4 Quality of Life Associations

Up to now we have explored how each quality of life indicator is associated with income inequality. It is equally important to disect how the quality of life indicators interplay with each other.

Figure 4.1 illustrates the distribution of each indicator by region. The colored-polygons represent the density for a given value. The box and whisker plot within each colored-polygon depicts the non-outlier quartile values. Note the different axes scales for each indicator.

Figure 4.1: Inequality to Quality of Life

Figure 4.2: Relationship between Individual Variables

This three dimensional scatterplot can be used to examine how each of the three quality of life indicators we examined relate to each other in different regions. For example, to view the relationship between birth rate and educational discrepancy, one could rotate the view to directly ‘above’ the plot, looking straight down the suicide rate axis.

Looking from this view, we can see that higher birth rates are correlated to higher educational discrepancy across Africa and Asia, although in the case of Asia most of that correlation is driven by a few central Asian countries (Afghanistan, Yemen, Iraq,Pakistan). Europe appears to buck this trend, although the scale, both in terms of birth rate and educational discrepancy, are extremely low by comparison.

Suicide rates tend to drop with higher birthrates, and increase around neutral educational discrepancy, although both of these trends appear somewhat exaggerated in this graph due to how each continent clusters. Africa and Asia tend to stretch the educational discrepancy scale, causing Europe and the Americas to look like a dense mass around 0. Combined with Europe’s generally high suicide rate, this makes the correlation appear stronger than it really is.

Our analysis thus far has disregarded how the variables of interest and Gini coefficient change over time. The set of line plots below depict the time-related trends in each of the variables from 2000 to the present.

Figure 4.3: Quality of Life over Time

Initially, we focused on only three continents: Africa, Asia, and Europe. (This view can be obtained by double-clicking on Africa and then single-clicking Asia and Europe.) For the most part, all four variables across the three continents exhibit a downward trend across time. Additionally, in the plots of education discrepancy, birth rate, and Gini coefficient, countries in Africa have the largest magnitudes, those in Europe have the smallest values, and those in Asia are in the middle.

We then added North America and South America to our analysis. (This view can be obtained by single-clicking North America and South America.) Other than the suicide rate in South America, the four variables tend to exhibit a downward trend across time for all five continents. The primary observation, however, is how North America and South America have high income inequality relative to what is expected from their low birth rates and low levels of education inequality.

Oceania and the Seven Seas consist of a small number of countries and, thus, contain noise that make it hard to identify time-related trends for education inequality. That said, the time-related trends for birth rate and income inequality for these two regions are similar to those observed across the other five continents.

Ultimately, we observe that all four variables across each continent have decreased over time, on average and with few exceptions. Additionally, the line plots extend our prior analysis to reveal that the associations between education inequality, birth rate, and income inequality have held over time.