The real-time revolution

Enter third-wave economics

How the pandemic reshaped the dismal science

As part of his plan for socialism in the early 1970s, Salvador Allende created Project Cybersyn. 

The Chilean president’s idea was to offer bureaucrats unprecedented insight into the country’s economy. 

Managers would feed information from factories and fields into a central database. 

In an operations room bureaucrats could see if production was rising in the metals sector but falling on farms, or what was happening to wages in mining. 

They would quickly be able to analyse the impact of a tweak to regulations or production quotas.

Cybersyn never got off the ground. 

But something curiously similar has emerged in Salina, a small city in Kansas. 

Salina311, a local paper, has started publishing a “community dashboard” for the area, with rapid-fire data on local retail prices, the number of job vacancies and more—in effect, an electrocardiogram of the economy.

What is true in Salina is true for a growing number of national governments. 

When the pandemic started last year bureaucrats began studying dashboards of “high-frequency” data, such as daily airport passengers and hour-by-hour credit-card-spending. 

In recent weeks they have turned to new high-frequency sources, to get a better sense of where labour shortages are worst or to estimate which commodity price is next in line to soar. 

Economists have seized on these new data sets, producing a research boom (see chart 1). 

In the process, they are influencing policy as never before.

This fast-paced economics involves three big changes. 

First, it draws on data that are not only abundant but also directly relevant to real-world problems. 

When policymakers are trying to understand what lockdowns do to leisure spending they look at live restaurant reservations; when they want to get a handle on supply-chain bottlenecks they look at day-by-day movements of ships. 

Troves of timely, granular data are to economics what the microscope was to biology, opening a new way of looking at the world.

Second, the economists using the data are keener on influencing public policy. More of them do quick-and-dirty research in response to new policies. Academics have flocked to Twitter to engage in debate.

And, third, this new type of economics involves little theory. 

Practitioners claim to let the information speak for itself. 

Raj Chetty, a Harvard professor and one of the pioneers, has suggested that controversies between economists should be little different from disagreements among doctors about whether coffee is bad for you: a matter purely of evidence. 

All this is causing controversy among dismal scientists, not least because some, such as Mr Chetty, have done better from the shift than others: a few superstars dominate the field.

Their emerging discipline might be called “third wave” economics. 

The first wave emerged with Adam Smith and the “Wealth of Nations”, published in 1776. 

Economics mainly involved books or papers written by one person, focusing on some big theoretical question. 

Smith sought to tear down the monopolistic habits of 18th-century Europe. 

In the 20th century John Maynard Keynes wanted people to think differently about the government’s role in managing the economic cycle. 

Milton Friedman aimed to eliminate many of the responsibilities that politicians, following Keynes’s ideas, had arrogated to themselves.

All three men had a big impact on policies—as late as 1850 Smith was quoted 30 times in Parliament—but in a diffuse way. 

Data were scarce. 

Even by the 1970s more than half of economics papers focused on theory alone, suggests a study published in 2012 by Daniel Hamermesh, an economist.

That changed with the second wave of economics. 

By 2011 purely theoretical papers accounted for only 19% of publications. 

The growth of official statistics gave wonks more data to work with. 

More powerful computers made it easier to spot patterns and ascribe causality (this year’s Nobel prize was awarded for the practice of identifying cause and effect). 

The average number of authors per paper rose, as the complexity of the analysis increased (see chart 2). 

Economists had greater involvement in policy: rich-world governments began using cost-benefit analysis for infrastructure decisions from the 1950s.

Second-wave economics nonetheless remained constrained by data. 

Most national statistics are published with lags of months or years. 

“The traditional government statistics weren’t really all that helpful—by the time they came out, the data were stale,” says Michael Faulkender, an assistant treasury secretary in Washington at the start of the pandemic. 

The quality of official local economic data is mixed, at best; they do a poor job of covering the housing market and consumer spending. 

National statistics came into being at a time when the average economy looked more industrial, and less service-based, than it does now. 

The Standard Industrial Classification, introduced in 1937-38 and still in use with updates, divides manufacturing into 24 subsections, but the entire financial industry into just three.

The mists of time

Especially in times of rapid change, policymakers have operated in a fog. 

“If you look at the data right now…we are not in what would normally be characterised as a recession,” argued Edward Lazear, then chairman of the White House Council of Economic Advisers, in May 2008. 

Five months later, after Lehman Brothers had collapsed, the imf noted that America was “not necessarily” heading for a deep recession. 

In fact America had entered a recession in December 2007. 

In 2007-09 there was no surge in economics publications. 

Economists’ recommendations for policy were mostly based on judgment, theory and a cursory reading of national statistics.

The gap between official data and what is happening in the real economy can still be glaring. 

Walk around a Walmart in Kansas and many items, from pet food to bottled water, are in short supply. 

Yet some national statistics fail to show such problems. 

Dean Baker of the Centre for Economic and Policy Research, using official data, points out that American real inventories, excluding cars and farm products, are barely lower than before the pandemic.

There were hints of an economics third wave before the pandemic. 

Some economists were finding new, extremely detailed streams of data, such as anonymised tax records and location information from mobile phones. 

The analysis of these giant data sets requires the creation of what are in effect industrial labs, teams of economists who clean and probe the numbers. 

Susan Athey, a trailblazer in applying modern computational methods in economics, has 20 or so non-faculty researchers at her Stanford lab (Mr Chetty’s team boasts similar numbers). 

Of the 20 economists with the most cited new work during the pandemic, three run industrial labs.

More data sprouted from firms. 

Visa and Square record spending patterns, Apple and Google track movements, and security companies know when people go in and out of buildings. 

“Computers are in the middle of every economic arrangement, so naturally things are recorded,” says Jon Levin of Stanford’s Graduate School of Business. 

Jamie Dimon, the boss of JPMorgan Chase, a bank, is an unlikely hero of the emergence of third-wave economics. 

In 2015 he helped set up an institute at his bank which tapped into data from its network to analyse questions about consumer finances and small businesses.

The Brexit referendum of June 2016 was the first big event when real-time data were put to the test. 

The British government and investors needed to get a sense of this unusual shock long before Britain’s official gdp numbers came out. 

They scraped web pages for telltale signs such as restaurant reservations and the number of supermarkets offering discounts—and concluded, correctly, that though the economy was slowing, it was far from the catastrophe that many forecasters had predicted.

Real-time data might have remained a niche pursuit for longer were it not for the pandemic. 

Chinese firms have long produced granular high-frequency data on everything from cinema visits to the number of glasses of beer that people are drinking daily. 

Beer-and-movie statistics are a useful cross-check against sometimes dodgy official figures. 

China-watchers turned to them in January 2020, when lockdowns began in Hubei province. 

The numbers showed that the world’s second-largest economy was heading for a slump. 

And they made it clear to economists elsewhere how useful such data could be.

Vast and fast

In the early days of the pandemic Google started releasing anonymised data on people’s physical movements; this has helped researchers produce a day-by-day measure of the severity of lockdowns (see chart 3). 

OpenTable, a booking platform, started publishing daily information on restaurant reservations. 

America’s Census Bureau quickly introduced a weekly survey of households, asking them questions ranging from their employment status to whether they could afford to pay the rent.

In May 2020 Jose Maria Barrero, Nick Bloom and Steven Davis, three economists, began a monthly survey of American business practices and work habits. 

Working-age Americans are paid to answer questions on how often they plan to visit the office, say, or how they would prefer to greet a work colleague. 

“People often complete a survey during their lunch break,” says Mr Bloom, of Stanford University. 

“They sit there with a sandwich, answer some questions, and that pays for their lunch.”

Demand for research to understand a confusing economic situation jumped. 

The first analysis of America’s $600 weekly boost to unemployment insurance, implemented in March 2020, was published in weeks. 

The British government knew by October 2020 that a scheme to subsidise restaurant attendance in August 2020 had probably boosted covid infections. 

Many apparently self-evident things about the pandemic—that the economy collapsed in March 2020, that the poor have suffered more than the rich, or that the shift to working from home is turning out better than expected—only seem obvious because of rapid-fire economic research.

It is harder to quantify the policy impact. 

Some economists scoff at the notion that their research has influenced politicians’ pandemic response. 

Many studies using real-time data suggested that the Paycheck Protection Programme, an effort to channel money to American small firms, was doing less good than hoped. 

Yet small-business lobbyists ensured that politicians did not get rid of it for months. 

Tyler Cowen, of George Mason University, points out that the most significant contribution of economists during the pandemic involved recommending early pledges to buy vaccines—based on older research, not real-time data.

Still, Mr Faulkender says that the special support for restaurants that was included in America’s stimulus was influenced by a weak recovery in the industry seen in the OpenTable data. 

Research by Mr Chetty in early 2021 found that stimulus cheques sent in December boosted spending by lower-income households, but not much for richer households. 

He claims this informed the decision to place stronger income limits on the stimulus cheques sent in March.

Shaping the economic conversation

As for the Federal Reserve, in May 2020 the Dallas and New York regional Feds and James Stock, a Harvard economist, created an activity index using a range of real-time economic data. 

The St Louis Fed used data from Homebase to track employment numbers daily. 

Both showed shortfalls of economic activity in advance of official data. 

This led the Fed to communicate its doveish policy stance faster.

Speedy data also helped frame debate. 

Everyone realised the world was in a deep recession much sooner than they had in 2007-09. 

In the imf’s overviews of the global economy in 2009, 40% of the papers cited had been published in 2008-09. In the overview published in October 2020, by contrast, over half the citations were for papers published that year.

The third wave of economics has been better for some practitioners than others. 

As lockdowns began, many male economists found themselves at home with no teaching responsibilities and more time to do research. 

Female ones often picked up the slack of child care. 

A paper in Covid Economics, a rapid-fire journal,finds that female authors accounted for 12% of economics working-paper submissions during the pandemic, compared with 20% before. 

Economists lucky enough to have researched topics before the pandemic which became hot, from home-working to welfare policy, were suddenly in demand.

There are also deeper shifts in the value placed on different sorts of research. 

The Economist has examined rankings of economists from ideas repec, a database of research, and citation data from Google Scholar. 

We divided economists into three groups: “lone wolves” (who publish with less than one unique co-author per paper on average); “collaborators” (those who tend to work with more than one unique co-author per paper, usually two to four people); and “lab leaders” (researchers who run a large team of dedicated assistants). 

We then looked at the top ten economists for each as measured by repec author rankings for the past ten years.

Collaborators performed far ahead of the other two groups during the pandemic (see chart 4). 

Lone wolves did worst: working with large data sets benefits from a division of labour. 

Why collaborators did better than lab leaders is less clear. 

They may have been more nimble in working with those best suited for the problems at hand; lab leaders are stuck with a fixed group of co-authors and assistants.

The most popular types of research highlight another aspect of the third wave: its usefulness for business. 

Scott Baker, another economist, and Messrs Bloom and Davis—three of the top four authors during the pandemic compared with the year before—are all “collaborators” and use daily newspaper data to study markets. 

Their uncertainty index has been used by hedge funds to understand the drivers of asset prices. 

The research by Messrs Bloom and Davis on working from home has also gained attention from businesses seeking insight on the transition to remote work.

But does it work in theory?

Not everyone likes where the discipline is going. 

When economists say that their fellows are turning into data scientists, it is not meant as a compliment. 

A kinder interpretation is that the shift to data-heavy work is correcting a historical imbalance. 

“The most important problem with macro over the past few decades has been that it has been too theoretical,” says Jón Steinsson of the University of California, Berkeley, in an essay published in July. 

A better balance with data improves theory. 

Half of the recent Nobel prize went for the application of new empirical methods to labour economics; the other half was for the statistical theory around such methods.

Some critics question the quality of many real-time sources. 

High-frequency data are less accurate at estimating levels (for example, the total value of gdp) than they are at estimating changes, and in particular turning-points (such as when growth turns into recession). 

In a recent review of real-time indicators Samuel Tombs of Pantheon Macroeconomics, a consultancy, pointed out that OpenTable data tended to exaggerate the rebound in restaurant attendance last year.

Others have worries about the new incentives facing economists. 

Researchers now race to post a working paper with America’s National Bureau of Economic Research in order to stake their claim to an area of study or to influence policymakers. 

The downside is that consumers of fast-food academic research often treat it as if it is as rigorous as the slow-cooked sort—papers which comply with the old-fashioned publication process involving endless seminars and peer review. 

A number of papers using high-frequency data which generated lots of clicks, including one which claimed that a motorcycle rally in South Dakota had caused a spike in covid cases, have since been called into question.

Whatever the concerns, the pandemic has given economists a new lease of life. 

During the Chilean coup of 1973 members of the armed forces broke into Cybersyn’s operations room and smashed up the slides of graphs—not only because it was Allende’s creation, but because the idea of an electrocardiogram of the economy just seemed a bit weird. 

Third-wave economics is still unusual, but ever less odd.  

Joe Biden’s bid for banks that serve the people

Republicans have called his nominee for comptroller of the currency a ‘radical’ — she is, but not in the way they think

Rana Foroohar 

© Matt Kenyon

One of the great failings of Democratic governments in the US in recent decades has been their inability to get ahead of financial markets via smart regulation.

Republicans should have been doing this as well, of course, but the public don’t expect it from them to the same extent. 

The conservative position is still that markets know best, even as it becomes quite clear that public markets no longer allocate capital in ways that are the most productive, or even understandable. 

Just look at the value and highly concentrated make-up of the S&P 500 today versus the state of the real economy.

But Joe Biden’s administration is taking on this issue, most recently with the nomination of Saule Omarova to the position of comptroller of the currency. 

This job includes the supervision of national banks and a variety of key jobs within that, from dealing with mortgage and housing issues to thinking about the role of the dollar in global markets.

Omarova, a Cornell University professor, has already come under fire from financiers and conservatives. 

They should worry — she’s one of the most qualified regulators to come along in a while and a perfect fit for the moment, since she has a rare talent for spotting systemic risk and asymmetries in markets.

For example, her 2013 paper on the problems inherent in banks both owning and trading commodities, “The Merchants of Wall Street: Banking, Commerce, and Commodities”, sparked serious interest in the topic. 

In 2013 and 2014, she played a key role in hearings around banks such as Goldman Sachs, JPMorgan and Morgan Stanley stockpiling and trading commodities at the same time.

The hearings brought to light the Kafkaesque loopholes of a system in which Goldman Sachs, for example, could get around rules about trading and owning physical commodities simultaneously, simply by moving aluminium back and forth between different warehouses.

As Omarova pointed out to me at the time, the key argument that financial institutions used to defend their right to both own and trade commodities — including the notion that they needed to own physical oil and trade oil derivatives to better understand the market — also demonstrated the unfair advantages, such as access to insider information, that such ownership brings.

This asymmetry of information problem has only become worse with the rise of fintech, cryptocurrencies and the entry of Big Tech into the financial industry. 

There are companies such as stablecoin issuer Tether, for example, that have become big enough to spark worries at the Fed about their systemic risk.

If the Biden administration lets fintech get out of control in a similar vein to how former presidents Bill Clinton and Barack Obama allowed derivatives to spread risk in all sorts of opaque ways, it will be terrible for Democrats politically. 

It would clash with Biden’s “work not wealth” mantra. 

It would also potentially allow Big Tech to snap up community banks, which are the only institutions in finance still lending to ordinary people.

Fortunately, Omarova — like the Securities and Exchange Commission’s Gary Gensler — comes with expertise in both the risks and opportunities inherent in cryptocurrency and digital dollars. 

Her paper “The People’s Ledger” offers up ideas for how the Fed could use digital dollars and direct banking with citizens via digital wallets to better allocate funds to the right individuals and institutions during crises.

Some worry that this puts too much power in the hands of the Fed as it would bypass conventional banks. 

But at least the Fed might have a better chance of getting support to the right people.

Omarova’s idea also implies the possibility of highly targeted monetary policy, done directly via the Fed without commercial banks (who, frankly, would often rather trade than lend) in the middle. 

This might start to tackle the issue of the wide disparities within regional economies in the US.

It is significant that Omarova’s paper opens with the populist William Jennings Bryan’s 1896 “cross of gold” speech, in which he pled for a monetary system that served the interests of working people and the country as a whole. 

Many thought the 2008 financial crisis was a moment for finance to become re-moored within the real economy. 

But thanks in part to major bank lobbying, that was not to be.

Omarova has faced an egregious campaign of slander from Wall Street lobbyists and Republicans. 

Born in Kazakhstan, she studied Marxism (among other things) before coming to the US. 

Critics call her “a radical”, and she is, although not in the way they think. 

While understanding the ideology of class warfare isn’t a bad thing for any establishment figure these days, Omarova is a law professor, not a socialist.

More importantly, she is someone who is laser-focused on “bringing the financial markets back in service to the real economy”, as she put it to me during an interview in 2016. 

She’s not interested in nibbling on technocratic issues at the margins, but in taking on the big questions about who and what the financial system should serve. 

The markets are already asking and answering these questions. 

Regulators should be too.

Inflation Expectations Could Be a Freudian Slip for the Fed

Central banks would be ill-advised to tighten monetary policy just because consumers say they think inflation will be higher in 10 years’ time

By Jon Sindreu

Data suggest that households mostly don’t form independent long-run expectations, they gradually revise them based on present inflation./ PHOTO: TING SHEN/ZUMA PRESS

The fate of financial markets may depend on central banks’ analysis of consumer “inflation expectations.” 

They risk venturing into Freudian pseudoscience more than sound psychology.

Inflation is running above 6% in the U.S. and could well go higher. 

After recent doubts, though, Western central banks have gone back to stressing the temporary factors driving the consumer-price index higher. 

Right now, investors seem to believe them: Stocks buckled under inflation fears earlier this week, but have since recovered. 

Bonds are pricing in very low interest rates for decades, despite a near-term rate rise.

Is inflation about excess demand, and not just supply bottlenecks? 

Some on Wall Street think so, since production of many key products is now above 2019 levels. 

But as a report by the Bank for International Settlements showed Thursday, there is no clear answer to how high demand is overall: It is running ahead of supply because consumption remains abnormally geared toward goods rather than services. 

Likewise, there are staffing shortages despite labor participation remaining below pre-Covid levels.

Left with little clarity, central banks may base their final call on a familiar boogeyman: inflation expectations.

Officials emphasize that these remain more or less anchored. 

Market measures of inflation expectations in the U.S. have only leapt in the short and medium term. 

The University of Michigan’s surveys of households show a similar pattern but, concerningly, even expectations five to 10 years ahead are now ticking up.

It is well known that, when inflation is very high, workers take it into account when timing big purchases and negotiating salaries. 

During the inflationary spiral of the late 1970s, however, policy makers extended this notion by incorporating inflation expectations into their models as a self-fulfilling prophecy that is itself the cause of inflation. 

Central banks became obsessed with manipulating psychology to keep these expectations on target.

But as a recent paper by Federal Reserve economist Jeremy Rudd pointed out, there is little science behind this idea.

Inflation can’t magically appear or disappear based on what households think: Workers need power to extract pay rises from employers.

Even then, research from the 1980s showed that, while expectations were playing a role in cost-of-living adjustments pushed by unions, they moved in lockstep with actual inflation.

This makes sense, because the data overwhelmingly suggests that households are ill-informed about macroeconomics. 

Rather than form independent long-run expectations, they gradually revise them based on present inflation. 

They can still react to it by changing how they spend, research shows—though often in ways that contradict the theory—but there is no reason to believe they put more stock in 10-year-ahead rather than three-year-ahead expectations. 

All of these have proven of little use when predicting inflation.

Inflationary spirals disappeared after the 1990s not because workers received psychological therapy, but because they lost the political leverage to keep pushing wages above productivity growth. 

Some investors posit that a post-Covid era of activist and distributive fiscal policy will change the balance of power again, but it is still a hypothesis: The U.S. private-sector unionization rate is just 6% and, while workers are getting paid more, this is in the context of a surge in corporate profits.

Whether an inflationary spiral is coming or not, central banks would be unwise to read too much into expectations, which are sure to keep grinding up if inflation remains elevated, rather than focus on labor-market reports. Sometimes a cigar is just a cigar.

Should You Buy a Home in the US?

Even at currently elevated US home-price levels, buying still makes sense for those who are set on ownership. But buyers need to be sure that they can accept what could be a rather bumpy and disappointing long-term path for home values.

Robert J. Shiller

NEW HAVEN – A few days ago, I got an email from a man who chastised me for my skepticism about investing in housing today. 

He identified himself as a former US Air Force pilot during the Vietnam War who subsequently became a stockbroker and banker before retiring recently. 

“You, as an educated person,” he wrote, “should be helping and promoting the ownership of real estate.”

He was responding to my warning about a bubble in home prices in many places around the world. 

According to the latest S&P CoreLogic Case-Shiller Home Price Indices, US home prices rose at a record rate of 19.7% in the last year, and now look very unstable. 

They might increase further for a while, but that may be followed by serious declines.

Still, my correspondent was at least partly right about what I should say to the public about homeownership. 

In particular, we should recognize its big-picture effect on our lives, despite the recent extreme price volatility.

But investing in housing in booming locations may not be as safe a long-term bet as many seem to think. 

Prospective US home buyers might logically assume that their tenure in a house will outlast any interruption to an upward trend in home prices, enabling them eventually to benefit from new highs. 

After all, real home prices in the United States fell by 36% nationally from December 2005 to February 2012, because of the Great Recession, but then increased by 71% to a level 10% above their 2005 peak.

However, I have been arguing for years that the US housing market’s performance since 2005 is not the only relevant example of long-term home-price trends. 

My historical data show that real US national home prices were sometimes lower in the 1990s than in the 1890s. 

Over that century, cities spread out to cheaper land, and building tools, technology, and transportation became more efficient.

Moreover, land itself is still cheap: the average cost today of one acre (0.4 hectares) of US farmland – onto which one can easily squeeze four or five houses – is only $3,380. 

Yes, farmland may be far away from cities, but history shows that cities start sprouting up in new places as the population increases.

Nevertheless, the Air Force pilot turned banker disagreed with my real-estate outlook. 

“In this country, like all developed countries, real estate is at the root of wealth as measured by money value,” he wrote. 

“It has been that way for at least a thousand years, and there is no indication we are creating any more real estate.”

So, let’s imagine that, for the past 1,000 years, home prices had beaten the US stock market’s average 7% annual return (after reinvesting dividends) in the twentieth century. 

During that time, these home prices, after compounding, would have increased by a factor of 24 followed by 28 zeroes.

But hardly any homes from a millennium ago remain today, and hardly anyone would want to live in those that have survived. 

Furthermore, the land they sat on is often no longer valuable. 

In Biblical times, for example, Ephesus in western Turkey was a coastal city with magnificent buildings. 

But its once-valuable harbor has since silted, so that the city’s ruins are now miles from the sea.

It is mostly true that we are not creating any more real estate, if we consider land only in the strict sense of the term. 

Land creation, as in the case of Dubai’s artificial archipelagos, is not a scalable solution. 

But we are essentially adding new space by developing high-rise apartments, creating virtual land in the form of online conference services and electronic storage, and improving transportation so that people can live in remote areas with cheap land.

The emailer then recounted his own experiences in the US housing market:

“The first house we bought was in 1971 for $19,000, now worth over $300K, second house for $34K, now worth over $400K, third house for $130K, now worth over $450K, fourth house for $190K, now worth $435K, fifth house for $305K, sold for $800K three years later, the current house bought for $300K (downsizing in retirement) and worth $450K.”

According to his numbers, the value of the first house has increased by a factor of 15.8 (300,000/19,000). 

But over that 50-year period, the US consumer price index has risen by a factor of 6.7, which means that the real value of the home did little more than double. 

And the compounded annual real price return over those five decades is only 1.7%.

Finally, he noted, “even the tax laws favor owning real estate.” 

That is true. 

There is often a tax subsidy to homeownership; in most countries, imputed rent on owner-occupied housing is not subject to income tax. 

But this tax subsidy does not appear to be growing, and so does not justify continued increases in home prices.

But I take the emailer’s moral imperative seriously. 

Even at currently elevated US home-price levels, buying still makes sense for those who are set on homeownership and want to get on with their lives. 

Homeownership can activate a predilection for community, long-term friendships with neighbors, and a sense of security and permanence.

Moreover, buying a house with a mortgage serves as a self-control mechanism that helps people to save more. 

The discipline imposed on young homeowners by regular amortizing mortgage payments is a key driver of retirement saving. 

And buyers can hedge some of their risk in the home price index futures market.

Make no mistake: homeownership clearly has its benefits. 

But people who really want to buy now need to be sure that they can accept what could be a rather bumpy and disappointing long-term path for home values.

Robert J. Shiller, a 2013 Nobel laureate in economics, is Professor of Economics at Yale University and the co-creator of the Case-Shiller Index of US house prices. He is the author of Irrational Exuberance, Phishing for Phools: The Economics of Manipulation and Deception (with George Akerlof), and Narrative Economics: How Stories Go Viral and Drive Major Economic Events.