Séminaires doctorants

Séminaires doctorants

Working Conditions and Mental Health

Date : Jeudi | 2023-02-15 à 12h30
Lieu : Salle des thèses

Lien TEAMS : Cliquer ici pour rejoindre le séminaire doctorant du LÉO

Aynur KHALILZADEH (LEO, Université d’Orléans)

Work significantly influences individual well-being and autonomy, with growing acknowledgment of the detrimental effects of poor working conditions on physical and mental health. Psychosocial factors, encompassing both protective resources and risk factors, play a pivotal role in shaping mental and emotional well-being within the social environment. Despite global recognition of these risks, understanding their relative importance and interaction with individual characteristics remains limited. Leveraging data from the French Monitoring of Employees' Exposure to Occupational Risks 2017 survey (SUMER 2017), our research investigates the complex interplay between working conditions and mental health among 20,605 employees in France. Utilizing scientifically validated questionnaires, including the PHQ-9, we assess workers' mental health and exposure to psychosocial risks. Our findings reveal a significant association between exposure to psychosocial risks and depressive disorders, with logistic regression analysis highlighting the predictive power of these risks on mental health outcomes. Moreover, our study identifies gender and occupational disparities in psychosocial risk exposure, emphasizing the need for tailored interventions to promote worker well-being. This comprehensive analysis enhances our understanding of the nuanced relationships between psychosocial factors, mental health outcomes, and their distribution across diverse demographic and occupational dimensions.

Impact des institutions sur les politiques et stratégies de lutte contre la corruption et de promotion de la bonne gouvernance : Cas de la Guinée

Date : Jeudi | 2024-02-08 à 12h30
Lieu : Salle des thèses

Lien TEAMS : Cliquer ici pour rejoindre le séminaire doctorant du LÉO

Saikou Amadou DIALLO (LEO, Université d’Orléans - ANLCPBG Guinée Conakry)

Le développement de politiques publiques et de stratégies nationales dans les pays en développement est essentiellement basé sur la volonté des décideurs politiques. De même, la faiblesse des institutions dans ces pays nécessite la construction/reconstruction d’institutions stables et pérennes. Ainsi, l’utilisation des données, des faits est prépondérante dans les prises de décisions et dans le développement des politiques nationales sur des thèmes aussi important que la lutte contre la corruption et la promotion de la bonne gouvernance. Dans le cadre de cette thèse, une enquête sur l’utilisation des paiements électroniques et une autre sur la perception de la corruption ont été réalisées en Guinée. Les résultats de ces enquêtes permettront d’évaluer l’impact de la construction d’institutions stables et pérennes sur la lutte contre la corruption dans les pays en développement. De même, les résultats permettront d’évaluer l’impact de la digitalisation notamment les paiements électroniques ainsi que l’inclusion financière sur la réduction de la corruption. Enfin, l’impact des bonnes pratiques de gouvernance sur le développement socio - économique de la Guinée sera evalué.

Recommender Systems Unplugged: Effects of explaining algorithmic recommendations in music consumption, an experimental approach

Date : Jeudi | 2024-02-01 à 12h30
Lieu : Salle des thèses

Lien TEAMS : Cliquer ici pour rejoindre le séminaire doctorant du LÉO

Julien M'BARKI (Chaire PcEn, Université Paris 1 Panthéon-Sorbonne)

Music streaming services make massive use of algorithms in their music recommender systems (MRS) to guide users to tracks they are likely to enjoy. However, the black-box nature of these algorithms makes them difficult for users to understand, both in terms of how they work and the music they predict. The field of explainable AI (XAI), and in particular its “explanation” side, has emerged to make the uses of AI (including MRSs) more comprehensible to users. This paper aims to observe, using an experimental method, whether the explanation of an MRS algorithm induces a change in listening behavior on music streaming services. In a theoretical framework, we model two types of users' behaviors, namely “study” and “browse” behaviors. We then test in the lab the explanation effects on these behaviors by explaining two simplified MRSs, taking into account only certain music recommendation criteria.

First-year PhD. Students Presentation

Date : Jeudi | 2023-01-25 à 12h30
Lieu : Salle B.103

Lien TEAMS : Cliquer ici pour rejoindre le séminaire doctorant du LÉO



S P E C I A L       S E S S I O N


Three new PhD. Students from the LEO (Université d'Orléans) will present their thesis projects. Each of them will give a 15 minutes presentation followed by a 15 minutes discussion :

  • Alexandra Amani (Econometrics Team)
  • Anne-Cécile Baptiste Giuliana (International Economics/Sustainable Development Team)
  • Flavien Vilbert (Macro/Finance Team)


Managing a Lazy Investment: Being Actively Passive

Date : Jeudi | 2024-01-18 à 12h30
Lieu : Salle B103

Lien TEAMS : Cliquer ici pour rejoindre le séminaire doctorant du LÉO

Indigo JONES (LEO, Université d’Orléans)

While passively managed financial instruments do not require intervention from their investors, some investors actively manage them anyway. To understand why and how we use a novel micro-level dataset of 6,247 robo-advisor clients who made 9,250 changes to their investment portfolios between 2015 and 2022. Micro-level demographic and financial variables as well as macro-level market returns and volatility are factors in the decision to change one's passively managed portfolio. In addition, how these changes affected investors' returns are studied. A counterfactual test showed that on average accounts which adjusted their portfolio allocation outperformed identical hypothetical accounts in which no changes were made, but this result was not replicated in the field using a more constrained dataset including only realized gains (i.e., closed accounts).