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This work examines theoretical issues, as well as practical developments in statistical inference related to econometric models and analysis. This work offers discussions on such areas as the function of statistics in aggregation, income inequality, poverty, health, spatial econometrics, panel and survey data, bootstrapping and time series.
Amartya Sen "Equality," I spoke the word As if a wedding vow Ah, but I was so much older then, I am younger than that now. Thus sang Bob Dylan in 1964. Approbation of equality varies not only with our age (though it is not absolutely clear in which direction the values may shift over one's life time), but also with the spirit of the times. The 1960s were good years for singing in praise of equality. The spirit of the present times would probably be better reflected by melodies in admiration of the Federal Reserve System. And yet the technical literature on the evaluation and measurement of economic inequality has grown remarkably over the last three decades. Even as actual economic policies (especially in North America and Europe) have tended to move towards focusing on virtues other than the avoidance of economic inequality, the professional literature on assessing and gauging economic inequality has taken quite a jump forward. A great many different problems have been addressed and effectively sorted out, and new problems continue to be posed and analyzed. The Contents: A Review Jacques Silber has done a great service to the subject by producing this collection of admirablyhelpful and illuminating papers on different aspects of the measurement of income inequality. The reach of this collection is quite remarkable. Along with a thorough overview from the editor himself, the major areas in this complex field have been carefully examined and accessibly discussed.
A comprehensive account of economic size distributions around the world and throughout the years In the course of the past 100 years, economists and applied statisticians have developed a remarkably diverse variety of income distribution models, yet no single resource convincingly accounts for all of these models, analyzing their strengths and weaknesses, similarities and differences. Statistical Size Distributions in Economics and Actuarial Sciences is the first collection to systematically investigate a wide variety of parametric models that deal with income, wealth, and related notions. Christian Kleiber and Samuel Kotz survey, compliment, compare, and unify all of the disparate models of income distribution, highlighting at times a lack of coordination between them that can result in unnecessary duplication. Considering models from eight languages and all continents, the authors discuss the social and economic implications of each as well as distributions of size of loss in actuarial applications. Specific models covered include: Pareto distributions Lognormal distributions Gamma-type size distributions Beta-type size distributions Miscellaneous size distributions Three appendices provide brief biographies of some of the leading players along with the basic properties of each of the distributions. Actuaries, economists, market researchers, social scientists, and physicists interested in econophysics will find Statistical Size Distributions in Economics and Actuarial Sciences to be a truly one-of-a-kind addition to the professional literature.
What new theories, evidence, explanations, and policies have shaped our studies of income distribution in the 21st century? Editors Tony Atkinson and Francois Bourguignon assemble the expertise of leading authorities in this survey of substantive issues. In two volumes they address subjects that were not covered in Volume 1 (2000), such as education, health and experimental economics; and subjects that were covered but where there have been substantial new developments, such as the historical study of income inequality and globalization. Some chapters discuss future growth areas, such as inheritance, the links between inequality and macro-economics and finance, and the distributional implications of climate change. They also update empirical advances and major changes in the policy environment. - The volumes define and organize key areas of income distribution studies - Contributors focus on identifying newly developing questions and opportunities for future research - The authoritative articles emphasize the ways that income mobility and inequality studies have recently gained greater political significance
The Encyclopedia of Health Economics offers students, researchers and policymakers objective and detailed empirical analysis and clear reviews of current theories and polices. It helps practitioners such as health care managers and planners by providing accessible overviews into the broad field of health economics, including the economics of designing health service finance and delivery and the economics of public and population health. This encyclopedia provides an organized overview of this diverse field, providing one trusted source for up-to-date research and analysis of this highly charged and fast-moving subject area. Features research-driven articles that are objective, better-crafted, and more detailed than is currently available in journals and handbooks Combines insights and scholarship across the breadth of health economics, where theory and empirical work increasingly come from non-economists Provides overviews of key policies, theories and programs in easy-to-understand language
Have gaps in health outcomes between the poor and better off grown? Are they larger in one country than another? Are health sector subsidies more equally distributed in some countries than others? Are health care payments more progressive in one health care financing system than another? What are catastrophic payments and how can they be measured? How far do health care payments impoverish households? Answering questions such as these requires quantitative analysis. This in turn depends on a clear understanding of how to measure key variables in the analysis, such as health outcomes, health expenditures, need, and living standards. It also requires set quantitative methods for measuring inequality and inequity, progressivity, catastrophic expenditures, poverty impact, and so on. This book provides an overview of the key issues that arise in the measurement of health variables and living standards, outlines and explains essential tools and methods for distributional analysis, and, using worked examples, shows how these tools and methods can be applied in the health sector. The book seeks to provide the reader with both a solid grasp of the principles underpinning distributional analysis, while at the same time offering hands-on guidance on how to move from principles to practice.
Eight papers, both theoretical and applied, on the concept of equality of opportunity which says that a society should guarantee its members equal access to advantage regardless of their circumstances, while holding them responsible for turning that access into actual advantage by the application of effort.
Gini's mean difference (GMD) was first introduced by Corrado Gini in 1912 as an alternative measure of variability. GMD and the parameters which are derived from it (such as the Gini coefficient or the concentration ratio) have been in use in the area of income distribution for almost a century. In practice, the use of GMD as a measure of variability is justified whenever the investigator is not ready to impose, without questioning, the convenient world of normality. This makes the GMD of critical importance in the complex research of statisticians, economists, econometricians, and policy makers. This book focuses on imitating analyses that are based on variance by replacing variance with the GMD and its variants. In this way, the text showcases how almost everything that can be done with the variance as a measure of variability, can be replicated by using Gini. Beyond this, there are marked benefits to utilizing Gini as opposed to other methods. One of the advantages of using Gini methodology is that it provides a unified system that enables the user to learn about various aspects of the underlying distribution. It also provides a systematic method and a unified terminology. Using Gini methodology can reduce the risk of imposing assumptions that are not supported by the data on the model. With these benefits in mind the text uses the covariance-based approach, though applications to other approaches are mentioned as well.