

Asteroid Defense and Types of Public Goods
From hurricanes to asteroids, how should we determine what steps to take to avert catastrophe?
Like other types of disaster defense, protection against asteroids is a public good. Indeed, George Mason University economists Tyler Cowen and Alex Tabarrok devoted an episode of their popular online economics program, Marginal Revolution University, to asteroids as a case study in why markets tend to undersupply public goods.
In the early 1980s, economist Jack Hirshleifer at the University of California, Los Angeles proposed categories for public goods. One type is “summation” goods, which depend on the collective effort of all participants to succeed. An example would be reducing greenhouse gasses in the atmosphere: Action taken by one country to cut emissions would not be sufficient if other countries continue to pollute. This is the classic public good, and economic theory predicts that it will be underprovided by voluntary participants due to the presence of free riding.
In contrast, what Hirshleifer calls a “best-shot” good can be successfully provided by one party acting alone. Asteroid defense is an example of this; only one successful interception is necessary to protect everyone. In theory, this could make the provision of such a good more likely. Wealthy nations have the most to lose economically from an asteroid strike and are in a better economic position to fund defensive measures unilaterally. Other factors certainly play a role in such decisions, but developed nations like the United States and the United Kingdom and the broader European Union have been the most active in funding efforts to track and defend against NEOs.
On the other hand, free-riding problems could be even more pronounced with best-shot goods, as Hirshleifer found in experiments conducted with Glenn Harrison of Georgia State University. But in a bit of good news, Hirshleifer and Harrison also found that individuals contributed more to all classes of public goods than simple theory would have predicted.
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