M2 Economics

Econometrics
Stata / R / Python

The first part introduces causality in economics, key treatment-effect concepts, and differences-in-differences (DID) methods, including extensions to staggered and non-staggered policies using alternative estimators such as Callaway–Sant’Anna and de Chaisemartin–D’Haultfoeuille. The second part covers supervised machine learning algorithms (penalized regression, random forests, boosting, neural networks) and their use in applied economics. The course also introduces text-as-data methods in NLP. Practical R / Python sessions complement theoretical lectures.

The course trains students to build credible causal identification strategies for policy evaluation and to implement them using econometric tools and R. By the end of the course, students should be able to estimate treatment effects using differences-in-differences methods (in both simple and advanced multi-period settings) and to apply supervised machine learning algorithms to causal inference tasks.

Assessment consists of two graded homework assignments including theoretical questions, simulations and empirical applications using R or Python. Teaching combines theory with hands-on computational practice.

Roth et al. (2023); de Chaisemartin & D’Haultfoeuille (2022);
Varian (2014); Mullainathan & Spiess (2017); Gentzkow, Kelly & Taddy (2019); Gaillac & L'Hour (2025)

The course begins with international business-cycle facts and the intertemporal approach to the balance of payments. It then covers capital accumulation in open economies, the small open-economy RBC model, emerging market business cycles, and exchange-rate dynamics. Depending on time, more advanced topics may include capital controls, policy credibility and balance-of-payments crises, financial frictions, and sovereign default.

The course aims to teach students how to build and analyze open-economy macroeconomic models for policy purposes. By the end of the course, students should be able to understand the mechanisms driving international business cycles, incorporate financial frictions into open-economy frameworks, and confront model-based predictions with empirical evidence on major global macroeconomic issues.

Assessment consists of a written exam. Lecture slides include topic-specific references and updated reading lists.

Uribe & Schmitt-Grohé (2017), Open Economy Macroeconomics, Princeton University Press.
Obstfeld & Rogoff (1996), Foundations of International Macroeconomics, MIT Press.

The course covers the foundations of experimental and behavioral economics, experimental methodology, and applications to individual decision making under risk and uncertainty, including expected utility, prospect theory, time discounting and bounded rationality. It then examines decision making in social interactions through game theory, bargaining, and non-standard social preferences, followed by evidence on social behavior and enforcement mechanisms. The final part applies experimental tools to real-world issues such as insurance, health and environmental prevention through laboratory, field and survey-based experiments.

The course aims to provide students with a solid understanding of experimental and behavioral economic methods and their applications in fields such as health, environment, development, insurance and food. Students should learn how to design an experiment, formulate an appropriate research question, collect and analyze experimental data, and interpret behavioral patterns in individual and social decision making.

Assessment is based on a written and oral project in which students design, implement and analyze an experiment.

Dhami (2016); Eeckhoudt, Gollier & Schlesinger (2005); Elster (2007); Gollier (2004); Holt (2019); Kagel & Roth (2016); Kahneman (2012); Schram & Ule (2019).

This course requires an economics training at level M1, with basis in intertemporal
macroeconomics and welfare economics. Mathematical skills at level L2 (optimization,
differential equations) are expected.

The course follows the chronological development of sustainability theory, beginning with the Dasgupta–Heal–Solow model and the emergence of sustainability concerns. It then covers the axiomatic foundations of intertemporal welfare criteria, explores a broad set of sustainability criteria and their practical implications, and examines how these frameworks inform environmental pricing and sustainability accounting. The course also includes an animated debate on growth versus degrowth and concludes with individual presentations of research articles.

The course aims to provide students with a deep understanding of the theoretical foundations of intergenerational equity within dynamic macroeconomics. Students will learn how different intertemporal welfare criteria—such as discounted utility, maximin and other axiomatic approaches—lead to different optimal growth paths and policy recommendations. They will also gain knowledge of sustainability concepts, long-term policy evaluation, and sustainability accounting as practiced by institutions like the World Bank. The course further develops understanding of macroeconomic models with natural resources and their implications under alternative normative criteria.

The course consists of eight 3-hour sessions, with regular homework in the form of readings or exercises. Assessment depends on class size: for small groups, evaluation is based on individual presentations; for larger groups, it consists of annotated research articles and a final test.

Dasgupta & Heal (1974); Solow (1974); Chichilnisky, Heal & Beltratti (1995); Chichilnisky (1996);
Cairns & Long (2006); Alvarez-Cuadrado & Long (2009);
Cairns & Martinet (2014); Long & Martinet (2018).

This course requires a strong background in public economics (M1 level), dynamics analysis (M1 level) and growth theories (Undergraduate).

The course is structured around three main blocks. It begins with R&D behavior and cooperation in oligopolies, including spillovers, joint labs and scope economies. It then examines green R&D and optimal environmental policy instruments, comparing taxes and standards, time-consistent versus pre-commitment policies, and cooperative environmental innovation. The last block focuses on the interaction between competition and environmental policy, covering R&D cooperation under competition law, green innovation within antitrust frameworks, corporate social responsibility, and the European Green Deal.

The course aims to provide students with an advanced understanding of how firms’ R&D behavior interacts with environmental regulation, using tools from Industrial Organization. Students are expected to understand the role of spillovers, joint ventures and economies of scope in R&D; to analyze green innovation and optimal environmental policy; and to evaluate the compatibility and potential tensions between competition policy and environmental objectives, especially within the context of recent European initiatives such as the Green Deal.

Students must attend and actively participate in all lectures and complete the exams to validate the course. Additional readings are provided for each chapter.

Amir et al. (2018, 2023); d’Aspremont & Jacquemin (1988); Kamien, Muller & Zang (1992);
Lambertini, Poyago-Theotoky & Tampieri (2017); McDonald & Poyago-Theotoky (2017);
Moner-Colonques & Rubio (2016); Montero (2002); Ouchida & Goto (2016, 2022);
Petrakis & Xepapadeas (2001); Poyago-Theotoky (2007);
Strandholm et al. (2018); Schinkel & Spiegel (2017);
Schinkel, Spiegel & Treuren (2022); Schinkel & Treuren (2024).

Microeconomics

The course begins with methods for modeling information and a version of Blackwell’s theorem for comparing information structures. It then covers correlated equilibria and their implementation via cheap talk. The course moves on to asymmetric information games and generalizations of correlated equilibrium, including communication and Bayes correlated equilibria. Students then study canonical cheap talk models, Bayesian persuasion, and recent research on cheap talk with transparent motives, which bridges communication games and information design. Additional topics may be included depending on student interest.

The course aims to introduce students to modern methods in information economics and advanced game-theoretic tools. By the end of the course, students should understand how to model information structures, compare them using concepts such as Blackwell’s theorem, and analyze equilibrium concepts including correlated equilibrium, communication equilibrium and Bayes correlated equilibrium. They will also gain familiarity with cheap talk models, Bayesian persuasion, and recent developments linking communication motives with information design.

Myerson, Roger B. "Communication, correlated equilibria and incentive compatibility." Handbook of game theory with economic applications 2 (1994): 827-847.

Forges, Françoise. "Correlated equilibria and communication in games." Complex Social and Behavioral Systems: Game Theory and Agent-Based Models (2020): 107-118.

Bergemann, Dirk, and Stephen Morris. "Information design: A unified perspective." Journal of Economic Literature 57.1 (2019): 44-95.

Kamenica, Emir. "Bayesian persuasion and information design." Annual Review of Economics 11 (2019): 249-272.

The course covers major themes in education economics, starting with education expenditure, returns to education and education as an economic decision. It then examines school-related topics such as teachers, class size, resources and compensatory policies, followed by public versus private schools. Additional modules address the role of families, school choice and segregation, non-cognitive skills, peer effects and gender gaps in education and STEM.

The course aims to introduce students to key questions in the economics of education, including how individuals make schooling decisions and how teachers, schools, families and peers influence educational outcomes. Students are expected to learn how to read and interpret recent research and public debates in the field. They should also develop critical thinking skills and the ability to formulate theoretically grounded research questions.

Behaghel, L., Grenet, J., Gurgand, M. (2023). Économie de l’éducation, La Découverte.

Hanushek, E., Machin, S., Woessmann, L. (2023). Handbook of the Economics of Education.

The course covers mathematics for growth (optimal control, dynamic programming, bifurcation theory), standard and extended growth theories (optimal growth, externalities, resources), the One Health framework (demographics, pollution, pandemics), the political economy of populism, and finally the role of artificial intelligence in shaping new growth paradigms and potential existential risks.

The course aims to introduce students to dynamic models of economic growth and to the broad concept of capital in its physical, human, natural and social dimensions. It emphasizes the links between economic growth and One Health, showing how economic activity interacts with ecological systems, epidemiological dynamics and social structures.

This course requires an economics training at level M1, in particular in game theory and international trade. Mathematical skills at level L2 (optimization) are expected.

The course is organized into four main blocks: canonical game-theoretic models of participation in international environmental agreements and their extensions; empirical evaluation of such agreements through econometric studies; theory of trade agreements, including terms-of-trade externalities and the rationale for GATT/WTO; and empirical assessments of trade agreements. Teaching is shared across instructors, with students presenting empirical articles in several sessions.

The course aims to provide students with a theoretical and empirical understanding of international negotiations, with a focus on environmental agreements and trade treaties. Students should learn how to characterize successful agreements, evaluate their stability, and assess welfare and emission outcomes relative to non-cooperative benchmarks. They should also be able to identify potential weaknesses in agreements and detect whether such flaws appear in real-world cases.

Assessment is based on in-class presentations of research articles and a final exam.

Barrett (1994, 1997, 2005); Bagwell & Staiger (2001); Bayramoglu, Finus & Jacques (2018); Finus (2008); Finus & McGinty (2019); Habla & Winkler (2013); Maggi (2014); Marchiori, Dietz & Tavoni (2017); Pavlova & De Zeeuw (2013).

This course is both theoretical and empirical. It therefore presumes familiarity with basic economic theory (growth models, macroeconomic stabilization mechanisms, microeconomic principles).

It also requires some background in the econometrics of evaluation and panel data.
This course is complementary to the Development Microeconomics course (M1).

The course covers six thematic areas: structural transformation and the role of agriculture; mobility and trade frictions; development and the environment, including climate change, water scarcity, and resource extraction; labor markets in developing countries and informality; migration, remittances and the “new economics of migration”; and finally, aspirations, hope and poverty traps. Each topic combines stylized facts with recent theoretical and empirical contributions.

The course aims to equip students with advanced theoretical and empirical tools used in development economics. Students should become familiar with major debates and methodological approaches in the field, gain the ability to critically engage with key research articles, and learn to synthesize and communicate academic and policy-relevant findings.

Assessment consists of presentations and a written policy brief to be submitted. The course mixes lectures with student-led discussions of recent research.

Bustos et al. (2016); Gollin et al. (2013); Blakeslee et al. (2020); Greenstone & Jack (2015); Tombe & Zhu (2019);
Todaro & Smith (Economic Development); Fields (2011); Basu (1999); Maloney (2004);
Clemens & McKenzie (2014); De Haas (2012); Rapoport & Docquier (2006); Stark & Bloom (1985);
Genicot & Ray (2020); Bernard & Taffesse (2014); Lybbert & Wydick (2018)

The course covers a wide range of topics organized into six parts. It begins with foundational questions such as why people vote, then examines representation, political selection, accountability and party politics. It expands to politics beyond voting, including lobbying and protests, and then studies autocracies and political transitions. The final part focuses on technology and politics, addressing technological change, media, and emerging questions around AI and political systems (“AI-cracy”).

The course aims to provide students with a solid understanding of the main theoretical models of political economy and with the analytical tools needed to study political behavior, institutions and policy formation. Students are expected to learn how economists analyze political processes, how empirical strategies are used in the literature, and how to critically evaluate both classic and recent research. The course also strengthens students’ ability to situate research contributions within the broader field.

Assessment includes an in-class presentation (25 percent), class participation (25 percent, including debate moderation), and a research proposal (50 percent). Students must choose readings for presentation via a Google form and select a debate session. The research proposal includes a draft and a final version and must articulate a research question, literature gap, and methodological approach.

Prerequisites: master 1 level course in Macroeconomics.

Lecture 1 : Introduction ; The financing of the economy Lecture 2 : Finance, development, and economic growth Lecture 3 : Macroeconomic theories and finance Lecture 4 : Monetary policy, before and after the Great Financial Crisis Lecture 5 : External adjustment and global imbalances Lecture 6 : Financial crises Lecture 7 : Global financial cycle (1) : characterization and determinants Lecture 8 : Global financial cycle (2) : economic policies

Based on theoretical contributions but also empirical applications, the course provides an overview of the recent academic macro literature devoted to financial issues.

A the end of the course, students will have (i) a borad overview of the macro theoretical approaches to financial frictions and (ii) an accurate understanding of contemporaneous macro-financial dynamics and their policy implications. On the methodolgocial ground, the course will train students skills for reading and analyzing resarch papers. Student will be encouraged to evaluate and critique research within the field.

Arcand, J.-L., Berkes, E., and Panizza, U., 2015, “Too much finance?”, Journal of Economic Growth, 20:105–148. Bernanke, B., Gertler M. , and Gilchrist, S., 1999, “The financial accelerator in quantitative business cycle framework,” Handbook of Macroeconomics, ch. 21, Vol 1, part C. Gourinchas, P.-O., and Rey, H., 2014, “External Adjustment, Global Imbalances, Valuation Effects”, Handbook of International Economics, chap 10, Vol 4. Kiyotaki N. and Moore J., 1997,” Credit Cycles,” Journal of Political Economy, 105(2). Kumhof, M. , Ranciere, R. , Winant, P. , 2015. Inequality, leverage and crises: The case of endogenous default.” American Economic Review 105 (3), 1217–1245 Levine, R. (2005) Finance and growth: theory and evidence. Handbook of Economic Growth, ch. 12, pp. 866–934.

• Handbook of International Economics, volume 6, Chapters 1, 5, 6, and 7 (2022)

The course is organized into three main parts. The first part covers matching markets, including school choice, housing markets, kidney exchange and refugee resettlement. The second part introduces mechanism design, addressing optimal selling procedures, social choice, implementation, and optimal mechanisms. The third part applies these concepts to auction design, with case studies on online auctions, keyword auctions, and spectrum auctions.

The course aims to provide students with the theoretical foundations of market design and to equip them with the tools needed to initiate research projects in this area. Students should learn how markets can be structured to allocate scarce resources efficiently and fairly, both through price mechanisms and through non-price procedures such as matching. They are expected to understand how these tools apply to a wide range of economic policy fields, from school choice and housing to auctions for government bonds, energy markets, CO₂ permits, or digital platforms.

Assessment is based on writing a referee report for an academic paper, combined with participation in class. The paper may be theoretical, applied, or empirical. A reading list is provided for each lecture.

Bichler, M. (2017). Market Design, Cambridge University Press.
Börgers, T. (2015). An Introduction to the Theory of Mechanism Design, Oxford University Press.
Haeringer, G. (2019). Market Design: Auctions and Matching, MIT Press.
Krishna, V. (2009). Auction Theory, Academic Press.
Roth, A., Sotomayor, M. (1990). Two-Sided Matching, Cambridge University Press.

1. The course begins with core concepts: definitions and properties of networks, models of network formation, and games played on networks. It then covers applications of theoretical models to topics such as peer effects, information flows, and technology adoption. The second part focuses on empirical analysis, including the estimation of network externalities, the design and interpretation of network experiments, and contrasts between risk-sharing mechanisms and information transmission.

The course aims to provide students with a solid understanding of how networks shape economic behavior and outcomes. By the end of the course, students should be able to analyze key game-theoretical models of networks and understand the main econometric challenges involved in working with network data. They will gain the ability to connect theoretical models with empirical applications and interpret network effects in real-world economic settings.

Assessment is based on a written referee report on one selected paper. Depending on class size, short in-class presentations may also be organized and may account for up to 30 percent of the final grade.

Jackson, M. (2008). Social and Economic Networks, Princeton University Press.

Basic knowledge of international trade theories

• Good microeconomic and econometric skills

The course covers six main topics: an overview of spatial inequalities; the core-periphery model and mechanisms behind firm and household agglomeration; the bell-shaped pattern of spatial development; foundations of urban economic theory; the benefits and costs of city living (productivity, housing prices, pollution); and the evaluation of place-based policies. Each lecture combines theory with empirical evidence to explain the emergence, performance and policy challenges of cities and regions.

The course aims to train students to understand the economic forces driving spatial inequalities, city formation and regional agglomeration. By the end of the course, students should be able to analyze the costs and benefits of cities, apply theoretical tools from urban and regional economics, identify relevant spatial datasets, and implement econometric strategies to evaluate the causal impact of urban, housing and transport policies on spatial disparities.

Assessment consists of a written exam (50 percent) and a team project in which students write and defend a referee report on an unpublished research paper related to the course (50 percent).

Cheshire, Nathan & Overman (2014); Combes, Mayer & Thisse (2008); Crozet & Lafourcade (2009); Fujita & Thisse (2013); Glaeser (2012); Lafourcade & Mayneris (2017); O’Sullivan (2019); Zenou (2009).

• Extensions of the NK model: Sticky Wages; Two Agents models; Heterogenous Agents Models
• Adjustment Cost of Investment in RBC and NK Models
• The effects of Public Infrastructures Spending
• Public Debt and Fiscal Consolidation
• The impacts of an Energy Shock
• Topics in open and international Macroeconomics

By the end of the course, students will be able to:

• Construct and extend macroeconomic models starting from standard benchmark frameworks.
• Implement, simulate, and solve these models using computational tools such as Matlab, Dynare, and Python.
• Analyze model outcomes and use simulation results to address macroeconomic questions and policy issues.

The course combines lectures with hands-on computational sessions. Students work through model extensions step by step, learning to implement and simulate them using software tools such as Matlab, Dynare, and Python. Practical coding exercises, guided tutorials, and supervised problem-solving sessions enable students to develop autonomy in building and analyzing macroeconomic models.

Short project outlining the topic of the Master's thesis, the expected methodology and a short review of the literature.
This document, between 2 and 10 pages long, must be submitted to the thesis supervisor before the end of the first semester.

Students must produce a written document of 20 to 50 pages presenting an individual research project. The thesis should follow the structure of an academic article.

The research—whether theoretical or empirical—must fall within the field of economic analysis. It should rely on a clearly defined methodology and provide an original contribution to the topic studied. The work is carried out over the academic year under the supervision of a faculty member teaching in the Master’s program.

A thesis defence must take place before the end of the semester. The jury, composed of at least two faculty members from the Master, assigns the final grade.

As an alternative to the thesis, students may complete an internship and submit an internship report. The internship must be approved by the M2 coordinator.