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by
James K. Galbraith
Qifei Wang The University of Texas Inequality Project http://utip.gov.utexas.edu
We gratefully acknowledge the support of the Ford Foundation
The U.T. Inequality Project:
The U.T. Inequality Project Measures Global Pay Inequality
Uses Simple Techniques that Permit Up-to-Date Measurement at Low Cost
Shows How Inequality Has Risen Under Globalization
Is Developing National and Regional Data Sets for Many Countries
Estimates Global Income Inequalities
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We use Theil’s T statistic, measured across sectors within each country, region or province, to show the evolution of economic inequality.
The components of the statistic provide a measure of the contribution of each 'province-sector cell' to inequality. This measure takes account both of the relative income of the cell and its size in relation to the whole employed population.
The method permits us to map changes in the flow of incomes across regions and across sectors very accurately through time, using national data sources andamp; without relying on sample surveys. General Technique
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A brief review of the Theil Statistic: n ~ employment; mu ~ average income; j ~ subscript denoting group 'The Theil Element'
China:
China
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1987
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2000
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These stacked line graphs show the contribution to inequality in China of each sector in each region.Two facts emerge clearly: the rise of monopolized activities such as transport, utilities and banking, especially in the richer areas, and the general relative decline of manufacturing and construction. 1987 2000 Note that the relative position of education and science has held up much better in China than in Russia. The decline in agriculture has also not been as severe.
Conclusions:
Conclusions Inequality rose in both Russia and China
Regional inequalities rose most sharply
Liberalization favored monopolies and the export regions and sectors
Agriculture lost ground
In China, the capacity to provide key public services withstood the pressures of liberalization much better than in Russia
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