Sports stars born early in the year

One of the more interesting pieces of evidence in the nature or nurture debate is the that athletes on professional sports teams tend to have a higher proportion of players born early in the year. Malcolm Gladwell documented this phenomenon for ice hockey players in his book Outliers. The basic idea is that when young, those born earlier in the year are bigger and faster than their peers and, as a result, tend to get more game time, are selected for further development and so on. This ongoing cycle amplifies the original difference. (The precise time of the year can change if the age cut-off is based on another date, but the same concept still holds.)

This morning I went through the current player lists of the Australian Rules Football teams and saw a similar result. Around 30 per cent more players were born in the first quarter of the year than the final quarter (although as a quick google discovered, I was not the first to have done this). Still, there is nothing like playing with the data and seeing it with your own eyes.

This is clearly evidence in favour of the nurture side of the debate. There are also a few possible policy responses. Gladwell talks of setting up parallel sports leagues for children, with one having a different birthday cut-off (say, mid year). Another suggestion might be to spend less time trying to pick future stars at such a young age. Within a single country, there is probably not much benefit to these policies, but for international sports, could this give a country an edge by ensuring that talented individuals born later in the year have a chance?

There is some evidence for this effect in academic pursuits, with the difference in test results between children born earlier and later in the year enough to differentiate who might be selected in a gifted academic program. The longer term effects of this is not so clear however. What would be a good dataset to test whether someone born later in the year is under-represented in an academic field?

To me, whether this all matters depends on whether this effect extends beyond the top end of the bell curve. Whether a few dozen professional sports players make it to the big league or not is of no great social consequence. If this effect was throughout all levels of society, with those born later in the year being invested in less than the early birds, there may be some serious misallocation of resources and sub-optimal use of human resources.

I have one further hypothesis regarding this scenario: the long term effect is larger in sport than in other areas. This is based on two ideas. Firstly, it comes from some scepticism on my part about the benefits of “gifted child” programs and the like. Secondly, in the area of sports, if you are shorter or slower, you tend not to be picked for the sports team or not given the ball. In academic areas, you still have to do your math problems, sit through the test and undergo most of the academic training that the “gifted children” do.  I’m not fully convinced of my hypothesis, but if there was a dataset to examine the birth effect for academic related outcomes in adulthood, it would be worth a look.

Should we tax education?

Over the last few weeks, Bryan Caplan of Econlog has engaged in a debate with his former teacher Bill Dickens over the social value of education. Brian’s position is that  education is largely used for signalling rather than skill acquisition. While some signalling is good (matches students and employers), it is privately optimal to far exceed the social optimal. This excessive signalling consumes resources for limited social return, so we should stop subsidising it and possibly consider taxing it. I find myself leaning towards Brian’s position – particularly in relation to senior high school and university/college education.

A recent Economist daily chart reflects the waste from the subsidisation of education, with over 20 per cent of university graduates in the OECD working in low-skilled jobs. In the United States and Canada, it is over 30 per cent. Some of this is choice, and you would expect that group to be the first to cut back on education if they were required to pay the full cost.

The element of Brian’s position that interests me is what the signalling environment would look like in the absence of subsidised education. Education is not only costly in terms of money, but it also consumes a large amount of time. Is the time commitment required for an accurate signal? Once education is no longer subsidised, will other signals that are less intensive in the time required emerge? IQ can be determined through tests. How long does someone need to engage in an education to show persistence, courteousness and reliability? Could an intense one-year course substitute?

I would also expect to see a distinction emerge between those courses with a larger signalling element and those in which skills are genuinely acquired. This would flow on to the costs (salaries) associated with those skills.

The last things, and my hope, is that it would give us cheaper plumbers. That is a touch flippant, but if fewer people move into education as the price has increased, perhaps some of them will get those other unsubsidised skills and trades and push the price of them down.

The 2010 Australian election betting market

Over the course of this increasingly interesting Australian election, I have followed the betting markets. I want a quick proxy of the likely election outcome without the requirement of monitoring the news. I try to avoid overdosing on short term news, preferring to read books and articles that someone has put some thought into. However, being unable to completely detach myself, I have had a quick peak at the betting markets each day. Everyone else can absorb and collate the events of the day for me.

During the course of the campaign leading up to the election (and this is from memory), Labour was generally ahead, with the Coalition paying over $3 at some points. On the night of the election, the polls in Eastern Australia closed at 6pm EST. At 9.30pm EST after some of the results had come in, the odds on a coalition victory had blown out to $3.80 on Betfair. When I returned two hours later after watching a movie, the Coalition was favourite (paying around $1.40) with the most likely outcome a hung parliament and the balance of power in the hands of a few rural independents.

That result has played out for the last two weeks, with the three rural independents possibly announcing who they will support tomorrow. Over those two weeks, the Coalition has slipped out of favouritism and is now paying $3.20 on Betfair (they have been as high as $3.40 today).

This raises the interesting question of the accuracy of the betting market in predicting an election outcome. On the one hand, it is wrong to say that the market was “wrong”. The implied odds of a Coalition victory during most of the campaign was around 25 per cent. If those odds are accurate, the Coalition should win one in four elections in those circumstances. Unfortunately, since our sample size is one, this election result does not help us in assessing the accuracy of those particular odds. On the other hand, I am not sure that the markets have provided a useful guide, particularly in the last two weeks. I might have just been better off completely detaching myself until the new Prime Minister is announced.

This highlights a question worth exploring. Over the last few years, I have seen a lot of comments and articles about how the betting markets are generally right. In an article on election markets, Andrew Leigh and Justin Wolfers noted that in 2001, the Centrebet favourite won in 43 out of 47 marginal seats, while they won 24 out of 32  in 2004. The implied Centrebet odds for 11 of the seats was between 50 and 60 percent for the favourite (that is from a visual inspection of Figure 6 in another article by Wolfers and Leigh). Another dozen or so seats were between 60 and 70 percent. To have seats in those probability ranges and get 43 out of 47 right suggests that the odds were far too conservative. The favourite should have been more favoured and a larger payout available for the underdog.

The predictive power of marshmallows

I have gone through the back catalogue of podcasts for WNYC’s Radiolab for a couple of months now. I got into it after ABC radio substituted it for the Science Show for two weeks in late June. It is sensational – great content and entertaining.

I just listened to the Radiolab podcast on Walter Mischel’s marshmallow experiment . The basic idea of the experiment concerned testing the ability of four-year olds to delay gratification. The experimenters left the children in a featureless room with a marshmallow (or Oreo cookie in later experiments) with a promise of more if they waited. The interesting outcome from the experiment has been how the ability to delay is a strong indicator of future success. For example, the average difference between those who waited 10 seconds and those who waited 15 minutes was around 210 points on the SAT. 210 points is roughly the difference between the 60th and 85th percentile.

I was familiar with this experiment from earlier readings, but was not aware on how this had become such a strong indicator of future success. Mischel and his colleagues are still following a number of the subjects, so there should be more to come.

So what we can take from this experiment? One (optimistic) possibility is that by training children to delay gratification (those who delayed used a variety of tricks) there may be lifelong benefits. Another possibility, and the one to which I lean, is that the experiment is an indicator of a broader personality trait, and that teaching a technique to delay gratification in such a case will not have much long-term impact. I consider that it is more like testing children for IQ – you can do something but it will take some effort.

An interesting question is whether patience is a cause of the delayer’s future success or a symptom of the underlying cause. For example, is this test simply a proxy for intelligence, with more intelligent children able to foresee the consequences of their actions and devise methods to help them delay?

It's a risky business attracting a mate

Last week, ABC’s Catalyst had a story on skateboarders taking extra risks based on the presence of an attractive researcher. This was based on article published earlier in the year (Ronay, R. & von Hippel, W. (2010). The presence of an attractive woman elevates testosterone and physical risk-taking in young men. Social Psychological and Personality Science, 1, 57-64).

I haven’t been able to access the article yet, but in the Catalyst story, von Hippel proposed that it could be explained through the role risk taking plays as a signal of fitness. It demonstrates skill or (in case of failure) robustness.

Another evolutionary explanation, and one that applies particularly to young males, was put forward in 1979 by Rubin & Paul (An Evolutionary Model of Taste for Risk, Economic Inquiry, 17:4).They noted that adolescents, having attracted zero mates, have little to lose from risk seeking activity. By taking the risk, they have a chance of increasing their number of mates from zero. Failure to take the risk leaves them with zero mates with a probability of one. The ‘risky’ activity is not risky from the perspective of the desired result.

An extension of the skateboarding experiment to test this other hypothesis could involve using older males or males with long-term partners. It would be interesting to see their testosterone response compared to the young, single cohort.

If this hypothesis were true, you would expect to see more risk taking where there were, say, an excess of males or some males monopolising the females. Some cross-society analysis could be interesting.