Course 2019-2020 a.y.


Department of Economics

Course taught in English
Go to class group/s: 31
CLMG (6 credits - II sem. - OP  |  SECS-P/05) - M (6 credits - II sem. - OP  |  SECS-P/05) - IM (6 credits - II sem. - OP  |  SECS-P/05) - MM (6 credits - II sem. - OP  |  SECS-P/05) - AFC (6 credits - II sem. - OP  |  SECS-P/05) - CLELI (6 credits - II sem. - OP  |  SECS-P/05) - ACME (6 credits - II sem. - OP  |  SECS-P/05) - DES-ESS (6 credits - II sem. - OP  |  SECS-P/05) - EMIT (6 credits - II sem. - OP  |  SECS-P/05) - GIO (6 credits - II sem. - OP  |  SECS-P/05) - DSBA (6 credits - II sem. - OP  |  SECS-P/05) - PPA (6 credits - II sem. - OP  |  SECS-P/05) - FIN (6 credits - II sem. - OP  |  SECS-P/05)
Course Director:

Classes: 31 (II sem.)

Class-group lessons delivered  on campus

Suggested background knowledge

A basic knowledge of econometrics is strongly recommended. Basic knowledge on the use of STATA or similar computer software is also recommended.

Mission & Content Summary


This course covers theoretical and empirical developments on Microeconometrics, with a focus on program evaluation. It provides students with basic skills to conduct rigorous estimation of the impact of governmental/aid agencies programs. Moreover, it conveys the theoretical background to test implications or assumptions of microeconomic models and to understand empirical applications in several applied fields, such as development, labor, health and education. The course has an applied focus, but theoretical material is broadly covered. Estimation techniques and econometric theory are discussed during lecture; with each method being motivated by a series of empirical papers. The problem sets focus on helping students understand how these methods can be applied to real world data.


  • The course begins with a discussion about reduced form and structural form analysis, the counterfactual notion of causality and the differences between estimation and identification.
  • The main methodological part is devoted to the estimation of causal relationships, including experimental and non-experimental techniques (matching, instrumental variables, regression discontinuity and panel data).

Intended Learning Outcomes (ILO)


At the end of the course student will be able to...
  • Differentiate the notions of causality and correlation, identification and estimation.
  • Understand the assumptions behind the different impact evaluation methodologies.
  • Decide what empirical strategy to use when evaluating a program.
  • Learn how to recover causal relationships using experimental and non-experimental techniques.


At the end of the course student will be able to...
  • Be able to read empirical papers trying to estimate causal effects.
  • Use impact evaluation techniques to solve problems with real world data.
  • Conduct or help conduct rigorous estimation of the imapct of governmental/aid agencies programs.

Teaching methods

  • Face-to-face lectures
  • Exercises (exercises, database, software etc.)
  • Case studies /Incidents (traditional, online)
  • Group assignments


  • Exercises (Exercises, database, software etc.):  students have to solve 4 problem sets in groups of 5. Problem sets consist on applying the methods studied in class to the analysis of real world data (datasets are provided) using the statistical software Stata.  The tutor of the class helps students learn advanced features of this statistical software.
  • Case Studies:  after the discussion of the theory behind each methodology, we cover an empirical paper using that methodology to answer relevant policy questions.
  • Group Assignments: students present one recent working paper that covers the methods studied in class, and all the class discusses the assumptions and contributions.

Assessment methods

  Continuous assessment Partial exams General exam
  • Written individual exam (traditional/online)
  • Group assignment (report, exercise, presentation, project work etc.)


Final Exam (50%):

  • Students are allowed to bring to the exam up to 3 sheets of paper (up to A4 size) written on the two sides with anything they want.

Problem sets (40%):

  • 4 problem sets.
  • All of them must be submitted.
  • Can be prepared and submitted in groups of 5 students.

Presentation (10%):

  • One presentation in Groups of 5 students. Students can choose one of the applied topics (Experiments, Matching, IV, RDD, DID, Standard Errors) to present a paper of their choice that uses the discussed methodology.

Teaching materials


  • The main compulsory material is based on Lecture Notes provided for each topic and posted online.
  • For each topic, a long list of recently published papers is provided (all of them are optional)
  •  We discuss material from the following textbooks (Most of the material in AP and IR is relevant for the course, but not compulsory. We cover only selected chapters of CT and W).

  • J. ANGRIST, J. PISCHKE, Mostly Harmless Econometrics, Princeton University Press. [AP], 2009.
  • G. IMBENSI, D. RUBIN,  Causal Inference for Statistics, Social and Biomedical Sciences. An Introduction, University Press. [IR], 2015.
  • CAMERON, A. COLIN, K. PRAVIN, TRIVEDI, Microeconometrics. Methods and Applications, Cambridge University Press, New York. [CT], 2005. 
  • CAMERON, A. COLIN, K. PRAVIN, TRIVEDI, Microeconometrics Using Stata, Stata Press, 2009.
  • J. WOOLDRIGE,  Econometric Analysis of Cross Section and Panel Data, MIT Press, 2nd Edition. [W], 2010. 
Last change 15/04/2019 12:02