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E-raamat: Econometric Evaluation of Socio-Economic Programs: Theory and Applications

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Econometric Evaluation of Socio-Economic Programs: Theory and ApplicationsSuurem pilt
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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 and regression-discontinuity-design and are given practical guidelines for selecting and applying suitable methods for specific policy contexts.

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This book gives a comprehensive account of micro-econometric techniques for such evidence-based programme evaluation in the social sciences. gives an up-to-date account of the subject and a comprehensive description of each technique, covering both theoretical aspects and issues of application such as model checking, accuracy and reliability of results, and computation including related software. book is well written and clearly arranged and can be recommended to all statisticians with an interest in programme evaluation in the social sciences. (Peter Hackl, Statistics Papers, Vol. 57, 2016)

1 An Introduction to the Econometrics of Program Evaluation
1(48)
1.1 Introduction
1(6)
1.2 Statistical Setup, Notation, and Assumptions
7(11)
1.2.1 Identification Under Random Assignment
13(1)
1.2.2 A Bayesian Interpretation of ATE Under Randomization
14(3)
1.2.3 Consequences of Nonrandom Assignment and Selection Bias
17(1)
1.3 Selection on Observables and Selection on Unobservables
18(6)
1.3.1 Selection on Observables (or Overt Bias) and Conditional Independence Assumption
19(2)
1.3.2 Selection on Unobservables (or Hidden Bias)
21(1)
1.3.3 The Overlap Assumption
22(2)
1.4 Characterizing Selection Bias
24(5)
1.4.1 Decomposing Selection Bias
27(2)
1.5 The Rationale for Choosing the Variables to Control for
29(4)
1.6 Partial Identification of ATEs: The Bounding Approach
33(4)
1.7 A Guiding Taxonomy of the Econometric Methods for Program Evaluation
37(3)
1.8 Policy Framework and the Statistical Design for Counterfactual Evaluation
40(3)
1.9 Available Econometric Software
43(1)
1.10 A Brief Outline of the Book
44(5)
References
45(4)
2 Methods Based on Selection on Observables
49(112)
2.1 Introduction
50(1)
2.2 Regression-Adjustment
51(16)
2.2.1 Regression-Adjustment as Unifying Approach Under Observable Selection
51(5)
2.2.2 Linear Parametric Regression-Adjustment: The Control-Function Regression
56(5)
2.2.3 Nonlinear Parametric Regression-Adjustment
61(2)
2.2.4 Nonparametric and Semi-parametric Regression-Adjustment
63(4)
2.3 Matching
67(33)
2.3.1 Covariates and Propensity-Score Matching
68(2)
2.3.2 Identification of ATEs Under Matching
70(2)
2.3.3 Large Sample Properties of Matching Estimator(s)
72(4)
2.3.4 Common Support
76(1)
2.3.5 Exact Matching and the "Dimensionality Problem"
76(2)
2.3.6 The Properties of the Propensity-Score
78(2)
2.3.7 Quasi-Exact Matching Using the Propensity-Score
80(3)
2.3.8 Methods for Propensity-Score Matching
83(5)
2.3.9 Inference for Matching Methods
88(6)
2.3.10 Assessing the Reliability of CMI by Sensitivity Analysis
94(2)
2.3.11 Assessing Overlap
96(2)
2.3.12 Coarsened-Exact Matching
98(2)
2.4 Reweighting
100(13)
2.4.1 Reweighting and Weighted Least Squares
100(5)
2.4.2 Reweighting on the Propensity-Score Inverse-Probability
105(5)
2.4.3 Sample Estimation and Standard Errors for ATEs
110(3)
2.5 Doubly-Robust Estimation
113(1)
2.6 Implementation and Application of Regression-Adjustment
114(12)
2.7 Implementation and Application of Matching
126(20)
2.7.1 Covariates Matching
126(2)
2.7.2 Propensity-Score Matching
128(14)
2.7.3 An Example of Coarsened-Exact Matching Using cem
142(4)
2.8 Implementation and Application of Reweighting
146(15)
2.8.1 The Stata Routine treatrew
146(5)
2.8.2 The Relation Between treatrew and Stata 13's teffects ipw
151(3)
2.8.3 An Application of the Doubly-Robust Estimator
154(3)
References
157(4)
3 Methods Based on Selection on Unobservables
161(68)
3.1 Introduction
161(2)
3.2 Instrumental-Variables
163(17)
3.2.1 IV Solution to Hidden Bias
164(2)
3.2.2 IV Estimation of ATEs
166(6)
3.2.3 IV with Observable and Unobservable Heterogeneities
172(3)
3.2.4 Problems with IV Estimation
175(5)
3.3 Selection-Model
180(8)
3.3.1 Characterizing OLS Bias within a Selection-Model
181(2)
3.3.2 A Technical Exposition of the Selection-Model
183(4)
3.3.3 Selection-Model with a Binary Outcome
187(1)
3.4 Difference-in-Differences
188(14)
3.4.1 DID with Repeated Cross Sections
189(5)
3.4.2 DID with Panel Data
194(4)
3.4.3 DID with Matching
198(1)
3.4.4 Time-Variant Treatment and Pre-Post Treatment Analysis
199(3)
3.5 Implementation and Application of IV and Selection-Model
202(14)
3.5.1 The Stata Command ivtreatreg
203(2)
3.5.2 A Monte Carlo Experiment
205(3)
3.5.3 An Application to Determine the Effect of Education on Fertility
208(4)
3.5.4 Applying the Selection-Model Using etregress
212(4)
3.6 Implementation and Application of DID
216(13)
3.6.1 DID with Repeated Cross Sections
216(5)
3.6.2 DID Application with Panel Data
221(5)
References
226(3)
4 Local Average Treatment Effect and Regression-Discontinuity-Design
229
4.1 Introduction
229(3)
4.2 Local Average Treatment Effect
232(16)
4.2.1 Randomization Under Imperfect Compliance
232(1)
4.2.2 Wald Estimator and LATE
233(4)
4.2.3 LATE Estimation
237(2)
4.2.4 Estimating Average Response for Compliers
239(3)
4.2.5 Characterizing Compliers
242(1)
4.2.6 LATE with Multiple Instruments and Multiple Treatment
243(5)
4.3 Regression-Discontinuity-Design
248(22)
4.3.1 Sharp RDD
249(5)
4.3.2 Fuzzy RDD
254(5)
4.3.3 The Choice of the Bandwidth and Polynomial Order
259(7)
4.3.4 Accounting for Additional Covariates
266(1)
4.3.5 Testing RDD Reliability
267(2)
4.3.6 A Protocol for Practical Implementation of RDD
269(1)
4.4 Application and Implementation
270
4.4.1 An Application of LATE
270(15)
4.4.2 An Application of RDD by Simulation
285(22)
References
307
Giovanni Cerulli is researcher at CNR-IRCrES (National Research Council of Italy - Research Institute on Sustainable Economic Growth). He took a degree in Statistics and a PhD in Economic Sciences from Sapienza University of Rome. His research deals with both theoretical and applied econometrics of program evaluation, including dose-response models, program evaluation with peer effects, and software development for quantitative evaluation purposes. He boasts a consolidated expertise in the evaluation of R&D and innovation policies. Giovanni Cerulli is editor-in-chief of the International Journal of Computational Economics and Econometrics (IJCEE), and coordinator of GRAPE (Research Group on the Analysis of Economic Policies). His publications have appeared in prestigious peer-reviewed scientific journals.
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