The aim of this project is to explore the impact of artisanal mines (legal or illegal) on the economic development of Sub-Saharan African countries from 2000 to 2021. This project proposes to collect exhaustive information for all Sub-Saharan African countries on the opening/closing of artisanal mines with GPS coordinates. I make use of satellite image time series and machine learning tool, namely convolutional neural network, to identify precisely the location of artisanal mines. Equipped with this original dataset, I will address the first order question of the local impact of artisanal mining, at a very large scale. Notably, I will study the local impact of artisanal mines on individual income, health, migration and conflict.

This project lies at the intersection of the political economy literature and the theory of optimal taxation. Its objective is to initiate an analysis on the links between inequalities, migration, and democracy. The first working package focuses on income redistribution, with an emphasis on middle classes. The second working package’s ambition is to estimate the impact of the tax and transfer system, and its media coverage, on far-right parties’ voting shares. This analysis will shed light on the determinants of the rising propensity of the middle class to vote in favor of these far-right parties. In the first working package, we first aim at measuring the evolution of pre-tax and post-tax income inequality and evaluating the redistributive impact of taxes and transfers on inequality in France since the beginning of the XXth century. We will then conduct two complementary analysis of behavioral responses to taxation among top income earners. The first study will analyze how taxation affects the decision of top income earners to emigrate. The second study will estimate how the design of income taxes may influence labor supply responses and/or the use of tax optimization strategies. Next, we will use the estimated behavioral responses to determine the top of the Laffer curve, that is, the rate of taxation at which tax revenue is maximized. Current top tax rates ranging below this estimated tax rate would suggest the existence of unexploited margins of redistribution, and therefore opportunities to implement tax reforms that would redistribute a part of the tax burden from the middle class to top-income earners. Such reforms, consistent with economic efficiency, would also improve the equity of the tax-and -transfer system. Finally, we turn our analysis into their political feasibility. We will explore this issue by providing theoretical foundations to the political process underlying redistributive policies in a context of international migration. In the second working package, we will first evaluate the effect of taxation and its evolution on the far-right vote in France. This quantitative analysis will allow us to compare the effect of taxation with that of other socio-economic determinants of the far-right vote identified by the literature, such as unemployment, exposure of low skill workers to import competition, or immigration. Using a structural model of vote, we will then conduct a counterfactual exercise allowing us to quantify the impact of tax reforms favoring the middle class on the far-right vote. We will finally analyze the role of media (traditional or social) on the evolution of the far-right vote. More precisely, we will estimate the effect of the media coverage of tax evasion or tax optimization strategies implemented by individuals or firms on the far-right vote.

The ability to infer cause-effect relations is an important facet of learning. In particular, learning the causal effect of our behaviors provides the basis for rational decision-making and allows people to engage in meaningful life and social interactions. A number of psychological theories have been proposed to account for the internal representations mediating causal learning. However, an integrated understanding linking causal learning theories and brain systems is still lacking. The CausaL project will investigate the neural and computational bases of causal learning in the context of goal-directed behaviors (action-outcome causal relations). To do so, we will need to lift two key barriers. The first is the lack of neurocomputational models that formalise psychological theories into predictions about the brain computations. The second is the lack of clear understanding of the brain dynamics supporting action-outcome causal learning. Two preliminary studies of our group have set the ground for lifting these barriers and demonstrated the feasibility of the project. The CausaL project will pursue this work along two Tasks. In Task 1, we will confront two leading neurocomputational learning frameworks: Active Inference (AI) and Reinforcement Learning (RL). Both approaches can be used to formalise the relation between decision variables predicted by causal learning theories and learning behaviors in humans, but differ in the underlying conceptual structure. Put simply, whereas the first is a Bayesian approach aiming at the minimization of uncertainty during explorative and exploitative behavior, the latter formalizes learning as a process maximizing cumulative rewards. By means of simulations of ideal agents and comparison with behavioral data collected from a large cohort of participants (N=180), we will develop neurocomputational models of action-outcome causal learning. We will then compare AI and RL models in their ability to accurately explain empirical behavioral patterns, choice patterns and causal scores. Finally, we will exploit the “best” models to yield predictions about the underlying neural computations. In Task 2, we will test AI and RL models on brain data and study how learning-related brain regions interact. In fact, it is now acquainted that action-outcome causal learning is a brain network phenomenon. However, it is still unclear how fronto-striatal regions dynamically interact to support learning computations. We will investigate brain data to test whether the internal representations predicted by psychological theories and implemented in AI and RL models (for example, conditional probabilities between actions and outcomes, causal beliefs) are encoded in: i) functional connectivity dynamics and directional influences between learning-related brain regions, by means of magnetoencephalography studies in healthy participants and intracranial stereo-electroencephalography in epileptic patients; ii) distinct spatial patterns of brain activaty along fronto-striatal territories, by means of functional and diffusion magnetic resonance imaging (MRI). The combination of functional and structural brain data will reveal how causal learning emerges from interplay between large-scale functional interactions and anatomo-functional gradients along fronto-striatal circuits. To conclude, the CausaL project offers the extraordinary opportunity to link theoretical models of action-outcome causal learning, behavior and brain network dynamics, the so-called cognitive architectures of causal learning (CausaL).

Revisiting Saint-Simonianism as an Innovative Utopia Summary The SAINT-SIMONISME 18-21 project is a multidisciplinary rediscovery of the largest utopian thought of the 19th century, designed and developed in its practical form between 1810 and 1880. It generated intellectual and practical innovations to deal with the uncertainties and risks of a changing world. Saint-Simonianism is examined through two different but interconnected ways. The first way is by retrospectively reinterpreting, back and forth across centuries (18th-21st), the great questions that Saint-Simonian thought confronted: new methods of organization and production, social justice, family and gender equality, the end of ideology and religious revival, the rejection of an independent economic discipline... The second way is by examining afresh the original writings — which are often hard to get through – with a modern gaze. With multi-version critical editions, it reanalyzes these texts and brings out potential for present-day application.

Macroeconomists often assume that fluctuations of inflation and employment are associated with social costs. A central bank can use monetary policy to contain fluctuations in a country’s inflation and employment rates. In an economy affected by supply shocks, however, the central bank faces a tradeoff between stabilizing inflation and employment. The private sector, on the other hand, can theoretically absorb supply shocks by proper adjustments of wages and prices. Private responses to supply shocks should be more efficient, because they can address asymmetric shocks and relieve the central bank from stabilizing employment, so that it can achieve an efficient stabilization of inflation. But, price and wage adjustments are associated with a coordination problem, because prices in different sectors are strategic complements and the optimal response to an exogenous shock depends on the responses of other private agents. Central banks have the ability of solving the coordination problem by affecting product demand via the interest rate. However, monetary policy can just address aggregate shocks and may crowd out private responses that are, however, necessary for convergence to equilibrium after asymmetric shocks. We, thus, identify two sources of interactions: - Strategic substitution between central bank’s stabilization policy and private sector’s reaction: there is a conflict between the central bank and the private sector, since active stabilization is costly, although both parties benefit from stabilizing employment. - Strategic complementarity between actions of private sector’s agents: wage or price adjustments to macroeconomic shocks in one sector raise the incentives to adjust wages or prices in other sectors of the economy. In this research project, we will use laboratory experiments to generate data on a game that follows a modern macro model. The advantage of this approach is that we can implement different policy regimes in treatments that are otherwise equal and, thus, isolate the effects of transparency, cheap talk, and commitments to rules. Our specific research question is which policy regimes are best-suited to resolve the conflict of interests between central bank and a decentralized private sector in stabilizing employment and minimize the welfare costs of exogenous shocks.