Introduction

When it comes to quality of life on a global scale, it is imperative to look at the basic needs which are available to the people of individual countries. Using data from The World Bank, I am interested to figure out the factors that influence the life expectancy of people all around the world. The three variables that are directly correlated to life expectancy I believe are: health expenditure as a percentage of GDP, access to improved drinking water sources, and access to improved sanitation facilities. The data I will be analyzing has been refined to only include countries which have complete information on the variables I have just mentioned. This leaves us with 179 countries to observe. Furthermore, I have taken the average of the 179 countries between the years of 1995 to 2012 for the sake of efficiency. The world bank has five classifications for countries: low income, lower middle income, upper middle income, high income (NON-OECD), and high income (OECD).

This graph depicts that there is a clear positive trend between income and life expectancy. The lower income countries have greater ranges and there are a few anomalistic data points in upper middle income and high income countries which can be removed to improve the results of the regression analysis.

There is a clear positive correlation between health expenditures as a percenteage of GDP and life expectancy, up to about 10% of GDP. Beyond that, there are outliars.

The above two graphs portray that access to clean water and access to improved saniation increase significantly moving towards 100%.

library(stargazer)
## 
## Please cite as: 
## 
##  Hlavac, Marek (2014). stargazer: LaTeX code and ASCII text for well-formatted regression and summary statistics tables.
##  R package version 5.1. http://CRAN.R-project.org/package=stargazer
stargazer(m0,m1,m2,m3,m4,m5, type="html")
Dependent variable:
life
(1) (2) (3) (4) (5) (6)
health 0.198 -1.027** -0.201 0.004 0.036 0.162
(0.166) (0.402) (0.466) (0.312) (0.277) (0.183)
water 0.185*** -0.075 0.086 0.192** 0.703*** 0.824
(0.049) (0.098) (0.095) (0.090) (0.069) (1.019)
san 0.266*** 0.142** 0.219*** 0.216*** 0.349*** 0.976***
(0.019) (0.054) (0.045) (0.064) (0.077) (0.295)
Constant 28.717*** 61.452*** 42.309*** 33.144*** -28.113*** -102.486
(3.861) (9.066) (7.613) (9.679) (9.179) (102.570)
Observations 179 31 46 53 20 29
Log Likelihood -548.827 -92.526 -142.153 -163.541 -42.873 -55.637
Akaike Inf. Crit. 1,105.654 193.051 292.306 335.083 93.746 119.274
Note: p<0.1; p<0.05; p<0.01

1= All countries included 2= Low income 3= Lower Middle Income 4= Upper Middle Income 5= High (Non-OECD) Income 6= High (OECD) Income

In terms of statistical significance access to sanitation and water are the most significant when explaining life expectancy. Health expenditure as a percentage of GDP does not seem to be as important of a factor as access to sanitation and water.

Conclusion

The results of this analysis tell us that water and improved saniation hold greater weight than health expenditure when looking at life expentancy rates on a global scale. This can allude to the fact that countries are spending too much of their funding towards areas that may not be assisting in the overall quality of life for their citizens. Water is the most important factor for all 179 countries (p<.05) for greater life expectancy. Furthermore, this analysis can further be improved by filtering for anomalies, specifically in the health expenditure dataset.