A recent meta-analysis by three economists, commonly, if misleadingly, referred to as the Johns Hopkins study, suggested that lockdowns had little to no effect on Covid-19 mortality. Critics accused the authors of ideological bias in interpreting, or even manipulating the evidence to fit their pre-existing beliefs.  One might think that by this point, figuring out the effectiveness of lockdowns would be a simple matter of looking at the data, the facts. But while the question might seem like a purely empirical one, it’s not, argue Lucie White and Philippe van Basshuysen.

A recent meta-analysis by three economists suggesting that lockdowns have had “little to no effect on COVID-19 mortality” has generated an intense and polarised reaction across the internet and media outlets. Various potential flaws with the study have been, by now, well documented. But there have also been suggestions that the authors did not only make mistakes in their study, but had deliberately manipulated the evidence to fit with their own biased anti-lockdown preconceptions. On the other side, commentators made accusations that the study had been unduly ignored because it contradicted pre-existing narratives about the efficacy of lockdowns.

___

___

SUGGESTED READING Were the Covid measures worth it? By Stephen John Strong disagreement about the efficacy of lockdowns is nothing new. The mathematical models that were used to justify initial lockdown decisions have been pilloried as deeply flawed and as vastly overestimating the amount of COVID deaths, while others have maintained that lockdowns were a necessary means of saving millions of lives. But we might think that this disagreement could be definitively resolved once we had access to data – and could actually see, after the fact, how well lockdowns had worked. It’s concerning, then, that the availability of empirical work seems to have done very little to resolve the disagreement. The polarised response continues, and is simply shifted to accusations of bias in how the data is interpreted.