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Measurement of income inequality and poverty is essential for any country to check the social welfare of the people and growth of the country. Lorenz curve and corresponding Gini index are the most popular indices of income inequality. However, there are certain measures which despite possessing interesting characteristics are not used often for measuring the income inequality. In this book, some other measures of income inequality like mean of Lorenz curve, family of inequality measures, Generalized Lorenz Curve, Absolute Lorenz Curve, Bonferroni curve, Cumulated mean income curve and associated indices are discussed. The properties of these inequality curves and indices are explored. In statistical inference, the asymptotic distribution of the inequality curves, testing of inequality curves, size and power of the test, confidence intervals are also proposed. The relationships of these inequality measures with some concepts of the reliability are established. This work will be of great interest to applied researchers which will encourage them to pursue further research work.
This book is a printed edition of the Special Issue "Econometrics and Income Inequality" that was published in Econometrics
"Prof. Nitis Mukhopadhyay and Prof. Partha Pratim Sengupta, who edited this volume with great attention and rigor, have certainly carried out noteworthy activities." - Giovanni Maria Giorgi, University of Rome (Sapienza) "This book is an important contribution to the development of indices of disparity and dissatisfaction in the age of globalization and social strife." - Shelemyahu Zacks, SUNY-Binghamton "It will not be an overstatement when I say that the famous income inequality index or wealth inequality index, which is most widely accepted across the globe is named after Corrado Gini (1984-1965). ... I take this opportunity to heartily applaud the two co-editors for spending their valuable time and energy in putting together a wonderful collection of papers written by the acclaimed researchers on selected topics of interest today. I am very impressed, and I believe so will be its readers." - K.V. Mardia, University of Leeds Gini coefficient or Gini index was originally defined as a standardized measure of statistical dispersion intended to understand an income distribution. It has evolved into quantifying inequity in all kinds of distributions of wealth, gender parity, access to education and health services, environmental policies, and numerous other attributes of importance. Gini Inequality Index: Methods and Applications features original high-quality peer-reviewed chapters prepared by internationally acclaimed researchers. They provide innovative methodologies whether quantitative or qualitative, covering welfare economics, development economics, optimization/non-optimization, econometrics, air quality, statistical learning, inference, sample size determination, big data science, and some heuristics. Never before has such a wide dimension of leading research inspired by Gini's works and their applicability been collected in one edited volume. The volume also showcases modern approaches to the research of a number of very talented and upcoming younger contributors and collaborators. This feature will give readers a window with a distinct view of what emerging research in this field may entail in the near future.
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.
Presented are new methods and new empirical studies on the subject of income inequality and poverty. The purpose of the book is to explore new ways to analyze recent trends in income inequality and poverty, both from the perspective of quantifying poverty and inequality and quantifyig the impact of various factors on the trends in inequality and poverty. The novelty lies in the diversity of empirical approaches used and customers will benefit from learning about different methods.
This book deals with the theoretical and practical problems involved in measuring the extent of inequality. The book covers modern theoretical developments in inequality analysis, and shows how the way we think about inequality has been shaped by classic contributions in economics and related disciplines.
Abstract: The inequality dataset compiled in the 1990s by the World Bank and extended by the United Nations has been both widely used and strongly criticized. The criticisms raise questions about conclusions drawn from secondary inequality datasets in general. The authors develop techniques to deal with national and international comparability problems intrinsic to such datasets. The result is a new dataset of consistent inequality series, allowing them to explore problems of measurement error. In addition, the new data allow the authors to perform parametric non-linear estimation of Lorenz curves from grouped data. This in turn allows them to estimate the entire income distribution, computing alternative inequality indexes and poverty estimates. Finally, the authors use their broadly comparable dataset to examine international patterns of inequality and poverty.
Consists of papers related to the theme of the dynamics of inequality and poverty that are subdivided into four separate parts. This volume examines inequality and poverty over time, the intergenerational transfer of poverty, inequality over time, and measurement issues. The chapters discuss inequality and poverty in developed countries.