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[url=] | Handbook on Impact Evaluation: Quantitative Methods and Practice | ||||||||||
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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 View table of contents >> ForewordPreface 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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