Hexbyte – Tech News – Ars Technica | To understand population growth, culture matters

Hexbyte – Tech News – Ars Technica |

Fertility factors —

Getting it wrong has implications for healthcare, infrastructure, and climate.

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Working out how many babies will be born each generation is wickedly complicated.

Samir KC is on a mission to get people thinking differently about population growth. The basic idea of predicting future population size is so simple a child could do it. The reality of getting an accurate estimate is fiendishly complex, however, requiring intimate knowledge of how factors like education and migration will affect a given region.

“It’s very easy to do statistical extrapolation,” says KC, a professor at Shanghai University. But accuracy demands local expertise: “You need to understand a lot of things, and not everything is in the data. Only local demographers that are experts in that country can give you the right inputs.”

In a paper published in PNAS last week, KC and his colleagues show how predictions of India’s population over the next century can vary widely, depending on what data gets baked into the calculations. Population data plays a crucial role in planning for healthcare, education, and infrastructure (and in the longer term, climate change), so that variability has clear real-world implications.

Hexbyte – Tech News – Ars Technica | Forecasting the future

If you wanted to build a very simple population growth estimate, it might look something like this: we have 10 women and 10 men now, and we expect each of the women to have four children (it can be pretty tricky to tell how many children men have). So, in 20 years’ time, there’ll be an extra forty people, making a population of 60 overall.

Obviously, that’s way too simple to be of any use at all. For one thing, those people will be different ages—so some of them will be too young to have kids, and some will be too old. For another, some of those people will die, including some of the newborns. So you need to build the age structure of the population, as well as the mortality rates at different ages, into your model.

A particularly thorny challenge is figuring out the fertility rate: how many children the average, hypothetical woman can be expected to have over the course of her life. If you just take the average fertility rate across the country, you’d have a simple but functional population model. But if you zoom in on the country and look at what’s going on in different regions, the fertility rate can look very different in different places.

The tricky thing is that fertility rates work like compound interest, where a small difference in interest rate can add up to a huge difference over time. Say there’s one region with a really high fertility rate, and one wi

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