Bukowski, Rajagopalan and their contributors seek to cross both analytical and geographic boundaries in the study of why and how authority shifts both within and beyond the modern nation-state. They develop a conceptualization of the re-distribution of authority, that is, when the capacity of governmental and societal units involved in carrying out the tasks and responsibilities of governance change over time, relative to each other. They argue that this is a more comprehensive alternative to extant conceptualizations used to study the shifting of authority, such as decentralization, regionalism, or federalism. Nine diverse cases are then presented: Pakistan, India, Sri Lanka, the United States, Russia, Spain, Portugal, Senegal, and South Africa. Each case addresses the questions: Which are the factors that explain the re-distribution of authority? Under what conditions are some of these factors more important than others?
Despite the diversity of the cases in both geographic location and levels of economic and political development, four major explanatory factors emerge as common across all nine cases: identity-related claims, economic imperatives, considerations of administrative efficiency, and political agency. Moreover, discerning the complex interaction of these factors is necessary in understanding the re-distribution of authority in both its centralizing and decentralizing forms, across all levels of governance. Of particular interest to scholars, students, and policy researchers involved with international relations, comparative politics, public administration, political development, and state formation, and ethnonational politics.
Economic inequality has become a focus of prime interest for economic analysts and policy makers. This book provides an integrated approach to the topics of inequality and personal income distribution. It covers the practical and theoretical bases for inequality analysis, applications to real world problems and the foundations of theoretical approaches to income distribution. It also analyses models of the distribution of labour earnings and of income from wealth. The long-run development of income - and wealth - distribution over many generations is also examined. Special attention is given to an assessment of the merits and weaknesses of standard economic models, to illustrating the implications of distributional mechanisms using real data and illustrative examples, and to providing graphical interpretation of formal arguments. Examples are drawn from US, UK and international sources.
In this book theories of distribution and growth after Keynes are reviewed and assessed. The first part presents a comprehensive overview of the main contributions including the approaches by Harrod and Domar, old and new neoclassical theories including the fundamental capital controversy critique, the post-Keynesian contributions of Kaldor, Pasinetti, Thirlwall and Robinson, as well as the approaches by Kalecki and Steindl. The second part of the book develops neo- and post-Kaleckian models, gradually introducing saving from wages and international trade, technological progress, money, interest and credit. It also explores issues of 'financialisation' and presents the empirical results of model applications.
There are now more refugees and internally displaced persons (IDPs) uprooted by conflict than at any time since the genocides in Rwanda and Bosnia in the early 1990s. In addition, hundreds of thousands of people are displaced every year by natural disasters. In International Organization for Migration (IOM) Bradley provides an accessible, incisive introduction to this important but under-examined agency.
By providing an accessible introduction to IOM and its work in the field of forced migration, alongside rigorous analysis of the organization's evolution, practices, and contemporary challenges, the proposed volume is essential reading for students and scholars of international relations, migration and international organizations.
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation is devoted to a new paradigm for evolutionary computation, named estimation of distribution algorithms (EDAs). This new class of algorithms generalizes genetic algorithms by replacing the crossover and mutation operators with learning and sampling from the probability distribution of the best individuals of the population at each iteration of the algorithm. Working in such a way, the relationships between the variables involved in the problem domain are explicitly and effectively captured and exploited.