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, regression-discontinuity-design, and synthetic control method, and are given practical guidelines for selecting and applying suitable methods for specific policy contexts.
The second revised and extended edition features two new chapters on some recent development of difference-in-differences. Specifically, chapter 5 introduces advanced difference-in-differences methods when many times are available and treatment can be either time-varying or fixed at a specific time. Chapter 6 introduces the synthetic control method, a treatment effect estimation approach suitable when only one unit is treated. Both chapters present applications using the software Stata.
Author(s): Giovanni Cerulli
Series: Advanced Studies in Theoretical and Applied Econometrics, 49
Edition: 2
Publisher: Springer
Year: 2022
Language: English
Pages: 388
City: Berlin
Preface to the Second Edition
Preface to the First Edition
Acknowledgments to the Second Edition
Acknowledgements to the First Edition
Contents
1 An Introduction to the Econometrics of Program Evaluation
1.1 Introduction
1.2 Statistical Setup, Notation, and Assumptions
1.2.1 Identification Under Random Assignment
1.2.2 A Bayesian Interpretation of ATE Under Randomization
1.2.3 Consequences of Nonrandom Assignment and Selection Bias
1.3 Selection on Observables and Selection on Unobservables
1.3.1 Selection on Observables (or Overt Bias) and Conditional Independence Assumption
1.3.2 Selection on Unobservables (or Hidden Bias)
1.3.3 The Overlap Assumption
1.4 Characterizing Selection Bias
1.4.1 Decomposing Selection Bias
1.5 The Rationale for Choosing the Variables to Control for
1.6 Partial Identification of ATEs: The Bounding Approach
1.7 A Guiding Taxonomy of the Econometric Methods for Program Evaluation
1.8 Policy Framework and the Statistical Design for Counterfactual Evaluation
1.9 Available Econometric Software
1.10 A Brief Outline of the Book
References
2 Methods Based on Selection on Observables
2.1 Introduction
2.2 Regression-Adjustment
2.2.1 Regression-Adjustment as Unifying Approach Under Observable Selection
2.2.2 Linear Parametric Regression-Adjustment: The Control-Function Regression
2.2.3 Nonlinear Parametric Regression-Adjustment
2.2.4 Nonparametric and Semi-parametric Regression-Adjustment
2.3 Matching
2.3.1 Covariates and Propensity-Score Matching
2.3.2 Identification of ATEs Under Matching
2.3.3 Large Sample Properties of Matching Estimator(s)
2.3.4 Common Support
2.3.5 Exact Matching and the ``Dimensionality Problem''
2.3.6 The Properties of the Propensity-Score
2.3.7 Quasi-exact Matching Using the Propensity-Score
2.3.8 Methods for Propensity-Score Matching
2.3.9 Inference for Matching Methods
2.3.10 Assessing the Reliability of CMI by Sensitivity Analysis
2.3.11 Assessing Overlap
2.3.12 Coarsened-Exact Matching
2.4 Reweighting
2.4.1 Reweighting and Weighted Least Squares
2.4.2 Reweighting on the Propensity-Score Inverse-Probability
2.4.3 Sample Estimation and Standard Errors for ATEs
2.5 Doubly-Robust Estimation
2.6 Implementation and Application of Regression-Adjustment
2.7 Implementation and Application of Matching
2.7.1 Covariates Matching
2.7.2 Propensity-Score Matching
2.7.3 An Example of Coarsened-Exact Matching Using cem
2.8 Implementation and Application of Reweighting
2.8.1 The Stata Routine treatrew
2.8.2 The Relation Between treatrew and Stata's teffects ipw
2.8.3 An Application of the Doubly-Robust Estimator
References
3 Methods Based on Selection on Unobservables
3.1 Introduction
3.2 Instrumental-Variables
3.2.1 IV Solution to Hidden Bias
3.2.2 IV Estimation of ATEs
3.2.3 IV with Observable and Unobservable Heterogeneities
3.2.4 Problems with IV Estimation
3.3 Selection-Model
3.3.1 Characterizing OLS Bias within a Selection-Model
3.3.2 A Technical Exposition of the Selection-Model
3.3.3 Selection-Model with a Binary Outcome
3.4 Difference-in-Differences
3.4.1 DID with Repeated Cross Sections
3.4.2 DID with Panel Data
3.4.3 DID with Matching
3.5 Implementation and Application of IV and Selection-Model
3.5.1 The Stata Command ivtreatreg
3.5.2 A Monte Carlo Experiment
3.5.3 An Application to Determine the Effect of Education on Fertility
3.5.4 Applying the Selection-Model Using etregress
3.6 Implementation and Application of DID
3.6.1 DID with Repeated Cross Sections
3.6.2 DID Application with Panel Data
References
4 Local Average Treatment Effect and Regression-Discontinuity-Design
4.1 Introduction
4.2 Local Average Treatment Effect
4.2.1 Randomization Under Imperfect Compliance
4.2.2 Wald Estimator and LATE
4.2.3 LATE Estimation
4.2.4 Estimating Average Response for Compliers
4.2.5 Characterizing Compliers
4.2.6 LATE with Multiple Instruments and Multiple Treatment
4.3 Regression-Discontinuity-Design
4.3.1 Sharp RDD
4.3.2 Fuzzy RDD
4.3.3 The Choice of the Bandwidth and Polynomial Order
4.3.4 Accounting for Additional Covariates
4.3.5 Testing RDD Reliability
4.3.6 A Protocol for Practical Implementation of RDD
4.4 Application and Implementation
4.4.1 An Application of LATE
4.4.2 An Application of RDD by Simulation
4.4.3 An Application of RDD to Real Data
References
5 Difference-in-Differences with Many Pre- and Post-Treatment Times
5.1 Introduction
5.2 The TVDIFF Model
5.2.1 Testing the ``Common Trend'' Assumption
5.2.2 An Application of the TVDIFF Model
5.3 The TFDIFF Model
5.3.1 Generalization to More Than Three Times
5.3.2 Testing the Parallel-Trend Assumption
5.3.3 An Application of the TFDIFF Model
5.4 Parallel-Trend Test in the Presence of Effect Anticipation
5.5 Conclusion
References
6 Synthetic Control Method
6.1 Introduction
6.2 The SCM Model
6.3 SCM Inference
6.4 Application
6.5 Conclusions
References
