Even decisions that claim the use of scientific models are ultimately political calls of judgement
One refrain of the ongoing, and hopefully now abating, global covid pandemic has been a cry for “evidence-based policymaking". The idea is seductive. By this proposition, public policy ought to be based purely on the basis of the best empirical evidence (specifically, data) interpreted through the lens of the current state-of-the-art understanding of the underlying scientific (or other relevant) theory. Pointedly, policy decisions ought not to be based, it holds, on the basis of ideological persuasion, common sense, political (such as electoral) considerations or any policymaker’s whim.
The most pertinent pandemic example has been the rationale for lockdowns. Given that the covid virus is transmissible through human contact, as well as airborne transmission, the so-called ‘agent-based’ models of epidemiologists constructed counterfactual scenarios of the infection rate and other relevant criteria, comparing what would happen with business as usual, as against lockdowns of differing levels of stringency. Given that all the models predicted a sharp reduction in infection rates with a lockdown rather than without, policymakers the world over opted for it, with re-opening evidently being calibrated based on the counterfactual predictions of the same models.
All of this sounds plausible. However, the claim that important policy decisions, such as lockdown, are purely evidence-based and scientific is a myth perpetrated upon a gullible public. The garb of science is a perfect blame-avoidance mechanism for political decisions that lead to the loss of incomes and livelihoods as a result of locking down. “I’m just doing what the science says I should do," is the usual reply to critics.
Yet, this cannot be the whole story. For one thing, sticking to our lockdown example, those epidemiological models focus only on the disease, but do not incorporate the economic, social or psychic costs of the action taken. Typically, unless one is very lucky, a real-world policy choice will involve trade-offs: a more stringent lockdown, other things being equal, will lead to a more rapid ‘flattening of the curve’ of infection, while at the same time resulting in a greater drop in economic activity in the short to medium run. While it is possible in theory to make monetary estimates for gains from reduced infections and for losses from an economic contraction, thus allowing for an ostensibly scientific cost-benefit calculus, in practice, evaluating such a trade-off requires that a policymaker attach a weight to economic, social and psychic costs of a lockdown, as against the public health benefits of a reduced infection rate. This invariably comes down to a judgement call on policy.
Given the proliferation of different valid models, and the proliferation of different weights that could be assigned to benefits as against costs, it is feasible to come up with different recommendations in exactly the same situation. Given uncertainty over the science—for instance, recall the fierce debates about a year ago on the likelihood of herd immunity—this further expands the scope of possible outcomes. Additionally, when one factors in the behavioural response of the public, such as the risk of a stringent lockdown inducing people to gather indoors and thus producing a self-defeating end result, the possibilities become literally endless.
I have discussed this latter problem previously, in reference to what economist Sam Peltzman has called the “offsetting" effect, which can nullify or reverse the beneficial effect of a well-intentioned policy because its behavioural impact was overlooked and people were assumed to be like robots who can be fitted into a mechanistic model. A celebrated research finding of Peltzman is a good illustration of that effect as well as the hubris of ostensibly scientific policymaking. He found that mandating seat-belt use in cars did not result in reduced traffic fatalities, as studies based on crash test dummies had suggested, because of the tendency of human beings to drive more rashly when they felt safer.
In reality, when faced with conflicting recommendations of science, a policy decision is ultimately a political one, based on the judgement of policymakers. And, once politics comes into the picture, any policymaker will consider the political—in particular, electoral—impacts of any policy that is picked. Will the public tolerate a lengthy lockdown? Will there be a blowback at the next election? Such considerations inevitably enter the political calculus. We are very far away from the pristine apolitical fiction of evidence-based policymaking.
I do not intend this to be a nihilistic tale. The lack of conditions that make space for genuinely evidence-based policymaking should not mean that anything goes. In particular, while science by itself cannot provide all the answers, it importantly constrains the scope of sensible policy options. Thus, for instance, if someone believes that drinking cow urine would cure covid, a belief which is completely at variance with our scientific understanding of the virus, a policy response based on this belief would be doomed to fail. Any sensible approach to policymaking would rule out such irrational beliefs as a possible basis for policy.
However, even ruling out irrational and provably false beliefs as a foundation for public policy, this still leaves a large range of possible options that are based on a slew of credible models. In the end, it comes to down political judgement. Evidence-based policymaking is a myth and must be recognized as such.