The Correlation Between Socioeconomic and Environmental Factors on Life Expectancy in the United States
Session Number
Project ID: BHVSO 18
Advisor(s)
Patrick Kearney, Illinois Mathematics and Science Academy
Discipline
Behavioral and Social Sciences
Start Date
20-4-2022 8:50 AM
End Date
20-4-2022 9:05 AM
Abstract
There are many factors that can impact an individuals’ health, such as proximity to services, access to nature, occupational opportunities, and more. It was hypothesized that factors such as population density, education, civilian labor force, poverty, mortality, public transportation, air quality, and unemployment rates differ between urban and rural areas and influence longevity in humans. However, previous research dove deeper into analyzing counties in Illinois, New York, California, Texas, and Florida and found that rather than the aforementioned factors, average income was the main determinant of longevity in individuals. With this knowledge, we will expand our data to cover all counties in the United States to examine if average income is the main determinant of longevity, or whether it was the main determinant for those fives tates. With this new data, we will generate numerous econometric regression models to test for correlation between the previously mentioned factors, average income, and the life expectancies of urban, suburban, and rural counties in all US states. Using the quantitative results reflected in these models will allows us to conclude how intensely the conditional factors and average income will affect the health and life expectancy of the citizens who live there.
The Correlation Between Socioeconomic and Environmental Factors on Life Expectancy in the United States
There are many factors that can impact an individuals’ health, such as proximity to services, access to nature, occupational opportunities, and more. It was hypothesized that factors such as population density, education, civilian labor force, poverty, mortality, public transportation, air quality, and unemployment rates differ between urban and rural areas and influence longevity in humans. However, previous research dove deeper into analyzing counties in Illinois, New York, California, Texas, and Florida and found that rather than the aforementioned factors, average income was the main determinant of longevity in individuals. With this knowledge, we will expand our data to cover all counties in the United States to examine if average income is the main determinant of longevity, or whether it was the main determinant for those fives tates. With this new data, we will generate numerous econometric regression models to test for correlation between the previously mentioned factors, average income, and the life expectancies of urban, suburban, and rural counties in all US states. Using the quantitative results reflected in these models will allows us to conclude how intensely the conditional factors and average income will affect the health and life expectancy of the citizens who live there.