The global downturn of 2020 is probably the most quantifiedon record. Economists, firms and statisticians seeking to gauge the depth of the collapse in economic activity and the pace of the recovery have seized upon a new dashboard of previously obscure indicators. Investors eagerly await the release of mobility statis- tics from tech companies such as Apple or Google, or restaurant- booking data from OpenTable, in a manner once reserved for offi- cial inflation and unemployment estimates. Central bankers pep- per their speeches with novel barometers of consumer spending. Investment-bank analysts and journalists tout hot new measures of economic activity in the way that hipsters discuss the latest bands. Those who prefer to wait for official measures are regarded as being like fans of u2, a sanctimonious Irish rock group: stuck behind the curve as the rest of the world has moved on.

The main attraction of real-time data to policymakers and in- vestors alike is timeliness. Whereas official, so-called hard data, such as inflation, employment or output measures, tend to be re- leased with a lag of several weeks, or even months, real-time data, as the name suggests, can offer a window on today’s economic conditions. The depth of the downturns induced by covid-19 has put a premium on swift intelligence. The case for hard data has al- ways been their quality, but this has suffered greatly during the pandemic. Compilers of official labour-market figures have strug- gled to account for furlough schemes and the like, and have plas- tered their releases with warnings about unusually high levels of uncertainty. Filling in statisticians’ forms has probably fallen to the bottom of firms’ to-do lists, reducing the accuracy of official output measures.

In some countries with dodgy official statistics, economists have no choice but to rely on alternative indicators (see Middle East & Africa section). In the rich world, though, official figures are still the benchmark for high-quality economic information. The methodologies used to construct them are, in the main, transpar- ent and they have track records dating back decades, over the course of several economic cycles. The same cannot be said about many of the indicators that are currently in vogue.

Take, for example, the mobility data from Apple and Google that have drawn so much attention in financial markets. The tech

firms should be commended for making the figures available so quickly, and at a level of granularity that allows for a detailed look at travel patterns. But the numbers need to be treated as what they are—a measure of mobility—and not a proxy for overall economic activity. They may reveal that more people are returning to work- places, but not whether they were previously working from home or out of the labour force altogether. Nor can they show whether commuters are spending more or less on their coffees and sand- wiches. Both Apple and Google present their figures relative to a pre-pandemic benchmark of travel in January. That made sense in February and March. Now, however, it could mislead. The latest mobility reports show that visits to non-food retail stores in some European countries are above those in January. But spending hab- its often have a seasonal pattern, which needs to be taken into ac- count. Oxford Economics, a consultancy, cautions that consumer spending in Europe is usually 5-15% higher in July than in January.

You might think that figures on debit- and credit-card transac- tions provide a better estimate of household spending. In June Andy Haldane, the Bank of England’s chief economist, pointed to a bounce-back in one such measure as evidence that Britain’s recov- ery from the depths of lockdown was “so far, so v”. But even here the signal is blurred. With many businesses keen to avoid cash transactions to prevent the spread of infection, card spending may be inflated by a substitution away from physical money. Even in countries where contactless payment is common, cash was still more likely to be used in small-value transactions before the pan- demic. Adjusting for this shift is especially tricky.

Real-time indicators with a narrower focus, such as measures of seated diners in restaurants or job vacancies posted on recruit- ment websites, probably provide an accurate gauge of activity in smaller pockets of the economy. But these are of limited use to policymakers trying to see the big picture. Part of the problem is that, as most of the data are collected by smartphones and con- sumer-facing websites, most real-time measures shine a light on consumers’ spending. But, though household spending is the sin- gle largest component of gdp, it is the smaller, more volatile com- ponents that tend to drive the business cycle. Companies’ capital spending is trickier to measure in real time than restaurant vis- its—but much more important to overall economic performance.

No better than the real thing The value of real-time measures will be tested once the swings in economic activity approach a more normal magnitude. Mobility figures for March and April did predict the scale of the collapse in gdp, but that could have been estimated just as easily by stepping outside and looking around (at least in the places where that sort of thing was allowed during lockdown). Forecasters in rich countries are more used to quibbling over whether economies will grow at an annual rate of 2% or 3% than whether output will shrink by 20% or 30% in a quarter. Real-time measures have disappointed before. Immediately after Britain’s vote to leave the European Union in 2016, for instance, the indicators then watched by economists pointed to a sharp slowdown. It never came.

Real-time data, when used with care, have been a helpful sup- plement to official measures so far this year. With any luck the best of the new indicators will help official statisticians improve the quality and timeliness of their own figures. But, much like u2, the official measures have been around for a long time thanks to their tried and tested formula—and they are likely to stick around for a long time to come. 7

Real-time dangerFree exchange

Why novel and timely measures of economic activity should be treated with caution

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