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Über dieses Buch

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.

Inhaltsverzeichnis

Frontmatter

1. Introduction

Abstract
Policy evaluation and programme choice are important tools for informed decision-making, for example with respect to active labour market programmes, welfare-to-work programmes, vocational training programmes, entrepreneurship promotion schemes, educational programmes, tuition subsidies, sickness rehabilitation programmes or disease prevention programmes. Both, policy evaluation and programme choice, are not ends in themselves but indispensable steps towards the efficient allocation of resources and towards the improvement of existing policies. Whereas the evaluation of programmes and policies is mainly concerned with an overall assessment of impact, benefits and costs, programme choice is directed towards achieving the optimal allocation of individuals to the programmes. Together they form a unified whole: For an ex-post assessment of policy impact and allocation efficiency and for deriving recommendations for how programmes should be modified and for which individuals should participate in which programmes. To take full account of their potential, policy evaluation and programme choice should be incorporated into an integrated system of resources planning and participant allocation, which would assist in assigning individuals to their optimal programmes (Statistically Assisted Programme Selection, SAPS).
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2. Programme Evaluation and Treatment Choice — An Overview

Abstract
Policy and programme evaluation in a wide sense is concerned with measuring how far a policy or a programme has achieved its intended aims. A policy is hereafter defined as a bundle of R different programmes. This includes the case of evaluating a single programme (R = 2, participation versus non-participation) and evaluating multiple programmes (R > 2). One example of policies consisting of multiple programmes are active labour market policies, which often comprise various public employment programmes, on-the-job training, retraining, classroom training, job search assistance, wage subsidies etc. Another example are rehabilitation policies for the re-integration of people with long-term illnesses, which may consist of different forms of vocational workplace training, vocational schooling, medical rehabilitation and social and psychological programmes. In the following, often the neutral term treatment will be used synonymously for programme, since the methods presented here are not restricted to the evaluation of social policies but apply similarly to, for example, the evaluation of the effectiveness of medical drugs or of different schooling choices, or of the effects of participation in the military. Since participation in a policy is often voluntary, or since full compliance in a ’mandatory’ policy might not always be enforceable, the set of different treatments usually includes a ’no-programme’ or ’non-participation’ option. As it is assumed that all individuals are untreated before participation in the policy, i.e. that they had not participated previously in the programmes,1 this ’non-participation’ treatment is often special in the sense that it is the treatment most similar to the situation before participation in the policy. To illustrate this asymmetry, the treatment set will be indexed by r ∊ {0,..,R − 1}, i.e. consisting of a ’non-participation’ treatment (r = 0) and R − 1 active treatments. In the case of the evaluation of a single programme the treatment set consists of r = 0 (non-participation) and r = 1 (participation).
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3. Nonparametric Covariate Adjustment in Finite Samples

Abstract
In this chapter the finite-sample properties of various estimators of the covariate-adjusted mean (2.25) are investigated. Estimates of (2.25) are essential ingredients for policy evaluation under the control-for-confounding-variables and under the difference-in-difference identification approaches (discussed in Section 2.1.4). Hence precise estimation of covariate-adjusted means is of importance, particularly if average treatment effects are analyzed for smaller subpopulations.1 In addition, estimation of covariate-adjusted means is also central for deriving individually optimal treatment choices in Chapter 4.
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4. Semiparametric Estimation of Optimal Treatment Choices

Abstract
In this chapter semiparametric estimation of the conditional expected potential outcomes E[Y r |X] is considered, which are the central ingredients to the derivation of optimal treatment choices, as discussed in Section 2.2. The previous chapter has shown that nonparametric regression, particularly SG matching, is suited for estimating average treatment effects in small samples or for small subpopulations. However the task of estimating treatment effects on an individual level is even more demanding, since the characteristics vector X usually must contain many covariates to identify the outcomes E[Y r |X] by the conditional independence assumption (2.4). Fully nonparametric estimation of E[Y r |X] might then be very imprecise. As an alternative, a semiparametric approach is developed in this chapter, which combines nonparametric SG matching on a subpopulation level with parametric specifications.
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5. Conclusions

Abstract
This book has reviewed a variety of aspects of programme evaluation and treatment choice. An overview over nonparametric identification and estimation of average treatment effects has been given in the framework of multiple treatment evaluation. Different estimators of the mean counterfactual outcomes have been examined and their finite sample properties have been investigated. In addition, the optimal allocation of individuals to the programmes has been discussed and a two-step semiparametric estimation method of optimal programme choices has been developed and applied to Swedish rehabilitation programmes. The main results can be summarized in two sets of conclusions.
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Backmatter

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