Document Type


Journal/Book Title/Conference

Economics Research Institute Study Paper




Utah State University Department of Economics

Publication Date



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

First Page


Last Page



Recent studies indicate that the economic value of a single year of life is about $70,000, and increases in life expectancy in the U.S. since 1950 are worth $12,000 or Inore per person per year. Hence, understanding the determinants of life expectancy have looked at relatively small cohorts of people over time, or have looked at mortality or life expectancy have looked at mortality or life expectancy at the national or state level. Recently, the authors con1pleted a study of the determinants of life expectancies for n1ales and females in Mountain States counties for 1990. The current study updates the earlier work by identifying the detern1inants of female and Inale life expectancies by county for 2000 and by examining changes in the impacts of various determinants between 1990 and 2000. By looking at county-level life expectancies, we can take into account the large variability in life expectancies within states that is largely obscured by looking only at differences in life expectancy between states. In this study, we develop and test a model of the ilnpact of demographic, economic, educational, social, and geographic factors on n1ean life expectancy by county for males and females born in 2000. We find that the percentage of population on rural farms, the percentage of Inarried households, the level of education, the percentage speaking a language other than English in the home, the percentage foreign-born, and county elevation have significant positive effects on life expectancy for both n1ales and females; while the percentage of population below the poveliy level, viole~t crilne rate, population density, unemploYlnent rate, and latitude have significant negative effects. Income has a nonlinear effect on life expectancy, whereas household size has a positive impact on average male life expectancy, but a negative ilnpact on average female life expectancy.