Teaching
Quantitative Methods for Policy Evaluation
(Previously Called Policy Evaluation During a Crisis. Taught to Sciences Po Paris Undergraduates in the Spring of 2023 and 2025)
The 21st Century has witnessed a dramatic return of state interventionism in response to a series of sudden and unexpected crises – be that the 2008 financial crash, the COVID-19 pandemic, or the inflationary shock post the Ukrainian war. Yet it is often very hard to gauge the true effect of such government responses.
Using this crisis context as an overarching frame, this class seeks to introduce students to quantitative methods employed in evaluating the efficacy of public policy. In the first two weeks, the course focuses on the basics of working with data, emphasizing the importance of how data is designed (longitudinal or cross-sectional) as well as the level of data collection (ecological or individual). In weeks 3 to 8, the focus turns to empirical methodology: how researchers design natural experiments to estimate the average treatment effect of a policy. Beginning with simple OLS regression, the course advances to studying three basic approaches: difference-in-differences, regression discontinuity design, and instrumental-variables regression. Finally, during weeks 9 to 12, the course concentrates on examples of applied research. In this final part of the course, an examination will be made as to how researchers connect these methods with important debates in public policy and social theory.
Throughout the course, there is a strong emphasis on adopting a hands-on approach, using the R programming language. This course seeks to give students the basic tools to be able to: manipulate and clean data, perform simple analyses (both descriptive and analytical), as well as visualize their results in an aesthetically pleasing way. But more than that, this course tries to give students the confidence to explore R on their own, understanding that programming is a skill developed over many years through persistent practice.
Quantitative Methods I & II
(Taught with Martin Aranguren to students in the sociology master's at Sciences Po Paris in the Fall of 2023 and the Spring of 2024)
This course provides students with a set of skills to apply quantitative methods to diverse types of data in order to address sociological issues.
In the first semester, students learn to use the statistical language R and get acquainted with basic quantitative methods. They do not need a strong mathematical, statistical, or computing background to succeed in this course. Topics include data visualizations, summary statistics, controlled experiments vs. observational studies, sampling, testing for the independence of two variables; comparing means and an introduction to linear models.
In the second semester, students deepen their knowledge of linear modelling techniques. Topics include, OLS regression, logit and probit models, multi-level modelling, fixed effects modelling, and a light introduction to causal methods with observational data.
Race, Discrimination, and Racial Inequalities on Both Sides of the Atlantic
(TA for Mirna Safi, Tom Di Prete, and Marissa Thompson. Taught to Columbia and Sciences Po Paris Undergraduates in the Fall of 2023)
This course focuses on race, discrimination and racial inequalities. The course will address three key questions: (1) What is race as perceived in the U.S. and Europe, and what are the sources of racial inequalities? (2) What does social science research tell us about patterns and trends of racial inequalities? (3) What policies can alleviate racial inequalities? The course will systematically adopt comparative perspectives focusing on the North American and European contexts in particular. We will also address research on race and racial inequality within an interdisciplinary lens, particularly building on sociology, economics, and social psychology. We propose a one-semester course that is divided into three sections: concepts, evidence, and policy. The first four weeks will use a comparative approach to focus on understanding how race is defined and measured across the U.S. and European contexts, the mechanisms through which racial inequality persists in each context, and contemporary attitudes towards minority and immigrant groups. Next, we will examine evidence on racial inequality across six sectors: education, the labor market, income and wealth, policing and criminal justice, housing and space, and everyday discrimination. Readings in these weeks will encompass empirical work that has documented the extent to which structural inequality shapes opportunity and outcomes in both contexts. We integrate both academic articles and popular news pieces to illustrate these differences. Finally, we will include two weeks that focus on policy. The first policy session will focus on anti-discrimination laws and affirmative action policies, while the second session will feature a discussion/debate among students about how equity might best be achieved in each context.