HANGZHOU/MILAN – COVID-19 is an unprecedented challenge for human society and the global economy. The pandemic has already taken more than 360,000 lives worldwide, and inflicted massive negative shocks on incomes, output, and employment. The challenge for policymakers is to strike a proper balance between containing the virus and creating the conditions for economic recovery.That is no easy task. While key measures such as testing, contact tracing, and social distancing happen to align well with both overarching goals, measuring real-time progress in each dimension is difficult. Direct measures like GDP tend to arrive with a significant lag, which makes it harder to determine when to reopen various economic sectors and activities.The Mobility WindowFortunately,
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HANGZHOU/MILAN – COVID-19 is an unprecedented challenge for human society and the global economy. The pandemic has already taken more than 360,000 lives worldwide, and inflicted massive negative shocks on incomes, output, and employment. The challenge for policymakers is to strike a proper balance between containing the virus and creating the conditions for economic recovery.
That is no easy task. While key measures such as testing, contact tracing, and social distancing happen to align well with both overarching goals, measuring real-time progress in each dimension is difficult. Direct measures like GDP tend to arrive with a significant lag, which makes it harder to determine when to reopen various economic sectors and activities.
The Mobility Window
Fortunately, there is an immediately observable, high-frequency indicator of COVID-19’s economic impact: mobility data, which can serve as a proxy for the broader contraction in economic activity around the globe. Following this insight, we have calculated mobility on the basis of aggregated, anonymized data published by Google, Apple, AMAP, and Baidu.
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Google, for example, publishes mobility information on retail and recreation, groceries and pharmacies, and workplaces, which we have combined into a single index. Apple publishes mobility data on driving, transit, and walking. And AMAP and Baidu publish location-based services (LBS) mobility data. Most important, these data are updated frequently – either on a weekly or even daily basis.
We have tracked the extent of mobility (as a percentage of its level in normal times) across 131 economies. Google data was the primary source for 129 economies, Apple data for Russia, and AMAP and Baidu data for mainland China.
Among the 19 countries and regions that have announced first-quarter GDP, we find that three-quarters of the variation in GDP growth can be explained by differences in mobility during this period (Figure 1). This variability across countries results from the fact that the virus arrived at different times during the quarter, and triggered mobility responses of varying speed and intensity.
To be sure, mobility is only one indicator of economic contraction. Risk avoidance by individuals, companies, and other institutions also could play a role in depressing economic activity, even in the absence of mandated lockdowns. But as a variable that captures the state of economic activity, mobility has several major advantages.
First, it is one of the few big-data metrics that both captures current activities and is available in more than 130 economies on a daily basis. Second, it is an endogenous variable, in the sense that it reflects both the impact of lockdowns and people’s choices, which often are motivated by risk aversion. And, third, it appears to capture a substantial portion of GDP variation across economies and over time.
Though a “pandemic economy” is rather unusual, it has identifiable features and operates according to clear patterns. In the early stages, the outbreak must be contained at the expense of mobility and productive economic activity. Failing that, a recovery cannot be realistically considered.
Owing to the tension between health outcomes and economic goals, the recovery will be much slower than the precipitous economic free fall that occurs when lockdowns are imposed. This general pattern has been confirmed in a wide range of countries (Figure 2).
Generally speaking, a sharp and deep contraction is followed by a period (of variable duration) in which the economy remains depressed as the virus is brought under control (the trough). This phase is then followed by an S-shaped recovery – a slow but steady acceleration in growth, followed by a deceleration as output approaches its pre-pandemic levels. This last deceleration reflects the fact that some sectors (air travel, sporting events) are difficult to restore, given the continued need for social distancing.
The main economic challenge lies in shortening the free-fall phase through early detection and containment (a challenge that most countries have failed to meet). Then the goal is to reduce the time spent in the trough while also making satisfactory progress on containing the virus. Eventually, the economy will decouple from the outbreak as new infections subside.
An effective, widely deployed vaccine would, of course, accelerate the recovery, even allowing for a U-shaped recovery if it arrived soon enough. As matters stand, that scenario seems unlikely.
To balance virus containment with support for the economic recovery, policymakers need better tools to measure and monitor the evolution of the global pandemic economy. With this goal in mind, Luohan Academy recently launched a Global Pandemic-Economic Tracker (PET), which aims to provide government leaders, businesses, and the general public with a deeper understanding of the general patterns of the pandemic economy’s processes, the tradeoffs that occur at different stages, and other challenges that await.
Charting the Pandemic Economy
The PET offers a window into a wide range of economies. In the graphs below, the vertical axes show the magnitude of the contraction, the estimated current levels of economic activity as a percentage of pre-pandemic levels, according to daily mobility data. The horizontal axes follow the number of days it takes for confirmed infection cases to double, as of the time of measurement. Doubling days (DD) is a proxy for the rate at which the virus is spreading within the population; the larger the DD, the lower the rate of spread.
Each graph also has a dashed vertical line showing the average DD for economies that have already met our recovery condition: 19 days. When an economy has gone three consecutive days in which the number of COVID-19 recoveries exceeds the number of confirmed new cases, we take that to mean that the outbreak has been met with an effective medical response. Thus, a country to the left of the dashed vertical line is less likely, on average, to have the virus under control, regardless of what is happening in its economy.
The time dimension in the graphs is also important to grasp, because the economic damage in terms of lost income, output, and increased unemployment depends on both the depth of the contraction and its duration. If the PET graphs for two countries look similar but the speed of transition is higher for one, that country will be in better shape. It will have experienced less balance-sheet damage; its programs to buffer the shock will be shorter-lived and less costly; and its deficits and sovereign debt increments will be lower.
As of May 20, at least 45 countries and regions had entered the recovery period, though most are still far from restoring normal economic activities. More than half of the countries analyzed still have not entered the recovery period after suffering the pandemic for more than two months.
The First Wave
The first wave of the pandemic hit mostly East Asian countries, with China’s trajectory representing the pandemic-economy curve very well. It took China 30 days to reach the bottom, at about 80% of normal economic activity, and this rapid lockdown more or less paid off.
By May 20, China had been in the pandemic economy for 124 days, during which time its DD steadily increased. Its economic activity has returned to 98% of its pre-pandemic level, and quarterly economic growth is on track to be positive in April-June. With strong stimulus packages, China should continue to register positive growth in the second half of the year. Note, however, that strong growth does not necessarily imply full recovery.
Several other East Asian countries and regions performed even better, owing to their early-detection regimes and other swift policy actions. South Korea, Hong Kong, and Taiwan have all recovered to more than 95% of normal activity levels. Their contractions at the start were less severe, and their DDs have all exceeded 50 days (meaning it takes more than seven weeks for the number of infections to double).
Overall, the lesson from East Asia (Figure 4) is that the faster and more decisively a country moves to contain the virus in the early stages, the smaller a price it has to pay, in terms of both public health and economic loss. Counterfactual models applied to other countries such as the United States reach the same conclusion.
Unfortunately, second-wave countries learned this lesson the hard way (Figure 5). Italy, for example, has been in the pandemic economy for 85 days. It took 62 days to satisfy our proxy recovery condition, and it has remained in a deep contraction for...