Publications

This study develops a new theoretical framework to explain how secularity influences migration aspirations in the Arab world. We argue that secular individuals incur significant psychological costs when living in highly religious societies. This value incongruence pushes them to seek out more secular environments, whereas strongly religious individuals face higher cultural costs of moving and thus prefer to stay. We derive testable hypotheses on how individual secularity and socio-political secularity act as push pull factors for different communities and migration destinations. We then test these hypotheses using 2018 2019 Arab Barometer data from eleven MENA countries. We construct original indices for individual secularity and socio-political secularity via multiple correspondence analysis. Consistent with our theory, probit and instrumental-variable probit estimates show that secular individuals are significantly more likely to express intentions to emigrate particularly to highly secular Western countries. Among Muslim majority populations, both individual and socio-political secularity increase the desire to migrate, whereas among Christian minorities only individual secularity has this effect. Moreover, secularity drives regular migration aspirations, with no measurable impact on irregular migration except in the case of religiously unaffiliated “nones,” who exhibit a heightened willingness to migrate by any means. These findings contribute to the migration literature by emphasizing the substantial, yet previously underexplored, influence of secular beliefs and practices on migratory behavior in the Arab context.

The livelihoods of 130 to 270 million people depend on artisanal mining. Artisanal mining is a labour-intensive, often illegal, extractive activity. We combine geological knowledge and a source of exogenous temporal variation to construct the first proxy for artisanal gold mining in Africa—the main form of artisanal mining. We establish that an increase in the potential value of artisanal mining is a significant driver of deforestation. The historical increase in the gold price accounts for 8% of forest loss across the continent and, within the subset of gold-suitable areas, 28 %. In parallel, artisanal mining increases local economic wealth and may provide an alternative livelihood should a weather shock jeopardise agricultural output. Finally, mining-induced deforestation seems rooted more in the direct clearing of trees for the activity than in indirect deforestation triggered by increased local demand.

ABSTRACT Power outages hinder economic development. Although their effects are less pronounced in developed countries, they are more severe in developing nations. Recent literature suggests, in addition to traditional strategies, the adoption of energy management measures as an adaptation strategy to power outages. This study evaluates, in the context of Sub‐Saharan Africa, the impact of adopting energy management measures while also considering traditional strategies such as backup generators. The analysis is based, on the one hand, on the 2023 World Bank Enterprise Survey dataset and, on the other hand, on the implementation of a Probit model to identify the determinants of adoption, as well as a 2SLS model to address endogeneity in impact measurement. The findings reveal that, although implementing energy management measures incurs costs, their adoption significantly increases business turnover and net income, particularly in the industrial sector. Conversely, although adopting a generator backup has a positive impact on turnover, it reduces net income in the service sector. Its impact on net income in the industrial sector is not even statistically significant. It is generally recognized in the ISO 50001 standard that, aside from optimizing energy consumption within businesses, the adoption of energy management measures also contributes to reducing the ecological footprint. By adopting this approach, businesses can enhance their resilience to power outages while simultaneously reducing their environmental impact.

This study analyzes the impact of international climate agreements-specifically the Kyoto Protocol-on the trade in environmental goods over the period 1997-2021. Using entropy balancing and a sample of 112 countries, we show that the Kyoto Protocol led to an overall increase in the trade of environmental goods among Annex B countries. During this period, the protocol notably stimulated imports, although it did not significantly boost exports prior to 2016. The analysis also highlights heterogeneities linked to countries' technological capacities, economic structures, and institutional characteristics.

This lecture note offers a rigorous introduction to Support Vector Machines (SVMs) at the crossroads of geometry, convex optimization, and kernel methods. We review Euclidean geometry and Rosenblatt’s perceptron, then develop the large-margin classifier: primal/dual formulations, KKT conditions, and the role of support vectors. Kernelization is formalized through RKHS and the representer theorem, enabling nonlinear decision boundaries. Extensions include soft-margin SVM, SVR, LS-SVM, multiclass strategies (OvR/OvO), and probability calibration (sigmoid, isotonic). The final part gathers practical modeling principles and hyperparameter tuning. The course targets Master’s-level students with background in statistical learning, functional analysis, linear algebra, and optimization; technical sections and further readings are flagged throughout.

This article has two primary objectives: 1) to propose a new parametric model of volatility and 2) to identify the days with the most significant volatility shocks, while exploring the events that triggered these shocks. We analyze financial volatility during the global financial crisis and the COVID-19 pandemic, utilizing high-frequency financial data and an improved model, which incorporates asymmetry and handles outliers, to estimate volatility and determine the timing of the largest shocks within the day. For instance, for the global financial crisis, major volatility shocks coincide with events such as the bankruptcy of Lehman Brothers and the failure of the Emergency Economic Stabilization Act. However, the largest shock occurred on February 27, 2007, which, despite coinciding with market events like a Chinese stock market crash and Freddie Mac's tighter subprime loan policy, was primarily caused by a computer glitch. Our analysis underscores the value of high-frequency data in modeling financial volatility and identifying the key events driving volatility shocks.