Syllabus-Firms’ Strategies and Markets (9 classes-3hours each)
Claire Chambolle
This course aims at equipping students to understand complex strategies that firms apply to maximize their profits. Emphasis is given to pricing, advertising and innovation strategies of firms in a static and/or a dynamic perspectives. The range of strategies analyzed is wide because firms operate in diverse market organizations (various competitive environment, direct sales vs indirect sales to consumers) and demand conditions (uncertainty, consumers’ heterogeneity, imperfect information of consumers about prices and products).
The methodology used is Microeconomic Theory and Industrial Organization. Each class will contain a case study 0.5 hours, 1,5 hours of theory and 1hour of exercises.
L'objectif de ce module est d'équiper les étudiants avec des outils spécialisés pour mener des évaluations économiques quantitatives pertinentes pour les affaires de concurrence et les questions de régulation. Il couvrira les techniques clés pour analyser empiriquement les marchés. Nous utiliserons des exemples basés sur des données provenant d'industries sélectionnées en France et à l'international. Le contenu du cours sera basé sur une discussion de certains articles académiques sélectionnés afin d'introduire la théorie et ses applications pratiques aux affaires de concurrence et de régulation.
Le cours combinera des conférences et des séances pratiques au cours desquelles des jeux de données réelles d'industries seront utilisés pour réaliser des estimations économétriques dans R. Des devoirs réguliers permettront également de pratiquer de manière autonome les méthodes empiriques enseignées pendant les conférences.
This course analyzes Artificial Intelligence through the lens of microeconomics, industrial organization, and applied econometrics, following Joshua Gans' The Economics of Artificial Intelligence. Core topics include:
Infrastructure & Inputs: Data centers, environmental resources, data as a non-rival good, privacy, and intellectual property.
Competition & Regulation: Network effects, loss-leading, open-source dynamics, and the EU AI Act.
Economic Impacts: Labor market displacement/augmentation, firm productivity, algorithmic bias, consumer search, and the economics of generative art.
A core component of the course is the empirical replication and extension of an existing economics paper on AI. Students will utilize the LLM and computational tools taught in the TD sessions to process data and run econometric analyses. Potential data sources include the AIOE Database, employment surveys, AI-simulated datasets for algorithmic bias, or web-scraped data from AI platforms.
Grading is based entirely on group work (groups of 5):
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50% — Literature Presentations: Graded on analytical comprehension, presentation materials, discussion engagement, and a 1-page (A4) synthesis.
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50% — Empirical Replication Project: Graded on the originality of the empirical variation, correct implementation of LLM/econometric tools, and critical assessment of the original paper's methodology. The final session (Session 6) is dedicated to presenting these projects.
This course introduces students to web scraping techniques, covering how to extract data from HTML, navigate both static and dynamic websites, and interact with private and public APIs. Students will also be introduced to key unsupervised and supervised machine learning methods used in econometrics, focusing on dimension reduction and detecting heterogeneity in treatment effects. The course emphasizes hands-on learning, with evaluation based on group projects where students collect data from a website of their choice and analyze the data to answer a question of scientific relevance.
