Econometric Evaluation of Socio-Economic Programs

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 and regression-discontinuity-design and are given practical guidelines for selecting and applying suitable methods for specific policy contexts.

Presents an introduction to the econometrics of program evaluation Offers a step-by-step guide to modern econometric techniques for program evaluation Demonstrates real and simulated data samples to illustrate ex-post evaluation design Includes supplementary material: sn.pub/extras

Auteur

Giovanni Cerulli is researcher 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 both theoretical and applied econometrics of program evaluation, including dose-response models, program evaluation 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.

Contenu
Preface.- 1 An Introduction to the Econometrics of Program Evaluation.- 2 Methods Based on Selection on Observables.- 3 Methods Based on Selection on Unobservables.- 4 Local Average Treatment Effect and Regression-Discontinuity-Design.