Ewen Gallic

Faculty ,  Aix-Marseille Université , Faculté d'économie et de gestion (FEG)

Econometrics, Finance and mathematical methods
Gallic
Status
Assistant professor
Research domain(s)
Econometrics, Environmental economics
Thesis
2017, University of Rennes 1
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CV
Contact
ewen.gallic[at]univ-amu.fr
Address

AMU - AMSE
5-9 Boulevard Maurice Bourdet, CS 50498
​13205 Marseille Cedex 1

Website
http://egallic.fr/
Abstract We study the impact of socioeconomic factors on two key parameters of epidemic dynamics. Specifically, we investigate a parameter capturing the rate of deceleration at the very start of an epidemic, and a parameter that reflects the pre-peak and post-peak dynamics at the turning point of an epidemic like coronavirus disease 2019 (COVID-19). We find two important results. The policies to fight COVID-19 (such as social distancing and containment) have been effective in reducing the overall number of new infections, because they influence not only the epidemic peaks, but also the speed of spread of the disease in its early stages. The second important result of our research concerns the role of healthcare infrastructure. They are just as effective as anti-COVID policies, not only in preventing an epidemic from spreading too quickly at the outset, but also in creating the desired dynamic around peaks: slow spreading, then rapid disappearance.
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Abstract Many problems ask a question that can be formulated as a causal question: what would have happened if...? For example, would the person have had surgery if he or she had been Black? To address this kind of questions, calculating an average treatment effect (ATE) is often uninformative, because one would like to know how much impact a variable (such as the skin color) has on a specific individual, characterized by certain covariates. Trying to calculate a conditional ATE (CATE) seems more appropriate. In causal inference, the propensity score approach assumes that the treatment is influenced by , a collection of covariates. Here, we will have the dual view: doing an intervention, or changing the treatment (even just hypothetically, in a thought experiment, for example by asking what would have happened if a person had been Black) can have an impact on the values of . We will see here that optimal transport allows us to change certain characteristics that are influenced by the variable whose effect we are trying to quantify. We propose here a mutatis mutandis version of the CATE, which will be done simply in dimension one by saying that the CATE must be computed relative to a level of probability, associated to the proportion of x (a single covariate) in the control population, and by looking for the equivalent quantile in the test population. In higher dimension, it will be necessary to go through transport, and an application will be proposed on the impact of some variables on the probability of having an unnatural birth (the fact that the mother smokes, or that the mother is Black).
Keywords Quantiles, Optimal Transport, Mutatis Mutandis, Counterfactual, CATE, Conditional Average Treatment Effects, Causality
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Abstract A binary scoring classifier can appear well-calibrated according to standard calibration metrics, even when the distribution of scores does not align with the distribution of the true events. In this paper, we investigate the impact of postprocessing calibration on the score distribution (sometimes named "recalibration"). Using simulated data, where the true probability is known, followed by real-world datasets with prior knowledge on event distributions, we compare the performance of an XGBoost model before and after applying calibration techniques. The results show that while applying methods such as Platt scaling, Beta calibration, or isotonic regression can improve the model's calibration, they may also lead to an increase in the divergence between the score distribution and the underlying event probability distribution.
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Abstract This paper investigates the dynamic effects of weather shocks on monthly agricultural production in Peru, using a Local Projection framework. An adverse weather shock, measured by an excess of heat or rain, always generates a delayed negative downturn in agricultural production. The magnitude and duration of this downturn depend on several factors, including the type of crop and the timing of the shock. On average, a weather shock—a temperature shock—can cause a monthly decline of 5% to 15% in agricultural production. The response exhibit important heterogeneity across time, crop, and season dimensions, with hysteresis suggesting farmers’ adaptation over time. At the macroeconomic level, weather shocks are recessionary and entail a decline in inflation, agricultural production, exports, exchange rate and GDP.
Keywords Weather shocks, Agriculture, Local projections, VAR
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Abstract A collection of insurance datasets from real insurers or mutual companies, mostly from Europe and North America. Datasets can be used to model and understand risks in both life and non-life insurance.
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Abstract 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.
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Abstract 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.
Keywords Agriculture, Business Cycles, Weather shocks, Climate change
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Abstract 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 xixe 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 xixe siècle.
Keywords Fertility, Genealogy, Collaborative data, Historical demography, Mortality, Migration, Démographe historique, Fécondité, Généalogie, Migration, Longévité, Données collaboratives
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Abstract 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 2.5 million (unique) individuals. Using the geographical coordinates of the birthplaces of 25,485 ancestors born in France between 1800 and 1804 and those of their descendants (24,516 children, 29,715 grandchildren and 62,165 great-grandchildren), we study migration between generations at several geographical scales. We start with a broad scale that of the departments, to reach a much finer one, that of the cities. Our results are consistent with those of the literature traditionally based on the parish or civil status registers. The results show that the use of collaborative genealogy data not only makes it possible to support previous findings of the literature, but also to enrich them.
Keywords 19th Century, Migration, Collaborative data, Genealogy
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Abstract Anxiety and depression may have serious disabling consequences for health, social, and occupational outcomes for people who are unaware of their actual health status and/or whose mental health symptoms remain undiagnosed by physicians. This article provides a big picture of unrecognised anxiety and depressive troubles revealed by a low score on the Mental Health Inventory-5 (MHI-5) with the help of machine learning methods using the 2012 French National Representative Health and Social Protection Survey (Enquête Santé et Protection Sociale, ESPS) matched with yearly healthcare consumption data from the French Sickness Fund. Compared to people with no latent symptoms who did not declare any depression over the last 12 months, those with unrecognised anxiety or depression were found to be older, more deprived, more socially disengaged, at a higher probability of adverse working conditions, and with higher healthcare expenditures backed, to some extent, by chronic conditions other than anxiety or mood disorder.
Keywords Tree-based methods, SHAP values, Workplace outcomes, Healthcare consumption, Mental health inventory-5 MHI-5, Unrecognised mental disorders
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Abstract The assessment of binary classifier performance traditionally centers on discriminative ability using metrics, such as accuracy. However, these metrics often disregard the model's inherent uncertainty, especially when dealing with sensitive decision-making domains, such as finance or healthcare. Given that model-predicted scores are commonly seen as event probabilities, calibration is crucial for accurate interpretation. In our study, we analyze the sensitivity of various calibration measures to score distortions and introduce a refined metric, the Local Calibration Score. Comparing recalibration methods, we advocate for local regressions, emphasizing their dual role as effective recalibration tools and facilitators of smoother visualizations. We apply these findings in a real-world scenario using Random Forest classifier and regressor to predict credit default while simultaneously measuring calibration during performance optimization.
Keywords Calibration, Binary classification, Local regression
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Abstract This paper investigates the dynamic effects of weather shocks on monthly agricultural production in Peru, using a Local Projection framework. An adverse weather shock, measured by an excess of heat or rain, always generates a delayed negative downturn in agricultural production. The magnitude and duration of this downturn depend on several factors, including the type of crop and the timing of the shock. On average, a weather shock—a temperature shock—can cause a monthly decline of 5% to 15% in agricultural production. The response exhibit important heterogeneity across time, crop, and season dimensions, with hysteresis suggesting farmers’ adaptation over time. At the macroeconomic level, weather shocks are recessionary and entail a decline in inflation, agricultural production, exports, exchange rate and GDP.
Keywords Weather shocks, Agriculture, Local projections, VAR
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Abstract 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.
Keywords SIR models, Phenomenological models, Double sigmoid models, Sanitary policies, Herd immunity, ICU capacity constraint
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Abstract 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.
Keywords Dependence, Joint life insurance, Family history, Genealogy, Grandparents-grandchildren, Annuities, Collaborative data, Information, Whole life insurance, Parents-children
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Abstract 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 xixe 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 xixe siècle.
Keywords Fertility, Genealogy, Collaborative data, Historical demography, Mortality, Migration, Démographe historique, Fécondité, Généalogie, Migration, Longévité, Données collaboratives
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Abstract A l'ère du numérique, les données peuvent être collectées massivement, de manière collaborative et à moindre coût. Les sites de généalogie fleurissent sur Internet pour proposer à leurs utilisateurs de reconstituer en ligne leur arbre généalogique. Le travail de collecte et de saisie effectué par ces utilisateurs peut potentiellement être réutilisé en démographie historique pour compléter la connaissance du passé de nos ancêtres. Dans notre étude, utilisons les enregistrements concernant 2 457 450 individus français ou d'origine française ayant vécu au XIX e siècle. Dans un premier temps, nous étudions la qualité de ces données. Nous mettons en évidence la présence de biais importants, notamment concernant le genre des individus. Les femmes sont sous-représentées dans les données comparativement aux hommes. Des biais relatifs à la fécondité sont également observés. En dépit de ces limites dont souffrent les données collaboratives de généalogie, nous montrons dans un deuxième temps qu'il est possible de retrouver des résultats connus dans la littérature en démographie historique. Plus particulièrement, nous exploitons les dates de naissance et de décès afin d'examiner la mortalité des individus présents dans la base de données. Nous exploitons également la richesse des caractéristiques spatiales contenues dans les arbres généalogiques pour analyser les migrations internes en France.
Keywords Longevity, Collaborative data, Genealogy, Généalogie, Données collaboratives, Longévité, Migration, XIXe siècle, R
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Abstract 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.
Keywords Agriculture, Business Cycles, Climate change, Weather shocks
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