20607 - METHODS AND TOOLS FOR POLICY ANALYSIS

Basic knowledge of algebra, statistics and of the statistical software STATA required

- The course introduces students to the main tools used for data analysis and applied empirical research, focusing on identifying and estimating causal effects. Most work in empirical social sciences 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. - By the end of the course, students should have a firm grasp of research designs 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 blends in some basic econometric theory, reading of empirical papers and application with STATA. - Students who do not have an intermediate level experience with Stata and Statistics should attend the course 20684 STATA PREPARATORY COURSE and 20356 PRECORSO DI STATISTICA / STATISTICS - PREPARATORY COURSE in August.

• The ideal experiment and the potential outcomes framework.
• The simple linear regression model.
• Instrumental variables

• Panel data: fixed effects

• Difference-in-Differences

• Regression Discontinuity Design

• Shift-share instruments

• 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.

• Prepare and manipulate data for empirical analysis
• Choose a research design suitable for a given research question and compatible with the available data.

• Apply and code 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.

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

• Each teaching session will contemporaneously blend in basic econometric theory, a discussion of empirical papers and practical applications with STATA.
• The course syllabus will contain information on required readings, including a number of research papers.
• Students will be randomly assigned to groups to develop a simple research project. We will provide a set of readily available datasets. Groups will autonomously 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.
• Students will write a paper draft summarizing their project

Assessments will be written and will make no difference between attending and non-attending students. Exams will take place in person on paper.

Questions and exercises will focus on the understanding of econometric concepts, empirical evidence and intuition, as opposed to memorization of mathematical formulas. You are expected to be able to apply the empirical methods we learn in class to new empirical contexts in the exam, to read and interpreted analysis outputs generated by STATA, to type STATA commands aimed at performing given analyses, to choose which method is most appropriate for a given empirical context and research question, to give policy suggestions based on given empirical evidence.

Exams will cover also STATA coding, the content of academic papers and of the in-class presentations of your classmates.

Two options available for the assessment, based on student’s preference:

1. Written Exam + Research project in team: The exam can either be taken in two partials or in one final exam at the end of the course. Research project in team will be evaluated with an in-class presentation of the research project plus the grading of a draft and of the STATA code used to generate the results. If your presentation score ends up being below your score in the written exam, then the written exam is worth 100%. Therefore, if you decide to present you can only improve the score obtained in the written exam.
    
2. Written exam (either two partial exams or one comprehensive exam) worth 100% of final score.

Cunningham, Scott. ”Causal inference: The mixtape.” (2021). https://mixtape.scunning.com/

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

A useful reference for applications in Stata is the following:

Cameron, C. and P.K. Trivedi. Microeconometrics Using Stata, First or Second Edition (Stata Press 2010, 2022). Available at Bocconi library.

For Students with little previous experience in empirical data analysis or for students who want to review basic statistical concepts and data analysis fundamentals, I strongly recommend to regularly refer to the following book:

A. Colin Cameron. ANALYSIS OF ECONOMICS DATA: AN INTRODUCTION TO ECONOMETRICS (2022). Hard copy available at Bocconi library and Pdf available at  https://cameron.econ.ucdavis.edu/aed/ for  USD 6.99