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.

Fun with Statistics

After a conversation with hannah about populations and demographics I decided to have a look for myself. The UN has a great database with information on just about every country. Because we were talking about populations of young men, I started by getting trend data on age and gender for the twenty largest countries in the world. We focused on young men because there’s a conventional wisdom meme that claims large populations of young men lead to wars.

Here’s the link to the entire spreadsheet:
http://spreadsheets.google.com/pub?key=tIIoL4lufmnKMrtTr9fClqw&output=html

The first page has some interesting statistics comparing the populations across countries and then there is one page for each country, in order from largest on down. All the raw numbers are in thousands. So in the first table (T1) on the front page, for T1-China:2000 it has the number 9731 – that’s 9,731,000 people. More specifically, those 9.7 million people are all young men, ages 15-29, that did not have a corresponding woman of the same age. On the individual country pages, I called this statistic “extra men.”

The UN output format isn’t the greatest in the world. It took some time moving the numbers around to bring out the relevant data. The second table (T2) on the front page is labeled “USA 2000-Baseline.” Here I took the number of extra men in the US in the year 2000 and set that as 1.00 – then I divided all the other figures on extra men by that number from T1-USA:2000 (1196) to help put the numbers of the other countries in perspective. For example, right now in 2010, the US has 1.25 times as many extra young men compared to back in 2000. Remember, this isn’t the raw number of young men, this is the difference between the number of young men and the number of young women. A change from 1 up to 1.25 is a fairly subtle shift. Compare the USA’s current number, 1.25, with China’s, 13.71, and your eyes might pop a bit. Right now, China has nearly 11 times as many extra young men compared to the US.

How much of that can be accounted for by difference in population size? Well, China’s population is about 4.2 times larger than ours. That should indicate that something different is going on between the US and China. People often say that China is missing women. There may be some error in statistics reported in China due to the “one child” policy, but I don’t really know much about that. More likely, the aberration is due to infanticide of baby girls which has been reported on in numerous places. The statistics seem to imply something similar has taken place in India. We shouldn’t think that the US’s numbers are rock bottom. I haven’t looked at a way to find the “normal” balance between young men and women yet, but the US has quite a few "extra men" as well.

So what about that “young men = war” idea? Well, these stats don’t tell us too much so far. The fact is that I would need to look at historical data to see just how we should interpret that claim. Does that mean that large groups of “extra men” increase the chance of war? Or does it mean that a population, as a whole, which has more young men is more likely to go to war? If that’s the case, Iran looks far more dangerous than China. They have the youngest, most-male population among the 20 largest countries in the world. Has there been any trouble in Iran lately? Another reason that historical data would be important here is that all around the world, medical care is improving and populations are getting older. There will be fewer spikes in the numbers of young men. So is the phenomena in question a matter of a magical tipping point – you get X percent of young men and you will have a war? Or is it a relative issue where the most lopsided countries in the world get to light the firecrackers? Furthermore, what if aging populations just lead to stretches in the longevity of our social roles. “30 is the new 20, son, so go grab your gun and your balaclava!”

For the information that statistics can tell us, they sure leave us with a lot of questions.

A Classy Guy

Jim Tressel is a winner and a total class act. He has done nothing but bring honor to the The Ohio State University and its football program. And he's kind of funny, too. "I think they're all sober."

Congratulations to Coach Tressel and the Ohio State Buckeyes, 2010 Rose Bowl Champions!