Econometrics of Program Evaluation Using Stata

Delivered by Dr. Giovanni Cerulli

Course Overview

This course equips participants with both theoretical insights and practical skills essential for effectively applying modern micro-econometric methods in policy evaluation and causal counterfactual modeling. It addresses scenarios involving "selection on observables" and "selection on unobservables" assumptions. The covered methodologies include Regression adjustment (both parametric and nonparametric), Matching (on covariates and propensity score), Reweighting, Double-robust methods, and Difference-in-differences methods.

Upon completion of the course, participants will have the capability to independently design and manage evaluations, considering both observable and unobservable selection biases. This includes identifying the policy framework, gathering and handling relevant datasets, employing suitable econometric methods, and interpreting results.

The practical applications of these skills extend across diverse policy contexts, such as finance and banking, the labor market, enterprise investment activities, education policy, regional cooperation, and incentives for business research and development. Importantly, the methodologies learned in the course are versatile and can be applied to any field of study – such as medicine, epidemiology and health-related disciplines – aiming to estimate the post-implementation impact of a specific intervention/treatment on targeted outcomes, especially when setting perfect randomization is difficult or impossible.

The course will provide various instructional examples on real datasets.

Learning Outcomes:

• Theoretical and Applied Understanding of Micro-econometric Methods:
• Participants will gain both theoretical and applied knowledge of modern micro-econometric methods for policy evaluation and causal counterfactual modeling. This includes a deep understanding of approaches such as regression adjustment (parametric and nonparametric), matching (on covariates and on propensity score), reweighting, double-robust methods, and difference-in-differences methods. 

• Competence in Evaluation Design and Dataset Management:
• Participants will be equipped with the skills to set up and manage a correct evaluation design under assumptions of both "selection on observables" and "selection on unobservables." This involves the identification of the policy framework, collection and management of suitable datasets, and the use of appropriate econometric methods.

• Interpretation of Results and Application Across Policy Contexts:
• The course will enable participants to interpret results effectively, fostering the ability to apply micro-econometric methods in various policy contexts. Potential applications include finance and banking, labor markets, enterprise investment activities, education policy, regional cooperation, and incentives for business research and development. Also, the learnt  methodologies can be applied to several types of medical trials in which perfect randomization is not achievable.

Real-world applications:

• Informed Policy Decision-Making:
• Participants will be equipped to contribute to informed policy decision-making by applying micro-econometric methods. This includes estimating the ex-post impact of policy interventions in diverse fields such as finance, labor markets, education, and regional cooperation. 

• Applying Techniques in Various Policy Contexts:
• The course's coverage of different micro-econometric methods allows participants to apply these techniques across a wide range of policy contexts. This versatility enables them to address the specific challenges associated with estimating the impact of policy interventions in various fields. 

• Use of Real Datasets for Practical Examples:
• The course provides practical examples using real datasets, allowing participants to bridge theoretical knowledge with real-world applications. This hands-on experience enhances their ability to implement micro-econometric methods in practical settings.