Publications
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).
The Balassa-Samuelson effect is still an important phenomenon in the theory of economic development, as Balassa states, "As economic development is accompanied by greater inter-country differences in the productivity of tradable goods, differences in wages and service prices increase, and correspondingly so do differences in purchasing power parity and exchange rates." To the best of our knowledge, the Balassa-Samuelson effect has not been formally examined in the framework of optimal growth theory. By embedding the Balassa-Samuelson's original model in an optimal growth model setting, we investigate the validity of the Balassa-Samuelson effect in such a case and show that the Balassa-Samuelson effect follows from one of the properties of the optimal steady state.
This chapter reviews the recent Bayesian literature on poverty measurement together with some new results. Using Bayesian model criticism, we revise the international poverty line. Using mixtures of lognormals to model income, we derive the posterior distribution for the FGT, Watts and Sen poverty indices, for TIP curves (with an illustration on child poverty in Germany) and for Growth Incidence Curves. The relation of restricted stochastic dominance with TIP and GIC dominance is detailed with an example based on UK data. Using panel data, we decompose poverty into total, chronic and transient poverty, comparing child and adult poverty in East Germany when redistribution is introduced. When panel data are not available, a Gibbs sampler can be used to build a pseudo panel. We illustrate poverty dynamics by examining the consequences of the Wall on poverty entry and poverty persistence in occupied West Bank.
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Si la présence d’un enfant pénalise l’accès à l’emploi des jeunes mères, la monoparentalité n’aggrave pas leur capacité d’accès à un premier emploi. En revanche, être mère isolée retarde l’accès au CDI à temps complet des femmes les moins diplômées, et donc leur insertion durable, à l’inverse des plus diplômées.
In September 2021, the World Health Organization decided to implement stronger air quality guidelines for protecting health, based on the last decade of research. Ambient air pollution (AAP) was already the first environmental risk to health in terms of number of premature deaths, and this decision suggests that the risk was seriously underestimated. This chapter covers the relationship between AAP and health from an economic perspective. The first part presents the major regulated air pollutants and their related health effects, the way population exposure is measured, and the individual vulnerability and susceptibility to AAP-related effects. Then, the main approaches that estimate the relationships between health effects and air pollutants are covered: pure observational and interventional/quasi-experimental studies. Up-to-date reviews of the most robust relationships, and of the main findings of interventional/causal inference methods, are detailed. Next, impact assessments studies are tackled and some recent global assessments of health impacts due to AAP are presented. Once calculated, the health impacts can be expressed in monetary terms to enter the decision-making process. The relevant approaches for valuing market and nonmarket health impacts – market prices, revealed and stated preferences – are critically outlined, and their adequation with the AAP context examined. Finally, the economic health-related impacts of AAP are presented and discussed, with specific sections devoted to the necessity of an interdisciplinary approach and inequity-related issues at national and international levels. This chapter concludes with a widening of the perspective that tackles interactions between AAP on the one hand and climate change and indoor pollution on the other hand.
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