Life Cycle Savings Data Correlation

Impact of Population Age on Financial Variables

The dataset “LifeCycleSavings” is included in base R. We will be using it to analyze correlation of population age to the savings and disposable income of the population.

attach(LifeCycleSavings)

Description of the contents of the Dataset:

A data frame with 50 observations on 5 variables.

[,1] sr numeric aggregate personal savings [,2] pop15 numeric % of population under 15 [,3] pop75 numeric % of population over 75 [,4] dpi numeric real per-capita disposable income [,5] ddpi numeric % growth rate of dpi

For the purpose of this analysis we will be utilized the two age variables (pop15 and pop75), and two of the financial variables (dpi, sr)

Young People and Disposable Income (dpi)

How does the percentage of young people in a society affect finances? Let us look at a scatterplot of pop15 versus dpi:

Not looking good for the young folk. A clear negative coorelation between younger people in the population and disposable income in the population.

Quantified:

cor(dpi,pop15)
## [1] -0.7561881

Young People and Savings

How about savings? We might expect to see similar results as we did with disposable income. Let us look at another scatterplot, this time with sr (numeric aggregate personal savings) plotted with pop15:

The scatterplot gives picture not as clear as the previous ones. Quanitifying the correlation:

cor(sr,pop15)
## [1] -0.4555381

We see a moderate negative correlation. In real world terms this means that while the presence of young people in a population has a strong negative correlation on disposable income (ask a Parent, kids are expensive), it has a weaker (although still present) negative correlation on savings.

Elderly Population and Disposable Income

If a young population has a negative correlation with disposable income and personal savings, does an older population have the opposite effect? Let us look at the same plots as above, but this time using the pop75 (% of population over 75) as the age variable.

First lets look at Disposable Income:

Correlation seems clearly positive. Let us quantify it:

cor(dpi, pop75)
## [1] 0.7869995

Strong positive correlation between the % of 75+ year olds in a population and the per capita disposable income. This means that older people have significantly more spending money.

Elderly Population and Personal Savings

Does this trend affect savings as well? Not a clear picture, let’s quantify it:

cor(sr,pop75)
## [1] 0.3165211

A weak correlation at best. In real world terms this means that elderly people don’t have an impact on the overall savings per capita as they do on the overal disposable income per capita. This could mean that elderly people don’t have savings, because they are spending it on their day to day lives.

Conclusions

We have shown that:

  • Having more young people in a population strongly correlates to having less disposable income in a population.
  • Having more young people in a population moderately correlates to having less savings in a population.
  • Having more old people in a population strongly correlates to having more disposable income in a population.
  • Having more old people in a population weakly correlates to having more savings in a population.