Who can predict job growth by sector in the next 10 years if the BLS can’t?

Derek Thompson points out that 2002 predictions by the Bureau of Labor Statistics about job growth by sector for the next ten years turned out to be quite wrong:

What did BLS get right? At least two things: the unstoppable growth in health-care jobs (which it expects to continue) and the steady growth in leisure and hospitality.

What did it miss? Everything else, in particular (a) the boom in mining, led by the natural-gas revolution, (b) the utter collapse of the publishing industry, and (c) the Great Recession, which wiped out half-a-decade of economic growth. BLS thought we’d create 20 million non-farm jobs last decade. We created about six million. That’s a 13-million-job gap. 

Essentially, the BLS failed to anticipate the real-world surprises, which is another way of saying it is not psychic. It extrapolated the recent past (health care was expanding, housing was booming, the economy was recovering from a mild recession), baked in global and demographic trends, and voila, put out a plausible projection of the next ten years. This is a perfectly sensible way to predict the future. But then the real world intervened.

This isn’t supposed to be a post about how the BLS forecasting models are bad. It’s supposed to be a post about how predicting the future is impossible, even though predictions play a starring role in discussions about finance and government.

I think Thompson draws the right conclusions here: it isn’t necessarily about jobs but more about the difficulties governments and other organizations have in predicting even ten years into the future. The world is a complex place and this should push us to think about what we can know moving forward. This would be a great point to inject the writings of Nassim Taleb who has argued in several books that this is a huge problem: there are plenty of people, like on Wall Street or in Washington, who think the future is clear enough to risk a lot. Granted, the BLS isn’t going to lose much if their predictions are wrong but it could have a big effect on others. One example: students looking at what majors to select. In recent years, there are more and more articles that talk about the job fields expected to grow in the future. The argument is that students need to make sure they study for employable careers, particularly with rising college costs. But, they may pick a college or a major based on predictions that aren’t necessarily correct. Perhaps this lack of predictive ability is a good argument for liberal arts schools.

Knowing the difficulties of making long-term predictions, what can the average citizen do? Taleb would suggest hedging our bets, perhaps risking some when the negative effects won’t be that bad. (Taleb lays out this investing strategy in Antifragile: put a good amount of money in safe investments and then risk some in places where the payoff could be huge but you aren’t going to lose much if it doesn’t pan out.)

More appealing measurements of the American economy

The Economist looks at several ways in which the US federal government calculates certain economic statistics that might make our economic situation look most appealing. Here is their conclusion:

Conspiracy theorists might conclude that the American government is trying to nip and tuck its way to attractiveness. The persistent downward revisions to GDP growth do look suspicious. But in other areas American number-crunchers seem to believe that their measures are better; indeed, history shows that European statistical agencies have often later adopted their methods. The world’s biggest economy is also much less bothered about the international comparability of its numbers than smaller European countries. True, when the statisticians at the IMF or the OECD produce comparative data, they do so on the basis of standardised definitions. The snag comes if investors fail to grasp that official national figures can show the American economy in an overly flattering light.

Complex numbers, such as these, can be difficult to operationalize or calculate but they also need to be interpreted. Economic experts may know about these methodological differences and can account for these but I’m guessing that the average citizen of the US or European countries has less of an idea about what is going on.

Another US figure that has recently attracted methodological attention is unemployment. While the US unemployment rate has undoubtedly risen in the economic crisis of recent years, it has its own quirks. One part that has been discussed in that people have to be actively looking for work in the last 4 weeks and once people move beyond that cut-off point, they are no longer counted as being unemployed. Another area involves those who work less than full-time but want full-time work and could be classified as “underemployed.” (You can see how the Bureau of Labor Statistics calculates unemployment here.)

(It is also interesting in this story that they compare the calculation of these statistics to cosmetic surgery, apparently an important marker of American culture.)

Sociologists less susceptible to offshoring

According to ResumeBear, the Bureau of Labor Statistics has compiled a list of the occupations that are likely to be sent offshore. Sociologists are not very likely to be offshored, with a score of 7 on a susceptibility score (which ranges from 16 on the high end to 4 on the low end).