We sports economists have become accustomed to inflated economic impact statements made in documents used to support subsidies for sports. Economists Craig Depken and J. C. Bradbury have recent posts on their blogs about this issue. First, in a post over at Division of Labour, Craig posts an excerpt from a Miami Herald article on the economic impact of Super Bowls in Miami.
Advocates of the Super Bowl as an economic engine dismiss its academic skeptics as using complicated formulas to obscure the obvious. And they note that the reports bashing NFL figures bring the professors coveted media coverage as the big game approaches.
Simplicity? You want simplicity? J.C. has a story about simplicity over at his Sabernomics blog. He writes about a call he got from a reporter regarding the economic impact of the Richmond (Va.) Flying Squirrels. An executive with the Flying Squirrels claims that the team generated $40 million in economic impacts in the Richmond area. J.C. wondered how they could come up with such a big number, so he asked the reporter about the methodology used in the report. The reporter responded:
“That figure, he said, is based on a Minor League Baseball formula that takes the amount of revenue generated by the organization and multiplies it by five.”
It doesn’t get much simpler than that.
The US Bureau of Economic Analysis uses input-output modeling to calculate multipliers for specific products in a whole host of geographic locations. You want the multiplier for butter in Columbia, Mo.? The BEA has one. Are you interested in a multiplier for the state of Minnesota? The BEA has got you covered. From my experience in using these multipliers, most of these BEA multipliers are around 2.
But we must account for the fact that spending on sports largely comes from within the local region. Such spending represents spending redistribution, not creation, and the resulting multiplier is closer to 0.
But, of course, the economist’s methodology is so complicated that it obscures the obvious.
I do not deny the complexity of our models. One reason that they are complicated is that observable economic outcomes, whether we are talking about ticket prices, personal income measures, employment numbers, etc. are the product of a complicated process. Just like a tornado is the product of an extremely complicated atmospheric process, so economic outcomes are the product of extremely complicated market processes.
Another reason our methods are complicated is that we are dealing with samples, samples often not under our direct control. When we try account for the random process inherent in these samples, the process gets even more messy. Yes, our models are complicated. But that’s because the world is a complicated place.
Maybe next time we should multiply our numbers by 5 and be done with it and wait to see our names in the papers.