Empirical methods for inequality analysis

A.Y. 2021/2022
Overall hours
Learning objectives
The purpose of this course is to introduce the students to the empirical methods commonly used to analyze several forms of inequality --- e.g. income inequality, educational inequality, inequality in the labor market, inequality inside the firm --- and the policies that can be undertaken to tackle them. The course is structured in three modules. In a first, more theoretical, module students will learn the main methods used in counterfactual impact evaluation and policy evaluation. Each method will be illustrated through an application with the STATA software. In a second, more applied, module students will learn how to measure poverty and inequality and how to deal with inequality decomposition using STATA and several datasets (e.g., the Luxembourg Income Study, the Bank of Italy's Survey of Household Income and Wealth). The third module will be focused on the analysis of gender inequality. This module will provide an overview of the recent literature in economics on different dimensions of the gender gaps (e.g. the gender pay gap), with a focus on the econometric methods used to evaluate gender differentials and some policies aimed at mitigating them.
Expected learning outcomes
By the end of the course students must acquire the following skills: 1) learning the main concepts related to the measurement and analysis of inequality and of the policies aimed at reducing it; 2) learning how to use statistical software and real data to analyze inequality and evaluate public policies which may impact individuals and firms; 3) being able to critically read the academic literature and policy reports related to inequality.
Course syllabus and organization

Single session

Lesson period
First trimester
Lectures and classes will be delivered in Microsoft Teams in a dedicated channel named "Empirical methods for inequality analysis - 2021/22". Teaching material will be posted on the ARIEL website of the course.
Course syllabus
First module
In the first module students will be introduced to:
- Causation and Counterfactual Impact Evaluation (CIE) methods
- Using OLS to assess causality and its limitations
- Randomized Control Trials (RCTs)
- Instrumental Variables (IVs)
- Difference-in-Differences (DID)
- Regression Discontinuity Design (RDD)
and their implementation with STATA.

Second module
This module deals with inequality decomposition. It will take as a reference text the LIS Annual report (forthcoming September 2021) and will use the remote access Lissy system (https://www.lisdatacenter.org/) to replicate most of the analysis contained in the report.
- Measuring inequality and poverty (theory and statistical methods)
- Measuring inequality and poverty (empirical applications)
- Worldwide trends in inequality and poverty (chapter 1 of LIS AR)
- Replicating trends in LIS using Lissy (https://www.lisdatacenter.org/data-access/lissy/)
- Earnings inequality along various dimensions (education, gender and age) (chapter 2 of LIS AR)
- Replicating decompositions in LIS using Lissy (https://www.lisdatacenter.org/data-access/lissy/)
- Inequality in wages and in worked hours (paper with Penalosa and Vivan)
- Inequality in schooling and competences using PIAAC data (paper with Gualtieri)
- Inequality of opportunity (paper with Bussolo and Peragine)
- Inequality of opportunity measured in the Italian case using SHIW

Third module
This module will provide an overview of some recent literature in economics on different gender gaps dimensions, with a focus on econometric methods used to evaluate gender differentials and policies aimed at mitigating them.

- The gender gap in labor market outcomes: Trends across countries
- Traditional explanations: Human capital and discrimination
- Psychological traits
- Culture
- Glass ceiling and public policies
Prerequisites for admission
Students must be familiar with the main concepts learned in an undergraduate course in descriptive and inferential statistics. Taking this course after having passed "Advanced labour economics + personnel economics" and "Data analysis and statistics" is strongly advised.
Teaching methods
The course is based both on traditional lectures in which the main economic and statistical concepts related to inequality and policy evaluation are explained, and on more applied sessions in which students learn how to replicate existing empirical work using STATA and to set up and write a research paper or an analytical report. STATA is available to students through UNIMI's campus license.
Teaching Resources
First module
Angrist, J. and Pischke, J.-S. (2015), Mastering 'Metrics: The Path from Cause to Effect, MIT Press.
A reading list with papers/articles will be provided during the course and posted on the ARIEL website.

Second module
A reading list will be provided during the course and posted on the ARIEL website.

Third module
A reading list will be provided during the course and posted on the ARIEL website.
Assessment methods and Criteria
At the end of the course students must write a research paper or an analytical report on a given topic related to inequality. In the paper, students must apply some of the empirical methods they learned in the course. Both the paper with the final analysis and the dataset and programs that reproduce the results in the paper must be handed in. The paper can be single-authored or can be coauthored in groups of two or three students. The paper should contain a declaration of the contribution of each author to the whole analysis.
SECS-P/01 - ECONOMICS - University credits: 9
Lessons: 60 hours
Thursday 17.00-19.00 on MS Teams or in person unless stated differently in this website (please write to me for confirmation). This week office hours will be on Oct 19, 18-19.30. If slots are all occupied there will be an extra session on Oct. 20, same
MS Teams