چکیده:
This paper analyzes the effects of socio-economic factors on life expectancy.
Using multiple regression analysis, the paper shows that there is a positive
strong correlation between life expectancy as an independent variable and per
capita income, health expenditures, literacy rate and daily calorie intake.
Also, it shows that there is a negative strong correlation between life
expectancy and number of people per doctor. Using dummy variables, the paper
shows that there exist some unrecognized or recognized but not quantifiable
factors which affect life expectancy in African countries. Finally the paper
concludes that human development requires an increasing investment in the
socio-economic sectors.
خلاصه ماشینی:
"Using multiple regression analysis, the paper shows that there is a positive strong correlation between life expectancy as an independent variable and per capita income, health expenditures, literacy rate and daily calorie intake.
858 N =120 Where: e = life expectancy ln(y) = log of per capita income lit = literacy rate ln(cal) = log of caloric consumption Preston''''s results show that all variables have the expected sign, but that the coefficient of daily caloric intake (measured as a deviation from 1500) does not approach statistical significance.
In this section we analyze the effects of per capita income and some human indicators such as nutrition, education and health on the variations of life expectancy in a cross-sectional framework.
Our specific model is the following: LE=B1+B2 (PCGNP)+B3(HE)+B4 (ALR)+ B5 (DCS)+ B6 (TPPD)+u (1) Where: LE = life expectancy at birth PCGNP = per capita GNP HE = health expenditure (% of GNP) ALR = adult literacy rate DCS = daily calorie supply of food (% of requirement) TPPD = thousands of people per doctor U = stochastic term This model is perfectly linear.
Although there are not perfect theoretical justifications for all included explanatory variables, economic literature indicates the influence of education, nutrition and health on life expectancy of people.
Historically different models have been used by researchers to illustrate the true relationship, if any, between the level of life expectancy and other economic variables such as per capita income and some human development indicators such as education, nutrition and health subject to the data restrictions and insufficient theoretical justifications."