Insegnamento a.a. 2020-2021

20607 - METHODS AND TOOLS FOR POLICY ANALYSIS

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:
HECTOR SOLAZ SANTOS

Classes: 24 (I sem.)
Instructors:
Class 24: HECTOR SOLAZ SANTOS


Suggested background knowledge

PREREQUISITES

Familiarity with basic algebra required and comfort with basic statistics

Mission & Content Summary

MISSION

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 unemployment to increase? Does listening to hate-speech on the radio make people more likely to participate to a genocide? Do longer prison sentences deter crimes? While one would ideally run a 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.

CONTENT SUMMARY

  • The ideal experiment and the potential outcomes framework.
  • The simple linear model.
  • Randomized controlled trials
  • Laboratory experiments
  • Instrumental variables

  • Panel data: fixed effects, Difference-in-Differences, synthetic control models

  • Regression Discontinuity Design

  • Additional topics (time permitting)


Intended Learning Outcomes (ILO)

KNOWLEDGE AND UNDERSTANDING

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

  • Recognize the experimental methods used in the social sciences

APPLYING KNOWLEDGE AND UNDERSTANDING

At the end of the course student will be able to...

  • Device a research design suitable for a given research question.

  • Develop experiments to test hypothesis.

  • 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

DETAILS

The learning experience in this course includes traditional lectures and class discussions. The course syllabus will contain information on required readings, including a number of research papers. Students will receive group projects covering the main topics in the syllabus (4-5 assignments). Students can work on group projects with others.


Assessment methods

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

ATTENDING STUDENTS

  • Written Exam (70% of the final grade): The exam can either be taken in two partials (35% each) or in one final exam at the end of the course.
  • Home assignments (20% of the final grade): The assignments develop the students’ ability to apply the methods taught during the course in practical situations emerging in data analysis.
  • Active class participation (10% of the final grade).

NOT ATTENDING STUDENTS

Written Exam (100% of the final grade)


Teaching materials


ATTENDING AND NOT ATTENDING STUDENTS

 

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.

 

In some of the assignments students will be asked to solve problems in Stata. 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 08/10/2020 17:17