Impact Evaluation of Development Interventions
A Practical Guide
Published on 1. December 2017
216 pages
978-92-9261-059-3 (ISBN)
Description
Impact evaluation is an empirical approach to estimating the causal effects of interventions, in terms of both magnitude and statistical significance. Expanded use of impact evaluation techniques is critical to rigorously derive knowledge from development operations and for development investments and policies to become more evidence-based and effective. To help backstop more use of impact evaluation approaches, this book introduces core concepts, methods, and considerations for planning, designing, managing, and implementing impact evaluation, supplemented by examples. The topics covered range from impact evaluation purposes to basic principles, specific methodologies, and guidance on field implementation. It has materials for a range of audiences, from those who are interested in understanding evidence on "what works" in development, to those who will contribute to expanding the evidence base as applied researchers.
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Howard White | David A. Raitzer | Asian Development Bank
Impact Evaluation of Development Interventions
A Practical Guide
Book
12/2017
Asian Development Bank
€54.70
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Content
- Cover
- Title
- Copyright
- Contents
- Tables, Figures, and Boxes
- Foreword
- Acknowledgments
- Abbreviations
- 1. Introduction: Impact Evaluation for Evidence-Based Development
- 1.1 Why Does Impact Evaluation Matter?
- 1.2 The Purposes of Impact Evaluation
- 1.3 What Questions Can Impact Evaluation Answer?
- 1.4 Impact Evaluation in the Project Cycle
- 1.5 Impact Evaluation, Evaluation, and Economic Analysis
- 1.6 About This Book
- 2. Using Theories of Change to Identify Impact Evaluation Questions
- 2.1 Introduction
- 2.2 What Is a Theory of Change?
- 2.3 Issues Raised by the Theory of Change
- 2.4 Constructing the Theory of Change
- 2.5 Applying the Theory of Change
- 3. The Core Concepts of Impact Evaluation
- 3.1 What Is Impact Evaluation?
- 3.2 Identifying Control and Comparison Groups
- 3.3 Biases and Challenges for Causal Inference
- 3.4 Time Dimension of Impacts
- 3.5 Unit of Assignment, Treatment, and Analysis
- 3.6 Different Impact Measures - Impacts for Whom?
- 3.7 Internal and External Validity
- 4. Randomized Controlled Trials
- 4.1 Introduction
- 4.2 Why Randomize?
- 4.3 Types of Randomized Controlled Trial Designs
- 4.4 Steps in Implementing a Randomized Controlled Trial
- 4.5 Reporting Randomized Controlled Trials
- 4.6 Working with Randomized Controlled Trials in Practice
- 4.7 Summary
- 5. Nonexperimental Designs
- 5.1 Introduction
- 5.2 Difference-in-Differences Estimates
- 5.3 Synthetic Controls
- 5.4 Propensity Score Matching
- 5.5 Propensity Score Weighting and Double Robust Techniques
- 5.6 Regression Discontinuity Design and Interrupted Time Series
- 5.7 Instrumental Variables
- 5.8 Endogenous Treatment and Control Function Approaches
- 5.9 Endogenous Switching Regressions
- 5.10 Summary
- 6. What and How to Measure: Data Collection for Impact Evaluation
- 6.1 The Importance of High-Quality Data
- 6.2 Determining What Data Are Needed
- 6.3 Data Sources
- 6.4 Designing a Survey
- 6.5 Sampling
- 6.6 Managing Data Collection
- 7. Sample Size Determination for Data Collection
- 7.1 Power Calculations: An Introduction
- 7.2 What Is Power?
- 7.3 Power Calculations for Simple Study Designs
- 7.4 Power Calculations for Cluster Designs
- 7.5 The Danger of Uwnderpowered Studies
- 7.6 Aids for Considering and Performing Power Calculations
- 8. Managing the Impact Evaluation Process
- 8.1 Introduction to Managing Impact Evaluations
- 8.2 Planning Impact Evaluations
- 8.3 Selecting an Impact Evaluation Design
- 8.4 Timing and Budgeting for Impact Evaluation
- 8.5 Where Help for Impact Evaluation Can Be Obtained
- 8.6 Interpreting and Presenting Impact Evaluation Findings
- 8.7 Conclusions: Twoard Evidence-Based Directions for Development
- Appendix 1. Application of Estimation Methods for Impact Evaluation
- Section 1: The Potential Outcomes Framework
- Section 2: Randomized Controlled Trials
- Section 3: Difference-in-Differences and Fixed Effects Models
- Section 4: Synthetic Controls
- Section 5: Propensity Score Based Approaches (Matching, Weighting, and Double Robust Estimators)
- Section 6: Instrumental Variables Based Approaches (Two-Stage Least Squares, Endogenous Treatment Regressions, and Endogenous Switching Regressions)
- Section 7: Regression Discontinuity Design
- Appendix 2. Designing Field Surveys
- Section 1: Types of Data Needed for Impact Evaluations
- Section 2: Sampling and Representation
- Section 3: Additional Considerations for Sample Size and Power Calculations
- Section 4: Survey Instrument Design
- Section 5: Survey Implementation
- Section 6: Data Management
