

This article assesses the extent to which the 2007 financial crisis has affected the implementation of conventional monetary policy in the euro area. This question is addressed within a theoretical framework based on the New Keynesian synthesis model that prevailed before the 2007 crisis. We observe that the crisis has strongly reduced the performance of conventional policy after the deterioration of the trade-off between the variance of inflation and that of activity (as defined by the Taylor curve), and after the deterioration of its effectiveness (as measured from the deviation from the Taylor curve resulting from a strong increase in the contribution of the output gap). The interest rate values simulated by our model show that the ECB should have set interest rates lower than those observed, and negative at the end of the period. New unconventional instruments are in fact necessary in order to make up for a monetary policy practice that was focused primarily on price stability, in a calm macroeconomic environment.

In this paper, we investigate a technique inspired by Ripley’s circumference method to correct bias of density estimation of edges (or frontiers) of regions. The idea of the method was theoretical and difficult to implement. We provide a simple technique – based of properties of Gaussian kernels – to efficiently compute weights to correct border bias on frontiers of the region of interest, with an automatic selection of an optimal radius for the method. We illustrate the use of that technique to visualize hot spots of car accidents and campsite locations, as well as location of bike thefts.

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.

In this paper, we investigate (and extend) Ripley's circumference method to correct bias of density estimation of edges (or frontiers) of regions. The idea of the method was theoretical and difficult to implement. We provide a simple technique -- based of properties of Gaussian kernels -- to efficiently compute weights to correct border bias on frontiers of the region of interest, with an automatic selection of an optimal radius for the method. We illustrate the use of that technique to visualize hot spots of car accidents and campsite locations, as well as location of bike thefts.

In this paper, we investigate (and extend) Ripley's circumference method to correct bias of density estimation of edges (or frontiers) of regions. The idea of the method was theoretical and difficult to implement. We provide a simple technique -- based of properties of Gaussian kernels -- to efficiently compute weights to correct border bias on frontiers of the region of interest, with an automatic selection of an optimal radius for the method. We illustrate the use of that technique to visualize hot spots of car accidents and campsite locations, as well as location of bike thefts.