Making sense of Arab labor markets: the enduring legacy of dualism

It is well-established that Arab labor markets share certain common characteristics, including an oversized public sector, high unemployment for educated youth, weak private sector dependent on government welfare for their survival, rapid growth in educational attainment, but much of it focused on the pursuit of formal credentials rather than productive skills, and low and stagnant female labor force participation rates. I argue in this paper that all of these features can be explained by the deep and persistent dualism that characterizes Arab labor markets as a result of the use of labor markets by Arab regimes as tool of political appeasement in the context of the authoritarian bargain social contract that they have struck with their citizens in the post-independence period. Even as fiscal crises have long destabilized these arrangements in most non-oil Arab countries, culminating in the dramatic political upheavals of the Arab spring revolutions, the enduring legacy of dualism will continue to strongly shape the production and deployment of human capital in Arab economies for some time. This will undoubtedly pose serious challenges to any efforts to transform these economies into dynamic, rapidly growing and more equitable globally competitive economies

A review on probabilistic graphical models in evolutionary computation

Thanks to their inherent properties, probabilistic graphical models are one of the prime candidates for machine learning and decision making tasks especially in uncertain domains. Their capabilities, like representation, inference and learning, if used effectively, can greatly help to build intelligent systems that are able to act accordingly in different problem domains. Evolutionary algorithms is one such discipline that has employed probabilistic graphical models to improve the search for optimal solutions in complex problems. This paper shows how probabilistic graphical models have been used in evolutionary algorithms to improve their performance in solving complex problems. Specifically, we give a survey of probabilistic model building-based evolutionary algorithms, called estimation of distribution algorithms, and compare different methods for probabilistic modeling in these algorithms

Assessing the Impact of Support Policies for Energy Efficient Technology in Switzerland

This study uses data of a representative sample of Swiss firms to assess the effectiveness of policy measures directed at the diffusion of energy effcient technology (EET). Three different outcome variables (number of EET applications implemented, CO2 reduction, and EET related investment) are analysed using methods based on the estimated propensity score in an attempt to overcome the problem of non-random assignment. I conclude that, even after controlling for non-random assignment, diffusion support from the two institutions taken into consideration has indeed been beneficial in spurring adoption of energy saving technology and in reducing emissions of CO2. Additionally, an estimator for Average Treatment Effects on the Treated (ATT) that directly relies on the propensity score has been found to produce better results, in terms of efficiency, than the widely used Nearest Neighbour Matching (NNM) procedure