Anno accademico
2021/2022 Programmi anni precedenti
Titolo corso in inglese
Codice insegnamento
EM1420 (AF:339298 AR:181546)
In presenza
Crediti formativi universitari
Livello laurea
Laurea magistrale (DM270)
Settore scientifico disciplinare
1° Periodo
Anno corso
Spazio Moodle
Link allo spazio del corso
Policy evaluation can be divided in ex-ante and ex-post evaluations. This course will focus on the latter, namely present evaluations of past efforts to achieve policy goals. Prospective evaluation aims at estimating the causal-effects of policy interventions such as anti COVID-19 programs, anti poverty programs, anti obesity programs etc.
Students will learn how to identify the causal-effect of such policies and the reason why the causal effect cannot be identified by the data alone but rather needs identifying assumptions that come with substantive knowledge of the phenomenon under investigation, as well as an depth understanding of the research design. Students will be able to understand the advanced and most used methods,then use them to critically assess studies that focus on the effects of crucial policy reforms. We will look at popular questions in labor economics such as returns to schooling, health economics issues such as healthcare interventions, urban economics phenomena such as housing vouchers meant to analyse neighborhood effects, and many other policy issues.
Students are required to have knowledge of probability theory, inferential statistics, and linear regression models.
A an introduction to causal inference
Directed Acyclic Graphs: to understand the causal relational in policy
Policy evaluation via Regression Discontinuity approach:Regression discontinuity (RD) analysis is a rigorous non-experimental approach that can be used to estimate program impacts in situations in which candidates are selected for treatment based on whether their value for a numeric rating exceeds a designated threshold or cut-point. Over the last two decades, the regression discontinuity approach has been used to evaluate the impact of a wide variety of social programs.
Evaluating a reform using instrumental variables, the issue of weak instruments, homogeneous versus heterogeneous treatment effects: You will learn the methods of instrumental variables in order to attempt to assess a causal relationship between outcomes and treatments, and understand the issue of weak instruments. The methods will be used to study the case of parental methamphetamine abuse & foster care and the returns to schooling.
Exploiting longitudinal data and study policy interventions: You will learn methods that are able to exploit the spatio-tempo dimension of the phenomena under investigation.
The power of Difference-in Difference to study policy interventions: You will learn the quasi experimental identification strategy to estimate causal effects of different reforms
Policy evaluation through the lenses of Synthetic Control: You will learn the quasi experimental identification strategy to estimate causal effects of different reforms
Cunningham S. (2021) Causal Inference, Yale University Press
Angrist D. J and Pischke J.S (2009) Mostly Harmless Econometrics: An Empiricist's Companion Paperback
A selection of scientific papers will be provided.
The exam will be written and students must answer to all questions.
Lecturers will be delivered on site and material uploaded on the system.
Programma definitivo.
Data ultima modifica programma: 06/08/2021