# Publications

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.

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.

This paper develops a theoretical framework to think about employees' effort choices, and applies this framework to assess the ability of existing experimental designs to identify the effect of pay inequality on worker effort. The analysis shows that failure to control for a number of confounds—such as reciprocity towards the employer in multi-lateral gift-exchange games (vertical fairness), or the incentive to increase effort when feeling underpaid under piece rates (income targeting)—may lead to inaccurate interpretation of evidence of treatment effects. In light of these findings, the paper provides a set of recommendations on how to improve identification in the design of controlled experiments in the future.

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty—nonstandard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for more reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants.

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.

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).