Border controls

Should Britain Have Tried to Contain the Virus Using Border Controls?

In his testimony to the Health and Science select committees, Dominic Cummings heavily criticised the Government’s handling of the pandemic. One of the biggest mistakes, he argued, was the failure to impose border controls:

Obviously, we should have shut the borders in January. We should have done exactly what Taiwan did… Yes, that has some disruption, but the kind of cost-benefit ratio is massively, massively out of whack, and at least it is worth a try, like lots of things. At least you try it … If it doesn’t work, you still have the whole nightmare to deal with, anyway. 

However, Cummings somewhat absolved the Government of blame on this score – at least with the respect to the period before April – insofar as all the scientists were advising against border controls:

He was told, and we were all told repeatedly, that the advice is not to close the borders, because essentially it would have no effect… you cannot blame the Prime Minister directly. That was the official advice. The official advice was, categorically, that closing the borders will have no effect.

Cummings’ testimony is consistent with the evidence from SAGE meetings in January and February of last year. For example, the minutes of a meeting on January 22nd record that “NERVTAG does not advise port of entry screening”.

Another factor Cummings mentioned, as to why the Government didn’t impose border controls, is political correctness:

At this time, another group-think thing was that it was basically racist to call for closing the borders and blaming China, the whole Chinese new year thing and everything else. In retrospect, I think that was just obviously completely wrong.

What should we make of Cummings’ argument that border controls were at least “worth a try” in January? On the face of it, the argument seems very reasonable. In the best-case scenario, we could have achieved the same outcomes as New Zealand – zero excess mortality and just a small decline in GDP. And in the worst-case scenario, we’d have been in the same situation as otherwise. 

However, the latter outcome – being no worse-off – isn’t necessarily the worst-case scenario. A potentially even worse scenario is if we’d contained the virus until the autumn, and then experienced a major epidemic at the same time as the NHS came under its normal winter pressures. 

This was in fact one of the reasons why scientists were initially advising against both border controls and lockdowns. Cummings was apparently told:

Even if we therefore suppress it completely, all that you are going to do is get a second peak in the winter when the NHS is already, every year, under pressure … If you try and flatten it now, the second peak comes up in the wintertime and that is even worse than the summer.

This argument should not be dismissed out of hand. Several of the European countries with the highest death tolls – Poland, Bulgaria, Czechia – are ones that escaped the first wave, only to get clobbered in the second. (Of course, there may be several reasons for the high death tolls in these countries; I’m not suggesting the epidemic’s timing is the only one.)

Deciding whether to impose border controls therefore represents a trade-off between the benefits of buying time and/or achieving containment versus the risks of postponing the epidemic until the winter. 

Border Controls, Not Lockdowns, Explain the Success of Denmark, Norway and Finland

I’ve previously explained why “we have to compare Sweden to its neighbours” isn’t a convincing argument for lockdowns. However, the argument keeps cropping up on social media. So I’ll have another go.

As I noted in my previous post, Sweden has had more deaths than the other Nordic countries – whether you use ‘confirmed COVID-19 deaths per million people’ or age-adjusted excess mortality. 

However, this doesn’t mean that lockdowns are what account for the divergent mortality trends. In other words, it doesn’t follow that if Sweden had locked down at the same time as its neighbours, then it would have seen many fewer deaths from COVID-19.

Even if you believe that lockdowns were the main factor behind the other Nordics’ low death rates (and they probably weren’t), the epidemic was already more advanced in Sweden by the time its neighbours locked down. And since lockdowns don’t have much impact unless case numbers are low (as in Australia and New Zealand), locking down probably wouldn’t have made a big difference. 

Moreover, there’s good reason to believe that lockdowns weren’t the main factor behind the other Nordic’s low death tolls. Rather, the main factor was probably border controls.

Let’s examine what each country did during the first wave, using the Oxford Blavatnik School’s COVID-19 Government Response Tracker. (I will ignore Iceland, since it’s a small island in the middle of the Atlantic ocean, and its geographic advantages are obvious.) 

Recall that the Blavatnik School’s database includes several measures of government restrictions. I will focus on mandatory workplace closures, mandatory stay-at-home orders, and restrictions on international travel (i.e., border controls). 

Let’s start with mandatory stay-at-home orders. None of the Nordics had any days of mandatory stay-at-home orders during the first wave. (This is in contrast to the U.K., which was hit much harder than all four Nordics, and had a mandatory stay-at-home order in place between March 23rd and May 12th.)

Now mandatory workplace closures. Norway did introduce these quite early on March 12th. However, Denmark did not introduce them until March 18th – just five days before the U.K. And Finland did not introduce them until April 14th – more than three weeks after the U.K.

These comparisons reveal that the other Nordics did not lock down particularly hard or particularly early. Indeed, all three had less strict lockdowns than the U.K. (which saw many more deaths during the first wave). Finland’s success is particularly difficult to explain with reference to lockdowns since the country did not introduce any real measures until after the peak of infections.