economic boom thesis

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Events, news & press, the ten causes of the reagan boom: 1982-1997.

In the United States the fifteen-year economic expansion that began in 1982, now called "the long boom" by economists, is the greatest economic boom in history--and it is still going.

Ten major factors that caused that boom are

• The vanished threat of nuclear war
• The spread of capitalism
• The computer revolution
• Control of government spending
• Deregulation
• Stable monetary policy
• Steady economic policy
• The U.S. capital base
• The superiority of the U.S. economy

The Mystery of the U.S. Economy

One of the great mysteries of our time seems to be why the U.S. economy is so good. An article on the front page of the Wall Street Journal in early 1997 summed it up nicely: "Pinch me," the reporter wrote. "A lot of good things are inexplicably happening. The economy tops the list. Economists remain mystified why."  

It's no mystery that the current state of the U.S. economy is good. The numbers are undeniable. The U.S. economy grew almost 4 percent in the fourth quarter of 1996. Inflation is low, interest rates are low, and job creation continues at a slow, but steady, pace.  

The mystery is why it is so good. What factors brought about this relatively blissful economic state? And will those factors continue to operate in the future?  

In some quarters there is increasing concern about the long-term prospects for prosperity and special concern about the stock market. Alan Greenspan, the chairman of the Federal Reserve, has even warned about the "irrational exuberance" of today's investors.  

A lot of this uncertainty about the future stems from the fact that much of the good news we are now enjoying was unexpected. Few, if any, economists predicted the kind of real economic growth we have been enjoying. And just a few months ago most economists, and even those in the Office of Management and Budget, were forecasting low rates of economic growth--less than 2.5 percent--right on out to the end of this century.  

The forecasting track record of economists does not inspire much confidence in their current or future predictions. I think the reason they have been consistently wrong is not because they are dumb (they are not) or not because they don't work hard (they do) but rather because they do not pay enough attention to a number of noneconomic factors that have been driving the U.S. economy.  

In February 1996, in a talk about the long-term prospects for the U.S. economy, I said that the prospects for prosperity were brighter than ever before in our history. I remember that prediction particularly well because, at least so far, it has turned out to be right.  

The analysis I used at the time was not standard. I relied on a few factors--political and technological ones--that have a powerful effect on economic activity but are impossible to measure with precision. Since then I have added several more factors to the list.  

Economists have a tendency to analyze only what is available in neat, historical tables. They seem to prefer a precise, irrelevant answer to a more relevant one fraught with uncertainty.  

The econometric models, whose mathematical complexity once seemed to hold the promise of more-reliable forecasting, have been disappointing failures. There are just too many factors that determine future economic activity that are impossible to quantify.  

John Maynard Keynes and Adam Smith  

A little more than sixty years ago, in his influential book The General Theory of Employment, Interest, and Money , John Maynard Keynes addressed the question of the necessary conditions for economic prosperity. Although much of what passes for Keynesian economics today is of dubious worth, Keynes was a perceptive thinker, and a number of his earlier insights have been forgotten. For anyone familiar with the jargon in today's academic economic journals, perhaps the most striking thing about The General Theory is how much of it is written in clear, eloquent English, with virtually no use of mathematics.  

Keynes argued that a large part of our economic activities "depend on spontaneous optimism rather than on a mathematical expectation, whether moral or hedonistic or economic." He went on to write that "most, probably, of our decisions to do something positive, the full consequences of which will be drawn out over many days to come, can only be taken as a result of animal spirits--of a spontaneous urge to action rather than inaction, and not as the outcome of a weighted average of quantitative benefits multiplied by quantitative probabilities." Further, he argued, "if the animal spirits are dimmed and the spontaneous optimism falters . . . enterprise will fade and die," and he concluded with this: "economic prosperity is excessively dependent on a political and social atmosphere which is congenial to the average business man." Another even more famous economist, Adam Smith, expressed much the same view some 180 years earlier, in 1755, when he wrote that "little else is required to carry a state to the highest degree of opulence from the lowest barbarism, but peace, easy taxes, and a tolerable administration of justice; all the rest being brought about by the natural course of things."  

The Best of Times, the Worst of Times  

If you step back for a moment and take a look at our current degree of prosperity, it can be a bit puzzling. Don't we have a crushing federal debt and an annual deficit that stretches endlessly into the future? Aren't we losing thousands of jobs because of corporate downsizing and foreign competition? Isn't our educational system pretty dismal, with many young men and women having difficulty just reading and doing basic math? Isn't crime rampant; welfare and high rates of illegitimacy destroying families; millions of illegal aliens overrunning our borders; and drug use rising dangerously among teenagers?  

But why then is the stock market, perhaps the best barometer of confidence in America's future, rising to new records day after day? I think the simple answer is that, despite the serious problems this country has, there are more powerful, positive factors that are overwhelming them.  

Let's go back to the stock market again. If you look at the path of the stock market over the past hundred years or so, a remarkable story emerges--particularly since the end of World War II. The last thirty or so years are most interesting--and relevant--and maybe even instructive.  

Let's go back to 1965. The Dow Jones average had risen to 1,000. Seventeen years later, after dropping sharply from time to time (down to about 600 in 1975), it had struggled back to 1,000. For seventeen years it was basically flat. In retrospect it is not surprising. Weighed down by the Vietnam War, a heavy tax burden, rampant inflation, and the possibility of a nuclear war between the Soviet Union and the United States, the stock market went--nowhere.  

But something happened in 1982 and the stock market took off in its strongest, steadiest rise in history. In the past fifteen years it has risen from less than 1,000 to 7,000 in March 1997--a sevenfold increase. But why? What was so different between the past fifteen years and the previous seventeen?  

I can't think of anyone who, back in 1982, predicted this explosion of wealth. And the last fifteen years have not been entirely friendly toward economic growth. Didn't President Bush push through a big tax increase in 1990? Didn't President Clinton get another big tax increase in 1993? Aren't we still running huge federal deficits? And yet the stock market keeps on climbing.  

Remember what Keynes said about those "animal spirits," that spontaneous urge to action that depends on optimism, rather than mathematical expectations? Well, there are political and technological factors that can either be congenial--or not--to the average businessperson.  

Ten Factors That Cause Economic Prosperity  

For some time now I have been compiling a list of what I think are the fundamental factors that can have a powerful effect--either positive or negative--on our economic prosperity.  

I like to call it my Economic Prosperity Sea Level list.

Think of economic activity in the United States as a great ocean, where the deeper and calmer the water, the better it is for the investors who sail on it.  

Most of the economic activity we watch and analyze is comparable to waves, which can run from mild to choppy to dangerous. When the airline unions strike or Alan Greenspan cautions or a severe drought damages agriculture, we can get economic wave action that can affect the market--but only temporarily. The water still remains deep.  

This sea of prosperity can also be affected by economic tides. We call them recessions and booms . These ebbs and flows of economic activity are of much longer duration, and the water level does change. But most of us are used to this kind of cyclic economic activity and are confident that when the tide goes out it will--soon--come back in.  

But then there are radical changes that happen infrequently, perhaps only once in our lifetime, that affect not the economic wave or tide action but the very level of the sea itself. Think of it as either a melting ice cap or global warming, but when the economic sea level rises or falls the consequences can be dramatic.  

Today the economic sea of prosperity is rising slowly, inexorably. The factors causing this rise for the past fifteen years or so are not easily measurable. There are no neat historical tables that chart their course. But they all have a telling impact on economic prosperity. So far I have ten factors on the list.  

The first three on the list, and perhaps the most important, are the requisites for prosperity that Adam Smith observed in 1755, namely, peace, justice, and easy taxes.  

Number One  

The most important fundamental factor driving the new prosperity of the past fifteen years is the disappearance of the threat of an all-out nuclear war between the Soviet Union and the United States.  

When the historians of the future write of the 1980s they will probably note that President Reagan's greatest legacy was not his economic policies but his bold confrontation of the Soviet Union, which led to the end of the cold war and nuclear arms reduction.  

The historians may not discern any clear relationship between the fading away of the nuclear threat and prosperity, but people will. The absence of the threat of nuclear war stretches out people's time horizons. If they believe they will live longer, their personal discount rates decline, which will be reflected in a downward trend in long-term interest rates.  

Some intriguing, though little noticed, studies have been done on the relationship between personal savings rates and the fear of nuclear war. In the September 1993 issue of the American Economic Review two professors, Joel Slemrod of the University of Michigan and Bruce Russett of Yale University, argue that  

Someone who believes that a "world" or "nuclear" war is likely to occur within the next ten years or so would be expected to have a much higher discount rate for benefits in that time period than someone who believes war is unlikely.

In their studies they show a remarkable correlation between the settings of the Bulletin of the Atomic Scientists' "Doomsday Clock"--that rough evaluation of the threat of nuclear war--and savings rates in the United States.  

Simply put, they found that a decreased fear of nuclear war is likely to increase personal savings, and which is likely to decrease long-term interest rates. And low long-term interest rates are good for economic prosperity.

Number Two  

The next item on the list is the mushrooming growth of capitalism throughout the world. In terms of justice, capitalism is the most just system for business ever devised. Private property and the rule of law create an environment that simply does not exist in any statist society.  

During the 1980s the idea of communism died. The philosophy of communism and its cousin, socialism, became intellectually bankrupt. That stunning intellectual collapse led not only to the breaking up of the Soviet empire and the withering away of the threat of world nuclear war but also to an explosion of capitalism in nation after nation. Nothing is better for economic prosperity than the sure economic justice of capitalism.  

Now, true, there are still a lot of live Communists and Socialists who are madder than hell about it and doing everything they can to stop the inevitability of the new capitalism sweeping the world. But the economic policies of countries flow from what people believe is right and what people believe will work. And as long as capitalism, not some form of statism, is the idea that people embrace, capitalism will continue to grow and strengthen. And as long as this trend toward free market justice continues, so will the prospects for economic prosperity.  

Number Three  

The third item on the list is easy taxes. The recent tax increases by Clinton and Bush have tended to make us forget just how high tax rates used to be. When President Reagan took office in 1981 the top marginal personal income tax rate was 70 percent.  

We have retreated a bit from the low tax rates of the mid-1980s, but compared to where we were before we began the fifteen-year prosperity ride we are now on--tax rates are still relatively low.  

Number Four  

Fourth on the list is the computer/communication revolution. Powered by spectacular technological progress in communications and computers, the entire world of business and finance has been compressed and speeded up. During the past fifteen years we have seen astounding advances in the processing and transmission of information.  

It is as if someone had a big can of WD-40 (that all-purpose lubricant) and was spraying it on the machinery of free enterprise, making all of it hum and whir with increasing efficacy. The personal computer, cellular phones and pagers, faxes and copying machines, and now the Internet--all of them are great for prosperity.  

Number Five  

Next on the list is the control of government spending. During the past fifteen years this has been the most intractable and disappointing part of our economic policy, but even this now seems to be coming under better control. Primarily due to defense cuts that flowed from winning the cold war, the federal deficit has been reduced this year to 1.4 percent of our gross domestic product, the lowest level in more than twenty years. Today both political parties are committed to a balanced budget--at least by the year 2002--and the current debate is over whether or not we should include a balanced budget requirement in our Constitution.  

Number Six  

Then there is the government regulation of business. Overall, the onset of new government regulations slowed considerably over the past fifteen years. There was even a significant amount of deregulation. Remember when the controls on gasoline and oil were lifted in 1981--and the gas lines miraculously disappeared.  

Except for that brief flirtation with turning our health care system over to federal control in 1993, the days of big new government regulations appear to be over. Reasonable, minimal government regulation is a key element in any recipe for economic prosperity.  

Number Seven  

Monetary policy is next on the list. During the past fifteen years our country has been blessed with two of the best leaders the Federal Reserve System has seen: Paul Volcker and Alan Greenspan. Overall, monetary policy has been stable and predictable and inflation has been low, which has been a powerful factor in ensuring steady economic growth. That kind of sound, dependable monetary policy is essential for long-term economic prosperity.  

Number Eight  

On my list of things that lift the "animal spirits" is something that, if done properly, is barely noticed. That is policy consistency.  

The past fifteen years or so have not seen a totally consistent economic policy but, compared to what we saw in earlier decades, it has--overall--been very, very good. The control of government spending has improved, tax rates have stayed close to the low levels set in the early 1980s, regulatory pressure has eased, and monetary policy has been superb and steady.  

It can be argued that even a bad economic policy, if consistent, will eventually allow fair results as people get to know it and figure out how to work around it. But an economic policy that is both good and consistent is good for prosperity.  

Number Nine  

The next item on my list is our stock of capital. Any nation's level of prosperity is dependent on the base from which it begins. During the past fifteen years an enormous amount of wealth has been added to America's economic base--so much that it is perhaps impossible to comprehend.  

Our economic base is worth tens of trillions of dollars and is still growing. Our stock of factories and homes, of schools and roads and every other kind of tangible asset you can think of has increased tremendously. Our intellectual capital has increased perhaps even more, especially in such critical areas as computer technology and software.  

The base of our industrial machine dwarfs that of any other nation on earth. With that kind of capital it's hard not to be prosperous  

Number Ten  

The last point is superiority. In this new global economy the United States economy may not be perfect, but, taking all relevant factors into consideration, it is simply the best.  

If anyone doubts the superiority of the U.S. economy try this exercise. If you wanted to invest or start a business in any other society in the world where would you go? Canada? Russia?? Japan? Cuba? Germany? China?  

Being the best means we draw investment from all over the world from those concerned with an optimum blend of opportunity and safety. And, in a free enterprise world, being the best is great for economic prosperity.  

So if you look back at the course of the past fifteen years and examine the major changes in politics and technology that have occurred, some of the mystery of why the stock market has gone so high disappears. The investors may not have had an econometric model to guide them, but, on balance, they seem to have known what they were doing.  

An even more interesting question is to what degree this economic prosperity will continue in the future. There is no crystal ball; it is a matter of personal judgment that only an individual can make.  

But if we run down the list of the ten factors that raised the sea level of economic activity during the past fifteen years or so, we probably can make an informed judgment as to whether or not those factors will continue to be in play in the months and years ahead. And from that maybe we can deduce our own personal forecasts.  

Here's the list again:

• The vanished threat of nuclear war. The vanished threat of a world nuclear war should hold for a long time to come. There will be an increasing threat of an accidental attack or one from a small, rogue state, but the construction of a small missile defense system could greatly reduce even that threat.  
• The spread of capitalism. Shows no signs of abating. No new statist theories on the intellectual horizons.  
• Easy taxes. At least for the next four years the chances of any major tax increase in the United States are close to zero, especially if the Republicans continue to control Congress. Both parties are now committed to some form of a tax decrease. We may even get a major reduction in the capital gains tax rate.  
• The computer revolution. Shows no sign of abating. If anything, it may be speeding up.  
• Control of government spending. This is the most difficult to predict, but at least for the next few months things look pretty good. In fact, the prospects for correcting the consumer price index are increasing, which could mean savings of hundreds of billions of dollars.  
• Deregulation. There is not even a whiff of any major new government regulatory programs. Pressure still strong to reform and reduce regulation .  
• Stable monetary policy. Alan Greenspan is good for another four years. After that it depends a good deal on who succeeds him .  
• Steady economic policy. There seems to be a developing unanimity on the key policies necessary for economic growth. The five pillars are (a) spending control, (b) low taxes, (c) reasonable regulation, (d) sound monetary policy, and (e) consistency.  
• The U.S. capital base. Every year it just gets bigger and more powerful. No sign of any decline, either in real or intellectual terms.  
• The superiority of the U.S. economy. The U.S. economy is now more than twice the size of its closest competitors. Even with prodigious advances by other nations it would be decades before even a Japan or a Germany could begin to even draw close.

The more you examine the powerful political and technological factors affecting the U.S. economy, the less mystery there is about its robust condition. The U.S. economy is good for good reasons.  

The Future  

Where the U.S. economy will be a year from now, or five years from now, depends heavily on powerful political and technological forces that affect, as Keynes would say, the "urge to action" of men and women in the financial and economic community. But right now, toward the end of 1997, the prospects are uncommonly bright.  

The U.S. economy is the most powerful economic engine yet devised. Its power just keeps on growing and shows no sign of abating in the foreseeable future. If those ten factors that have raised the level of the economic sea in the past fifteen years stay positive, we should not be surprised to see the Dow Jones average hit 10,000 or higher before we see the end of the twentieth century.  

As President Reagan once said, "You ain't seen nothing yet."  

Luncheon talk by Martin Anderson to the Franklin Templeton Group on March 6, 1997, in Newport Beach, California. Moderator: Paul Kangas of the Nightly Business Report for the Public Broadcasting System.

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11 facts on the economic recovery from the COVID-19 pandemic

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Mitchell barnes , mitchell barnes former research analyst - the hamilton project lauren bauer , and lauren bauer fellow - economic studies , associate director - the hamilton project wendy edelberg wendy edelberg director - the hamilton project , senior fellow - economic studies.

September 29, 2021

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Despite the headwinds created by the Delta COVID-19 variant, the economy is recovering. Economic growth during the pandemic has generally surpassed consensus expectations while households and businesses have maintained a surprising amount of activity and spending while social distancing.

The strength in economic output was, in part, a result of the enormous legislative response to both the pandemic and to the human hardship it caused. This includes laws passed in 2020 and 2021 by Congress, chief among them the Coronavirus Aid, Relief, and Economic Security Act (CARES Act), the Consolidated Appropriations Act, and the American Rescue Plan Act. Successive rounds of substantial fiscal support have boosted economic activity since March 2020 and are projected to continue to do so through 2023. To give a sense of the potential impact of federal action on the economy, Edelberg and Sheiner (2021a) estimated that a package of similar magnitude to the American Rescue Plan would boost economic output by 4 percent in 2021 and 2 percent in 2022.

These 11 facts on the economic recovery from the COVID‑19 pandemic build on much of The Hamilton Project’s work over the past year and a half.

• Since the onset of the pandemic, The Hamilton Project has provided guidance to policymakers on the fiscal policy response writ large ( Edelberg and Sheiner 2020 , 2021a , 2021b ; Shambaugh 2020a , 2020b , 2020c , 2020d , 2020e ).
• In the summer and fall of 2020, The Hamilton Project published essays from leading economic thinkers projecting how COVID-19 would change the economy ( Autor and Reynolds 2020 ; Edelberg and Shambaugh 2020 ; Hardy and Logan 2020 ; Rose 2020 ; Stevenson 2020 ), provided interim evidence on the state of the economy ( Bauer, Broady, et al. 2020 ), and published rapid evaluations and policy proposals on nutrition assistance ( Bauer, Pitts, et al. 2020 ) and small business ( Hamilton 2020 ).
• The Hamilton Project has focused on the disparate impact of the pandemic and its economic consequences on women ( Bauer 2021 ; Bauer, Buckner, et al. 2021 ; Bauer, Estep, and Yee 2021 ), communities of color ( Aaronson, Barnes, and Edelberg 2021 ; Broady et al. 2021 ; Grooms, Ortega, and Rubalcaba 2020 ), and frontline essential workers ( Nunn, O’Donnell, and Shambaugh 2020 ; O’Donnell 2020 ).
• The Hamilton Project has focused on providing research regarding critical policy areas, including food insecurity ( Bauer 2020a , 2020b ; Bauer and Schanzenbach 2020 ), housing insecurity ( Broady, Edelberg, and Moss 2020 ; Edelberg et al. 2021 ), and labor market distress ( Aaronson and Edelberg 2020 ; Bauer, Dube, et al. 2021 ; Bauer, Edelberg, and Parsons 2020 ; Gilarsky, Nunn, and Parsons 2020 ; Nunn 2020 ; Nunn and O’Donnell 2020 ; Nunn, Parsons, and Shambaugh 2020 ).
• The Hamilton Project has commissioned policy proposals to rethink the social insurance system ( Barnes et al. 2021 ), including unemployment insurance ( Dube 2021 ), paid leave ( Byker and Patel 2021 ), housing ( Collinson, Ellen, and Keys 2021 ), child care ( Davis and Sojourner 2021 ), workforce development ( Holzer 2021 ), and postsecondary education ( Arum and Stevens 2020 ).

Based on this body of work and the facts in this paper, we draw the following conclusions at this point in the economic recovery. First, the initial rapid economic recovery and expected slowing creates risks that policymakers should heed. Second, fiscal support has been essential to accelerating the recovery. Third, greater federal investment in infrastructure, both physical and human, is key to improving the country’s longer-term economic prospects.

The Economic Recovery

With the ongoing effects of fiscal support, pent-up demand from consumers for face-to-face services, and the strength in labor markets and asset prices, economic growth is poised to be strong for the remainder of 2021. Indeed, the Congressional Budget Office (CBO) projects that real GDP will grow 7.4 percent from the fourth quarter of 2020 to the fourth quarter of 2021 (CBO 2021c). Moreover, CBO predicts that, by the middle of 2022, real GDP will exceed its sustainable level by 2.5 percent. The sustainable level of GDP, also known as potential output, is not a ceiling. Instead, it is the estimated level of output, given current laws and underlying structural factors, that the economy can achieve without putting upward pressure on inflation. As the factors boosting growth in the short term begin to wane, real GDP growth is expected to slow significantly.

CBO’s projection is subject to a great deal of uncertainty. In particular, the resurgence in the pandemic stemming from the Delta variant, vaccine hesitancy, and the slowness in vaccinating children ages 12 and younger appear to have dampened the growth of consumer demand and employment. Recent data suggest that the latest COVID-19 wave might be waning. However, if the Delta variant—or others that take its place—continue to affect consumer behavior and supply chains, the economic recovery will be notably slower.

In addition, although consensus projections are for a soft landing, including a couple of quarters with GDP roughly moving sideways, the slowdown could be more abrupt and painful than those projections suggest. There are actions that Congress could take to help avoid a painful slowdown in activity—both by fine-tuning the timing of spending and by focusing resources on policies that boost potential output. For example, changes in policy that repurpose fiscal support from boosting current aggregate demand to policies that would boost the economy’s potential (such as federal investment in infrastructure that would increase labor supply and human capital) would increase the chances of a soft landing, in part by raising the landing area to a higher level.

The Uneven Nature of the COVID-19 Pandemic and Economic Recovery

As of September 26, 2021, more than 687,000 people in the United States have died from COVID-19; and more than 4.7 million have died worldwide (Johns Hopkins 2021). At the onset of COVID-19, the virus displayed clear geographic trends, beginning in densely populated coastal cities then spreading to more rural parts of the country (Desjardins 2020). With the pandemic first hitting the Northeast, in April of 2020 New York and New Jersey accounted for more than 60 percent of deaths and more than 40 percent of hospitalizations from COVID-19. The Delta variant and vaccine hesitancy have changed the geographic patterns: as shown in figure A-1, since mid-July 2021 patients hospitalized with COVID-19 in the South have risen to account for nearly two‑thirds of the US total, with half of those patients in Florida and Texas (broken out from the rest of the region in the figure).

The economic downturn caused by the pandemic has created widely different experiences across sectors and demographic groups. In the spring of 2020, spending on consumer services sharply contracted and has yet to fully recover. Indeed, of the 22 million total jobs lost in March 2020, nearly 19 million were in service-providing businesses, including a decline of 8 million in leisure and hospitality. Leisure and hospitality has added back more than 6.5 million jobs so far; as a result, it is still 10 percent short of returning to its pre-pandemic level, and even farther below its expected level in the absence of the pandemic. Other industries, such as financial services, that experienced shallower dips in employment during the onset of the pandemic, have also been the quickest to recover as their workforces were better able to shift to remote work.

Those sector dynamics disproportionately hurt women, non-white workers, lower-wage earners, and those with less education (Stevenson 2020). Because workers among those groups were more likely to be employed in the services sector, and in particular in the leisure and hospitality sector, they experienced job losses at much higher rates. For example, the gap in the rates of unemployment between Black and white men jumped from 3 percentage points to 6 percentage points during the initial downturn. By July, that gap had partially fallen back and was 4 percentage points.

The uneven recovery is also evident when we focus on consumer spending at retail establishments. Between February and April 2020, overall retail sales sank 22 percent before quickly recovering to their pre-pandemic level just a few months later. As people began social distancing, spending shifted to at-home consumption, benefiting businesses like online retailers, grocery stores, and suppliers of building and garden materials. Indeed, spending on total retail sales has averaged 16 percent higher than its pre-pandemic level so far this year. At the same time, some categories of retail sales were severely depressed until showing signs of recovery in March of this year; those include in-person dining and spending on clothes, electronics, and appliances.

Overall, the pandemic continues to weigh on aggregate demand for goods and services. In addition, bottlenecks and supply shortages have created challenges for businesses to meet consumer demand for some products, particularly as consumer demand has shifted wildly. Also, the pace of hiring has not kept up with the pace of labor demand, as job matching has been held back by a number of factors described below.

Those developments have led to a notable increase in inflation. Because prices fell in 2020, one-year changes from August 2020 to August 2021 overstate the increase in inflation since the pandemic began. Instead, focusing on the annualized rate of inflation since February 2020 shows that inflation through August 2021 (as measured by the core consumer price index) was 3.1 percent, substantially lower than the one-year trend but still higher than any annual increase since the early 1990s.

There are two primary reasons why the rise in inflation is unlikely to persist. First, the significant shifts in demand and bottlenecks are a function of the recent, temporary pace of economic activity. For example, demand for automobiles recovered quickly during the pandemic to high levels even as production was curtailed, in part due to disruptions in the supply chain for critical semiconductors. The result has been a sharp increase in prices for new and used vehicles. Second, as production is increased (with normalization of global supply chains) and growth in demand abates, inflation should slow overall.

Nonetheless, certain factors will continue to create inflationary pressure; even with the slowdown, economic activity over the next year or so will continue to exceed the sustainable level. We might also see price spikes in certain services as demand shifts. For example, from March 2021 through July sales at restaurants were up 14 percent while sales at building materials and garden stores were down 11 percent. Such changes could lead to price surges at restaurants that more than offset softer prices at stores selling building materials and garden supplies. In addition, the rapid rise we have seen in home prices will likely translate into significantly higher rental costs across the country.

Fact 1:  In the second quarter of 2021, GDP returned to its pre-pandemic level.

Since the economy hit bottom in the second quarter of 2020, economic growth has surpassed consensus expectations formed at the beginning of the pandemic. As a result, in the second quarter of 2021 real GDP exceeded its pre-pandemic level. With economic growth boosted by the ongoing effects of the fiscal support enacted by Congress in 2020 and 2021, pent-up demand from consumers for face-to-face services, and the strength in labor markets and asset prices, real GDP appears on track to grow at the rapid pace of roughly 6 percent in 2021. To be sure, the Delta variant threatens that projection. However, even in the initial stages of the pandemic, when people had far less information and fewer mitigation resources, consumer spending and overall economic activity was remarkably resilient.

The surprising strength in GDP and the improvements in expectations are evident from CBO’s upward revisions to its projections (shown in figure 1). In the third quarter of 2020 the level of GDP was 4.8 percent above the projection that CBO published at the beginning of that quarter. Moreover, since July 2020 CBO has revised up projected GDP for 2023 by nearly 7 percent, where the projected level of GDP at the end of 2023 is now 2 percent above CBO’s pre-pandemic forecast. Nonetheless, through 2023 the cumulative shortfall in real output relative to a pre-pandemic projection is expected to total about $400 billion in 2012 dollars (CBO 2020a, 2021c). Note that CBO’s projections show a soft landing, with real GDP showing only modest growth by late 2022. The slowdown could be more abrupt and painful than those projections suggest.

Fact 2: The sharp decline in employment in spring 2020, which was largely concentrated in the services sector, has only partially reversed.

Figure 2 shows the percent difference in overall employment from the peak month prior to recent economic downturns through the month where employment recovered to its previous business cycle peak. Across the labor market, employment is still down 5.3 million from February 2020 and down about 9 million from where trends in employment were headed to prior to the pandemic.

From February to April of 2020, employment declines in the leisure and hospitality sector accounted for about 40 percent of the total 22 million jobs that were lost. Conversely, a partial recovery in that sector has fueled employment growth since then. Overall, from February through July of this year, monthly employment rose by more than 700,000 on average. In August that pace slowed significantly, however. The resurgence of the pandemic likely held back the recovery in the leisure and hospitality sector, which saw no net gain in employment in August. In that sector, employment is still down 1.7 million jobs from February 2020.

In comparison to previous recessions, the COVID-19 recession has been worse for the services sector relative to the goods sector. Consider the average outcomes across the four recessions from 1981 to 2019, 18 months from when the different recessions began: employment in the service sector was 1 percent below its pre-recession peak and employment in the goods sector was 10 percent below its peak. In contrast, as of August 2021 employment in the service sector was still 4 percent below its February 2020 level and employment in the goods sector was 3 percent below.

Fact 3: Millions of workers are no longer eligible for Unemployment Insurance.

Over the summer of 2021 in some states, and in the first week of September 2021 in the remainder of states, enhanced UI expired. That set of policies had significantly expanded eligibility to workers not covered by regular UI (Pandemic Unemployment Assistance [PUA]), extended the number of weeks that a worker could receive UI (Pandemic Emergency Unemployment Compensation [PEUC]), and increased the generosity of benefits (Federal Pandemic Unemployment Compensation [FPUC]). Prior to the CARES Act, which created PUA, PEUC, and FPUC, only 30 percent of workers were eligible for unemployment compensation.

Figure 3 shows the total number of unemployed workers superimposed over weekly continued UI claims for regular UI benefits and Extended Benefits, which automatically extends weeks of eligibility based on a state’s economic conditions, as well as claims for emergency programs: PUA and PEUC.

Note that the level of unemployment greatly underestimates the number of people who lost jobs during the pandemic. To be described as officially unemployed, a person must be actively looking for work; however, millions of people effectively have left the labor force since March 2020 but were eligible for the expanded UI benefits. At the time that the emergency programs expired, there was a gap of more than 5.5 million workers who were in the labor market and unemployed, but not receiving UI. We project that gap to close only modestly through the end of this year.

Fiscal support has helped people prioritize their health over labor market income, which was certainly one of the goals when the support was put in place in the spring of 2020 and when it was reauthorized several times. Preliminary analysis suggests that UI generosity had a modest effect on recipients’ job-finding rates (Petrosky-Nadeau and Valletta 2021). Nonetheless, we see no compelling evidence that the cancellation of those benefits so far has led to significant increases in aggregate employment (Coombs et al. 2021; Pardue 2021). On the other hand, the abrupt elimination of access to UI benefits for millions of people creates financial hardship for those who are unable to work owing to health risks or other constraints.

Fact 4: The number of job openings and the number of workers quitting their jobs is higher now than in the past 20 years.

Despite job openings being their highest since the end of 2000 (the earliest available data), several factors are holding down employment gains. One factor is that the share of workers quitting jobs each month is at a series high. As figure 4 shows, the quit rate generally moves with the job opening rate, since workers are more likely to switch jobs in a strong labor market. Moreover, in the current environment the composition of labor demand is changing, and workers may be taking time to move from temporary jobs they took during the pandemic. Taken together, record job openings, the slowness of job matching, and the depressed level of labor force participation has created wage pressure, particularly for workers in the service sector, for younger workers, and for workers with less formal education.

In addition to the depressed rate of job matching, also worrying is the lack of recovery in the labor force participation rate, which is the share of the population working or actively seeking work. That rate fell from 63 percent to 60 percent between February and April of last year, when nearly 8 million workers left the labor force. The participation rate recovered about halfway by June 2020, but has remained stubbornly depressed since then.

Factors unique to the pandemic have disproportionately affected labor force participation among certain groups even if these changes do not meaningfully affect aggregate levels (Furman, Kearney, and Powell 2021). For example, among mothers of elementary school–aged children—which is the demographic likely bearing the brunt of school closures (Amuedo-Dorantes et al. 2020)—the share that is employed fell more than that of mothers who did not have children in elementary school (Bauer, Dube, et al. 2021). Consequently, addressing the child-care crisis moves in the right direction but will not on its own make up the ground that has been lost in aggregate labor force participation.

Fact 5: Even with recent jumps in inflation, lower income workers are seeing increases in real wages.

Upward pressure on wages has been good news, particularly for low-income workers and workers in certain industries. As shown in figure 5, wages for those in the bottom quartile of the wage distribution are up 7.0 percent from their pre-pandemic level, or 4.6 percent at an annual rate. That rate of growth is close to what that group experienced in 2019, when the consensus held that the labor market was relatively tight. Some sectors have seen particularly strong wage gains. For example, over the past 12 months average hourly earnings in the leisure and hospitality sector have grown nearly twice as fast as the overall private industry average. Other sectors seeing strong gains in hourly earnings include retail trade, transportation and warehousing, and financial activities.

Because of recent increases in the rate of inflation, workers’ purchasing power is not rising as fast as nominal wages. Price increases in recent months led to declines in real wages from March to June 2021. Those declines partly offset increases in real wages earlier in the pandemic for wage-earners in the bottom quartile, when inflation was soft and nominal wages were rising. In July and August real wages for that group notably accelerated. Overall, from February 2020 to August 2021 real wages for the bottom quartile have risen 2.4 percent, or 1.6 percent at an annual rate. That is considerably below the rate we saw in 2019 when real wage growth was 2.4 percent at an annual rate for the bottom quartile. Moreover, real wages are roughly unchanged for those in the highest quartile, in contrast to a gain of 0.8 percent in 2019.

Fact 6: Post-pandemic, income after government taxes and transfers, as well as household saving, have been above their recent trends.

Disposable personal income (DPI, or total aftertax income) in 2020 and so far in 2021 has been higher than if DPI had simply grown at its trend rate of the previous five years. In aggregate, DPI has so far been higher than trend by a total of $1.4 trillion since the start of the pandemic.

In 2020 weak aggregate compensation of employees was more than offset by a sharp increase in government benefits, leaving total DPI a cumulative $630 billion above its trend level over the course of that year (figure 6). As a result of additional dispensation of government social benefits to households in January and March of this year, DPI has been higher, on average, by about $115 billion each month since January than if it had grown at its trend pace. Since March of this year those benefits have come down sharply but remain elevated. Under current law, the boost to DPI should fully wane by early next year. (See Alcala Kovalski et al. 2021 for related information about the waning fiscal support.)

As a result of the significant boosts to DPI and restrained services spending during the pandemic, aggregate household saving has skyrocketed. In every month from March 2020 through April of this year, the rate of saving was higher than in the past four decades; in some months it was roughly double the previous post–World War II peak. In total, households have roughly $2.5 trillion more in savings than if DPI and spending had grown in line with trend rates in the five years prior to the pandemic. Moreover, home prices and stock market prices have surged, leading to large increases in household wealth. Those resources will help to finance the pent-up demand for forgone spending. Ultimately, households will view the increase in savings and wealth as financial resources to support long-term, relatively steady consumer spending.

Fact 7: Fiscal support led to a reduction in poverty in 2020.

By the Official Poverty Measure (OPM), poverty increased from 10.5 percent to 11.4 percent from 2019 to 2020. After taking into account the enormous fiscal support provided to households in 2020, the percentage of the US population in poverty, as measured by the Supplemental Poverty Measure (SPM), fell from 12 percent to 9 percent (figure 7). While poverty as measured by the SPM is typically lower than OPM for children, 2020 was the first time that SPM was lower than the OPM overall.

The two policies that had the most significant effects relative to earlier years, because they were the most changed from prior policy, were the expansion of unemployment compensation and checks to households. If Congress had not enacted relief for families, SPM poverty would have risen to 12.7 percent rather than falling to 9.1 percent.

Another factor behind the decrease in poverty was the relatively strong wage growth for those at the bottom of the income distribution who remained employed (see fact 5). Notably, those wage gains came on the heels of strong wage growth in 2018 and 2019, when the tight labor market benefited lower-wage workers.

In 2021 continued fiscal support—particularly the full refundability of and the increase in the child tax credit and increases to the Supplemental Nutrition Assistance Program (SNAP) maximum benefit—as well as the continued labor market recovery should help to lift households out of poverty. Sustained progress in reducing post-tax-and-transfer poverty as measured in the SPM is possible through making permanent some of the policies enacted to counter the COVID-19 recession.

Fact 8: To date, 36 states have made progress in catching up on delinquent rent and mortgage payments.

To help Americans struggling to make mortgage and rent payments in the midst of a sharp contraction in labor income in the spring of 2020, policymakers put in place several relief programs. Those programs initially took the form of foreclosure and eviction moratoria and later also included financial support.

Delinquent mortgage borrowers experiencing economic hardships related to the pandemic, who had a federally backed mortgage, which includes mortgages backed by Federal Housing Administration, Veterans Administration, Fannie Mae, and Freddie Mac loans, were automatically eligible for forbearance through September 30, 2021. The government put in place help for mortgage servicers who are generally required to make payments to investors regardless of whether borrowers are delinquent. According to the Federal Reserve Bank of New York, forbearance plans disproportionately benefitted low-income borrowers, especially those holding FHA-insured loans and those living in disadvantaged neighborhoods (Haughwout, Lee, Scally, and van der Klaauw 2021). In addition, Congress’s American Rescue Plan provided nearly $10 billion to help homeowners who were behind on their mortgage and utility payments.

The federal eviction moratorium expired in August 2021, although some states have extended such protections. The federal government has allocated $46.5 billion in relief to help renters make their back payments and to help landlords who are owed those payments. State and local grantees had provided only $5.1 billion of the first $25 billion allocated for emergency rental assistance through July 2021 and news reports (Siegel 2021) suggest distribution of aid continues to be slow, even with recent US Department of the Treasury (2021) guidance to expedite delivery. With regard to the money that was distributed in the first quarter of 2021, more than 60 percent of households who received aid had household incomes under 30 percent of typical incomes in their geographic area.

Nonetheless, the broader fiscal support and the partial recovery in the labor market has helped to reduce the number of people who are behind on their payments. Figure 8 shows how much progress has been made in getting caught up on rent or mortgage payments by state, from each state’s peak to the most recent data spanning July and August. Three-quarters of the states reached their highest share of missed rent or mortgage between December 2020 and March 2021. Since peaking, the share of residents who reported missing rent or mortgage payments is lower in 36 states by statistically significant amounts.

Fact 9: The strength in durable goods spending and weakness in spending on consumer services stands in sharp contrast to previous recoveries.

Together, social distancing and substantial support to households led to a surge in spending on durable goods even as households curtailed spending on services—a dramatic departure from behavior in more-typical recessions. As shown in figure 9a, overall real spending on goods initially sank 13 percent from February to April of 2020, but then quickly rose and had exceeded its pre-pandemic level by June. This rise included purchases such as vehicles, household furniture, and recreational equipment; after adjusting for inflation, so far in 2021 purchases of those durable goods have averaged 25 percent higher than pre-pandemic spending. In contrast, spending on services—many of those being face-to-face transactions such as live entertainment and dining at restaurants—fell steeply during the pandemic. Real services spending dropped more than 20 percent in the spring of 2020 and has yet to recover to pre-pandemic levels.

These patterns diverge from prior recessions. In most prior recessions, spending on durable goods remains subdued for an extended period, as in the case of the Great Recession where 18 months into the recovery, goods expenditures remained 7 percent below the pre-recession peak. In addition, figure 9b shows that, in each of the prior three recessions, spending on services temporarily plateaued in the first year of recovery before resuming growth. But in none of these prior recessions did services dip below their pre-recession levels for any sustained period—another sign of the uniqueness of the COVID-19 recession.

In recent months, demand has begun shifting back toward services as people begin resuming normal activities. From March to July, goods purchases declined moderately, while spending on services climbed 3 percent; notably, spending on live entertainment, hotels, and public transportation collectively increased by 35 percent over those four months.

Fact 10: Retail inventories are unsustainably low.

Through August 2021, much of the consumer demand for goods has been met by drawdowns of inventory. As shown in figure 10, the retail inventory-to-sales ratio spiked at the beginning of the pandemic when spending plummeted. Since then, however, the ratio has fallen precipitously. This is particularly true for the automotive sector, where shortages of semiconductors have constrained production. Even outside of that sector, production has been insufficient to keep up with demand. Indeed, unfilled orders and delivery times are elevated across the manufacturing sector. Disruptions in global supply chains have been a continuing obstacle, in particular backlogs at ports that have increased the cost of shipping to historic highs.

On the one hand, capacity utilization in the manufacturing sector has recovered close to its pre-pandemic level. On the other hand, historical patterns and recent surveys of manufacturers suggest that they will increase utilization well beyond that level to replenish inventories as demand recovers.

In addition to investment in inventories, survey data suggest that investment to expand capacity and productivity is poised to increase. Private investment in equipment and structures has partially rebounded since the second quarter of 2020 but has not yet returned to pre-pandemic trends. As of the first quarter of 2021, investment in business equipment had rebounded as a share of potential output, but additional investment is required to make up for lost investment during the pandemic. A rebound in investment in structures is more than accounted for by investment in residential structures; in fact, investment in residential structures as a share of output is back to levels not seen since 2007. Nonresidential structure investment, however, is still down as a share of potential output.

Fact 11: There were more new business applications and fewer bankruptcies in 2020 and 2021 than in 2018 and 2019.

Newly created businesses appear to be a major source of production of the goods and services that households are demanding. Figure 11a shows new business applications of firms that the Census Bureau characterizes as having a high propensity to employ workers. Since the summer of 2020, we have seen the highest level of applications since the agency began to track the series in 2004. Applications have perhaps reflected new business opportunities in the wake of the pandemic. The prospective new businesses are concentrated in online retail, which makes up a third of the increase in total new applications, and in service sector industries, which suffered some of the largest employment losses early last year (Haltiwanger 2021).

In the past year and a half, fewer firms have failed than initially feared, due in part to fiscal support like the Paycheck Protection Program that offered forgivable loans to small- and medium-sized businesses. Figure 11b compares cumulative commercial bankruptcies for the past four years. The full year 2020 ended with 17 percent fewer bankruptcies than in 2019, while 2021 is currently on track to record the fewest commercial bankruptcy filings since at least 2012 (when the data became available). More specifically, Chapter 7 filings and Chapter 13 filings, which represent asset liquidation and those of sole proprietorships, were 16 percent and 45 percent lower in 2020 than 2019, respectively. In contrast, Chapter 11 filings, which historically have reflected the reorganizations of large firms, jumped 29 percent in 2020. That increase also likely reflects legislation enacted in February 2020 and then expanded under the CARES Act, which offered smaller businesses the ability to reorganize under Chapter 11 and thus remain viable.

Although the business sector appears to have done well overall, some acutely affected sectors have seen more closures. For example, early evidence shows an elevated rate of exits for heavily COVID-affected businesses, such as barber shops and hair salons (Crane et al. 2021).

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Home > STUDENTS > THESES > 955

Masters Theses

Trends of employment-based health insurance: why did the coverage for private-sector workers decrease during economic boom.

Yan Ni , Eastern Illinois University

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Ni, Yan, "Trends of employment-based health insurance: Why did the coverage for private-sector workers decrease during economic boom" (2005). Masters Theses . 955. https://thekeep.eiu.edu/theses/955

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Economics Theses and Dissertations

This collection contains theses and dissertations from the Department of Economics, collected from the Scholarship@Western Electronic Thesis and Dissertation Repository

Theses/Dissertations from 2024 2024

Essays on Development Economics , Rowena Cornelius

Essays on Credit Risks and Financial Frictions , Jihyun Kim

Essays on Firms and Climate Change , Emmanuel Murray Leclair

Essays on Life Cycle Skill Growth and Wage Dynamics: Understanding the Impact of Skill Levels, Personality Traits, and Job Search , Tommas Trivieri

Theses/Dissertations from 2023 2023

Essays on Monetary Economics , Duhyeong Kim

Essays on Macroeconomics , Mauricio Torres Ferro

Essays on the Economics of Immigration , Phuong Vu

Theses/Dissertations from 2022 2022

Essays on Industrial Organization and Health Economics , Cecilia S. Diaz Campo

Essays on Financial Shocks and External Debt , Jafar El Armali

Revealed Preference Analysis: Theory and Applications , Charles Gauthier

Essays on Conflict Mediation , Ali Kamranzadeh

Essays On Market Design And Auctions , Mingshi Kang

Essays on the Economics of Education , Enrique Martin Luccioni

Essays on Disability and the Labour Market , Robert Geoffrey Millard

Essays on the Economics of Education , Marco Pariguana

Essays on the All-Pay Auction , Henk Schouten

Theses/Dissertations from 2021 2021

Three essays in International Economics , Francisco Adame

Peer Effects and Social Networks in an MBA Program , Zinaida Foltin

Three Essays on Climate and Development , Samantha Goertz

Three Essays on Individual and Household Responses to Information, Liquidity, and Policy Shocks , Brian C. Held

Essays in financial asset pricing , Dillon Ross Huddleston

Essays in Financial Econometrics and Machine Learning , Fred Liu

Essays on Firms in International Trade , Aldo Sandoval Hernandez

Essays on Private Information and Monetary Policy , Zijian Wang

Theses/Dissertations from 2020 2020

Relaxing the Rational Expectations Assumption: Data-based and Model-based Approaches , Yifan Gong

Essays on Family Economics , Hyeongsuk Jin

Essays on Share Repurchases and Boom-Bust Cycles , Bohan Li

Essays on Student Loans and Returns to Skill , Qian Liu

A Language Barrier To Human Capital Development: The Case Of Guatemalan Students , Fidel Pérez Macal

Characterizing the Value and Effect of Perceptiveness in Various Game-Theoretic Settings , Terrence Adam Rooney

Essays on Criminal Behaviour, Human Capital Formation, and Mental Health , Diego F. Salazar

Adding Data-driven Modelling to Causal Inference And Financial Economics , Sha Wang

Essays on Housing Markets , Yuxi Yao

Theses/Dissertations from 2019 2019

Essays on Microeconomic Problems in Multilateral Settings , Ke Xian AuYong

Essays in Economics of Education: Teacher Labour Markets and Earnings of University Graduates , Tomasz M. Handler

Essays on the Economics of Digital Piracy , Zhuang Liu

Essays on College Majors and Skills , Yuki Onozuka

Theses/Dissertations from 2018 2018

Essays in International Economics: the Trade-Creation Effect of Migration , Miguel Cardoso

Essays on Parental Leave and Family Labour Supply , Youjin Choi

Non-Linear Time Series Modelling with Applications to Equity and Fixed Income Markets , Galyna Grynkiv

Essays on Crime, Education, and Employment , Maria Antonella Mancino

Theses/Dissertations from 2017 2017

Essays in International Economics: Decomposing Episodes of Large Growth in International Trade , Brandon K. Malloy

Essays on Policies Related to Immigration, School Choice, and Crime , Georgy Orlov

Essays on Debt in Macroeconomics , James G. Partridge

Informal Hiring Patterns with Endogenous Job Contacts , Deanna Walker

Essays on Growth and Input Misallocation in China , Wenya Wang

Theses/Dissertations from 2016 2016

Essays on Human Capital Complementarities , Hiroaki Mori

Essays on Human Capital and Inequality , Youngmin Park

Volatility Modelling with Applications to Equity and Foreign Exchange Markets , Sergii Pypko

Essays on Applied Microeconomics , Jin Zhou

Theses/Dissertations from 2015 2015

College-High School Wage and Human Capital Price Differentials, and the Role of Mobility for Local Wages in the U.S. , Eda Bozkurt

On the Economics of Climate Change and its Effects , Aaron B. Gertz

Theses/Dissertations from 2014 2014

Essays on Labor Market in Indonesia , Xue Dong

Essays on Innovation and Consumer Credit , David E. Fieldhouse

Essays on Portfolio Optimization, Simulation and Option Pricing , Zhibo Jia

Role of Search, Human Capital and Learning in Occupational Mobility and Immigrant Assimilation , Masashi Miyairi

Theses/Dissertations from 2013 2013

Essays on Mechanism Design and the Informed Principal Problem , Nicholas C. Bedard

Essays on Worker Promotion and Wage Growth , Hugh Cassidy

Essays in Macroeconomics of Development , Douwere Eric Grekou

News, Copulas and Independence , Ivan Medovikov

Essays on Skilled Workers and Economic Development , Dozie Okoye

Theses/Dissertations from 2012 2012

Essays on Informal Labor Markets , Javier Cano Urbina

Volatility, Duration, and Value-at-Risk , Pujun Liu

Essays on Capital Gains, Household Consumption and Corporate Payout Policy , Chris Mitchell

Essays on Health Insurer and Provider Interactions , Daniel Montanera

Essays on International and Environmental Economics , Jacob Wibe

Essays on Financial Return and Volatility Modeling , Jing Wu

Theses/Dissertations from 2011 2011

Essays On Entrepreneurial Financing , Ye Jia

A Collection of Portfolio Management Issues , Mike McCausland

Essays on International Trade , Kai Xu

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©1878 - 2016 Western University

The Official Journal of the Pan-Pacific Association of Input-Output Studies (PAPAIOS)

• Open access
• Published: 09 September 2023

Implications of AI innovation on economic growth: a panel data study

• Julius Tan Gonzales   ORCID: orcid.org/0009-0002-4008-5961 1  

Journal of Economic Structures volume  12 , Article number:  13 ( 2023 ) Cite this article

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The application of artificial intelligence (AI) across firms and industries warrants a line of research focused on determining its overall effect on economic variables. As a general-purpose technology (GPT), for example, AI helps in the production, marketing, and customer acquisition of firms, increasing their productivity and consumer reach. Aside from these, other effects of AI include enhanced quality of services, improved work accuracy and efficiency, and increased customer satisfaction. Hence, this study aims to gauge the impact of AI on the economy, specifically on long-run economic growth. This study conjectures a positive relationship between AI and economic growth. To test this hypothesis, this study makes use of a panel dataset of countries from 1970 to 2019, and the number of AI patents as a measure of AI. A text search query is performed to distinguish AI patents from other types of innovations in a public database. Employing fixed effects and generalized method of moments (GMM) estimation, this paper finds a positive relationship between AI and economic growth, which is higher than the effect of the total population of patents on growth. Furthermore, other results indicate that AI’s influence on growth is more robust among advanced economies, and more evident towards the latter periods of the dataset.

1 Introduction

The developments in computer science and digital technology, including artificial intelligence (AI) and machine learning, naturally led to their application in key sectors such as healthcare, finance, manufacturing, and transport. Footnote 1 Their increasing use in industries has opened up questions as to whether these technologies may have an impact on economic variables. In neoclassical and endogenous economic growth models, for example, technical change brings about increases in productivity, leading to economic growth. Hence, breakthroughs in computing technology should also entail increases in growth rates.

In particular, the last 60 years have witnessed a shift in production from traditional inputs to more information and communications technology (ICT)-based, capital-intensive tools. The introduction of modern computers and the Internet in the early 1990s, and more recently, AI, led to changes in the methods of production. In keeping with the rise of new technologies, Zeira ( 1998 ) proposed an economic growth model adopting technological innovations that reduce labor inputs but require more capital. Footnote 2 Furthermore, more recent empirical studies on the subject have found these technologies as potential sources of economies of scale (e.g., Nightingale 2000 ; Wang et al. 2011 ; Nchake and Shuaibu 2022 ). Thus, it is only expected that advancements in ICT may have a positive effect on overall productivity and economic growth.

This study continues this line of research by exploring the relationship between technical change, as manifested by modern developments in science and technology, and economic growth. Several economic papers have been published on the subject, of proxying technology with various forms of knowledge and ICT measures (e.g., patents and scientific journals, Internet penetration, computer ownership, etc.). This paper is similar to past academic papers, yet with a special focus on AI as a newer form of technology.

Estimation results reveal a positive and significant impact of AI on long-run economic growth in a cross-country panel dataset. The magnitude of AI's effect on growth is also higher than that of total patents. Furthermore, the contribution of AI to growth is more robust both for advanced economies and for the latter half of the period considered in the estimation.

2 Literature review

Endogenous growth models are central to much of the existing literature on technology and economic growth. Arrow ( 1962 ) associates technology as a “by-product of ordinary production” through knowledge accumulation, aptly termed “learning-by-doing.” The process of learning through repetition and experience should manifest itself in increases in productivity, thus creating opportunities for economic growth. Footnote 3 Arrow ( 1962 ) assumes that technical change, born out of knowledge and experience, is embodied in new physical capital, which then enters the production process and improves “productive efficiency.” Footnote 4

The literature on technology and growth follows this line of thought, assuming that technological change increases capital productivity. In line with the Schumpeterian tradition, Zeira ( 1998 ) presents a theoretical framework involving intermediate goods in production. Technology adoption increases the intensity of capital and replaces labor in the production process. Technology is then adopted if it increases output; however, as technology requires more capital input, not all countries can keep up with the technological frontier. Footnote 5 The disparities in the levels of technology across countries then result in differences in overall output and productivity.

Meanwhile, Acemoglu and Restrepo ( 2018 ) have constructed a “task-based” framework, treating automation and the creation of new tasks as types of technological innovation. Both types of technology are necessary to increase productivity. Initially, Acemoglu and Restrepo ( 2018 ) considered that all tasks could be done by labor, whereas “lower-indexed” tasks could and would be automated. Footnote 6 However, automation requires some capital investment, thus raising the share of capital and decreasing the share of labor in production. This is counterbalanced, though, by creating new and more sophisticated tasks, where labor has a “comparative advantage.” In the long run, there is a “stable, balanced growth path” where the two types of innovations coexist and grow at the same rate. Footnote 7

The aforementioned theoretical works help explain the relationship between modern science and ICT developments and economic growth. However, empirical evidence on more recent forms of technological innovation, such as AI and machine learning, is still limited. This can be attributed to an insufficient amount of data both at the firm and at macro levels, especially when dealing with long-run growth. Footnote 8 This study is an attempt to contribute to the body of literature on this subject despite limitations on data availability. In the following discussion in this section, this paper will revisit some recent publications regarding the relationship between technical change and economic growth, using common scientific knowledge and technology variables.

In empirical studies, the number of patents and scientific journals are common measures of technological innovation. In the Schumpeterian context, patents represent ownership of monopoly rents from the invention of new technology. Firms aim for exclusive rights over these monopoly rents; thus, new technologies that improve productivity are continuously invented, while dismantling obsolete ones in the process—the so-called “creative destruction.” As better technologies are created, firms become more productive, possibly achieving increasing returns to scale status. Footnote 9 Hence, countries with higher concentrations of patents may signal higher productivity and levels of production, and of course, national growth. Footnote 10

On the other hand, scientific journals index the level of research and development (R&D). Based on standard growth models, technical progress is a product of knowledge accumulation, which is made possible through continuous R&D efforts. According to Kim and Lee ( 2015 ), academic articles as a measure of scientific knowledge have been regarded as a contributor to economic growth, citing scientific journals published by institutions and universities as sources of “patents and industrial technology.” Assuming academic knowledge from journal articles can be transformed into concrete technological inputs for production, published research should then also contribute to overall productivity and growth. Footnote 11

However, Kim and Lee ( 2015 ) conclude that it is patents and not scientific journals that contribute to economic growth. They considered academic articles to be sources of scientific knowledge, whereas patents are embodiments of technological knowledge. Technological knowledge, though, is more a product of the private R&D efforts of firms than of scientific research from academic institutions. Using panel data estimation and evidence from Latin American economies, Kim and Lee ( 2015 ) found an insignificant effect of scientific knowledge, while patents indicated significant and positive impacts on economic growth.

Studies about patents and growth are numerous, often arriving at similar results (e.g., Lach 1995 ; Sinha 2008 ; Kim et al. 2012 ). In an earlier work modeling innovation and entrepreneurship with economic growth, Wong et al. ( 2005 ) found a significant and positive effect of patent grants as an indicator of innovation on country growth rates. In contrast, recent studies such as those by Sweet and Eterovic ( 2019 ) and Blind et al. ( 2022 ) found no significant effect of patents on economic growth.

In another recent study, Nguyen and Doytch ( 2022 ) found a positive and significant effect of total patents on economic growth for advanced economies, but the magnitude of the effect of the technology variable weakens for emerging economies. Footnote 12 Moreover, ICT patents only contribute to economic growth among advanced economies. In addition, the authors found that total patents, regardless of domain, are not significant in the long run, but ICT patents remain positive and significant.

On the other hand, studies on the effect of scientific research, measured by the number of scientific journals, on cross-country growth tend to be mixed. As mentioned previously, Kim and Lee ( 2015 ) discovered no significant impact of scientific knowledge from academic articles on growth. Meanwhile, Ntuli et al. ( 2015 ) found differing results in determining causality between research output and growth among OECD countries. Research output exhibits “unidirectional causality” on growth in some countries such, as the United States, Finland, Hungary, and Mexico, but is negligible in other OECD members.

Existing literature suggests a weak or ambiguous relationship between academic research and national growth (e.g., Inglesi-Lotz et al. 2014 ; Hatemi-J et al. 2016 ). Footnote 13 In contrast, Solarin and Yen ( 2016 ) obtained a positive relationship between research publications and economic growth using a cross-country panel dataset. They found that the effect was significant “irrespective of whether the focus is on developed countries or developing nations.” However, Solarin and Yen ( 2016 ) noted that the impact on growth is stronger in advanced economies.

Interestingly, Mueller ( 2006 ) found that research output may be favorable to local economic performance. Mueller ( 2006 ) analyzed the impact of private industry and university R&D, along with measures of entrepreneurship and university-industry relations, Footnote 14 on regional aggregate output in West Germany. Regression results have established individual, positive effects of each variable on regional economic performance.

Further, at the firm level, “intangible assets” such as “R&D, goodwill, brand equity, patents, copyrights, software, licenses, image, and organization” are “enhancers” of total factor productivity (TFP) (Nakatani 2021 ). Comparing firms within the ICT sector across five countries, intangible assets revealed a significant impact, though differing in magnitude, on the productivity of ICT firms across countries. Footnote 15 This can be attributed to some countries already being at the forefront of the global technology frontier. Hence, the additional effect of intangible assets on firm productivity diminishes (Nakatani 2021 ).

This study, however, is more interested in a specific technological innovation different from ICT, namely AI, including machine learning. With AI swiftly becoming the new general-purpose technology (GPT) (Trajtenberg 2018 ), comparisons between AI and previous technologies, particularly ICT, have been raised (Lu and Zhou 2021 ). However, AI is considered to “impact a broader range of sectors,” leading to “different implications at the aggregate level” and an “unpredictable future development.” Furthermore, ICT is known to require high investments in capital over long periods, whereas AI can leverage data and cloud services that can help lower capital investments. These differences could potentially lead to a distinct “pathway” for AI adoption, different from that of previous technologies (Lu and Zhou 2021 ).

Because of the scarcity of data, there is a dearth of empirical evidence on the topic of AI as a driver of economic growth. Nonetheless, this article attempts to determine this relationship using an available measure that can indicate the level of AI per country.

2.1 What is AI?

AI encompasses a broad category of technology, and there is not a single, widely accepted definition. However, international organizations have similar definitions of AI. The European Parliamentary Research Service (EPRS), for example, refers to AI as machines that perform “human-like cognitive processes,” namely, “learning, understanding, reasoning and interacting.” As a general-purpose technology, AI can take many forms such as a “technical infrastructure (i.e., algorithms), a part of the (production) process, or an end-user product” (Szczepański 2019 ). Hence, in contrast with traditional technologies that automate routine processes, AI technologies even go further to mimic human activities that require cognition, and their application and use are not limited to the production process.

Meanwhile, the International Telecommunication Union (ITU) broadly defines AI as “self-learning, adaptive systems.” Accordingly, there are several “approaches” in defining AI, namely: (1) in terms of “technologies, techniques and/or approaches” such as “a neural network approach to machine translation”; (2) in terms of “purpose,” which include facial and image recognition; (3) in terms of “functions,” such as the “ability to understand language, recognize pictures, solve problems, and learn”; and (4) in terms of “agents or machines or algorithms” such as robots and self-driving cars (International Telecommunication Union 2023 ).

Furthermore, Montagnier and Ek ( 2021 ) cite several definitions of AI by individual countries and organizations such as the European Commission and the OECD. For instance, the OECD defines AI as a “machine-based system” that can “make predictions, recommendations, or decisions” and “operate with varying levels of autonomy” (Yeung 2020 ). Additionally, the European commission ( 2021 ) provides some examples of AI, which include “chatbots” and “virtual assistants,” “face recognition systems,” “machine translation software,” “data analysis based on machine learning,” “autonomous robots,” and “autonomous drones.” On the other hand, national statistics institutions such as the French Institut national de la statistique et des études économiques (INSEE) ( 2019 ) describe AI as “technologies” that can perform “cognitive tasks traditionally performed by humans,” whereas Statistics Sweden ( 2020 ) notes that physically, AI may be “purely software based or embedded in hardware.”

Because of its broad definition and the lack of a single, universally accepted descriptor of AI, classifying existing AI technologies is also a difficult task. In spite of this, Sarker ( 2022 ) categorized AI into five types, which include analytical, functional, interactive, textual, and visual. Footnote 16 However, the most commonly heard terms in AI are the “techniques” used in developing intelligent and smart systems in various real-world application areas.” Sarker ( 2022 ) identified at least ten “potential categories,” namely:

Machine learning,

Neural network and deep learning (including generative AI),

Data mining, knowledge discovery, and advanced analytics,

Rule-based modeling and decision-making,

Fuzzy logic-based approach,

Knowledge representation, uncertainty reasoning, and expert system modeling,

Case-based reasoning,

Text mining and natural language processing,

Visual analytics, computer vision, and pattern recognition,

Hybrid approach, searching, and optimization.

While each AI technique has its scope and specific applications, it is often that existing technologies are combinations and applications of various categories. Thus, grouping AI systems according to specific types or techniques is not always feasible. Moreover, AI development is a wide and ongoing practice, and more and newer forms of AI technologies are continuously produced over time. For example, ChatGPT, a form of generative AI technology that employs deep learning, was released to the public in 2022, and quickly became a groundbreaking AI technology due to its ability to interact with individuals and provide “comprehensive and practical responses” (Marr 2023 ). Footnote 17 ChatGPT is built upon “foundational large language models” (LLMs), which go beyond conventional natural language algorithms.

In addition, AI development may be unique to its industry due to the nature of AI itself. Coiera ( 2019 ) identifies three main stages of AI development, termed “miles.” The “first mile” consists of data acquisition, pre-processing, or “cleaning.” The “middle mile” includes “developing and testing the technical performance of different algorithms” that are built using the data acquired in the first stage. After all tests and tuning are completed, an AI system enters the last mile, where it is “embedded in real-world processes and tested for impact on real-world outcomes.”

However, each stage of AI development has its challenges. The first mile entails “foundational challenges,” such as “gathering and curating” huge amounts of high-quality data. Acquiring large amounts of data presents a potential “bottleneck,” and “translates into a roadblock to technology application.” Meanwhile, the middle mile involves the difficulties of “data-driven algorithm development,” such as “biases, replicability, causal inference, avoiding overfitting on training data, and enhancing the generalizability of any models and algorithms” (Coiera 2019 ). Footnote 18

Finally, and likely the hardest task, occurs in the third mile. As it turns out, “AI does not do anything on its own”; therefore, AI systems must somehow “connect” to the real world. Simply, the impact of an AI system must be “consequential” and “meaningful.” For example, the current setting does not necessitate better diagnoses of cancer but “more nuanced” and “less aggressive” approaches to detection and management. Hence, the last mile refers to the implementation of AI itself in real-world processes. AI implementation faces a plethora of challenges, which can be classified under “measurement,” “generalization and calibration,” and “local context” (Coiera 2019 ). Footnote 19

2.2 AI and economic growth

AI drives economic growth by stimulating gains both from the supply side and the demand side. AI can drive business productivity through (1) automation of processes with the use of robots and “autonomous vehicles,” and (2) improvements in the existing labor force by equipping them with AI technologies. On the other hand, AI can generate an increase in consumer demand with the availability of “personalised and/or higher-quality” products and services. Accordingly, it is expected that AI could contribute up to USD 15.7 trillion to the global economy in 2030 (Rao and Verweij 2017 ).

Furthermore, the contributions of AI may be specific to the sectors where it is applied, such as manufacturing, health, finance, energy, and transport. For example, AI supports healthcare services through early detection and diagnosis of illnesses, identification of “potential pandemics and tracking incidence,” and “imaging diagnostics” in radiology and pathology. Meanwhile, AI contributions to the financial sector include applications for fraud detection and anti-money laundering. Also, AI developments such as “robo-advice” make “customized investment solutions” possible in managing financial goals and optimizing clients’ funds. In addition, AI enables “autonomous trucking and delivery,” traffic control systems, and improved security in the transport sector (Rao and Verweij 2017 ).

Recently, Lu ( 2021 ) built a theoretical framework that traces the impact of AI on endogenous growth. Lu ( 2021 ) likens AI to human capital accumulation, “as it can learn and accumulate knowledge by itself.” Secondly, AI is a “nonrival input,” which can be used in production without having it “detract from its ability to accumulate AI.” This implies that AI is disembodied from physical capital, and should be considered a separate input. Footnote 20 Moreover, Lu ( 2021 ) unveils a balanced growth path in the three-sector endogenous growth model, where production and factors including AI grow at the same rate. Footnote 21

Using provincial data from China, He ( 2019 ) estimated the effect of AI on regional economic growth. Unlike most innovation studies on ICT and growth, He ( 2019 ) makes use of fixed assets investment in ICT to GDP as a measure of AI, Footnote 22 rather than AI-specific patents or published articles. Similarly, Fan and Liu ( 2021 ) tested AI as a tool for the sustainable economic development of Chinese provinces. Footnote 23 The results in both studies are consistent with theories on the growth-enhancing capability of AI.

Furthermore, Yang ( 2022 ) evaluated the effect of both AI and non-AI patents on firm-level productivity and employment in Taiwan. Both types of patents were found to improve productivity and employment among Taiwanese electronic firms. Estimation results revealed that both AI and non-AI patents contribute to TFP, and the difference in elasticities between the two patent types is insignificant. Moreover, when TFP is replaced by labor productivity, the estimated coefficient for AI patents is lower than in the model with TFP as a dependent variable. Yang ( 2022 ) suggested that this can be attributed to AI technology having a “greater effect on capital productivity,” which is consistent with the frameworks of Arrow ( 1962 ) and Zeira ( 1998 ).

At present, there are limited empirical works regarding AI as an engine of economic growth, primarily because of the unavailability of data. Footnote 24 Though extant literature on the topic finds a positive relationship between AI technology and economic growth, general sentiment suggests the effect of AI on growth is complex (He 2019 ) and difficult to measure. Intuitively, this can be because of its multifaceted role as an input to production. Still, with the increasing use of AI across countries and industries, this article seeks to measure the impact of AI on national growth rates amidst empirical constraints.

3 Theoretical framework

This study follows an endogenous growth framework. An endogenous model of economic growth often starts with the basic Cobb–Douglas function. However, this study also takes into account human capital as an input to production:

where \(Y\) is the total output, \(K\) stands for capital, \(L\) for labor, and \(H\) is human capital. The elasticities of output to capital, labor, and human capital are denoted by \(\alpha\) , \(\beta\) , and \(\gamma\) , respectively. Meanwhile, \(A\) is the level of knowledge, or as proposed by Jones and Williams ( 1998 ), the stock of ideas, available in an economy.

To obtain the output per unit of labor, Eq. ( 1 ) is divided on both sides by \(L\) . Multiplying the right-hand side with \(\frac{{L}^{\alpha +\gamma }}{{L}^{\alpha +\gamma }}=\frac{{L}^{\alpha }}{{L}^{\alpha }}\cdot \frac{{L}^{\gamma }}{{L}^{\gamma }}=1\) and assuming constant returns to scale, \(\alpha +\beta +\gamma =1\) , results in Eq. ( 2 ):

For simplicity, the per unit of labor variables are replaced by small letters, as with Eq. ( 3 ):

The technology factor \(A\) is seen as the available knowledge stock at time \(t\) . Romer ( 1990 ) proposed that since knowledge is a nonrival input, all researchers can utilize existing knowledge stock at the same time. Summing across all individual efforts in research yields Eq. ( 4 ):

where \(R\) is the research effort or resources devoted to research. The function is assumed to be increasing in \(R\) , as more research leads to more ideas. Jones and Williams ( 1998 ), though, noted that Eq. ( 4 ) may be increasing or decreasing in \(A\) , depending on how previous ideas affect current research.

A basic (and crucial) assumption is that the parameter \(\theta\) is assumed to be 1, to show that the increase in \(R\) results in an increase in new ideas. Footnote 25 Meanwhile, the coefficient \(\delta\) depicts the productivity of research, as proposed by Romer ( 1990 ) and Jones and Williams ( 1998 ).

To estimate Eq. ( 3 ), the equation is transformed into its natural log form. Further, the differenced natural logged form of Eq. ( 3 ) is obtained to calculate the growth rate:

The growth rate of \(y\) is defined as \({g}_{y}=\frac{\dot{y}}{y}\) , where \(\dot{y}=\frac{dy}{dt}\) . The term \(\dot{y}\) represents the difference, or change, in output per worker between two time periods (the change in \(t\) ). Mathematically, the growth rate can be further expressed as \({g}_{y}=\frac{dy/dt}{y}=\frac{d\mathrm{ln}y}{dt}= \frac{\mathrm{ln}{y}_{t}-{\mathrm{ln}y}_{t-s}}{s}\) . Therefore, dividing Eq. ( 5 ) by the change in \(t\) yields the growth rate equation: Footnote 26

Substituting Eq. ( 4 ) for the value of \(\dot{A}\) in Eq. ( 6 ) and simplifying the resulting equation yields:

Finally, the growth rate of \(y\) can be written as:

This study focuses on determining the relationship between AI innovation and economic growth. Thus, the variable \(R\) is proxied by the level of AI innovation in the economy, given by the amount of AI patents published within a certain period. Notably, this is slightly different from the theoretical specification, which indicates \(R\) as inputs or resources devoted to research (e.g., R&D expenditure, share of labor assigned to R&D, etc.). In general, patents are precisely the output of these R&D efforts. The choice of R&D input, such as the number of researchers, or output, such as the number of patents, in economic analysis, has been discussed by Griliches ( 1998 ). Ultimately, this decision depends on the size of the error terms in the relationships among patents, research, and knowledge stock. Footnote 27 Moreover, Griliches ( 1998 ) conjectures that if the “stochastic component” of knowledge stock is captured to some extent by patenting, using patents may have some “value added” over the use of common research inputs as an indicator of knowledge.

Patents embody the quantity, type, inventiveness, and complexity of innovation created in a given time (Griliches 1998 ). Although not without disadvantages, patents can serve as a good indicator of technical knowledge. More importantly, patent data are more readily available for analysis than research input measures, especially for AI. Hence, this study makes use of the number of AI and total patents as a proxy for R&D.

Furthermore, while the model discussed in this section explains how traditional research translates to economic growth, the current model might not fully encapsulate the effect of AI. Footnote 28 As stated previously, the employed model assumes constant returns to research (and by extension, AI). However, because of the nonrivalry of data and the possibility of AI “outpacing” human intelligence, continuous AI invention may exhibit increasing returns, further leading to a “technological singularity,” or explosion of growth rates (Aghion et al. 2018 ). Exploring empirical evidence of such a mechanism is beyond the scope of this study; however, it is a highly recommended topic for future research. Footnote 29

4 Data and methodology

For this study, the primary challenge to perform econometric analysis is obtaining data that can measure the level of AI in a cross-country, panel dataset format. As discussed in the previous sections, the most common indicator of technological innovation is patent publications. Therefore, this study uses AI patents as a measure of AI.

Data for AI patents are available from the Google Patents Public Data, provided by the Information for Industry, Inc. (IFI) CLAIMS Patent Services. To identify AI patents, a text search query was performed in the patents database. The text search includes common words or phrases related to AI, such as “artificial intelligence,” “face recognition,” “virtual assistant,” “machine learning,” etc. Footnote 30 Meanwhile, data for the dependent and control variables are sourced from the United Nations (UN) Department of Economic and Social Affairs Statistics Division and the World Bank.

This study echoes the econometric models of Wong et al. ( 2005 ), Kim and Lee ( 2015 ), and He ( 2019 ) among others. Estimating Eq. ( 8 ) from the previous section, the econometric model follows the equation:

where \({\text{Growth}}_{it}\) is the annual average real GDP per capita growth rate of country \(i\) over a certain period \(t\) , i.e., five years, calculated by dividing the difference between the natural log value of end-of-period real GDP per capita (in USD and constant 2015 prices) and the natural log value of initial real GDP, by the number of years in period \(t\) . Hence, \({\text{Growth}}_{it}\) is the instantaneous growth rate of the real GDP of country \(i\) in period \(t\) .

The lagged variable of growth rate is added to control for any potential endogeneity brought by the omitted variable, in the case when a large influence on current growth by its lagged value is present. Likewise, the lagged value of real GDP per capita is included to test for the convergence effect between high-income and low-income countries. The lagged real GDP per capita refers to the 5-year average of real GDP per capita in the period \(t-1\) .

The variable \({\text{Patents}}_{it}\) stands for the level of AI innovation per country, measured by the total number of AI-related patents per million people in a 5-year average population within period \(t\) . This measure is the same intensity index used by Kim and Lee ( 2015 ) and was also converted into natural logarithms. Footnote 31 AI patents are then replaced by the total number of patents to determine the relationship between total technological innovation and economic growth (see Table 3 ). The expected sign of both patent variables is positive.

\({X}_{it}\) represents a set of control variables that include population growth, real gross capital formation growth rate per capita, real government expenditure growth rate per capita, trade openness, and inflation. Footnote 32 All control variables are 5-year average growth rates except for trade openness, which is the 5-year average ratio of trade volume (exports plus imports) to GDP. Footnote 33 The control variables appear in similar literature, such as in the seminal works of Grier and Tullock ( 1989 ) and Barro ( 1997 ), and in the more recent studies of Bassanini et al. ( 2001 ), Ulku ( 2004 ), Kim et al. ( 2012 ), and Fan and Liu ( 2021 ).

In addition to the control variables, an index using data for years of schooling and returns to education, obtained from the Penn World Table (PWT) by Feenstra et al. ( 2015 ), is taken as a proxy for human capital. Footnote 34 The index makes use of average years of schooling, while also considering decreasing returns to education. Despite this, the index, like other usual human capital measures, ignores cognitive skills, which may be more important in capturing the real effect of human capital (Feenstra et al. 2013 ). This measure also enters the model as a 5-year average growth rate.

Specific time period effects and advanced economic status are indicated using dummy variables. There are ten \(t\) periods in total consisting of five years each, spanning from 1970 to 2019. Advanced economies are countries with more than USD 10,000 of the 5-year average real GDP per capita. Finally, to control for any interaction effect between the level of economic development and patent creation, an interaction term between advanced economic status and patent variables was introduced. The expected sign of the interaction term is negative, implying a lower impact of patent creation on long-run growth among advanced economies.

Statistical treatment was initially done using ordinary least squares (OLS) and fixed effects in panel data. However, because of the inclusion of the lagged growth rate, the model is prone to the Nickell bias, which is unaccounted for in the fixed effects estimation of dynamic panels (Nickell 1981 ; Roodman 2009 ). In addition, bias due to reverse causality between growth rate and patents might be present in the model. Hence, the Anderson-Hsiao (AH) and generalized method of moments (GMM) estimation techniques are employed to minimize endogeneity issues (Arellano and Bond 1991 ; Arellano and Bover 1995 ; Blundell and Bond 1998 ). Footnote 35

The next section presents the results of the panel data regressions.

5 Results and discussion

Table 1 presents the descriptive statistics of the panel data, consisting of ten periods with 5-year intervals between 1970 and 2019. Because of data availability issues, the dataset used is an unbalanced panel data, as indicated by the unequal number of observations ( N ) and number of groups ( n ) across variables.

Five-year growth rates averaged around 1.30%, with a standard deviation of 3.55 across countries in the dataset. Footnote 36 Intuitively, high-income countries will typically have lower growth rates because of the convergence effect. To control for this effect, the estimations presented later include the 5-year average real GDP per capita variable from the previous period. The mean 5-year real GDP per capita is USD 11,990.25.

Table 2 summarizes the economic performance measures such as real GDP per capita and real GDP per capita growth rates, and technological progress in terms of AI and total patents per level of economic development. This follows the classification of Kim and Lee ( 2015 ), where countries with real GDP per capita above USD 10,000 are considered to be in an advanced development stage. Countries with real GDP per capita below the threshold are classified as less developed.

As expected, high-income countries post higher technology output, in terms of patents per million people, between the two income groups. With 165 countries in the dataset, less developed economies are the larger group of the two, and with slightly higher average real GDP per capita growth (1.39%). Illustratively, patent output and income per capita across countries are displayed in Fig.  1 .

AI patents (left), total patents (right), and average real GDP per capita, 1970–2019

Figure  1 depicts the patent publications and level of income per capita. In terms of patents, advanced economies such as Japan, the United States, Germany, South Korea, France, and China have had the highest output between 1970 and 2019. Overall, China has had the highest cumulative AI and total patents within the period, with 849,752 AI and 32,317,932 total patents. This is followed by Japan (365,409 AI and 18,965,778 total patents), and the United States (259,844 AI and 12,883,662 total patents), respectively.

Regardless, all countries started with low levels of AI and total patents in the early 1970s, as illustrated in Fig.  2 . While global AI and total patent counts have steadily increased since the 1970s, China has had a dramatic increase in the number of patents from 2000 onwards. This dwarfs the patent output of other advanced economies (see top panel of Fig.  2 ). The explosion of Chinese patents can be attributed to the growth of R&D expenditure, FDI, and patent subsidies in the country (Chen and Zhang, 2019). Footnote 37

AI patents (left) and total patents (right) of selected countries by period, 1970–2019

Meanwhile, the East Asian economies of Japan and South Korea, have led in terms of patent “intensity,” defined as the number of patents per million people (see bottom panel of Fig.  2 ). Japanese AI and total patents per million people have demonstrated sharp increases since the 1970s but generally declined by the mid-2000s. Footnote 38 On the other hand, South Korea has also witnessed substantial growth in both AI and total patents per million people since the early 1990s. This trend has continued in the subsequent periods, with South Korea eventually overtaking Japan by the early 2010s.

The main estimation results are presented in Table 3 . The estimation techniques used are panel OLS, fixed effects, AH, and GMM. Both models with AI patents and total patents were estimated; columns 1–4 estimate models with the log of AI patents per million people, while columns 5–8 test for the effect of the log of total patents per million people on the dependent variable, denoted by the 5-year average real GDP growth rate. Separate interaction terms between advanced economic status and the patent variables are also included.

As mentioned earlier, the lagged dependent variable in a dynamic panel regression is susceptible to the Nickell bias. Hence, both the AH and GMM estimations are employed to minimize this issue. While the lagged growth rate is positive in all models, it is only statistically significant in the AH estimation among the AI patents models (columns 1–4) and insignificant among the total patents models (columns 5–8). The lack of significance and small magnitude of the coefficients indicate the minimal impact of the first-order lagged growth rate on the contemporaneous growth rate.

Lagged real GDP per capita is statistically significant and negative in all models except in column 8, where it is negative but not significant. The results suggest a strong convergence effect as observed in extant literature. Similarly, population growth has a negative and significant effect on per capita growth in columns 1, 2, 3, and 5, but is insignificant in other estimations. The negative sign of population growth in some estimates is in line with growth theories, but the actual overall effect of population growth across countries is unclear. Kelley and Schmidt ( 1995 ) attribute this mixed result between population growth and economic growth in the long run to the “offsetting” mechanism of “intertemporal demographic effects.” Accordingly, population growth rates are characterized by strong autocorrelation; thus, cross-sectional evidence that uses contemporaneous indicators of population inevitably captures “both the negative impacts of current births and positive impacts of past births.” Footnote 39

Meanwhile, both gross capital formation and government expenditure growth rates manifest significantly positive effects in all models, implying that investments in physical capital and public infrastructure positively contribute to economic growth. Likewise, trade openness is statistically significant and positive in most equations, which is consistent with the existing growth literature. Inflation and human capital, however, are not significant in all models. The lack of significance of human capital can be attributed to (1) the limitations of the measure, and (2) the substitution of labor and/or human capital with AI as an input to production, as raised by Zeira ( 1998 ) and Lu ( 2021 ) among others.

The variables of interest, the extent of AI and total innovation, are taken as the log number of AI patents per million people. As depicted in Table 3 , AI patents have a significant and positive impact on economic growth in all models. This is consistent with the findings of other studies by Lu ( 2021 ) and Yang ( 2022 ). On the other hand, total patents also significantly and positively affect economic growth; however, the magnitude of the effect is lower than AI patents. This is somewhat consistent with the findings of Nguyen and Doytch ( 2022 ), wherein total patents do not display a significant impact on the long-run growth rate. In addition, Nguyen and Doytch ( 2022 ) conclude that ICT patents have a more significant impact on economic growth than other kinds of patents. Footnote 40

To address potential endogeneity either by omitted variables or reverse causality, the patent variables are instrumented in GMM estimations (columns 4 and 8). Footnote 41 Estimated coefficients of AI innovation seem to be consistent and significant at least at the 10% level. On the other hand, total innovation is significant at least at the 10% level in OLS, fixed effects, and AH, but insignificant in GMM.

Moreover, the level of economic development (advanced economy) variable is insignificant in all estimated models except in column 6. Meanwhile, the interaction term between the level of economic status and patent creation is negative and significant in columns 4 and 6. The negative sign implies that AI and overall innovation exhibit less impact on economic growth among advanced economies, which is similar to the convergence effect stated previously.

Furthermore, the Sargan-Hansen test provides the test for overidentifying restrictions for the GMM model. The p-values of the Sargan-Hansen statistic of the GMM models for AI and total patents are 0.426 and 0.581, respectively. Thus, the null hypothesis that the instruments are valid is not rejected. Footnote 42 Also, the Arellano-Bond AR(1) and AR(2) tests for GMM are presented for reference. The p-values of the AR tests indicate the presence of serial correlation only at the first differences. Footnote 43

Results indicate that AI-related innovation drives long-run economic growth. The wide applicability of AI across industries can be one reason for its positive contribution. AI systems can be implemented in manufacturing, ICT, transportation, finance, and medical services among other industries (Mou 2019 ). Self-learning and monitoring benefit the manufacturing sector by increasing precision and efficient utilization of physical capital, reducing defects and delays (Rao and Verweij 2017 ). More recent and practical forms of AI such as voice-to-text applications and speech recognition allow businesses to reach and respond to customers in real time (Mou 2019 ), inducing an increase in the volume of consumer transactions. AI technologies can be implemented in financial systems to detect fraudulent activities, preventing theft and loss (Bose 2006 ; Akhilomen 2013 ). Furthermore, predictive modeling with AI can analyze and manage traffic flow (Mou 2019 ; Yigitcanlar et al. 2020 ), which is notoriously known to cause negative externalities, more effectively.

5.1 Robustness checks

For additional robustness checks, periodic estimations in the dataset are also performed. The dataset is divided into two periods, 1970–1994 and 1995–2019, consisting of 25 years each. Due to limited AI and non-AI patent data, the 1970–1994 period has fewer observations. Additionally, most countries that published and applied for AI-related patents within this period are advanced economies, as shown in the number of groups ( n ) and the lack of an estimated coefficient for the “advanced” dummy in columns 2 and 3 in Table 4 . Footnote 44 Therefore, a comparison of impacts on long-run growth brought by technological innovation, specifically those on AI, between advanced and less advanced economies might not be intuitively useful for observations within this time frame.

Table 4 displays the estimation results of the models for both AI and total patents for the 1970–1994 period, whereas Table 5 provides the results of estimations for the period 1995–2019. As indicated in Table 4 , the effect of AI on growth is not statistically significant for the period 1970–1994, which can be due to (1) the limited number of observations, (2) the lesser number of AI patents, and (3) the relatively less technically advanced nature of AI innovation during this period. Interestingly, the effect of total patents is significant and positive during the same period, as shown in columns 5–8. Hence, the results suggest that other types of patents compared to early AI technologies might have had a more substantial effect on growth rates before 1995.

On the other hand, the estimated fixed effects, AH, and GMM coefficients are significant for AI patents in the 1995–2019 period in columns 2–4 in Table 5 . The significance of the estimates provides evidence of AI being a driver of long-run economic growth for the latter half of the time frame in the dataset. More surprisingly, the value of the coefficient of AI patents in the GMM model is relatively large compared to estimated parameters in other models. Meanwhile, the total patents variable is insignificant for long-run growth rate in all models, except in OLS in column 5. In addition, the interaction terms between patent creation and economic status are mostly insignificant in both periods. Hence, there is no clear distinction on the effect of patent creation between developed and less developed economies on long-run growth in both periods. Footnote 45

An obvious implication of the above results is that the effect of AI has become increasingly evident toward the latter years of the dataset, while innovations from other disciplines have extended relatively less impact on growth. Footnote 46 Notably, AI patent registration had started picking up by the mid-1990s, especially among advanced economies (see Fig.  2 ), which naturally contributed to a heightened impact of AI in the second half of the entire period. Arguably, the quality of AI technologies within this period has also improved compared to earlier forms of AI prior to the last two to three decades.

Furthermore, separate estimations between advanced and less advanced economies were also done, both for AI and total patents. As defined in the previous section, advanced countries are those with more than USD 10,000 of the 5-year average real GDP per capita. The results can be found in both Tables 6 and 7 .

The effect of AI is strongly and positively significant among advanced economies in columns 1–4 in Table 6 , but does not entail any implication on long-run growth among less advanced or emerging economies in Table 7 . Footnote 47 This suggests that viable infrastructures and institutions, which may only be available in developed countries, are necessary to leverage AI in the economy. This, in turn, translates into positive contributions to economic growth. More importantly, this finding resembles the theory proposed by Zeira ( 1998 ), which explains the differences in the type and level of technologies available across countries.

Meanwhile, total patents engender a quite similar effect on growth between advanced and less advanced economies. Coefficients of the patent variable are positive and significant in OLS, but negligible in fixed effects, AH, and GMM, which is true for both groups of countries. This indicates that total patents do not contribute to long-run economic growth regardless of a country's level of development. Thus, more specific, technical, and practical innovations, such as those of AI or ICT in nature, are more important than other types of innovations in terms of their effect on economic growth.

Finally, the possibility of an external instrument is not precluded and has been explored to further address endogeneity. As mentioned earlier, the estimated model is susceptible to bias, either due to omitted variables or bi-directional causality between patent creation and economic growth. Hence, aside from fixed effects and GMM, fixed effects estimation with instrumental variable (FE-IV) is also considered as a means of obtaining unbiased estimates.

The number of non-patent literature (NPL) cited by the patents is used as an instrument for both AI and total patents. NPL refers to the cited articles of a patent document that are not patents themselves (e.g., scientific publications, books, online sources, conference proceedings, etc.) to “justify” an invention’s “novelty.” Furthermore, NPL citations help gauge “the impact of scientific production cited in patents,” or conversely, “the technological impact of scientific publications” (Velayos-Ortega and Lopez-Carreño 2021 ).

Non-patent references contribute to patent creation by providing justification and a scientific foundation for the technology being patented. Hence, a rich amount of non-patent knowledge should positively contribute to patent creation. Scientific knowledge itself is vast and varied; however, only the cited literature in the patents themselves is specific and relevant to the inventions.

As expected, the direct effect of scientific publications, as a measure of scientific knowledge, on economic growth has been well-studied in the literature (e.g., Kim and Lee 2015 ; Solarin and Yen 2016 ; Maradana et al. 2017 ; Pinto and Teixeira 2020 ). While the non-significance of academic research on economic growth has been found in some studies, general sentiment and results still regard academic publications as a direct and positive contributor to growth. This notion casts some doubt on whether scientific literature can serve as a valid instrument for patent creation.

This study, however, suggests that for research output to be translated into an object of economic value, it has to be transformed first into an input (or intermediate good), to be used later in the production of other goods. Footnote 48 The knowledge contained in relevant and cited NPL, for example, is used by patent creators, or inventors, to create new products, services, modes of production, processes, frameworks, and/or other kinds of inventions used for enterprise building. Thus, the transformation of scientific knowledge into intermediate, technology-based capital goods is embodied in the patents themselves. Finally, the high patent output indicates the availability of technology that helps in the production of final goods, which then ultimately leads to economic growth. Footnote 49

The results of the FE-IV regression are shown in Table 8 , alongside the OLS, fixed effects, and GMM estimations. Due to the limited data on the instruments, the number of observations and groups in Table 8 is lower compared to the number of observations and groups in the main results (see Table 3 ). Footnote 50 For convenience, the same panel groups used in the FE-IV regression are used in the OLS, fixed effects, and GMM estimations as well to allow comparison of the estimates.

The log number of AI patents per million people is significantly positive in all models (at the 10% level in GMM and FE-IV), and the magnitudes of the AI coefficients are relatively consistent among the fixed effects, GMM, and FE-IV estimations in columns 2, 3, and 4 in Table 8 , respectively. Notably, the magnitudes of the coefficients are larger than the estimates in the main results in Table 3 . On the other hand, total patents are only significant and positive in OLS and fixed effects in columns 5 and 6. In addition, the magnitude of the coefficient of total patents in the FE-IV regression (column 8) is inconsistent with the other estimations.

Several tests were performed to check for the validity of the instruments in both the GMM and FE-IV models. The null hypothesis is not rejected for the Sargan-Hansen test for overidentifying restrictions in both the GMM and FE-IV estimations, suggesting no overidentification in the first-stage regressions. This is true for both the AI and total patents models (columns 3, 4, 7, and 8 in Table 8 ). Meanwhile, the null hypothesis of the Kleibergen-Paap test for weak instruments is rejected for the FE-IV estimates of both the AI and total patents models, implying the first stage FE-IV regressions are not underidentified. Hence, both tests seem to confirm the validity of the instruments used, especially for the FE-IV estimations.

The p-values of the endogeneity test, however, differ between the AI patents and total patents models in FE-IV (columns 4 and 8). Under the null hypothesis, the regressors, or the instruments, can be treated as exogenous. Rejection of the null hypothesis means the regressors are not exogenous and thus may not be considered acceptable instruments. According to the test, the null hypothesis is not rejected for AI patents but is rejected for the total patents model at standard significance levels. The result indicates that the validity of instruments and parameter estimates is only applicable to the former, whereas estimates for the latter model are likely inconsistent and biased.

Overall, the results of the main estimations and robustness checks reveal a strong positive relationship between AI innovation and long-run economic growth. This is consistent with the endogenous growth theories and with the findings of existing literature such as Kim and Lee ( 2015 ), He ( 2019 ), and Nguyen and Doytch ( 2022 ). On the other hand, total patents still contribute to long-run economic growth, albeit to a lesser extent compared to more technical innovations such as those developed in ICT. This is particularly true to more recent observations in the dataset. Moreover, AI has had a more robust and significant effect on the long-run growth among advanced economies, while total innovation exhibits almost no impact on growth for both advanced and emerging countries.

In addition, an IV estimation with fixed effects using cited NPL has been employed to further minimize the endogeneity issue. The FE-IV estimates are valid for the AI patents model, but not for the total patents model. The FE-IV estimates are also comparable with the results of other estimation techniques such as fixed effects and GMM, suggesting that cited non-patent references may serve as an instrument for specific types of patents such as those related to AI.

6 Conclusion

Innovations in AI have been around as early as the 1970s, but their application and impact have only been more apparent and pervasive in the last ten to twenty years. The huge surge in AI and total patent registrations by the turn of the century, alongside the obvious physical and non-physical embodiments of innovative technologies consumed daily, is evidence of how AI and related technologies have changed the economic landscape. Several companies, especially those in e-commerce, have been leveraging natural language processing to predict customer behavior to increase sales. Meanwhile, multinational companies rely on AI and machine learning to optimize their supply chains through predictive scheduling, demand forecasting, inventory and risk management, and predictive maintenance among many other purposes (Rao and Verweij 2017 ; Ashcroft 2023 ). In general, advancements in AI and related ICT technologies have ultimately helped in modernizing production processes, minimizing manual inefficiency, and enhancing overall customer experience across firms and industries.

This study sets out to determine the relationship between the level of AI innovation and long-run economic growth, using a panel dataset across countries between 1970 and 2019. The main finding demonstrates that there exists a positive and significant impact of AI patenting on average long-run economic growth. Additionally, the effect of AI is more apparent in the latter period, because of the increasing quantity and quality of AI innovation generated over time. Overall, the positive impact of AI found in this study is consistent with the results of other studies focusing on AI and growth such as those by He ( 2019 ), Fan and Liu ( 2021 ), and Yang ( 2022 ).

Meanwhile, there is also some evidence of the positive contribution of total patent creation on economic growth. This positive effect of patenting is consistent with the findings of Wong et al. ( 2005 ), Kim and Lee ( 2015 ), and Nguyen and Doytch ( 2022 ). The effect, however, is notably smaller and weaker compared to the effect of AI patents on growth. Total patents, however, have exhibited significantly positive effects in the earlier periods of the dataset. The muted effect of patent publication on long-run economic growth is similar to the results found by Chu et al. ( 2016 ), Blind et al. ( 2022 ), and Nguyen and Doytch ( 2022 ) in their studies.

Furthermore, the effect of AI on growth is more robust among advanced economies, which is in line with the theory of machine automation proposed by Zeira ( 1998 ). Because of differences in capital endowments, not all countries can keep up with the pace of a constantly shifting technological frontier. As AI requires physical, and oftentimes ICT-related capital and technical know–how, not all countries can implement and use AI technologies effectively. In the meantime, more developed economies can leverage AI in production and business operations because of the availability of knowledge and infrastructure that complement AI, which engenders a strong positive contribution of AI to economic growth.

Finally, cited non-patent references in AI patents may serve as a valid instrument for AI patent creation. The estimates obtained from the FE-IV regression are consistent with the fixed effects estimation and GMM, and are also supported by various tests on instrument validity. Further work on this topic is recommended to future researchers, either by discovering other possible instruments or expanding the use of the instrument to other types of patents and/or measures of innovation.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request. However, the datasets supporting the conclusions of this article are also available in the following repositories: Google Patents Data: https://console.cloud.google.com/bigquery?ws=!1m4!1m3!3m2!1spatents-public-data!2sgoogle_patents_research . Penn World Table: https://www.rug.nl/ggdc/productivity/pwt/?lang=en . United Nations Statistics: https://unstats.un.org/unsd/snaama/Downloads . World Bank Development Indicators: https://databank.worldbank.org/source/world-development-indicators .

Yang et al. ( 2021 ), Mahalakshmi et al. ( 2022 ), Sood et al. ( 2022 ), etc.

Zeira ( 1998 ) notes that standard economic growth models that involve technology adoption encourage the accumulation of capital. However, this may not be necessary as “new technology increases output for any combination of inputs”.

Arrow ( 1962 ) assumes a competitive equilibrium. However, Dasgupta and Stiglitz ( 1988 ) reason that learning possibilities can only translate into growth if “learning spillovers are complete.” In an oligopolistic market structure, for example, firms may not be able to learn “costlessly, completely, and instantaneously from the experience of others.”.

Arrow ( 1962 ) follows this idea from the standard neoclassical growth model. On the contrary, Bahk and Gort ( 1993 ) revealed that the effect of learning can also be disembodied from both capital and labor.

Zeira ( 1998 ) ascribes this to the wage differential across countries. Countries with lower wages tend to have lower productivity; hence, they are unable to afford the high requirements of capital to adopt the latest technology.

Low-indexed tasks refer to tasks that require minimal skill. In general, low-indexed tasks are assigned to low-skilled labor.

Acemoglu and Restrepo ( 2018 ) attribute the stability of the growth path to “self-correcting forces” of the factor prices. Furthermore, as both types of innovation advance at the same rate, the long-run growth rate path is characterized by a constant labor share.

This study also suffers from this problem. For example, the chosen measure for AI may still not fully and accurately capture its effect on growth.

See Aghion and Howitt ( 1990 ).

Chu et al. ( 2016 ), however, claim that “patent breadth” only promotes growth in the short run by raising the “profit margin of monopolistic firms” and providing “more incentives for R&D.” Accordingly, patents reduce growth in the long run but expand the total number of firms. Further, they state that an R&D subsidy is a more appropriate policy for “stimulating long-run economic growth.”.

Kim and Lee ( 2015 ), however, recognize that this might only be plausible among advanced countries, where viable “national innovation systems” and infrastructures enable scientific research to be put into effective commercial use.

This is similar to the finding of Kim et al. ( 2012 ), where they found no significant effect of patent intensity on growth among developing economies.

In relation to this, Lee et al. ( 2011 ) highlight a “mutual causation” between research publications and GDP among Asian economies. This causation, however, is less clear in Western countries.

Mueller ( 2006 ) distinguishes the types of R&D (private and university) from each other and estimated the impact of each separately. Also, the university-industry relation was measured by industry grants per researcher.

In the sample, Nakatani ( 2021 ) reveals an insignificant impact of intangible assets on TFP among South Korean firms, whereas a significant and positive contribution to TFP with the largest magnitude is found for ICT firms in the United Kingdom.

Analytical AI refers to technologies that help in the identification of “new insights, patterns, and relationships or dependencies” in data for decision-making. Functional AI executes or implements actions, instead of generating recommendations. Interactive AI enables “interactive communication” between the user and a smart system to provide user assistance (e.g., chatbots and smart personal assistants). Textual AI typically covers textual analytics and natural language processing. Finally, visual AI can be considered a “branch of computer science” that “trains” machines to learn images and visual data (Sarker 2022 ).

The period considered in the analysis may not cover recent generative AI such as ChatGPT, unless these inventions have been patented years before their release.

Aside from these challenges, training AI models is typically associated with enormous costs, both in time and resources.

Challenges in measurement gauge how well an AI performs its assigned tasks. On the other hand, generalization and calibration refer to the performance and replicability of an AI system to different populations or datasets. Local context encompasses the “act of fitting” new technology and its “goodness of fit” into a pre-existing organizational network (Coiera 2019 ).

This is similar to the Bahk and Gort ( 1993 ) model. Lu ( 2021 ) further adds that AI may replace human labor in the future, which subsequently has welfare implications.

The balanced growth path by Lu ( 2021 ) shows output, human capital, physical capital, AI, and consumption grow at the same rate.

Specifically, He ( 2019 ) measures AI as “the ratio of fixed assets investment in information transmission computer services and software industry to GDP.”.

Fan and Liu ( 2021 ) have developed an index to measure AI level based on three aspects, namely “infrastructure development, technology application, and market benefits.”.

On the other hand, Oxford Insights ( 2022 ) has developed an AI readiness index per country, available in the annual reports published since 2017. However, the current dataset lacks enough observation in terms of the time dimension. Thus, using the index was ruled out in favor of long-run analysis. Nonetheless, this study recommends using the index and/or other related AI measures for future research once more data are available.

Jones and Williams ( 1998 ) explored the idea of a non-constant return to \(R\) and \(A\) . They introduce additional parameters that represent “congestion externality,” “knowledge spillovers,” and “fishing out effects” in research, allowing the parameter \(\theta\) to fall between 0 and 1 and assume non-linearity in \(A\) .

Equation (6) is the (short-run) growth equation when \(dt=1\) . For long-run growth rates, the change in \(t\) is greater than 1 \((dt>1)\) to indicate longer periods.

Griliches ( 1998 ), however, acknowledges the difficulty of measuring these relationships, as knowledge stock is unobservable.

Lu and Zhou ( 2021 ) note that the definition of AI in theoretical models can be “very broad,” whereas empirical data tend to have “narrow” definitions, resulting in a gap between the two. Theoretical models typically depict AI as a type of automation, but continuous AI development may be capable of replacing even high-skilled labor. In addition, AI raises the question of what a “human being” is in economics, where the human being is often “narrowed down” to “labor” and an “optimization agent.” Aside from the current lack of clarity of whether AI is a “new production technology” or simply a new input of production, the question of which input (e.g., labor, human capital, or an “independent decision-making agent”) is AI used as a substitute for also persists.

In addition, Brynjolfsson et al. ( 2018 ) highlighted the “modern productivity paradox” in the age of AI. AI is indeed capable of many promising feats; however, productivity growth remained stagnant over the past decade. They attributed this inconsistency to several reasons, such as the difficulty of measuring AI capital because of its mostly intangible outputs, and the amount of time and resources required for the impact of technology to be fully reflected in productivity.

Because of the mode of data extraction, the AI patent variable may be prone to accuracy and measurement error. As much as possible, the list of common AI terms used in the text search has been exhaustive. Furthermore, some technical jargon may be shared among multiple branches of knowledge that include AI. Hence, the word list has been limited to the most common and specific AI terms. An exact search of the identified terms and/or phrases was then performed.

Several studies make use of R&D “intensity” as a measure of innovation (e.g. Jones and Williams 1998 ; Blind et al. 2006 ; Yanhui et al. 2015 ). Other examples of R&D intensity measures include patent applications per R&D expenditure, R&D over sales for firm-level data, number of researchers per million people, etc.

Because of data availability, this study makes use of the implicit price deflator (rather than the consumer price index) to calculate inflation.

Except for trade, all variables in \({X}_{it}\) are also expressed as instantaneous growth rates.

This comes from Wößmann ( 2003 ), who argues that common proxies for human capital such as school enrollment rates and average years of schooling either insufficiently or incorrectly model the “development effect” of human capital. Specifically, Wößmann ( 2003 ) explains that enrollment ratios are flow variables, and enrolled students are not yet part of the labor force, and thus are excluded from economic production. On the other hand, average years of schooling “misspecifies” human capital by placing the “same weight on any year of schooling” of a person, and does not input the “quality of education system.”.

The full list of variables is available in Table 12 in the appendix. Additional variables are considered (e.g., Internet users, non-patent literature) as part of the robustness checks. See Sect.  5.1 under Sect. 5 .

The presence of positive and negative outliers contributed to a relatively high standard deviation. Calculated five-year real GDP per capita growth rates range between − 24.52% and 23.59% in the dataset, across countries and periods.

The driving forces, however, have had specific and varying effects per type of patent filing. Chen and Zhang ( 2019 ) note that R&D spending generally boosts Chinese patent creation, while FDI is only robust for utility and design patents. Patent subsidies, on the other hand, have a positive effect on design patents.

The gradual decrease in domestic patenting was due to Japanese firms being selective in their patent registrations, focusing more on the quality than the number of filings (Japan Patent Office 2015 ).

Kelley and Schmidt ( 1995 ), however, note that this does not imply that demographic effects on per capita growth are unimportant. Empirical results only highlight the need to study the long-run dynamics between population growth and output growth more carefully.

Aside from the technology “intensity index” given by the log number of patents per million people, the log number of patents was also used directly in the estimations. Results of these estimations reveal similar results (positive and significant coefficient for AI patents, and weak significance for total patents).

The period (1970–2019) considered for estimation covers several socio-economic, political, and technological events (e.g., military conflicts, oil shocks, financial crises, Internet diffusion, etc.) that may have affected inter-country growth rates. All estimated models include time dummies; however, they may not fully capture the influence of external events on long-run growth rates. As part of the robustness checks, the average five-year growth rate of Internet users per country, for example, was included as a control variable. Results are available in Table 10 in the appendix.

The reliability of the Sargan-Hansen statistic, however, weakens as the number of instruments increases. Thus, the number of instruments was reduced to avoid this issue as much as possible. Roodman ( 2009 ) recommends that the total number of instruments should be less than the total number of individual units in a panel dataset. To reduce the number of instruments, the optimal number of lags is chosen per GMM estimation. A maximum of five lags is used, but the model should simultaneously satisfy the Sargan-Hansen and AR(2) tests, while also considering the explanatory power of the variable(s) of interest and control variables. Following these specifications, the AI patents model passes the Sargan-Hansen and AR(2) tests until the fifth-order lagged instruments, while the total patents model only passes both tests at the second lag; hence, the difference in the number of instruments (column 4 with 65 and column 8 with 32).

Unit root tests (Fisher-type based on augmented Dickey-Fuller) for unbalanced panel data were also performed to check for random walk. The tests revealed that the variables contain at least one stationary panel (the null hypothesis that all n panels contain unit roots is rejected). However, the test could not be performed for the log of AI patents per million people due to missing observations.

The “advanced” dummy variable was dropped due to collinearity.

A short-run analysis was also conducted to check for the short-run impacts of technical innovation on economic growth. Instead of a five-year average growth rate, the yearly real GDP growth rate per capita growth rate was used as the dependent variable. Likewise, annual levels and growth rates of the patent and control variables were used in the regressions. The results are available in Table 9 in the appendix.

Distinguishing the effect of AI from non-AI patents on growth might be a challenging task when using patents as an indicator of technological innovation. As AI becomes increasingly and deeply embedded in production tools and processes, any new invention might have some AI component in it. Hence, disembodying AI from the “non-AI” component of an invention, for example, to estimate AI’s true effect on growth might present a challenge for future research.

Because of the limited number of countries, the number of instruments (42) used in the GMM estimation among advanced economies for AI patents is relatively close to the number of individual panels n (53). While the number of instruments is still lower than the number of individual groups, it is more desirable to have the number of instruments as few as possible.

Although Pinto and Teixeira ( 2020 ) use research output instead of patents as a measure of knowledge as a good, the authors illustrate how research ultimately contributes to economic growth (see Fig.  1 of Pinto and Teixeira 2020 ).

Moreover, to strengthen the exogeneity assumption, only the NPL cited in the patents themselves is used as an instrument, rather than the entire population of scientific and academic publications. Hence, the instrument used has a direct causal link to patent creation and is more likely to manifest an effect on growth only through the patents.

Both the current and lagged values of the (natural log) number of non-patent literature are used as instruments to ensure the validity and precision of the FE-IV estimates. Also, the interaction term between advanced economic status and patent variable is instrumented. The first stage results are available in Table 11 in the appendix.

Acemoglu D, Restrepo P (2018) The race between man and machine: implications of technology for growth, factor shares, and employment. Am Econ Rev 108(6):1488–1542. https://doi.org/10.1257/aer.20160696

Article   Google Scholar  

Aghion P, Jones BF, Jones CI (2018) Artificial intelligence and economic growth. In: The economics of artificial intelligence: an agenda, pp. 237–282. University of Chicago Press. https://doi.org/10.3386/w23928

Aghion P, Howitt P (1990) A model of growth through creative destruction. Econometrica 60(2):323–351. https://doi.org/10.2307/2951599

Akhilomen J (2013) Data mining application for cyber credit-card fraud detection system. In: Industrial Conference on Data Mining, pp. 218–228. https://doi.org/10.1007/978-3-642-39736-3_17

Arellano M, Bond S (1991) Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Rev Econ Stud 58(2):277–297. https://doi.org/10.2307/2297968

Arellano M, Bover O (1995) Another look at the instrumental variable estimation of error-components models. J Econometr 68(1):29–51. https://doi.org/10.1016/0304-4076(94)01642-D

Arrow KJ (1962) The economic implications of learning by doing. Rev Econ Stud 29(3):155–173. https://doi.org/10.2307/2295952

Ashcroft S. Top 10 AI and ML supply chain firms & solutions.” Supply Chain Digital , May 24. 2023; Available at https://supplychaindigital.com/digital-supply-chain/top-10-ai-and-ml-supply-chain-solutions

Bahk B-H, Gort M (1993) Decomposing learning by doing in new plants. J Polit Econ 101(4):561–583. https://doi.org/10.1086/261888

Barro RJ (1997) Determinants of economic growth: a cross-country empirical study. MIT Press, Cambridge. https://doi.org/10.3386/w5698

Book   Google Scholar  

Bassanini A, Stefano S, Philip H (2001) Economic growth: the role of policies and institutions. Panel data evidence from OECD countries. In: OECD Economics Department Working Papers, No. 283, OECD Publishing, Paris. https://doi.org/10.1787/722675213381 .

Blind K, Edler J, Frietsch R, Schmoch U (2006) Motives to patent: empirical evidence from Germany. Res Policy 35(5):655–672. https://doi.org/10.1016/j.respol.2006.03.002

Blind K, Ramel F, Rochell C (2022) The influence of standards and patents on long-term economic growth. J Technol Transf 47(4):979–999. https://doi.org/10.1007/s10961-021-09864-3

Blundell R, Bond S (1998) Initial conditions and moment restrictions in dynamic panel data models. J Econometr 87(1):115–143. https://doi.org/10.1016/S0304-4076(98)00009-8

Bose R (2006) Intelligent technologies for managing fraud and identity theft. In: Third International Conference on Information Technology: New Generations (ITNG'06), pp. 446–451. https://doi.org/10.1109/ITNG.2006.78 .

Brynjolfsson E, Daniel R, Chad S (2018) Artificial intelligence and the modern productivity paradox: a clash of expectations and statistics. The economics of artificial intelligence: An agenda, pp. 23–57. University of Chicago Press. https://doi.org/10.3386/w24001 .

Chen Z, Zhang J (2019) Types of patents and driving forces behind the patent growth in China. Econ Model 80:294–302. https://doi.org/10.1016/j.econmod.2018.11.015

Chu AC, Furukawa Y, Ji L (2016) Patents, R&D subsidies, and endogenous market structure in a Schumpeterian economy. South Econ J 82(3):809–825. https://doi.org/10.1002/soej.12122

Coiera E (2019) The last mile: where artificial intelligence meets reality. J Med Internet Res 21(11):e16323. https://doi.org/10.2196/16323

Dasgupta P, Stiglitz J (1988) Learning-by-doing, market structure and industrial and trade policies. Oxf Econ Pap 40(2):246–268. https://doi.org/10.1093/oxfordjournals.oep.a041850

Fan D, Liu K (2021) The relationship between artificial intelligence and China’s sustainable economic growth: focused on the mediating effects of industrial structural change. Sustainability 13(20):11542. https://doi.org/10.3390/su132011542

Feenstra RC, Robert I, Marcel T (2015) The next generation of the Penn world table. Am Econ Rev 105(10):3150–3182. https://doi.org/10.1257/aer.20130954

Feenstra RC, Robert I, Marcel T (2013) PWT 8.0—a user guide. Available at: www.ggdc.net/pwt .

Google (2022) “Google Patents Public Data.” Available at: https://console.cloud.google.com/bigquery?ws=!1m4!1m3!3m2!1spatents-public-data!2sgoogle_patents_research

Grier KB, Tullock G (1989) An empirical analysis of cross-national economic growth, 1951–1980. J Monet Econ 24(2):259–276. https://doi.org/10.1016/0304-3932(89)90006-8

Griliches Z (1988) Patent statistics as economic indicators: a survey. R&D Productiv Econ Evid pp. 287–343. University of Chicago Press. https://doi.org/10.3386/w3301 .

Hatemi-J A, Ajmi AN, El Montasser G, Inglesi-Lotz R, Gupta R (2016) Research output and economic growth in G7 countries: new evidence from asymmetric panel causality testing. Appl Econ 48(24):2301–2308. https://doi.org/10.1080/00036846.2015.1117052

He Y (2019) The effect of artificial intelligence on economic growth: evidence from cross-province panel data. Korea J Artif Intell 7(2):9–12. https://doi.org/10.24225/kjai.2019.7.2.9

Inglesi-Lotz R, Balcilar M, Gupta R (2014) Time-varying causality between research output and economic growth in US. Scientometrics 100(1):203–216. https://doi.org/10.1007/s11192-014-1257-z

Institut national de la statistique et des études économiques (INSEE). 2019. “Technologies de l'information et de la communication et commerce électronique (TIC) 2019.” Available at: https://www.insee.fr/fr/metadonnees/source/fichier/TIC2019_questionnaire.pdf

International Telecommunication Union (2023) Artificial intelligence for good. Available at: https://www.itu.int/en/mediacentre/backgrounders/Pages/artificial-intelligence-for-good.aspx .

Japan Patent Office (2015) JPO Status Report 2015. Available at: https://www.jpo.go.jp/e/resources/report/statusreport/2015/ .

Jones CI, Williams JC (1998) Measuring the social return to R&D. Q J Econ 113(4):1119–1135. https://doi.org/10.2139/ssrn.2155

Kelley AC, Schmidt RM (1995) Aggregate population and economic growth correlations: the role of the components of demographic change. Demography 32(4):543–555. https://doi.org/10.2307/2061674

Kim YK, Lee K (2015) Different impacts of scientific and technological knowledge on economic growth: contrasting science and technology policy in East Asia and Latin America. Asian Econ Policy Rev 10(1):43–66. https://doi.org/10.1111/aepr.12081

Kim YK, Lee K, Park WG, Choo K (2012) Appropriate intellectual property protection and economic growth in countries at different levels of development. Res Policy 41(2):358–375. https://doi.org/10.1016/J.RESPOL.2011.09.003

Lach S (1995) Patents and productivity growth at the industry level: a first look. Econ Lett 49(1):101–108. https://doi.org/10.1016/0165-1765(94)00618-C

Lee L-C, Lin P-H, Chuang Y-W, Lee Y-Y (2011) Research output and economic productivity: a Granger causality test. Scientometrics 89(2):465–478. https://doi.org/10.1007/s11192-011-0476-9

Lu C-H (2021) The impact of artificial intelligence on economic growth and welfare. J Macroecon 69:103342. https://doi.org/10.1016/j.jmacro.2021.103342

Lu Y, Zhou Y (2021) A review on the economics of artificial intelligence. J Econ Surv 35(4):1045–1072. https://doi.org/10.1111/joes.12422

Mahalakshmi V, Nirzar Kulkarni KV, Pradeep Kumar K, Suresh Kumar D, Sree N, Durga S (2022) The role of implementing artificial intelligence and machine learning technologies in the financial services industry for creating competitive intelligence. Mater Today Proc 56:2252–2255. https://doi.org/10.1016/j.matpr.2021.11.577

Maradana RP, Pradhan RP, Dash S, Gaurav K, Jayakumar M, Chatterjee D (2017) Does innovation promote economic growth? Evidence from European countries. J Innov Entrep 6(1):1–23. https://doi.org/10.1186/s13731-016-0061-9

Marr B (2023) A short history of chatGPT: how we got to where we are today. Forbes, May 19. Available at: https://www.forbes.com/sites/bernardmarr/2023/05/19/a-short-history-of-chatgpt-how-we-got-to-where-we-are-today/?sh=1b6986bf674f

Montagnier P, Irene EK (2021) AI measurement in ICT usage surveys: a review. OECD Digital Economy Papers, No. 308, OECD Publishing, Paris. https://doi.org/10.1787/20716826 .

Mou X (2019) Artificial intelligence: investment trends and selected industry uses. International Finance Corporation 8. Available at: https://openknowledge.worldbank.org/handle/10986/32652

Mueller P (2006) Exploring the knowledge filter: How entrepreneurship and university–industry relationships drive economic growth. Res Policy 35(10):1499–1508. https://doi.org/10.1016/J.RESPOL.2006.09.023

Nakatani R (2021) Total factor productivity enablers in the ICT industry: a cross-country firm-level analysis. Telecommun Policy 45(9):102188. https://doi.org/10.1016/j.telpol.2021.102188

Nchake MA, Shuaibu M (2022) Investment in ICT infrastructure and inclusive Growth in Africa. Sci Afr 17:1–16. https://doi.org/10.1016/j.sciaf.2022.e01293

Nguyen CP, Nadia D (2022) The impact of ICT patents on economic growth: an international evidence. Telecommun Policy 46(5):102291. https://doi.org/10.1016/j.telpol.2021.102291

Nickell S (1981) Biases in dynamic models with fixed effects. Econometrica 49(6):1417–1426. https://doi.org/10.2307/1911408

Nightingale P (2000) Economies of scale in experimentation: knowledge and technology in pharmaceutical R&D. Ind Corp Chang 9(2):315–359. https://doi.org/10.1093/ICC/9.2.315

Ntuli H, Inglesi-Lotz R, Chang T, Pouris A (2015) Does research output cause economic growth or vice versa? Evidence from 34 OECD countries. J Am Soc Inf Sci 66(8):1709–1716. https://doi.org/10.1002/asi.23285

Oxford Insights (2022) Government AI Readiness Index 2022. Available at: https://www.oxfordinsights.com/government-ai-readiness-index-2022 .

Pinto T, Teixeira AAC (2020) The impact of research output on economic growth by fields of science: a dynamic panel data analysis, 1980–2016. Scientometrics 123(2):945–978. https://doi.org/10.1007/s11192-020-03419-3

Rao AS, Gerard V (2017) Sizing the prize: What’s the real value of AI for your business and how can you capitalise. PwC Publication, PwC: 1–30. Available at: https://www.pwc.com/gx/en/issues/analytics/assets/pwc-ai-analysis-sizing-the-prize-report.pdf

Romer PM (1990) Endogenous technological change. J Polit Econ 98(5):S71–S102. https://doi.org/10.1086/261725

Roodman D (2009) How to do xtabond2: an introduction to difference and system GMM in Stata. Stand Genomic Sci 9(1):86–136. https://doi.org/10.1177/1536867X0900900106

Sarker IH (2022) Ai-based modeling: techniques, applications and research issues towards automation, intelligent and smart systems. SN Comput Sci 3(2):158. https://doi.org/10.1007/s42979-022-01043-x

Sinha D (2008) Patents, innovations and economic growth in Japan and South Korea: evidence from individual country and panel data. Appl Econ Int Dev 8(1). Available at: https://ssrn.com/abstract=1308261

Solarin SA, Yen YY (2016) A global analysis of the impact of research output on economic growth. Scientometrics 108(2):855–874. https://doi.org/10.1007/s11192-016-2002-6

Sood SK, Keshav SR, Dheeraj K (2022) A visual review of artificial intelligence and Industry 4.0 in healthcare. Comput Electr Eng 101:107948. https://doi.org/10.1016/j.compeleceng.2022.107948

Statistics Sweden (2020) Artificial intelligence (AI) in Sweden 2019. Available at: https://www.scb.se/en/finding-statistics/statistics-by-subject-area/education-and-research/research/research-and-development-in-sweden/pong/statistical-news/artificial-intelligence-ai-in-sweden-2019/

Sweet C, Eterovic D (2019) Do patent rights matter? 40 years of innovation, complexity and productivity. World Dev 115:78–93. https://doi.org/10.1016/J.WORLDDEV.2018.10.009

Szczepański M (2019) Economic impacts of artificial intelligence (AI). European Parliamentary Research Service. Available at: https://www.europarl.europa.eu/RegData/etudes/BRIE/2019/637967/EPRS_BRI(2019)637967_EN.pdf

The European Commission (2021) Community Survey on ICT Usage and E-Commerce in Enterprises. Available at: https://circabc.europa.eu/sd/a/f9dc8b66-a429-49e2-ae01-f7424ec389f0/MQ_2021_ICT_ENT.pdf

The United Nations (2022) National Accounts—analysis of Main Aggregates (AMA). https://unstats.un.org/unsd/snaama/Downloads

The World Bank (2022) World Development Indicators. The World Bank Group. https://databank.worldbank.org/source/world-development-indicators

Trajtenberg M (2018) AI as the next GPT: a Political-Economy Perspective. No. w24245. National Bureau of Economic Research. https://doi.org/10.3386/w24245 .

Ulku H (2004) R&D, innovation, and economic growth: an empirical analysis. Int Monet Fund Working Papers 185:2–35. https://doi.org/10.5089/9781451859447.001

Velayos-Ortega G, López-Carreño R (2021) Non-patent literature. Encyclopedia 1(1):198–205. https://doi.org/10.3390/encyclopedia1010019

Wang L, Zhan J, Shi W, Liang Yi (2011) In cloud, can scientific communities benefit from the economies of scale? IEEE Trans Parallel Distrib Syst 23(2):296–303. https://doi.org/10.1109/TPDS.2011.144

Wong PK, Ho YP, Autio E (2005) Entrepreneurship, innovation and economic growth: evidence from GEM data. Small Bus Econ 24(3):335–350. https://doi.org/10.1007/S11187-005-2000-1

Wößmann L (2003) Specifying human capital. J Econ Surv 17(3):239–270. https://doi.org/10.1111/1467-6419.00195

Yang C-H (2022) How artificial intelligence technology affects productivity and employment: firm-level evidence from Taiwan. Res Policy 51(6):104536. https://doi.org/10.1016/j.respol.2022.104536

Yang T, Yi X, Shaowen Lu, Johansson KH, Chai T (2021) Intelligent manufacturing for the process industry driven by industrial artificial intelligence. Engineering 7(9):1224–1230. https://doi.org/10.1016/j.eng.2021.04.023

Yanhui W, Huiying Z, Jing W (2015) Patent elasticity, R&D intensity and regional innovation capacity in China. World Patent Inf 43:50–59. https://doi.org/10.1016/J.WPI.2015.10.003

Yeung K (2020) Recommendation of the council on artificial intelligence (OECD). Int Leg Mater 59(1):27–34. https://doi.org/10.1017/ilm.2020.5

Yigitcanlar T, Desouza KC, Butler L, Roozkhosh F (2020) Contributions and risks of artificial intelligence (AI) in building smarter cities: insights from a systematic review of the literature. Energies 13(6):1473. https://doi.org/10.3390/en13061473

Zeira J (1998) Workers, machines, and economic growth. Q J Econ 113(4):1091–1117. https://doi.org/10.1162/003355398555847

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The impact of economic booms on competitiveness

Readers Question: Why do countries that experience a boom risk losing international competitiveness?

An economic boom implies that an economy is growing above its long term trend rate . This means that the rate of economic growth is high, but there tend to be inflationary pressures because demand is growing faster than supply.

The impact of high growth and inflation will tend to cause a current account deficit and declining export competitiveness.

The labour market

In a boom, we will see a rapid fall in unemployment. The number of job vacancies will increase as firms try to hire more workers to meet rising demand. This invariably puts upward pressure on wages. Because demand for labour is high, workers are in a position to bargain for higher wages. This could be exacerbated by labour market imperfections, e.g. a shortage of key skilled workers could push up wages, even before full employment is reached. Rising wages will be a significant factor in causing higher unit labour costs and higher export prices. This will lead to a decline in competitiveness. This will be especially important if the country is producing labour-intensive goods, such as textiles.

Boom and inflation

A simple AD/ AS diagram shows that as the economy reaches full capacity, an increase in AD will cause inflation to increase significantly. This will push up wages, but also raw materials and the general cost of production. This will be reflected in higher export prices.

Example of boom countries and declining competitiveness

In the Euro, between 2001-2007, several southern European countries experienced strong growth, but also declining competitiveness. Because their exchange rates were fixed in the Euro, this resulted in large current account deficits – reflecting the loss of competitiveness. For example, Ireland and Spain both had strong economic growth leading up to 2007, but the boom contributed to rising labour costs and a decline in relative competitiveness.

Current account deficits an indicator of declining competitiveness.

UK boom of late 1980s.

After economic growth reached 5% in 1988, we  saw a sharp rise in inflation to nearly 10%.

This led to a decline in competitiveness. The UK current account deficit increased in this period due to two factors

• Rising import demand
• Declining export competitiveness

Evaluation of booms and competitiveness

It depends on the nature of the economic boom. If the boom is fuelled by higher consumer spending and rising asset prices, then we are more likely to see demand outstripping supply resulting in higher inflation. However, if we have a boom in investment and rising productivity, then a country may not see a decline in competitiveness. For example, China has been able to maintain high rates of economic growth without losing competitiveness. This is because the growth has been export led and consistent with low wage growth.

Also, countries can lose competitiveness due to supply side factors. Even countries with low economic growth will become uncompetitive if costs rise. For example, before 2007, Portugal and Greece had relatively modest rates of growth, but they experienced rising unit labour costs.

When measuring competitiveness, the current account only shows part of the picture. It is also necessary to look at wage costs, inflation to get a better picture, e.g. wage competitiveness

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A critical analysis of the impacts of COVID-19 on the global economy and ecosystems and opportunities for circular economy strategies

T. ibn-mohammed.

a Warwick Manufacturing Group (WMG), The University of Warwick, Coventry CV4 7AL, United Kingdom

K.B. Mustapha

b Faculty of Engineering and Science, University of Nottingham (Malaysia Campus), Semenyih, Selangor43500, Malaysia

c School of The Built Environment and Architecture, London South Bank University, London SE1 0AA, United Kingdom

K.A. Babatunde

d Faculty of Economics and Management, Universiti Kebangsaan Malaysia, Bangi, Selangor43600, Malaysia

e Department of Economics, Faculty of Management Sciences, Al-Hikmah University, Ilorin, Nigeria

D.D. Akintade

f School of Life Sciences, University of Nottingham, Nottingham NG7 2UH United Kingdom

g Kent Business School, University of Kent, Canterbury CT2 7PE, United Kingdom

h Faculty of Economics, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan

M.M. Ndiaye

i Department of Industrial Engineering, College of Engineering, American University of Sharjah, Sharjah, UAE

F.A. Yamoah

j Department of Management, Birkbeck University of London, London WC1E 7JL United Kingdom

k Sheffield University Management School (SUMS), The University of Sheffield, Sheffield S10 1FL, United Kingdom

• • COVID-19 presents unprecedented challenge to all facets of human endeavour.
• • A critical review of the negative and positive impacts of the pandemic is presented.
• • The danger of relying on pandemic-driven benefits to achieving SDGs is highlighted.
• • The pandemic and its interplay with circular economy (CE) approaches is examined.
• • Sector-specific CE recommendations in a resilient post-COVID-19 world are outlined.

The World Health Organization declared COVID-19 a global pandemic on the 11th of March 2020, but the world is still reeling from its aftermath. Originating from China, cases quickly spread across the globe, prompting the implementation of stringent measures by world governments in efforts to isolate cases and limit the transmission rate of the virus. These measures have however shattered the core sustaining pillars of the modern world economies as global trade and cooperation succumbed to nationalist focus and competition for scarce supplies. Against this backdrop, this paper presents a critical review of the catalogue of negative and positive impacts of the pandemic and proffers perspectives on how it can be leveraged to steer towards a better, more resilient low-carbon economy. The paper diagnosed the danger of relying on pandemic-driven benefits to achieving sustainable development goals and emphasizes a need for a decisive, fundamental structural change to the dynamics of how we live. It argues for a rethink of the present global economic growth model, shaped by a linear economy system and sustained by profiteering and energy-gulping manufacturing processes, in favour of a more sustainable model recalibrated on circular economy (CE) framework. Building on evidence in support of CE as a vehicle for balancing the complex equation of accomplishing profit with minimal environmental harms, the paper outlines concrete sector-specific recommendations on CE-related solutions as a catalyst for the global economic growth and development in a resilient post-COVID-19 world.

1. Introduction

The world woke up to a perilous reality on the 11th of March, 2020 when the World Health Organization (WHO) declared novel coronavirus (COVID-19) a pandemic ( Sohrabi et al., 2020 ; WHO, 2020a ). Originating from Wuhan, China, cases rapidly spread to Japan, South Korea, Europe and the United States as it reached global proportions. Towards the formal pandemic declaration, substantive economic signals from different channels, weeks earlier, indicated the world was leaning towards an unprecedented watershed in our lifetime, if not in human history ( Gopinath, 2020 ). In series of revelatory reports ( Daszak, 2012 ; Ford et al., 2009 ; Webster, 1997 ), experts across professional cadres had long predicted a worldwide pandemic would strain the elements of the global supply chains and demands, thereby igniting a cross-border economic disaster because of the highly interconnected world we now live in. By all accounts, the emerging havoc wrought by the pandemic exceeded the predictions in those commentaries. At the time of writing, the virus has killed over 800,000 people worldwide ( JHU, 2020 ), disrupted means of livelihoods, cost trillions of dollars while global recession looms ( Naidoo and Fisher, 2020 ). In efforts to isolate cases and limit the transmission rate of the virus, while mitigating the pandemic, countries across the globe implemented stringent measures such as mandatory national lockdown and border closures.

These measures have shattered the core sustaining pillars of modern world economies. Currently, the economic shock arising from this pandemic is still being weighed. Data remains in flux, government policies oscillate, and the killer virus seeps through nations, affecting production, disrupting supply chains and unsettling the financial markets ( Bachman, 2020 ; Sarkis et al., 2020 ). Viewed holistically, the emerging pieces of evidence indicate we are at a most consequential moment in history where a rethink of sustainable pathways for the planet has become pertinent. Despite this, the measures imposed by governments have also led to some “accidental” positive effects on the environment and natural ecosystems. As a result, going forward, a fundamental change to human bio-physical activities on earth now appears on the spectrum of possibility ( Anderson et al., 2020 ). However, as highlighted by Naidoo and Fisher (2020) , our reliance on globalization and economic growth as drivers of green investment and sustainable development is no longer realistic. The adoption of circular economy (CE) – an industrial economic model that satisfies the multiple roles of decoupling of economic growth from resource consumption, waste management and wealth creation – has been touted to be a viable solution.

No doubt, addressing the public health consequences of COVID-19 is the top priority, but the nature of the equally crucial economic recovery efforts necessitates some key questions as governments around the world introduce stimulus packages to aid such recovery endeavours: Should these packages focus on avenues to economic recovery and growth by thrusting business as usual into overdrive or could they be targeted towards constructing a more resilient low-carbon CE? To answer this question, this paper builds on the extant literature on public health, socio-economic and environmental dimensions of COVID-19 impacts ( Gates, 2020b ; Guerrieri et al., 2020 ; Piguillem and Shi, 2020 ; Sohrabi et al., 2020 ), and examines its interplay with CE approaches. It argues for the recalibration and a rethink of the present global economic growth model, shaped by a linear economy system and sustained by profit-before-planet and energy-intensive manufacturing processes, in favour of CE. Building on evidence in support of CE as a vehicle for optimizing the complex equation of accomplishing profit while minimizing environmental damage, the paper outlines tangible sector-specific recommendations on CE-related solutions as a catalyst for the global economic boom in a resilient post-COVID-19 world. It is conceived that the “accidental” or the pandemic-induced CE strategies and behavioural changes that ensued during coronavirus crisis can be leveraged or locked in, to provide opportunities for both future resilience and competitiveness.

In light of the above, the paper is structured as follows. In Section 2 , the methodological framework, which informed the critical literature review is presented. A brief overview of the historical context of previous epidemics and pandemics is presented in Section 3 as a requisite background on how pandemics have shaped human history and economies and why COVID-19 is different. In Section 4 , an overview of the impacts (both negative and positive) of COVID-19 in terms of policy frameworks, global economy, ecosystems and sustainability are presented. The role of the CE as a constructive change driver is detailed in Section 5 . In Section 6 , opportunities for CE after COVID-19 as well as sector-based recommendations on strategies and measures for advancing CE are presented, leading to the summary and concluding remarks in Section 7.

A literature review exemplifies a conundrum because an effective one cannot be conducted unless a problem statement is established ( Ibn-Mohammed, 2017 ). Yet, a literature search plays an integral role in establishing many research problems. In this paper, the approach taken to overcome this conundrum involves searching and reviewing the existing literature in the specific area of study (i.e. impacts of COVID-19 on global economy and ecosystems in the context of CE). This was used to develop the theoretical framework from which the current study emerges and adopting this to establish a conceptual framework which then becomes the basis of the current review. The paper adopts the critical literature review (CLR) approach given that it entails the assessment, critique and synthetisation of relevant literature regarding the topic under investigation in a manner that facilitates the emergence of new theoretical frameworks and perspectives from a wide array of different fields ( Snyder, 2019 ). CLR suffers from an inherent weakness in terms of subjectivity towards literature selection ( Snyder, 2019 ), prompting Grant and Booth (2009) to submit that systematic literature review (SLR) could mitigate this bias given its strict criteria in literature selection that facilitates a detailed analysis of a specific line of investigation. However, a number of authors ( Morrison et al., 2012 ; Paez, 2017 ) have reported that SLR does not allow for effective synthesis of academic and grey literature which are not indexed in popular academic search engines like Google Scholar, Web-of-Science and Scopus. The current review explores the impacts of COVID-19 on the global economy and ecosystems and opportunities for circular economy strategies, rather than investigating a specific aspect of the pandemic. As such, adopting a CLR approach is favoured in realising the goal of the paper as it allows for the inclusion of a wide range of perspectives and theoretical underpinnings from different sources ( Greenhalgh et al., 2018 ; Snyder, 2019 ).

Considering the above, this paper employed archival data consisting of journal articles, documented news in the media, expert reports, government and relevant stakeholders’ policy documents, published expert interviews and policy feedback literature that are relevant to COVID-19 and the concept of CE. To identify the relevant archival data, we focused on several practical ways of literature searching using appropriate keywords that are relevant to this work including impact (positive and negative) of COVID-19, circular economy, economic resilience, sustainability, supply chain resilience, climate change, etc. After identifying articles and relevant documents, their contents were examined to determine inclusions and exclusions based on their relevance to the topic under investigation. Ideas generated from reading the resulting papers from the search were then used to develop a theoretical framework and a research problem statement, which forms the basis for the CLR. The impact analysis for the study was informed by the I = P × A × T model whereby the “impact” (I) of any group or country on the environment is a function of the interaction of its population size (P), per capita affluence (A), expressed in terms of real per capita GDP, as a valid approximation of the availability of goods and services and technology (T) involved in supporting each unit of consumption.

As shown in the methodological framework in Fig. 1 , the paper starts with a brief review of the impacts of historical plagues to shed more light on the link between the past and the unprecedented time, which then led to an overview of the positive and negative impacts of COVID-19. The role of CE as a vehicle for constructive change in the light of COVID-19 was then explored followed by the synthesis, analysis and reflections on the information gathered during the review, leading to sector-specific CE strategy recommendations in a post-COVID-19 world.

Methodological framework for the critical literature review.

3. A brief account of the socio-economic impacts of historical outbreaks

At a minimum, pandemics result in the twin crisis of stressing the healthcare infrastructure and straining the economic system. However, beyond pandemics, several prior studies have long noted that depending on latency, transmission rate, and geographic spread, any form of communicable disease outbreak is a potent vector of localized economic hazards ( Bloom and Cadarette, 2019 ; Bloom and Canning, 2004 ; Hotez et al., 2014 ). History is littered with a catalogue of such outbreaks in the form of endemics, epidemics, plagues and pandemics. In many instances, some of these outbreaks have hastened the collapse of empires, overwhelmed the healthcare infrastructure, brought social unrest, triggered economic dislocations and exposed the fragility of the world economy, with a knock-on effect on many sectors. Indeed, in the initial few months of COVID-19 pandemic, it has become more evident that natural, accidental or intentional biological threats or outbreak in any country now poses an unquantifiable risk to global health and the world economy ( Bretscher et al., 2020 ).

Saunders-Hastings and Krewski (2016) reported that there have been several pandemics over the past 100 years. A short but inexhaustible list of outbreaks of communicable diseases include ‘the great plague’ ( Duncan-Jones, 1996 ; Littman and Littman, 1973 ), the Justinian plague ( Wagner et al., 2014 ), the Black Death ( Horrox, 2013 ), the Third Plague pandemic ( Bramanti et al., 2019 ; Tan et al., 2002 ), the Spanish flu ( Gibbs et al., 2001 ; Trilla et al., 2008 ), HIV/AIDS ( De Cock et al., 2012 ), SARS ( Lee and McKibbin, 2004 ), dengue ( Murray et al., 2013 ), and Ebola ( Baseler et al., 2017 ), among others. The potency of each of these outbreaks varies. Consequently, their economic implications differ according to numerous retrospective analyses ( Bloom and Cadarette, 2019 ; Bloom and Canning, 2004 ; Hotez et al., 2014 ). For instance, the Ebola epidemic of 2013-2016 created socio-economic impact to the tune of $53 billion across West Africa, plummeted Sierra Leone's GDP in 2015 by 20% and that of Liberia by 8% between 2013 and 2014, despite the decline in death rates across the same timeframe ( Fernandes, 2020 ).

As the world slipped into the current inflection point, some of the historical lessons from earlier pandemics remain salutary, even if the world we live in now significantly differs from those of earlier period ( McKee and Stuckler, 2020 ). Several factors differentiate the current socio-economic crisis of COVID-19 from the previous ones ( Baker et al., 2020 ), which means direct simple comparisons with past global pandemics are impossible ( Fernandes, 2020 ). Some of the differentiating factors include the fact that COVID-19 is a global pandemic and it is creating knock-on effects across supply chains given that the world has become much more integrated due to globalisation and advancements in technology ( McKenzie, 2020 ). Moreover, the world has witnessed advances in science, medicine and engineering. The modest number of air travellers during past pandemics delayed the global spread of the virus unlike now where global travel has increased tremendously. From an economic impact perspective, interest rates are at record lows and there is a great imbalance between demand and supply of commodities ( Fernandes, 2020 ). More importantly, many of the countries that are hard hit by the current pandemic are not exclusively the usual low-middle income countries, but those at the pinnacle of the pyramid of manufacturing and global supply chains. Against this backdrop, a review of the impact of COVID-19 is presented in the next section.

4. COVID-19: Policy frameworks, global economy, ecosystems and sustainability

4.1. evaluation of policy frameworks to combat covid-19.

The strategies and policies adopted by different countries to cope with COVID-19 have varied over the evolving severity and lifetime of the pandemic during which resources have been limited ( Siow et al., 2020 ). It is instructive that countries accounting for 65% of global manufacturing and exports (i.e. China, USA, Korea, Japan, France, Italy, and UK) were some of the hardest to be hit by COVID-19 ( Baldwin and Evenett, 2020 ). Given the level of unpreparedness and lack of resilience of hospitals, numerous policy emphases have gone into sourcing for healthcare equipment such as personal protective equipment (PPE) and ventilators ( Ranney et al., 2020 ) due to global shortages. For ventilators, in particular, frameworks for rationing them along with bed spaces have had to be developed to optimise their usage ( White and Lo, 2020 ). Other industries have also been affected, with shocks to their existence, productivity and profitability ( Danieli and Olmstead-Rumsey, 2020 ) including the CE-sensitive materials extraction and mining industries that have been hit by disruption to their operations and global prices of commodities ( Laing, 2020 ).

As highlighted in subsequent sub-sections, one of the psychological impacts of COVID-19 is panic buying ( Arafat et al., 2020 ), which happens due to uncertainties at national levels (e.g. for scarce equipment) and at individual levels (e.g. for everyday consumer products). In both instances, the fragility, profiteering and unsustainability of the existing supply chain model have been exposed ( Spash, 2020 ). In fact, Sarkis et al. (2020) questioned whether the global economy could afford to return to the just-in-time (JIT) supply chain framework favoured by the healthcare sector, given its apparent shortcomings in dealing with much needed supplies. The sub-section that follow examines some of the macro and micro economic ramifications of COVID-19.

4.1.1. Macroeconomic impacts: Global productions, exports, and imports

One challenge faced by the healthcare industry is that existing best practices, in countries like the USA (e.g. JIT macroeconomic framework), do not incentivise the stockpiling of essential medical equipment ( Solomon et al., 2020 ). Although vast sums were budgeted, some governments (e.g. UK, India and USA) needed to take extraordinary measures to protect their supply chain to the extent that manufacturers like Ford and Dyson ventured into the ventilator design/production market ( Iyengar et al., 2020 ). The US, in particular activated the Defense Production Act to compel car manufacturers to shift focus on ventilator production ( American Geriatrics Society, 2020 ; Solomon et al., 2020 ) due to the high cost and shortage of this vital equipment. Hospitals and suppliers in the US were also forced to enter the global market due to the chronic shortfall of N95 masks as well as to search for lower priced equipment ( Solomon et al., 2020 ). Interestingly, the global production of these specialist masks is thought to be led by China ( Baldwin and Evenett, 2020 ; Paxton et al., 2020 ) where COVID-19 broke out, with EU's supply primarily from Malaysia and Japan ( Stellinger et al., 2020 ). Such was the level of shortage that the US was accused of ‘pirating’ medical equipment supplies from Asian countries intended for EU countries ( Aubrecht et al., 2020 ).

France and Germany followed suit with similar in-ward looking policy and the EU itself imposed restrictions on the exportation of PPEs, putting many hitherto dependent countries at risk ( Bown, 2020 ). Unsurprisingly, China and the EU saw it fit to reduce or waive import tariffs on raw materials and PPE, respectively ( Stellinger et al., 2020 ). Going forward, the life-threatening consequences of logistics failures and misallocation of vital equipment and products could breathe new life and impetus to technologies like Blockchain, RFID and IoT for increased transparency and traceability ( Sarkis et al., 2020 ). Global cooperation and scenario planning will always be needed to complement these technologies. In this regard, the EU developed a joint procurement framework to reduce competition amongst member states, while in the US, where states had complained that federal might was used to interfere with orders, a ventilator exchange program was developed ( Aubrecht et al., 2020 ). However, even with trade agreements and cooperative frameworks, the global supply chain cannot depend on imports – or donations ( Evenett, 2020 ) for critical healthcare equipment and this realisation opens doors for localisation of production with consequences for improvements in environmental and social sustainability ( Baldwin and Evenett, 2020 ). This can be seen in the case of N95 masks which overnight became in such high demand that airfreights by private and commercial planes were used to deliver them as opposed to traditional container shipping ( Brown, 2020 ).

As detailed in forthcoming sections, a significant reduction in emissions linked to traditional shipping was observed, yet there was an increase in use of airfreighting due to desperation and urgency of demand. Nevertheless, several countries are having to rethink their global value chains ( Fig. 2 ) as a result of realities highlighted by COVID-19 pandemic ( Javorcik, 2020 ). This is primarily because national interests and protectionism have been a by-product of COVID-19 pandemic and also because many eastern European/Mediterranean countries have a relative advantage with respect to Chinese exports. As shown in Fig. 2 , the global export share which each of these countries has, relative to China's share of the same exports (x-axis) is measured against the economies of countries subscribing to the European Bank for Reconstruction and Development (EBRD) (y-axis). For each product, the ideal is to have a large circle towards the top right-hand corner of the chart.

A summary of how some Eastern European / Mediterranean countries have advantages over China on certain exports – based on the Harmonized Commodity Description and Coding System from 2018, where export volume is represented by dot sizes in millions of USD; Source: Javorcik (2020) .

4.1.2. Microeconomic impacts: Consumer behaviour

For long, there has been a mismatch between consumerist tendencies and biophysical realities ( Spash, 2020 ). However, COVID-19 has further exacerbated the need to reflect on the social impacts of individual lifestyles. The behaviour of consumers, in many countries, was at some point alarmist with a lot of panic buying of food and sanitary products ( Sim et al., 2020 ). At private level, consumer sentiment is also changing. Difficult access to goods and services has forced citizens to re-evaluate purchasing patterns and needs, with focus pinned on the most essential items ( Company, 2020 ; Lyche, 2020 ). Spash (2020) argued that technological obsolescence of modern products brought about by rapid innovation and individual consumerism is also likely to affect the linear economy model which sees, for instance, mobile phones having an average life time of four years (two years in the US), assuming their manufacture/repair services are constrained by economic shutdown and lockdowns ( Schluep, 2009 ). On the other hand, a sector like healthcare, which could benefit from mass production and consumerism of vital equipment, is plagued by patenting. Most medical equipment are patented and the issue of a 3D printer's patent infringement in Italy led to calls for ‘Open Source Ventilators’ and ‘Good Samaritan Laws’ to help deal with global health emergencies like COVID-19 ( Pearce, 2020 ). It is plausible that such initiatives/policies could help address the expensive, scarce, high-skill and material-intensive production of critical equipment, via cottage industry production.

For perspective, it should be noted that production capacity of PPE (even for the ubiquitous facemasks) have been shown by COVID-19 to be limited across many countries ( Dargaville et al., 2020 ) with some countries having to ration facemask production and distribution in factories ( San Juan, 2020 ). Unsurprisingly, the homemade facemask industry has not only emerged for the protection of mass populations as reported by Livingston et al. (2020) , it has become critical for addressing shortages ( Rubio-Romero et al., 2020 ) as well as being part of a post-lockdown exit strategy ( Allison et al., 2020 ). A revival of cottage industry production of equipment and basic but essential items like facemasks could change the landscape of global production for decades, probably leading to an attenuation of consumerist tendencies.This pandemic will also impact on R&D going forward, given the high likelihood that recession will cause companies to take short-term views, and cancel long and medium-term R&D in favour of short-term product development and immediate cash flow/profit as was certainly the case for automotive and aerospace sectors in previous recessions.

4.2. Overview of the negative impacts of COVID-19

The negative effects have ranged from a severe contraction of GDP in many countries to multi-dimensional environmental and social issues across the strata of society. In many respects, socio-economic activities came to a halt as: millions were quarantined; borders were shut; schools were closed; car/airline, manufacturing and travel industries crippled; trade fairs/sporting/entertainment events cancelled, and unemployment claims reached millions while the international tourist locations were deserted; and, nationalism and protectionism re-surfaced ( Baker et al., 2020 ; Basilaia and Kvavadze, 2020 ; Devakumar et al., 2020 ; Kraemer et al., 2020 ; Thunstrom et al., 2020 ; Toquero, 2020 ). In the subsections that follow, an overview of some of these negative impacts on the global economy, environment, and society is presented.

4.2.1. Negative macroeconomic impact of COVID-19

Undoubtedly, COVID-19 first and foremost, constitutes a ferocious pandemic and a human tragedy that swept across the globe, resulting in a massive health crisis ( WHO, 2020b ), disproportionate social order ( UN DESA, 2020 ), and colossal economic loss ( IMF, 2020 ). It has created a substantial negative impact on the global economy, for which governments, firms and individuals scramble for adjustments ( Fernandes, 2020 ; Pinner et al., 2020 ; Sarkis et al., 2020 ; Sohrabi et al., 2020 ; Van Bavel et al., 2020 ). Indeed, the COVID-19 pandemic has distorted the world's operating assumptions, revealing the absolute lack of resilience of the dominant economic model to respond to unplanned shocks and crises ( Pinner et al., 2020 ). It has exposed the weakness of over-centralization of the complex global supply and production chains networks and the fragility of global economies, whilst highlighting weak links across industries( Fernandes, 2020 ; Guan et al., 2020 ; Sarkis et al., 2020 ). This has had a direct impact on employment and heightened the risk of food insecurity for millions due to lockdown and border restrictions ( Guerrieri et al., 2020 ). To some extent, some of the interventional measures introduced by governments across the world have resulted in the flattening of the COVID-19 curve (as shown in Fig. 3 ). This has helped in preventing healthcare systems from getting completely overwhelmed ( JHU, 2020 ), although as at the time of writing this paper, new cases are still being reported in different parts of the globe. Fernandes (2020) and McKibbin and Fernando (2020) reported thatthe socio-economic impact of COVID-19 will be felt for many months to come.

Daily confirmed new COVID-19 cases of the current 10 most affected countries based on a 5-day moving average. Valid as of August 31st, 2020 at 11:46 PM EDT ( JHU, 2020 ).

Guan et al. (2020) submitted that how badly and prolonged the recession rattles the world depends on how well and quickly the depth of the socio-economic implications of the pandemic is understood. IMF (2020) reported that in an unprecedented circumstance (except during the Great Depression), all economies including developed, emerging, and even developing will likely experience recession. In its April World Economic Outlook, IMF (2020) reversed its early global economic growth forecast from 3.3% to -3 %, an unusual downgrade of 6.3% within three months. This makes the pandemic a global economic shock like no other since the Great Depression and it has already surpassed the global financial crisis of 2009 as depicted in Fig. 4 . Economies in the advanced countries are expected to contract by -6.1% while recession in emerging and developing economies is projected (with caution) to be less adverse compared to the developed nations with China and India expected to record positive growth by the end of 2020. The cumulative GDP loss over the next year from COVID-19 could be around $9 trillion ( IMF, 2020 ).

Socioeconomic impact of COVID-19 lockdown: (a) Comparison of global economic recession due to COVID-19 and the 2009 global financial crisis; (b) Advanced economies, emerging and developing economies in recession; (c) the major economies in recession; (d) the cumulative economic output loss over 2020 and 2021. Note: Real GDP growth is used for economic growth, as year-on-year for per cent change ( IMF, 2020 ).

With massive job loss and excessive income inequality, global poverty is likely to increase for the first time since 1998 ( Mahler et al., 2020 ). It is estimated that around 49 million people could be pushed into extreme poverty due to COVID-19 with Sub-Sahara Africa projected to be hit hardest. The United Nations’ Department of Economic and Social Affairs concluded that COVID-19 pandemic may also increase exclusion, inequality, discrimination and global unemployment in the medium and long term, if not properly addressed using the most effective policy instruments ( UN DESA, 2020 ). The adoption of detailed universal social protection systems as a form of automatic stabilizers, can play a long-lasting role in mitigating the prevalence of poverty and protecting workers ( UN DESA, 2020 ).

4.2.2. Impact of COVID-19 on global supply chain and international trade

COVID-19 negatively affects the global economy by reshaping supply chains and sectoral activities. Supply chains naturally suffer from fragmentation and geographical dispersion. However, globalisation has rendered them more complex and interdependent, making them vulnerable to disruptions. Based on an analysis by the U.S. Institute for Supply Management, 75% of companies have reported disruptions in their supply chain ( Fernandes, 2020 ), unleashing crisis that emanated from lack of understanding and flexibility of the several layers of their global supply chains and lack of diversification in their sourcing strategies ( McKenzie, 2020 ). These disruptions will impact both exporting countries (i.e. lack of output for their local firms) and importing countries (i.e. unavailability of raw materials) ( Fernandes, 2020 ). Consequently, this will lead to the creation of momentary “manufacturing deserts” in which the output of a country, region or city drops significantly, turning into a restricted zone to source anything other than essentials like food items and drugs ( McKenzie, 2020 ). This is due to the knock-on effect of China's rising dominance and importance in the global supply chain and economy ( McKenzie, 2020 ). As a consequence of COVID-19, the World Trade Organization (WTO) projected a 32% decline in global trade ( Fernandes, 2020 ). For instance, global trade has witnessed a huge downturn due to reduced Chinese imports and the subsequent fall in global economic activities. This is evident because as of 25 th March 2020, global trade fell to over 4% contracting for only the second time since the mid-1980s ( McKenzie, 2020 ). Fig. 5 shows a pictorial representation of impact of pandemics on global supply chains based on different waves and threat levels.

Impact of pandemics on global supply chains. Adapted from Eaton and Connor (2020) .

4.2.3. Impact of COVID-19 on the aviation sector

The transportation sector is the hardest hit sector by COVID-19 due to the large-scale restrictions in mobility and aviation activities ( IEA, 2020 ; Le Quéré et al., 2020 ; Muhammad et al., 2020 ). In the aviation sector, for example, where revenue generation is a function of traffic levels, the sector has experienced flight cancellations and bans, leading to fewer flights and a corresponding immense loss in aeronautical revenues. This is even compounded by the fact that in comparison to other stakeholders in the aviation industry, when traffic demand declines, airports have limited avenues to reducing costs because the cost of maintaining and operating an airport remains the same and airports cannot relocate terminals and runaways or shutdown ( Hockley, 2020 ). Specifically, in terms of passenger footfalls in airports and planes, the Air Transport Bureau (2020) modelled the impact of COVID-19 on scheduled international passenger traffic for the full year 2020 under two scenarios namely Scenario 1 (the first sign of recovery in late May) and Scenario 2 (restart in the third quarter or later). Under Scenario 1, it estimated an overall reduction of: between 39%-56% of airplane seats; 872-1,303 million passengers, corresponding to a loss of gross operating revenues between ~$153 - $ 231 billion. Under Scenario 2, it predicted an overall drop of: between 49%-72% of airplane seats; 1,124 to 1,540 million passengers, with an equivalent loss of gross operating revenues between ~$198 - $ 273 billion. They concluded that the predicted impacts are a function of the duration and size of the pandemic and containment measures, the confidence level of customers for air travel, economic situations, and the pace of economic recovery ( Air Transport Bureau, 2020 ).

The losses incurred by the aviation industry require context and several other comparison-based predictions within the airline industry have also been reported. For instance, the International Civil Aviation Organization ICAO (2020) predicted an overall decline ininternational passengers ranging from 44% to 80% in 2020 compared to 2019. Airports Council International, ACI (2020) also forecasted a loss of two-fifths of passenger traffic and >$76 billion in airport revenues in 2020 in comparison to business as usual. Similarly, the International Air Transport Association IATA (2020) forecasted $113 billion in lost revenue and 48% drop in revenue passenger kilometres (RPKs) for both domestic and international routes ( Hockley, 2020 ). For pandemic scenario comparisons, Fig. 6 shows the impact of past disease outbreaks on aviation. As shown, the impact of COVID‐19 has already outstripped the 2003 SARS outbreak which had resulted in the reduction of annual RPKs by 8% and $6 billion revenues for Asia/Pacific airlines, for example. The 6‐month recovery path of SARS is, therefore, unlikely to be sufficient for the ongoing COVID-19 crisis ( Air Transport Bureau, 2020 ) but gives a backdrop and context for how airlines and their domestic/international markets may be impacted.

Impact of past disease outbreaks on aviation ( Air Transport Bureau, 2020 ).

Notably, these predictions are bad news for the commercial aspects of air travel (and jobs) but from the carbon/greenhouse gas emission and CE perspective, these reductions are enlightening and should force the airline industry to reflect on more environmentally sustainable models. However, the onus is also on the aviation industry to emphasise R&D on solutions that are CE-friendly (e.g. fuel efficiency; better use of catering wastes; end of service recycling of aircraft in sectors such as mass housing, or re-integrating airplane parts into new supply chains) and not merely investigating ways to recoup lost revenue due to COVID-19.

4.2.4. Impact of COVID-19 on the tourism industry

Expectedly, the impact of COVID-19 on aviation has led to a knock-on effect on the tourismindustry, which is nowadays hugely dependent on air travel. For instance, the United Nation World Tourism Organization UNWTO (2020) reported a 22% fall in international tourism receipts of $80 billion in 2020, corresponding to a loss of 67 million international arrivals. Depending on how long the travel restictions and border closures last, current scenario modelling indicated falls between 58% to 78% in the arrival of international tourists, but the outlook remains hugely uncertain. The continuous existence of the travel restrictions could put between 100 to 120 million direct tourism-related jobs at risk. At the moment, COVID-19 has rendered the sector worst in the historical patterns of international tourism since 1950 with a tendency to halt a 10-year period of sustained growth since the last global economic recession ( UNWTO, 2020 ). It has also been projected that a drop of ~60% in international tourists will be experienced this year, reducing tourism's contribution to global GDP, while affecting countries whose economy relies on this sector ( Naidoo and Fisher, 2020 ). Fig. 7 depicts the impact of COVID-19 on tourism in Q1 of 2020 based on % change in international tourists’ arrivals between January and March.

The impact of COVID-19 on tourism in quarter 1of 2020. Provisional data but current as of 31st August 2020 ( UNWTO, 2020 ).

4.2.5. Impact of COVID-19 on sustainable development goals

In 2015, the United Nations adopted 17 Sustainable Development Goals (SDGs) with the view to improve livelihood and the natural world by 2030, making all countries of the world to sign up to it. To succeed, the foundations of the SDGs were premised on two massive assumptions namely globalisation and sustained economic growth. However, COVID-19 has significantly hampered this assumption due to several factors already discussed. Indeed, COVID-19 has brought to the fore the fact that the SDGs as currently designed are not resilient to shocks imposed by pandemics. Prior to COVID-19, progress across the SDGs was slow. Naidoo and Fisher (2020) reported that two-thirds of the 169 targets will not be accomplished by 2030 and some may become counterproductive because they are either under threat due to this pandemic or not in a position to mitigate associated impacts.

4.3. Positive impact of COVID-19

In this section, we discussed some of the positive ramifications of COVID-19. Despite the many detrimental effects, COVID-19 has provoked some natural changes in behaviour and attitudes with positive influences on the planet. Nonetheless, to the extent that the trends discussed below were imposed by the pandemic, they also underscore a growing momentum for transforming business operations and production towards the ideal of the CE.

4.3.1. Improvements in air quality

Due to the COVID-19-induced lockdown, industrial activities have dropped, causing significant reductions in air pollution from exhaust fumes from cars, power plants and other sources of fuel combustion emissions in most cities across the globe, allowing for improved air quality ( Le Quéré et al., 2020 ; Muhammad et al., 2020 ). This is evident from the National Aeronautics and Space Administration ( NASA, 2020a ) and European Space Agency ( ESA, 2020 ) Earth Observatory pollution satellites showing huge reductions in air pollution over China and key cities in Europe as depicted in Fig. 8 . In China, for example, air pollution reduction of between 20-30% was achieved and a 20-year low concentration of airborne particles in India is observed; Rome, Milan, and Madrid experienced a fall of ~45%, with Paris recording a massive reduction of 54% ( NASA, 2020b ). In the same vein, the National Centre for Atmospheric Science, York University, reported that air pollutants induced by NO 2 fell significantly across large cities in the UK. Although Wang et al. (2020) reported that in certain parts of China, severe air pollution events are not avoided through the reduction in anthropogenic activities partially due to the unfavourable meteorological conditions. Nevertheless, these data are consistent with established accounts linking industrialization and urbanization with the negative alteration of the environment ( Rees, 2002 ).

The upper part shows the average nitrogen dioxide (NO 2 ) concentrations from January 1-20, 2020 to February 10-25, 2020, in China. While the lower half shows NO 2 concentrations over Europe from March 13 to April 13, 2020, compared to the March-April averaged concentrations from 2019 ( ESA, 2020 ; NASA, 2020a ).

The scenarios highlighted above reiterates the fact that our current lifestyles and heavy reliance on fossil fuel-based transportation systems have significant consequences on the environment and by extension our wellbeing. It is this pollution that was, over time, responsible for a scourge of respiratory diseases, coronary heart diseases, lung cancer, asthma etc.( Mabahwi et al., 2014 ), rendering plenty people to be more susceptible to the devastating effects of the coronavirus ( Auffhammer et al., 2020 ). Air pollution constitutes a huge environmental threat to health and wellbeing. In the UK for example, between ~28,000 to ~36,000 deaths/year was linked to long-term exposure to air pollutants ( PHE, 2020 ). However, the reduction in air pollution with the corresponding improvements in air quality over the lockdown period has been reported to have saved more lives than already caused by COVID-19 in China ( Auffhammer et al., 2020 ).

4.3.2. Reduction in environmental noise

Alongside this reduction in air pollutants is a massive reduction in environmental noise. Environmental noise, and in particular road traffic noise, has been identified by the European Environment Agency, EEA (2020) to constitute a huge environmental problem affecting the health and well-being of several millions of people across Europe including distortion in sleep pattern, annoyance, and negative impacts on the metabolic and cardiovascular system as well as cognitive impairment in children. About 20% of Europe's population experiences exposure to long-term noise levels that are detrimental to their health. The EEA (2020) submitted that 48000new cases of ischaemic heart disease/year and ~12000 premature deaths are attributed to environmental noise pollution. Additionally, they reported that ~22 million people suffer chronic high annoyance alongside ~6.5 million people who experienceextreme high sleep disturbance. In terms of noise from aircraft, ~12500 schoolchildren were estimated to suffer from reading impairment in school. The impact of noise has long been underestimated, and although more premature deaths are associated with air pollution in comparison to noise, however noise constitutes a bigger impact on indicators of the quality of life and mental health ( EEA, 2020 ).

A recent study on the aftereffect of COVID-19 pandemic on exercise rates across the globe concluded that reduced traffic congestions and by extension reduced noise and pollution has increased the rate at which people exercise as they leveraged the ensued pleasant atmosphere. Average, moderate, and passive (i.e. people who exercised once a week before COVID-19) athletes have seen the frequency of their exercise regime increased by 88%, 38%, and 156% respectively ( Snider-Mcgrath, 2020 ).

4.3.3. Increased cleanliness of beaches

Beaches constitute the interface between land and ocean, offering coastal protection from marine storms and cyclones ( Temmerman et al., 2013 ), and are an integral part of natural capital assets found in coastal areas ( Zambrano-Monserrate et al., 2018 ). They provide services (e.g. tourism, recreation) that are crucial for the survival of coastal communities and possess essential values that must be prevented against overexploitation ( Lucrezi et al., 2016 ; Vousdoukas et al., 2020 ). Questionable use to which most beaches have been subjected have rendered them pollution ridden ( Partelow et al., 2015 ). However, due to COVID-19-induced measures, notable changes in terms of the physical appearance of numerous beaches across the globe have been observed ( Zambrano-Monserrate et al., 2020 ).

4.3.4. Decline in primary energy use

Global energy demand during the first quarter of 2020 fell by ~3.8% compared to the first quarter of 2019, with a significant effect noticeable in March as control efforts heightened in North America and Europe ( IEA, 2020 ). The International Energy Agency (IEA) submitted that if curtailment measures in the form of restricted movement continue for long and economic recoveries are slow across different parts of the globe, as is progressively likely, annual energy demand will plummet by up to 6%, erasing the last five years energy demand growth. As illustrated in Fig. 9 , if IEA's projections become the reality, the world could experience a plunge in global energy use to a level not recorded in the last 70 years. The impact will surpass the effect of the 2008 financial crisis by a factor of more than seven times. On the other hand, if COVID-19 is contained earlier than anticipated and there is an early re-start of the economy at a successful rate, the fall in energy could be constrained to <4% ( IEA, 2020 ). However, a rough re-start of the economy characterised by supply chain disruptions and a second wave of infections in the second half of the year could further impede growth ( IEA, 2020 ).

Annual rate of change in primary energy demand, since 1900, with key events impacting energy demand highlighted ( IEA, 2020 ).

Coal was reported to have been hit the hardest by ~8% in comparison to the first quarter of 2019 due to the impact of COVID-19 in China whose economy is driven by coal, reduced gas costs, continued growth in renewables, and mild weather conditions. Oil demand was also strongly affected, plummeting by ~5% in the first quarter driven mainly by restrictions in mobility and aviation activities which constitute ~60% of global oil demand ( IEA, 2020 ). For instance, global road transport and aviation activities were respectively ~50% and 60% below the 2019 average. Global electricity demand declined by >20% during full lockdown restrictions, with a corresponding spill over effect on the energy mix. Accordingly, the share of renewable energy sources across the energy supply increased due to priority dispatch boosted by larger installed capacity and the fact that their outputs are largely unconstrained by demand ( IEA, 2020 ). However, there was a decline for all other sources of electricity including gas, coal and nuclear power ( IEA, 2020 ).

4.3.5. Record low CO 2 emissions

Unprecedented reduction in global CO 2 emissions is another positive effect that can be attributed to the COVID-19 pandemic.The massive fall in energy demand induced by COVID-19 accounted for the dramatic decline in global GHG emissions. The annual CO 2 emissions have not only been projected to fall at a rate never seen before, but the fall is also envisioned to be the biggest in a single year outstripping the fall experienced from the largest recessions of the past five decades combined ( IEA, 2020 ).The global CO 2 emissions are projected to decline by ~8% (2.6 GCO 2 ) to the levels of the last decade. If achieved, this 8% emissions reduction will result in the most substantial reduction ever recorded as it is expected to be six times larger than the milestone recorded during the 2009 financial crisis, ( Fig. 10 ). Characteristically, after an economic meltdown, the surge in emissions may eclipse the decline, unless intervention options to set the economy into recovery mode is based on cleaner and more resilient energy infrastructure ( IEA, 2020 ).

Global energy-related emissions (top) and annual change (bottom) in GtCO 2 , with projected 2020 levels highlighted in red. Other major events are indicated to provide a sense of scale ( IEA, 2020 ).

4.3.6. Boost in digitalisation

The COVID-19 pandemic has been described as an opportunity to further entrench digital transformation without the ‘digitalism’ which is an extreme and adverse form of connectedness ( Bayram et al., 2020 ). Protecting patients from unnecessary exposure was a driver for telemedicine ( Moazzami et al., 2020 ) and virtual care would become the new reality ( Wosik et al., 2020 ). The necessity for social distancing under lockdown circumstances has also highlighted the importance (and need) for remote working ( Dingel and Neiman, 2020 ; Omary et al., 2020 ), which has had implications for broadband connectivity ( Allan et al., 2020 ) as well as reductions in transportation-related pollution levels ( Spash, 2020 ). The impact of COVID-19 on remote working and digitalisation of work is expected to constitute long-term implications for reduced fossil fuel consumption due to mobility and commuting ( Kanda and Kivimaa, 2020 ). Besides, the survival and thriving of many small business restaurants during the lockdown period depended on whether they had a digital resilience, via online platforms, through which they could exploit the home delivery market via Uber Eats ( Raj et al., 2020 ). For consumers, the pandemic has seen a noticeable increase in online orders for food in many countries such as: Taiwan ( Chang and Meyerhoefer, 2020 ); Malaysia ( Hasanat et al., 2020 ); Germany ( Dannenberg et al., 2020 ) as well as Canada ( Hobbs, 2020 ).

4.4. Unsustainability of current economic and business models amidst COVID-19

It is interesting to observe that while COVID-19 has led to a very steep reduction in air pollution in advanced economies due to reduced economic activity imposed by the lockdown, this pandemic-driven positive impact is only temporary as they do not reflect changes in economic structures of the global economy ( Le Quéré et al., 2020 ). The changes are not due to the right decisions from governments in terms of climate breakdown policies and therefore should not be misconstrued as a climate triumph. More importantly, life in lockdown will not linger on forever as economies will need to rebuild and we can expect a surge in emissions again. To drive home the point, we conducted a decomposition analysis of key drivers (accelerators or retardants) of four global air pollutants using Logarithmic Mean Divisia Index (LMDI) framework ( Ang, 2005 ; Fujii et al., 2013 ), with the results shown in Fig. 11 . The drivers of the pollutants considered based on the production side of an economy include: (i) economic activity effect, given thatemissions can increase or decrease as a result of changes in the activity level of the entire economy; (ii) industrialeconomy structure effect, based on the fact thatthe growth in emissions is a function of the changes in the industrial activity composition; (iii) emissions intensity effect, which can be improvements or deteriorations at the sectoral level, depending on theenergy efficiency (e.g. cleaner production processes) of the sector; (iv) fuel mix or fuel dependency effect, given that its composition influences the amount of emissions; and (v) emission factors effect, because these factors, for different fuel types, changes over time due toswitching from fossil fuels to renewables, for example.

Drivers of representative four (4) global pollutants: a) CO 2 emissions; b) NO x emissions; c) SO x emissions; d) CO emissions. All data for the decomposition analysis of the four pollutants were obtained from the WIOD database ( Timmer et al., 2012 ).

As shown in Fig. 11 a, for example, between 1995 and 2009, global change in CO 2 emission was 32%, where economic activity (+48%) and emission factor (+2%) acted as accelerators, while economic structure (-8%), emission intensity (-9%) and fuel mix (-1%) acted as retardants, of the global CO 2 emission dynamics and trajectory.This implies that although economic activities, as expected, alongside emission factor drove up emissions, however, the upward effect of both drivers was offset by the combined improvements of other driving factors namely economic structure, emission intensity, and fuel mix. Indeed, cutting back on flying or driving less as we have experienced due to COVID-19 contributed to ~8% in emission reduction, however, zero-emissions cannot be attained based on these acts alone. Simply put, emissions reduction cannot be sustained until an optimal balance across the aforementioned drivers informed by structural changes in the economy is attained. As Gates (2020a) rightly stated – the world should be using more energy, not less, provided it is clean.

Characteristically, after an economic meltdown, like the global recession in 2008, there is a surge in emissions ( Feng et al., 2015 ; Koh et al., 2016 ). The current social trauma of lockdown and associated behavioural changes tends to modify the future trajectory unpredictably. However, social responses would not drive the profound and sustained reduction required to attain a low-carbon economy ( Le Quéré et al., 2020 ). This is evident given that we live on a planet interlinked by networked product supply chains, multidimensional production technologies, and non-linear consumption patterns ( Acquaye et al., 2017 ; Ibn-Mohammed et al., 2018 ; Koh et al., 2016 ). Additionally, post COVID-19, the society may suffer from green bounce back– there appears to be an increasing awareness of climate change and air pollution because of this pandemic (though the linkages are non-causal). On the one hand this might promote greener choices on behalf of consumers, but on the other it may result in increased car ownership (at the expense of mass transit), driving up emissions. As such, establishing approaches that ensure an optimal balance between quality of life and the environmental burden the planet can bear is pertinent, if the boundaries of environmental sustainability informed by the principles of low-carbon CE are to be extended. In the next section, the role of the CE as a potential strategy for combating pandemics such as COVID-19 is discussed.

5. The role of circular economy

For long, the central idea of the industrial economy rests on the traditional linear economic system of taking resources, making products from them, and disposing of the product at the end of life. Experts referred to this as “extract-produce-use-dump”, “take-make-waste”, or “take-make-dispose” energy flow model of industrial practice ( Geissdoerfer et al., 2017 ; Kirchherr et al., 2017 ; MacArthur, 2013 ). However, the unlimited use of natural resources with no concern for sustainability jeopardizes the elastic limit of the planet's resource supply. For instance, Girling (2011) submitted that ~90% of the raw materials used in manufacturing become waste before the final product leaves the production plant while ~80% of products manufactured are disposed of within the first 6 months of their life. Similarly, Hoornweg and Bhada-Tata (2012) reported that ~1.3 billion tonnes of solid waste with a corresponding cost implication of $205.4 billion/year is generated by cities across the globe and that such waste might grow to ~2.2 billion tonnes by 2025, with a corresponding rate of $375.5 billion. This is further compounded by the fact that at the global level, the demand for resources is forecasted to double by 2050 ( Ekins et al., 2016 ).

Against this backdrop, the search for an industrial economic model that satisfies the multiple roles of decoupling of economic growth from resource consumption, waste management and wealth creation, has heightened interests in concepts about circular economy ( Ekins et al., 2016 ; MacArthur, 2013 ).In theory, CE framework hinges on three principles: designing out waste, keeping products and materials in use and regenerating the natural systems ( MacArthur, 2013 ). Practically, CE is aimed at: (i) emphasizing environmentally-conscious manufacturing and product recovery ( Gungor and Gupta, 1999 ); (ii) promoting the avoidance of unintended ecological degradation in symbiotic cooperation between corporations, consumers and government ( Bauwens et al., 2020 ); and (iii) shifting the focus to a holistic product value chain and cradle-to-cradle life cycle via promotion of product repair/re-use and waste management ( Duflou et al., 2012 ; Lieder and Rashid, 2016 ; Rashid et al., 2013 ).

Given the current COVID-19 pandemic, there has never been a more adequate time to consider how the principles of CE could be translated into reality when the global economy begins to recover. This is pertinent because the pandemic has further exposed the limitations of the current dominant linear economy regarding how it is failing the planet and its inhabitants, and has revealed the global ecosystem's exposure to many risks including climate breakdown, supply chain vulnerabilities and fragility, social inequality and inherent brittleness ( Bachman, 2020 ; Sarkis et al., 2020 ). The pandemic continues to amplify the global interlinkages of humankind and the interdependencies that link our natural environment, economic, and social systems ( Haigh and Bäunker, 2020 ). In the subsections that follow, the potentials of CE as a tool for: (i) climate change mitigation; (ii) crafting a more resilient economy, and ; (iii) facilitating a socially just and inclusive society, is briefly discussed.

5.1. Circular economy as a tool for climate breakdown mitigation

As highlighted in Section 4.3.5 , a CO 2 emission reduction of 8%, which in real terms implies an equivalent of ~172 billion tCO 2 will be released instead of ~187 billion tCO 2 , is indeed unprecedented. Nevertheless, the peculiar conclusion from the lockdown is that it still entails emissions of 92% of the initial value while there was restrictions to mobility and other related leisure activities. Measures for mitigating climate change have often been presented dramatically as a "prohibition of the nice things of life", but as shown, a cut-off of such an amount of nice things only delivers an 8% reduction. More importantly, it comes at a heavy cost of between $3,200/tCO 2 and $5,400/tCO 2 in the US, for example, based on data from the Rhodium Group ( Gates, 2020a ). In other words, the shutdown is reducing emissions at a cost between 32 and 54 times the $100/tCO 2 deemed a reasonable carbon price by economists ( Gates, 2020a ). This suggests that a completely different approach to tackling climate issue is required.

Accordingly, there is the need for a system that calls for greater adoption of a more resilient low-carbon CE model, given the predictions by experts that climate breakdown and not COVID-19 will constitute the biggest threat to global health ( Hussey and Arku, 2020 ; Watts et al., 2018a ; Watts et al., 2018b ). International bodies and country-level environmental policies have highlighted the fact that a significant reduction in GHG emissions cannot be achieved by transitioning to renewables alone but with augmentation with CE strategies. The demands side CE strategies such as (i)material recirculation (more high-value recycling, less primary material production, lower emissions per tonne of material); (ii)product material efficiency (improved production process, reuse of components and designing products with fewer materials); (iii)circular business models (higher utilisation and longer lifetime of products through design for durability and disassembly, utilisation of long-lasting materials, improved maintenance and remanufacturing), could reduce emissions whilst contributing to climate change mitigation ( Enkvist et al., 2018 ). CE principles, when adopted in a holistic manner provide credible solutions to the majority of the structural weaknesses exposed by COVID-19, offering considerable opportunities in competitiveness and long-term reduced GHG emissions across value chains. Investments in climate-resilient infrastructure and the move towards circular and low-carbon economy future can play the dual role of job creation while enhancing environmental and economic benefits.

5.2. Circular economy as a vehicle for crafting more resilient economies

Haigh and Bäunker (2020) reported that if we muddle through every new crisis based on the current economic model, using short-term solutions to mitigate the impact, future shocks will continue to surpass capacities. It is, therefore, necessary to devise long-term risk-mitigation and sustainable fiscal thinking with the view to shift away from the current focus on profits and disproportionate economic growth. Resilience in the context of the CE largely pertains to having optimized cycles (i.e. products are designed for longevity and optimized for a cycle of disassembly and reuse that renders them easier to handle and transform). Some cycles can be better by being closed locally (e.g. many food items), and for other cycles, a global value chain could be a better option (e.g. rare earth elements). Due to globalization, all cycles have become organized at the global level, diminishing resilience. COVID-19 has further shown how some particular cycles had the wrong scale level, as such, the adoption of CE can be seen as an invitation to reconsider the optimal capacity of cycles.

Sustainability through resilience thinking would have a positive and lasting impact as reported by the Stockholm Resilience Centre (2016) , which concluded that prosperity and sustainability cannot be accomplished without building “ resilient systems that promote radical innovation in economic policy, corporate strategy, and in social systems and public governance”. It calls for sustainability through resilience thinking to become an overarching policy driver and encourages the application of the principles of such thinking to enhance social innovation. Haigh and Bäunker (2020) concluded that when resilience thinking is employed as a guide, all innovations emanating from circular thinking would extend beyond focusing mainly on boosting the market and competitiveness and recognise the general well-being of the populace as an equal goal. As the global economy recovers from COVID-19, it has become more apparent that there is a strong sense of interconnectedness between environmental, economic and social sustainability ( Bauwens et al., 2020 ).

5.3. Circular economy as a facilitator of a socially just and inclusive society

Advanced economies have mainly focused on maintaining the purchasing power of households through the establishment of the furlough scheme (in the UK, for example). Most developing countries have also adopted a similar approach through the integration of containment measures with a huge increase in social protection spending. However, these intervention strategies in response to the pandemic have further revealed the social injustice and inequality between countries and communities given that the deployment of such strategy in advanced economies could devastate developing countries and communities ( Ahmed et al., 2020 ; Haigh and Bäunker, 2020 ). Guan and Hallegatte (2020) revealed that developing and underdeveloped economies face tougher and more challenging situation in comparison to their developed counterparts, because even under the assumption that social protection systems could fully replace income and shield businesses from bankruptcy, maintaining access to essential commodities is impossible if the country is lacking in production capabilities in the first place. Furthermore, in the underdeveloped world, the idea of working from home is very difficult due to the lack of infrastructure and access to health facilities is severely cumbersome. As such, short-term fixes adopted by governments cannot adequately address deep-rooted inequality and social injustice.

Accordingly, Preston et al. (2019) submitted that CE has the potential to minimise prevailing pressures and struggles regarding conflicts due to imbalanced distribution of resources, through participatory forms of governance that entails the inclusion of local stakeholders in resource management initiatives. This can be achieved through the adoption of CE strategy such as closed-loop value chains, where wastes are transformed into resources with the view to not only reduce pollution but to simultaneously aid the pursuance of social inclusion objectives. A number of companies are already embracing this idea. For instance, under the Food Forward SA initiative, “ the world of excess is connected with the world of need ” through the recovery of edible surplus food from the consumer goods supply chain and gets redistributed to the local community. This ensures loops are closed and the needy receive nourishment ( Haigh and Bäunker, 2020 ). With sufficient investment in the CE, developing countries can leapfrog their developed counterparts in digital and materials innovation to integrate sustainable production and consumption and low-carbon developments at the core of their economies. Additionally, Stahel (2016) reported that another benefit of the CE as a facilitator of a socially just and inclusive society is that it is likely to be more labour-intensive due to the variety of end-of-life products and the high cost of automating their processing compared to manual work. As such, CE can enable the creation of local jobs and “reindustrialisation of regions” ( Stahel, 2019 ) through the substitution of: manpower for energy, materials for (local) labour, and local workshops for centralised factories ( Stahel, 2019 ), while boosting the repair economy and local micro industries. Of course, not everybody will see this as a benefit, and many would like to see more automation, not less. However, this is a political/economic argument, not an engineering or scientific one. In the next section, barriers to CE in general and in the context of COVID-19 is discussed.

5.4. Barriers to CE in the context of COVID-19

On the surface, the benefits of CE should be obvious as it strives for three wins in the three dimensions of social, economic and environment impacts through a symbiotic vision of reduced material usage, reduced waste generation, extending value retention in products and designing products for durability. However, limiting barriers obviating the success of CE have existed around technical implementation, behavioural change, financial and intellectual investments, policy and regulations, market dynamics, socio-cultural considerations as well as operational cost of transforming from the linear economy to one based on circularity ( Friant et al., 2020 ). In more concrete terms, the barriers dwell within the ecosystem of actors (and the interactions within the actors) involved in the move towards CE ( Lieder and Rashid, 2016 ).

Pre-COVID-19, Korhonen et al. (2018) enumerated six fundamental factors hindering the promise of CE: (i) thermodynamic factors (i.e. limit imposed by material and energy combustion in recycling/re-manufacturing); (ii) complexity of spatial and temporal boundaries (i.e. material and energy footprints for a product cannot be easily reduced to a point in space and time for an in-depth analysis of environmental impacts); (iii) interlink of governance and nation's economy; (iv) consumer and organizational inertia (i.e. reluctance to embrace new way of doing things due to uncertainty about the success of business models as well as fuzziness around organizational culture and management models that rely on CE); (v) fragile industrial ecosystems (featuring the difficulty of establishing and managing intra-/inter-organizational collaboration along with local/regional authorities); and (vi) lack of consensus on what the many Rs (re-use, recycle, recover, repurpose, repair, refurbish, remanufacture) embedded in CE framework really means ( Kirchherr et al., 2017 ). Challenges in data sharing between product end points and stakeholders, complexity in the supply chain with unclear details of product biography over time, and prohibitive start-up investment costs have also been identified as CE barrier in other climes ( Jaeger and Upadhyay, 2020 ; Manninen et al., 2018 ). Other issues along similar lines were captured in the work by several other authors including Galvão et al. (2020) , Kirchherr et al. (2018) , Govindan and Hasanagic (2018) , De Jesus and Mendonça (2018) and many more.

The paradox of COVID-19 is grounded on creating a once in a lifetime opportunity to re-examine the difficulty of some of these barriers, but it also unveiled a new set of challenges. For instance, the sharing economy models that have been hitherto hailed as exemplars of CE strategy is now perceived differently by many urban dwellers because of the behavioural change embedded in “social distancing”, which is necessary to limit the spread of the virus. Although if concepts such as “access over ownership” or “pay for performance” service have become fully operational, they could have constituted a significant solution to offer flexibility. Additionally, it has been argued that COVID-19 will ‘disrupt some disruptors’ peer-to-peer (P2P) providers such as Airbnb, which has reported a 4.16% drop in local bookings for every doubling new COVID-19 cases ( Hu and Lee, 2020 ). In transportation, demand from ride-sharing modes could increase due to commuters wanting to minimise exposure to COVID-19 in mass transport systems like buses and trains ( Chandra, 2020 ). However, the risks of human-to-human transmission of COVID-19 for passengers not wearing facemask have been noted ( Liu and Zhang, 2020 ), including when either passengers or drivers in ride-hailing and car-sharing disruptors like Uber do not wear facemasks ( Wong et al., 2020 ).

Reducing emissions, in the long run, requires large investments, from both the public and private sectors, in low-carbon technologies and infrastructure in terms of both innovation and diffusion ( OECD, 2018 ). Given the downturn of the global economy due to COVID-19, the prospects of significant low-carbon investments from the private sector have significantly reduced compared to pre-COVID-19. This view is not just limited to the private sector, but also to the public sector, as echoed by Naidoo and Fisher (2020) . Hence, post COVID-19, accelerating progress towards CE still requires: (i) a decisive legal and financial championships from local, regional and national authorities; (ii) innovation across multiple domains (product design, production technologies, business models, financing and consumer behaviours); (iii) governments to promote green logistics and waste management regulations with reasonable incentives to aid producers and manufacturers in minimizing loss while maximizing value. It is therefore recommended that governments provide the much-needed policy framework that will eliminate some of aforementioned barriers to facilitate the urgent transition to CE. Doing this will build resilience for community response to future pandemic and it also aligns with some of the existing roadmaps for resource efficiency ( European Commission, 2011 ).

6. Opportunities for circular economy post COVID-19

COVID-19 has instigated a focus on vibrant local manufacturing as an enabler of resilient economy and job creation; fostered behavioural change in consumers; triggered the need for diversification and circularity of supply chains, and evinced the power of public policy for tackling urgent socio-economic crises. As we rise to the challenges imposed by COVID-19, the question is no longer should we build back better, but how. Consequently, going forward, crafting a roadmap for a sustainable future is as much about the governmental will to forge a new path to socio-economic growth as it is about local businesses joining forces with the consumers to enable the transition to CE. As already documented in the earlier sections of this paper, governments around the world have deployed many financial policy instruments to combat the short-term consequences of COVID-19 pandemic. Still, in the long-term, the adoption of circular economy principles across various technological frontiers holds the promise to bring about a desired technical and behavioural change that will benefit many nations around the world.

Specifically, adopting the CE principle will alleviate some of the detrimental effects of COVID-19 pandemic in the future. To mention just a few: (i) a national level adoption of CE will reduce the over-reliance on one country as the manufacturing hub of the world; (ii) a systematic shift away from the traditional polluting, energy-intensive, manufacturing-driven economy to a CE, based on renewable energy, smart materials, smart re-manufacturing, and digital technology will strengthen the fight against pollution; and (iii) the transition to CE will also spur local job creation along several of the axes of societal needs (e.g. built environment, mobility, health, consumables, etc.). Accordingly, in the subsections that follow, an overview of recommendations as well as policy measures, incentives, and regulatory support for advancing sector-specific CE strategies in a post-COVID-19 world is presented.

6.1. Local manufacturing and re-manufacturing of essential medical accessories

Disruptions due to COVID-19 has been attributed to unprecedented demand, panic buying, and intentional hoarding of essential medical goods for profit ( Bradsher and Alderman, 2020 ; Fischer et al., 2020 ). The shortage of many items was so dire in many countries that the principle of CE, such as re-use, is already been unwittingly recommended ( Gondi et al., 2020 ), by respectable bodies such as the US Centres for Disease Control and Prevention (CDC) ( Ranney et al., 2020 ). However, designed and produced from non-CE compliant processes, medical accessories such as PPE cannot be easily refurbished for re-use without leading to severe degradation in their efficiencies, as noticed for example, in the case of particulate respirators ( Liao et al., 2020 ). Accordingly, it is recommended that companies strive to establish competencies in eco-design and environmentally beneficial innovation to facilitate product re-use in the long run. Some of the desired competencies centre on design strategies for closing resource loops (e.g. designing for technological and biological cycles) as pioneered by McDonough and Braungart (2010) .

A detailed discussion of these competencies is also enunciated by Braungart et al. (2007) , where the authors differentiated between eco-efficiency (less desirable) and eco-effectiveness (the desired dream of CE), for companies to be compliant with the CE framework. Meanwhile, a starting point for companies to shift to eco-effectiveness at the product design level, which will facilitate product re-use, is to follow the five-step framework enumerated by Braungart et al. (2007) or to adopt the analytical framework to explore some of the key dimensions in eco-design innovations developed by Carrillo-Hermosilla et al. (2010) . During implementation, the preceding steps comport with the idea of eco-factories that take pride in design for effortless end-of-life product re-use and design for “upcycling” and remanufacturing ( Bocken et al., 2016 ; Herrmann et al., 2014 ; Ijomah, 2010 ), all of which falls under the umbrella of CE.

Another emerging evidence in favour of CE, also adopted inadvertently during this pandemic, is the ease with which several manufacturers have pivoted their factory floors to make different products in response to the shortage of medical accessories. Few examples of these companies in the UK include, but not limited to: AE Aerospace, which retooled its factory floor to produce milled parts for ventilators; Alloy Wire International re-purposed its machinery to make springs for ventilators; AMTICO (flooring manufacture) re-configured its production lines to make visors for front line workers; BAE Systems deployed its factory resources to produce and distribute over 40000 face shields; and BARBOUR (a clothing company) re-purposed to produce PPE for nurses ( Williamson, 2020 ).

6.2. CE strategies for managing hospital medical and general waste

Wastes generated by the healthcare industry (HCI) normally arouse concerns about operational, public, and environmental safety as a result of the awareness of the corrosive, hazardous, infectious, reactive, possibly radioactive, and toxic nature of the wastes’ composition ( Lee et al., 1991 ; Prüss-Üstün et al., 1999 ). Consequently, the management of the different categories of healthcare waste far removed from the traditional municipal wastes, falls under stringent national or local regulatory frameworks. Pre-COVID-19, the staggering scale of HCI waste is reported to reach into millions of tonnes per year and there have been many studies of national-level attempts at managing these wastes ( Da Silva et al., 2005 ; Insa et al., 2010 ; Lee et al., 1991 ; Oweis et al., 2005 ; Tudor et al., 2005 ). However, this problem is expected to worsen with the tremendous surge, in the last few months, in the volume of disposable medical hardware (PPE, masks, gloves, disposable gears worn by healthcare workers and sanitation workers as well as those contaminated by contacts with COVID-19 patients). Another allied problem is the troubling shift among consumers who now prioritize concerns for hygiene by leaning towards plastic packaging (e.g. in food delivery and grocery shopping) during this pandemic at the expense of environmental impacts ( Prata et al., 2020 ). Most of these products are derived from non-biodegradable plastics, and their disposal has not been given much thought. As a result, the management of these wastes has raised understandable angst in several quarters ( Klemeš et al., 2020 ; Xiao and Torok, 2020 ). Frustratingly, there is much less that can be done at the moment apart from devising judicious waste management policy for these potentially hazardous wastes.

The traditional steps concerning the treatment of HCI wastes (such as collection and separation, storage, transportation to landfill, and decontamination/disposal) suffer from many complications that make the management a challenging undertaking ( Windfeld and Brooks, 2015 ). To alleviate the complexity, the characterization of the physicochemical composition of HCI waste has become an important tool in devising crucial steps for setting up waste minimization and recycling programs ( Kaiser et al., 2001 ). This aligns with the objective of circular economy (CE), which prioritizes the prevention of waste, failing which it proposes the re-use/recyclability of materials from waste to close the loop.

Wong et al. (1994) reported that hospital wastes involve different types of materials: plastics (tubes, gloves, syringes, blood bags), metals (basins, aluminium cans), papers (towel papers, toilet papers, newspapers), cotton/textiles (drapes, table covers, diapers, pads, bandages), glass (bottles) etc. With this categorization in mind, a CE product design consideration that looks promising in the near future, as a way to avert some of the dangers that can be triggered by events such as COVID-19, is to increase the volume of recyclable materials and biodegradable bioplastics in the production of medical accessories. However, the reality is that not all medical gears and products can be derived from bio-plastics or recyclable materials, and some will inevitably continue to be fabricated with materials that need further downstream processing. Yet, the application of CE to the healthcare industry (HCI) remains a touchy subject. Understandably, health and safety concerns, as well as requirements to meet stringent regulations, tend to override the environmental gain from the 4R practice promoted by CE ( Kane et al., 2018 ). Nonetheless, the benefits of CE are starting to catch on in the HCI as a means of optimizing hospital supply chains and reduce overhead cost, all the while creating environmental benefits in the course of saving human lives.

Principally, the applications of CE in HCI, like in other fields, are tied to materials flow and an examination of the nature of wastes. Pioneering studies on hospital wastes characterizations ( Diaz et al., 2008 ; Eleyan et al., 2013 ; Özkan, 2013 ; Wong et al., 1994 ), revealed that close to 80% of the wastes can be classified as general wastes, while the remaining 20% falls under the infectious waste category ( WHO, 1998 ). A prevalent method of dealing with the two HCI waste categories has been incineration ( Wong et al., 1994 ). Although suitable for large volumes, incineration produces toxic pollutants such as heavy metals, dioxins, acid gases, and hydrogen chloride ( Yang et al., 2009 ). Consequently, pre-COVID-19, besides incineration, reducing or preventing the volume of wastes in both categories is also shaped by the adoption of green purchasing practices ( Wormer et al., 2013 ). While this may help in the short term, a holistic approach to confronting this problem is the adoption of CE, which can facilitate the shift towards eco-efficient HCI, starting with lifecycle evaluations of medical products to the proposal for re-usable medical instruments ( Cimprich et al., 2019 ; De Soete et al., 2017 ; Penn et al., 2012 ). Numerous CE strategies for healthcare waste management are detailed by Kane et al. (2018) and Voudrias (2018) . Undoubtedly, with COVID-19, there is an uptick in the percentage of waste under the infectious category due to hospitals taking various precautions to facilitate control of the pandemic ( Peng et al., 2020 ). Nevertheless, by subjecting the general waste category to proper sterilization procedure via any of thermal, microwave, bio-chemical sterilization, the huge potential from upcycling of the retrieved materials will edge towards fulfilling the promise of CE within the sector ( Yang et al., 2009 ).

6.3. Embracing resource efficiency in the construction and built environment

As with other economic sectors, COVID-19 has exposed the shortcomings of the built and natural environment's business-as-usual practices, highlighting the prevalence of poor-quality buildings, issues regarding affordability of decent housing and rigidity of the current building stocks ( EMF, 2020b ). Living in poor-quality houses and in small constricted energy inefficient homes, led to the in-house transmission of the virus in some cases ( Clair, 2020 ). This is particularly the case in poorer countries where inadequate access to sanitation amenities has prevented people from adopting best practices necessary for halting the transmission ( Andrew et al., 2020 ). These issues alongside the growing concern and awareness regarding the resource-wasting nature of the sector, present a strong case for rethinking it. The CE is well positioned to offer potential solutions to these problems.

CE can help balance behavioural challenges and opportunities from occupancy requirements. Humans spend up to 90% of their time indoors ( Marques et al., 2018 ; Pitarma et al., 2017 ). The pandemic has led to people spending more time at ‘home’ than at work, leading to massively underutilised office and business spaces, which is likely to increase due to on-going social distancing constraints ( Feber et al., 2020 ) or perhaps due to more organisation discovering the cost benefits of remote working. It is also plausible that upgrading of existing (or design of new) office and commercial spaces would require making them flexible and adaptable to cope with changing needs (e.g. occupant density, social distancing, ventilation, etc.) by using movable walls ( Carra and Magdani, 2017 ). Insufficient ventilation can increase the risk of infection to healthcare workers and susceptible patients in healthcare buildings, especially makeshift hospitals ( Chen and Zhao, 2020 ). The impact of these engineering measures on energy consumption of typical buildings and healthcare facilities needs to be considered because of social distancing measures, which may require a decrease in occupant density but an increase in ventilation rates. So, although energy recovery is high on the agenda for CE in the built environment ( Eberhardt et al., 2019 ), the additional requirement of more mechanical ventilation for less people will stretch the energy consumed by buildings. Some researchers have argued for buildings to avoid recirculation (essential for energy savings) and use 100% fresh outdoor air for mechanical ventilation systems ( Pinheiro and Luís, 2020 ). Such scenarios are likely to increase the adoption of renewable energy sources to support acceptable indoor air quality (IAQ).

The adoption of CE strategies such as material reuse and development of recycling infrastructure can facilitate value circulation and efficient use of resources within the built and natural environment, ensuring a more competitive and cost-effective post-COVID-19 recovery, while contributing to GHG emissions reduction and creating job opportunities ( EMF, 2020b ). For instance, a study by ARUP estimated that designing for steel reuse has the potential of generating savings of 6-27% and 9-43% for a warehouse and an office respectively, whilst constituting up to 25% savings on material costs ( SYSTEMIQ, 2017 ). The EU is leading in policy direction that would make it a legal requirement to introduce recycled content (i.e. material looping) in specific construction products, after the functionality and safety have been vetted ( European Commission, 2020 ). Such initiatives will encourage designers and researchers to incorporate material looping into their overall design strategy across the value chain to ensure they are fit for circulation ( Deloitte, 2020 ). This material looping has been shown to reduce disposal fees and generate new income streams from the secondary materials market ( Rios et al., 2015 ). It is an approach that would help reduce construction waste, which accounts for a third of all solid wastes in countries like India ( EMF, 2016 ). The adoption of digital material passports that supports end-to-end tracking of building materials has been reported by SYSTEMIQ (2017) to aid the identification of materials for reuse as they approach their end of first life, thereby allowing the longevity and encouraging tighter material looping.

COVID-19 in the context of CE will encourage prefabrication, design thinking and renovation. As the building industry moves towards the industrialisation of construction via prefabrication/offsite production, seven strategies have been suggested by Minunno et al. (2018) out of which the principle of designing for eventual disassembly and reuse is critical. With a combined smart and industrialised prefabrication (SAIP) process ( Abbas Elmualim et al., 2018 ), the intelligent performance and circularity of buildings can be boosted by advanced smart technologies ( Windapo and Moghayedi, 2020 ). The building of 1,000 bed Huoshenshan Hospital in Wuhan covering 34,000m 2 in ten days using modular pre-fabricated components, which can be disassembled and reused ( Zhou et al., 2020 ) has demonstrated the capability of the construction industry to deliver adaptable buildings in record time. But it is perhaps in the sphere of refurbishment and renovation that CE in the built environment would mostly be felt. A CE strategy that promotes repair and refurbishment is preferable to one which encourages recycling, since the economic and environmental value of a product is retained better by the former ( Sauerwein et al., 2019 ).

Renovation helps achieve carbon reduction targets while contributing to economic stimulation ( Ibn-Mohammed et al., 2013 ) . Retrofitting, refurbishing or repairing existing buildings leads to lower emission facilities, is less resource-intensive and more cost-effective than demolition or new construction ( Ardente et al., 2011 ; Ibn-Mohammed et al., 2014 ). Nevertheless, circular renovation of buildings must align with circular design thinking – as alluded to above, in terms of re-integrating materials back into the value chain – as well as the need to enhance material/product durability and energy efficiency ( Pomponi and Moncaster, 2017 ). In Europe, renovation of buildings decreases the residential sector's GHG emissions by 63%, with a reduction of up to 73% in the non-residential sector ( Artola et al., 2016 ). In meeting the emerging needs of the renovation sub-sector, digital infrastructure technologies (such as thermographic and infrared surveys, photogrammetry and 3D laser scanning, as well as BIM and Digital Twinning) will play a crucial role in ensuring the low carbon and energy-efficient future of the built environment ( ARUP, 2020 ).

6.4. Bio-cycle economy and the food sector

COVID-19 or not, the food sector is generally wasteful ( Dilkes-Hoffman et al., 2018 ), contributes to environmental degradation ( Beretta and Hellweg, 2019 ), disrupts nutrient flows due to the current linear nature of its value chain, thereby diminishing the nutritional quality of food ( Castañé and Antón, 2017 ). To address these issues, as part of a future resilience in the food sector, a number of CE levers applicable to the sector is highlighted: (i) closing nutrient loops through the adoption of regenerative agriculture ( Rhodes, 2017 ). The organic content of soil reflects its healthiness and propensity to produce nutritious crops. The adoption of regenerative agriculture can facilitate the preservation of soil health through returning organic matter to the soil in the form of food waste or composted by-products or digestates from treatment plants ( Sherwood and Uphoff, 2000 ); (ii) value recovery from organic nutrients through the adoption of anaerobic digestion facilities ( De Gioannis et al., 2017 ; Huang et al., 2017 ), which is related to controlled biogas production for onward injection into natural gas network or conversion to electrical energy ( Atelge et al., 2020 ; Monlau et al., 2015 ). This has the potential to transform ensuing methane from food waste into carbon-neutral energy; and (iii) the embrace of urban and peri-urban agriculture ( Ayambire et al., 2019 ; Lwasa et al., 2014 ; Opitz et al., 2016 ; Thebo et al., 2014 ), which entails the “ cultivation of crops and rearing of animals for food and other uses within and surrounding the boundaries of cities, including fisheries and forestry ”( EPRS, 2014 ). Indeed, by cultivating food in proximity to where it will be consumed, carbon footprint can be mitigated in numerous ways. For instance, through the adoption of urban agriculture, Lee et al. (2015) demonstrated GHG reduction of 11,668 t yr −1 in the transportation sector. The popularity of local farms has severely increased as a direct consequence of COVID-19, whereby people could experience the power of local food cycles and avoid perceived contamination risks in supermarkets. This will further bolster urban and peri-urban agriculture.

All the above-mentioned CE strategies will contribute towards the establishment of a better and more resilient future food system. However, in the context of COVID-19, transitioning to regenerative agricultural production processes and expanding food collection, redistribution and volarisation facilities constitute an integral part of a more resilient and healthy food system that allows greater food security and less wastage, post COVID-19 ( EMF, 2020a ). Investments towards accelerating regenerative agriculture offer economic benefits facilitated by reforms in food, land, and ocean use ( World Economic Forum, 2020 ). It also offer environmental benefits by supporting biologically active ecosystems ( EMF, 2020a ) and through numerous farming mechanisms including no-till farming, adoption of cover crops, crop rotations and diversification ( Ranganatha et al., 2020 ) as well as managed grazing for regenerative livestock rearing ( Fast Company, 2019 ). Similarly, expanding food collection, redistribution and volarisation facilities offers both economic and environmental benefits for the food system ( EMF, 2020a ). However, realising these benefits will require investment in: (i) physical infrastructure like cold chains that support the storage, processing, and supply of edible food, especially in low-income countries, and (ii) processing infrastructure for the collection and volarisation of waste food ( EMF, 2020a ). This will facilitate door-to-door waste food collection, offering avenues for municipal organic waste volarisation.

6.5. Opportunities for CE in the transport and mobility sector

Facilitating the movement of people, products and materials, transportation infrastructures are imperative to the success of circularity in the shift towards sustainable cities given its impact on the quality of life, the local environment and resource consumption ( Van Buren et al., 2016 ). As noted in an earlier section, the transport sector was one of the sectors most heavily impacted by COVID-19. Going forward, many CE strategies could be adopted as part of building a resilient transport sector. Development of compact city for effective mobility given their attributes in terms of being dense with mixed-use neighbourhoods and transit-oriented ( EMF, 2019 ), can create an enabling environment for both shared mobility options (e.g. trams, buses, ride-shares) and active mobility options (e.g. bicycling, walking) ( Chi et al., 2020 ; Shaheen and Cohen, 2020 ). This will help to re-organize urban fabric and promote intelligent use of transportation infrastructures ( Marcucci et al., 2017 ). However, the behavioural change embedded in “social distancing”, which is necessary to limit the contagion, may affect the perception of many urban dwellers about this. On the other hand, less compact cities require increased mobility infrastructure with a corresponding increase in operational vehicle use, leading to more traffic congestion, energy and resource depletion as well as pollution ( UN Habitat, 2013 ).

The use of urban freight strategies for effective reverse logistics and resource flows is also a viable CE strategy for the transport sector ( EMF, 2019 ) as it enables the provision of services in a manner that also supports similar priorities for economic growth, air quality, environmental noise and waste management ( Akgün et al., 2019 ; Kiba-Janiak, 2019 ). Beyond vehicles and infrastructure, the adoption of these strategies can enable the development of new technologies and practices such as virtualisation of products, digital manufacturing, waste collection, and sorting systems. Interestingly, innovative environmentally-friendly logistics solutions resting on the backbone of the CE framework are already materializing and being trialled in various capacities, including: urban consolidation centre (UCC) ( Johansson and Björklund, 2017 ), crowshipping ( Buldeo Rai et al., 2017a ; Rai et al., 2018 ) and off-hour delivery ( Gatta et al., 2019 ). UCC stresses the use of logistics facilities in city suburbs to ease good deliveries to customers ( Browne et al., 2005 ), while crowshipping is a collaborative measure that employs the use of free mobility resources to perform deliveries ( Buldeo Rai et al., 2017b ).

The availability of rich transport data (e.g. impacts of events on transport, commuter habits) and AI-enabled complex data processing technologies can be leveraged to inform the planning, management, and operations of transport networks over time. Real-time data can also be adopted for monitoring and for instant regulations of traffic flow based on route planning, dynamic pricing and parking space allocation. Noticeably, many of these innovative CE-related initiatives still need an efficient governance mechanism ( Janné and Fredriksson, 2019 ). However, coupling them with the deployment of environmentally efficient vehicles and superior technical solutions hinging on the internet-of-things will bring many nations closer to reaping the benefits of CE. Given that urban planning is most often within the remit of governmental agencies, they must therefore develop integrated pathways and strategies for urban mobility to ensure effective logistics and resource flows. Stakeholder engagements within the transport sector can also facilitate innovative solutions that enable better use of assets and big data solutions.

6.6. Sustaining improvements in air quality

Improvements in air quality is one of the positives recorded due to the COVID-19-imposed lockdown as transportation and industrial activities halted. To sustain such improvements, there is the need to facilitate a step change by ramping up the uptake of low emission vehicles through setting more ambitious targets for the embrace of electric vehicles, constructing more electric car charging points as well as encouraging low emissions fuels. This entails heightening investments in cleaner means of public transportation as well as foot and cycle paths for health improvements; redesigning of cities to ensure no proximity to highly polluting roads and the populace as well as preventing highly polluting vehicles from accessing populated areas using classifications such as clear air or low emission zones ( PHE, 2020 ).

Batteries constitute an integral part towards the decarbonisation of road transportation and support the move to a renewable energy system ( World Economic Forum, 2019 ). As such, it is important to establish a battery value chain that is circular, responsible and just, to realise the aforementioned transitions. This entails the identification of the ( World Economic Forum, 2019 ): (i) challenges inhibiting the scaling up of the battery value chain (e.g. battery production processes, risks of raw materials supplies); (ii) levers to mitigate the challenges such as a circular value chain (e.g. design for life extension, implementation of V1G and V2G and scaling up of electric shared and pooled mobility, coupling the transport and power sectors); sustainable business and technology (e.g. increasing the share of renewables and energy efficiency measures across the value chain, effective regulations and financial incentives to support value creation); and a responsible and just value chain based on a balanced view and interplay between environmental, social and economic factors. Indeed, cost-effective and sustainable batteries, as well as an enabling ecosystem for the deployment of battery-enabled renewable energy technologies backed with a dense infrastructure network for charging, will facilitate the transition towards broader acceptance of electric vehicles and by extension guarantees a sustained improvement in air quality ( Masiero et al., 2017 ; PHE, 2020 ; World Economic Forum, 2019 ).We recognize that if all cars are simply replaced by electricones, there will still be the same volume of traffic and an increased need for raw materials, posing significant social, environmental and integrity risks across its value chain. However, CE through the aforementioned levers can address these challenges and support the achievement of a sustainable battery value chain. This will entail lowering emission during manufacturing, eradicating human rights violations, ensuring safe working conditions across the value chain and improving reuse, recycling and remanufacturing ( World Economic Forum, 2019 ).

6.7. Digitalisation for supply chain resilience post COVID-19

Digitalisation of supply chains through leveraging disruptive digital technologies (DDTs) - technologies or tools underpinning smart manufacturing such as the Internet of Things (IoT), artificial intelligence, big data analytics, cloud computing and 3D printing - constitute an important step for companies to prepare for and mitigate against the disruptions and attain business resilience amidst global pandemics such as COVID-19. Circular supply chain value drivers’ entails elongation of useful lifespan and maximisation of asset utilisation. Intelligent assets value drivers entail gathering knowledge regarding the location, condition and availability of assets ( Morlet et al., 2016 ). Paring these drivers could provide a broad range of opportunities, which could change the nature of both products and business models, enabling innovation and value creation ( Antikainen et al., 2018 ; Morlet et al., 2016 ). For instance, big data analytics, when adopted properly can aid companies in streamlining their supplier selection processes; cloud-computing is currently being used to facilitate and manage supplier relationships; through automation and the IoT, logistics and shipping processes can be greatly enhanced ( McKenzie, 2020 ). Digitalisation enables predictive maintenance, preventing failures while extending the lifespan of a product across the supply chains. It therefore, constitutes an ideal vehicle for circular supply chains transitioning, providing opportunities to close material loops and improve processes ( Morlet et al., 2016 ; Pagoropoulos et al., 2017 ).

Indeed, COVID-19 has prompted renewed urgency in the adoption of automation and robotics towards mitigating against the disruptive impact on supply chains through restrictions imposed on people's movement. Numerous companies are taking advantage of this to automate their production lines. Prior to COVID-19, momentum towards adopting 5G mobile technology was mounting but delays caused by factors including anticipated use evaluations, security, competition and radio communications regulatory issues limited progress ( McKenzie, 2020 ). It is likely that the experience of COVID-19 may accelerate the provision of regulatory certainty for 5G, which will in turn fast-track the deployment of IoT-enabled devices for remote monitoring, to support supply chain resilience post COVID-19.

Despite the benefits of DDTs, tension exists between their potential benefits (i.e. ability to deliver measurable environmental benefits at an affordable cost), and the problems (i.e. heavy burden imposed during manufacturing and disposal phases of their lifecycle) they constitute, creating rebound effects. As such, the tension between the push for increasing digitalisation and the associated energy costs and environmental impacts should be investigated such that they do not exacerbate the existing problems of resource use and pollution caused by rapid obsolescence and disposal of products containing such technologies. This entails identifying, mapping and mitigating unintended consequences across their supply chains, whilst taking into account technological design embedded within green ethical design processes, to identify environmental sustainability hotspots, both in conception, application and end of life phases.

6.8. Policy measures, incentives and regulatory support for CE transitioning

Becque et al. (2016) in their analysis of the political economy of the CE identified six main types of policy intervention to facilitate, advance and guide the move to a CE by addressing either barriers that aim to fix the market and regulatory failures or encourage market activity. Some of the policy intervention options identified include: (i) education, information and awareness that entails the integration of CE and lifecycle systems thinking into educational curricula supported by public communication and information campaigns; (ii) setting up platforms for collaboration including public-private partnerships with ventures at the local, regional and national levels, encouraging information sharing as well as value chain and inter-sectoral initiatives, establishing research and development to facilitate breakthroughs in materials science and engineering, biomaterials systems etc.; (iii) introduction of sustainability initiatives in public procurement and infrastructure ; (iv) provision of business/financial/technical support schemes such as initial capital outlay, incentive programs, direct subsidies and financial guarantees as well as technical support, training, advice and demonstration of best practices; (v) regulatory frameworks such as regulation of products (including design), extension of warranties and product passports; strategies for waste management including standards and targets for collection and treatments, take-back systems and extended producer responsibility; strategies at the sectoral levels and associated targets for resource productivity and CE; consumer, competition, industry and trade regulations; introduction of standard carbon accounting standards and methodologies; and (vi) fiscal frameworks such as reductions of VAT or excise tax for products and services designed with CE principles.

7. Conclusion

COVID-19 has highlighted the environmental folly of ‘extract-produce-use-dump’ economic model of material and energy flows. Short-term policies to cope with the urgency of the pandemic are unlikely to be sustainable models in the long run. Nonetheless, they shed light on critical issues that deserve emphases, such as the clear link between environmental pollution and transportation/industrialization. The role of unrestricted air travel in spreading pandemics particularly the viral influenza types (of which COVID-19 is one) is not in doubt, with sectors like tourism and aviation being walloped (some airlines may never recover or return to profitability in a long time) due to reduced passenger volumes. The fallout will re-shape the aviation sector, which like tourism has been among the hardest to be hit economically, albeit with desirable outcomes for the reduction in adverse environmental impacts. Peer-to-peer (P2P) or sharing economy models (e.g. Uber, Airbnb) which have birthed a new generation of service providers and employees are found to be non-resilient to global systemic shocks.

The urgency of supply and demand led to a reduction in cargo shipping in favour of airfreights whose transatlantic cost/kg tripled overnight. This is matched by job losses, income inequalities, mass increase in global poverty levels and economic shocks across industries and supply chains. The practicability of remote working (once the domain of technology/service industries) has been tried and tested for specific industries/professions with its associated impacts on reduced commuting for workers. Remote healthcare/telemedicine/ and remote working, in general, is no longer viewed as unfeasible because it has been practiced with success over the best part of a four-month global lockdown period. There was a corresponding reduction in primary energy consumption due to the slowing and shutting down of production and economic activities, and the delivery of education remotely is also no longer questioned. The potential of automation, IoT, and robotics in improving manufacturing processes, as well as the use of cloud computing and big data analytics in streamlining supplier selection processes and management of supplier relationships and logistics are now better appreciated.

The inadequacies of modern healthcare delivery systems to cope with mass casualties and emergencies are universally acknowledged, primarily due to the incapacity of hospital JIT procurement process to provide essential medical and emergency supplies in vast quantities at short notice. This had deadly consequences with thousands of patients and healthcare workers paying the ultimate price for lack of planning and shortfalls in PPE inventory and critical care equipment. Protectionism and in-ward looking policies on exports and tariff reductions/waivers on the importation of raw materials and critical PPE have emphasized the importance of cooperation to cope with shortages, which evolved in tandem with profiteering, thereby emphasizing the role/need for cottage industries to help meet global production of essentials (facemasks, 3D printed parts/equipment, etc.). The increase in infectious hospital wastes due to the pandemic was necessitated by precautionary measures to control the transmission, but proper/advanced sterilization procedures via thermal, microwave, biochemical processes can help in upcycling discarded or retrieved materials and PPE.

Changes in consumer behaviour with social distancing have necessitated a huge increase in online purchasing, which has benefitted the big players but seriously harmed SMEs, who were not exploiting web-based product and service delivery. A CE-based resilience of the consumer food sector was found to require: (i) closing nutrient loops with the use of regenerative agriculture; (ii) value recovery from organic nutrients via anaerobic digestion facilities; (iii) adoption of urban and peri-urban agriculture; and (iv) expanding food collection, redistribution and volarisation facilities. It is believed that CE will facilitate a socially just and inclusive society,driven by the need for resilience and sustainability goals, which could see a rise in bio-economy and sharing economy (SE). The consequences of these would be felt in terms of global cooperation and mutual interests; long-term planning as well as the need to strike an optimum balance between dependence on outsourcing/importation and local manufacturing/productivity. A realignment of value chains is likely to occur because of countries with raw materials exploiting this pandemic for their sustainable growth, and a new world order not shaped by the technological superiority of super-powers is likely to emerge.

During the lockdown, offices and commercial spaces were massively underutilized and the need to increase ventilation rates, e.g. in hospitals is leading to more energy consumption. However, there are opportunities to (re)design buildings to have movable walls for adaptable use. The use of modular techniques for fast construction of buildings that can be disassembled and re-configured for new needs, as demonstrated in China, is likely to increase. Renovation and refurbishment will witness a renewed vigour as existing buildings get a new lease of life with reduced carbon emissions and new jobs being created. Nonetheless, integrating circularity (product durability, energy efficiency, recyclability, etc.) via design thinking is essential from the onset if all these potential benefits are to be achieved. Digital technologies will play a crucial role in ensuring the low carbon and energy-efficient future of the built environment.

Governments are recognizing the need for national-level CE policies in many aspects, such as: (a) reducing over-reliance on other manufacturing countries for essential goods as massive shortages forced the unwitting adoption of CE principles such as re-use; (b) intensive research into bio-based materials for the development of biodegradable products and the promotion of bio-economy; (c) legal framework for local, regional and national authorities to promote green logistics and waste management regulations which incentivize local production and manufacturing; and (d) development of compact smart cities for effective mobility (with social distancing considerations) as well as enabling environment for shared mobility options (e.g. ride-shares) and active mobility options (e.g. bicycling, walking).

Going forward, resilience thinking should guide lessons learnt and innovations emanating from circular thinking should target the general well-being of the populace and not merely focus on boosting the competitiveness, profitability or growth of businesses and national economies. The post-COVID-19 investments needed to accelerate towards more resilient, low carbon and circular economies should also be integrated into the stimulus packages for economic recovery being promised by governments, since the shortcomings in the dominant linear economic model are now recognized and the gaps to be closed are known.

Credit author statement

IMT, MKB and GJ conceived the idea. IMT developed the methodological notes. IMT, MKB, AZ & FH conducted the analysis. IMT, MKB, AZ, BKA, ADD, AA and FH designed the structure and outline of the paper. All authors contributed to the writing the paper, with comments and feedback from GJ and KSCL.

Declaration of Competing Interest

• Abbas Elmualim S.M., Chileshe N., Rameezdeen R. Construction and the circular economy: smart and industrialised prefabrication. Unmak. Waste Product. Consump. 2018:323. [ Google Scholar ]
• ACI Policy Brief – COVID-19: Relief measures to ensure the survival of the airport industry. Airport Council Int. 2020 [ Google Scholar ]
• Acquaye A., Feng K., Oppon E., Salhi S., Ibn-Mohammed T., Genovese A., Hubacek K. Measuring the environmental sustainability performance of global supply chains: A multi-regional input-output analysis for carbon, sulphur oxide and water footprints. J. Environ. Manag.187. 2017:571–585. [ PubMed ] [ Google Scholar ]
• Ahmed F., Ahmed N.e., Pissarides C., Stiglitz J. Why inequality could spread COVID-19. The Lancet Public Health. 2020; 5 :e240. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Air Transport Bureau . International Civil Aviation Organization (ICAO); Montréal, Canada: 2020. Effects of Novel Coronavirus (COVID‐19) on Civil Aviation: Economic Impact Analysis. [ Google Scholar ]
• Akgün E.Z., Monios J., Rye T., Fonzone A. Influences on urban freight transport policy choice by local authorities. Transport Policy. 2019; 75 :88–98. [ Google Scholar ]
• Allan J., Donovan C., Ekins P., Gambhir A., Hepburn C., Robins N., Reay D., Shuckburgh E., Zenghelis D. A net-zero emissions economic recovery from COVID-19. COP26 Univ. Netw. Brief. 2020 April. [ Google Scholar ]
• Allison A.L., Ambrose-Dempster E., Domenech Aparsi T., Bawn M., Casas Arredondo M., Chau C., Chandler K., Dobrijevic D., Hailes H.C., Lettieri P. The environmental dangers of employing single-use face masks as part of a COVID-19 exit strategy. UCL Open. 2020 Preprint. [ Google Scholar ]
• American Geriatrics Society American Geriatrics Society (AGS) policy brief: COVID‐19 and nursing homes. J. American Geriatrics Society. 2020 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Anderson R.M., Heesterbeek H., Klinkenberg D., Hollingsworth T.D. How will country-based mitigation measures influence the course of the COVID-19 epidemic? The Lancet. 2020; 395 :931–934. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Andrew A., Armand A., Augsburg B., Taveras I.K. Challenges of adopting coronavirus precautions in low-income countries. The IFS, Inst. Fiscal Stud. 2020 [ Google Scholar ]
• Ang B.W. The LMDI approach to decomposition analysis: a practical guide. Energy Policy33. 2005:867–871. [ Google Scholar ]
• Antikainen M., Uusitalo T., Kivikytö-Reponen P. Digitalisation as an enabler of circular economy. Procedia CIRP. 2018; 73 :45–49. [ Google Scholar ]
• Arafat S.Y., Kar S.K., Marthoenis M., Sharma P., Apu E.H., Kabir R. Psychological underpinning of panic buying during pandemic (COVID-19) Psychiatry Res. 2020 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Ardente F., Beccali M., Cellura M., Mistretta M. Energy and environmental benefits in public buildings as a result of retrofit actions. Renew. Sustain. Energy Rev.15. 2011:460–470. [ Google Scholar ]
• Artola I., Rademaekers K., Williams R., Yearwood J. Study for the iTRE Committee, Commissioned by DG for Internal Policies Policy Department A. Vol. 72. 2016. Boosting building renovation: What potential and value for Europe. [ Google Scholar ]
• ARUP . ARUP; 2020. Transform and Reuse:Low-Carbon Futures for Existing Buildings. [ Google Scholar ]
• Atelge M., Atabani A., Banu J.R., Krisa D., Kaya M., Eskicioglu C., Kumar G., Lee C., Yildiz Y., Unalan S. A critical review of pretreatment technologies to enhance anaerobic digestion and energy recovery. Fuel. 2020; 270 [ Google Scholar ]
• Aubrecht, P., Essink, J., Kovac, M., Vandenberghe, A.-S., 2020. Centralized and decentralized responses to COVID-19 in federal systems: US and EU comparisons. Available at SSRN 3584182.
• Auffhammer M., Burke M., Burney J., Hsiang S., Lobell D., Roberts M., Schlenker W. COVID-19 reduces economic activity, which reduces pollution, which saves lives. Global Food, Environment and Economic Dynamics (G-FEED), United States. 2020 [ Google Scholar ]
• Ayambire R.A., Amponsah O., Peprah C., Takyi S.A. A review of practices for sustaining urban and peri-urban agriculture: Implications for land use planning in rapidly urbanising Ghanaian cities. Land Use Policy. 2019; 84 :260–277. [ Google Scholar ]
• Bachman D. COVID-19 could affect the global economy in three main ways. Deloitte. 2020 [ Google Scholar ]
• Baker S.R., Bloom N., Davis S.J., Terry S.J. National Bureau of Economic Research; 2020. Covid-induced economic uncertainty. [ Google Scholar ]
• Baldwin R., Evenett S. CEPR Press; London: 2020. Covid-19 and Trade Policy: Why turning inward won't work. [ Google Scholar ]
• Baseler L., Chertow D.S., Johnson K.M., Feldmann H., Morens D.M. The pathogenesis of ebola virus disease. Annu. Rev. Pathol. 2017; 12 :387–418. [ PubMed ] [ Google Scholar ]
• Basilaia G., Kvavadze D. Transition to Online Education in Schools during a SARS-CoV-2 Coronavirus (COVID-19) Pandemic in Georgia. Pedag. Res.5. 2020 [ Google Scholar ]
• Bauwens T., Hekkert M., Kirchherr J. Circular futures: what will they look like? Ecol. Econ.175. 2020 [ Google Scholar ]
• Bayram M., Springer S., Garvey C.K., Özdemir V. COVID-19 digital health innovation policy: A portal to alternative futures in the making. OMICS J. Integrat. Biol. 2020 [ PubMed ] [ Google Scholar ]
• Becque, R., Roy, N., Hamza-Goodacre, D., 2016. The Political Economy of the Circular Economy-lessons to date and questions for research. San Francisco, pp. 1-16.
• Beretta C., Hellweg S. Potential environmental benefits from food waste prevention in the food service sector. Resour. Conserv. Recycl.147. 2019:169–178. [ Google Scholar ]
• Bloom D.E., Cadarette D. Infectious disease threats in the 21st century: strengthening the global response. Front. Immunol.10. 2019:549. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Bloom D.E., Canning D. Epidemics and economics. Interact. Glob. Chang. Hum. Health Scripta Varia. 2004; 106 :304–331. [ Google Scholar ]
• Bocken N.M., De Pauw I., Bakker C., van der Grinten B. Product design and business model strategies for a circular economy. J. Indust. Prod. Eng.33. 2016:308–320. [ Google Scholar ]
• Bown C.P. A CEPR Press; 2020. COVID-19: Demand spikes, export restrictions, and quality concerns imperil poor country access to medical supplies. VoxEU.org eBook, 31. [ Google Scholar ]
• Bradsher K., Alderman L. New York Times; 2020. The world needs masks. China makes them, but has been hoarding them. [ Google Scholar ]
• Bramanti B., Dean K.R., Walloe L., Chr Stenseth N. The third plague pandemic in Europe. Proc. Biol. Sci. 2019; 286 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Braungart M., McDonough W., Bollinger A. Cradle-to-cradle design: creating healthy emissions – a strategy for eco-effective product and system design. J. Clean. Prod.15. 2007:1337–1348. [ Google Scholar ]
• Bretscher, L., Hsu, A., Tamoni, A., 2020. The supply channel of uncertainty shocks and the cross-section of returns: evidence from the COVID-19 crisis. Available at SSRN 3588418.
• Browne M., Sweet M., Woodburn A., Allen J. Vol. 10. Transport Studies Group, University of Westminster; 2005. (Urban freight consolidation centres final report). [ Google Scholar ]
• Buldeo Rai H., Verlinde S., Merck J., Macharis C. Crowd logistics: an opportunity for more sustainable urban freight transport? Eur. Transp. Res. Rev. 2017; 9 (39) [ Google Scholar ]
• Buldeo Rai H., Verlinde S., Merckx J., Macharis C. Crowd logistics: an opportunity for more sustainable urban freight transport? Eur. Transp. Res. Rev.9. 2017:39. [ Google Scholar ]
• Carra G., Magdani N. Circular business models for the built environment. Arup BAM. 2017:1–44. [ Google Scholar ]
• Carrillo-Hermosilla J., Del Río P., Könnölä T. Diversity of eco-innovations: Reflections from selected case studies. J. Clean. Prod.18. 2010:1073–1083. [ Google Scholar ]
• Castañé S., Antón A. Assessment of the nutritional quality and environmental impact of two food diets: A Mediterranean and a vegan diet. J. Clean. Prod.167. 2017:929–937. [ Google Scholar ]
• Chandra, S., 2020. Speed, space and sustainability (3S) in transportation amid COVID-19 crisis. SSRN 3598501.
• Chang H.-H., Meyerhoefer C. National Bureau of Economic Research; 2020. COVID-19 and the Demand for Online Food Shopping Services: Empirical Evidence from Taiwan. [ Google Scholar ]
• Chen C., Zhao B. Makeshift hospitals for COVID-19 patients: where health-care workers and patients need sufficient ventilation for more protection. J. Hosp. Infect.105. 2020:98–99. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Chi M., George J.F., Huang R., Wang P. Unraveling sustainable behaviors in the sharing economy: An empirical study of bicycle-sharing in China. J. Clean. Prod. 2020 [ Google Scholar ]
• Cimprich A., Santillán‐Saldivar J., Thiel C.L., Sonnemann G., Young S.B. Potential for industrial ecology to support healthcare sustainability: Scoping review of a fragmented literature and conceptual framework for future research. J. Indust. Ecol.23. 2019:1344–1352. [ Google Scholar ]
• Clair A. Social Market Foundation (SMF); 2020. Homes, health, and COVID-19: how poor housing adds to the hardship of the coronavirus crisis. Online. [ Google Scholar ]
• Company M. A global view of how consumer behavior is changing amid COVID-19. McKinsey. 2020 [ Google Scholar ]
• Da Silva C., Hoppe A., Ravanello M., Mello N. Medical wastes management in the south of Brazil. Waste Manag.25. 2005:600–605. [ PubMed ] [ Google Scholar ]
• Danieli, A., Olmstead-Rumsey, J., 2020. Sector-specific shocks and the expenditure elasticity channel during the covid-19 crisis. Available at SSRN 3593514.
• Dannenberg, P., Fuchs, M., Riedler, T., Wiedemann, C., 2020. Digital transition by COVID‐19 pandemic? The German food online retail. Tijdschrift voor economische en sociale geografie. [ PMC free article ] [ PubMed ]
• Dargaville T., Spann K., Celina M. Opinion to address a potential personal protective equipment shortage in the global community during the COVID-19 outbreak. Polym. Degrad. Stab. 2020 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Daszak P. Anatomy of a pandemic. The Lancet. 2012; 380 :1883–1884. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• De Cock K.M., Jaffe H.W., Curran J.W. The evolving epidemiology of HIV/AIDS. Aids. 2012; 26 :1205–1213. [ PubMed ] [ Google Scholar ]
• De Gioannis G., Muntoni A., Polettini A., Pomi R., Spiga D. Energy recovery from one-and two-stage anaerobic digestion of food waste. Waste Manag.68. 2017:595–602. [ PubMed ] [ Google Scholar ]
• De Jesus A., Mendonça S. Lost in transition? Drivers and barriers in the eco-innovation road to the circular economy. Ecol. Econ.145. 2018:75–89. [ Google Scholar ]
• De Soete W., Jiménez-González C., Dahlin P., Dewulf J. Challenges and recommendations for environmental sustainability assessments of pharmaceutical products in the healthcare sector. Green Chem.19. 2017:3493–3509. [ Google Scholar ]
• Deloitte . Deloitte; 2020. Understanding the sector impact of COVID-19: Engineering and Construction sector; p. 2. [ Google Scholar ]
• Devakumar D., Shannon G., Bhopal S.S., Abubakar I. Racism and discrimination in COVID-19 responses. The Lancet. 2020; 395 :1194. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Diaz L.F., Eggerth L., Enkhtsetseg S., Savage G. Characteristics of healthcare wastes. Waste Manag.28. 2008:1219–1226. [ PubMed ] [ Google Scholar ]
• Dilkes-Hoffman L.S., Lane J.L., Grant T., Pratt S., Lant P.A., Laycock B. Environmental impact of biodegradable food packaging when considering food waste. J. Clean. Prod.180. 2018:325–334. [ Google Scholar ]
• Dingel J.I., Neiman B. National Bureau of Economic Research; 2020. How many jobs can be done at home? [ Google Scholar ]
• Duflou J.R., Sutherland J.W., Dornfeld D., Herrmann C., Jeswiet J., Kara S., Hauschild M., Kellens K. Towards energy and resource efficient manufacturing: A processes and systems approach. CIRP Anna.61. 2012:587–609. [ Google Scholar ]
• Duncan-Jones R.P. The impact of the Antonine plague. J. Roman Arch.9. 1996:108–136. [ Google Scholar ]
• Eaton J., Connor Y. How to strengthen your supply chain in the face of COVID-19 disruption: 8 Lessons for strengthening your supply chain today. Grant Thornton. 2020 Online. [ Google Scholar ]
• Eberhardt L.C.M., Birgisdottir H., Birkved M. IOP Conference Series: Materials Science and Engineering. IOP Publishing; 2019. Potential of circular economy in sustainable buildings. [ Google Scholar ]
• EEA . Vol. 2020. Publications Office of the European Union; Luxembourg: 2020. pp. 1–104. (Environmental noise in Europe — 2020. European Environment Agency (EEA)). [ Google Scholar ]
• Ekins, P., Hughes, N., Brigenzu, S., Arden Clark, C., Fischer-Kowalski, M., Graedel, T., Hajer, M., Hashimoto, S., Hatfield-Dodds, S., Havlik, P., 2016. Resource efficiency: Potential and economic implications.
• Eleyan D., Al-Khatib I.A., Garfield J. System dynamics model for hospital waste characterization and generation in developing countries. Waste Manag. Res.31. 2013:986–995. [ PubMed ] [ Google Scholar ]
• EMF . Ellen MacArthur Foundation; London: 2016. Circular economy in India: Rethinking growth for long-term prosperity; p. 86. [ Google Scholar ]
• EMF . Ellen MacArthur Foundation; 2019. Planning effective transport of people, products and materials; p. 6. [ Google Scholar ]
• EMF . Ellen MacArthur Foundation; 2020. 10 circular investment opportunities to build back better: food sector; p. 13. [ Google Scholar ]
• EMF . Ellen MacArthur Foundation; London: 2020. 10 circular investment opportunities to build back better: The built environment; p. 10. [ Google Scholar ]
• Enkvist P., Klevnäs P., Teiwik A., Jönsson C., Klingvall S., Hellberg U. Material Economics Sverige AB; Stockholm, Sweden: 2018. The circular economy–a powerful force for climate mitigation: transformative innovation for prosperous and low-carbon industry. [ Google Scholar ]
• EPRS . European Parliamentary Research Service (EPRS); 2014. Urban And Peri-Urban Agriculture. [ Google Scholar ]
• ESA . European Space Agency (ESA); 2020. Air pollution remains low as Europeans stay at home. Online. [ Google Scholar ]
• European Commission . European Commission; 2011. The Roadmap to a Resource Efficient Europe; p. 26. [ Google Scholar ]
• European Commission . European Commission; 2020. EU Circular Economy Action Plan: A new Circular Economy Action Plan for a Cleaner and More Competitive Europe. [ Google Scholar ]
• Evenett S.J. Flawed prescription: Export curbs on medical goods won't tackle shortages. COVID-19 and Trade Policy: Why Turning Inward Won't Work. 2020; 49 [ Google Scholar ]
• Fast Company . Fast Company; 2019. Is it possible to raise a carbon-neutral cow? [ Google Scholar ]
• Feber D., Lingqvist O., Nordigården D. McKinsey & Company McKinsey & Company; 2020. Shaping the next normal of packaging beyond COVID-19; p. 6. [ Google Scholar ]
• Feng K., Davis S.J., Sun L., Hubacek K. Drivers of the US CO2 emissions 1997-2013. Nat. Commun.6. 2015 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Fernandes, N., 2020. Economic effects of coronavirus outbreak (COVID-19) on the world economy. Available at SSRN 3557504.
• Fischer R., Morris D.H., van Doremalen N., Sarchette S., Matson J., Bushmaker T., Yinda C.K., Seifert S., Gamble A., Williamson B. Assessment of N95 respirator decontamination and re-use for SARS-CoV-2. medRxiv. 2020 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Ford T.E., Colwell R.R., Rose J.B., Morse S.S., Rogers D.J., Yates T.L. Using satellite images of environmental changes to predict infectious disease outbreaks. Emerg. Infect. Dis.15. 2009:1341. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Friant M.C., Vermeulen W.J., Salomone R. A typology of circular economy discourses: Navigating the diverse visions of a contested paradigm. Resour. Conserv. Recycl.161. 2020 [ Google Scholar ]
• Fujii H., Managi S., Kaneko S. Decomposition analysis of air pollution abatement in China: empirical study for ten industrial sectors from 1998 to 2009. J. Clean. Prod.59. 2013:22–31. [ Google Scholar ]
• Galvão G.D.A., Homrich A.S., Geissdoerfer M., Evans S., Ferrer P.S.s., Carvalho M.M. Towards a value stream perspective of circular business models. Resour. Conserv. Recycl.162. 2020 [ Google Scholar ]
• Gates B. COVID-19 is awful. Climate change could be worse. Clim. Coronavirus. 2020 GatesNotes. [ Google Scholar ]
• Gates B. Responding to Covid-19—a once-in-a-century pandemic? N. Engl. J. Med.382. 2020:1677–1679. [ PubMed ] [ Google Scholar ]
• Gatta V., Marcucci E., Delle Site P., Le Pira M., Carrocci C.S. Planning with stakeholders: Analysing alternative off-hour delivery solutions via an interactive multi-criteria approach. Res. Transp. Econ.73. 2019:53–62. [ Google Scholar ]
• Geissdoerfer M., Savaget P., Bocken N.M., Hultink E.J. The Circular Economy–A new sustainability paradigm? J. Clean. Prod.143. 2017:757–768. [ Google Scholar ]
• Gibbs M.J., Armstrong J.S., Gibbs A.J. Recombination in the hemagglutinin gene of the 1918" Spanish flu". Science. 2001; 293 :1842–1845. [ PubMed ] [ Google Scholar ]
• Girling R. Random House; 2011. Rubbish!: Dirt on Our Hands and Crisis Ahead. [ Google Scholar ]
• Gondi S., Beckman A.L., Deveau N., Raja A.S., Ranney M.L., Popkin R., He S. Personal protective equipment needs in the USA during the COVID-19 pandemic. The Lancet. 2020 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Gopinath G. Limiting the economic fallout of the coronavirus with large targeted policies. IMF. 2020 [ Google Scholar ]
• Govindan K., Hasanagic M. A systematic review on drivers, barriers, and practices towards circular economy: a supply chain perspective. Int. J. Prod. Res.56. 2018:278–311. [ Google Scholar ]
• Grant M.J., Booth A. A typology of reviews: an analysis of 14 review types and associated methodologies. Health Inform. Libr. J.26. 2009:91–108. [ PubMed ] [ Google Scholar ]
• Greenhalgh T., Thorne S., Malterud K. Time to challenge the spurious hierarchy of systematic over narrative reviews? Eur. J. Clin. Invest. 2018; 48 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Guan, D., Hallegatte, S., 2020. The containment divide: COVID-19 lockdowns and basic needs in developing countries.
• Guan, D., Wang, D., Hallegatte, S., Huo, J., Li, S., Bai, Y., Lei, T., Xue, Q., Davis, S.J., Coffman, D.M., 2020. Global economic footprint of the COVID-19 pandemic.
• Guerrieri V., Lorenzoni G., Straub L., Werning I. National Bureau of Economic Research; 2020. Macroeconomic Implications of COVID-19: Can Negative Supply Shocks Cause Demand Shortages? [ Google Scholar ]
• Gungor A., Gupta S.M. Issues in environmentally conscious manufacturing and product recovery: a survey. Comp. Indust. Eng.36. 1999:811–853. [ Google Scholar ]
• Haigh, L., Bäunker, L., 2020. Covid-19 and the circular economy: opportunities and reflections.
• Hasanat M.W., Hoque A., Shikha F.A., Anwar M., Hamid A.B.A., Tat H.H. The impact of coronavirus (Covid-19) on e-business in Malaysia. Asian J. Multidiscipl. Stud.3. 2020:85–90. [ Google Scholar ]
• Herrmann C., Schmidt C., Kurle D., Blume S., Thiede S. Sustainability in manufacturing and factories of the future. Int. J. Precis. Eng. Manuf.-Green Tech.1. 2014:283–292. [ Google Scholar ]
• Hobbs J.E. Food supply chains during the COVID‐19 pandemic. Can. J. Agricult. Econ./Revue canadienne d’agroeconomie. 2020 [ Google Scholar ]
• Hockley L. Coronavirus roundtable: How is the aviation industry responding to the COVID-19 pandemic? Int. Airport Rev. 2020 Online. [ Google Scholar ]
• Hoornweg, D., Bhada-Tata, P., 2012. What a waste: a global review of solid waste management.
• Horrox R. Manchester University Press; 2013. The Black Death. [ Google Scholar ]
• Hotez P.J., Alvarado M., Basáñez M.-G., Bolliger I., Bourne R., Boussinesq M., Brooker S.J., Brown A.S., Buckle G., Budke C.M. The global burden of disease study 2010: interpretation and implications for the neglected tropical diseases. PLoS Neglect. Tropic. Dis.8. 2014 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Hu M.R., Lee A.D. Airbnb, COVID-19 risk and lockdowns: global evidence. COVID-19 Risk Lockdowns Glob. Evid. 2020 (April 30, 2020) [ Google Scholar ]
• Huang W., Zhao Z., Yuan T., Huang W., Lei Z., Zhang Z. Low-temperature hydrothermal pretreatment followed by dry anaerobic digestion: A sustainable strategy for manure waste management regarding energy recovery and nutrients availability. Waste Manag.70. 2017:255–262. [ PubMed ] [ Google Scholar ]
• Hussey L.K., Arku G. Are we ready for it? Health systems preparedness and capacity towards climate change-induced health risks: perspectives of health professionals in Ghana. Clim. Dev.12. 2020:170–182. [ Google Scholar ]
• IATA Air transport & COVID-19 coronavirus. Int. Air Transp. Assoc. 2020 www.airlines.iata.org [ Google Scholar ]
• Ibn-Mohammed T. Application of mixed-mode research paradigms to the building sector: a review and case study towards decarbonising the built and natural environment. Sustain. Cities Soc.35. 2017:692–714. [ Google Scholar ]
• Ibn-Mohammed T., Greenough R., Taylor S., Ozawa-Meida L., Acquaye A. Operational vs. embodied emissions in buildings—A review of current trends. Energy Build.66. 2013:232–245. [ Google Scholar ]
• Ibn-Mohammed T., Greenough R., Taylor S., Ozawa-Meida L., Acquaye A. Integrating economic considerations with operational and embodied emissions into a decision support system for the optimal ranking of building retrofit options. Build. Environ.72. 2014:82–101. [ Google Scholar ]
• Ibn-Mohammed T., Reaney I., Koh S., Acquaye A., Sinclair D., Randall C., Abubakar F., Smith L., Schileo G., Ozawa-Meida L. Life cycle assessment and environmental profile evaluation of lead-free piezoelectrics in comparison with lead zirconate titanate. J. Eur. Cer. Soc. 2018 [ Google Scholar ]
• ICAO Effects of novel coronavirus (COVID-19) on civil aviation: economic impact analysis. Unit. Aviat. 2020 [ Google Scholar ]
• IEA . International Energy Agency, IEA Publications; 2020. Global Energy Review 2020:The impacts of the COVID-19 crisis on global energy demand and CO2 emissions; pp. 1–56. [ Google Scholar ]
• Ijomah W.L. Proceedings of the Institution of Civil Engineers-Waste and Resource Management. Thomas Telford Ltd; 2010. The application of remanufacturing in sustainable manufacture; pp. 157–163. [ Google Scholar ]
• IMF . The International Monetary Fund (IMF); Washington, DC: 2020. World Economic Outlook: The Great Lockdown; p. 37. -37. [ Google Scholar ]
• Insa E., Zamorano M., López R. Critical review of medical waste legislation in Spain. Resour. Conserv. Recycl.54. 2010:1048–1059. [ Google Scholar ]
• Iyengar K., Bahl S., Vaishya R., Vaish A. Challenges and solutions in meeting up the urgent requirement of ventilators for COVID-19 patients. Diab. Metabol. Syndr. Clin. Res. Rev. 2020 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Jaeger B., Upadhyay A. Understanding barriers to circular economy: cases from the manufacturing industry. J. Enterp. Inform. Manag. 2020 [ Google Scholar ]
• Janné M., Fredriksson A. Construction logistics governing guidelines in urban development projects. Constr. Innov. 2019 [ Google Scholar ]
• Javorcik B. Global supply chains will not be the same in the post-COVID-19 world. COVID-19 and Trade Policy: Why Turning Inward Won't Work. 2020; 111 [ Google Scholar ]
• JHU . Coronavirus Resource Center, Johns Hopkins University (JHU); 2020. MAPS & TRENDS: New Cases of COVID-19 In World Countries. [ Google Scholar ]
• Johansson H., Björklund M. Urban consolidation centres: retail stores’ demands for UCC services. Int. J. Phys. Distrib. Logist. Manag. 2017 [ Google Scholar ]
• Kaiser B., Eagan P.D., Shaner H. Solutions to health care waste: life-cycle thinking and“ green” purchasing. Environ. Health Perspect.109. 2001:205–207. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Kanda W., Kivimaa P. What opportunities could the COVID-19 outbreak offer for sustainability transitions research on electricity and mobility? Energy Res. Soc. Sci.68. 2020 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Kane G.M., Bakker C.A., Balkenende A.R. Towards design strategies for circular medical products. Resour. Conserv. Recycl.135. 2018:38–47. [ Google Scholar ]
• Kiba-Janiak M. EU cities’ potentials for formulation and implementation of sustainable urban freight transport strategic plans. Transp. Res. Procedia. 2019; 39 :150–159. [ Google Scholar ]
• Kirchherr J., Piscicelli L., Bour R., Kostense-Smit E., Muller J., Huibrechtse-Truijens A., Hekkert M. Barriers to the circular economy: evidence from the European Union (EU) Ecol. Econ.150. 2018:264–272. [ Google Scholar ]
• Kirchherr J., Reike D., Hekkert M. Conceptualizing the circular economy: An analysis of 114 definitions. Resour. Conserv. Recycl.127. 2017:221–232. [ Google Scholar ]
• Klemeš J.J., Fan Y.V., Tan R.R., Jiang P. Minimising the present and future plastic waste, energy and environmental footprints related to COVID-19. Renew. Sustain. Energy Rev.127. 2020 -109883. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Koh S., Ibn-Mohammed T., Acquaye A., Feng K., Reaney I., Hubacek K., Fujii H., Khatab K. Drivers of US toxicological footprints trajectory 1998–2013. Sci. Rep.6. 2016:39514. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Korhonen J., Honkasalo A., Seppälä J. Circular economy: the concept and its limitations. Ecol. Econ.143. 2018:37–46. [ Google Scholar ]
• Kraemer M.U., Yang C.-H., Gutierrez B., Wu C.-H., Klein B., Pigott D.M., du Plessis L., Faria N.R., Li R., Hanage W.P. The effect of human mobility and control measures on the COVID-19 epidemic in China. Science. 2020; 368 :493–497. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Laing T. The economic impact of the Coronavirus 2019 (Covid-2019): Implications for the mining industry. Extract. Indust. Soc. 2020 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Le Quéré C., Jackson R.B., Jones M.W., Smith A.J., Abernethy S., Andrew R.M., De-Gol A.J., Willis D.R., Shan Y., Canadell J.G. Temporary reduction in daily global CO 2 emissions during the COVID-19 forced confinement. Nat. Clim. Chang. 2020:1–7. [ Google Scholar ]
• Lee C., Huffman G., Nalesnik R. Medical waste management. Environ. Sci. Tech.25. 1991:360–363. [ Google Scholar ]
• Lee G.-G., Lee H.-W., Lee J.-H. Greenhouse gas emission reduction effect in the transportation sector by urban agriculture in Seoul, Korea. Landscape and Urban Plan.140. 2015:1–7. [ Google Scholar ]
• Lee J.-W., McKibbin W.J. National Academies Press; Washington, DC: 2004. Estimating the global economic costs of SARS, Learning from SARS: preparing for the next disease outbreak: workshop summary; p. 92. [ PubMed ] [ Google Scholar ]
• Liao L., Xiao W., Zhao M., Yu X., Wang H., Wang Q., Chu S., Cui Y. Can N95 respirators be reused after disinfection? How many times? ACS Nano. 2020 [ PubMed ] [ Google Scholar ]
• Lieder M., Rashid A. Towards circular economy implementation: a comprehensive review in context of manufacturing industry. J. Clean. Prod.115. 2016:36–51. [ Google Scholar ]
• Littman R.J., Littman M.L. Galen and the Antonine plague. Am. J. Philol.94. 1973:243–255. [ PubMed ] [ Google Scholar ]
• Liu X., Zhang S. COVID‐19: Face masks and human‐to‐human transmission. Influen. Other Respir. Virus. 2020 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Livingston E., Desai A., Berkwits M. Sourcing personal protective equipment during the COVID-19 pandemic. JAMA. 2020 [ PubMed ] [ Google Scholar ]
• Lucrezi S., Saayman M., Van der Merwe P. An assessment tool for sandy beaches: A case study for integrating beach description, human dimension, and economic factors to identify priority management issues. Ocean Coast. Manag. 2016; 121 :1–22. [ Google Scholar ]
• Lwasa S., Mugagga F., Wahab B., Simon D., Connors J., Griffith C. Urban and peri-urban agriculture and forestry: Transcending poverty alleviation to climate change mitigation and adaptation. Urban Climate. 2014; 7 :92–106. [ Google Scholar ]
• Lyche, H., 2020. Might a disaster trigger a new circular-economy?
• Mabahwi N.A.B., Leh O.L.H., Omar D. Human health and wellbeing: Human health effect of air pollution. Procedia-Soc. Behav. Sci.153. 2014:221–229. [ Google Scholar ]
• MacArthur E. Ellen MacArthur Foundation; Cowes, UK: 2013. Towards the circular economy, economic and business rationale for an accelerated transition. [ Google Scholar ]
• Mahler D.G., Lakner C., Aguilar R.A.C., Wu H. World Bank; Washington, D.C., United States: 2020. The impact of COVID-19 (Coronavirus) on global poverty: Why Sub-Saharan Africa might be the region hardest hit. [ Google Scholar ]
• Manninen K., Koskela S., Antikainen R., Bocken N., Dahlbo H., Aminoff A. Do circular economy business models capture intended environmental value propositions? J. Clean. Prod.171. 2018:413–422. [ Google Scholar ]
• Marcucci E., Le Pira M., Carrocci C.S., Gatta V., Pieralice E. 2017 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) IEEE; 2017. Connected shared mobility for passengers and freight: Investigating the potential of crowdshipping in urban areas; pp. 839–843. [ Google Scholar ]
• Marques G., Roque Ferreira C., Pitarma R. A system based on the internet of things for real-time particle monitoring in buildings. Int. J. Environ. Res. Public Health15. 2018 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Masiero G., Ogasavara M.H., Jussani A.C., Risso M.L. The global value chain of electric vehicles: A review of the Japanese, South Korean and Brazilian cases. Renew. Sustain. Energy Rev.80. 2017:290–296. [ Google Scholar ]
• McDonough W., Braungart M. North point press; 2010. Cradle to cradle: Remaking the way we make things. [ Google Scholar ]
• McKee M., Stuckler D. If the world fails to protect the economy, COVID-19 will damage health not just now but also in the future. Nat. Med.26. 2020:640–642. [ PubMed ] [ Google Scholar ]
• McKenzie B. Beyond COVID-19: supply chain resilience holds key to recovery. Oxford Econ.24. 2020:20. [ Google Scholar ]
• McKibbin, W.J., Fernando, R., 2020. The global macroeconomic impacts of COVID-19: Seven scenarios.
• Minunno R., O'Grady T., Morrison G.M., Gruner R.L., Colling M. Strategies for applying the circular economy to prefabricated buildings. Buildings. 2018; 8 :125. [ Google Scholar ]
• Moazzami B., Razavi-Khorasani N., Moghadam A.D., Farokhi E., Rezaei N. COVID-19 and telemedicine: Immediate action required for maintaining healthcare providers well-being. J. Clin. Virol. 2020 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Monlau F., Sambusiti C., Antoniou N., Barakat A., Zabaniotou A. A new concept for enhancing energy recovery from agricultural residues by coupling anaerobic digestion and pyrolysis process. Appl. Energy. 2015; 148 :32–38. [ Google Scholar ]
• Morlet A., Blériot J., Opsomer R., Linder M., Henggeler A., Bluhm A., Carrera A. Ellen MacArthur Foundation; 2016. Intelligent assets: Unlocking the circular economy potential; pp. 1–25. [ Google Scholar ]
• Morrison A., Polisena J., Husereau D., Moulton K., Clark M., Fiander M., Mierzwinski-Urban M., Clifford T., Hutton B., Rabb D. The effect of English-language restriction on systematic review-based meta-analyses: a systematic review of empirical studies. Int. J. Tech. Assess. Health Care28. 2012:138. [ PubMed ] [ Google Scholar ]
• Muhammad S., Long X., Salman M. COVID-19 pandemic and environmental pollution: A blessing in disguise? Sci. Total Environ. 2020 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Murray N.E.A., Quam M.B., Wilder-Smith A. Epidemiology of dengue: past, present and future prospects. Clin. Epidemiol.5. 2013:299. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Naidoo R., Fisher B. Nature Publishing Group; 2020. Reset Sustainable Development Goals for a pandemic world. [ PubMed ] [ Google Scholar ]
• NASA . National Aeronautics and Space Administration (NASA); 2020. Airborne Nitrogen Dioxide Plummets Over China. Online. [ Google Scholar ]
• NASA . NASA; 2020. Airborne Particle Levels Plummet in Northern India. [ Google Scholar ]
• OECD . OECD; Paris: 2018. Financing Climate Futures - Rethinking Infrastructure. [ Google Scholar ]
• Omary M.B., Eswaraka J., Kimball S.D., Moghe P.V., Panettieri R.A., Scotto K.W. The COVID-19 pandemic and research shutdown: staying safe and productive. J. Clin. Invest.130. 2020 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Opitz I., Berges R., Piorr A., Krikser T. Contributing to food security in urban areas: differences between urban agriculture and peri-urban agriculture in the Global North. Agricult. Hum. Val.33. 2016:341–358. [ Google Scholar ]
• Oweis R., Al-Widyan M., Al-Limoon O. Medical waste management in Jordan: A study at the King Hussein Medical Center. Waste Manag.25. 2005:622–625. [ PubMed ] [ Google Scholar ]
• Özkan A. Evaluation of healthcare waste treatment/disposal alternatives by using multi-criteria decision-making techniques. Waste Manag. Res.31. 2013:141–149. [ PubMed ] [ Google Scholar ]
• Paez A. Gray literature: An important resource in systematic reviews. J. Evid.‐Based Med.10. 2017:233–240. [ PubMed ] [ Google Scholar ]
• Pagoropoulos A., Pigosso D.C., McAloone T.C. The emergent role of digital technologies in the Circular Economy: A review. Procedia CIRP. 2017; 64 :19–24. [ Google Scholar ]
• Partelow S., von Wehrden H., Horn O. Pollution exposure on marine protected areas: a global assessment. Mar. Pollut. Bull.100. 2015:352–358. [ PubMed ] [ Google Scholar ]
• Paxton, N.C., Forrestal, D.P., Desselle, M., Kirrane, M., Sullivan, C., Powell, S.K., Woodruff, M.A., 2020. N95 respiratory masks for COVID-19: a review of the literature to inform local responses to global shortages.
• Pearce J.M. A review of open source ventilators for COVID-19 and future pandemics. F1000Research. 2020; 9 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Peng J., Wu X., Wang R., Li C., Zhang Q., Wei D. Medical waste management practice during the 2019-2020 novel coronavirus pandemic: Experience in a general hospital. Am. J. Infect. Control. 2020 S0196-6553(0120)30351-30355. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Penn E., Yasso S.F., Wei J.L. Reducing disposable equipment waste for tonsillectomy and adenotonsillectomy cases. Otolaryngol.–Head and Neck Surg.147. 2012:615–618. [ PubMed ] [ Google Scholar ]
• PHE . Public Health England (PHE); London: 2020. Review of interventions to improve outdoor air quality and public health; pp. 1–262. [ Google Scholar ]
• Piguillem F., Shi L. Einaudi Institute for Economics and Finance (EIEF); 2020. The optimal COVID-19 quarantine and testing policies, EIEF Working Papers Series 2004; p. 40. [ Google Scholar ]
• Pinheiro M.D., Luís N.C. COVID-19 Could Leverage a Sustainable Built Environment. Sustainability. 2020; 12 [ Google Scholar ]
• Pinner D., Rogers M., Samandari H. McKinsey & Company; 2020. McKinsey Quarterly: Addressing climate change in a post-pandemic world; pp. 1–6. [ Google Scholar ]
• Pitarma R., Marques G., Ferreira B.R. Monitoring indoor air quality for enhanced occupational health. J. Med. Syst.41. 2017 [ PubMed ] [ Google Scholar ]
• Pomponi F., Moncaster A. Circular economy for the built environment: A research framework. J. Clean. Prod.143. 2017:710–718. [ Google Scholar ]
• Prata J.C., Silva A.L.P., Walker T.R., Duarte A.C., Rocha-Santos T. COVID-19 pandemic repercussions on the use and management of plastics. Environ. Sci. Tech.54. 2020:7760–7765. [ PubMed ] [ Google Scholar ]
• Preston F., Lehne J., Wellesley L. CHATAM HOUSE, The Royal Institute of International Affairs; London: 2019. An Inclusive Circular Economy; Priorities for Developing Countries, Priorities for Developing Countries; pp. 1–82. [ Google Scholar ]
• Prüss-Üstün A., Prüss A., Giroult E., Townend W.K., Rushbrook P., Organization W.H. World Health Organization; 1999. Safe Management of Wastes from Health-care Activities. [ Google Scholar ]
• Rai H.B., Verlinde S., Macharis C. Shipping outside the box. Environmental impact and stakeholder analysis of a crowd logistics platform in Belgium. J. Clean. Prod.202. 2018:806–816. [ Google Scholar ]
• Raj, M., Sundararajan, A., You, C., 2020. COVID-19 and Digital Resilience: Evidence from Uber Eats. arXiv preprint arXiv:2006.07204.
• Ranganatha J., Waite R., Searchinger T., Zionts J. World Resources Institute; 2020. Regenerative Agriculture: Good for Soil Health, but Limited Potential to Mitigate Climate Change. [ Google Scholar ]
• Ranney M.L., Griffeth V., Jha A.K. Critical supply shortages—the need for ventilators and personal protective equipment during the Covid-19 pandemic. N. Engl. J. Med. 2020 [ PubMed ] [ Google Scholar ]
• Rashid A., Asif F.M., Krajnik P., Nicolescu C.M. Resource conservative manufacturing: an essential change in business and technology paradigm for sustainable manufacturing. J. Clean. Prod.57. 2013:166–177. [ Google Scholar ]
• Rees W.E. Footprint: our impact on Earth is getting heavier. Nature. 2002; 420 :267–268. [ PubMed ] [ Google Scholar ]
• Rhodes C.J. The imperative for regenerative agriculture. Sci. Progr.100. 2017:80–129. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Rios F.C., Chong W.K., Grau D. Design for disassembly and deconstruction-challenges and opportunities. Procedia Eng.118. 2015:1296–1304. [ Google Scholar ]
• Rubio-Romero J.C., del Carmen Pardo-Ferreira M., García J.A.T., Calero-Castro S. Disposable masks: Disinfection and sterilization for reuse, and non-certified manufacturing, in the face of shortages during the COVID-19 pandemic. Safe. Sci. 2020 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• San Juan, D.M., 2020. Responding to COVID-19 through socialist (ic) measures: a preliminary review. Available at SSRN 3559398.
• Sarkis J., Cohen M.J., Dewick P., Schröder P. A brave new world: lessons from the COVID-19 pandemic for transitioning to sustainable supply and production. Resour. Conserv. Recycl. 2020 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Sauerwein M., Doubrovski E., Balkenende R., Bakker C. Exploring the potential of additive manufacturing for product design in a circular economy. J. Clean. Prod.226. 2019:1138–1149. [ Google Scholar ]
• Saunders-Hastings P.R., Krewski D. Reviewing the history of pandemic influenza: understanding patterns of emergence and transmission. Pathogens. 2016; 5 :66. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Schluep M. UNEP; 2009. Recycling-from e-waste to resources: Sustainable innovation technology transfer industrial sector studies. [ Google Scholar ]
• Shaheen S., Cohen A. Elsevier; 2020. Mobility on demand (MOD) and mobility as a service (MaaS): early understanding of shared mobility impacts and public transit partnerships, Demand for Emerging Transportation Systems; pp. 37–59. [ Google Scholar ]
• Sherwood S., Uphoff N. Soil health: research, practice and policy for a more regenerative agriculture. Appl. Soil Ecol.15. 2000:85–97. [ Google Scholar ]
• Sim K., Chua H.C., Vieta E., Fernandez G. The anatomy of panic buying related to the current COVID-19 pandemic. Psychiatry Res. 2020 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Siow W.T., Liew M.F., Shrestha B.R., Muchtar F., See K.C. Springer; 2020. Managing COVID-19 in resource-limited settings: critical care considerations. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Snider-Mcgrath B. 2020. Exercise rates on the rise during COVID-19. [ Google Scholar ]
• Snyder H. Literature review as a research methodology: An overview and guidelines. J. Busi. Res.104. 2019:333–339. [ Google Scholar ]
• Sohrabi C., Alsafi Z., O’Neill N., Khan M., Kerwan A., Al-Jabir A., Iosifidis C., Agha R. World Health Organization declares global emergency: A review of the 2019 novel coronavirus (COVID-19) Int. J. Surg. 2020 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Solomon M.Z., Wynia M., Gostin L.O. Scarcity in the Covid‐19 pandemic. Hast. Cent. Rep.50. 2020:3. -3. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Spash C.L. ‘The economy'as if people mattered: revisiting critiques of economic growth in a time of crisis. Globalizations. 2020:1–18. [ Google Scholar ]
• Stahel W.R. The circular economy. Nature. 2016; 531 :435–438. [ PubMed ] [ Google Scholar ]
• Stahel W.R. Routledge; 2019. The circular economy: A user's guide. [ Google Scholar ]
• Stellinger A., Berglund I., Isakson H. How trade can fight the pandemic and contribute to global health. COVID-19 and Trade Policy: Why Turning Inward Won't Work. 2020; 21 [ Google Scholar ]
• Stockholm Resilience Centre . Stockholm Resilience Centre, Stockholm University; Stockholm: 2016. Through resilience thinking towards sustainability and innovation: recommendations for policy makers in the EU; pp. 1–20. [ Google Scholar ]
• SYSTEMIQ . SUN Institute Environment & Sustainability in collaboration with The Ellen MacArthur Foundation; 2017. ACHIEVING ‘GROWTH WITHIN’: A €320-billion circular economy investment opportunity available to Europe up to 2025; p. 149. [ Google Scholar ]
• Tan J., Liu Y., Shen E., Zhu W., Wang W., Li R., Yang L. Towards<< the atlas of plague and its environment in the People's Republic of China>>: idea, principle and methodology of design and research results. Huan jing ke xue= Huanjing kexue. 2002; 23 :1–8. [ PubMed ] [ Google Scholar ]
• Temmerman S., Meire P., Bouma T.J., Herman P.M., Ysebaert T., De Vriend H.J. Ecosystem-based coastal defence in the face of global change. Nature. 2013; 504 :79–83. [ PubMed ] [ Google Scholar ]
• Thebo A., Drechsel P., Lambin E. Global assessment of urban and peri-urban agriculture: irrigated and rainfed croplands. Environ. Res. Lett.9. 2014 [ Google Scholar ]
• Thunstrom, L., Newbold, S., Finnoff, D., Ashworth, M., Shogren, J.F., 2020. The benefits and costs of flattening the curve for COVID-19. Available at SSRN 3561934.
• Timmer M., Erumban A., Gouma R., Los B., Temurshoev U., de Vries G., Arto I. The world input-output database (WIOD): contents, sources and methods. WIOD Background. 2012 document available at www. wiod. org 40. [ Google Scholar ]
• Toquero C. Challenges and opportunities for higher education amid the COVID-19 pandemic: The Philippine Context. Pedag. Res.5. 2020 [ Google Scholar ]
• Trilla A., Trilla G., Daer C. The 1918 “Spanish Flu” in Spain. Clin. Infect. Dis.47. 2008:668–673. [ PubMed ] [ Google Scholar ]
• Tudor T., Noonan C., Jenkin L. Healthcare waste management: a case study from the National Health Service in Cornwall, United Kingdom. Waste Manag.25. 2005:606–615. [ PubMed ] [ Google Scholar ]
• UN DESA Everyone included: social impact of COVID-19. UN Depart. Econ. Soc. Aff. (UN DESA) 2020 [ Google Scholar ]
• UN Habitat . Taylor & Francis; 2013. Planning and design for sustainable urban mobility: Global report on human settlements 2013. [ Google Scholar ]
• UNWTO . World Tourism Organization; 2020. Impact assessment of the covid-19 outbreak on international tourism United Nation. [ Google Scholar ]
• Van Bavel J.J., Baicker K., Boggio P.S., Capraro V., Cichocka A., Cikara M., Crockett M.J., Crum A.J., Douglas K.M., Druckman J.N. Using social and behavioural science to support COVID-19 pandemic response. Nat. Hum. Behav. 2020:1–12. [ PubMed ] [ Google Scholar ]
• Van Buren N., Demmers M., Van der Heijden R., Witlox F. Towards a circular economy: The role of Dutch logistics industries and governments. Sustainability. 2016; 8 :647. [ Google Scholar ]
• Voudrias, E.A., 2018. Healthcare waste management from the point of view of circular economy. [ PubMed ]
• Vousdoukas M.I., Ranasinghe R., Mentaschi L., Plomaritis T.A., Athanasiou P., Luijendijk A., Feyen L. Sandy coastlines under threat of erosion. Nat. Clim. Chang.10. 2020:260–263. [ Google Scholar ]
• Wagner D.M., Klunk J., Harbeck M., Devault A., Waglechner N., Sahl J.W., Enk J., Birdsell D.N., Kuch M., Lumibao C. Yersinia pestis and the Plague of Justinian 541–543 AD: a genomic analysis. Lancet Infect. Dis.14. 2014:319–326. [ PubMed ] [ Google Scholar ]
• Wang P., Chen K., Zhu S., Wang P., Zhang H. Severe air pollution events not avoided by reduced anthropogenic activities during COVID-19 outbreak. Resour. Conserv. Recycl.158. 2020 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Watts N., Amann M., Arnell N., Ayeb-Karlsson S., Belesova K., Berry H., Bouley T., Boykoff M., Byass P., Cai W. The 2018 report of the Lancet Countdown on health and climate change: shaping the health of nations for centuries to come. The Lancet. 2018; 392 :2479–2514. [ PubMed ] [ Google Scholar ]
• Watts N., Amann M., Ayeb-Karlsson S., Belesova K., Bouley T., Boykoff M., Byass P., Cai W., Campbell-Lendrum D., Chambers J. The Lancet Countdown on health and climate change: from 25 years of inaction to a global transformation for public health. The Lancet. 2018; 391 :581–630. [ PubMed ] [ Google Scholar ]
• Webster R.G. Predictions for future human influenza pandemics. J. Infect. Dis.176. 1997:S14–S19. [ PubMed ] [ Google Scholar ]
• White D.B., Lo B. A framework for rationing ventilators and critical care beds during the COVID-19 pandemic. JAMA. 2020 [ PubMed ] [ Google Scholar ]
• WHO . 1998. Safe Management of Wastes from Health-Care Activities. 2014. [ Google Scholar ]
• WHO . World Health Organization; 2020. Coronavirus Disease (COVID-19) Pandemic. [ Google Scholar ]
• WHO, 2020b. Coronavirus disease (COVID-19): Situation Report – 107, pp. 1-17.
• Williamson J. Every UK manufacturer helping to produce PPE and equipment for NHS workers. The Manufacturer. 2020 [ Google Scholar ]
• Windapo A.O., Moghayedi A. Adoption of smart technologies and circular economy performance of buildings. Built Environ. Project Asset Manag. 2020 [ Google Scholar ]
• Windfeld E.S., Brooks M.S.-L. Medical waste management – A review. J. Environ. Manag.163. 2015:98–108. [ PubMed ] [ Google Scholar ]
• Wong H.J.Y., Deng Z., Yu H., Huang J., Leung C., Miao C. Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI-20)Demonstrations Track. 2020. A testbed for studying COVID-19 spreading in ride-sharing systems; pp. 5294–5296. [ Google Scholar ]
• Wong K.-F.V., Narasimhan R., Kashyap R., Fu J. Medical waste characterization. J. Environ. Health. 1994:19–25. [ Google Scholar ]
• World Economic Forum . World Economic Forum; Geneva: 2019. A Vision for a Sustainable Battery Value Chain in 2030: Unlocking the Full Potential to Power Sustainable Development and Climate Change Mitigation; pp. 1–52. [ Google Scholar ]
• World Economic Forum . World Economic Forum; 2020. New Nature Economy Report II: The Future of Nature and Business. [ Google Scholar ]
• Wormer B.A., Augenstein V.A., Carpenter C.L., Burton P.V., Yokeley W.T., Prabhu A.S., Harris B., Norton S., Klima D.A., Lincourt A.E. The green operating room: simple changes to reduce cost and our carbon footprint. Am. Surg.79. 2013:666–671. [ PubMed ] [ Google Scholar ]
• Wosik J., Fudim M., Cameron B., Gellad Z.F., Cho A., Phinney D., Curtis S., Roman M., Poon E.G., Ferranti J. Telehealth transformation: COVID-19 and the rise of virtual care. J. Am. Med. Inform. Assoc.27. 2020:957–962. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Xiao Y., Torok M.E. Taking the right measures to control COVID-19. The Lancet Infect. Dis.20. 2020:523–524. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Yang C., Peijun L., Lupi C., Yangzhao S., Diandou X., Qian F., Shasha F. Sustainable management measures for healthcare waste in China. Waste Manag. 2009; 29 :1996–2004. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Zambrano-Monserrate M.A., Ruano M.A., Sanchez-Alcalde L. Indirect effects of COVID-19 on the environment. Sci. Total Environ. 2020 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Zambrano-Monserrate M.A., Silva-Zambrano C.A., Ruano M.A. The economic value of natural protected areas in Ecuador: A case of Villamil Beach National Recreation Area. Ocean Coast. Manag.157. 2018:193–202. [ Google Scholar ]
• Zhou M., Chen Y., Su X., An L. Proceedings of the Institution of Civil Engineers-Civil Engineering. Thomas Telford Ltd; 2020. Rapid construction and advanced technology for a Covid-19 field hospital in Wuhan, China; pp. 1–29. [ Google Scholar ]

On the Post-Pandemic Horizon, Could That Be … a Boom?

Signs of economic life are picking up, and mounds of cash are waiting to be spent as the virus loosens its grip.

Credit... Maxime Mouysset

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By Ben Casselman

• Published Feb. 21, 2021 Updated June 3, 2021

The U.S. economy remains mired in a pandemic winter of shuttered storefronts, high unemployment and sluggish job growth. But on Wall Street and in Washington, attention is shifting to an intriguing if indistinct prospect: a post-Covid boom.

Forecasters have always expected the pandemic to be followed by a period of strong growth as businesses reopen and Americans resume their normal activities. But in recent weeks, economists have begun to talk of something stronger: a supercharged rebound that brings down unemployment, drives up wages and may foster years of stronger growth.

There are hints that the economy has turned a corner: Retail sales jumped last month as the latest round of government aid began showing up in consumers’ bank accounts. New unemployment claims have declined from early January, though they remain high . Measures of business investment have picked up, a sign of confidence from corporate leaders.

Economists surveyed by the Federal Reserve Bank of Philadelphia this month predicted that U.S. output will increase 4.5 percent this year , which would make it the best year since 1999. Some expect an even stronger bounce: Economists at Goldman Sachs forecast that the economy will grow 6.8 percent this year and that the unemployment rate will drop to 4.1 percent by December, a level that took eight years to achieve after the last recession.

“We’re extremely likely to get a very high growth rate,” said Jan Hatzius, Goldman’s chief economist. “Whether it’s a boom or not, I do think it’s a V-shaped recovery,” he added, referring to a steep drop followed by a sharp rebound.

The growing optimism stems from the confluence of several factors. Coronavirus cases are falling in the United States. The vaccine rollout, though slower than hoped, is gaining steam. And largely because of trillions of dollars in federal help, the economy appears to have made it through last year with less structural damage — in the form of business failures, home foreclosures and personal bankruptcies — than many people feared last spring.

The DealBook DC Policy Project with Andrew Ross Sorkin

What does the road to recovery look like? Join Janet Yellen, Mitt Romney, the leaders of CVS Health, Delta and others on Feb. 22-23. Free of charge, from anywhere in the world. Register today.

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The Transformation of the Texas Economy

Overview: Empresarios, Grangers, Railroaders, Wildcatters and Dot-commers

Two centuries ago Texas was a sparsely populated and desolate backwater on the distant frontier of the waning Spanish empire, decades away from joining the emerging nation that grew out of the thirteen British colonies on the eastern shore of North America.

Today the Texas economy is large, diverse and dynamic, boasting a gross state product (GSP) valued at almost $925 billion in 2005. If the Lone Star State were an independent country, it would have the tenth largest national economy in the world--just behind Spain and Canada, and ahead of Brazil, the Republic of Korea, and India--according to data from the World Bank.

The actual unfolding of the transformation of the Texas economy was wholly unpredictable at the beginning of the 1800s when the territory was still a dangerous, desolate and dusty outpost, well beyond the frontier of substantial European settlement. Only in the last decades of the 19th century did the potential of the territory as an economic and political powerhouse begin to reveal itself. But, the growth of the cotton industry, the development of railroads and the discovery of large quantities of oil were only the first of a series of dramatic transformations that would make the Lone Star State the economic powerhouse it is today.

A combination of unexpected bounty in the form of mineral wealth and other resources, growing integration into the national economy of the United States (which itself underwent its own series of transformations), the growth of the service economy and the high technology industry, and the state's increasingly important geographic position at one of the frontiers of an extensive free trade area that includes Mexico and Canada, have all contributed to the ongoing transformation of the state.

1800 to Independence: Empresarios and early settlement

In the early days of exploration and settlement by the Spanish, Texas represented a vast, unsecured, and sparsely populated territory with little immediate economic or political value. Over almost three centuries from approximately 1519 (when Spanish explorers first came to Texas) to 1800, the Spanish established only a few, relatively small settlements in the territory. Spain's military authority over that time was limited and uneven, sometimes eclipsed by aggressive and powerful indigenous groups like the Apaches and Comanches.

Vast spaces and sparse settlement made any claim to the territory tenuous. In 1803, only three years after the French wrested the territory of Louisiana from weak Spanish control, they sold it to the United States. The new owners then claimed that the territory's southwestern border was the Rio Grande (known to Mexicans as the Rio Bravo).

This raised Spanish concern that the territory west of the Sabine River needed to be populated with Spanish subjects--"facts on the ground," as we say today. The limited progress made by the Spanish in populating the Texas territory by the first decade of the 1800s easily came undone during the early struggles for independence from Mexico (1811 to 1813). By the time of Mexico's ultimate independence in 1821 the Texas territory had even fewer persons of Spanish descent than at the turn of the century--probably fewer than 5,000.

During the first two decades of the nineteenth century the people of the territory remained quite poor, even by frontier standards. The territory was too vast and under-populated for significant wealth generating commerce to thrive. The population and the economy was largely sustained by the Spanish military, which had sent garrisons to defend the territory from encroaching Anglos and hostile natives.

After independence a period of relative tranquility settled over Texas as the new Mexican government focused on establishing a constitution, laws and state-level administration. The territory of Texas was joined with Coahuila to become the state of Coahuila y Tejas.

Meanwhile, immigration from the United States--mainly from Tennessee--continued to swell the Anglo population. The settlement founded by Moses Austin in 1820 and later managed by his son Stephen grew steadily. Stephen sought and won approval for a law under the newly independent Mexican government that promoted the development of settlements by granting large tracts of land to agents who recruited colonists to the territory. This was known as the empresario system, and the agents were called empresarios.

Approximately 30 or more six-year empresario contacts were awarded beginning in 1825, providing compensation to the empresarios for up to 9,000 immigrant families. The empresario contracts covered vast areas of Texas territory, effectively denying the state government the authority over disposition of these lands for the six-year period of the contracts. These empresario contracts represented the main legal mechanism by which property in the public domain was put into private hands.

Still, because they provided land to settlers at very low cost, and required that the individual acquirers inhabit and cultivate the land, they had a broad democratizing effect. Concentration of land ownership and land speculation--common in other parts of the frontier in the United States--was largely absent in Coahuila y Tejas.

The late 1820s and 1830s were characterized by growing political tension despite--and perhaps because of--the deepening economic development in the territory. The population of Texas in 1820 was about 7,000, not much greater than it was in the first years of the century. But, during the colonization period after Mexican independence from Spain (1821-1835) the population of Texas grew at a considerable rate, if admittedly from a very low base. The non-native population grew more than ten-fold from about 2,000 at the time of Mexican independence to an estimated 20,000 in 1831.

Population growth through immigration primarily from the United States seemed to accelerate in the early 1830s despite the considerable political turmoil caused by factional struggles over political control of the huge expanse of territory that constituted the state of Coahuila y Tejas.

By 1834 the Texas population (including slaves) was estimated at 24,700. Just two years later in 1836--the year of Texas independence from Mexico--the non-native population was estimated at about 38,470. Including the estimated 14,200 natives brought the total population to well over 50,000.

Many factors on both sides of the U.S.-Mexico border--then formed by the Sabine River which separates the states of Texas and Louisiana today--contributed to the considerable growth in the number of colonists from the United States. Still, it seems that the much lower cost of land in Texas than in frontier areas of the United States, combined with the formal land grant system, were major factors.

Independence & Early Statehood: Cotton and slaves

From independence to early statehood the Texas population continued to swell, exceeding 212,000 inhabitants (154,034 whites, 58,161 slaves, and 397 freed African Americans) in the new state’s first U.S. Census in 1850.

Most of the new immigrants came from southern states, especially Tennessee, Virginia and Georgia. Representatives of these three states alone formed a majority in the constitutional convention that produced the state Constitution of 1845. Reflecting the Jacksonian political culture and agricultural economic interests of these settlers, that first state constitution prohibited banking and made the formation of private corporations very difficult.

These southern immigrants also brought with them their preference for and expertise in growing cotton. As cotton was a labor intensive crop, southern immigrants brought increasing numbers of slaves. A census of the state in 1848 reported 42,455 slaves in the state. But just two years later, the U.S. Census of 1850 counted 58,161 slaves.

In the ten years from 1849 to 1859 production of cotton multiplied more than sevenfold, from 58,073 bales (each weighing 500 pounds) to 431,645 bales. As cotton production blossomed, so did slavery – an Anglo American institution that had previously been only tolerated by Mexican authorities, and which was relatively limited at the time of independence. The Census of 1860 showed that sixty-four counties had 1,000 slaves or more, and all but eight of those counties produced 1,000 bales or more.

Civil War to World War II: "King Cotton," railroads and oil

From the end of the Civil War through the turn of the century, cotton production continued to increase dramatically as a result of several key developments. These included massive immigration from the deep South and Europe, removal of natives from prime cotton-growing areas, the invention of a new plow that more easily broke the thick black sod of the plains, the invention of barbed wire, the extension of railroads, the invention of cotton ginning (removal of seeds from cotton fibers and cleaning and baling of the lint), and perfection of cotton compressing at the side of railroads for easier shipping.

Clearly, "King Cotton" became a central feature of the Texas economy, attracting considerable investment capital, labor power, and technological development. Other industries within the broader agricultural sector also grew considerably in late nineteenth century Texas, including ranching, timber, and corn. Still, cotton was king until the 1920s when it began a decades long decline in importance caused by the drop in demand during the Great Depression, the loss of labor power during World War II, the rise of other centers of cotton production abroad, and federal efforts to hold down production to maintain prices.

As the railroads extended their reach in the late nineteenth century across the state to the panhandle and the high plains of west Texas, their influence grew. At first, the combination of more extensive railroad service and the relocation of cotton compresses from the seaports to rail sidings helped cotton farmers break the power of the port facility operators. But, hostility and political competition between farmers and ranchers on the one hand and the railroads on the other quickly grew.

Because railroads tend to be natural monopolies (in which the huge cost of investment makes it inefficient to have more than one service provider in a particular area), they tended to exercise enormous market power over their customers--the farmers and ranchers. The railroads' power to set rates was perceived as injurious to farmers and ranchers.

The struggle between railroads and their customers led to the victory of James Stephen Hogg in the gubernatorial election of 1890. Hogg ran chiefly on a populist platform whose main plank was the promise to regulate the railroads. In that same election a proposed amendment to the Texas constitution was ratified that permitted the creation of a railroad regulating body that among other things would regulate freight rates. Hogg made the first appointments to the new Texas Railroad Commission in 1891. Three years later in 1894 the Legislature made those positions elective.

The creation of the Railroad Commission represented the most significant and direct political clash between competing economic interests since the Civil War pitted slaveholding cotton growers against northern industrialists. Governor Hogg pushed through a series of laws, known as "Hogg's Laws," aimed at reining in the railroads, out-of-state corporations, and insurance companies.

As cotton began its long decline in the early decades of the twentieth century, oil began to assume increasing prominence. Though commercial oil exploration had enjoyed some limited success in the post-Civil War era, the industry did not make major discoveries until the late 1890s and the first years of the new century.

As sizable discovery followed sizable discovery, a fully integrated industry began to take shape, including pipelines and oil refineries on the Gulf Coast. Then, as Henry Ford and other manufacturers turned the automobile with its the internal combustion engine into an object of mass consumption, the Texas oil industry came into its own. By 1929 there was already one automobile in the state for every 4.3 of the almost six million Texans. Also by 1929, the four states of Texas, Oklahoma, Louisiana and Arkansas accounted for approximately 60 percent of oil production in the United States.

The success of the oil and natural gas industry helped diversify the state economy, which until the first quarter of the century was still dominated by agriculture. The dominance of that sector by cotton continued, but to a lesser degree than in the earlier period. Cotton prices had tumbled, while new fruits and vegetables, harvested by increasing numbers of migrant Mexican workers, grew in importance. While white and black Texans also worked in the itinerant farm labor pool, Mexicans became the backbone of the industry.

Modern Texas Economy: Travel, retail, and technology

Oil and natural gas production bestowed new economic importance and diversity on the Texas economy. Nevertheless, they only reinforced the state's reliance on the production of so-called "primary goods"--mining (including oil and natural gas production), timber, agriculture and ranching. It would take a World War, followed by a post-war national economic boom, to really build a significant base for industrial production, create several transportation hubs in the new air travel industry, and establish the state as a platform for high technology research and development.

The energy sector is still prominent among Texas industries, as are ranching, agriculture, and agriculture related industries like cotton ginning. But other industries such as airlines, travel and entertainment, and technology (including computers, aerospace, and telecommunications) have grown to considerable prominence.

In part these industries have enjoyed considerable encouragement and dollars from the federal government--particularly the aerospace industry. National and even international politics played important roles here. The arms and space races with Cold War foe the Soviet Union led to creation of the NASA facility near Houston. In turn, the aerospace industry has spawned growth in related industries such as telecommunications, information technology, and the airline industry and travel reservations industries. The airline and air travel industry has been a particular beneficiary of the fortuitous location of Texas roughly halfway between the two coasts, as well as from the state's burgeoning and increasingly urbanized population.

Some of the largest companies operating in the state in other important industries, such as retail and manufacturing (e.g., CVS Pharmacy, Office Depot, Safeway, Coca-Cola, and International Paper), generally are not headquartered in Texas. Still, these companies represent considerable levels of employment and production. The prominence of these out-of-state corporations in Texas reflects the increasingly integrated nature of the national economy.

This integration was facilitated in part by the creation of the interstate highway system, which was initiated under 1956 legislation creating the National System of Interstate and Defense Highways, ostensibly for the rapid movement of troops and materiel for defense of the national territory. The interstate highway system gave a considerable boost to the development of corporate restaurant chains. The prominence of numerous corporate chain restaurant companies (including Church's, Popeye's, Chili's, Red Lobster, and Luby's, and others) among the state's top employers in recent years confirms the symbiotic relationship of the dining and entertainment sector to the national highway system. The size of these corporate restaurant chains also reflects the explosion of suburbs and exurbs across the state, which was in turn facilitated by extensive highways.

The development of this emerging socio-economic complex--whose key components included petroleum, automobiles, highways, suburbanization, and chain retail and restaurants--was reinforced by forces already operating within the state. The Texas Good Roads and Transportation Association had already been established in 1932 to promote public expenditure on the building and maintenance of roads in Texas.

As early as 1946--well before President Eisenhower's push for a national highway system--the Good Roads Association was instrumental in pushing through the 1946 "Good Roads Amendment" to the Texas Constitution (see the chapter on the Texas Constitution). This amendment required that three-quarters of all revenue from state gasoline taxes be "used for the sole purpose of acquiring rights of way, constructing, maintaining, and policing... public road ways" and for the administration of traffic safety laws.

Although Texas-based companies are not so dominant in other economic sectors, they do represent some of the leading businesses in their industries. The high-technology sector includes such recognizable names as personal computer manufacturer Dell Computer Corporation (based in Round Rock, Texas), telecommunications giant AT&T (bought in 2005 by Southwestern Bell Communications headquartered in San Antonio), and chip maker Texas Instruments (Dallas). All three companies are among the top 100 employers in Texas.

In the retail sector several of the biggest employers are headquartered in the Lone Star State. These include: 7-Eleven, Inc. based in Dallas, J.C. Penney Company (which also owns Eckerd Drug) based in Plano, Radio Shack in Fort Worth, and Winn Dixie also based in Fort Worth. In the services and finance sectors two Texas based companies are notable for being large employers. Administaff is based in Kingwood, while Cullen Frost Bankers claims San Antonio as home.

This brief list of industries and companies conveys just how large and diverse the economy of Texas really is today.

Source: 

"Texas in the age of Mexican Independence." The Handbook of Texas Online. http://www.tsha.utexas.edu/handbook/online/articles/view/TT/nptsd.html. 

"Mexican Colonization Laws." The Handbook of Texas Online. http://www.tsha.utexas.edu/handbook/online/articles/view/MM/ugm1.html. 

"Census and Census Records." The Handbook of Texas Online. http://www.tsha.utexas.edu/handbook/online/articles/view/CC/ulc1.html. 

"Anglo American Colonization." The Handbook of Texas Online. http://www.tsha.utexas.edu/handbook/online/articles/view/AA/uma1.html. 

"Antebellum Texas." The Handbook of Texas Online. http://www.tsha.utexas.edu/handbook/online/articles/view/AA/npa1.html. 

"Cotton Culture." The Handbook of Texas Online. http://www.tsha.utexas.edu/handbook/online/articles/view/CC/afc3.html. 

The National Grange, http://www.nationalgrange.org/about/history.html. 

"Grange." The Handbook of Texas Online. http://www.tsha.utexas.edu/handbook/online/articles/view/GG/aag1.html. 

"Texas in the 1920s." The Handbook of Texas Online. http://www.tsha.utexas.edu/handbook/online/articles/view/TT/npt1.html.

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• Introduction

Four phases of an economic cycle

Cycling your investments through the phases, what sectors tend to perform better in each phase of the economic cycle, the bottom line.

Economic cycles: Investing through boom and bust

We refer to it by different names: boom and bust; expansion and contraction; growth and recession; and the proverbial bull and bear . What we’re talking about is the economic cycle, aka “business cycle.”

Economic cycles are the recurrent boom-and-bust phases that markets and economies typically exhibit. Think of it like a wave:

• Expanding from a trough,
• Peaking at the crest,
• Descending (“ contracting ”) from the high point, and
• Hitting bottom and recovering , where the wave begins anew.

The image of the cycle is easy to imagine, but what actually happens in the economy during its entire span? What causes economic cycles? And is it possible to position your investments to take advantage of the different phases?

• The economic cycle generally comprises four phases: expansion, peak, contraction, and recovery.
• The duration of economic cycles varies, making the phases difficult to time.
• Some sectors tend to outperform others during different phases of the cycle.

Although there are numerous theories explaining what causes economic cycles, most generally agree on the four phases: expansion, peak, contraction, and recovery.

Phase 1: Expansion. During the expansion phase, interest rates are often on the low side, making it easier for consumers and businesses to borrow money. The demand for consumer goods is growing, and businesses begin ramping up production to meet consumer demand. To increase production, businesses hire more workers or invest capital to expand their physical infrastructure and operations. Generally, corporate profits begin to rise along with stock prices. Gross domestic product (GDP) also begins rising as the economy gets its “boom” cycle underway.

Phase 2: Peak. At this stage, the economy reaches a maximum rate of growth. As consumer demand rises, there’s a point at which businesses may no longer be able to ramp up production and supply to match the increasing demand. Some companies may find it necessary to expand production capabilities, which entails more spending or investment. Businesses may also begin experiencing a rise in production costs (including wages), prompting some to transfer these costs over to the consumer via higher prices .

Consequently, businesses may begin to see a “topping-off” in profits despite charging higher prices. Other businesses will see decreasing profits due to higher manufacturing (input) costs or higher wage demands. Overall, inflationary pressures start to build up, or “bubble,” and the economy begins to overheat.

Typically, the Federal Reserve will hike interest rates to combat rising prices—making it more expensive to borrow money—in an attempt to cool the economy.

Phase 3: Contraction. Then the economic contraction begins. In this stage, corporate profits and consumer spending, particularly on discretionary (e.g., luxury) items, begins to fall. Stock values also decline as investors move their investments to “safer” assets such as Treasury bonds and other fixed-income assets , plus good ole cash. GDP contracts due to the decrease in spending. Production slows to match falling demand. Employment and income can also decline as businesses temporarily freeze hiring or resort to laying off workers. Overall, economic activity slows, stocks enter a bear market , and a recession typically follows.

Sometimes a recession is mild, but other contractions—such as the Great Depression —are particularly severe and long-lasting. In a depression, many businesses close up shop for good.

If the economy looks to be suffering a severe contraction, the Federal Reserve tends to lower interest rates so that consumers and businesses can borrow money on the cheap for spending and investment. Lawmakers may tweak tax policy and/or call on the Treasury Department to issue economic stimulus in order to stoke consumer spending and demand for goods and services.

Phase 4: Recovery. The recovery phase is when the economy hits its trough, bottoms out, and begins the cycle anew. Policies enacted during the contraction phase begin to bear fruit. Businesses that retrenched during the contraction begin to ramp up again. Stock values tend to rise as investors see greater potential returns in stocks than bonds. Production ramps up to meet rising consumer demand and with it, business expansion, employment, income, and GDP.

Can you use the economic cycle model as an actionable map to plot out investments? It’s a tempting prospect. After all, if you can identify the phases, all you have to do is match your market entries and exits to the start of each phase, right?

If only investing were that simple.

What makes it tricky is that the cycles vary in length. For example, from 1857 to 2020, we’ve seen a peak-to-trough cycle as short as two months and as long as 65 months, according to the National Bureau of Economic Research (NBER).

Economic cycle models are actionable, but it takes plenty of research, plus constant monitoring and the unavoidable hit-or-miss approach in timing your investments. It also helps to know which sectors tend to perform better in each phase of the economic cycle.

It’s rarely a good idea to go all-in on any sector. Even the most brilliant investing minds are wrong from time to time, so it’s best to keep those eggs spread among many baskets . But you can certainly change your allocations to try to capitalize on sectors that may soon outperform the market.

One way you can rebalance your portfolio during each phase of the economic cycle is to invest in sector-based exchange-traded funds (ETFs) . In this way, you can gain a bit more exposure to certain sectors , actively managing your portfolio across the cycle’s proverbial seasons.

Your washing machine runs through its cycles according to a preset schedule in terms of the order, the timing, and the length of each cycle. The business cycle? Not so much. There are too many variables that prevent its precise timing from being predictable.

However, the market has enough consistencies to allow us to anticipate and confirm certain sector outcomes during each phase of the economic cycle, give or take a few variations (and errors) in performance.

Expansion: Sectors fueling the engines of economic growth. When the economy is expanding, economic growth and expectations of continued growth are on the rise. Information technology, financials, communications, and consumer discretionary sectors tend to outperform as they help fuel the segments of the economy that drive expansion.

Peak: Investing at the summit. When the economy approaches its peak phase, demand and consumption begin to outpace production and supply, inflation tends to heat up, and the Fed typically begins raising interest rates to slow the economy.

Financials tend to outperform as banks benefit directly from higher interest rates. Energy and materials also perform well, as both continue to fuel growth even at the peak. Investors who are anticipating the next phase of the cycle may choose to take early positions in sectors or industries that are relatively “inelastic” (meaning, people need their products and services regardless of the state of the economy).

Contraction: Defensive sectors and safe haven assets. When contraction sets in, we typically see a bear market in stocks followed by an economic recession. Now’s a good time to go “defensive,” as those stocks historically perform much better. Defensive sectors include health care, consumer staples, and utilities. Investors also tend to load up on bonds or cash as a safe haven from stock market risk.

Recovery: Repositioning your portfolio for the bull’s return. After a bruising contraction phase, investors who get an early sense that the economic cycle is about to begin anew may be looking for stocks that can outperform in the early phases of recovery. These might include real estate and industrials. As the recovery gets going into a full-on expansion, you’d go back to phase 1 again.

Throughout the cycle, the name of the game is to anticipate the next phase and to get in early enough to take advantage of what could become the next outperformers.

There’s a saying that time in the market is much better than timing the market. Although you don’t want to place big and over-concentrated bets on sectors based solely on market timing, you can optimize your portfolio returns through careful and nuanced rebalancing . Using the economic cycle as a model and map for rebalancing your investments can help you navigate large-scale market changes with a greater deal of certainty.

Just bear in mind that the economy is a dynamic process whose driving factors are rife with particularities and variations. This means that you can follow the map, but don’t forget to keep your eye on the road, and always mind your surroundings.

• Business Cycle Dating | nber.org
• How the Business Cycle Happens | mises.org
• [PDF] Sector Business Cycle Analysis | ssga.com

134 Economics Thesis Topics: Ideas for Outstanding Writing

Writing a thesis is not an easy task. For most of the students, it can be even intimidating, especially when you do not know where to start your research.

Here, we have provided an economics thesis topics list. After all, everyone knows that choosing the right idea is crucial when writing an academic paper. In economics, it can combine history, math, social studies, politics, and numerous other subjects. You should also have solid foundations and a sound factual basis for a thesis. Without these elements, you won’t be able to master your research paper.

The issue is:

It is not always clear what could be seen as an excellent economics thesis topic. Our experts can assist you with this challenge. This list contains some outstanding examples to get you started.

• ⭐ Thesis in Economics
• 🔥 Supreme Thesis Topics
• 👍 Bachelor’s Thesis
• 😲 Master’s Thesis

📊 Microeconomics

📈 macroeconomics.

• 🤔 Developmental
• 👨‍💼 Behavioral
• 💼 Financial
• 🌱 Agricultural
• 🤝‍ Sociology
• 📚 Ph.D. Topics
• 📝 How to Pick a Topic

⭐ What Does a Thesis in Economics Look Like?

A good thesis in economics is a blend between an empirical paper and a theoretical one. One of the essential steps in choosing a topic in economics is to decide which one you will write.

You may write, research, analyze statistical data and other information. Or build and study a specific economic model.

Or why not both!

Here are some questions you can ask when deciding what topic to choose:

• What has already been written on this topic?
• What economic variables will my paper study?
• Where should I look for the data?
• What econometrics techniques should I use?
• What type of model will I study?

The best way to understand what type of research you have to do is to write a thesis proposal. You will most probably be required to submit it anyway. Your thesis supervisor will examine your ideas, methods, list of secondary and primary sources. At some universities, the proposal will be graded.

After you get the initial feedback, you will have a clear idea of what to adjust before writing your thesis. Only then, you’ll be able to start.

🔥 Supreme Economics Thesis Topics List

• Fast fashion in India.
• The UK housing prices.
• Brexit and European trade.
• Behavioral economics.
• Healthcare macroeconomics.
• COVID-19’s economic impact.
• Global gender wage gap.
• Commodity dependence in Africa.
• International trade – developing countries.
• Climate change and business development.

👍 Economics Bachelor’s Thesis Topics

At the U.S. Universities, an undergraduate thesis is very uncommon. However, it depends on the Department Policy.

The biggest challenge with the Bachelor’s Thesis in economics concerns its originality. Even though you are not required to conduct entirely unique research, you have to lack redundant ideas.

You can easily avoid making this mistake by simply choosing one of these topics. Also, consider visiting IvyPanda essays database. It’s a perfect palce to conduct a brainstorming session and come up with fresh ideas for a paper, as well as get tons of inspiration.

• The impact of the oil industry on the economic development of Nigeria. The oil industry is vital for the economic development of Nigeria. In this thesis, students can discuss the notion of the resource curse. Analyze the reasons why general people are not benefiting from the oil industry. Why did it produce very little change in the social and economic growth of the country?
• Sports Marketing and Advertising: the impact it has on the consumers.
• Economic opportunities and challenges of investing in Kenya .
• Economic Development in the Tourism Industry in Africa. Since the early 1990s, tourism significantly contributed to the economic growth of African countries. In this thesis, students can talk about the characteristics of the tourist sector in Africa. Or elaborate on specific countries and how their national development plans look like.
• Globalization and its significance to business worldwide .
• Economic risks connected to investing in Turkey .
• The decline in employment rates as the biggest American economy challenge .
• The economics of alcohol abuse problems. In this thesis, students can develop several essential issues. First, they can examine how poverty is connected to alcohol abuse. Second, they can see the link between alcohol consumption and productivity. To sum up, students can elaborate on the economic costs of alcohol abuse.
• Causes and solutions for unemployment in Great Britain.
• Parallel perspective on Global Economic Order: China and America. This thesis can bring a comparative analysis of the economies to a new level. China and The US are the world’s two largest economies. These two countries have a significant impact on the global economic order. So, looking at the set of institutions, policies, rules can be constructive.
• The new international economic order after COVID-19
• Financial stability of the banking sector in China.
• New Electronic Payment Services in Russia.
• The influence of culture on different entrepreneurial behaviors.
• The impact of natural cultural practices on entrepreneurial activity.
• The relationships between national culture and individual behavior.
• The main reasons for salary inequalities in different parts of the U.S.

😲 Economics Master’s Thesis Topics

Student life can be fascinating, but it comes with its challenges. One of which is selecting your Master’s thesis topic.

Here is a list of topics for a Master’s thesis in economics. Are you pursuing MPhil in Economics and writing a thesis? Use the following ideas as an inspiration for that. They can also be helpful if you are working on a Master’s thesis in financial economics.

• The impact of visual aid in teaching home economics.
• The effect of income changes in consumer behaviors in America.
• Forces behind socio-economic inequalities in the United States. This thesis can explore three critical factors for socio-economic differences in the United States. In the past 30 years, social disparities increased in the United States. Some of the main reasons are technology, trade, and institutions.
• The relationships between economic growth and international development.
• Technological innovations and their influence on green and environmental products.
• The economics of non-solar renewable energy .

• The economic consequences of terrorism . Terrorism not only takes away lives and destroys property but also widely affects the economy. It creates uncertainty in the market, increases insurance claims, slows down investment projects, and tourism. This thesis can address all of the ways in which terrorism can affect economies.
• Corporate Social Responsibility (CSR) implementation in the Oil and Gas Industry in Africa.
• Use of incentives in behavioral economics.
• Economic opportunities and challenges of sustainable communities .
• Economics of nuclear power plants.
• Aid and financial help for emerging markets. This topic is very versatile. Students can look at both the positive and the adverse effects that funding has on the development. There are plenty of excellent examples. Besides, some theories call international help a form of neocolonialism.
• Multinational firms impact on economic growth in America .
• The effect of natural disasters on economic development in Asia.
• The influence of globalization on emerging markets and economic development.

📑 More Economics Thesis Topics: Theme

For some students, it makes more sense to center their search around a certain subject. Sometimes you have an econ area that interests you. You may have an idea about what you want to write, but you did not decide what it will be.

If that’s the case with you, then these economics thesis topics ideas are for you.

• An analysis of the energy market in Russia.
• The impact of game theory on economic development.
• The connection between minimum wage and market equilibrium.
• Gender differences in the labor market in the United States. This topic can shed light on gender differences in the labor market in the United States. In the past years, the overall inequality in labor in the markets decreased. However, there is still a lot of work that can be done.
• Economic reasons that influence the prices of oil .
• Relationship between the Lorenz curve and the Gini coefficient.
• Challenges of small businesses in the market economy.
• The changes in oil prices: causes and solutions . Universal economic principles do not always apply to the sale and purchase of the oil. The same happens with its cost. In the thesis, talk about what affects the prices. What are the solutions that can be implemented?
• The economic analysis of the impact of immigration on the American economy.

• Economic inequality as a result of globalization . Economic inequality becomes even more apparent on the global level. There is a common belief that globalization is the cause of that. Discuss what can be the solutions to these problems. This topic is vital to minimize the gap between the rich and the poor.
• The economic explanation of political dishonesty .
• Effect of Increasing Interest rates costs in Africa .
• The connection between game theory and microeconomics.
• Marketing uses in microeconomics.
• Financial liability in human-made environmental disasters.
• Banks and their role in the economy. Banks are crucial elements of any economy, and this topic covers why. You can explain how banks allow the goods and services to be exchanged. Talk about why banks are so essential for economic growth and stability.
• Inflation in the US and ways to reduce its impact.
• The connection between politics and economics.
• Income Dynamics and demographic economics.
• US Market Liquidity and macroeconomics.
• Macroeconomics and self-correction of the economy .
• The American economy, monetary policy, and monopolies .
• The importance of control in macroeconomics. One of the central topics in macroeconomics is grouped around the issue of control. It is quite reasonable that control over money and resources should become a topic of discussion.
• Analysis of Africa’s macroeconomics and its performance.
• Economics of education in developing markets.
• Problems and possible solutions for Japan macroeconomics .
• Comparative analysis of British macroeconomics concerning the US .
• Public policies and socio-economic disparities.
• The world problems through macroeconomic analysis. Indeed, macroeconomics is very complicated. There are many influences, details, and intricacies in it. However, it allows economists to use this complex set of tools to examine the world’s leading problems today.

• The connection between employment interest and money.

🤔 Development Economics

• Economics of development . This topic is very rich in content. First, explain what it is. Then pay particular attention to domestic and international policies that affect development, income distribution, and economic growth.
• The relation between development and incentive for migration.
• The impact of natural disasters on the economy and political stability of emerging markets.
• The economic consequences of population growth in developing countries.
• The role of industrialization in developing countries . The industrialization has been connected with the development. It promotes capital formation and catalyzes economic growth in emerging markets. In this thesis, you can talk about this correlation.
• Latin American economic development.
• Gender inequality and socio-economic development .
• Problems of tax and taxation in connection with economic growth.
• The economic impact of terrorism on developing markets.
• Religious decline as a key to economic development. Not everyone knows, but a lot of research has been done in the past years on the topic. It argues that decreased religious activity is connected with increased economic growth. This topic is quite controversial. Students who decide to write about it should be extra careful and polite.

👨‍💼 Behavioral Economics

• Risk Preferences in Rural South Africa.
• Behavioral Economics and Finance .
• Applied behavioral economics in marketing strategies. If you want to focus your attention on marketing, this topic is for you. Behavioral economics provides a peculiar lens to look at marketing strategies. It allows marketers to identify common behaviors and adapt their marketing strategies.
• The impact of behavioral finance on investment decisions.
• Behavioral Economics in Child Nutrition Programs in North Texas.
• Guidelines for Behavioral Economics in Healthcare Sector.
• Cognitive and behavioral theories in economics .
• Cross-cultural consumer behavior and marketing communication. Consumers are not only affected by personal characteristics, but also by the culture they are living in. This topic focuses on the extent it should determine marketing strategy and communication.
• Behavior implications of wealth and inequality.

• Optimism and pessimism for future behavior.

💼 Financial Economics

• Financial Economics for Infrastructure and Fiscal Policy .
• The use of the economic concept of human capital. Students can focus on the dichotomy between human and nonhuman capital. Many economists believe that human capital is the most crucial of all. Some approach this issue differently. Therefore, students should do their research and find where they stand on this issue.
• The analysis of the global financial crisis of 2020s. Share your thoughts, predictions, ideas. Analyze the economic situation that affects almost everyone in the world. This thesis topic will be fresh and original. It can help to start a good and fruitful conversation.
• The big data economic challenges for Volvo car.
• The connection between finance, economics, and accounting.
• Financial economics: Banks competition in the UK .
• Risk-Taking by mutual funds as a response to incentives.
• Managerial economics and financial accounting as a basis for business decisions.
• Stock market overreaction.

🌱 Agricultural Economics

• Agricultural economics and agribusiness.
• The vulnerability of agricultural business in African countries.
• Agricultural economics and environmental considerations of biofuels .
• Farmer’s contribution to agricultural social capital.
• Agricultural and resource economics. Agricultural and resource economics plays a huge role in development. They are subdivided into four main characteristics which in this topic, students can talk about: – mineral and energy resources; – soil resources, water resources; – biological resources. One or even all of them can be a focus of the thesis.
• Water as an economic good in irrigated agriculture.
• Agriculture in the economic development of Iran.
• The US Agricultural Food Policy and Production .
• Pesticides usage on agricultural products in California.

• An analysis of economic efficiency in agriculture. A lot of research has been done on the question of economic efficiency in agriculture. However, it does not mean there is no place for your study. You have to read a lot of secondary sources to see where your arguments can fit.

🤝‍Economic Sociology

• Theory, approach, and method in economics sociology.
• Economic sociology of capitalism. While economists believe in the positive effect capitalism has on the economy, the social effect is quite different. The “economic” part of the issue has been studied a lot. However, the sociology of it has been not. This thesis can be very intriguing to read.
• Political Economy and Economic Sociology.
• Gender and economic sociology .
• Progress, sociology, and economics.
• Data analysis in economics, sociology, environment .
• Economic sociology as a way to understand the human mind.
• Economic sociology of money.
• Economics, sociology, and psychology of security.
• Major principles of economic sociology. In the past decade, economic sociology became an increasingly popular field. Mainly due to it giving a new view on economics, human mind, and behavior. Besides, it explores relationships between politics, law, culture, and gender.

📚 The List of Ph.D. Topics in Economics

If you decide to go to grad school to do your Masters, you will likely end up getting a Ph.D. as well. So, with this plan in mind, think about a field that interests you enough during your Masters. Working with the same topic for both graduate degrees is easier and more effective.

This list of Ph.D. Topics in Economics can help you identify the areas you can work on.

• Occupational injuries in Pakistan and its effect on the economy. Injuries are the leading cause of the global burden of disability. Globally, Pakistan was ranked 9th populated country with a large number of unskilled workers. In this dissertation, consider the link between occupational injuries and their effects on the economy.
• The study of the Philippines’ economic development.

• Financial derivatives and climate change .
• Econometric Analysis of Financial Markets.
• Islamic Banking and Financial Markets .
• Health economics and policy in the UK.
• Health insurance: rationale and economic justification. In this dissertation, students can find different ways to explain and justify health insurance. Starting to philosophical to purely economic grounds. In the past years, there was a lot of discussion regarding the healthcare system for all. What are some of the economic benefits of that?
• Colombian economy, economic growth, and inequality.
• Benefits of mergers and acquisitions in agribusiness.
• Methods to measure financial risks when investing in Africa.
• The significance of financial economics in understanding the relationship between a country’s GDP and NDP.
• Network effects in cryptocurrency. Cryptocurrencies are not new anymore. However, it is still an original subject for a dissertation. Students can decide to choose several crypto coins and evaluate the importance of the network effect. This effect is particularly significant for Bitcoin. Explain why.
• The comparison of the Chinese growth model with the American growth model.
• An economic justification versus political expediency.
• Pollution Externalities Role in Management Economics .

📝 How to Select an Economics Thesis Topic

As your academic journey is coming to an end, it’s time to pick the right topic for your thesis. The whole academic life you were preparing to undertake this challenge.

Here is the list of six points that will help you to select an economics thesis topic:

• Make sure it is something you are genuinely interested in. It is incredibly challenging to write something engaging if you are not interested in the topic. So, choose wisely and chose what excites you.
• Draw inspiration from the previous student’s projects. A great place to start is by looking at what the previous students wrote. You can find some fresh ideas and a general direction.
• Ask your thesis advisor for his feedback. Most probably, your thesis advisor supervised many students before. They can be a great help too because they know how to assess papers. Before meeting with your professor, do some basic research, and understand what topic is about.
• Be original, but not too much. You do not want to spend your time writing about a project that many people wrote about. Your readers will not be interested in reading it, but your professors as well. However, make sure you do not pick anything too obscure. It will leave you with no secondary sources.
• Choose a narrow and specific topic. Not only will it allow you to be more original, but also to master a topic. When the issue is too broad, there is just too much information to cover in one thesis.
• Go interdisciplinary. If you find yourself interested in history, philosophy, or any other related topic, it can help you write an exceptional thesis in economics. Most of your peers may work on pure economics. Then, the interdisciplinary approach can help you to stand out among them.

Thank you for reading the article to the end! We hope this list of economics thesis topics ideas could help you to gather your thoughts and get inspired. Share it with those who may find it useful. Let us know what you think about it in the comment section below.

🔗 References

• Economics Thesis Topics List: Seminars Only
• How To Pick A Topic For Your Economics Research Project Or Master’s Thesis: INOMICS, The Site for Economists
• What Do Theses and Dissertations Look Like: KU Writing Center, the University of Kansas
• Writing Economics: Robert Neugeboren with Mireille Jacobson, University of Harvard
• Economics Ph.D. Theses: Department of Economics, University of Sussex Business School, IDEAS_RePEc
• World Economic Situation and Prospects 2018: United Nations
• Undergraduate Honors Theses: Department of Economics, University of California, Berkeley
• Economics Department Dissertations Collection: Economics Department, University of Massachusetts Amherst
• Topics for Master Theses: Department of Economics, NHH, Norwegian School of Economics
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The dilemma I faced in getting Thesis proposal for my M Phil programme is taken away. Your article would be a useful guide to many more students.Thank you for your guidance.

Thanks for the feedback, John! Your opinion is very important for us!

I wants it for msc thesis

These are very helpful and concise research topics which I have spent days surfing the internet to get all this while. Thanks for making research life experience easier for me. Keep this good work up.

Thank you, Idris!

Glad to hear that! Thank you for your feedback, Idris!

Excellent research

For research

A very well written, clear and easy-to-read article. It was highly helpful. Thank you!

Thanks for your kind words! We look forward to seeing you again!

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