EA - Value of Life: VSL Estimates vs Community Perspective Evaluations by Joel Tan

Link to original articleWelcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Value of Life: VSL Estimates vs Community Perspective Evaluations, published by Joel Tan on September 6, 2022 on The Effective Altruism Forum. Summary The value of life, which we need to know if we are to correctly prioritize between interventions, is poorly captured by value of statistical life (VSL) estimates, and instead is better captured by community perspective evaluations. Value of Statistical Life Estimates VSL estimates may involve stated preferences of survey respondents (i.e. from asking them their willingness to pay to avert some risk) or revealed preference (e.g. looking at how much people are willing to pay for airbags in cars, or at how much they need to be compensated to work in a riskier job). Regardless, the general idea behind the VSL approach is that we have the following implicit equation: Compensation needed = Probability of death Badness of death Hence, if we elicit the compensation needed, we can then just divide by the probability of death to get the badness of death. There are numerous flaws in this approach, however: Problems leading to the misestimation of the compensation needed Humans are not hyperrational, and it's not as if survey respondents, or people taking up risky jobs, will bother to literally think about how much they subjectively value their own life in monetary terms, and then use the additional probability of death to calculate the amount of compensation needed to get them to undertake a risky course of action. Whatever number they throw out in response to surveys, or whatever risk premium they implicitly accept in taking up jobs, therefore, will just not be a good guide to the actual compensation needed, and cannot be used to estimate the badness of death. People exhibit insensitivity with respect to changes in small risks, reporting same or similar compensation amounts for risks that differ even in magnitudes. Hence, even if people were calculating the sums needed to compensate them for a risk, they may not be doing so accurately. Estimates may be made under duress (e.g. workers may fear quitting a risky job due to a lack of alternatives). The upshot of this is that the true equation people are implicitly solving is "Value from avoiding unemployment >= Probability of death Badness of death", in which case the actual risk premium does not necessarily equal the compensation needed, since even if the former were below the latter people would still choose the risk so long as the value of avoiding unemployment were sufficiently high. This potentially leads to the systematic understatement of the compensation needed and hence the badness of death, if the value from avoiding unemployment > compensation needed > actual risk premium. People may substitute in the judgement of others when estimating the compensation needed, rather than using their own, true subjective monetary value of life (e.g. workers leaving it up to their boss to decide what risks are reasonable, even if the bosses do not value their lives as much as they themselves would). Estimates may be confounded by moral considerations (e.g. workers like firefighters may take up jobs not because they are appropriately compensated for it from a purely self-interested perspective, but because it benefits others sufficiently that they see it as worth doing, the risk to themselves notwithstanding). And so, parallel to the duress case, the true equation people are implicitly solving is "Social benefits >= Probability of death Badness of death", and there is systematic understatement of the compensation needed and hence the badness of death, if the social benefits > compensation needed > actual risk premium. Estimates may be confounded by social considerations (e.g. workers valuing the social conditions in which the risk occurs, with them finding risks more acceptable when the...