Document Type

Article

Journal/Book Title/Conference

Economics Research Institute Study Paper

Volume

2

Publisher

Utah State University Department of Economics

Publication Date

2006

Rights

Copyright for this work is held by the author. Transmission or reproduction of materials protected by copyright beyond that allowed by fair use requires the written permission of the copyright owners. Works not in the public domain cannot be commercially exploited without permission of the copyright owner. Responsibility for any use rests exclusively with the user. For more information contact the Institutional Repository Librarian at digitalcommons@usu.edu.

First Page

1

Last Page

12

Abstract

Previous studies on life expectancy by U.S. county have found large differences among counties in life expectancy at birth for both males and females. Various determinants of these differences have been identified, including economic, education, demographic, social, geographic, climatic, and environmental factors. This preliminary study uses life expectancy by county to calculate the relative inequality in life expectancy within states. Gini coefficients for life expectancy are calculated for each state, and separate Gini coefficients are calculated for men and for women. The Gini coefficients are obtained from county average life expectancies by weighting each county life expectancy by county population, then calculating the Gini coefficient from the resulting Lorenz curve. A model of the determinants of within-state life expectancy inequality is identified and tested using regression analysis. Dependent variables in the model include Gini coefficients for various economic, demographic, and social factors calculated from county data in the same mamler as are the life-expectancy Gini coefficients. Economic variables in the model include Gini coefficients for income and poverty level. Demographic variables include Gini coefficients for percent of county population white, percent urban, and age. Social variables include Gini coefficients for educational attainment. Environmental variables include pollution incides. Separate regressions are also run for male and female life expectancy Gini coefficients. It is found that relative inequality in population life expectancy within states increases with relative inequality within states in poverty rate, urbanization, percent white, air pollution and age. Relative inequality in female life expectancy within states increases with relative inequality in poverty rate, percent white, and air pollution; and relative inequality in male life expectancy within states increases with relative inequality in poverty rate, education, and percent white.

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