Interaction of Sanitation and Water:

quantile(eff, c(.025,.975))
##       2.5%      97.5% 
## -0.1956852  0.1856182

-> Based on these results, we have failed to reject the null hypothesis as our confidence interval includes the number 0.

Interaction of Life Expectancy and Health Expenditure as a Percentage of GDP:

quantile(eff, c(.025,.975))
##      2.5%     97.5% 
## -1530.104  1613.146

-> The above is a histogram of population on life average and health expenditure. Based on these results, we have failed to reject the null hypothesis as our confidence interval includes the number 0.

Regression Results

stargazer(count2, type = "html")
Dependent variable:
pop
life 0.111***
(0.00000)
health -0.141***
(0.00001)
water 0.055***
(0.00000)
san -0.049***
(0.00000)
Constant 9.236***
(0.0002)
Observations 179
Log Likelihood -9,426,191,937.000
Akaike Inf. Crit. 18,852,383,884.000
Note: p<0.1; p<0.05; p<0.01


I have created a new variable for population which will be used for the rest of this analysis. I have gathered the most updated numbers on population for the 173 countries I am using in this analysis. The table above shows some interesting results. First off, as life expectancy increases, the overall population increases which is intuitive. However, something I have been proving over the past few weeks is that as countries spend more money on health expenditure as a percentage of GDP, the population actually DECREASES. This means that health expenditure MIGHT not carry as much weight as is often perceived. Next, as access to improved water increases, so does the population naturally since people are not dying due to lack of water. Lastly, for each increase in access to improved sanitation conditions, life expectancy actually decreases. After further research into this, it seems that this variable might introduce the most noise in the data as “improved sanitation” can mean something very different when compared to first and third world countries, hence I speculate this might be a confounding variable. However, despite these things the entire analysis is still statically significant.

Simulation Graphs

Note: To create a more concise looking report I will be showing the code only for the first simulation of the independent variables for this analysis.


Graph 1:

san.range <- min(laveragepop$san):max(laveragepop$san)
x <- setx(reg4, san = san.range)
s <-sim(reg4, x = x)

sExpect <- (s$qi)
sExpect <- (sExpect$ev)
sExpect <- as.matrix(sExpect)
sExpect <- as.data.frame(sExpect)  
sExpect <- melt(sExpect, measure = 1:85)

select(sExpect, variable, value) %>% 
  group_by(variable) %>% 
  ggplot(aes(variable, value)) +   
  geom_point(shape = 21, color = "gray30", alpha = I(0.05)) +  
  stat_smooth(aes(group=1), method = "lm") +
  scale_x_discrete(breaks = c("V1", "V42", "V85"), labels =c("0","42", "85"))+
  xlab("Range of Access to Improved Sanitation") +
  ylab("Expected Values: E(Y|X)") +
  ggtitle("Simulation of Access to Improved Sanitation") +
  theme_bw()

According to the regression above, sanitation is negatively correlated to population. Meaning as a larger portion of a given country has access to improved sanitation, population goes down. Logically this is counter-intuitive. One cause for this might be that large countries such as China and India where large portions of their populations do not have access to improved sanitation. Moreover, it depends on how the world bank classifies sanitation. If the baseline of improved sanitation is developed countries, then a significant portion of the world’s population will be sub-par.


Graph 2:

The results of this regression are very intuitive because as more people in a given country have access to clean water, there will be less people dying due to dehydration. Policy makers should keep this variable in mind as it is the most significant variable in terms of determine overall quality of life.


Graph 3:

The graph above is showing that an increased in health expenditure, decreases population. At first glance this is telling us that spending more money on health expenditure may not be helpful towards increasing the overall life expectancy of people. However, developed countries for instance have very large GDP so health expenditure is a very small number compared to the overall. In comparison, smaller countries might have a smaller GDP in general, but a larger percentage of that GDP going towards health expenditure. Thus, this indicator may not be a good reflection of these variables, hence the conflicting results.


Graph 4:

The graph above displays that as life expectancy goes up, so does the population. Once countries have established how to keep life expectancy up, dealing with the increase in population needs to be a top priority for all countries.

Conclusion

Despite the conflicting results within the health and sanitation variables, the numbers are still significant. This gives us a fuller understanding of how life expectancy influences other factors. However, in order to a get a more complete story of this phenomenon future research should look at a different way to measure health expenditure as a percentage of GDP. Additionally, because there is a lot of ambiguity around the sanitation variable as access to improved sanitation varies greatly by country this variable should be treated different for low income, lower middle income, upper middle income, high income (NON-OECD), and high income (OECD) countries.