Publications

La plupart des informations présentées ci-dessous ont été récupérées via RePEc avec l'aimable autorisation de Christian Zimmermann
Explicit solutions for the asymptotically-optimal bandwidth in cross-validationJournal articleKarim M. Abadir et Michel Lubrano, Biometrika, pp. asae007, 2024

We show that least squares cross-validation methods share a common structure which has an explicit asymptotic solution, when the chosen kernel is asymptotically separable in bandwidth and data. For density estimation with a multivariate Student t(ν) kernel, the cross-validation criterion becomes asymptotically equivalent to a polynomial of only three terms. Our bandwidth formulae are simple and noniterative thus leading to very fast computations, their integrated squared-error dominates traditional cross-validation implementations, they alleviate the notorious sample variability of cross-validation, and overcome its breakdown in the case of repeated observations. We illustrate our method with univariate and bivariate applications, of density estimation and nonparametric regressions, to a large dataset of Michigan State University academic wages and experience.

Subsampling under distributional constraintsJournal articleFlorian Combes, Ricardo Fraiman et Badih Ghattas, Statistical Analysis and Data Mining: The ASA Data Science Journal, Volume 17, Issue 1, pp. e11661, 2024

Some complex models are frequently employed to describe physical and mechanical phenomena. In this setting, we have an input X\ X \ in a general space, and an output Y=f(X)\ Y=f(X) \ where f\ f \ is a very complicated function, whose computational cost for every new input is very high, and may be also very expensive. We are given two sets of observations of X\ X \, S1\ S_1 \ and S2\ S_2 \ of different sizes such that only fS1\ f\left(S_1\right) \ is available. We tackle the problem of selecting a subset S3⊂S2\ S_3\subset S_2 \ of smaller size on which to run the complex model f\ f \, and such that the empirical distribution of fS3\ f\left(S_3\right) \ is close to that of fS1\ f\left(S_1\right) \. We suggest three algorithms to solve this problem and show their efficiency using simulated datasets and the Airfoil self-noise data set.

Spatial earnings inequalityJournal articleChristian Schluter et Mark Trede, The Journal of Economic Inequality, 2024

Earnings inequality in Germany has increased dramatically. Measuring inequality locally at the level of cities annually since 1985, we find that behind this development is the rapidly worsening inequality in the largest cities, driven by increasing earnings polarisation. In the cross-section, local earnings inequality rises substantially in city size, and this city-size inequality penalty has increased steadily since 1985, reaching an elasticity of .2 in 2010. Inequality decompositions reveal that overall earnings inequality is almost fully explained by the within-locations component, which in turn is driven by the largest cities. The worsening inequality in the largest cities is amplified by their greater population weight. Examining the local earnings distributions directly reveals that this is due to increasing earnings polarisation that is strongest in the largest places. Both upper and lower distributional tails become heavier over time, and are the heaviest in the largest cities. We establish these results using a large and spatially representative administrative data set, and address the top-coding problem in these data using a parametric distribution approach that outperforms standard imputations.

Exposure to worrisome topics can increase cognitive performance when incentivized by a performance goalJournal articleTimothee Demont, Daniela Horta-Sáenz et Eva Raiber, Scientific Reports, Volume 14, Issue 1, pp. 1204, 2024

Worrisome topics, such as climate change, economic crises, or pandemics including Covid-19, are increasingly present and pervasive due to digital media and social networks. Do worries triggered by such topics affect the cognitive capacities of young adults? In an online experiment during the Covid-19 pandemic (N=1503), we test how the cognitive performance of university students responds when exposed to topics discussing (i) current adverse mental health consequences of social restrictions or (ii) future labor market hardships linked to the economic contraction. Moreover, we study how such a response is affected by a performance goal. We find that the labor market topic increases cognitive performance when it is motivated by a goal, consistent with a ‘tunneling effect’ of scarcity or a positive stress effect. However, we show that the positive reaction is mainly concentrated among students with larger financial and social resources, pointing to an inequality-widening mechanism. Conversely, we find limited support for a negative stress effect or a ‘cognitive load effect’ of scarcity, as the mental health topic has a negative but insignificant average effect on cognitive performance. Yet, there is a negative response among psychologically vulnerable individuals when the payout is not conditioned on reaching a goal.

Education politics, schooling choice and public school quality: the impact of income polarizationJournal articleMajda Benzidia, Michel Lubrano et Paolo Melindi-Ghidi, International Tax and Public Finance, pp. 1-29, 2024

What is the role of income polarization for explaining differentials in public funding of education? To answer this question, we provide a new theoretical modelling for the income distribution that can directly monitor income polarization. It leads to a new income polarization index where the middle class is represented by an interval. We implement this distribution in a political economy model with endogenous fertility and public/private educational choices. We show that when households vote on public schooling expenditures, polarization matters for explaining disparities in public education funding across communities. Using micro-data covering two groups of school districts, we find that both income polarization and income inequality affect public school funding with opposite signs whether there exist a Tax Limitation Expenditure (TLE) or not.

Autoregressive conditional betasJournal articleF. Blasques, Christian Francq et Sébastien Laurent, Journal of Econometrics, Volume 238, Issue 2, pp. 105630, 2024

This paper introduces an autoregressive conditional beta (ACB) model that allows regressions with dynamic betas (or slope coefficients) and residuals with GARCH conditional volatility. The model fits in the (quasi) score-driven approach recently proposed in the literature, and it is semi-parametric in the sense that the distributions of the innovations are not necessarily specified. The time-varying betas are allowed to depend on past shocks and exogenous variables. We establish the existence of a stationary solution for the ACB model, the invertibility of the score-driven filter for the time-varying betas, and the asymptotic properties of one-step and multistep QMLEs for the new ACB model. The finite sample properties of these estimators are studied by means of an extensive Monte Carlo study. Finally, we also propose a strategy to test for the constancy of the conditional betas. In a financial application, we find evidence for time-varying conditional betas and highlight the empirical relevance of the ACB model in a portfolio and risk management empirical exercise.

Optimal Infrastructure after Trade Reform in IndiaJournal articlePriyam Verma, Journal of Development Economics, Volume 166, pp. 103208, 2024

Lower tariffs typically raise productivity, production, and trade, increasing the benefits from building infrastructure. Infrastructure spending by governments should therefore increase after countries open up to trade. I test this hypothesis empirically using a trade reform in India and find that a 1 percentage point reduction in tariffs increased states’ infrastructure spending by 0.5% between 1991 and 2001. To understand the mechanisms behind my empirical findings, I develop and calibrate a multi-region model of international trade, private capital accumulation, and infrastructure spending, in which each government chooses such spending to maximize their state’s welfare. I find if governments choose infrastructure following the reform optimally, infrastructure would have increased by 60% on average. The actual increase, based on my empirical findings, was about 29%. Counterfactual exercises show that raising aggregate infrastructure towards its optimal following the trade reform will result in state GDP to increase by 7% points on average.

Exit Polls and Voter Turnout in the 2017 French ElectionsJournal articleAlberto Grillo et Eva Raiber, Revue économique, Volume Pub. anticipées, Issue 7, pp. 14-31, 2024

Lors des élections françaises, les médias belges et suisses interfèrent régulièrement en publiant des sondages et des prédictions avant la fermeture des bureaux de vote. Nous utilisons la précocité et le degré de confiance inhabituels des sondages au second tour de l’élection présidentielle de 2017 pour étudier leurs effets sur la participation électorale. Notre analyse compare les taux de participation à différents horaires, aux premier et second tours, et par rapport aux élections de 2012 et 2022. Les résultats montrent une baisse significative de la participation après la publication des sondages à la sortie des urnes. L’effet s’élève à 1,1 point de pourcentage dans l’analyse en triples differences avec l’élection de 2022 et il est plus fort dans les départements limitrophes de la Belgique. Nous constatons également un léger effet underdog pouvant réduire la marge de victoire jusqu’à 1 point de pourcentage.

Optimal Transport for Counterfactual Estimation: A Method for Causal InferenceBook chapterArthur Charpentier, Emmanuel Flachaire et Ewen Gallic, In: Optimal Transport Statistics for Economics and Related Topics, Nguyen Ngoc Thach, Vladik Kreinovich, Doan Thanh Ha et Nguyen Duc Trung (Eds.), 2024, pp. 45-89, Springer Nature Switzerland, 2024

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 $$\boldsymbol{x}$$x, 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 $$\boldsymbol{x}$$x. 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).

État des lieux de l’enseignement de l’éducation thérapeutique du patient dans la formation initiale des sages-femmes françaisesJournal articleEmilie Ohayon, Claire Marchand, David Naudin et Sébastien Riquet, Éducation thérapeutique du patient / Therapeutic patient education, Volume 15, Issue 1, pp. 10206, Forthcoming

Objectives This study aims to establish an inventory of the teaching of Therapeutic Patient Education (TPE) in the initial training of French midwives. Method: A descriptive quantitative study was conducted in France. An online questionnaire comprising 27 questions was distributed to 35 French midwifery schools. Results: Out of 19 schools that responded to the survey, 11 taught TPE, 8 did not address it in training. This teaching is mainly transversal. The obstacles to the teaching of TPE are the current density of the program, the absence of a text regulating this teaching and the difficulties in circumscribing the field of TPE in relation to that of prevention, promotion and health education. The simulation is used in only one school. Discussion: This survey shows a willingness of educational teams to invest in the teaching of health education, including TPE. For this, it is a question of strengthening the training of teachers in order to clarify the areas of intervention of the midwife calling for health promotion, prevention and health education; to offer specific internships to students and to use simulation. Extending the duration of initial training is an opportunity to plan specific teaching and to discuss the place of the health service.