Applied Computational Social Sciences
Data-Intensive Governance - PSL Institute
Une expertise de recherche en sciences sociales articulée avec des capacités en sciences des données pour :
- Renforcer la pertinence de la recherche académique sur les grandes problématiques sociétales
- Eclairer la décision publique et privée
- Favoriser une meilleure gouvernance collective
Prochaines sessions des groupes de travail
2024-05-06 at 17:30h - Public governance working group
Lena Song (University of Illinois Urbana-Champaign)
Making Public Law (joint with Elliott Ash, Aniket Kesari, Suresh Naidu, and Dominik Stammbach)
Abstract: Laws have increasingly become difficult for the public to understand due to their complexity and the decline in trusted legal journalism. We build a novel AI-powered legal summarizer to produce easy-to-read summaries of the reasoning in judicial opinions. We study the effects of these summaries in three related experiments. First, we show in a survey experiment that, compared to existing expert-written summaries, AI-generated simple summaries of U.S. Supreme Court judicial opinions are more accessible to the public and more easily understood by non-experts. They help respondents understand the key features of a ruling, and have higher perceived quality, especially for respondents with less formal education. Second, we study how these summaries affect policy attitudes and institutional legitimacy in a large-scale survey experiment around the release of the affirmative action decision in June 2023. While the summaries enhance understanding of judicial opinions across issues, they have mixed effects on the acceptance of court decisions. Finally, we explore the effect of these summaries on public discourse in a social media experiment.
2024-06-03 at 17:30h - Public governance working group
Berkeren Büyükeren (EIEF and LUISS)
Endogenous Local Government Formation and Nation Building (joint with Serhii Abramenko)
Abstract: How does local government amalgamation affect public goods provision, economic activity, and nation building? We focus on an administrative reform in Ukraine between 2015-2020, during which smaller local councils (LCs) had the opportunity to voluntarily amalgamate in order to keep a substantially larger portion of their tax revenues and gain greater autonomy over the local administration. By investigating the determinants of the willingness to amalgamate, we show that some of the pre-reform characteristics, such as tax revenues, the share of native Russian speakers, and political preferences, were not substantial predictors of the amalgamation. We first show that the reform positively impacted district level personal income tax collection. Secondly, by employing previously unused data on standardized college entrance exam results, we estimate a staggered difference-in-differences model and show that the reform did not affect the Ukrainian test scores significantly. For math results, it led to a 0.07 standard deviation decrease after four years of exposure. Utilizing the same staggered design, we show that the reform led to 0.06 standard deviations increase in log nightlight intensity per capita after four years of exposure. Finally, using a nationally representative repeated cross-sectional survey, we document the greater self-identification as Ukrainian as opposed to Russian in rural areas.
Partenaires
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Contact : bruno.chavesferreira@dauphine.psl.eu