Life Expectancy at Birth = 62.9 years + (0.0001095 * GNP per Capita) + (1.4555274 * Annual Population Growth) + (-3.623246 * Fertility Rate) + (-0.066892 * AIDS) + (-0.016498 * Tuberculosis) + (0.1662502 * School Enrollment Rate) + (0.0524011 * Access to Safe Water) + (-0.035922 * Forest and Woodlands) + (-0.557085 *Annual Rate of Deforestation)
Multicollinearity was removed. For example, telephone lines were highly correlated with other modes of communication such as radio, televisions, newspapers, as well as GNP per Capita, GNP growth, average annual rate of inflation and electricity consumption. Due to their excessive multicollinarities, these two variables, phones and GNP (PPP), were removed.
Although developed, industrial countries tend to have higher life expectancies at birth than underdeveloped countries, this is not always the case. One example, as mentioned earlier, is the United States of America, which ranks very highly in terms of development, but whose life expectancy is not extremely high. An observation of our data set also reveals that there are several less developed countries that have extremely high life expectancies, such as Jamaica, Kuwait and Singapore, which have life expectancies of 74.1, 75.4, and 77.1 years, respectively. These discrepancies result in the removal of country development.
The following variables have been found to exert the greatest significance: GNP per Capita, Population Growth, Fertility Rate, AIDS, Tuberculosis, School Enrollment Rate, Access to Safe Water, Forest and Woodlands, and Rate of Deforestation. All have p-values less than 0.020.
Life expectancy is positively correlated to GNP per capita, population growth, fertility, enrollment, and access to safe water and negatively correlated to AIDS, tuberculosis, forest and woodland percentage, and rate of deforestation. The soundness of these results was discussed in detail following the stepwise regression. To improve the model, and increase the its explanatory capability (increase R2), we might consider additional variables that were excluded such as ethnicity and diet.
So a country with GNP per capita was $200,000 and fertility was 2 and annual population growth was 1% and there was no Aids or TB and 100% school enrollment and 100% access to safe drinking water and no deforestation would have a predicted life expectancy of about 100 years.
So a country with GNP per capita was $100,000 (in year 2000 dollars) and fertility was 2 and annual population growth was 1% and there was no Aids or TB and 100% school enrollment and 100% access to safe drinking water and no deforestation would have a predicted life expectancy of about 90 years.
A 2% growth rate from a base of 100,000 GNP per capita would add 0.228 years.
A 5% growth rate from a base of 100,000 GNP per capita would add 0.45 years.
A 6% growth rate from a base of 100,000 GNP per capita would add 0.66 years.
A 2% growth rate from a base of 50,000 GNP per capita would add 0.11 years.
A 5% growth rate from a base of 50,000 GNP per capita would add 0.2785 years.
A 6% growth rate from a base of 50,000 GNP per capita would add 0.33 years.
So richer countries get more benefit from a percentage growth rate in per capita income. It is the absolute amount of increase per capita that has the impact.
A roughly $9000 per capita increase in income would be equal to a 1 year increase in life expectancy.
If Monaco at an inflation adjusted $150,000 per capita in year 2000 dollars had 6% GNP per capita growth they would add 1 year. A $300,000 per capita in year 2000 dollar country would need to have 3% GNP per capita growth to get to add 1 year life expectancy at birth.
The formula and correlations do not hold beyond the ranges of the statistical analysis.
But it is interesting how increasing per capita wealth while controlling the other factors leads to life expectancy in the 90 to 100 year range at a national level with achievable levels of per capita income.
Brian Wang is a Futurist Thought Leader and a popular Science blogger with 1 million readers per month. His blog Nextbigfuture.com is ranked #1 Science News Blog. It covers many disruptive technology and trends including Space, Robotics, Artificial Intelligence, Medicine, Anti-aging Biotechnology, and Nanotechnology.
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