In the multidimensional well-being literature, it has been long advocated that it is important to consider how the different well-being domains interact. Nevertheless, none of the existing approaches is useful to tackle this issue. In this paper, we show that the statistical technique of Bayesian Networks is an intuitive and powerful instrument that allows to graphically model the dependence structure among the different dimension of well-being. Moreover, Bayesian Networks can be used to understand the effectiveness of given interventions addressed to one or more dimensions, as well as to design more effective policies to reach the desired outcome. The new approach is illustrated with an empirical application based on data for a selection of Western and Eastern European countries.
The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
Read-across is a non-testing data gap filling technique which provides information for toxicological assessments by inferring from known toxicity data of compound(s) with a "similar" property or chemical profile. The increased usage of read-across was driven by monetary, timing and ethical costs associated with in vivo testing, as well as promoted by regulatory frameworks to minimize new animal testing (e. g., EU-REACH). Several guidance documents have been published by ECHA and OECD providing guidelines on how to perform, assess and docu-ment a read-across study. In parallel, much effort was invested by the scientific community to provide good readacross practices and structured frameworks to enhance validity of read-across justifications. Nevertheless, readacross is an evolving method with several open issues and opportunities. A brief review is here provided on key developments on the use of read-across, regulatory and scientific expectations, practical hurdles and open challenges.
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