Following the data is all very well, but how should we decide which data to follow?
Nate Silver argues that the total data are more important than the marginal data. There is more virus transmission in restaurants than in aeroplanes.
The average American spends something like 5 hours per year on a plane. The mask mandate might be good or bad at the margin, but it is very unlikely to make a major difference in the overall course of the coronavirus.
— Nate Silver (@NateSilver538) April 19, 2022
The average American eats at a restaurant about 250 times per year, whereas they travel by airplane about 2.5 times per year. Dining out and general nightly social activity is a much, much, much bigger contributor to COVID spread than air travel.
— Nate Silver (@NateSilver538) April 19, 2022
Several people have challenged this, including Carl Bergstrom.
Imagine thinking that this is what data-driven reasoning looks like
— Carl T. Bergstrom (@CT_Bergstrom) April 19, 2022
The first point I want to make is about risk. When calculating the risk of transmission in aeroplanes, the level of risk somewhere else is not really relevant. As Bergstrom points out, the calculation should be based simply on the costs and benefits of wearing masks in that particular setting.
To explain: whether or not to take some safety precaution requires a cost-benefit analysis.
— Carl T. Bergstrom (@CT_Bergstrom) April 19, 2022
Nate forgot the cost side of the equation.
Wingsuit jumping be far less common than motorcycle riding, but that's hardly an argument against safety precautions for squirrel-suiters.
The problem here, of course, is determining the cost of wearing masks. If some people regard mask-wearing as a minor inconvenience, while others regard it as a major infringement of their human rights, what sort of data would be relevant to this calculation? Or if mask-wearing impedes communication to some extent, can we quantify this effect?
The broader question is whether we should be looking at total costs and benefits, or marginal costs and benefits? In the case of mask-wearing, if we assume that most people possess a usable mask, then the marginal cost of wearing one might simply include the increased frequency of washing or replacing it, plus whatever psychological, physiological or social costs we can determine.
But for some people, opposition to mask-wearing is much more fundamental than that. It is not about the costs and benefits of mask-wearing in a particular setting, but an overall objection to living in the kind of society where mask-wearing is mandated at all. Some people even object (violently) to the sight of other people wearing masks. So any kind of mask-wearing may cause social division, therefore incurring a sociopolitical cost, and this needs to be set against the overall benefits of controlling the transmission of disease.
Nate Silver acknowledges that a mask-wearing mandate may be useful at the margin, but questions its overall value. Presumably people aren't going to wear masks in restaurants while they are eating. So his tweet seems to be asking what is the point of making them wear masks on planes?
This then brings us onto a question about policy, and the extent to which policy can be evidence-based. Is it better to have a consistent policy on mask-wearing across different settings, or to focus policy on those areas that are regarded as the highest risk? And should policies be optimized for effectiveness (what will have the greatest effect on suppressing transmission of the virus) or social acceptability (what will most people accept as reasonable)? Nate Silver's data may be relevant to this calculation, at least to the extent that they influence people's opinions.
While there are some particularly emotive features of this example, it raises some important general points about data-driven reasoning, especially in relation to cost-benefit calculations and evidence-based policy.
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