Document Type

Article

Journal/Book Title/Conference

Economics Research Institute Study Paper

Volume

18

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

21

Abstract

The purpose of this study is to determine the significant factors that affect the distribution of mortality by county in the United States, by using mortality data from the Multiple Cause of Death File of the National Center for Health Statistics from 1985 to 1994. These data are used to calculate distributions of mortality for men and women in each county by year. Gin coefficients are determined and used in a multiple regression model to ascertain the determinants of the distribution of mortality within counties. State and year effects are identified for the entire period, but the availability of data on the independent variables in the model is limited to census years. Hence, the complete model of determinants of the distribution of mortality is tested for 1990. Previous studies of the determinants of life expectancy, mortality, and the distribution of life expectancy suggest a number of variables that should be included in a model of the determinants of mortality at the county level. The level of education attained, mean income, poverty rate, unemployment rate, household size, population density, urbanization, and racial composition are among the variables that are expected to be important determinants of the distribution of mortality in United States counties. State effects and year effects are identified through the use of appropriate dummy variables.

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