The second problem with Wolfers’ estimate is his assumption of a $10 million value per life saved. This is the economists’ usual estimate of what we call the “Value of a Statistical Life.” A standard methodology for computing the VSL is to estimate the risk premium that workers earn for taking jobs in risky occupations. A typical number is an extra $1,000 annually for taking on an added 1-in-10,000 chance of dying in a year. If 10,000 workers are each paid to take on that added 1-in-10,000 risk, then the “expected” number of deaths (expected in a probabilistic sense, that is, the probability of death multiplied by the number at risk) is 1. So economists multiply that $1,000 by 10,000 workers to get the “Value of a Statistical Life.” The result: $10 million.
When I taught that concept in my cost/benefit analysis course, it was always in a context where a few lives were saved or lost. It breaks down when we’re talking about a million lives. I didn’t realize that until I read a post by economist Luigi Zingales of the University of Chicago. He estimated how much GDP we should be willing to give up to save 7.2 million people from dying of COVID-19. His 7.2 million lives lost is grossly overstated. But that’s not the point. What if it were true? He showed, using an apparently conservative $9 million per life saved, that we should be willing to give up $64.8 trillion, which is three years of GDP. The Zingales estimate amounts to an unintentional reductio ad absurdum. If we cut GDP to zero for three years we would do … what? Grow gardens and, in most of the country, live in very cold houses in the winter? In that case, over 100 million lives would be lost, which is 14 times his 7.2 million estimate. When your model tells you that because of the high value of life, you should be willing to give up 100 million lives to save 7.2 million lives, there’s something wrong with your model. Wolfers did not have an answer for that. The bottom line is that for a large number of lives like one million, a $10 million value of life is far too high.
Justin’s discussion of least-cost avoider got me thinking further. Here’s that section:
When I made these points in the debate, Wolfers responded that he and his family should be able to go to a public park and not have to risk getting the disease from others. His is a valid point. But during the debate, he made another point that undercuts his response. He argued that employers who open up should be held liable if one worker gives the disease to another because, he said, the employer is the “least-cost avoider.”
The least-cost avoider is the entity that has the least cost of avoiding a harm from an externality. It’s often hard to tell how someone got the disease and it’s not at all clear that the employer should be liable. Wolfers responded that workers and customers don’t have deep pockets but employers do. The idea that there are millions of employers with deep pockets is absurd. Apple, Facebook, Microsoft, and Alphabet (owner of Google) do. But even in normal times a large percent of employers have shallow pockets and those are no doubt shallower after 6 to 7 weeks of lockdown.
Yet the Wolfers invocation of the least-cost avoider principle got me thinking. If you’re an older person with co-morbidities, who is the least-cost avoider: young people or you? It’s probably you.