The Real-Time Revolution and Digital Economics in the COVID Era

April 19, 2022

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Out of all the awfulness created by the COVID-19 global pandemic, a few unexpected silver linings have emerged. One of them is in the field of economics, which in the past year has quietly undergone a revolution, a revolution that mirrors one that is happening in the business world.

To an outsider, economics is a field dominated by numbers and statistics. However, as the Economist magazine pointed out in its recent cover story, “Instant Economics: The Real-Time Revolution,” there has long been a startling shortage of timely data and statistics in the actual practice of economics — especially its most-critical and glamorous speciality, economic forecasting.

the economist shruit covid post

(The Economist: A real-time revolution will up-end the practice of macroeconomics)

Governments use macroeconomic forecasts to guide their policymaking. Will another interest rate cut jumpstart a flagging economy? How much unemployment will result from raising the minimum wage to X dollars per hour?

Prior to the 20th century, classical economists — think Adam Smith or Thomas Malthus — created grand, unified theories. However, data was so scarce and spotty that their treatises read more like philosophy than modern economics. More than half of the economics papers published in the 1970s lacked any numerical data, according to the Economist. Even today, key statistics such as national GDP or unemployment rates take weeks and months to collect, revise, and finalize. More complex figures such as productivity rates take even longer.

That time frame is ok for economics professors, but too slow for policymakers. The problem remains two-fold: official government statistics take too long to emerge, especially in crises, and the levers at the disposal of policymakers are too blunt and slow.

“Traditional government statistics weren’t really all that helpful — by the time they came out, the data were stale,” a former U.S. assistant treasury secretary told the Economist.

Faced with this data dilemma, some economists retreat back to theory and ideology. Supply siders pushed for cutting taxes and regulations, while demand-siders argued for higher taxes and government spending.

Others mined real-time indicators such as stock and bond market prices. While these have the virtue of mining the wisdom of crowds, they are also vulnerable to a whole set of accuracy-reducing factors: market manipulation, unwarranted investor confidence or panic, issues particular to one company or industry, etc.

Stale Data Costs Trillions of Dollars

“It is only a slight exaggeration to say that central banks are flying blind,” wrote the Economist. As a result, “bad and late data can lead to policy errors that cost millions of jobs and trillions of dollars in lost output.”

And that’s exactly what happened during the 2008 recession. As TV talking heads referred to stale economic data showing everything was A-OK, housing prices plummeted, foreclosures skyrocketed, and the economy tanked. Banks were too big to fail, until they suddenly weren’t. The lack of reliable, fresh data led to bad policy decisions that worsened the recession.

When COVID-19 hit, a new wave of economists and policymakers were determined to avoid the mistakes of 2008.

“Without the time to wait for official surveys to reveal the effects of the virus or lockdowns, governments and central banks have experimented, tracking mobile phones, contactless payments, and the real-time use of aircraft engines,” wrote the Economist. “Instead of locking themselves in their studies for years writing the next ‘General Theory,’ today’s star economists, such as Raj Chetty at Harvard University, run well-staffed labs that crunch numbers.” If Netflix knows exactly which shows are trending, why can’t policy-makers get a better pulse of the economy as things unfold?

Fresher Analytics for Faster Actions

Where is this new wave of economists getting these data sets?

Fresher analytics means faster actions. The first analysis of the effect of America’s $600 stimulus checks was published in mere weeks. Within a month, the UK government confirmed that a policy to bring customers back to restaurants also increased the number of COVID infections. Economists confirmed the large number of workers taking their jobs on the road in part from social media posts embracing #vanlife.

“The age of bewilderment is starting to give way to a greater enlightenment,” declared the Economist.

And that has led to targeted, quickly-deployed economic policies. The American stimulus bill included special support for restaurants due in part to the OpenTable.com data. In Hong Kong, the government is sending cash electronically to the digital wallets of its citizens, cash that will expire if not spent by a certain date. Using analytics, similar instant cash handouts can be automatically sent out to poor people who have lost their jobs without the need for them to file any paperwork. Or loans could be instantly offered to businesses that are determined to be low bankruptcy risks.

Contrast that with broad-based monetary policies such as interest rate cuts, which take multiple quarters to take full effect, have many unintended side effects, and can lead to major victories — or be massive misses.

Digital Economics for More Accurate, Transparent Policies

Economists and government policymakers haven’t caught up to cutting-edge businesses that have made the transformation into digital enterprises. Data latency is the most obvious area. To ensure data reliability and quality, Google, OpenTable and others are still publishing their datasets overnight, rather than pumping out live streams.

But more live and real-time sources of data are emerging. India recorded 25.6 billion real-time electronic payments last year. IoT sensors are being fitted to machines and objects at a fast rate. And more than 50 countries, including China, are trialing central bank digital currencies (CBDCs), also known as GovCoins, as supplements to paper money. Unlike Bitcoin and other anonymity-promising cryptocurrencies, GovCoins will be trackable by their government issuers — a boon for policymakers, though a minus for privacy-concerned individuals.

There are other potential pitfalls of the new digital economics. Signalling directional changes in the economy is great, but quantifying actual GDP output or unemployment rates is a harder problem. There are perpetual issues of data relevance and data drift. Is a downturn in Uber car sharing trips a distant early warning of a global recession, or simply the result of a company misstep? And is data published by corporations tainted with an optimistic bias? These are all legitimate concerns, and ones with which our customers in the business world rightly must also wrestle.

Still, “these trends will intensify as technology permeates the economy,” writes the Economist. That means more and fresher datasets that can be combined in creative ways to produce quick but informative economic policy insights.

This mirrors exactly what I see in the business world. The ongoing shift from historical analytics using data warehousing to real-time analytics using more modern data stacks has unlocked a wealth of opportunities for businesses to make smarter, data-driven decisions faster than ever.

“The real-time revolution promises to make economic decisions more accurate, transparent, and rules-based,” writes the Economist. I couldn’t agree more.


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