By Giovanni Cerulli

This ebook presents complicated theoretical and utilized instruments for the implementation of contemporary micro-econometric ideas in evidence-based application evaluate for the social sciences. the writer provides a finished toolbox for designing rigorous and powerful ex-post application evaluate utilizing the statistical software program package deal Stata. for every approach, a statistical presentation is constructed, by means of a pragmatic estimation of the therapy results. by utilizing either actual and simulated facts, readers turns into conversant in overview suggestions, corresponding to regression-adjustment, matching, difference-in-differences, instrumental-variables and regression-discontinuity-design and are given functional instructions for choosing and using appropriate tools for particular coverage contexts.

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**Extra resources for Econometric Evaluation of Socio-Economic Programs: Theory and Applications**

**Sample text**

In this case, in fact, the BIAS (À80) outweighs the value of DIM (50), thus leading to a final negative value of αa (À30). It is now quite clear that randomness is the way in which nature balances samples. On the contrary, when some nonrandomness is at work, sample unbalances can be pervasive and evaluation trickier. It is also more evident at this stage that the knowledge of the unbalance and of the strength of the predicting power of the x-factors on Y are key to restore unbiased results. But what happens when x is not observable?

From a policy assessment perspective, this is a limitation, as relying only on the effect in a subgroup can be insufficient for understanding the effect of a given program. Nevertheless, if we restrict our attention to the set of untreated (D ¼ 0), we can properly identify at least ATENT since: ATENT ¼ ð6 þ 4Þ=2ÀÀð5 þ 8Þ=2 ¼ À1:5 ð1:47Þ while ATET—for the same reason of ATE—is not identifiable. Thus, as a general rule, the identification of ATET just requires that p(D ¼ 1 | x) < 1 and that of ATENT that p(D ¼ 1 | x) > 0 (or, equivalently, p(D ¼ 0 | x) < 1 ).

On the contrary, the bounding approach responds to the need of sensibly reducing the number of relied upon assumptions. But in doing so, this approach implies interval rather than point estimates of the effects that might be poorly informative for a policymaker to assess the actual effectiveness of a policy program. Nevertheless, this seems to deal more with the ontology of evaluation exercises than only with technical considerations. In line with the mainstream literature, however, this book will focus on the more traditional non-bounding approach to treatment effect estimation.