Publications

This study examines how epidemic outbreaks influence foreign direct investment (FDI) inflows in developing countries, with a particular focus on whether healthcare systems can act as buffers during such shocks. Using a panel dataset of 98 countries from 2000 to 2022, the analysis combines two-way fixed effects (FE) models and the Local Projection Method (LPM). The analysis is structured in three parts: First, fixed-effects regressions assess the average effect of 20 major epidemics on FDI, revealing that diseases such as Ebola, MERS, Lassa Fever, and Leptospirosis significantly reduce investment inflows. Second, local projection methods trace the short-and medium-term responses of FDI to health shocks by transmission type. The results show varying recovery patterns: while FDI tends to rebound after direct contact or mosquito-borne outbreaks, airborne diseases cause more persistent declines. Third, the study explores whether stronger healthcare systems can mitigate these negative effects. Results suggest that countries with a higher density of nurses experience less severe FDI losses during outbreaks, particularly for diseases transmitted through direct contact or bodily fluids. These findings underline the importance of healthcare investment not only for public health but also for economic resilience. By distinguishing effects across disease types and highlighting the moderating role of health infrastructure, this study offers practical insights for policymakers seeking to safeguard investment flows during times of crisis.

Environmental concerns are becoming more pressing as the climate emergency intensifies, posing a major challenge for many governments: increasing green investments to promote better adaptation and resilience to climate events, while maintaining fiscal discipline. This raises the question of whether governments that operate under fiscal rules tend to safeguard environmental spending in light of the climate emergency, or whether they are more inclined to scale it back to meet their fiscal targets, given that such investments require substantial public funding. Using data covering 31 advanced economies between 1995 and 2021, we find robust evidence that the strengthening of fiscal rules significantly reduces environmental spending, in particular debt rules and expenditure rules. Moreover, the adverse impact of fiscal rules on environmental expenditures is amplified during election periods, whereas it is mitigated in the presence of sound past fiscal conditions, the Kyoto Protocol, and stringent environmental policies. Further analysis reveals that although fiscal rules tend to reduce environmental spending, they are associated with greater efficiency in such spending.

Based on the 2020 International Social Survey Program (ISSP) IV Environment Module, this paper examines how individual characteristics influence the willingness to pay to protect the environment. We contribute to the literature by highlighting the role of social status: the higher respondents place themselves on the social scale, the more they are willing to pay; and of prosocial orientation: the more willing respondents are to take action for the environment independently of others, the more they are willing to pay. We underline that the impact of social status, i.e., respondents' subjective perception of their position on the social scale, remains significant even after controlling for income and education levels. Our results suggest that, in order to strengthen individuals' willingness to pay for the environment, two types of policies may be effective: (1) policies aimed at influencing subjective social status and its determinants -for example, exposing professional groups to narratives framing their position as socially valued, reducing perceived inequalities, enhancing individuals' sense of control over life events, and fostering orientation toward long-term planning; and (2) policies aimed at shaping prosocial orientation, that is, convincing individuals that personal actions are meaningful even when they are not widely adopted by others. Besides, based on our results, climate policies can be successful when imposing net costs on the following groups: individuals who rank themselves in the top 40% of the population (social status); the 50% of individuals who agree to act without waiting for others to do so (prosocial orientation). By contrast, climate policies that impose net costs on the complementary segments of the population are likely to face disapproval.

This paper studies whether profile-based explanations influence investors' acceptance of algorithmic risk recommendations in a randomized controlled trial embedded directly in the platform's interface of a leading French robo-advisor. Users were assigned either to see graphical explanations of the drivers underlying their recommended risk score and associated portfolio or to receive the standard interface with no explanation. Our results, obtained in a real-world setting with actual clients of a FinTech, do not support the adherence gains from increased transparency that are widely anticipated in the literature. Overall, providing profile-based explanations is not found to increase acceptance of the recommended profile nor raise users' engagement with the platform. However, we find a heterogeneous treatment effect as profilebased explanations lead to a greater downward deviation among desktop users who have already deviated to safer-than-recommended portfolios, but this pattern disappears once users' experience of the platform is taken into account. We observe non-causal evidence in both conditions that behavior is shaped primarily by the digital context and experience: phone and first-time users are more likely to accept the portfolio recommendation than desktop and returning users. While such transparency-enhancing profile-based explanations are informative, they are not a universal lever for adherence, suggesting that explanation design should be tested and tailored across device types and users' experience.

The Digital Services Act and the AI Act adopted by European institutions require algorithmic decisionmaking systems to meet transparency obligations through the provision of explanations of their functioning. As algorithmic decision-making systems increasingly shape individuals' economic and social lives, this paper experimentally tests whether adding a non-technical explanation to a neutral system description affects public acceptance. The study relies on a large-scale survey experiment on nationally representative adult samples in France, Germany, and Italy in which each respondent evaluates six algorithmic and AI systems spanning finance, health, public services, employment, online commerce, and digital media. We measure the economically relevant dimensions of adoption and legitimacy, including beliefs, evaluative attitudes, and willingness to delegate decisions. Explanations yield measurable, though modest, increases in willingness to delegate. A mechanism-consistent decomposition shows that these effects arise primarily through improved attitudes toward the systems, while direct effects and belief shifts play a secondary role. Overall, explanations reliably move acceptance in the intended direction, but do not eliminate persistent concerns, especially those related to privacy. The results highlight both the promise and limits of information disclosure as a regulatory tool.

It is generally considered that industrialization is accompanied by an increase in the skills of the workforce. However, this complementarity between human and material capital is controversial, particularly in the early phases of the industrial revolution. It is indeed possible that mechanization, by promoting the division of labor, led to a simplification of tasks and thus to a lower demand for skilled labor. This is the so-called de-skilling hypothesis. The aim of this article is to test this hypothesis using Prussian data from the first half of the 19th century. Industrialization is measured at the county level by the number of workers in the metallurgy sector in 1849. An identification strategy based on the county's proximity to coal mines reveals a negative causal impact of industrialization on schooling rates. This effect seemed to persist right up to the eve of German unification.