By Marco Caliendo
The assessment of labour industry and different public regulations has turn into more and more very important lately. In an period of tight govt budgets, an intensive research of those measures is crucial. This publication offers a finished assessment and evaluation of the main correct microeconometric evaluate tools. It specializes in the preferred propensity rating matching process and offers huge information for its implementation. within the moment a part of the ebook, this technique is used to guage the employment results of activity construction schemes at the partaking contributors in Germany. in keeping with a wide administrative dataset of over 11,000 individuals, the learn permits to attract policy-relevant conclusions for the 1st time.
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Additional info for Microeconometric Evaluation of Labour Market Policies (Lecture Notes in Economics and Mathematical Systems)
We will show that matching is a more ﬂexible way of estimating treatment eﬀects and that regression estimates do not exploit the richness of this identifying assumption. Since matching algorithms and estimators are the main topic of the next section, we will restrict the discussion here to presenting the basic ideas of matching and regression under unconfoundedness and to comparing both approaches. 1 The Basic Idea of Matching Under Unconfoundedness The method is based on the identifying assumption that conditional on some covariates X, the outcome Y is independent of D.
By contrast, simple regression analysis weights the X−heterogeneous eﬀects proportionally to the variance of treatment status at X, that is proportional to P (X) · [1 − P (X)]. 5. Finally, another point worth mentioning is the scale of the outcome variable. g. linear or probability regression models. In the matching approach the construction of the matched sample is severed from the scale of the outcome 38 1 Introduction in Programme Evaluation variable. Only when calculating diﬀerences in means one has to account for the scale.
Therefore, each estimator is only consistent in a certain restrictive environment. As Heckman, LaLonde, and Smith (1999) note, all estimators would identify the same parameter only if there is no selection bias at all. We will start with three basic evaluation principles, namely the beforeafter, cross-section and the diﬀerence-in-diﬀerences estimator. They are still widely used and therefore we will discuss their properties and inherent identifying assumptions. Except the before-after estimator, all estimators make some comparisons of treated and untreated individuals.