What can total utilitarians learn from empirical estimates of the value of a statistical life?
This post was inspired by Carl Shulman's blog post from last month—if you have time, read that first, since this is basically a response to it. My goal here is to combine
- Empirical studies of how much people are willing to pay to reduce their risk of death, and
- The "total utilitarian" assumption that potential people are as important as existing people, and the value of an additional person is independent of the number of preexisting people
- An additional (quite strong!) assumption that the utility gain from being born and becoming an adult is the same as the utility loss from a premature adult death
Suppose everyone has identical preferences, and only two variables affect expected utility: their probability of survival and their income
. Since von Neumann–Morgenstern utility functions are invariant under affine transformations, we can define the utility of being dead as 0 and still have one degree of freedom left (two utility functions are equivalent iff they are related by a positive linear transformation). Fixing a reference (minimum) income level
, we can always the write the utility function as
,
where is some function defined on
with
. This condition ensures that
is the utility at the minimum income. For instance, if utility from income is logarithmic, we can let
. A logarithm with any other base can be turned into
by a linear transformation, so the choice of base doesn't matter.
We can infer from empirical estimates of the value of a statistical life if we have a hypothesis for the form of
—so total utilitarians should pay a lot of attention to these estimates! If you're willing to pay
for a small relative increase in your probability of survival,
(as opposed to an absolute increase
), then your value of life is defined as
.
If your utility from income takes the same form as and you're rational, then it's also true that
.
In other words, the value of life is the marginal rate of substitution between income and log survival probability. So
and
.
In the case of , we have
.
$6 million is a reasonable estimate (although on the low side) for the value of a statistical life. is in units of income, so the $6M estimate needs to be translated into an income stream. At an interest rate of 3% over 40 years, this will require payments of ~$257,582 per year. If the $6M estimate was for people making $50,000 a year, then
. With
at $300 per year, this gives us
. It's just a coincidence that
is so close to 0: slightly different parameters will shift
substantially away from that point. I biased all my parameter estimates (except for the interest rate, which I understand very poorly) so that
would have a downward bias, so if my estimates are wrong
is probably higher.
I'm not going to draw any conclusions about what a total utilitarian should do, since there are many problems with this method of estimation:
- The value-of-statistical-life studies are from high-income countries, so it's questionable to extrapolate to very low incomes.
- Utility from income probably isn't logarithmic, since people exhibit relative risk aversion.
- The value of
depends strongly on the interest rate.
- I assumed that somebody with $300 per year has the same life expectancy as someone with $50K per year. This isn't as big of a problem as it seems. If they live half as long, you can compare two $300/year people versus one $50K/year person and get a similar result.