Looking at lockdowns

The reopening of pubs in the UK this weekend marks an important milestone in the easing of lockdown conditions (though the Scots will have to wait until 15 July). It is 103 days since English residents have been able to take advantage of their inalienable right to drink alcohol in their local pub. This is historically unprecedented: As far back as anyone knows, pubs have always remained open. The last time there was an assault on people’s right to buy alcohol on licensed premises was during World War I when the government reckoned that drunkenness was undermining the war effort (a highly questionable assertion). This led to the imposition of limits on opening times, which lasted until the 1980s, but the pubs nonetheless remained open.

There is little doubt that the lockdown has been the most stringent imposition on individual freedoms in living memory and it has been repeated across the world, with news bulletins showing pictures of empty shopping malls and motorways that would normally otherwise be packed. In an attempt to compare the extent of the lockdowns across different countries, I am indebted to the work of academics at the Blavatnik School of Government at Oxford University who have constructed lockdown indices across more than 160 countries. The index is composed of a series of individual policy response indicators based on a range of indicators (interested readers are referred to the working paper for more detail, which can be found here).

The data are collated over the period since 1 January 2020 and chart 1 shows the maximum value of the index over the course of this year-to-date. Based on the random sample of 16 countries used here, the UK ranks fairly low down in terms of lockdown stringency. It is notable, however, that it is slightly ahead of Germany yet Germany had a very low death rate from Covid-19 infections[1] (10.8 per 100,000 of population versus 65.6 in the UK). Italy introduced the most stringent set of measures and recorded a high number of deaths (57.6 per 100,000). As has been well documented elsewhere, Sweden’s lockdown was relatively relaxed and although the death rate is still relatively high it is still below that of the UK and Italy at 52.1 per 100,000. Clearly, the maximum extent of the lockdown is not very meaningful as an indicator of the severity of the Covid-19 outbreak.

 

A more useful indicator might be the average measure of the index which also takes duration into account. But here, too, there is no ordinal ranking from lockdown severity to Covid-19 mortality rates. Chart 2 indicates that Italy’s average lockdown is significantly higher than that of Germany but mortality rates are too. To make the point more clearly, the scatter plots in chart 3 clearly indicate only a limited negative relationship between the extent of the lockdown and Covid mortality or infection rates.

This obviously raises the question of how useful have the lockdowns been as a defence mechanism against Covid-19. The trick is not to think in terms of levels but rates of change. Or to put it another way, how much worse would infection rates have been in the absence of lockdowns? Here we seem to be on safer ground for there is a very strong relationship between the extent and duration of the lockdown and the rate of infections (chart 4). In the three European examples chosen here, it is notable that Italy introduced the lockdown long before infections peaked whereas the UK clamped down quite some time after infections started to pick up, thus supporting the view that the UK was late in acting which contributed to the surge in mortality.

Lockdowns do, of course, have a significant economic cost. The lockdown indices on their own are not particularly useful and have to be augmented with other indicators such as mobility trends and specific country characteristics to render useful information. The Bank of England has done some sophisticated modelling work using the lockdown index as one input and concluded that the data across a range of countries is consistent with a decline in world GDP of around 15% over the first half of 2020 (chart 5).

Prior to this weekend the UK lockdown index was still fairly high in comparison to a number of other countries. That is appropriate given that Covid-related deaths are still high in a European comparison. However, they are well down from their previous highs and given the improvements in recent weeks, it is probably appropriate to begin a limited form of easing. But striking the right balance is virtually impossible. Some think that the infection rates are too high to justify any form of lockdown whilst others believe it should have been lifted long ago. We will only know whether the current policy stance is the right one when we can assess the trends in Covid infections.

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[1] I use mortality rates rather than the number of reported infections because this is dependent on the breadth of the testing programme which differs across countries.

The limits of modelling

The British government has made it clear throughout the Covid 19 crisis that it has been “following the science.” But at this relatively early stage of our understanding of the disease there is no single body of knowledge to draw on. There is a lot that epidemiologists agree on but there are also areas where they do not. Moreover, the science upon which the UK lockdown is based is derived from a paper published almost two months ago when our understanding of Covid was rather different to what we know now. I was thus fascinated by this BBC report by medical editor Deborah Cohen, who posed questions of the current strategy and interviewed experts in the field who expressed some reservations about how the facts are reported. Whilst the report gave an interesting insight into epidemiology, it also reminded me of the criticism directed at economic forecasting.

One of the most interesting issues to arise out of the discussion was the use of models to track the progression of disease. The epidemiologists quoted were unanimous in their view that models were only useful if backed up by data. As Dame Deirdre Hine, the author of a report on the 2009 H1N1 pandemic pointed out, models are not always useful in the early stages of a pandemic given the lack of data upon which they are based. She further noted that “politicians and the public are often dazzled by the possibilities that modelling affords” and that models often “overstate the possibilities of deaths in the early stages” of a pandemic due to a lack of data. As Hine pointed out, epidemiological models only start to become useful once we implement a thorough programme of tracing and tracking people’s contacts, for only then can we start to get a decent handle on the spread of any disease.

This approach has great parallels with empirical macroeconomics where many of the mainstream models used for analytical purposes are not necessarily congruent with the data. Former member of the Bank of England Monetary Policy Committee Danny Blanchflower gave a speech on precisely this topic back in 2007 with the striking title The Economics of Walking About. The objective of Blanchflower’s speech was to encourage policymakers to look at what is going on around them rather than uncritically accept the outcomes derived from a predetermined set of ideas, and to put “the data before the theory where this seems warranted.”

I have always thought this to be very sensible advice, particularly in the case where DSGE models are used for forecasting purposes. These models are theoretical constructs based on a particular economic structure which use a number of assumptions whose existence in the real world are subject to question (Calvo pricing and rational expectations to name but two). Just as in epidemiology, models which are not consistent with the data do not have a good forecasting record. In fact, economic models do not have a great track record, full stop. But we are still forced to rely on them because the alternative is either not to provide a forecast at all, or simply make a guess. As the statistician George Box once famously said, “all models are wrong, but some are useful.”

Epidemiologists make the point that models can be a blunt instrument which give a false sense of security. The researchers at Imperial College whose paper formed the basis of the government’s strategy might well come up with different estimates if, instead of basing their analysis on data derived from China and Italy, they updated their results on the basis of latest UK data. They may indeed have already done so (though I have not seen it) but this does not change the fact that the government appears to have accepted the original paper at face value. Of course, we cannot blame the researchers for the way in which the government interpreted the results. But having experienced the uncritical media acceptance of economic forecasts produced by the likes of the IMF, it is important to be aware of the limitations of model-driven results.

Another related issue pointed out by the epidemiologists is the way in which the results are communicated. For example, the government’s strategy is based on the modelled worst case outcomes for Covid 19 but this has been criticised for being misleading because it implies an event which is unlikely rather than one which close to the centre of the distribution. The implication is that the government based its strategy on a worst case outcome rather than on a more likely outcome with the result that the damage to the economy is far greater than it needed to be. That is a highly contentious suggestion and is not one I would necessarily buy into. After all, a government has a duty of care to all its citizens and if the lives of more vulnerable members of society are saved by imposing a lockdown then it may be a price worth paying.

But it nonetheless raises a question of the way in which potential outcomes are reported. I have made the point (here) in an economics context that whilst we need to focus on the most likely outcomes (e.g. for GDP growth projections), there are a wide range of possibilities around the central case which we also need to account for. Institutions that prepare forecast fan charts recognise that there are alternatives around the central case to which we can ascribe a lower probability. Whilst the likes of the Bank of England have in the past expressed frustration that too much emphasis is placed on the central case, they would be far more concerned if the worst case outcomes grabbed all the attention. The role of the media in reporting economic or financial outcomes does not always help. How often do we see headlines reporting that markets could fall 20% (to pick an arbitrary figure) without any discussion of the conditions necessary to produce such an outcome? The lesson is that we need to be aware of the whole range of outcomes but apply the appropriate weighting structure when reporting possible outcomes.

None of this is to criticise the efforts of epidemiologists in their efforts to model the spread of Covid 19. Nor is it to necessarily criticise the government’s interpretation of it. But it does highlight the difficulties inherent in forecasting outcomes based on models using incomplete information. As Nils Bohr reputedly once said, “forecasting is hard, especially when it’s about the future.” He might have added, “but it’s impossible without accurate inputs.”

Stresses and strains

Was the government too complacent?

The outbreak of Covid-19 will go undoubtedly down as one of the most traumatic social and economic upheavals of our time. At the time of writing, more than 200,000 people worldwide are recorded as having died and the true figure is undoubtedly higher. More will undoubtedly succumb. But as tragically high as these figures are, it is possible to imagine a far worse pandemic. A typical pandemic would be expected to strike more evenly across the age spectrum than Covid-19 which has predominantly impacted on those aged over 50. You do not have to be a virologist to imagine an even more terrifying disease which is more virulent and infectious than Covid-19. Indeed, the threat of such a pandemic is one of the natural disasters which form a key element in national disaster planning across the world.

Fortunately, such outbreaks are rare but precisely because of that it is so easy to become complacent about the risks which they pose. However, in what now seems like propitious timing, a year before we had even heard of Covid-19, a group of epidemiologists conducted a study to assess the preparedness of global health systems in the event of a global epidemic. They constructed an Epidemic Preparedness Index (EPI) covering 188 countries and based on five key metrics:  overall economic resources; public health communications; infrastructure; public health systems and institutional capacity. According to the authors, “the most prepared countries were concentrated in Europe and North America, while the least prepared countries clustered in Central and West Africa and Southeast Asia.”

All countries have expressed concerns that the outbreak of the disease would overwhelm their health systems, which is why they have imposed a lockdown to spread out the incidence of infections. Health experts are unanimous in their belief that containment and mitigation strategies are the first line of defence to combat a pandemic. Italy was one of the first countries, aside from China, to implement a lockdown on 9 March. At that time it had recorded 7,375 cases and 366 deaths. As of today, it has recorded 197,675 cases and 26,644 fatalities. The UK imposed a lockdown two weeks later than Italy, on 23 March, at which time it had recorded 5,683 cases and 281 fatalities. Almost five weeks later it has recorded 148,377 cases and 20,319 fatalities.

The debate in the UK focuses very heavily on the fact that the government was too late in implementing the lockdown and that it should have learned from the Italian experience. By the time it adopted this strategy, when its figures were similar to those in Italy two weeks previously, it was already too late and the path of the disease was effectively predetermined. There certainly does appear to be a lot of evidence to suggest the British government was reluctant to take such a dramatic measure although others suggest that the scientific advisers were slow to respond.

Either way it appears that the delay in implementing the lockdown played a role in allowing Covid-19 to become more widespread than it need have been although it is easy to be wise after the event. Indeed when Germany implemented a lockdown on 23 March, it had recorded 24,774 cases (more than either the UK or Italy at the same stage) but just 94 fatalities. It is thus likely that future research will concude that some governments were too slow to deploy their first line of defence. But this is not the whole story.

Or is it the lack of spending?

National health systems act as the second line of defence, offering options ranging from testing to intensive care. At this point the degree of funding provided to the health system really starts to come into its own. According to data compiled by the OECD, the UK had fewer medical staff per 1000 of population than many other European nations (see chart below). Although the proportion of doctors is below the OECD average, it is not too far out of line with other EU countries. But the number of nursing staff is somewhat lower. This might partially explain, for example, why the UK has been so slow in rolling out mass testing. To the extent that a shortage of trained medical staff at a time of emergency puts pressure on existing staff as overstretched resources are stretched more thinly, there is some evidence to suggest that funding constraints over the last decade have added to the strains facing the British NHS in recent weeks. Indeed, despite making great play of the fact that a number of temporary hospitals have been opened to add additional capacity to the health system, there have been complaints that there are simply not enough trained staff to provide the requisite services.

I have noted the strains on various parts of the public sector on numerous occasions in recent years and have pointed out the issues facing NHS funding (here, for example). In theory, of course, the NHS was protected from the worst of the austerity but there was still a slowdown in the rate of funding which meant that the supply of health care has not kept pace with demand. In terms of what the service offers, it can be regarded as efficient in an international context. For example, the NHS operates its critical care facilities with an 84% utilisation rate (higher than all other OECD countries bar Ireland, Israel and Canada, see chart below). But this also means that there is limited spare capacity to cope with emergencies. When it comes to the overall capacity of the system, the UK also has fewer intensive care beds per head of population than the OECD average.  

It is hard to avoid the conclusion that the NHS entered the Covid-19 crisis with the bare minimum of resources. For anyone who doubts the strains that the medical profession operate under in normal times, I highly recommend the book by former doctor Adam Kay, This is Going to Hurt, which is a litany of the humorous, bizarre and tragic circumstances routinely encountered by the medical profession. Anecdotal evidence gathered from my own discussions with medical personnel in recent years suggests that the strains intensified during the worst of the government’s austerity programme.

On the basis that demand for health services is infinite, some serious questions will have to be asked once the crisis is over as to what we require of health services in future and how we expect to pay for  them. It is pretty certain that no government will be able to deny funds to the NHS in the near future. Therefore, either spending in non-health related areas will have to be cut or taxes will have to rise. I even suggested a couple of years ago that a hypothecated tax to fund health spending might be something we need to consider. Whatever options we finally choose, the public will accept nothing less than a new deal for the NHS. The era of austerity is over although the question of how to pay for it all will be the subject of future posts.

Whistling in the dark or shining a light?

The global picture

As the official bodies begin to put out their growth forecasts for 2020 and 2021 the magnitude of the hit facing the global economy following the Covid-19 shutdown is becoming increasingly clear. The IMF’s latest projections suggest that global GDP will contract this year by 3%, rebounding by 5.8% in 2021. We have not seen anything like it in 90 years since the Great Depression, when world activity is estimated to have fallen by 10% between 1930 and 1932 with three successive annual declines of 3% or more. For the record, it took six years for output to regain its pre-crash highs. The IMF is suggesting that next year we will be able to put all of this behind us and push output back above pre-crash levels. I remain highly sceptical.

The good news, as the IMF points out, is that we do not currently have the degree of protectionism and beggar-thy-neighbour policies of the early-1930s which made the downturn so much worse than it needed to be. But economic nationalism is clearly back in fashion, and Donald Trump’s decision to halt US funding to the World Health Organisation during the greatest public health threat in a century is indicative of the febrile sentiment currently at play (not to mention the fact that it is probably one of the dumbest of petty acts and says a lot about Trump’s way of doing business, but in the interests of politeness to my American friends I will leave it there). Interestingly, the IMF’s forecast makes it clear that whilst output in emerging markets will rebound quickly, the advanced economies will not recoup their output losses in 2021. Indeed, EM economies take a relatively small hit with output projected to fall by only 1% this year and surging by 6.6% next year. My concern with this is that many EMs are export-driven economies, and if the developed world is growing relatively slowly, the demand for EM exports may not recover sufficiently quickly to drive the expected global growth surge.

The big imponderable is how deep will be the scars left by the current shutdown? The cause of the economic collapse is simply that much economic activity is prohibited as lockdowns came into force which has resulted in many people having to remain at home. Such impacts will ripple throughout the economy in as-yet unpredictable ways, and whilst fiscal and monetary policies have been turned up to the max they can only mitigate and not totally offset the economic damage. For example, even though interest rates are at rock bottom levels everywhere, this is no guarantee that people will want to borrow when the worst of the crisis is past. Nor will lenders necessarily be willing to grant credit to those individuals and businesses who are struggling to stay afloat if they are perceived to be a bad credit risk. This puts banks in a difficult position. Whilst they were perceived as the bad guys a decade ago, they want to be seen to be making a positive contribution today. But they also have a duty to their shareholders whose returns have taken a beating, and who will not thank them for any big rise in loan-loss provisions.

So far, all of this has been predicated on the assumption that the Covid-19 crisis can be compressed into the second quarter of 2020. This is far from a certainty. Much will depend on what form of exit strategy is adopted by governments: How long will it take to reopen the economy even if the threat passes relatively quickly if the process is staggered over several stages? Then there is the question of whether the viral threat will indeed pass so suddenly. Scientific evidence suggests that social distancing measures may have to remain in place until 2022 and vigilance maintained until 2024, neither of which are conducive to a sudden pickup in activity. For the record, the IMF did conduct alternative scenarios. In one of the worst case outcomes, the assumption of a longer Covid-19 outbreak in 2020 together with a renewed outbreak in 2021, results in a level of GDP next year which is 8% below the baseline discussed above. This would imply an output loss of more than 5% over two years which starts to look more like a 1930s outcome.

The local picture

Closer to home, the UK Office for Budget Responsibility came out with an illustrative scenario earlier this week which suggested UK GDP could collapse by 13% in 2020, with a 35% contraction in Q2 alone, which is followed by a rebound of 18% in 2021 (chart below). To put that into context, this would be the largest annual contraction in GDP since 1709 when the Great Frost wiped out agricultural output. The projected rebound in 2021 would also be the largest since 1704 (apparently). Even allowing for the fact that the historical data are subject to a huge degree of uncertainty, the OBR figures suggest the most volatile swings in output for over 300 years. Like the IMF (whose predictions for UK growth in 2020 and 2021 are a more modest -6.5% and +4.0% respectively), the OBR figures effectively assume that there will be no economic scarring although I doubt very much that if the OBR’s awful 2020 forecast is realised there will be much of a rebound next year.

Predictably, the IMF and OBR projections were met with the usual scepticism from those who have nothing better to do than criticise the forecasting efforts of others. I am not going to jump on that bandwagon. After all, these forecasts are produced because there is a need to have some basis for planning. What would the sceptics rather we do? Produce nothing and trust to luck by making it up as we go along? Just imagine the howls of rage if governments were not prepared for the worst case outcomes. But it does raise a question as to how such analysis should be treated at a time when predicting the future is little more than guesswork. The OBR made it clear that its analysis was a scenario, not a forecast, yet the media treated it as if it were a forecast. You may ask what is the difference? The answer is that a scenario is a conditional assessment based on a “what-if” approach whereas a forecast is typically viewed as an unconditional, what-will-happen event.

Obviously this is a fine distinction but it is important. The OBR is not suggesting in its analysis that it believes the outcome will necessarily be realised but it is an attempt to highlight the economic risks. Arguably there are better ways to do it. It could, for example, have prepared a range of outcomes along the same lines as the IMF and not chosen to discuss one illustrative case which runs the risk of being treated as an unconditional forecast. As former BoE insider Tony Yates pointed out on Twitter, the criticism levelled at the OBR is “the kind of thing that makes policy bodies nervous about being as transparent as they should be to help us hold them to account.  The BoE was paralysed by this nervousness, and made themselves hard to scrutinise.”

The one thing we know is that all forecasts produced in the current uncertain environment will be wrong in some way. They should be viewed as an attempt to shine some light in the dark, however feeble. In truth, the ordinary voter does not care about GDP growth but when you tell them it is a proxy for the path of employment and incomes, we are then talking about something meaningful for them. As a final thought, when the IMF and OBR are so far apart in their views on the UK, this is an indication that the light cast by the forecast insight is dim indeed.