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Handbook on Impact Evaluation: Quantitative Methods and Practice

Authors:
Shahidur R. Khandker, Gayatri B. Koolwal, and Hussain Samad
Publisher: World Bank
Copyright: 2010
ISBN-13: 978-0-8213-8028-4
Pages: 280; ebook

View the electronic edition

Comment from the Stata technical group

In Handbook on Impact Evaluation: Quantitative Methods and Practices, Shahidur Khandker, Gayatri Koolwal, and Hussain Samad provide an excellent, relatively nontechnical introduction to the estimation and interpretation of treatment effects. While this book is aimed at practitioners in development economics, it will also be useful to graduate students and researchers who need background and intuition for the modern, more technical literature. The authors also provide a succinct introduction to Stata for the purpose of estimating treatment effects.

To illustrate the technical points, Khandker, Koolwal, and Samad use many case studies and intuitive examples drawn both from their own work and from development economics. The authors' ability to make technical information easy to understand is a definite strength of this book.

The breadth of coverage is impressive: after distinguishing quantitative impact evaluation from other forms of program evaluation, the authors provide introductions to the counterfactual model, the random-assignment model, propensity-score matching for selection on observables, and several methods for selection on unobservables.

The authors do not address many important technical details, but each chapter contains a good bibliography for readers who need more information. The authors provide a high-level introduction that will serve as an excellent outline or jump-off point per the reader’s needs.


Table of contents

Foreword
Preface
About the Authors
Abbreviations
Part 1   Methods and Practices
1. Introduction
References
2. Basic Issues of Evaluation
Summary
Learning Objectives
Introduction: Monitoring versus Evaluation
Monitoring
Setting Up Indicators within an M&E Framework
Operational Evaluation
Quantitative versus Quantitative Impact Assessments
Quantitative Impact Assessment: Ex Post versus Ex Ante Impact Evaluations
The Problem of the Counterfactual
Basic Theory of Impact Evaluation: The Problem of Selection Bias
Different Evaluation Approaches to Ex Post Impact Evaluation
Overview: Designing and Implementing Impact Evaluations
Questions
References
3. Randomization
Summary
Learning Objectives
Setting the Counterfactual
Statistical Design of Randomization
Calculating Treatment Effects
Randomization in Evaluation Design: Different Methods of Randomization
Concerns with Randomization
Randomized Impact Evaluation in Practice
Difficulties with Randomization
Questions
Notes
References
4. Propensity Score Matching
Summary
Learning Objectives
PSM and Its Practical Uses
What Does PSM Do?
PSM Method in Theory
Application of the PSM Method
Critiquing the PSM Method
PSM and Regression-Based Methods
Questions
Notes
References
5. Double Difference
Summary
Learning Objectives
Addressing Selection Bias from a Different Perspective: Using Differences as Counterfactual
DD Method: Theory and Application
Advantages and Disadvantages of Using DD
Alternative DD Models
Questions
Notes
References
6. Instrumental Variable Estimation
Summary
Learning Objectives
Introduction
Two-Stage Least Squares Approach to IVs
Concerns with IVs
Sources of IVs
Questions
Notes
References
7. Regression Discontinuity and Pipeline Methods
Summary
Learning Objectives
Introduction
Regression Discontinuity in Theory
Advantages and Disadvantages of the RD Approach
Pipeline Comparisons
Questions
References
8. Measuring Distributional Program Effects
Summary
Learning Objectives
The Need to Examine Distributional Impacts of Programs
Examining Heterogeneous Program Impacts: Linear Regression Framework
Quantile Regression Approaches
Discussion: Data Collection Issues
Notes
References
9. Using Economic Models to Evaluate Policies
Summary
Learning Objectives
Introduction
Structural versus Reduced-Form Approaches
Modeling the Effects of Policies
Assessing the Effect of Policies in a Macroeconomic Framework
Modeling Household Behavior in the Case of a Single Treatment: Case Studies on School Subsidy Programs
Conclusions
Note
References
10. Conclusions
Part 2   Stata Exercises
11. Introduction to Stata
Data Sets Used for Stata Exercise
Beginning Exercise: Introduction to Stata
Working with Data Files: Looking at the Content
Changing Data Sets
Combining Data Sets
Working with .log and .do Files
12. Randomized Impact Evaluation
Impacts of Program Placement in Villages
Impacts of Program Participation
Capturing Both Program Placement and Participation
Impacts of Program Participation in Program Villages
Measuring Spillover Effects of Microcredit Program Placement
Further Exercises
Notes
13. Propensity Score Matching Technique
Propensity Score Equation: Satisfying the Balancing Property
Average Treatment Effect Using Nearest-Neighbor Matching
Average Treatment Effect Using Stratification Matching
Average Treatment Effect Using Radius Matching
Average Treatment Effect Using Kernel Matching
Checking Robustness of Average Treatment Effect
Further Exercises
Reference
14. Double-Difference Method
Simplest Implementation: Simple Comparison Using “ttest”
Regression Implementation
Checking Robustness of DD with Fixed-Effects Regression
Applying the DD Method in Cross-Sectional Data
Taking into Account Initial Conditions
The DD Method Combined with Propensity Score Matching
Notes
Reference
15. Instrumental Variable Method
IV Implementation Using “ivreg” Command
Testing for Endogeneity: OLS versus IV
IV Method for Binary Treatment: “treatreg” Command
IV with Fixed Effects: Cross-Sectional Estimates
IV with Fixed Effects: Panel Estimates
Note
16. Regression Discontinuity Design
Impact Estimation Using RD
Implementation of Sharp Discontinuity
Implementation of Fuzzy Discontinuity
Exercise
Answers to Chapter Questions
Appendix: Programs and .do Files for Chapters 12–16 Exercises
Index
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