Fun with Statistics 2

I had so much fun with that last set of number that I decided to try this again. This time I rearranged the data so that I could look at generational sets over the course of time. Each generation contains five years worth of people. And all the data comes in five-year intervals from 2000 to 2015 – that’s four data points for each generational group.

You can see here that each generation gets four rows. This generation group started out in the year 2000 from the age 0-4. Then in 2005 they were between 5-9 – unfortunately not everyone made it, the generation shrank by about 500,000. As best I can tell, there are just two things that change these generational number, mortality and migration. I don’t know how to separate out those two effects yet. If I was really ambitious I might be able to find some migration numbers, but I took a quick look around and didn’t find anything helpful or reliable. The generation that started out age 0-4 in 2000 will be 15-19 in 2015. It’s expected to shrink from 95,371,000 down to 94,289,000 – that’s a loss of 1,082,000 people due to death and migration. It shrank by about 1.13% from the 2000 level.

That’s the number that I finished with. The generational shift should show us how much each generation has had to contend with change, loss, and general struggle. Again, you can access the complete spreadsheet here. Each country has its own page and I’ve ordered the generational loss numbers in a convenient manner. Not surprisingly they all end the same way no matter what country you look at. By the time you’re looking at 80 and 90 year old people, mortality rate approaches 100%. The really interesting information is at the other end – from 0 up to about age 49 or 54.

https://spreadsheets.google.com/ccc?key=0AqpZ3vUyL2GvdFBjemx1UU83aEVFTktIT0h2aWpPZUE&hl=en

Those are the ages where people really move around. The US gets a good number of people coming in – apparently more than other nations our size. India and China are visions of stability. Relative to their native populations, migration doesn’t appear to have a significant impact on their numbers. I would guess the losses that appear in the numbers are mostly due to mortality because they are so stable across age groups. My guess is that when the numbers are more sporadic across generation groups, it is due to migration patterns. Among this group, Saudi Arabia, Iran, and Vietnam stand out. You can see these contrasts in the graph below.

So it seems that there are approximately three kinds of countries. There are gainers, losers, and countries that are stable. The gainers, like the US, Germany, and Saudi Arabia have economies with a strong demand for more manual labor – they’re importing workers. The losers have cheaper-labor economies where workers might have incentive to seek better employment opportunities somewhere else. The stable countries are probably more self-contained economically relative to their size. They could be smaller counties without a lot of immigration or emigration, or they could be larger countries that have nowhere to send workers in such numbers that look meaningful against their total population (China and India.)

I think this info is pretty interesting so my next mini-project is going to expand the data I’ve used to look at these numbers. I know that the effects in my generational loss numbers are mixed, but I think that’s ok. These numbers should give us some information about stability because whether someone has moved away or passed away, their social circle needs to contend with their absence. We’ll see where this goes.