2,809 research outputs found

    Defining pro-poor growth

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    .Poverty, pro-poor

    Burden Sharing Emissions and Climate Change: A Theoretic Welfare Approach

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    The approximated cost-benefit function of pollution abatement from two integrated assessment models are employed in constructing of social welfare functions (SWF). Following a normative approach and evaluating equally the environmental goods in rich and poor countries, furthermore using distributional weights, a relation between elasticity of marginal utility epsilon and inequality aversion parameter gamma is established. By maximizing the global social welfare, the optimal pollution abatement level are found. The relation between the income elasticity of marginal utility epsilon and the inequality aversion parameter gamma allow to narrow the variation of epsilon for a particular value of gamma. As a consequence, smaller variation for optimal abatement levels are obtained, which allows to inspect if the Kyoto abatement objectives respect the requirement of evaluating equally the environmental goods in rich and poor countries.cost-benefit analysis, distributional weights, global warming, welfare theory, integrated assessment modeling

    EU - Information and Communication Technology (ICT) and e-learning in Education Project - Phase II

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    The training needs analysis was conducted beteeen February and April 2015 for the EU funded project: ICT in Education in Kosovo. The processes required to perform the traning needs analysis have been. The design of a framework of competences; The identification of target groups; The creation and implementation of an online survey to assess the competence of education sector personnel against the competences contained in the framework; The collation, preparation and analysis of the survey data; and Reporting the research findings.European Union Office in KosovoEuropeAid/133846/C/SER/X

    The Demands of Inclusive Growth: Lessons from South Asia (The Mahbub Ul Haq Memorial Lecture)

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    This paper examines the concept of inclusive growth, compares and contrasts it with related concepts such as pro-poor growth and equitable growth, and analyses the recent experience of South Asia through the lens of this concept. A common experience of the region is that spells of rapid growth have been marked by accelerated poverty reduction on the one hand rising income inequality on the other. The contrasting movements in poverty and inequality render intriguing the question of whether South Asia has experienced inclusive growth or not. The reduction in poverty suggests inclusiveness, while the rise in inequality suggests otherwise. The implication is that the growth process has been inclusive in some dimensions but not in others. Closer examination shows that in each country of the region horizontal equity (between groups) has been served better than vertical equity (within groups). Thus, while the growth process has opened up plentiful opportunities for most groups of people to enjoy the benefits of growth, thereby making poverty reduction possible at an accelerated pace, in every group some individuals have failed to link up with the growth process, thereby exacerbating inequality. The problem was that within each group some individuals lacked the skills and endowments required to integrate with the growth process. Improving the ‘integrability’ of these people is an essential demand of inclusive growth.Inclusive Growth, Poverty, Inequality, South Asia

    Expanding Voice and Accountability Through the Budgetary Process

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    human development, democracy

    The case of two self-enforcing international agreements for environmental protection

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    Abstract Non-cooperative game theoretical models of self-enforcing international environmental agreements (IEAs) that employ the cartel stability concept of d'Aspremont et al. (1983) frequently use the assumption that countries can sign a single agreement only. We modify the assumption by considering two self-enforcing IEAs. Extending a model of Barrett (1994a) on a single self-enforcing IEA, we demonstrate that there are many similarities between one and two self-enforcing IEAs. But in the case of few countries and high environmental damage we show that two self-enforcing IEA work far better than one self-enforcing IEA in terms of both welfare and environmental equalityKeywords: self-enforcing international environmental agreements; non-cooperative game the- ory; stability; nonlinear optimization

    Smartphone apps usage patterns as a predictor of perceived stress levels at workplace

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    Explosion of number of smartphone apps and their diversity has created a fertile ground to study behaviour of smartphone users. Patterns of app usage, specifically types of apps and their duration are influenced by the state of the user and this information can be correlated with the self-reported state of the users. The work in this paper is along the line of understanding patterns of app usage and investigating relationship of these patterns with the perceived stress level within the workplace context. Our results show that using a subject-centric behaviour model we can predict stress levels based on smartphone app usage. The results we have achieved, of average accuracy of 75% and precision of 85.7%, can be used as an indicator of overall stress levels in work environments and in turn inform stress reduction organisational policies, especially when considering interrelation between stress and productivity of workers

    Benchmarking machine learning models on multi-centre eICU critical care dataset

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    Progress of machine learning in critical care has been difficult to track, in part due to absence of public benchmarks. Other fields of research (such as computer vision and natural language processing) have established various competitions and public benchmarks. Recent availability of large clinical datasets has enabled the possibility of establishing public benchmarks. Taking advantage of this opportunity, we propose a public benchmark suite to address four areas of critical care, namely mortality prediction, estimation of length of stay, patient phenotyping and risk of decompensation. We define each task and compare the performance of both clinical models as well as baseline and deep learning models using eICU critical care dataset of around 73,000 patients. This is the first public benchmark on a multi-centre critical care dataset, comparing the performance of clinical gold standard with our predictive model. We also investigate the impact of numerical variables as well as handling of categorical variables on each of the defined tasks. The source code, detailing our methods and experiments is publicly available such that anyone can replicate our results and build upon our work.Comment: Source code to replicate the results https://github.com/mostafaalishahi/eICU_Benchmar
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