The Great 2020s Covid-19 pandemic? Or how about the 2019-23 Global Pandemic? Or (and let’s hope not) the First Coronavirus Pandemic? History will decide what to call it. It will also decide the dates on which it started and finished. Until then, we can choose for ourselves.
For me, the pandemic started 3 years ago today. On 24 January 2020 I read Pandemics and the economy by New Zealand economist and prolific blogger Michael Reddell. He posted:
Who knows quite what will happen with the current coronavirus. But experts in such matters seem pretty confident (resigned?) that one day there will be [a] virus that really takes hold and causes significant infection, disruption, and probably loss of life across a wide range of countries …
The prime focus in much discussion around pandemics is (understandably) on the potential loss of life, but in a modern economy a serious pandemic could have major economic consequences, less because of the loss of life itself … than because of the disruption, the fear, and the voluntary or semi-compulsory social distancing that would be put in place to try to minimise the risk of the virus spreading or of particular individuals contracting it.
I’d heard about an unusual flu in Wuhan, China, before reading Michael’s post. But mentally I’d put it in the same category as SARS, MERS, etc. — tragic for those involved, but pretty much irrelevant at the global scale. Michael’s post was the first hint, for me, that this event could conceivably be a big one. So a big shout-out to Michael for prescience, and also for his early identification of the (now obvious) fact that a global health crisis would inevitably be accompanied by a global economic crisis.
On 28 February, just one month after Michael’s post, NZ had its first case of Wuhan Flu, now officially designated “COVID-19”. One month after that, on 25 March, NZ went into “lockdown” — a new term for all of us, at least as applied to a country.
It was not until two-and-a-half years later (in September 2022), that the regulatory framework supporting lockdowns was dismantled, and most remaining behavioural and movement restrictions were lifted. Covid persists, as can be seen in these graphs, but it is now treated (from a policy perspective) as just one of many viral diseases.
Covid will keep researchers busy for decades
Covid will keep researchers, including economists, busy for decades. Even those working on non-Covid research projects will face tricky data management issues — just what to do with those weird datapoints from the early 2020s?
But in the shorter term, I think that the 3-year mark, and the return of something like 2019 “normality” to most of our lives, is a great point to reflect on the pandemic.
In this post I’d like to offer some reflections on the pandemic, and what I think we — as humans, New Zealanders, and as economists, have learned.1 Though perhaps that's too high a bar, given the degree to which opinions about Covid are personal, political and strongly held. So, rather, these thoughts are more what I think we should have learnt.
Please feel free to submit your own reflections to Asymmetric Information. You could draft a post (email the editor), or write a comment on this post.
Also note that the NZAE published a Covid-specific volume of New Zealand Economic Papers in 2022 — COVID-19: Economic Implications for New Zealand and the Pacific.
Vaccine tech development was very impressive
It remains extraordinary that the world could “discover” a new virus and have an effective vaccine in mass production within a year. As at 17 January 2023:
69.2% of the world population has received at least one dose of a COVID-19 vaccine.
13.21 billion doses have been administered globally, and 1.94 million are now administered each day.
25.9% of people in low-income countries have received at least one dose.2
In the public policy sphere, a significant contribution to this extraordinary feat was Operation Warp Speed. Vaccines were an under-developed aspect health technology, largely due to a tricky combination of market and government failures.3 Operation Warp Speed used government pre-commitments to overcome these failures, with impressive results.
The best strategy is always obvious in retrospect
Hindsight is 20/20. Knowing what we know now, the best playbook to emerge from Covid with a low death toll would seem to be:
live on a island;
secure the borders before the virus becomes well established;
place just enough restrictions on the populace to control disease spread (i.e. manage the reproduction rate down to below 1);
quickly get new outbreaks under control (e.g. from failures of border control);
use the available time to build up capacity in the health system, especially ICU capacity for treating respiratory disease;
keep all the above in place until an effective vaccine is available, then quickly vaccinate the (willing) population; and
once the vaccination campaign reaches diminishing returns, re-open the borders and deal with the resulting “wave” of infections as best as possible.
NZ followed parts of the above playbook. Generally speaking, it performed better at the items towards the top of the list, and less well on those further down.
Inter-country comparisons are tricky, and not necessarily fair. Not all countries were lucky enough to be islands (either geographic islands like NZ, Iceland or Taiwan, or functional islands like South Korea). And many had significant Covid incursions before it was clear that border closures might be necessary or worthwhile (e.g. the UK, Italy). Such countries had fewer options, and their best-available playbook differs. It remains an open question, for example, whether the much-maligned “Swedish experiment" ended up better or worse than that of surrounding countries.
Uncertainty started very high, then reduced over time
Uncertainty was very high in the early phases of the pandemic. For me this has four implications:
There was significant value in actions that keep future options open.4 (Of course, there was also uncertainty about which actions met that criterion.)
It is best to be generous to those who made decisions under high degrees of uncertainty. Hindsight bias makes it very difficult to forget what we now know, but they didn’t at the time.
There is no single source of wisdom. Over the medium-to-long term, plurality of analysis & advice from those in a wide range of professional and academic disciplines, refined by contest and cooperation, finds the best solutions to complex problems.
Early decisions, approaches and messages should never be set in stone. Even if they appear to have worked at the time, they can almost certainly be improved upon as more information comes to light.
Authorities in many countries (not just NZ) appeared to prioritise messaging consistency and simplicity in over plurality of debate. They were dealing with a real fear — that the public might start to distrust them and thus not comply with public health instructions and restrictions on behaviour. But this prioritisation risked groupthink and policy stasis.
Isolation has its own costs
In apparent defiance of Michael Reddell’s observation that a pandemic would be both a health and an economic crisis, NZ’s response was driven by epidemiologists. NZ economists were largely absent from the decision-making process, and left to making observations from the sidelines. Those observations were not always welcomed.
The epidemiologists asserted that their preferred policies would lead to lowest Covid-related health costs, and this would automatically lead to the lowest economic costs. Those assertions were not backed up with argument or credible analysis.
Some of the costs of Covid policies were predictable at the time, others are just becoming clear now. Cost-benefit analysis was, and remains, the best tool for disciplined decision making that most fully accounts for costs alongside benefits. It’s early and comprehensive rejection by NZ decision makers during Covid is difficult to excuse.
The final global pandemic?
Next time will be different. This pandemic — the worst since the "Spanish Flu” a hundred years earlier — occurred at an interesting juncture in human history. Cheap, widespread PCR testing, a technology unavailable during previous pandemics, made it very easy to determine who was, and who wasn’t, actually infected with Covid.
But, while technology had developed to the point we could easily detect Covid, we were limited to disease-control measures that had advanced little from those used in Milan, Italy in the 1370s. And this situation was only partially remediated by Covid vaccines and Covid-specific treatments (e.g. Paxlovid).
My prediction? Next time a previously unknown human pathogen emerges, biotechnology will have advanced to the point that vaccine development and production will occur in weeks. It will be little more than a blip, cut off well before it spreads far enough to be considered a pandemic.
By Dave Heatley
As an aside, I was blogging about the pandemic during 2020, while at the NZ Productivity Commission. The Pandemic Economics blog recently disappeared from the Commission’s website, but you can still view it via the Internet Archive’s Wayback Machine. You can view my posts here, and make your own judgement on my 2020 self’s prescience (or lack of it).
Edouard Mathieu, Hannah Ritchie, Lucas Rodés-Guirao, Cameron Appel, Charlie Giattino, Joe Hasell, Bobbie Macdonald, Saloni Dattani, Diana Beltekian, Esteban Ortiz-Ospina, & Max Roser (2020). Coronavirus Pandemic (COVID-19). Published online at OurWorldInData.org.
These failures manifested as private companies avoiding investment in vaccine development. They did this because they were unsure of (1) whether the disease would arise or not; and (2) whether, in the event of an actual pandemic, governments would (a) confiscate the intellectual and commercial property private companies had developed; and/or (b) use regulation or monopsony power to force prices below those necessary to recover the cost of vaccine development, production and stockpiling, plus the cost of unsuccessful development.
A topic explored in Benjamin Davies & Arthur Grimes (2022). COVID-19, lockdown and two-sided uncertainty, New Zealand Economic Papers, 56:1, 49-54, DOI: 10.1080/00779954.2020.1806340
Replying to Shaun Hendy's comment, posted below.
Shaun is completely correct to criticise my point: “place just enough restrictions on the populace to control disease spread (i.e. manage the reproduction rate down to below 1)”. I have no excuse for over-summarising the much more complex position I have held since May 2020, when I advocated the use of CBA to choose Alert Levels (see https://www.productivity.govt.nz/assets/Documents/cost-benefit-analysis-covid-alert-4/92193c37f4/A-cost-benefit-analysis-of-5-extra-days-at-COVID-19-at-alert-level-4.pdf).
That said, Shaun’s proposed solution appears to confuse CBA with cost-effectiveness analysis. For example: “In a CBA one should look for controls that minimise the total cost (economic + health)” is consistent with CBA thinking, whereas “bring Reff as close as possible to zero” and “more economically efficient in bringing case numbers down to a desired level” is about cost-effectiveness, i.e. choosing the cheapest way to achieve an exogenously specified goal. CBA is always an attempt to maximise net social benefits, not the pursuit of other goals.
CBA, properly applied, will not lead to a universal answer as to whether a short period of harsh controls has higher net benefits than does a longer period of laxer controls. CBA analysis will be specific to circumstances, as both costs and benefits change with circumstances. Further, in addition to the factors that Shaun mentions, the analysis will be sensitive to:
A. The proportion of the population with Covid at the start of the relevant period. This is because the costs of controls apply to the whole population, whereas benefits, measured as avoided Covid-related health costs, scale with the numbers infected.
B. The expected time horizon for the realisation of benefits. As NZ learnt during 2020 and 2021, the benefits of “elimination” were truncated every time a border breach resulted in a behind-the-border outbreak. An analysis that doesn’t take this into account will likely over-estimate benefits.
C. The expected counterfactual. Evidence from overseas suggests that a significant proportion of the population adopted voluntary controls in jurisdictions with relatively lax controls, especially when A was high. (Similarly, compliance with harsh controls was patchy, especially when A was low.) This makes determining the effect of specific packages of controls on Reff tricky.
All else equal, a high A will favour harsher controls. Over time, as controls reduce A, the optimal set of controls will change towards laxer ones. So, the output of a CBA analysis should be an optimally staged path, rather than a single “best” package of measures.
A longer time horizon B would favour harsher controls. At the other extreme, a low A combined with a very high chance of an externally sourced outbreak, say 1 week from now, would make harsh controls all but pointless in the intervening period.
Admittedly, my May 2020 analysis was inadequate with respect to B and C.
Without a properly specified and conducted CBA that incorporates these additional factors, I think it is premature to offer the conclusion that “Alert Level 3 is roughly 70% more economically expensive in controlling an outbreak than Alert Level 4”, in the specific circumstances NZ faced during the two 2020 outbreaks. And even such a CBA would not support a more universal conclusion.
A comment from Shaun Hendy, via email:
I read Dave Heatley’s recent piece on the pandemic with interest. I note that his suggestion here:
"* place just enough restrictions on the populace to control disease spread (i.e. manage the reproduction rate down to below 1);”
is probably not correct in general, although I would be interested in seeing his argument for this.
These kinds of issues can be addressed using a dynamical model of disease spread. In the simplest models the decay time for case numbers scales as T/(1-Reff) where Reff < 1 and T is the generation time. Thus, placing just enough controls to bring Reff below one leads to a long decay time i.e. controls have to be held in place for a long time. In contrast, using very strong controls to bring Reff as close as possible to zero will reduce the time that controls need to be in place. In a CBA one should look for controls that minimise the total cost (economic + health). Stronger controls will reduce health costs but *may* also reduce economic cost if they only need to be in place for short times. When this is the case there is no trade-off between health and economic costs and a CBA would suggest using the strongest controls practically available.
If we assume that controls have a fixed daily cost (this is the approach Treasury has taken for example) then the total cost of imposing a set of controls is the daily cost (c, say) times the number of days that the controls need to be imposed. This time period scales as T/(1-Reff), so we can compare two control regimes to see which is more economically efficient in bringing case numbers down to a desired level via the ratio (c1/(1-R1))/(c2/(1-R2)).
To use some real data, we can compare Alert Levels 3 and 4, using Treasury’s costing (~18% and 30% daily GDP respectively) and estimates of Reff from the March-April 2020 and August 2020 wild-type COVID-19 outbreaks (I use TPM’s branching process fits here of 0.77 and 0.35 respectively). This suggests that Alert Level 3 is roughly 70% more economically expensive in controlling an outbreak than Alert Level 4. Alert Level 4 also minimises cases and corresponding health costs, so a decision-maker using a CBA should prefer it to Alert Level 3.