Upcoming seminars:

01/10/2025 

Noémie MARTIN - Carbon Taxes, Transport, and Inequalities: A Heterogeneous-Agent Perspective (view poster)

PhD Supervision: Etienne Billette de Villemeur, Heyjin Park (Université de Montréal) 

Abstract:
Climate regulations are essential to reducing greenhouse gas (GHG) emissions but often impose unequal burdens across households. The U.S. transport sector, now the largest source of GHG emissions, illustrates these distributional challenges particularly well. Low-income households emit far less than wealthier households in absolute terms, yet they devote a much larger share of their income to fuel expenditures, making them more vulnerable to price-based policies. Structural inequalities, such as limited access to public transit, further constrain households’ ability to adapt to carbon pricing. This paper examines the incidence of carbon taxes on transport across heterogeneous households, focusing on the role of heterogeneity in preferences from income and residential location. We also assess how alternative revenue-recycling schemes can redistribute the burden and mitigate regressive effects. We build a model of household transport use and conduct simulations of carbon tax implementation. By analyzing both economic and spatial heterogeneity, the paper contributes to understanding how carbon pricing can be designed to balance efficiency, effectiveness, and equity.

08/10/2025 

Claire MORRIER - Which Teaching Practices Increase Students' Test Scores? A French Case Analysis (view poster)

PhD Supervision: Quentin DAVID, Manon GARROUSTE and Nina GUYON | Research  axis: Decision-making | Discussant: Gero DASBACH

Abstract:
Teachers play a crucial role in shaping students' performance. However, the impact of teaching practices on student achievement is still debated in the literature. This paper exploits data from the Trends in International Mathematics and Science Study (TIMSS) for 4th-grade students in France, taking advantage of the fact that in primary school, French pupils have the same teacher for both mathematics and sciences. Using a within-student, within-teacher between subject identification strategy, I assess the effect of teaching practices on students’ test scores. Specifically, increasing the use of homework assignments by 20 percentage points increases student test scores by 2% of a standard deviation. Increasing individual practice in class also raises students test score by a same proportion. Practicing in class or at home works better when teachers use cognitive activation techniques rather than traditional techniques. These findings are robust to controls for students' and classrooms' subject specific perceptions of teacher quality. Further investigations show that results are partly driven by the effect of cognitive activation on confidence and interest in the subject.

12/11/2025 

Mohamed CHARHBILI - TBA

10/12/2025 

Camille NAUDY - TBA

21/01/2026 

Lena BELKESSA - TBA

11/02/2026 

11/03/2026

08/04/2026

13/05/2026 

10/06/2026 

08/07/2026 

Previous seminars:

17/09/2025  

Gero DASBACH - Autocratic Manipulation of Regional GDP vs. Alternative Welfare Measures (view poster)

PhD Supervision: Quentin DAVID, Abel FRANCOIS (University of Strasbourg) | Co-authors: David STADELMANN, Lara BIESKE | Research  axis: Decision-making | Discussant: Louis MAROLLEAU

Abstract:
This paper explores the manipulation of regional GDP data in non-democratic states and offers a novel approach to mitigating such biases. We extend the work of Martinez (2022) by examining how regional GDP figures are systematically distorted in autocratic regimes, particularly in regions with high levels of corruption. Our results demonstrate that GDP manipulation is more pronounced where regional corruption intersects with autocratic governance, although the impact varies across regions. Notably, we observe a pronounced manipulation in African regions, where both national-level autocracy and local corruption amplify data distortions. To address these issues, we propose using subjective welfare measures as a more reliable alternative. Our findings suggest that subjective indicators, such as those from the Global Data Lab and the International Wealth Index, significantly reduce manipulation, as they are less prone to distortion compared to GDP figures. This is especially true in African countries, where subjective data from surveys like the Afrobarometer reveal a more accurate reflection of welfare levels. This paper contributes to the literature by highlighting the geographic and political dimensions of GDP manipulation and offering a robust method for improving data reliability