Course 2019-2020 a.y.

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:
AUDINGA BALTRUNAITE

Classes: 24 (I sem.)
Instructors:
Class 24: AUDINGA BALTRUNAITE


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.
  • Instrumental variables.
  • Randomized controlled trials and natural experiments.
  • 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.

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.
  • 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.
  • Recognize challenges emerging in empirical research papers.

Teaching methods

  • Face-to-face lectures
  • Guest speaker's talks (in class or in distance)
  • Exercises (exercises, database, software etc.)
  • Individual assignments
  • Group assignments

DETAILS

The learning experience in this course includes traditional lectures and class discussions. The course syllabus contains information on required readings, including a number of research papers (they should be read in advance so as to facilitate active participation).

  • Some lectures are held by invited guest speakers, presenting their empirical research papers, primarily on topics in political economics and political science. The talks are followed by a discussion of empirical challenges faced when answering the research question and how they were addressed. This allow students to better grasp the practical applications of the empirical tools learned in class.
  • Students receive group assignments covering the main topics in the syllabus (4-5 assignments). Students can work on these assignments with others, but to receive credit every student should be able to master all questions tackled in the assignment.
  • Individual assignements are distributed on the voluntary basis for students willing to gain more credit for class participation. 

Assessment methods

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

ATTENDING STUDENTS

In order to evaluate the acquisition of the above-mentioned learning outcomes, the assessment of attending students comprises two components:

  1. Written exam (70% of the final grade). The exam can either be taken in two partial exams (counting 35% each) or in one final exam at the end of the course.
  2. Home assignment (20% of the final grade). The assignments develop students ability to apply the methods taught during the course in practical situations emerging in data analysis.
  3. Active class participation (10% of the final grade). 

NOT ATTENDING STUDENTS

In order to evaluate the acquisition of the above-mentioned learning outcomes, the assessment of non-attending students are based on the written exam (100%).


Teaching materials


ATTENDING STUDENTS

  • J. ANGRIST, S. PISCHKE, Mostly Harmless Econometrics, Princeton University Press, 2008.
  • Additional textbooks and readings are indicated in the detailed syllabus.
  • In some of the assignments students are asked to solve problems in Stata. A useful reference for applications in Stata is the following: C. CAMERON, P.K. TRIVEDI, Microeconemetrics Using Stata, Revised Edition, Stata Press, 2010.

NOT ATTENDING STUDENTS

  • J. ANGRIST, S. PISCHKE, Mostly Harmless Econometrics, Princeton University Press, 2008.
  • Additional textbooks and readings are indicated in the detailed syllabus.
Last change 05/06/2019 22:42