Programme Evaluation and Treatment Choice Volume 524. Questo articolo non è disponibile.Programme Evaluation and Treatment Choice Volume 524

Policy evaluation and programme choice are important tools for informed decision-making, for the administration of active labour market programmes, training programmes, tuition subsidies, rehabilitation programmes etc. Whereas the evaluation of programmes and policies is mainly concerned with an overall assessment of impact, benefits and costs, programme choice considers an optimal allocation of individuals to the programmes. This book surveys potential evaluation strategies for policies with multiple programmes and discusses evaluation and treatment choice in a coherent framework. Recommendations for choosing appropriate evaluation estimators are derived. Furthermore, a semiparametric estimator of optimal treatment choice is developed to assist in the optimal allocation of participants.

1 Introduction.- 2 Programme Evaluation and Treatment Choice — An Overview.- 2.1 Introduction in Programme Evaluation.- 2.1.1 Potential Outcomes.- 2.1.2 Stable-Unit-Treatment-Value Assumption.- 2.1.3 Average Treatment Effects and Selection Bias.- 2.1.4 Identification of Average Treatment Effects.- 2.1.5 Estimation of Mean Counterfactual Outcomes.- 2.2 Optimal Treatment Choice.- 2.2.1 Definition of Optimal Treatment.- 2.2.2 Profiling and Targeting of Programmes in Practice.- 2.2.3 Estimating the Optimal Treatment.- 2.3 Nonparametric Regression.- 2.3.1 Nearest Neighbours and Local Polynomial Regression.- 2.3.2 Properties of Local Polynomial Regression.- 3 Nonparametric Covariate Adjustment in Finite Samples.- 3.1 Potential Efficiency Gains of Local Polynomial Matching.- 3.1.1 Simulation Results at the Optimal Bandwidth Value.- 3.1.2 Sensitivity to the Bandwidth Value.- 3.2 Approximation to the MSE and Bandwidth Choice.- 3.2.1 Bandwidth Choice.- 3.2.2 MSE Approximation of Local Polynomial Matching.- 3.2.3 Approximation Accuracy in Finite Samples.- 3.3 Data-driven Bandwidth Choice by Cross-Validation.- 3.4 Matching with Unknown Propensity Score.- 4 Semiparametric Estimation of Optimal Treatment Choices.- 4.1 Estimation of Conditional Expected Potential Outcomes.- 4.1.1 Semiparametric GMM Estimator.- 4.1.2 Monte Carlo Simulation.- 4.2 Optimal Choice and Swedish Rehabilitation Programmes.- 5 Conclusions.- A Appendix.- B Appendix.- C Appendix.- MSE-Approximation for Local Polynomial Matching.- Additional Tables to Chapter 3.- D Appendix.- D.1 Simulated Mean Squared Error for Sample Size 40.- D.2 Simulated Mean Squared Error for Sample Size 200.- D.3 Simulated Mean Squared Error for Sample Size 1000.- D.4 MSE Approximation: Kernel Matching, Sample Size 200.- D.5 MSE Approximation: Kernel Matching, Sample Size 1000.- D.6 MSE Approximation: Local Linear, Sample Size 200.- D.7 MSE Approximation: Local Linear, Sample Size 1000.- E Appendix.- E.1 Asymptotic Properties of the GMM Estimator.- E.2 Power of the J-tests — Additional Monte Carlo Results.- E.3 Additional Tables to Swedish Rehabilitation Programmes.- References.