Most people in the knowledge producing industry in academia, foundations, media or think tanks are not Bayesians. This makes it difficult to know how Bayesians should go about deferring to experts.
Many experts are guided by what Bryan Caplan has called ‘myopic empiricism’, also sometimes called scientism. That is, they are guided disproportionately by what the published scientific evidence on a topic says, and less so by theory, common sense, scientific evidence from related domains, and other forms of evidence. The problem with this is that, for various reasons, standards in published science are not very high, as the replication crisis across psychology, empirical economics, medicine and other fields has illustrated. Much published scientific evidence is focused on the discovery of statistically significant results, which is not what we ultimately care about, from a Bayesian point of view. Researcher degrees of freedom, reporting bias and other factors also create major risks of bias.
Moreover, published scientific evidence is not the only thing that should determine our beliefs.
I will now discuss some examples where the experts have taken views which are heavily influenced by myopic empiricism, and so their conclusions can come apart from what an informed Bayesian would say.
Scepticism about the efficacy of masks
Leading public health bodies claimed that masks didn’t work to stop the spread at the start of the pandemic.1 This was in part because there were observational studies finding no effect (concerns about risk compensation and reserving supplies for medical personnel were also a factor).2 But everyone also agrees that COVID-19 spreads by droplets released from the mouth or nose when an infected person coughs, sneezes, or speaks. If you put a mask in the way of these droplets, your strong prior should be that doing so would reduce the spread of covid. There are videos of masks doing the blocking. This should lead one to suspect that the published scientific research finding no effect is mistaken, as has been confirmed by subsequent research.
Scepticism about the efficacy of lockdowns
Some intelligent people are sceptical not only about whether lockdowns pass the cost-benefit analysis, but even about whether lockdowns reduce the incidence of covid. Indeed, there are various published scientific papers suggesting that such measures have no effect.3 One issue such social science studies will have is that the severity of a covid outbreak is positively correlated with the strength of the lockdown measures, so it will be difficult to tease out cause and effect. This is especially in cross-country regressions where the sample size isn’t that big and there are dozens of other important factors at play that will be difficult or impossible to properly control for.
As for masks, given our knowledge of how covid spreads, on priors it would be extremely surprising if lockdowns don’t work. If you stop people from going to a crowded pub, this clearly reduces the chance that covid will pass from person to person. Unless we want to give up on the germ theory of disease, we should have an extremely strong presumption that lockdowns work. This means an extremely strong presumption that most of the social science finding a negative result is false.
Scepticism about first doses first
In January, the British government decided to implement ‘first doses first’ - an approach of first giving out as many first doses of the vaccine as possible before giving out second doses. This means leaving a longer gap between the two doses - from 12 weeks rather than 21 days. However, the 21 day gap was what was tested in the clinical trial of the Oxford/AstraZeneca vaccine. As a result, we don’t know from the trial whether spacing out the doses has a dramatic effect on the efficacy of the vaccine. This has led expert groups, such as the British Medical Association and the WHO, to oppose the UK’s strategy.
But again, on the basis of our knowledge of how other vaccines work and of the immune system, it would be very surprising if the immune system response did decline substantially when the gap between the doses is increased. To borrow an example from Rob Wiblin, the trial also didn’t test whether people turned into a unicorn two years after receiving the vaccine, but that doesn’t mean we should be agnostic about that possibility. Subsequent evidence has confirmed that the immune response doesn’t drop off after the longer delay.
Scepticism about whether the AstraZeneca works on the over 65s
Almost half of all EU countries have forbidden the use of the Oxford/AstraZeneca vaccines on the over 65s. This was in part because the sample of over 65s in the initial study was not conclusive enough to form a judgement on efficacy for that age group. But there was evidence from the study that the AZ vaccine is highly effective for under 65s. Given what we know about similarities across the human immune system, it is very unlikely that the AZ vaccine has 80% efficacy for under 65s but drops off precipitously for over 65s. To make useful judgments about empirical research, one has to make some judgments about external validity, which need to be informed by non-study considerations. The myopic empiricist approach often neglects this fact.
Denying that the minimum wage affects demand for labour
Prior to the 1990s, almost all economists believed that the minimum wage would cause firms to economise on labour either by causing unemployment or reducing hours. This was on the basis of Economics 101 theory - if you increase the price of something, then demand for it falls. In the 1990s, some prominent observational studies found that the minimum wage did not in fact have these effects, which has caused much more sanguine views about the employment effects of the minimum wage among some economists. See this recent IGM poll of economists on the employment effects of a minimum wage increase in the US - many respondents appeal to the empirical evidence on the minimum wage when justifying their conclusion.
“An increase to $15/hour is a big jump, and I'm not sure we have the data to know what the effect on employment would be.”
“Evidence is that small increases in min. wage (starting from US lows) don't have large disemployment effects. Don't know what $15 will do”
“The weight of the evidence does not support large job loss. But I'm above extra nervous about setting min $15/hr during the pandemic.”
“Research has shown modest min. wage increases do not increase unemployment. But going from $6 to $15 in the current situation is not modest.”
“Evidence on employment effects of minimum wages is inconclusive, and the employment losses may well be small.”
A lone economist - Carl Shapiro - digs his heels in and sticks to theory
“Demand for labor is presumably downward sloping, but the question does not ask anything about magnitudes.”
As Bryan Caplan argues, there are several problems with the myopic empiricist approach:
- There are strong grounds from theory to think that any randomly selected demand curve will slope downward. The only way minimum wages wouldn’t cause firms to economise on labour is if they were a monopsonistic buyer of labour, which just doesn’t seem to be the case.
- The observational evidence is very mixed and is almost always testing very small treatment effects - increases in the minimum wage of a few dollars. Given the quality of observational studies, we should expect a high number of false negatives if enough studies are conducted.
- The literature on the impact of immigration on the wages of native workers suggests that the demand curve for labour is highly elastic, which is strongly inconsistent with the view that minimum wages don’t damage employment.
- Most economists agree that many European countries have high unemployment due to regulations that increase the cost of hiring workers - minimum wages are one way to increase the costs of hiring workers.
- Keynesians think that unemployment is sometimes caused by nominal downward wage rigidity, i.e. that nominal wages fail to fall until the market clears. This view is very hard to reconcile with the view that the minimum wage doesn’t cause firms to economise on labour.
Scepticism about the effects of mild drinking while pregnant
There are a lot of observational studies that show pretty conclusively that moderate or severe drinking while pregnant is extremely bad for babies: doctors will try to get pregnant alcoholic drug addicts off alcohol before getting them off heroin. However, observational studies have struggled to find an effect of mild drinking on birth outcomes, which has led some experts to argue that mild drinking in pregnancy is in fact safe.4
Given what we know about the effects of moderate drinking while pregnant, and theoretical knowledge about the mechanism of how it has this effect, we should have a very strong presumption that mild drinking is also mildly bad for babies. The reason observational studies struggle to find an effect is that they are searching for an effect that is too close to zero to distinguish the signal from the noise, and there are million potential confounders. The effect is still very likely there.
Nutritional epidemiology tries to tease out the effect of different foods on health. It has produced some extraordinary claims. John Ioannidis describes the effect of different foods found in meta-analyses
“the emerging picture of nutritional epidemiology is difficult to reconcile with good scientific principles. The field needs radical reform… Assuming the meta-analyzed evidence from cohort studies represents life span–long causal associations, for a baseline life expectancy of 80 years, eating 12 hazelnuts daily (1 oz) would prolong life by 12 years (ie, 1 year per hazelnut), drinking 3 cups of coffee daily would achieve a similar gain of 12 extra years, and eating a single mandarin orange daily (80 g) would add 5 years of life. Conversely, consuming 1 egg daily would reduce life expectancy by 6 years, and eating 2 slices of bacon (30g) daily would shorten life by a decade, an effect worse than smoking.”
As Ioannidis notes: “These implausible estimates of benefits or risks associated with diet probably reflect almost exclusively the magnitude of the cumulative biases in this type of research, with extensive residual confounding and selective reporting”. Sticking to an enlightened common sense prior on nutrition is probably a better bet.
2. Implications for deference
I have outlined some cases above where hewing to the views of certain experts seems likely to lead one to mistaken beliefs. In these cases, taking account of theory, common sense and evidence from other domains leads one to a different view on crucial public policy questions. This suggests that, for Bayesians, a good strategy would be to defer to the experts on what the published scientific evidence says, and let this be one input into one’s all-things-considered judgement about a topic.
For example, we might accept that some studies find limited effects of masks but also discard that evidence given our other knowledge.
Many subject matter experts are not experts on epistemology - on whether Bayesianism is true. So, this approach does not obviously violate epistemic modesty.
1. For an overview of the changing guidance, see this Unherd article by Stuart Ritchie.
2. For an overview see Greenhalgh. For example, “A preprint of a systematic review published on 6 April 2020 examined whether wearing a face mask or other barrier (goggles, shield, veil) prevents transmission of respiratory illness such as coronavirus, rhinovirus, tuberculosis, or influenza.11 It identified 31 eligible studies, including 12 randomised controlled trials. The authors found that overall, mask wearing both in general and by infected members within households seemed to produce small but statistically non-significant reductions in infection rates. The authors concluded that “The evidence is not sufficiently strong to support the widespread use of facemasks as a protective measure against covid-19”11 and recommended further high quality randomised controlled trials.” Trisha Greenhalgh et al., ‘Face Masks for the Public during the Covid-19 Crisis’, BMJ 369 (9 April 2020): m1435, https://doi.org/10.1136/bmj.m1435.
3. For an overview of the sceptical literature, see this website.
4. For example, Emily Oster, an economist and author of the popular book Expecting Better argues that there is “little evidence” that one to two drinks per week causes harm to the foetus, as discussed in this Vox piece.