Winning the Game of Life

A few weeks ago I took a dive into the empirics of life expectancy. How can we live longer, I asked, without consuming more energy?

In the past, we’ve used fossil fuels as a crutch. To increase life expectancy, we’ve consumed loads more energy. Figure 1 shows the trend across all countries.

Figure 1: Life expectancy vs. energy use per capita. I’ve plotted here international data relating life expectancy at birth to energy use per capita. Lines represent the path through time of various countries from 1960–2015. The black line shows the average trend. [Sources and methods]

Although using more energy does increase life expectancy, it’s not a sustainable option for the future. Increasing energy use by an order of magnitude (from 10 GJ to 100 GJ per person) only gives about 20 extra years of life. That’s not efficient. And it’s certainly not sustainable.

So the question is, how can we increase life expectancy without using more energy? To answer this question, it’s helpful to define something called the ‘energy-life-expectancy residual’. This is the difference between a country’s life expectancy and the international trend.

Figure 2 shows some examples. Here the black line is the international trend between life expectancy and energy use per capita. I’ve highlighted 4 countries that depart sharply from this trend. The average Nigerian, for instance, lives 12 fewer years than expected from the energy-life-expectancy trend. The average Costa Rican, in contrast, lives about 10 years longer than expected from the trend. Similar discrepancies occur at higher energies. The average Russian lives 4 years less than expected from the international trend. The average Netherlander lives 7 years longer than expected.

Figure 2: Same energy use, different life expectancy. Grey dots show the energy-life-expectancy data for all countries. On top of this data, I’ve highlighted two pairs of countries that have similar energy use, but different life expectancies. The ‘life-expectancy residual’ is the deviation from the international trend. [Sources and methods].

Winners in the game of life

In Living the Good Life … Without Killing the Planet, I took a stab at seeing what causes these energy-life-expectancy residuals. The results sparked much discussion. A common request from readers was to see which countries were doing ‘best’ and which were doing ‘worse’.

Here’s what the data says.

Figure 3 shows the countries that are winning the game of life. These are the 10 countries that have most improved their energy-life-expectancy residuals.

Let’s unpack what this means. First of all, it’s not about the overall increase in life expectancy. Rather, I’m measuring the change in life expectancy relative to the international trend. In other words, the countries in Figure 3 have beat the average.

Figure 3: Ten countries that beat the energy-life-expectancy trend. Here are the 10 countries with the greatest increase in the energy-life-expectancy residual. I’ve ranked panels (from top to bottom) in descending order. In each panel, the grey line shows the international trend between energy use and life expectancy. [Sources and methods]

Most of these countries have drastically increased life expectancy without using more energy. Some countries (like Tajikistan and North Korea) have even managed to increase life expectancy while decreasing energy use. Seeing North Korea on this list is a bit surprising, given its bizarre brand of kleptocratic communism. Living longer, it seems, doesn’t require freedom or free markets.

I’m not an expert on international development, so I’d like to hear your thoughts about what’s going on here. Why are these countries winning the game of life?

Losers in the game of life

Now let’s turn the table and look at the countries that are losing the game of life. Figure 4 shows the 10 countries whose life expectancy has decreased the most relative to the international trend.

Figure 4: Ten countries that fell short of the energy-life-expectancy trend. Here are the 10 countries with the greatest decrease in the energy-life-expectancy residual. I’ve ranked panels (from top to bottom) in ascending order. In each panel, the grey line shows the international trend between energy use and life expectancy. [Sources and methods]

Notice that other than Eswatini and Namibia, all of these countries have increased their life expectancy in absolute terms. But they have done so slower than the international trend. In other words, greater energy use has paid off less than expected.

Interestingly, half of these countries are island nations (St. Vincent, Seychelles, Tonga, St. Lucia, Grenada). These nations had higher life expectancies to begin with. When they increased their energy use, it seems that life expectancy regressed towards the mean.

And what’s going on in Eswatini? (It’s also known as Swaziland.) From 1990 to 2007, Swazi life expectancy collapsed — decreasing from over 60 years to less than 45 years. I’m no expert, but I suspect we’re seeing the results of the HIV epidemic. According to Wikipedia, a quarter of all Swazi adults are HIV positive — the highest rate anywhere.

Zero sum, by definition

I’ll close by noting that the game of life, as I’ve defined it here, is zero sum. I’m measuring life expectancy relative to the international trend, which means that there are always going to be winners and losers. Because of this fact, I don’t think that beating the international trend should necessarily be a policy goal. It would be perverse for all countries to try to ‘beat the average’, since by definition, that’s impossible. Still, it’s interesting to see which countries do better (in relative terms) than others. The harder task is to figure out why this is happening.

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Sources and methods

Energy use data is from the World Bank series EG.USE.PCAP.KG.OE. I’ve added an estimate for the energy consumed though food (2000 kcal per person per day). Life expectancy (at birth) is from the World Bank series SP.DYN.LE00.IN.

I calculate the energy-life-expectancy trend (in Figures 1 and 2) using a locally-weighted polynomial regression. Residuals are then measured as deviations from this regression.

I’ve defined ‘winners’ and ‘losers’ as follows. In each country, I measure the energy-life-expectancy residual in the first available year and last available year. ‘Winners’ have the greatest difference between the two observations. ‘Losers’ have the smallest (i.e. most negative) difference between the two observations.


  1. Just took a quick look at the history of life expectancy changes in Senegal. Here is the best resource I could find:

    The increase in life expectancy was greatest during the 1970’s and early 1980’s due mostly to decreases in child mortality from measles and malaria. Vaccination programs suppressed measles and new drugs suppressed malarial death.

    Increases in life expectancy stopped altogether in the late 1980’s through the first half of the 1990’s. At first glance this would seem to correspond with the onset of HIV as a cause of increasing adult death, but the rate of infection in Senegal has always been very low. It would take more research to determine the cause of this period of stagnating increase in life expectancy.

    From the mid 1990’s life expectancy continued increasing again, although at a lower rate than in the 70’s. I have not attempted to find out why.

    I think it is safe to say that improvements in public health measures, particularly vaccination programs were a big part of the increase in life expectancy. No real surprise.

  2. HI Blair,

    From looking at your graphs I am starting to doubt the strength of the relationship between life expectancy at birth and energy use per capita. Almost every one of the top 10 improvers in life expectancy at birth had very little increase in per capita energy use. Maybe graph the changes in life expectances from 1990 to 2007 vs the changes in energy use per capita from 1990 to 2007 for each country?

    Now I know that life expectancy at birth is very sensitive to infant mortality, maybe much of what we see in the graphs is due to lower infant mortality. I can see (potentially) how that could be improved with a low expenditure of energy.

    If the graph was redone for life expectancy at 40 years old you would remove the infant and childhood mortality and focus on the changes is mortality for mature adults. That could be really interesting.

    Again thanks for all your interesting work

    • Hi Jim,

      The relation between life expectancy and energy use isn’t really in doubt. The international trend has thousands of data points backing it up. Of course, once your start looking at individual countries, they’ll diverge from the trend. That’s exactly the point that I wanted to show here. I went looking for countries that diverged most from the international trend. So obviously when you do that you find no correlation between life expectancy and energy use. But this doesn’t negate the international trend. It just highlights the noise.

  3. Blair, your thousand yard stare has infected me. (LOL)

    I just thought of neat way to divide up the countries that might help illuminate the relationship between energy use per capita and the things it most directly impacts.

    There seems to be a fair number of countries in which the energy use per capita did not go up very much (a few looked like they went down in energy use) and other countries where the energy use per capita went up a lot. What happened in those countries who’s energy expenditures per person went up substantially that did not happen in countries who’s energy expenditures per person stayed mostly the same?

    I think the time series data for each country might tell us a different story than simple cross country evaluations

    So graph the change in energy use from 1990 to 2007 on the x axis.
    Verses the change in your dependent variable on the y axis…. Is there a relationship there?

    • So you mean within-country analysis vs between country analysis. Yes, sounds interesting. I’ll put it on my (always growing) to-do list for empirical research.

  4. One graph will still show all the countries in the world, each point on the graph would be one countries CHANGES in energy use on the X axis and the CHANGES in the dependent variable on the Y axis.

    So take the 2007 values for each country and subtract the 1990 values and graph them vs the changes in energy use.

    I think by doing this you control for differences between counties, because the data in your graph would come from the differences in a time series for each country. ( i am not totally sure on that point, been way too long sense i have done any social science research.)

    anyway – Thanks so much for the work you do. I really do find it interesting.

  5. Does average life expectancy motivate anyone’s decision-making? I’m curious what kind of impact these insights can lead to.

    If citizens of wealthy nations were actually concerned with global welfare and long-term stability of society and the biosphere, this would hopefully improve decisions about foreign aid and government investment in industrial technologies, or something like that. If they weren’t completely captured by the short-term attention spans of investors and consumers (and xenophobes and reactionaries, etc).

    I’m not saying this isn’t important. Hopefully it will inform the viewpoints of somebody consequential, eventually!

    In the meantime, I wonder if there are ways to appeal to the obsessions and fixations of the powerful and self-absorbed. The invisible hand may be an obsolete fantasy, but at some point, we have to appeal to self-interest, and do what we can to try to enlighten it.

    How does the life expectancy of people in other nations affect the people in wealthy nations? I’m sure it must, even while I suspect most people don’t care about it at all.

    • The correlation between energy use and lifespan should affect everyone’s plans. If lifespan generally goes up as energy expenditure per capita goes up, then lifespan should go down when energy availability goes down. Since the bulk of current energy availability is from non-sustainable sources, everyone should be prepared to live in a world with much less energy and much shorter average lifespans.

      If societies are going to collectively manage that preparation process, figuring out how to maximize lifespan per unit energy by looking at other countries’ situations would be very helpful, especially if there are examples of countries that do very well in keeping people alive with very little energy.

      But since I doubt that people in power are even thinking about the ephemeral nature of the fossil fuel era, I would recommend that everyone figure out on their own how to keep body and soul together when energy decline causes a deterioration in the smooth functioning of advanced industrial economies. When times get tough, some people will be in better position to deal with hard times than others. As the old cliche goes, “Failing to plan is planning to fail”.

  6. Sorry I’m late to this. Just one thought so far (regarding life span v. energy consumption): in this hyper-connected world, would it not be possible for populations of one country to profit from the energy use of another country without that use per capita showing in the national numbers? So if scientists in one country, buoyed in part by massive energy availability, develop a vaccine for malaria, do not the citizens of other, energy-poor but malaria infested countries, benefit from that development via life expectancy increases?

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