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Relationship between normal and excessive personal income differentiation and regional economic performance indicators

Relationship between normal and excessive personal income differentiation and regional economic... The paper uses A.Yu. Shevyakov’s approach to the decomposition of the Gini coefficient into normal and excessive inequalities, the full deflation method for determining real income and real labor productivity, correlation and regression analysis, and the construction of Cobb–Douglas production functions. Three differentiation levels of normal and excessive inequalities (poverty line, social minimum line, and social well-being line) are proposed. For Russian regions in 2013, the study has found an inverse relationship between real per capita income and the Gini coefficient of excessive inequality, as well as a direct relationship between real per capita income and the Gini coefficient of normal inequality, except for the three most affluent regions, which show a reverse trend of decline in normal inequality. Correlations of normal and excessive inequalities with general development indicators, composition and dynamics of the population, the structure of income and gross regional product, sectoral structure of the economy, and the population’s wealth are revealed. A positive correlation for normal inequality and a negative correlation for excessive inequality with real output in Russian regions are confirmed based on the inclusion of Gini coefficients in the five-factor interregional Cobb–Douglas production function. It is concluded that excessive inequality, on the one hand, is a consequence of the low development level and, on the other hand, suppresses production possibilities and incentives. Normal inequality promotes economic development, which is first accompanied by a growth in normal inequality, followed by a decline. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Regional Research of Russia Springer Journals

Relationship between normal and excessive personal income differentiation and regional economic performance indicators

Malkina, M.
Regional Research of Russia , Volume 7 (2) – Jun 24, 2017
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References (24)

Publisher
Springer Journals
Copyright
Copyright © 2017 by Pleiades Publishing, Ltd.
Subject
Economics; Regional/Spatial Science
ISSN
2079-9705
eISSN
2079-9713
DOI
10.1134/S2079970517020058
Publisher site
See Article on Publisher Site

Abstract

The paper uses A.Yu. Shevyakov’s approach to the decomposition of the Gini coefficient into normal and excessive inequalities, the full deflation method for determining real income and real labor productivity, correlation and regression analysis, and the construction of Cobb–Douglas production functions. Three differentiation levels of normal and excessive inequalities (poverty line, social minimum line, and social well-being line) are proposed. For Russian regions in 2013, the study has found an inverse relationship between real per capita income and the Gini coefficient of excessive inequality, as well as a direct relationship between real per capita income and the Gini coefficient of normal inequality, except for the three most affluent regions, which show a reverse trend of decline in normal inequality. Correlations of normal and excessive inequalities with general development indicators, composition and dynamics of the population, the structure of income and gross regional product, sectoral structure of the economy, and the population’s wealth are revealed. A positive correlation for normal inequality and a negative correlation for excessive inequality with real output in Russian regions are confirmed based on the inclusion of Gini coefficients in the five-factor interregional Cobb–Douglas production function. It is concluded that excessive inequality, on the one hand, is a consequence of the low development level and, on the other hand, suppresses production possibilities and incentives. Normal inequality promotes economic development, which is first accompanied by a growth in normal inequality, followed by a decline.

Journal

Regional Research of Russia – Springer Journals

Published: Jun 24, 2017

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