The Five Eyes nations are Australia, Canada, New Zealand, the United Kingdom, and the United States. The nations comprise the core Anglosphere and have an intelligence alliance. The designation “Five Eyes” (FVEY) applies to this well-known and infamous alliance.
Edward Snowden described the FVEY as a “supra-national intelligence organisation that does not answer to the known laws of its own countries” [ref].
I speculate that because their shared history and heritage, these nations might be more similar in terms of social and public health trends than say another group of five nations roughly comparable in population size and economic development. We’ll see what the data says.
The Five Other nations for this comparison are Italy, Japan, Mexico, Norway, and Russia. Next tab has recent popluation estimates (from Wikipedia) for all.
| Five Eyes | ||
|---|---|---|
| Australia | 25,949,300 | |
| Canada | 38,436,447 | |
| New Zealand | 5,139,470 | |
| United Kingdom | 67,081,000 | |
| United States | 331,893,745 | |
| Total | 468,499,962 | |
| Five Other | ||
| Italy | 60,317,116 | |
| Japan | 125,440,000 | |
| Mexico | 126,014,024 | |
| Norway | 5,402,171 | |
| Russia | 145,478,097 | |
| Total | 462,651,408 | |
Popluation estimates from most recent data available @ Wikipedia.
Dear TSD students for Spring 2022: please browse this example dashboard. Your final project will be generally similar.
The base gapminder data set from Data Science Labs. Additional Gapminder data sets directly downloaded from Gapminder.org, and available under a CC-BY 4.0 license.
Used in the two correlation graphs and related linear models, the Per Capita GDP (PPP) variable defined as “Gross domestic product per person adjusted for differences in purchasing power (in international dollars, fixed 2011 prices, PPP based on ICP).” Source: http://gapm.io/dgdppc.
Data for Choropleth maps available from Mapping Gapminder to Tidyverse, an improved version of the ggplot2::map_data("world") data set. Available under CC0 (Public Domain).
Project data sets and worksheets @Github Repo.
21 February 2022
Thomas J. Haslam, PhD
We see from the graphs that the strict assummptions for a linear model (lm) are not met, but modeling gapminder typically starts with an lm. So I include it if only to test some standard assumptions.
That said, the adjusted R-squared value for Five Eyes: 0.9734; for Five Other, 0.8352. As the model results do show, it turns out that Per Capita GDP (PPP) matters considerably less than one might think. The USA, for example, which has the highest Per Capita GDP (PPP), lags the other Five Eyes nations. (For the lm, Australia is the baseline nation – and first in overall outcomes).
Once a nation has reached a certain level of economic development, it seems clearly that public health policies, access to medical care and resources, and even typical diet and lifestyles matter more. Again, the USA leads in wealth but trails in health! New Zealand, with roughly 70% the Adjusted PPP Per Capita GDP of the USA over the time frame studied, consistently out performs the USA for Life Expectancy.
In Five Other, Russia is the clear anomaly – violating our assumption that things generally improve over time. (For this time frame, NO). Life Expectancy crashed after the fall of the USSR, but began declining in the late 1960s! It started improving consistently in 2005, but now (2016, last year plotted) still stands just roughly two years above the early 1960s level. Otherwise, Mexico made considerable gains; and Japan and Norway switched places, with Japan now first and Norway a close third. (For the lm, Italy is the baseline nation – and second overall in best outcomes).
Finally, please note that the variable Year obviously includes a number of lurking variables: improvements in technology, medical treatment, and more. It also generally correlates with Per Capita GDP, which is measured annually and more often than not increases rather than decreases.
When we compare the map for 1960 to that of 2016, we find several important contrasts. First, our high range for Average Life Expectancy in years has gone from the low 70s to the low to mid 80s. Progress indeed.
China, in part because of 三年大饥荒 (the Great Chinese Famine), had an Average Life Expectancy of roughly 30.5 years in 1960. In 2016, 76.5 years. An improvement of 46 years!
Much of Sub-Saharan Africa is still in the lower range. But many nations that were in the 30s or 40s in 1960 are now in the high 50s or low 60s in 2016. So also considerable gains.
South America and Central America also show strong improvements – significant gains in Average Life Expectancy – nearly across the board.
All in all, the global overview is good news, albeit not distributed equally across all continents and nations.
For the Five Eyes nations, in the 1960s we have 5 out of 10 comparisons in which the average life expectancy between the two nations for that decade has no difference that makes a difference. Otherwise, we do see the USA trailing all other Five Eyes nations; Canada slightly ahead of Australia.
For the 2000s, the USA now trails by an increased margin all around; and Australia is the best performer, and now meaningfully ahead of New Zealand. We also have only three null comparisons – so more gaps all around. Greater divergence in Life Expectancy.
For the Five Other nations, Russia has clearly suffered as it now trails all other nations – including Mexico, on which it once outperformed significantly. Mexico has strongly improved. Outside of Russia, Mexico still trails the other three nations – but significantly less so. Finally, Japan barely outperforms both Italy and Norway. But the three nations are very close.
Excepting Russia (again), the Five Other ANOVA results show strong and general improvement over the past 50 years (1960 start; 2009 end). Excluding Russia, in the 2000s, no other nations are +/- 7 years apart. In the 1960s, we had four out of ten comparisons in which the nations had 9+/- years or greater difference in average Life Expectancy. Only one of which included Russia, and that was Russia being 9.5 years on average higher than Mexico.
Please note that the Energy numbers stand for Kilogram of Oil Equivalent Per Capita. (Source @ Gapminder.org/data).
We might reasonably assume a connection between increased Energy consumption per capita, quality of life, and hence Life Expectancy. The data does NOT support this assumption!
Italy and Japan, two of our nations with the best Life Expectancy outcomes, have actually have lower Energy consumption per capita in 2014 versus 1990. Australia and Norway, two nations also with strong Life Expectancy outcomes, show a moderate to mild increase in Energy consumption per capita.
The UK and the USA show moderate decreases in Energy consumption per capita, but not stellar outcomes in Life Expectancy.
So “going Green” so far seems unrelated to Life Expectancy outcomes. Of our seven nations with an average Life Expectancy of 80 years or higher in year 2014, three of them are below 3500 in Energy; three are between 4500 and 5600; and one, Canada, at 7900.