2019-10-12_The_Economist_

The EconomistOctober 12th 2019 Finance & economics 77

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Buttonwood Tale risk

E

veryone knows, or thinks they

know, the story of the Wall Street

shoeshine boy. In 1929 Joseph Kennedy,

patriarch of the Boston-Irish political

clan, had an epiphany while his shoes

were being cleaned. When the boy who

shined his shoes offered him stock tips,

he realised the stockmarket was about to

implode. Kennedy promptly sold all his

shares and took a short position, betting

that the market would fall. When it

crashed that October he made a killing.

In his new book, “Narrative Econom-

ics”, Robert Shiller, a Nobel laureate,

offers this tale as an example of a conta-

gious narrative that becomes part of folk

wisdom. A story need not be accurate to

spread. Mr Shiller searched archives of

newspapers from the period, and could

find no record of it. But he did find a

similar kind of story in the Minneapolis

Morning Tribune. The stockmarket, it

said, could not yet have peaked because

“we do not hear of the chamber maids

and bootblacks who have cleaned up

fortunes by lucky plays.” That story was

published in 1915.

Whatever their provenance, says Mr

Shiller, it matters which kinds of narra-

tives are contagious and why. The ones

that catch on have the power to influence

behaviour. Stories sway decisions to hire

or fire; to buy or sell; to spend or save.

These individual choices, writ large,

move markets and drive the business

cycle. Fundamentals such as prices and

profits are just one part of the reckoning.

The stories that people tell themselves

and each other matter at least as much.

To wield such influence, economic

narratives must first become popular.

Epidemiology offers a model for how

they take hold. Disease epidemics are

hump-shaped when plotted on a graph.

In the rising phase, the rate of increase of

newly infected people (the contagion rate)

is faster than the recovery rate plus the

death rate. When the recovery rate exceeds

the contagion rate, the epidemic falls off. It

is the same with stories. A growing num-

ber of “infected” people spread the narra-

tive; later on comes a period of lost interest

and forgetting.

The most contagious economic narra-

tives drive boom-and-bust cycles. Such

narratives have common features. They

tend to be oversimplified models of reality

and thus catchy. Their success may owe to

a “super-spreader”, perhaps a celebrity,

capable of infecting many people. And

they are often part of a narrative cluster,

which adds weight to their plausibility.

The stockmarket boom of the 1990s was

powered by an array of stories: the tri-

umph of capitalism; the rise of the in-

ternet; the decline of inflation; and so on.

Some of the most contagious narratives

are newer, more resistant variants of old

ones. Behind every property boom is a

mutation of the eternal narrative about the

scarcity value of land. “Who could think of

tilling or being contented with a hundred

acres of land, when thousands of acres in

the broad west were waiting for occu-

pants,” says a tract documenting the

follies of America’s land boom of the

1830s. The global housing boom that led

up to the Great Recession of 2007-09 was

driven by narratives that persuaded

people to think of their homes as specu-

lative investments in scarce land.

A science of economic narratives, of

the kind Mr Shiller calls for, would re-

quire high-quality data. It would need

regular surveys designed to draw out

people’s justifications for their economic

decisions. But interpreting even good

data would be tricky. Narratives tend to

be ignored by economists because their

links to events are complex and vari-

able—as Mr Shiller himself notes. Any

official data on narratives would, once

published, surely become part of the

narrative itself.

The most prominent economic narra-

tives today are not cheery. A monthly

survey conducted by Bank of America

finds that two-fifths of fund managers

expect a recession in the next year. The

same proportion thinks the trade dispute

between America and China will never

be resolved. Besides the trade war, fund

managers list the impotence of central

banks and a bubble in bond markets as

their biggest worries.

Take these messages, add to them

bleak surveys of business confidence

worldwide, and you might decide to

batten down the hatches for a coming

storm. If so, you may still be troubled by

a nagging doubt, a sense that the story

does not quite add up. The usual end-of-

cycle euphoria, which causes companies

to make unwise investments and draws

greenhorns into speculative assets, is not

there. The chambermaids and bootblacks

have gone missing.

How stories can help explain booms and busts

ers are millennials or younger. The average

age of a Klarna customer is 32. A quarter of

millennials in Australia have used After-

pay, compared with 16% of adults.

Until recently they lent only for pur-

chases at selected outlets. For Afterpay and

Klarna, these included Anthropologie, a

home-goods and clothing store, and asos,

an online retailer. But this is changing. In

May Klarna launched an app allowing

shoppers to pay in instalments at any re-

tailer. On October 7th Affirm introduced an

app that pre-approves users for credit they

can spend anywhere.

In their latest iteration, such firms seem

to approach the territory of credit cards.

But Mr Levchin insists that they retain the

crucial distinction that makes them a bet-

ter bet for customers: pre-arranged repay-

ment schedules for each purchase, which

cap the amount they will pay.

At present losses are modest—around

1% of the value of financed sales at After-

pay, for example. But the firms are burning

through cash to finance expansion, and

few have yet lived through a recession.

There are reasons to be sanguine about

their prospects during one, however. They

have grown sufficiently large that regula-

tors are keeping a close eye on them. They

must interact constantly with banks, who

intermediate their loans. If regulators had

concerns, they could simply tell banks to

stop doing business with them.

And their sophisticated credit evalua-

tion uses big data and proprietary models

to evaluate how much debt an applicant

can bear. Customers are turned away if a

loan seems beyond their means, and bal-

ances are low. Their methods have done

well so far, but as the global economy weak-

ens they will face a tougher test. 7