Hans Rosling on population growth and other, related things.
Category: Demographics
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Second hand water
Randall Munroe, over at xkcd, does some really funny cartoons, and is a physicist by training. Every week he answers a hypothetical question: this week it’s
How much of the Earth’s currently-existing water has ever been turned into a soft drink at some point in its history?
His answer, by the way, is “not much”. On the other hand, almost all of it has been drunk by a dinosaur.
One of the things I really like about these “what-if” answers is the way they demonstrate one of the important aspects of modelling: working out what’s significant and what’s not. And significance depends very much on what the purpose of the model is. Often, Munroe can make some really sweeping assumptions that are clearly not borne out in practice, but are equally clearly the right approximations to make for his purposes. And sometimes he says that he doesn’t know what assumption to make.
An example of a sweeping assumption comes in the answer to
How close would you have to be to a supernova to get a lethal dose of neutrino radiation?
where he assumes that you’re not going to get killed by being incinerated or vaporised.
And in answering the question
When, if ever, will Facebook contain more profiles of dead people than of living ones?
the difficult assumption is whether Facebook is a flash in the pan, and stops adding new users, or whether it will become part of the infrastructure, and continue adding new users for ever (or at least for 50 or 60 years). There are also some sweeping demographic assumptions, of course.
(and while you’re at it, read the one on stirring tea)
I’m reminded of two things here. The first is doing mechanics problems in A-level maths: there was nothing difficult about the maths involved, the trick was all in recognising the type of problem. Was it a weightless, inelastic string, or a frictionless surface? It was all about building a really simple model.
The second is those Google interview questions we used to hear so much about, like how many golf balls fit in a school bus, or how many piano tuners there are in the world. The trick with these is to come up with a really simple model and then make reasonable guesses for the assumptions. And, of course, be aware of your model’s limitations.
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The aging silver lining
A few days ago I noted the difficulty of thinking in terms of dependency ratios: being economically active is a continuum, rather than black or white. There’s another side to the story, too. An aging population can provide opportunity, not only by producing products that appeal directly to a growing segment of the population, but also by providing services to help care for them.
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Dependency: it’s not black or white
Frances Coppola makes some interesting points about dependency ratios, sparked by this article from The Economist. We often see charts showing the proportion of the population aged over 65 compared to those between 16 and 64, based on the assumption that the former aren’t working and the latter are.
The trouble is, as Frances points out, that the assumption is a massive over simplification. At the younger end, there are a lot of young people in education. In the middle, you’ve got the unemployed and disabled, those not working through choice, and those that are working but who also receive benefits. And at the older end there are increasing numbers of people who are both working and drawing pensions. Being economically active is not an all or nothing state.
Frances argues that, on the whole, there are few people over 65 who are not partially or fully dependent. But the main reason that the raw ratio is misleading is the large number of younger people who are also partially or fully dependent.
The dependency ratio is a crude measure that takes no account of the actual economic contributions made by people in different circumstances and at different stages in their lives. A few over-65s working mainly part-time to top up their state pensions doesn’t invalidate the ONS’s dependency ratio calculation. But a large number of people dependent on state benefits to top up their wages does. We don’t just have a demographic problem. We have a low wage problem.