96359 - DATA SCIENCE FOR POLICY ANALYSIS

Conoscenze e abilità da conseguire

The aim of the course is to build capacity in using data to inform evidence-based decision-making. The course will provide students with a broad overview of tools and methods for data analysis and applied empirical research, with a particular focus on the estimation of causal effect of public policies. Students will analyze real-word datasets and will be guided through case studies from a variety of policy domains. By the end of the course students will be able to perform a basic – yet rigorous – analysis of data to better understand policy choices. They will gain enough data science literacy to interpret and judge the quality of existing empirical research and to communicate the results to decision makers and the public.

Contenuti

The main topics are:

• What is a policy evaluation: ex-ante and ex-post evaluation
• Monitoring, outcome, output and impact evaluation
• Observed, experimental and quasi-experimental data
• Steps in conducting an evaluation analysis
• Counterfactual
• Conditioning and instrumental variables
• Regression Discontinuity Design
• Difference-in-difference methods
• Propensity Score Matching method

 

Note on prerequisites: students are required to have a foundational understanding of the R programming language and basic concepts of simple linear regression models. These prerequisites ensure that participants can effectively engage with the course material and fully benefit from the advanced topics covered.

Testi/Bibliografia

The course is mainly based on:

• Gertler, Paul J., Sebastian Martinez, Patrick Premand, Laura B. Rawlings, and Christel M. J. Vermeersch. Impact Evaluation in Practice, second edition. Washington, DC: Inter-American De velopment Bank and World Bank, 2016

Free download available at: https://www.worldbank.org/en/programs/sief-trust-fund/publication/impact-evaluation-in-practice

Other books:

• Angrist, Joshua D., and Jörn-Steffen Pischke. Mastering 'Metrics: The Path from Cause to Effect. Princeton University Press, 2014.
• Cunningham, S. (2021). Causal inference: The mixtape. Yale University Press.

Other useful materials, such as lecture notes and chapters/articles, will be provided through the e-learning platform Virtuale.

Metodi didattici

The course consists of a combination of theoretical lectures, applied case studies and quantitative tutorials.

Modalità di verifica e valutazione dell'apprendimento

Despite the course being split into two modules, there is a single grade.

During the course, students will be provided with available datasets and will be required to write an essay dealing with the data analysis and result interpretation.

The essay must be completed solely by groups consisting of one to three students. It must be submitted by the official day of the exam.

The essay template is available on Virtuale.

The grade will take into account the completeness of the analysis, the appropriateness of the terminology used and the clarity of the results.

Strumenti a supporto della didattica

Additional materials (such as scientific papers, slides, datasets..) will be provided during the lessons and the e-learning platform Virtuale will enable access to these contents.

Orario di ricevimento

Consulta il sito web di Meri Raggi

Consulta il sito web di Silvia De Nicolò