000			05323nam a2200157Ia 4500
020			_a0821380281
082			_a001.42 _bKHA
100			_aKhandker, Shahidur & others
245			_aHandbook on impact evaluation : quantitative methods and practices
260			_aWashington D.C. _bWorld Bank _c2010
300			_axx,239p.
500			_aCONTENTS Forewordxiii Preface xv About the Authors xvii Abbreviations xix Part 1 Methods and Practices 1 1. Introduction 1 References 6 2. Basic Issues of Evaluation 7 Summary 1 Learning Objectives 1 Introduction: Monitoring versus Evaluation 8 Monitoring 8 Setting Up Indicators within an M&E Framework 9 Operational Evaluation 16 Quantitative versus Qualitative Impact Assessments 18 Quantitative Impact Assessment: Ex Post versus Ex Ante Impact Evaluations 20 The Problem of the CounterfactuaL 22 Basic Theory of Impact Evaluation: The Problem of Selection Bias 25 Different Evaluation Approaches to Ex Post Impact Evaluation 27 Overview: Designing and Implementing Impact Evaluations 28 Questions 29 References 30 3. Randomization 33 Summary 33 Learning Objectives 33 Setting the34 Statistical Design of Randomization 34 Calculating Treatment Effects 35 Randomization in Evaluation Design: Different Methods of Randomization 38 Concerns with Randomization 38 Randomized Impact Evaluation in Practice 39 Difficulties with Randomization 47 Questions 49 Notes 51 References 51 4. Propensity Score Matching 53 Summary 53 Learning Objectives 53 PSM and Its Practical Uses 54 What Does PSM Do? 54 PSM Method in Theory 55 Application of the PSM Method 58 Critiquing the PSM Method 63 PSM and Regression-Based Methods 64 Questions 66 Notes 67 5. Double Difference 71 Summary 71 Learning Objectives 71 Addressing Selection Bias from a Different Perspective : Using Differences as Counterfactual 71 DD Method: Theory and Application 72 Advantages and Disadvantages of Using DD 76 Alternative DD Models 78 Questions 82 Notes 84 References. 84 6. Instrumental Variable Estimation 87 Summary 87 Learning Objectives 87 Introduction .87 Two-Stage Least Squares Approach to IVs 89 Concerns with IVs 91 Sources of IVs 95 Questions 99 Notes 100 References 100 7. Regression Discontinuity and Pipeline Methods 103 Summary 103 Learning Objectives 103 Introduction 104 Regression Discontinuity in Theory 104 Advantages and Disadvantages of the RD Approach 108 Pipeline Comparisons 110 Questions 111 References 112 8. Measuring Distributional Program Effects 115 Summary 115 Learning Objectives 115 The Need to Examine Distributional Impacts of Programs 115 Examining Heterogeneous Program Impacts : Linear Regression Framework 116 Quantile Regression Approaches 118 Discussion: Data Collection Issues 124 Notes 125 References 125 9. Using Economic Models to Evaluate Policies 127 Summary 127 Learning Objectives 127 Introduction 127 Structural versus Reduced-Form Approaches 128 Modeling the Effects of Policies 130 Assessing the Effects of Policies in a Macroeconomic Framework 131 Modeling Household Behavior in the Case of a Single Treatment : Case Studies on School Subsidy Programs 133 Conclusions 135 Note 136 References 137 10. Conclusions 139 Part 2 Stata Exercises 143 11. Introduction to Stata 145 Data Sets Used for Stata Exercises 145 Beginning Exercise: Introduction to Stata 146 Working with Data Files: Looking at the Content 151 Changing Data Sets 158 Combining Data Sets 162 Working with .log and .do Files 164 12. Randomized Impact Evaluation 171 Impacts of Program Placement in Villages 171 Impacts of Program Participation 173 Capturing Both Program Placement and Participation 175 Impacts of Program Participation in Program Villages 176 Measuring Spillover Effects of Microcredit Program Placement 177 Further Exercises 178 Notes 179 13. Propensity Score Matching Technique 181 Propensity Score Equation: Satisfying the Balancing Property 181 Average Treatment Effect Using Nearest-Neighbor Matching 185 Average Treatment Effect Using Stratification Matching 186 Average Treatment Effect Using Radius Matching 186 Average Treatment Effect Using Kernel Matching 187 Checking Robustness of Average Treatment Effect 187 Further Exercises 188 Reference 188 14. Double-Difference Method 189 Simplest Implementation: Simple Comparison Using ttest 189 Regression Implementation 190 Checking Robustness of DD with Fixed-Effects Regression 192 Applying the DD Method in Cross-Sectional Data 193 Taking into Account Initial Conditions 196 The DD Method Combined with Propensity Score Matching 198 Notes 201 Reference 201 15. Instrumental Variable Method 203 IV Implementation Using the ivreg Command 203 Testing for Endogeneity: OLS versus IV .205 IV Method for Binary Treatment: treatreg Command 206 IV with Fixed Effects: Cross-Sectional Estimates 207 IV with Fixed Effects: Panel Estimates 208 Note 209 16. Regression Discontinuity Design 211 Impact Estimation Using RD 211 Implementation of Sharp Discontinuity 212 Implementation of Fuzzy Discontinuity .214 Exercise 216 Answers to Chapter Questions 217 Appendix: Programs and .do Files for Chapter 12-16 Exercises 219 Index 231
890			_aUnited States
891			_aSP/IAPM
999			_c14044 _d14044
650			_aValue analysis (Cost control) _aEconomic assistance--Evaluation _aEconomic development projects--Evaluation
650			_aUrbanization -- India
650			_aUrban policy -- India
650			_aAménagement du territoire -- Inde
650			_aUrbanisation -- Inde.
650			_aPolitique urbaine -- Inde.