Workers in developing countries are already jittery with worries ranging from “rebound” outbreaks and lay-offs to the onset of cabin fever.

As if workers don’t have enough on their minds, the covid-19 pandemic is resurfacing another concern: the one about technology’s impact on the future of work. Specifically, recent research suggests that the deepening recession is likely to bring a surge of labour-replacing automation.

But what’s the connection between recessions and automation? On the surface, the implacable infiltration of automation into the economy would seem to be a steady, longer-term trend rather than an immediate fact. Likewise, it might seem intuitive that any rise in unemployment in the coming months will make human labour relatively cheaper, thus slowing companies’ move to technology. 

Yet that’s not actually how automation works. Unfortunately for the workers poised to be affected, robots’ infiltration of the workforce doesn’t occur at a steady, gradual pace. Instead, automation tends to happen in bursts, concentrated especially in bad times such as in the wake of economic shocks when humans become relatively more expensive as firms’ revenues rapidly decline. At these moments, employers can benefit by shedding less-skilled workers and replacing much of what they do with technology, while often investing in higher-skilled workers, which increases labour productivity as a recession tapers off.

Ultimately, such cycles of workforce reallocation may well improve the efficiency of the economy for the longer-term good of society. But along the way, and in the nearer-term, such cyclical surges of technology adoption tend to be disruptive, unequal and unhelpful to the cause of maintaining employment during a crisis.

Nor are these disruptions only speculative. Several economists have extensively documented the cyclical, selective nature of automation and employment disruption. Nir Jaimovich of the University of Zurich and Henry E. Siu of the University of British Columbia have reported that over three recessions in the past 30 years a whopping 88% of job loss took place in “routine”, highly automatable occupations—suggesting that automation accounted for “essentially all” of the jobs lost in the crises. Separately, Brad J. Hershbein of the W.E. Upjohn Institute and Lisa B. Kahn of the University of Rochester looked at almost 100m online job postings before and after the Great Recession and found that firms in hard-hit metropolitan areas were steadily replacing workers who performed automatable “routine” tasks with a combination of technology and skilled workers. So, even as robots replace workers during boom times at places such as Amazon and Walmart, their influx surges during recessions—not great news for the world’s anxious workers.

Given the importance of social distancing and sanitation it seems likely that the coronavirus recession will see an accentuated embrace of automation technologies, whether in the form of kiosk ordering in restaurants, checkout-free shopping or robotic sanitation machines. Considering that the past decade has seen more automation and artificial intelligence (AI) applications readied for effective use than ever, it seems clear that the next few years will see more rather than less automation as the economy slows.

Cyclical surges of technology adoption tend to be disruptive, unequal and unhelpful to the cause of maintaining employment during a crisis.

As to who may be vulnerable to automation-related dislocation in the recession, the reach of technology may be wider this time round.

As my 2019 assessment of US automation trends suggests, it’s likely that low-income workers, the young and workers of colour will be some of the most vulnerable. That’s because the epidemic and subsequent automation surge is likely to affect the most “routine” occupations—jobs in areas such as production, food service or transportation.

 

 

And yet that is only part of the story. During this crisis, AI may play a larger role than before as an array of algorithmic applications take on countless office functions that would be said to require higher-level “intelligence”, whether it be planning, predicting, classifying, reasoning or problem-solving. In that vein, assessments developed by Stanford scholar Michael Webb in partnership with my team suggest that if AI surges further into the economy during the crisis it may affect better-paid, white-collar or professional workers more than the less-educated, lower-wage workers who have tended to be most affected by robots and software. 

 

Workers in higher-wage occupations will generally be more exposed to AI than lower-wage workers. The curve tapers at the 90th percentile, suggesting that the most elite workers—such as CEOs—may be somewhat protected.

 

That doesn’t mean that the world’s white-collar workers should all be fearing for their jobs. But it does mean that the relatively more fortunate “telework” class—market research analysts, middle managers, programmers or financial analysts—could also find themselves more involved with new and disruptive technologies. In that sense, no group of workers may be entirely immune this time around.   

As to what all of this means for the future, the potential for a covid-19 automation surge reinforces the fact that the pandemic recession won’t just bring an end to a decade of plentiful jobs. More starkly, the downturn is likely to usher in a new bout of structural change in the labour market and its demand for skills. 

If it continues for a while, the downturn could induce firms in food service, retail and administrative work to restructure their operations towards greater use of technology and higher-skilled workers. And it could introduce new waves of “digital transformation” into the world’s offices. From beleaguered, lower-skilled employees to professional workers, these changes will no doubt complicate an already daunting return to normality.

 

Mark Muro is a senior fellow at the Brookings Institution in Washington, DC.

The views and opinions expressed in this article are those of the author and do not necessarily reflect the views of The Economist Group or any of its affiliates. The Economist Group cannot accept any responsibility or liability for reliance by any person on this article or any of the information, opinions or conclusions set out in the article. 

Data has become a crucial battleground in the war against coronavirus, as many countries have used sophisticated methods for gathering and analysing information on individuals’ behaviour to monitor and manage the pandemic. 

This could lead to a lasting shift in how we think about data and its governance. Here I set out what a new social contract around data might consist of—and how might move beyond the frustrating vagueness that has characterised much of the debate so far.

 

The binary debate of the 2010s

For the past decade the public discourse around data has been squeezed into a binary framework. On one side were big organisations—governments and large companies—harvesting data on an unprecedented scale. They provided little transparency or consideration for privacy—but demonstrated benefits in valuable products and services. Against them grew activists who argued for new rights and restrictions to put data under the control of citizens.

Covid-19 has now shown the limits of both data hubris and data restriction. Smart use of data from multiple sources can undoubtedly be in the public interest. But it’s clearer than ever that strong rules will be needed to prevent the abuse of power.

We may be headed towards a new social contract around data that combines three distinct elements: first, new norms of data minimisation and privacy by design; second, strong laws to punish abuses; and third, a new generation of regulators and institutions charged with maximising the public value derived from data. If we can get this right, we’ll see radically more data sharing where there is a public interest in doing so, and less where there isn’t. But the details will be all-important. 

 

 

Innovations in the crisis

The prompt is the extraordinary innovation fuelled by the crisis. China moved first, using mobile phone data to track the millions who left Wuhan in the hours before the city was cut off. Alipay and WeChat’s HealthCode (which also drew on self-reporting and medical records) were then used to give people red, yellow or green status to determine their freedom of movement depending on whether they had been near infected individuals. Taiwan also used mobile phone data to track people who had been infected and manage their quarantines. 

Singapore relied on a combination of its TraceTogether app and teams performing investigations and interviews to determine who needed to be tested. South Korea used smartphone data, credit card payments and other sources to trace contact between individuals (and sparked controversy when transparency about people’s travel patterns uncovered illicit affairs). 

Covid-19 has shown the limits of both data hubris and data restriction. Smart use of data from multiple sources can undoubtedly be in the public interest, but it’s clearer than ever that strong rules will be needed to prevent the abuse of power.

Each approach was slightly different. But all of these countries were aggressive in pulling data together to contain the crisis. Nothing comparable has been implemented by Western countries, but many are now trying to copy them. In the UK, for example, much effort is going into an NHS app that asks people to report their symptoms (or lack thereof) on a regular basis. It’s hoped that a majority of the population will engage with the scheme to accelerate the end of lockdown. 

New apps aren’t technically needed since smartphones automatically know where they are. Intelligence agencies and phone companies can easily track the proximity of individuals (and in Israel the intelligence agency Shin Bet has been active in using location data to track infections).

 

Design dilemmas

Despite these existing capabilities, the crisis is introducing important design and technical choices. Tracing can be done using either Bluetooth or phone network geolocation. Bluetooth is, in principle, more decentralised and leaves more control in the hands of citizens, though it creates its own problems if it’s always on—a challenge Google and Apple are working on.

Another choice is whether to anonymise the data that’s collected. Europe’s DP-3T (Decentralised Privacy-Preserving Proximity Tracing) project is attempting to shield the identities of those affected by covid-19 using randomisation and Bluetooth technology. The initiative aims to allow those with the virus to anonymously alert others of exposure risk while keeping their own identity hidden from the authorities. This is appealing—but at a certain point there is no avoiding the need to identify people and ensure that they are showing up for tests.  Here we come up against the unavoidable tension between individual rights and the collective interest, and the need for governance mechanisms to judge how that trade-off should be made in different conditions. There will be even harder judgments to make about using data to manage certification of immunity. 

As these experiments unfold in front of our eyes the crisis is bringing to the surface all the big questions that will need to be answered if we’re to make the most of data and AI over the next decade. It has already prompted some hand-wringing by prominent thinkers such as Yuval Harari and Shoshana Zuboff, though it’s striking that they have very little to say about possible solutions. So what could a more permanent settlement around data look like?

 

A new social contract around data

I expect that it will combine three apparently very different, but complementary, elements. First, we will need new approaches to technology design that build in data minimisation. We have become used to digital tools that gather and share data on an extraordinary scale, but mainly for the benefit of a handful of big commercial platforms. Google really does know more about you than you do. But this is not inevitable; it is the result of choices. The alternative route promotes data minimisation and says that companies and governments should only gather what they need. Some of the projects in the EU’s DECODE programme have been experimenting with ways of doing this—for example, allowing that if you book a hotel room there is no need for the hotel to know all of your passport or banking details. My guess is that data minimisation and privacy by design will increasingly become the norm, but with clear provisions of greater data gathering where there is clear-cut public interest. 

Second, we will continue to need laws that are strong enough to penalise abuses and flexible enough to adapt to changing pressures and technologies. The EU's General Data Protection Regulation (GDPR), implemented in 2018, has become a de facto standard and, contrary to the complaints of Silicon Valley, has turned out to be quite flexible. It allows, for example, employers to gather data on which employees need to be self-isolating because of symptoms but with strict rules as to what they can do with it. The European Data Protection Board acknowledged that an emergency like this is a "legal condition which may legitimise restrictions of freedoms provided these restrictions are proportionate and limited to the emergency period" and Article 9 allows the processing of personal information without consent if it’s necessary to protect “against serious cross-border threats to health”. It’s clearer than ever that every country will need laws of this kind, and there is now little chance of the UK, post-Brexit, moving far away from GDPR.

Third, we will need new institutions, design to protect trust and make judgments about trade-offs. The crisis has confirmed the glaring lack of institutions with the skills and authority to be trusted guardians of data and data linking, including the kinds of data that are being gathered for covid-19 responses. Currently this is an empty space. Although some countries have information commissioners, they hardly ever appear on the evening news discussing big events or privacy trade-offs in this space. Consider revelations like the Cambridge Analytica scandal which have all been driven by whistleblowers and the media not by public regulators.  

The crisis has confirmed the lack of institutions with the skills and authority to be trusted guardians of data.

Yet history tells us that when powerful new technologies arise we cannot rely solely on law or design, which on their own cannot help us make judgments about trade-offs. Instead it’s the combination of law, design and accountable institutions that gives us confidence our interests are being protected.

We take the role of institutions for granted in relation to now-quotidian technologies like the car, and in finance—where complex ecosystems of regulation and law manage the subtleties of pensions, insurance, equities, savings and banking. I expect that we will see a comparable complexity in data to provide visible institutions to work out, in the public interest, the balance of issues around options like an NHS app.

The solutions will have to be complex because the issues are. Some data we can control, such as choosing whether to have an app that for the public benefit tracks our human contact. But other data we can’t control, including the traces our phones leave automatically. There is a similar complexity in the latent value of data. Some of it is only valuable to me, like most of what’s on a Fitbit health and activity tracker. But other data has huge public value, including tracing the behavioural patterns of the virus to help us be better prepared next time.

Into this space I expect we will see the creation of an array of different kinds of data trust, including trusts responsible for the myriad decisions needing to be made concerning health data. During crises it is public data trusts that become all the more important, requiring visible and accountable bodies in positions of management.

This is a debate that has hardly started, as the still vague comments from many leading opinion-formers confirms. Hopefully covid-19 will force the pace to a more sophisticated public debate and towards a more durable social contract that gives us the benefits of smart technologies as well as reliable protections against misuse.

 

Geoff Mulgan CBE is professor of collective intelligence, public policy and social innovation at the University College London department of science, technology, engineering and public policy, and the former chief executive of Nesta, the UK's innovation foundation.

The views and opinions expressed in this article are those of the author(s) and do not necessarily reflect the views of The Economist Group or any of its affiliates. The Economist Group cannot accept any responsibility or liability for reliance by any person on this article or any of the information, opinions or conclusions set out in the article. 

 

The theory of punctuated equilibrium, proposed in 1972 by biologists Stephen Jay Gould and Niles Eldredge, holds that populations of living organisms tend to experience a significant amount of evolutionary change in short, stressful bursts of time. 1Gould and Eldredge argued that evolution isn’t a constant, gradual process—it occurs during episodes when species are in environments of high tension or especially crisis.

The human species is going through such a period right now: the covid-19 pandemic. The profound pressures that individuals, organisations and societies face in this crisis are accelerating the fourth Industrial Revolution (4IR), blurring the boundaries between the physical, digital and biological worlds.2 The current state of emergency compels us to consider the necessity of structural shifts in our relationship with the environment and how we conduct ourselves as a global community. 

The pandemic is forcing all of us to appreciate how much we rely on 21st-century technologies—artificial intelligence, the internet of things, social media, digital learning platforms, augmented and virtual reality, drones, 3D printing and so much more—to keep us healthy and to transform economies. The unprecedented context is simultaneously driving us to become far more reliant on breakthrough digital, biological and physical technologies and far more inventive about how we can use these emerging technologies to create value in new ways. 

More than 7bn people live in countries that have implemented extraordinary restrictions on the movement of people,3 and more than a third of the world is under stringent lockdown.4 In response, systems that have resisted change for decades have gone virtual. Video conferencing as the primary means of co-working? Old news. Remote learning? More than 1.5bn students are doing that today.5 Organisations from all sectors are building new technical capabilities, harnessing digital technologies and evolving their business models at a pace unimaginable only months ago. 

The virus is crowding new technology paradigms into healthcare everywhere. Networks of epidemiologists are tracking the coronavirus using low-cost gene-sequencing technologies6  which are also driving some of the most promising vaccine candidates.7 Researchers and medics are using machine learning to search repositories of scholarly articles published about covid-19, such as the 47,000 articles indexed by the covid-19 Open Research Dataset (CORD-19) Explorer.8 Informal networks of hobbyists and manufacturing firms are using 3D printers to make tens of thousands of face shields to help protect front-line medical workers.9  And in an unprecedented move, Apple and Google have partnered to invent a contact tracing application embedded in the operating systems for smartphones.10

This explosion in innovation started when covid-19 threw humankind into uncharted waters. During historical periods where the equilibrium has been dramatically disturbed, organisations and economies have struggled to survive. 

But we are technological beings who purposefully—and at scale—adapt the environment to our needs. Scientists have called our current epoch “the Anthropocene” because humans are the overwhelming force shaping the planet’s ecosystems. Hence, those who successfully adapt won’t just thrive in the accelerated 4IR—they will shape it. 

The question is, into what?

A critical choice that humans will have to make is how to re-engage with a natural world that has been better off as a result of the pandemic. 

Environmental activist Greta Thunberg was “striking to disrupt the system”. 11 The pandemic has done just that and is revealing what it means—and what it costs—to dramatically drop carbon emissions.12 Passing one of our climate’s “tipping points” could involve costs that are orders of magnitude higher.13

Will the massive stimulus packages being rolled out by governments around the world include significant 4IR re-skilling for the newly unemployed, advancing a global green economy?14

Or, in the frantic rush to get “back to normal,” will nations relax environmental standards and justify wastefulness in the name of short-term economic growth?

The pandemic is demonstrating the extent to which high levels of collaboration are required for deeply interconnected societies to manage—and recover from—complex, exponential systemic crises. The fact that viruses are borderless is just another reason why humans need to invest in dramatically re-tooled principles and mechanisms for global co-operation.

This crisis should spur us all to explore a new form of globalisation for the 21st century, one that prioritises collective investment in global public goods—including technological and ethical goods—to the benefit of all.15 Such global integration must enable diverse stakeholders from across the public, private and non-profit sectors worldwide to work more effectively and sustainably together.

The pandemic has several silver linings. One of them is the chance to experiment with technologies and co-operative approaches across borders that could lead to safer, more sustainable and more inclusive global futures. 

The scientific collaboration, purpose-driven hacking16 and political leadership that will bring us out of the pandemic are precisely the tools that can unlock success in reducing inequality, adapting societies to the impacts of climate change and restoring our natural environment to a more balanced state. We must create a new punctuated equilibrium that maximizes 4IR benefits inclusively and sustainably.

The covid-19 pandemic is a major test for us as a species: a transformational window of opportunity. Will we seize it?

 

Sanjeev Khagram would like to thank co-author Nicholas Davis. As a professor of practice at Thunderbird School of Global Management at Arizona State University and managing director of SWIFT Partners, a technology and innovation consultancy, Davis focuses on supporting organizations by finding value-creating opportunities to put humans at the centre of the Fourth Industrial Revolution.

 

[1] S J Gould, N Eldredge, “Punctuated Equilibria: The Tempo and Mode of Evolution Reconsidered”, Paleobiology, Vol. 3, No. 2, pages 115-151, 1977. http://www.johnboccio.com/courses/SOC26/Bak-Sneppan/07_Gould.pdf [2] K Shwab, “The Fourth Industrial Revolution: what it means, how to respond”, World Economic Forum, January 14th 2016. https://www.weforum.org/agenda/2016/01/the-fourth-industrial-revolution-what-it-means-and-how-to-respond/ [3] P Connor, “More than nine-in-ten people worldwide live in countries with travel restrictions amid COVID-19”, Pew Research Centre, April 1st 2020. https://www.pewresearch.org/fact-tank/2020/04/01/more-than-nine-in-ten-people-worldwide-live-in-countries-with-travel-restrictions-amid-covid-19/ [4] J Kaplan, L Frias, M McFall-Johnsen, “A third of the global population is on coronavirus lockdown — here's our constantly updated list of countries and restrictions”, Business Insider, [Accessed April 21st 2020]. https://www.businessinsider.com/countries-on-lockdown-coronavirus-italy-2020-3?r=DE&IR=T [5] “COVID-19 Educational Disruption and Response”, UNESCO, [Accessed April 21st 2020]. https://en.unesco.org/covid19/educationresponse [6] K Finley, “Data Sharing and Open Source Software Help Combat Covid-19”, Wired, March 13th 2020. https://www.wired.com/story/data-sharing-open-source-software-combat-covid-19/ [7] T Thanh Le, Z Andreadakis, A Kumar et al., “The COVID-19 vaccine development landscape”, Nature, April 9th 2020. https://www.nature.com/articles/d41573-020-00073-5 [8] “CORD-19 Explorer”, Allen Institute for AI, [Accessed April 21st 2020]. https://cord-19.apps.allenai.org [9] N Frandino, “3D printers forge face shields for fight against the coronavirus”, Reuters, April 3rd 2020. https://www.reuters.com/article/us-health-coronavirus-3d-printing-volunt/3d-printers-forge-face-shields-for-fight-against-the-coronavirus-idUSKBN21L1EU [10] M Gurman, “Apple, Google Bring Covid-19 Contact-Tracing to 3 Billion People”, Bloomberg, April 10th 2020. https://apple.news/AHY0me9nbTnequX80tNawgw [11] ““We Are Striking to Disrupt the System”: An Hour with 16-Year-Old Climate Activist Greta Thunberg”, Democracy Now!, September 11th 2019. https://www.democracynow.org/2019/9/11/greta_thunberg_swedish_activist_climate_crisis [12] M Stone, “Carbon emissions are falling sharply due to coronavirus. But not for long.”, National Geographic, April 3rd 2020. https://www.nationalgeographic.com/science/2020/04/coronavirus-causing-carbon-emissions-to-fall-but-not-for-long/ [13] T M Lenton, J Rockström, O Gaffney et al., “Climate tipping points—too risky to bet against”, Nature, November 27th 2019. https://www.nature.com/articles/d41586-019-03595-0 [14] S Khagram, “Global Climate Restoration for People, Prosperity and Planet: $Trillions in Market Opportunities and Economic, Social, Environmental Benefits”, Thunderbird School of Global Management, January 2020. https://thunderbird.asu.edu/sites/default/files/khagram-gcr-market-report-2020_0.pdf [15] See for example https://www.weforum.org/whitepapers/global-technology-governance-a-multistakeholder-approach [16] “Fighting a Global Crisis”, Global Hack, April 9-12th 2020. https://theglobalhack.com

 

The views and opinions expressed in this article are those of the author(s) and do not necessarily reflect the views of The Economist Group or any of its affiliates. The Economist Group cannot accept any responsibility or liability for reliance by any person on this article or any of the information, opinions or conclusions set out in the article.