Publications

Résumé We revisit the question of colonial legacies in education by focusing on quality rather than quantity. We study Cameroon, a country where a Francophone education system with French colonial origins coexists with an Anglophone system with British colonial origins. This allows us to investigate the impact of different teaching practices on students' test scores. We find that pupils schooled in the Francophone system perform better in mathematics in Grade 5, with test scores higher by two thirds of a standard deviation. Thanks to detailed school survey data, we are able to account for a wide array of inputs of the education production function, such as the economic and social conditions of students, the material conditions of the schools and classrooms, as well as some information on the teachers' practices and pedagogical culture. We find that Francophone schools have better classroom equipment and that Francophone teachers use more vertical teaching methods, but that these differences cannot explain why Francophone students perform better in mathematics. In the end, we cannot pin down the exact mechanism behind our result.
Mots clés Africa, Colonial legacies, School quality, Education
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Résumé We examine the links between age, risk tolerance, and impatience in a large French representative sample. We combine elicited preferences data based on an incentivized web experiment and stated preferences data based on self-reported surveys. Our findings highlight distinct patterns: when considering stated preferences, both risk tolerance and impatience exhibit a decline with age. Higher risk tolerance is associated with higher impatience, and this relationship strengthens with age in the financial domain. In contrast, our analysis of elicited measures uncovers a different dynamic. Specifically, risk tolerance tends to increase with age, while age exhibits no significant influence on impatience. Furthermore, individuals endowed with higher risk tolerance tend to demonstrate lower levels of impatience, irrespective of their age.
Mots clés Age, Elicited preferences, Risk preferences, Stated preferences, Time preferences
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Résumé Based on a unique database (data on 2529 bank-firm relationships of 403 firms from 2012 to 2018) provided by the Central Bank of Tunisia, this article analyses the impact of the intensity and duration of bank-firm relationship on loan quality. By estimating a panel ordered probit model, the results show that the intensity of the lending relationship has a positive (negative) impact on high (medium or low) quality loans. In addition, the duration of the bank-firm relationship increases the probability of low-quality loans. We also find that the impact of relationship lending on loan quality differs according to the level of profitability of the firm. Low and non-performing firms tend to have longer and closer bank relationship, whereas it is the opposite for performing firms. Our results suggest that in an emerging market concentrated around a few banks, longer and closer banking relationships are mainly in favour of low and non-performing firms, reflecting adverse selection and strong moral hazard.
Mots clés Banks, Relationship lending, Credit registry, Tunisia
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Résumé 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.
Mots clés Elections, Self-selection, Candidates, Persistence, Competition, Gender
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Résumé We rationalize the observed short-run differences in corporate and long-term government bond yields in an financial-accelerator model with frictions that restrict changes in portfolio shares. We estimate the model on quarterly data for the Euro Area from 1999 to 2019, and show that the portfolio friction parameter is positive and significant. Portfolio frictions not only generate a time-varying wedge between the two returns that fits the data, but also raise the volatility of return differentials, and the precautionary motive of savers. As a result, the macroeconomic effects of uncertainty shocks are amplified by portfolio frictions.
Mots clés Financial Accelerator, Uncertainty shocks, Portfolio frictions
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Résumé We use a distinctive methodology that leverages a fixed population of Twitter users located in France to gauge the mental health effects of repeated lockdown orders. To do so, we derive from our population a mental health indicator that measures the frequency of words expressing anger, anxiety and sadness. Our indicator did not reveal a statistically significant mental health response during the first lockdown, while the second lockdown triggered a sharp and persistent deterioration in all three emotions. Our estimates also show a more severe deterioration in mental health among women and younger users during the second lockdown. These results suggest that successive stay-at-home orders significantly worsen mental health across a large segment of the population. We also show that individuals who are closer to their social network were partially protected by this network during the first lockdown, but were no longer protected during the second, demonstrating the gravity of successive lockdowns for mental health.
Mots clés Mental health, Twitter data, Well-being, Lockdown, COVID-19
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Résumé Purpose: The article examines stock index price responses in Brazil, Chile, and Mexico to those in the US, Spain, and four European countries during three sub-periods surrounding the neoliberal reforms of the 1990s: 1988 to 1994, 1995 to 1998, and 1999 to 2004. Design/methodology/approach: The methodology is empirical and uses time series analysis, in particular impulse response functions (IRFs) derived using vector autoregression (VAR) models. Main Findings: It finds that equity markets became more interconnected as countries opened to international trade and capital flows and that there was an increasing impact of Spain on Latin American equity markets. Stronger economic linkages (more trade and foreign direct investment) between Spain and these countries, especially in Brazil, seem to explain increased equity market interconnectedness. Research limitations/implications: The study limitations are, in general, the same that apply to the VAR methodology, and in particular, to missing control variables or to possible bias in the selection of the subsample periods used as historical benchmarks. Originality/value: To our knowledge, no other work showed that there was an increasing impact of Spain on Latin American equity markets during the neoliberal reform period by using IRFs and VAR models.
Mots clés Emerging markets, VAR modeling, Stock markets interdependence, Spain, Latin America
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Résumé Two recent contributions have found conditions for large dimensional networks or systems to generate long memory in their individual components. We build on these and provide a multivariate methodology for modeling and forecasting series displaying long range dependence. We model long memory properties within a vector autoregressive system of order 1 and consider Bayesian estimation or ridge regression. For these, we derive a theory-driven parametric setting that informs a prior distribution or a shrinkage target. Our proposal significantly outperforms univariate time series long-memory models when forecasting a daily volatility measure for 250 U.S. company stocks over twelve years. This provides an empirical validation of the theoretical results showing long memory can be sourced to marginalization within a large dimensional system.
Mots clés Model Forecasting, Vector autoregressive, Ridge regression, Bayesian estimation
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Résumé Aims: we propose a sociotechnical taxonomy for the analysis of socioeconomic disruptions caused by technological innovations. Methodology: a transdisciplinary principled approach is used to build the taxonomy through categorization and characterization of technologies using concepts and definitions originating from cybernetics, occupational science, and economics. The sociotechnical taxonomy is then used, with the help of logical propositions, to connect the characteristics of different categories of technologies to their socioeconomic effects, for example their externalities. Results: we offer concrete illustrations of concepts and uses, and an Industry 5.0 case study as an application of the taxonomy. We suggest that the taxonomy can inform the analysis of opportunities and risks related to technological disruptions, specially of those that result from the rise of cognitive machines.
Mots clés Cognitive machines, Sociotechnical taxonomy, Occupational science, Artificial intelligence, Technological disruptions, Industry 50, Externalities, Skillreplacing, Skill-enhancing, Cognitive technology, Physical technology, Autonomous, Automatic, Technological innovations
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Résumé 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 (
Mots clés Hyperparameter tuning, Multi-output regression, Design of Experiments
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