Publications

Résumé The modernisation theory of regime change is often perceived to be a murky paradigm, lacking theoretical or empirical foundations. In response, we clarify the links between education and regime change.More specifically, we propose that education contributes indirectly to the collapse of autocratic regimes because educated people engage in non-violent (civil) resistance that reduces the effectiveness of the security apparatus. We empirically test the validity of this 'defanging effect' of education. We indeed find that the combination of high autocracy and high education levels tends to trigger non-violent campaigns, which in turn increases the likelihood of a regime change, likely to be associated with political liberalisation.
Mots clés Regime change, Modernisation, Education, Democratisation, Civil resistance, Autocracy
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Résumé Background It is commonly believed that Africa largely evaded the worst of the COVID-19 pandemic, with fewer cases than other continents. However, regional comparisons that ignore differences in testing intensity may misrepresent dynamics. Studying the spread and case-fatality relationship during COVID-19 across WHO regions requires explicitly adjusting for time-varying test volumes. Methods We build a weekly panel dataset spanning May 2020 to December 2021 for the WHO regions: Africa, Eastern Mediterranean, South-East Asia, the Americas, Western Pacific, and Europe. Data on tests, confirmed cases, and COVID-19-attributed deaths were sourced from Our World in Data. We apply a novel metric that corrects for fluctuating test volumes to quantify week-to-week acceleration in infections and in mortality. We then compare the frequency, magnitude, and timing of these acceleration episodes across regions.Results Accounting for testing dynamics, we show that Africa exhibits multiple infectionacceleration episodes whose magnitude and frequency match those in other regions. Mortality accelerations in Africa closely follow infection surges, with an average lag of ten weeks. A positive correlation between infection acceleration in Africa and the Americas further indicates synchrony. These findings hold when using a larger secondary dataset of 140 countries. Conclusions Contrary to prevailing assumptions, Africa was not spared from the pandemic's severe dynamics. Infection surges were on par with those elsewhere and were followed by mortality accelerations. These results underscore that accounting for testing variability is essential to accurately assess pandemic progression, and they highlight the urgent need to strengthen surveillance and healthcare capacity across all regions.The relatively low number of reported COVID-19 cases and deaths in Africa has prompted debates about whether the continent was spared the worst of the pandemic, a phenomenon described by some as the African "puzzle" 1,2 or "paradox" 3 . Early media reports and research articles speculated that Africa's younger population, lower population density in rural areas, and prior experience with infectious diseases and their pharmaceutical treatments might have mitigated the severe impacts observed in other regions.However, emerging evidence from seroprevalence studies indicates that far more individuals in Africa were exposed to SARS-CoV-2 than is reflected in official surveillance data, especially during the pandemic's first 2 years. For example, the ratio of seroprevalence to confirmed cases has been estimated to be as high as 100:1 4 . This gap between seroprevalence estimates and reported cases grew as the pandemic continued 5 , suggesting substantial under-reporting in surveillance data. For example, while 6 mention low testing rates as a likely source of under-reporting in their discussion of the
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Résumé Multi-criteria decision analysis in databases has been actively studied, especially through the Skyline operator. Yet, few approaches offer a relevant comparison of Pareto optimal, or Skyline, points for high cardinality result sets. We propose to improve the dp-idp method, inspired by tf-idf, a recent approach computing a score for each Skyline point, by introducing the concept of dominance hierarchy. As dp-idp lacks efficiency and does not ensure a distinctive rank, we introduce the RankSky method, the adaptation of Google’s well-known PageRank solution, using a square stic matrix, a teleportation matrix, a damping factor, and then a row score eigenvector and the IPL algorithm. For the same reasons as RankSky, and also to offer directly embeddable in DBMS solution, we establish the TOPSIS-based CoSky method, derived from both information research and multi-criteria analysis. CoSky automatically ponderates normalized attributes using the Gini index, then computes a score using Salton’s cosine toward an ideal point. By coupling multilevel Skyline to dp-idp, RankSky or CoSky, we introduce DeepSky. Implementations of the improved version of dp-idp, RankSky and CoSky are evaluated experimentally using generated synthetic data sets. All of the proposed methods highlight relevance and performance: dp-idp with dominance hierarchy seems twice as efficient as the original while RankSky provides a fast robust usual approach transposed to Skyline’ ranking, and CoSky offers a far more effective solution than any other method.
Mots clés Multiple-criteria decision analysis Skyline Information retrieval Ranking
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Résumé Multi-criteria decision analysis in databases has been actively studied, especially through the Skyline operator. Yet, few approaches offer a relevant comparison of Pareto optimal, or Skyline, points for high cardinality result sets. We propose to improve the dp-idp method, inspired by tf-idf, a recent approach computing a score for each Skyline point, by introducing the concept of dominance hierarchy. As dp-idp lacks efficiency and does not ensure a distinctive rank, we introduce the RankSky method, the adaptation of Google’s well-known PageRank solution, using a square stic matrix, a teleportation matrix, a damping factor, and then a row score eigenvector and the IPL algorithm. For the same reasons as RankSky, and also to offer directly embeddable in DBMS solution, we establish the TOPSIS-based CoSky method, derived from both information research and multi-criteria analysis. CoSky automatically ponderates normalized attributes using the Gini index, then computes a score using Salton’s cosine toward an ideal point. By coupling multilevel Skyline to dp-idp, RankSky or CoSky, we introduce DeepSky. Implementations of the improved version of dp-idp, RankSky and CoSky are evaluated experimentally using generated synthetic data sets. All of the proposed methods highlight relevance and performance: dp-idp with dominance hierarchy seems twice as efficient as the original while RankSky provides a fast robust usual approach transposed to Skyline’ ranking, and CoSky offers a far more effective solution than any other method.
Mots clés Ranking, Information retrieval, Skyline, Multiple-criteria decision analysis
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Résumé This study provides a life-course analysis of the relationship between self-employment, health, and health care use among individuals aged 50 and older in Europe. Using data from the Survey of Health, Ageing, and Retirement in Europe (SHARE), we apply first-difference and dynamic panel data models that go beyond standard approaches in mitigating endogeneity concerns. Our findings show that the self-employed enjoy better health at younger ages, consistent with a selection effect. In addition, they experience a steeper decline in physical health over time. We also document two distinct phases of health care use: during working life, the self-employed are more likely to be hospitalised, suggesting delayed care until acute needs arise; after retirement, the number of medical visits increases, consistent with a lower opportunity cost of care.
Mots clés Self-employment, SHARE, Health care use, Health
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Résumé Background: Breast cancer and its treatment may contribute to an increased risk of unemployment, influenced by both disease-related factors and socioeconomic determinant. Few longitudinal studies have examined employment outcomes among women diagnosed with cancer. This retrospective study investigated long-term employment among breast cancer survivors (BCS) and assessed disease specific and socioeconomic factors associated with employment. Design and methods: Registry-based data included working age BCS in Norway 2004–2008 alive at 6 years follow-up ( N = 3560). The employment status on each BCS was compared to two matched non-cancer controls ( N = 7081) by means of logistic regression analyses with marginal effects. Separate analyses by employment status at the time of diagnosis were conducted. Results: Among BCS employed at diagnosis, 73.7%, 71.5% and 71.8% of BCS were in employment at 1, 2 and 6 years after diagnosis, respectively. BCS employed at diagnosis had significantly lower probability of being employed at all follow-up time points, compared to controls. BCS outside employment at the time of diagnosis experienced lower probability of employment compared to controls. BCS with secondary or higher education had higher probability of employment compared to BCS with basic education, and BCS living in families with children were more likely to enter employment during follow-up compared to BCS without children. Conclusions: BCS employed at diagnosis had a subsequent risk of unemployment, and BCS not employed at diagnosis had lower probability of entering employment. Additional risk factors are high age, low education, and being single without children. Significance for public health: The risk of unemployment after a breast cancer diagnosis was increased. Job loss is costly economically and socially, both for individuals and for society. Early focus on employment particularly among employees with low education and with little family support may alleviate this problem.
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Résumé This paper studies the main determinants of bilateral financial flows in the euro area to achieve sustainable and fair financing opportunities. We revisit the modern theory of the optimal currency area considering the impact of heterogeneity in inequality measures, within and across countries, on cross border financial flows. To do so, we introduce financial and social fragmentation in gravity models of European capital flows. We use data from 19 Eurozone countries from 2000 to 2021 and show how fragmentation impacts capital flows, namely foreign direct investment, cross-border loans as well as portfolios, equity and bond flows. Since capital is, in principle, free to flow in the Eurozone, our analysis directly identifies the roles of potential sources of fragmentation: social inequalities, lack of market openness, and domestic regulations such as macroprudential controls. Overall, our results show that financial integration in Europe entails more capital flows of any type while social fragmentation across European countries is detrimental to capital flows, no matter which type. This is strong evidence of the importance of financial and social fragmentation in the Eurozone on the distribution of capital.
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Résumé Drift and volatility are two mainsprings of asset price dynamics. While volatilities have been studied extensively in the literature, drifts are commonly believed to be impossible to estimate and largely ignored in the literature. This paper shows how to detect drift using realized autocovariance implemented on high-frequency data. We use a theoretical treatment in which the classical model for the efficient price, an Itō semimartingale possibly contaminated by microstructure noise, is enriched with drift and volatility explosions. Our theory advocates a novel decomposition for realized variance into a drift and a volatility component, which leads to significant improvements in volatility forecasting.
Mots clés Volatility Forecasting, Serial Covariance, High-frequency Data, Drift
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Résumé Not all barrels of oil are created equal: their extraction varies in both private cost and carbon intensity. Leveraging a comprehensive micro-dataset on world oil fields, alongside detailed estimates of carbon intensities and private extraction costs, this study quantifies the additional emissions and costs from having extracted the “wrong” deposits. We do so by comparing historical deposit-level supplies to counterfactuals that factor in pollution costs, while keeping annual global consumption unchanged. Between 1992 and 2018, carbon misallocation amounted to at least 11.00 gigatons of CO2-equivalent (GtCO2eq), incurring an environmental cost evaluated at $2.2 trillion (US$ 2018). This translates into a significant supply-side ecological debt for major producers of high-carbon oil. Looking forward, we estimate the gains from making deposit-level extraction socially optimal at about 9.30 GtCO2eq, valued at $1.9 trillion, along a future aggregate demand pathway coherent with the objective of net-zero emissions in 2050, and document unequal reserve stranding across oil nations.
Mots clés Carbon mitigation, Stranded assets, Misallocation, Oil
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