Publications

This paper studies differences across genders in the re-contesting decisions of politicians following electoral wins or defeats. Using close races in mixed-gender French local elections, we show that women are less likely to persist in competition when they lose compared to male runners-up, but are equally or more prone than male winners to re-contest when they win. Differences in observable characteristics or in the expected electoral returns of running again cannot fully account for these gender gaps in persistence. In contrast, evidence suggests that results are driven by behavioural explanations such as cross-gender differences in candidates’ attitudes toward competition, or by political parties behaving differently toward female and male candidates for a given electoral outcome. Additionally, we provide evidence that a woman’s victory encourages former female challengers to re-contest but does not trigger the entry of new female candidates.

With the low adherence to voluntary mutual health insurance, Senegal’s policymakers have sought to understand the feasibility of compulsory health insurance membership. This study aims to measure the acceptability of mandatory membership in community-based mutual health insurance (CBHI) and to understand its possible administrative modalities. The study consists of a national survey among a representative population sample selected by marginal quotas. The survey was conducted in 2022 over the phone, with a random composition method involving 914 people. The questionnaire measured the socio-economic characteristics of households, their level of acceptability concerning voluntary and compulsory membership, and their level of confidence in CBHIs and the health system. Respondents preferred voluntary (86%) over mandatory (70%) membership of a CBHI. The gap between voluntary and compulsory membership scores was smaller among women (p = 0.040), people under 35 (p = 0.033), and people with no health coverage (p = 0.011). Voluntary or compulsory membership was correlated (p = 0.000) to trust in current CBHIs and health systems. Lack of trust in the CBHI management has been more disadvantageous for acceptance of the mandatory than the voluntary membership. No particular preference emerged as the preferred administrative channel (e.g. death certificate, identity card, etc.) to enforce the mandatory option. The results confirmed the well-known challenges of building universal health coverage based on CBHIs—a poorly appreciated model whose low performance reduces the acceptability of populations to adhere to it, whether voluntary or mandatory. Suppose Senegal persists in its health insurance approach. In that case, it will be essential to strengthen the performance and funding of CBHIs, and to gain population trust to enable a mandatory or more systemic membership.

In the Design of Experiments, we seek to relate response variables to explanatory factors. Response Surface methodology (RSM) approximates the relation between output variables and a polynomial transform of the explanatory variables using a linear model. Some researchers have tried to adjust other types of models, mainly nonlinear and nonparametric. We present a large panel of Machine Learning approaches that may be good alternatives to the classical RSM approximation. The state of the art of such approaches is given, including classification and regression trees, ensemble methods, support vector machines, neural networks and also direct multi-output approaches. We survey the subject and illustrate the use of ten such approaches using simulations and a real use case. In our simulations, the underlying model is linear in the explanatory factors for one response and nonlinear for the others. We focus on the advantages and disadvantages of the different approaches and show how their hyperparameters may be tuned. Our simulations show that even when the underlying relation between the response and the explanatory variables is linear, the RSM approach is outperformed by the direct neural network multivariate model, for any sample size (<50) and much more for very small samples (15 or 20). When the underlying relation is nonlinear, the RSM approach is outperformed by most of the machine learning approaches for small samples (n ≤ 30).

We study how firm premia influence the gender wage gap for 21 European countries. We use a quadrennial harmonized matched employer–employee dataset to estimate gender-specific firm premia. Subsequently, we decompose the firm-specific wage premia differential into within- and between-firm components. On average, the former accounts mainly for the decline in the pay gap between 2002 and 2014. We pay particular attention to the development of each component by age group, and find that the between-firm component is associated with an increase in the gender pay gap over age. The decomposition of firm premia allows us to investigate how institutional settings relate to each component. We associate the within-firm component with collective bargaining at the national and firm levels, and the between-firm component with family policies. Decentralized wage bargaining is associated with a larger within-firm pay gap, whereas family policies incentivizing women to return to employment after family formation are linked to a smaller between-firm component.