"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."
https://www.econlib.org/end-the-lockdowns-now/
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."
https://www.econlib.org/end-the-lockdowns-now/