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This book provides advanced theoretical and applied tools for the implementation of modern micro-econometric techniques in evidence-based program evaluation for the social sciences. The author presents a comprehensive toolbox for designing rigorous and effective ex-post program evaluation using the statistical software package Stata. For each method, a statistical presentation is developed, followed by a practical estimation of the treatment effects. By using both real and simulated data, readers will become familiar with evaluation techniques, such as regression-adjustment, matching, difference-in-differences, instrumental-variables, regression-discontinuity-design, and synthetic control method, and are given practical guidelines for selecting and applying suitable methods for specific policy contexts.
The second revised and extended edition features two new chapters on some recent development of difference-in-differences. Specifically, chapter 5 introduces advanceddifference-in-differences methods when many times are available and treatment can be either time-varying or fixed at a specific time. Chapter 6 introduces the synthetic control method, a treatment effect estimation approach suitable when only one unit is treated. Both chapters present applications using the software Stata.
List of contents
Chapter 1. An Introduction to the Econometrics of Program Evaluation.- Chapter 2. Methods Based on Selection on Observables.- Chapter 3. Methods Based on Selection on Unobservables.- Chapter 4. Local Average Treatment Effect and Regression-Discontinuity-Design.- Chapter 5. Di erence-in-di erences with many pre- and post-treatment times.- Chapter 6. Synthetic Control Method
About the author
Giovanni Cerulli is research director at CNR-IRCrES (National Research Council of Italy - Research Institute on Sustainable Economic Growth). He took a degree in Statistics and a PhD in Economic Sciences from Sapienza University of Rome. His research deals with two main subjects: causal inference (including program evaluation), and machine learning. He has developed both theoretical and applied econometric models for program evaluation, including dose-response models, treatment effect estimation with peer effects, and software development for quantitative evaluation purposes. He boasts a consolidated expertise in the evaluation of R&D and innovation policies. Giovanni Cerulli is editor-in-chief of the "International Journal of Computational Economics and Econometrics" (IJCEE), and coordinator of GRAPE (Research Group on the Analysis of Economic Policies). His publications have appeared in prestigious peer-reviewed scientific journals.
