Publications

Résumé In this paper, we provide a framework in which a stationary bubble can exist on a portfolio of dividend-yielding assets. Consistent with standard asset pricing theory, this portfolio bubble is defined as the difference between the portfolio market price and the present value of its future dividend stream. This bubble can coexist with a positive stationary fundamental value, without requiring the collapse of the latter over time. This result is obtained in an exchange overlapping generations economy featuring both newly issued and pre-existing financial assets that depreciate over time, and jointly constitute the asset portfolio. The introduction of new assets in each period decouples the return on bubbles from the effective discount rate applied to dividends. As a result, stationary equilibria can exist with both a positive bubble and a positive fundamental component in the portfolio value. Finally, our framework also allows us to discuss the role of the substitutability between financial assets on the level of bubbles and fundamental values.
Mots clés Fundamental value, Financial assets, Rational bubbles
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Résumé This article analyzes the impact of income inequality on environmental policy in the presence of green consumers. We first perform an empirical analysis using a panel of European countries over the period 1995–2021. The results show a negative relationship between inequality and public environmental expenditure, which is weaker with higher inequality. We also find a negative correlation between environmental expenditure and green consumption, that highlights the substitutable nature of the relationship between the two variables. We next develop a model with two main ingredients: citizens with different income capacities have access to two commodities that differ in terms of environmental impact, and they vote on the environmental policy. In equilibrium, the population is divided into two groups, conventional vs green consumers. An increase in inequality raises the marginal cost of policy through size and composition effects. The higher the equilibrium tax, the larger the overall effect. This provides us with an explanation of the main empirical result.
Mots clés Green consumption, Environmental public expenditure, Inequality, Income distribution
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Résumé Objectives 1. To develop a deep-learning segmentation model for automated measurement of maximal aortic diameter (D max ) and volumes of aortic dissection components: true-lumen (TL), circulating false-lumen (CFL), and thrombus (Th) on CT angiography (CTA). 2. To assess the predictive value of these measures for adverse aortic remodeling in residual aortic dissection (RAD).Materials and methods This retrospective study included 322 patients from two centers. The segmentation model was trained on 120 patients (Center 1) and tested on an internal dataset (30 patients, Center 1) and an external dataset (10 patients, Center 2) in terms of Dice Similarity Coefficient (DSC). The model extracted D max , global false-lumen volume (FL Glo = CFL + Th), and local false-lumen volume (FL Loc , measured 3 cm around the largest diameter). Clinical validation was performed on 83 patients from Center1 (internal validation, 2-year follow-up) and 79 patients from Center2 (external validation, 4.5-year follow-up). ResultsThe segmentation model achieved high accuracy (Center 1, DSC: 0.93 TL, 0.93 CFL, 0.87 Th; Center 2, DSC: 0.92 TL, 0.93 CFL, 0.84 Th) with strong agreement between automated and manual measurements. Aortic remodeling occurred in 39/83 patients (46.9%) from Center1 and 33/79 patients (41.7%) from Center2. Aortic remodeling occurred in 39/83 patients (47%) from Center1 and 33/80 (42%) from Center2. FL Loc outperformed D max and FLGlo (Center 1: AUC = 0.83, 0.73, and 0.76; Center 2: AUC = 0.77, 0.64, and 0.70). At optimal thresholds, FL Loc showed good predictive performance (Center 1: Sensitivity = 0.87, Specificity = 0.68). Conclusion Deep-learning segmentation provides accurate aortic measurements. Local false-lumen volumes predict adverse aortic remodeling in RAD better than diameter and global false-lumen volumes. Key PointsQuestion In residual aortic dissection (RAD) after type-A dissection, early identification of high-risk patients on initial CT angiography is crucial for endovascular treatment decisions. Findings False-lumen local volumes (3 cm around aortic dissection maximal diameters), obtained with an automatic deeplearning method, predict adverse remodeling better than diameter or global false-lumen volumes. Clinical relevance A deep-learning segmentation method of aortic dissection components on CTA, enabling automatic measurements of diameters and volumes is feasible. It provides local false-lumen volumes, a better predictive marker of adverse aortic remodeling than the currently used diameters and global volumes.
Mots clés Aortic dissection, Computed tomography angiography, Deep-learning, Prognosis, Computer-assisted image processing
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Résumé Introducing a new measure of scientific proximity between private firms and public research groups and exploiting a multi-billion euro financing program of academic clusters in France, we provide causal evidence of local spillovers from academic research to firms in the private sector. Our main estimate suggests that each euro spent in academic research through this program spurred an additional 0.81 euros in private R&D expenditures. We also show that this shock increased the average quality of patents. We exploit reports produced by funded clusters, complemented by data on firm creation, labor mobility and R&D public–private partnerships, to provide evidence on the channels for these spillovers. We discuss the policy implications of funding academic research to stimulate private R&D.
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Résumé To the best of our knowledge, this study provides the first broad-scale, nation-wide analysis of a set of long-term morbidity effects of air pollution and assessment of their economic impacts in France. We used the Health Risk Assessment method and the latest concentration-response functions, both from the World Health Organization (WHO). The economic analysis - a comprehensive cost of illness approach - includes direct health and non-health care costs, indirect and intangible costs. Beyond its impact on mortality, the study shows that lowering PM2.5 levels over time in France would produce substantial health and well-being benefits by reducing the onset of several diseases. Documented here as attributable to long-term exposure to anthropogenic PM2.5 on average in a given year are 20 % of new cases of respiratory diseases in children and 7–11 % of new cases of respiratory, cardiovascular or metabolic diseases in adults. We also show that reducing PM2.5 concentrations to WHO’s air-quality guideline (AQG) levels would reduce morbidity attributable to this anthropogenic pollution by up to 75 %. Finally, if average PM2.5 levels were reduced to their anthropogenic thresholds, annual benefits to health and well-being for the diseases studied would total €201812.88 billion. Given these findings, complying with WHO AQG would reduce mortality and morbidity attributable to air pollution in France and help achieve the objective of WHO’s Global Action Plan for the Prevention and Control of Noncommunicable Diseases, namely a one-third reduction in the risk of dying from a chronic disease by 2030.
Mots clés Economic assessment, Health risk assessment, Long term impacts, Morbidity, PM25, Air pollution
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Résumé This study examines the saving behavior of a regret-averse agent within a two-period model. The analysis demonstrates that disproportionate aversion to large regrets induces a pseudo effect resembling probability weighting. In particular, the agent assigns greater weight to states in which significant saving regret might arise. As a result, regret aversion encourages precautionary saving when income shocks are sufficiently negatively skewed but diminishes or even reverses precautionary saving when they are not. The exact skewness condition under which the agent saves more than a discounted utility counterpart is characterized in the context of small binary risks. Notably, this condition becomes more restrictive as the traditional measure of absolute prudence increases. A simulation involving large income shocks further confirms that the qualitative insights derived from the small-risk case extend to broader scenarios, highlighting that regret aversion can substantially influence saving behavior when income risks are skewed.
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Résumé Recurring statistical issues such as censoring, non-random selection and heteroskedasticity often impact the analysis of observational data from natural and human processes. We investigate the potential advantages of models based on quantile regression (QR) for addressing these issues, with a particular focus on non-market valuation data. First, we provide analytical arguments showing how QR can tackle these issues. Second, we show by means of a Monte Carlo experiment how censored QR (CQR)-based methods perform compared to standard models with selection both accounted for and not accounted for in the modeling. Incidentally, we propose an alternative to the standard estimation procedure for the CQR model with selection, which divides computation time by about 100. Third, we apply these four models to a French contingent valuation survey on flood risk. Our findings suggest that selection-censored models are useful for simultaneously tackling issues often present in observational and human data. In addition, the CQR models give a better picture of the heterogeneity of the coefficients, but the computational complexity of the CQR-selection model does not seem to be offset by better performance.
Mots clés Selection model censored quantile regression Monte Carlo experiment nonmarket valuation flood
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Résumé This paper is essentially based on the assumption that policies supporting investment in intermittent renewable technologies cannot be contingent on meteorological events causing this intermittence. This decision was taken by most policymakers to avoid overly complex policy prescriptions. But in doing so, the first-best energy mix may be out of reach. We compare, in a unified second-best setting, the feed-in tariff, renewable premiums and tradable green certificates policy. We consider a “two-period, S-state” model. The S states reflect intermittency. Production decisions for renewable electricity are taken prior to the resolution of the uncertainty while the fossil-fuel sector adjusts its decision in each state. Retailers buy electricity on a state-dependent wholesale market which they deliver to consumers according to a fixed-tariff or a real-time-pricing contract. All these elements matter in the efficiency assessment of these policies.
Mots clés Tradable green certificates, Renewable premiums, Feed-in tariff, Renewables, Intermittency
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Résumé We propose the stepwise Cauchy combination test (StepC), a new procedure for multiple testing with dependent test statistics and sparse signals. Unlike the global version, StepC pinpoints which p-values drive rejections, while maintaining strong familywise error control. It is less conservative under dependence and more powerful than conventional multiple testing corrections. In simulations and in applications to drift burst detection and testing for nonzero alphas, StepC consistently boosts power and yields more meaningful rejections, making it a practical alternative for large-scale financial datasets.
Mots clés Nonasymptotic approximation, Sequential rejection, Multiple hypothesis testing, Familywise error, Dependence
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Résumé This paper revisits the question of whether fixed and mobile Internet expenditures are substitutable or complementary. We estimate a demand system using French household expenditure data to compute price elasticities for different categories of goods. The results indicate that fixed and mobile Internet expenditures are complementary in France. This complementarity effect increases with income level. We then develop a simple theoretical model showing that depending on the characteristics of fixed and mobile data tariffs, fixed and mobile Internet expenditures can exhibit non-substitutability or even complementarity.
Mots clés QUAIDS demand system, Household behavior, Internet expenditure
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