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Econometric Evaluation of Socio-Economic Programs, Second Edition

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Author:	Giovanni Cerulli
Publisher:	Springer
Copyright:	2022
ISBN-13:	978-3-662-65944-1
Pages:	376; hardcover

Author:	Giovanni Cerulli
Publisher:	Springer
Copyright:	2022
ISBN-13:
Pages:	376; eBook
Price:	$0.00

Author:	Giovanni Cerulli
Publisher:	Springer
Copyright:	2022
ISBN-13:
Pages:	376; Kindle
Price:	$

Comment from the Stata technical group

Econometric Evaluation of Socio-Economic Programs by Giovanni Cerulli, is an excellent introduction to estimating treatment effects from observational data. This book provides readers with the theoretical and practical knowledge required to design rigorous and precise program evaluations. The statistical models that stem from each evaluation technique are thoroughly discussed and followed by empirical examples illustrating their implementation in Stata. The wide array of models covered can be fit with the commands teffects, etregress, etpoisson, eteffects, didregress, and hdidregress. They can also be fit with the community-contributed commands treatrew and ivtreatreg.

The book first overviews the econometrics of program evaluation and the concept of treatment effect. It then shows how to estimate treatment effects under the assumption of conditional independence between treatment and potential outcomes. Methods that drop this assumption are presented next, thereby allowing for endogenous treatment effects. The following chapters cover more advanced topics, including local average treatment effects and regression discontinuity designs. The second edition features two new chapters on advanced difference-in-differences methods and synthetic control methods.

The author provides a nice mix of intuition, mathematics, and Stata examples. Professors, graduate students, practitioners, and researchers will find this book useful in the classroom and for self-study in preparation for research projects.

View table of 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 13 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

Econometric Evaluation of Socio-Economic Programs, Second Edition

Comment from the Stata technical group

Table of contents

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Econometric Evaluation of Socio-Economic Programs, Second Edition

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Table of contents

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