Insegnamento a.a. 2022-2023


Department of Social and Political Sciences

Course taught in English
Go to class group/s: 24
PPA (8 credits - I sem. - OB  |  SECS-P/02)
Course Director:

Classes: 24 (I sem.)

Lezioni della classe erogate in presenza

Suggested background knowledge


Familiarity with basic algebra required and comfort with basic statistics

Mission & Content Summary


The course introduces students to the main tools used for data analysis and applied empirical research, focusing on identifying and estimating causal effects. Almost any work in empirical economics (and social science, in general) is about questions of cause and effect such as: Which are the economic returns of one additional year of schooling? Do democratic institutions promote economic development? Does imposing a female policy-maker through gender quotas cause a change in policy? Does raising the minimum wage cause employment to decrease? Do longer prison sentences deter crimes? While one would ideally run a randomized controlled experiment to answer these questions, this is often not possible. Therefore, special methods and techniques have been developed in social science research. The mission of the course is to provide background on issues that arise when analyzing social science data and a guide for tools that are useful for applied research. By the end of the course, students should have a firm grasp of the types of research design that can lead to convincing analysis and be able to go through the multiple stages of empirical research: searching for interesting questions, devising an appropriate research design, collecting the data, and implementing the analysis. The course includes practical sessions with Stata. Students who do not have an intermediate level experience with Stata are strongly adviced to attend the course 20684 STATA PREPARATORY COURSE.


  • The ideal experiment and the potential outcomes framework.
  • The simple linear regression model.
  • Randomized controlled trials
  • Instrumental variables
  • Panel data: fixed effects

  • Difference-in-Differences

  • Regression Discontinuity Design

  • Shift-share instruments

Intended Learning Outcomes (ILO)


At the end of the course student will be able to...
  • Understand the main econometric methods used in empirical research.
  • Identify the basic properties of estimators and the conditions under which they apply
  • Understand the principles behind applied empirical methods used in the social sciences

  • Structure sensible research hypotheses to answer specific research/policy questions.


At the end of the course student will be able to...
  • Develop experiments to test research hypotheses.
  • Choose a research design suitable for a given research question and compatible with the available data.
  • Apply statistical software to conduct regression analyses.

  • Interpret and present the findings of econometric analysis.

  • Critically engage with texts and journal articles which involve empirical work, recognizing the problems encountered when dealing with data in practice.

Teaching methods

  • Face-to-face lectures
  • Exercises (exercises, database, software etc.)
  • Group assignments


  • The learning experience in this course includes traditional and online lectures and class discussions.
  • The course syllabus will contain information on required readings, including a number of research papers.
  • Students will attend practice sessions to develop their knowledge of STATA.
  • Students will be randomly assigned to groups to develop a simple research project. We will provide a set of readily available datasets. Groups will identify a research question suitable for the data and then choose empirical methods effective to answer the research question.
  • Students will learn how to effectively summarize their research process and present their research project in class in front of their peers.

Assessment methods

  Continuous assessment Partial exams General exam
  • Written individual exam (traditional/online)
  x x
  • Group assignment (report, exercise, presentation, project work etc.)
  x x
  • Active class participation (virtual, attendance)
  • Peer evaluation

Teaching materials


Cunningham, Scott. ”Causal inference: The mixtape.” (2021).


Angrist, J., and S. Pischke. Mostly Harmless Econometrics (Princeton University Press, 2008).


Additional textbooks and readings will be indicated in the detailed and during the lectures.


A useful reference for applications in Stata is the following:


Cameron, C. and P.K.Trivedi. Microeconemetrics Using Stata, Revised Edition (Stata Press 2010).

Last change 10/06/2022 10:09