We propose to build on this foundation and find a way to learn not just from crises but even during the crisis itself. We argue for this position not just in the context of the COVID-19 pandemic but also toward the ultimate goal of improving our ability to handle things we can’t foresee—that is, to become more resilient.
To find the upside of emergencies, we first looked at the economic effects of a tidy little crisis, a two-day strike that partially disrupted service of the London Underground in 2014. We discovered that the approximately 5 percent of the commuters who were forced to reconsider their commute ended up finding better routes, which these people continued after service was restored. In terms of travel time, the strike produced a net benefit to the system because the one-off time costs of the strike were less than the enduring benefits for this minority of commuters.
Why had commuters not done their homework beforehand, finding the optimal route without pressure? After all, their search costs would have been quite low, but the benefits from permanently improving their commute might well have been large. Here, the answer seems to be that commuters were stuck in established yet inefficient habits; they needed a shock to prod them into making their discovery.
Icelandic volcano eruption of 1973.
Photo-Illustration: Chad Hagen; Original Photo: Bettmann/Getty Images
A similar effect followed the eruption of a long-dormant Icelandic volcano in 1973. For younger people, having their house destroyed led to an increase of 3.6 years of education and an 83 percent increase in lifetime earnings, due to their increased probability of migrating away from their destroyed town. The shock helped them overcome a situation of being stuck in a location with a limited set of potential occupations, to which they may not have been well suited.
As economists and social scientists, we draw two fundamental insights from these examples of forced experimentation. First, the costs and benefits of a significant disruption are unlikely to fall equally on all those affected, not least at the generational level. Second, to ensure that better ways of doing things are discovered, we need policies to help the experiment’s likely losers get a share of the benefits.
Because large shocks are rare, research on their consequences tends to draw from history. For example, economic historians have argued that the Black Death plague may have contributed to the destruction of the feudal system in Western Europe by increasing the bargaining power of laborers, who were more in demand. The Great Fire of London in 1666 cleared the way, literally, for major building and planning reforms, including the prohibition of new wooden buildings, the construction of wider roads and better sewers, and the invention of fire insurance.
History also illustrates that good data is often a prerequisite for learning from a crisis. John Snow’s 1854 Broad Street map of cholera contagion in London was not only instrumental in identifying lessons learned—the most important being that cholera was transmitted via the water supply—but also in improving policymaking during the crisis. He convinced the authorities to remove the handle from the pump of a particular water source that had been implicated in the spread of the disease, thereby halting that spread.
Four distinct channels lead to the benefits that may come during a disruption to our normal lives.
China enters world markets as major exporter of industrial products.
Photo-Illustration: Chad Hagen; Original Photo: Tao Images/Alamy
Habit disruption occurs when a shock forces agents to reconsider their behavior, so that at least some of them can discover better alternatives. London commuters found better routes, and Icelandic young people got more schooling and found better places to live.
Selection involves the destruction of weaker firms so that only the more productive ones survive. Resources then move from the weaker to stronger entities, and average productivity increases. For example, when China entered world markets as a major exporter of industrial products, production from less productive firms in Mexico was reduced or ceased altogether, thus diverting resources to more productive uses.
Weakening of inertia occurs when a shock frees a system from the grip of forces that have until now kept it in stasis. This model of a system that’s stuck is sometimes called path dependence, as it involves a way of doing things that evolved along a particular path, under the influence of economic or technological factors.
The classic example of path dependence is the establishment of the conventional QWERTY keyboard standard on typewriters in the late 19th century and computers thereafter. All people learn how to type on existing keyboards, so even a superior keyboard design can never gain a foothold. Another example is cities that persist in their original sites even though the economic reasons for founding them there no longer apply. Many towns and cities founded in France during the Roman Empire remain right where the Romans left them, even though the Romans made little use of navigable rivers and the coastal trade north of the Mediterranean that became important in later centuries. These cities have been held in place by the man-made and social structures that grew up around them, such as aqueducts and dioceses. In Britain, however, the nearly complete collapse of urban life after the departure of the Roman legions allowed that country to build new cities in places better suited to medieval trade.
Coordination can play a role when a shock resets a playing field to such an extent that a system governed by opposing forces can settle at a new equilibrium point. Before the Great Boston Fire of 1872, the value of much real estate had been held down by the presence of crumbling buildings nearby. After the fire, many buildings were reconstructed simultaneously, encouraging investment on neighboring lots. Some economists argue that the fire created more wealth than it destroyed.
A shock may free a system from path dependence—the grip of forces that have until now kept it in stasis
The ongoing pandemic has set off a scramble among economists to access and analyze data. Although some people have considered this unseemly, even opportunistic, we social scientists can’t run placebo-controlled experiments to see how a change in one thing affects another, and so we must exploit for this purpose any shock to a system that comes our way.
What really matters is that the necessary data be gathered and preserved long enough for us to run it through our models, once those models are ready. We ourselves had to scramble to secure data regarding commuting behavior following the London metro strike; normally, such data gets destroyed after 8 weeks. In our case, thanks to Transport for London, we managed to get it anonymized and released for analysis.
In recent years, there has been growing concern over the use of data and the potential for “data pollution,” where an abundance of data storage and its subsequent use or misuse might work against the public interest. Examples include the use of Facebook’s data around the 2016 U.S. presidential election, the way that online sellers use location data to discriminate on price, and how data from Strava’s fitness app accidentally revealed the sites of U.S. military bases.
Given such concerns, many countries have introduced more stringent data-protection legislation, such as the EU General Data Protection Regulation (GDPR). Since this legislation was introduced, a number of companies have faced heavy fines, including British Airways, which in 2018 was fined £183 million for poor security arrangements following a cyberattack. Most organizations delete data after a certain period. Nevertheless, Article 89 of the GDPR allows them to retain data “for scientific or historical research purposes or statistical purposes” in “the public interest.” We argue that data-retention policies should take into account the higher value of data gathered during the current pandemic.
The presence of detailed data is already paying off in the effort to contain the COVID-19 pandemic. Consider the Gauteng City-Region Observatory in Johannesburg, which in March 2020 began to provide governmental authorities at every level with baseline information on the 12-million-strong urban region. The observatory did so fast enough to allow for crucial learning while the crisis was still unfolding.
The Great Boston Fire of 1872.
Photo-Illustration: Chad Hagen; Original Photo: Universal Images Group/Getty Images
The observatory’s data had been gathered during its annual “quality of life” survey, now in its 10th year of operation, allowing it to quantify the risks involved in household crowding, shared sanitation facilities, and other circumstances. This information has been cross-indexed with broader health-vulnerability factors, like access to electronic communication, health care, and public transport, as well as with data on preexisting health conditions, such as the incidence of asthma, heart disease, and diabetes.
This type of baseline management, or “baselining,” approach could give these data systems more resilience when faced with the next crisis, whatever it may be—another pandemic, a different natural disaster, or an unexpected major infrastructural fault. For instance, the University of Melbourne conducted on-the-spot modeling of how the pandemic began to unfold during the 2020 lockdowns in Australia, which helped state decision-makers suppress the virus in real time.
When we do find innovations through forced experimentation, how likely are those innovations to be adopted? People may well revert to old habits, and anyone who might reasonably expect to lose because of the change will certainly resist it. One might wonder whether many businesses that thrived while their employees worked off-site might nonetheless insist on people returning to the central office, where managers can be seen to manage, and thereby retain their jobs. We can also expect that those who own assets few people will want to use anymore will argue for government regulations to support those assets. Examples include public transport infrastructure—say, the subways of New York City—and retail and office space.
One of the most famous examples of resistance to technological advancements is the Luddites, a group of skilled weavers and artisans in early 19th-century England who led a six-year rebellion smashing mechanized looms. They rightly feared a large drop in their wages and their own obsolescence. It took 12,000 troops to suppress the Luddites, but their example was followed by other “machine breaking” rebellions, riots, and strikes throughout much of England’s industrial revolution.
Resistance to change can also come from the highest levels. One explanation for the low levels of economic development in Russia and Austria-Hungary during the 19th century was the ruling class’s resistance to new technology and to institutional reform. It was not that the leaders weren’t aware of the economic benefits of such measures, but rather that they feared losing a grip on power and were content to retain a large share of a small pie.
The conventional QWERTY keyboard.
Photo-Illustration: Chad Hagen; Original Photo: Jonathan Weiss/Alamy
Clearly, it’s important to account for the effects that any innovation has on those who stand to lose from it. One way to do so is to commit to sharing any gains broadly, so that no one loses. Such a plan can disarm opposition before it arises. One example where this strategy has been successfully employed is the Montreal Protocol on Substances That Deplete the Ozone Layer. It included a number of measures to share the gains from rules that preserve the ozone layer, including payments to compensate those countries without readily available substitutes who would otherwise have suffered losses. The Montreal Protocol and its successor treaties have been highly effective in meeting their environmental objectives.
COVID-19 winners and losers are already apparent. In 2020, economic analysis of social distancing in the United States showed that as many as 1.7 million lives might be saved by this practice. However, it was also found that about 90 percent of the life-years saved would have accrued to people older than 50. Furthermore, it is not unreasonable to expect that younger individuals should bear an equal (or perhaps greater) share of the costs of distancing and lockdowns. It seems wise to compensate younger people for complying with the rules on social distancing, both for reasons of fairness and to discourage civil disobedience. We know from stock prices and spending data that some sectors and firms have suffered disproportionately during the pandemic, especially those holding stranded assets that must be written off, such as shopping malls, many of which have lost much of their business, perhaps permanently. We can expect similar outcomes for human capital. There are ways to compensate these parties also, such as cash transfers linked to retraining or reinvestment.
There will almost certainly be winners and losers as a result of the multitude of forced experiments occurring in workplaces. Some people can more easily adapt to new technologies, some are better suited to working from home or in new settings, and some businesses will benefit from less physical interaction and more online communication.
Consider that the push toward online learning that the pandemic has provided may cost some schools their entire business: Why would students wish to listen to online lectures from their own professors when they could instead be listening to the superstars of their field? Such changes could deliver large productivity payoffs, but they will certainly have distributional consequences, likely benefitting the established universities, whose online platforms may now cater to a bigger market. We know from the history of the Black Death that if they’re big enough, shocks have the power to bend or even break institutions. Thus, if we want them to survive, we need to ensure that our institutions are flexible.
To manage the transition to a world with more resilient institutions, we need high-quality data, of all types and from various sources, including measures of individual human productivity, education, innovation, health, and well-being. There seems little doubt that pandemic-era data, even when it’s of the most ordinary sort, will remain more valuable to society than that gathered in normal times. If we can learn the lessons of COVID-19, we will emerge from the challenge more resilient and better prepared for whatever may come next.
Editor’s note: The views expressed are the authors’ own and should not be attributed to the International Monetary Fund, its executive board, or its management.
This article appears in the August 2021 print issue as “What We Learned From the Pandemic.”
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