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 in figuring 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. These three things are the basic fundamentals citizens in each country should be granted.

Conveniently enough, the data has been collected in such a way that health expenditure as a percentage of GDP excludes information on improved drinking water and sanitation facilities. This works out quite well when regressing these three variables on life expectancy. 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 in order to provide a comprehensive report on the data.

The World Bank has five classifications for countries: low income, lower middle income, upper middle income, high income (NON-OECD), and high income (OECD). Historically, OECD countries contain those which are dedicated to stimulate economic progress and world trade. They are a forum of countries describing themselves as committed to democracy and the market economy. Furthermore, OECD countries have been rated as high income nations, whereas non-OECD countries are still relatively well off compared to most nations but are not part of this particular forum.

Figures

Figure 1. This figure portrays that there is a clear positive correlation between health expenditure as a percentage of GDP and life expectancy, up to about 10% of GDP. Beyond that, there are out-liars. This signifies the importance of health expenditure up to a certain point and once that limit has been reached, life expectancy stabilizes. Researchers can conclude from this that health expenditure might not be the driving force behind life expectancy after all.

Figure 2. Figure 2 portrays the correlation between life expectancy and access to clean water. From the figure above, it is obvious to note the importance clean water has on people around the globe. Countries in which all their citizens have access to clean water have the highest life expectancy.

Figure 3. Similar to figure 2, figure 3 shows another significant trend. Access to improved sanitation increases significantly moving towards 100%. From the looks of it, access to improved sanitation is probably a stronger driving force behind life expectancy than access to clean water. This makes sense on a fundamental level because without access to improved sanitation, citizens wouldn’t have access to improved water conditions.

Figure 4. This graph depicts that there is a clear positive trend between income and life expectancy. The lower income countries have greater ranges of life expectancy and there are far less anomalistic data points in upper middle income and high income countries. Low income nations have the least number of years in terms of life expectancy.


Table 1.

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


Table 1. This tables shows life expectancy regressed upon the three chosen independent variables collectively. This has been broken down as follows:

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. Furthermore, health expenditure as a percentage of GDP does not seem to be as important of a factor as access to sanitation and water.

Further Analysis

Figure 6. The above probability density graph illustrates that the data is skewed by class. In other words, high-income(OECD) countries make up about 20% of the entire data-set. This is most likely due to research methodology and the notion that data is more readily available for high-income(OECD) nations. Additionally, it is interesting to note the relationship between density and life expectancy. This alludes to the notion that there is not enough available data on life expectancy in lower income countries.

Conclusion

The results of this analysis tell us that water and improved sanitation hold greater weight than health expenditure when looking at life expectancy 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) when it comes to greater life expectancy. After a further analysis it became evident that about 20% of the data was comprised of high-income(OECD) nations. This further depicts how much data is readily available on this topic as lower income nations comprised the least amount of data. Sociologists should focus on establishing better research methodology tools in order to increase the amount of data pulled from low income nations. In conclusion, the order of importance in relation to life expectancy is: access to improved sanitation facilities, access to improved drinking water sources, and health expenditure as a percentage of GDP. These results have been novice in the sense that common knowledge has alluded to the order of importance be in reverse for life expectancy. However these findings suggest the basic fundamentals that are most valuable when it comes to life expectancy on a global scale.