Black-gold rush

T

he commodity-traders who feature in “The World for Sale” are not the kind who yell orders at each other in the ring of the London Metal Exchange. Javier Blas and Jack Farchy, journalists at Bloomberg News, are instead interested in the small band of mostly private companies that move bulk commodities from there to here. It is a fascinating and revealing story, largely because of where “there” is: usually a place where many people would prefer not to do business, run by characters they would prefer not to do it with. A handful of swashbucklers became billionaires by overcoming such qualms. 

The opportunities to make these riches came in three big waves. During the 1960s and 1970s commodity­traders were pivotal in breaking the established Anglo­Ameri­

can oil cartel known as the “seven sisters”

and establishing a new one in opec. For­

tunes were made in the wake of the free­

for­all that followed the collapse of the So­

viet Union. And after 2001 these figures were instrumental in the integration of China into the global trading order, sup­

plying vast quantities of raw materials from Africa and elsewhere. Along the way, as the authors describe, some traders helped apartheid­era South Africa sidestep economic sanctions, sold oil for Saddam Hussein’s Iraq and funnelled dollars to Vla­

dimir Putin’s Russia. There are tales in the book of breathtaking trades, such as ship­

ments of rebel oil from war­torn Libya or deals bartered amid the brutal “aluminium wars” in the Russia of the 1990s. 

The wonder is that so few firms, owned by a tiny number of people, were able to grab such a hold on global commodity­

trading with so little oversight. Vitol, a Dutch trading house, became the largest independent oil­distributor under Ian Tay­

lor, its British boss, in part by being venturesome in former Soviet republics.

Many of the industry’s branches sprang from a single tree—Philipp Brothers, a trading outfit based in America but with roots in Hamburg, at which a young Marc Rich made his name as a trader. 

Rich saw more clearly than others how emerging Middle Eastern petro­states would reshape the oil market. He set up Marc Rich & Co in 1974, after a breach with The World for Sale. By Javier Blas and Jack Farchy. Oxford University Press;

416 pages; $29.95. Random House Business; £20

70 Books & arts The Economist February 27th 2021

I

f you frequentlyGoogle language­

related questions, whether out of interest or need, you’ve probably seen an advertisement for Grammarly, an auto­

mated grammar­checker. In ubiquitous YouTube spots Grammarly touts its ability not only to fix mistakes, but to improve style and polish too. Over more than a decade it has sprawled into many applications: it can check emails, phone messages or longer texts composed in Microsoft Word and Google Docs, among other formats.

Does it achieve what it purports to?

Sometimes. But sometimes Grammarly doesn’t do what it should, and some­

times it even does what it shouldn’t.

These strengths and failings hint at the essence of language and the peculiarity of human intelligence, as opposed to the artificial sort as it stands today. 

Begin with the strengths. In a rough piece of student writing, Johnson count­

ed 14 errors. Grammarly flagged five. For example, it sensibly suggested inserting a hyphen in “post cold war [world]”. It spotted a missing “the” in the phrase

“with [the] European economy”. And it noticed an absent “about” in “wondering [about] the state of Europe”. By using Grammarly, the author of this essay could have avoided some red ink.

On the other hand, Grammarly has a problem with false positives, calling out mistakes that are not. The other two suggestions were not disastrous, but neither did they relate to “critical errors”

as Grammarly maintains. In the as­

sertion that enlargement had “created a fatigue” within the European Union, Grammarly needlessly suggested delet­

ing the “a”. In another error­ridden sen­

tence it recommended removing a com­

ma, which fixed none of the problems.

This false­positive tendency is not a

deal­breaker for reasonably skilled writers who just want a second pair of eyes; you can dismiss any suggestion you like. But truly struggling scribblers might not know when Grammarly’s ideas would make their prose worse rather than better.

Then there are the false negatives, or the mistakes Grammarly fails to notice.

Depending on the text, Grammarly can seem to miss more errors than it marks.

The company’s chief executive, Brad Hoover, describes it as a “coach, not a crutch”—which sets expectations more appropriately than some of the ads do. 

Artificial­intelligence systems like Grammarly are trained with data; for instance, translation software is fed sen­

tences translated by humans. Grammarly’s training data involve a large number of standard error­free sentences (so it knows what good English should look like) and human­corrected sentences (so the soft­

ware can find the patterns of fixes that human editors might make). Developers also manually add certain rules to the patterns Grammarly has taught itself. The software then looks at a user’s prose: if a

string of words seems ungrammatical, it tries to spot how the putative mistake most closely resembles one from its training inputs.

All this shows how far artificial “in­

telligence” is from the human kind (which Grammarly wants to correct to

“humankind”). Computers outpace humans at problems that can be cracked with pure maths, such as chess. Ad­

vances in language technology have been impressive in, for example, speech rec­

ognition, which involves another sort of statistical guess—whether or not a stretch of sound matches a certain string of words. One Grammarly feature that works fairly well is sentiment analysis. It can rate the tone of an email before you send it, after being trained on texts that have been assessed by humans, for ex­

ample as “admiring” or “confident”.

But grammar is the real magic of language, binding words into structures, binding those structures into sentences, and doing so in a way that maps onto meaning. And at this crucial structure­

meaning interface, machines are no match for humans. Computers can parse (grammatical) sentences fairly well, labelling things like nouns and verb phrases. But they struggle with sentences that are difficult to analyse, precisely because they are ungrammatical—in other words, written by the kind of per­

son who needs Grammarly. 

To correct such prose requires know­

ing what the writer intended. But com­

puters don’t work in meaning or in­

tention; they work in formulae. Humans, by contrast, can usually understand even rather mangled syntax, because of the ability to guess the contents of other minds. Grammar­checking computers illustrate not how bad humans are with language, but just how good.

Grammar’s resistance to artificial intelligence illuminates the nature of language

Johnson The human touch 

the Philipp Brothers’ brass over the profits from a bet on Iranian oil. He became the model for a new style of commodity­trad­

er—a bold risk­taker on personal terms with unsavoury leaders. His firm in turn spawned Trafigura and Glencore, a mining giant that listed in London in 2011.

“The World for Sale” has an elegiac air.

Rich died in 2013; Taylor in 2020. Ivan Gla­

senberg, the hard­charging boss of Glen­

core, will retire this year. Deeper factors are at work, too. The rapid growth in China’s economy in the first decade of this century, which spurred a commodity “super­cycle”,

will not be repeated. And public disquiet over the ethics of dealing with shady re­

gimes is growing. Rich spent two decades as a fugitive from American justice be­

cause of his business with Iran; other com­

modity­traders have since had expensive brushes with the law. America has used the dollar’s role as a global currency to enforce economic sanctions more effectively. 

Meanwhile the suppliers and custom­

ers of the traders have become more astute.

The super­profits of the middlemen were in part based on superior private knowl­

edge. It is much harder to make so much

money from arbitrage now that informa­

tion flows so freely.

Still, the instinct to buy low and sell high will endure. Peddling stuff dug up from the ground may always be a mucky business, but if it becomes too unrefined for Western tastes, others are bound to step in. As Messrs Blas and Farchy say, the lead­

ing hawker of Iranian crude is now Zhuhai Zhenrong, a Chinese trading house. “The Chinese”, Taylor told the authors in 2019,

“probably are willing to take much more risk than we are.” The seeds of a sequel to this gripping book lie somewhere here. n

The Economist February 27th 2021

Economic & financial indicators

72

Economic data

Gross domestic product Consumer prices Unemployment Current-account Budget Interest rates Currency units

% change on year ago % change on year ago rate balance balance 10-yr gov't bonds change on per $ % change

latest quarter* 2020† latest 2020† % % of GDP, 2020† % of GDP, 2020† latest,% year ago, bp Feb 24th on year ago

United States -2.5 Q4 4.0 -3.5 1.4 Jan 1.3 6.3 Jan -2.5 -14.9 1.4 nil -

China 6.5 Q4 10.8 2.3 -0.3 Jan 2.5 5.2 Dec‡§ 1.7 -5.8 3.1 §§ 45.0 6.45 9.0

Japan -1.2 Q4 12.7 -5.3 -0.6 Jan nil 2.9 Dec 3.5 -12.2 nil -8.0 106 4.5

Britain -7.8 Q4 4.0 -9.9 0.7 Jan 1.0 5.1 Nov†† -2.8 -14.3 0.7 9.0 0.71 8.4

Canada -5.2 Q3 40.5 -5.3 1.0 Jan 0.8 9.4 Jan -2.1 -13.5 1.3 12.0 1.26 5.6

Euro area -5.0 Q4 -2.4 -7.6 0.9 Jan 0.3 8.3 Dec 2.6 -9.2 -0.3 18.0 0.82 12.2

Austria -4.0 Q3 54.6 -7.4 0.9 Jan 1.4 5.8 Dec 2.1 -8.6 -0.1 25.0 0.82 12.2

Belgium -4.7 Q4 0.8 -6.2 0.3 Jan 0.4 5.8 Dec -1.1 -9.4 -0.1 14.0 0.82 12.2

France -5.0 Q4 -5.3 -8.3 0.6 Jan 0.5 8.9 Dec -2.3 -10.9 -0.1 11.0 0.82 12.2

Germany -3.6 Q4 1.4 -5.3 1.0 Jan 0.4 4.6 Dec 6.8 -4.8 -0.3 18.0 0.82 12.2

Greece -9.6 Q3 9.5 -9.9 -2.0 Jan -1.3 16.2 Nov -6.6 -9.2 1.0 nil 0.82 12.2

Italy -6.6 Q4 -7.7 -8.9 0.4 Jan -0.1 9.0 Dec 3.2 -11.3 0.7 -29.0 0.82 12.2

Netherlands -2.9 Q4 -0.5 -4.4 1.6 Jan 1.1 3.6 Jan 8.4 -6.1 -0.3 4.0 0.82 12.2

Spain -9.1 Q4 1.6 -11.0 0.5 Jan -0.3 16.2 Dec 0.7 -12.3 0.3 9.0 0.82 12.2

Czech Republic -5.3 Q3 1.2 -5.7 2.2 Jan 3.2 3.2 Dec‡ 3.6 -6.5 1.6 18.0 21.4 8.6

Denmark -3.8 Q3 2.4 -4.0 0.6 Jan 0.4 4.4 Dec 8.5 -3.6 -0.2 30.0 6.13 12.2

Norway -0.6 Q4 2.6 -1.3 2.5 Jan 1.3 5.0 Nov‡‡ 1.3 -4.2 1.4 2.0 8.45 10.4

Poland -1.8 Q3 -2.8 -2.8 2.7 Jan 3.4 6.5 Jan§ 3.6 -7.9 1.4 -56.0 3.72 6.5

Russia -3.4 Q3 na -3.1 5.2 Jan 3.4 5.8 Jan§ 2.0 -3.8 7.0 87.0 73.8 -11.3

Sweden -2.6 Q4 2.0 -3.0 1.6 Jan 0.5 9.3 Jan§ 4.8 -3.5 0.4 47.0 8.31 17.1

Switzerland -1.6 Q3 31.9 -3.0 -0.5 Jan -0.7 3.5 Jan 9.1 -3.7 -0.3 50.0 0.91 7.7

Turkey 6.7 Q3 na 0.4 15.0 Jan 12.3 12.9 Nov§ -5.4 -3.4 12.8 64.0 7.21 -15.1

Australia -3.8 Q3 14.0 -2.9 0.9 Q4 0.9 6.4 Jan 1.2 -7.3 1.6 66.0 1.26 19.8

Hong Kong -3.0 Q4 0.7 -5.8 1.9 Jan 0.3 7.0 Jan‡‡ 6.2 -7.6 1.3 -1.0 7.75 0.7

India -7.5 Q3 125 -7.0 4.1 Jan 6.6 6.5 Jan 1.2 -9.3 6.2 -22.0 72.3 -0.5

Indonesia -2.2 Q4 na -2.2 1.6 Jan 2.0 7.1 Q3§ -1.6 -7.2 6.4 -6.0 14,085 -1.5

Malaysia -3.4 Q4 na -5.3 -0.2 Jan -1.1 4.8 Dec§ 4.8 -7.4 3.0 -2.0 4.04 4.7

Pakistan 0.5 2020** na -2.8 5.7 Jan 9.5 5.8 2018 0.1 -8.1 10.1 ††† -108 159 -2.9

Philippines -8.3 Q4 24.4 -9.4 4.2 Jan 2.6 8.7 Q4§ 3.4 -7.8 3.6 -69.0 48.6 4.8

Singapore -2.4 Q4 15.9 -5.8 0.2 Jan -0.2 3.2 Q4 18.2 -13.9 1.3 -32.0 1.32 6.1

South Korea -1.3 Q4 4.4 -1.0 0.6 Jan 0.5 5.7 Jan§ 3.8 -5.7 1.9 43.0 1,112 9.7

Taiwan 5.1 Q4 5.8 3.0 -0.2 Jan -0.2 3.8 Dec 13.8 -1.5 0.4 -15.0 27.9 9.4

Thailand -4.2 Q4 5.4 -6.1 -0.3 Jan -0.8 1.5 Dec§ 3.7 -6.4 1.6 70.0 30.0 5.5

Argentina -10.2 Q3 61.7 -9.7 38.5 Jan‡ 42.0 11.7 Q3§ 0.6 -8.6 na na 89.6 -31.1

Brazil -3.9 Q3 34.6 -4.4 4.6 Jan 3.2 14.1 Nov§‡‡ -0.7 -15.8 8.1 138 5.43 -19.3

Chile -9.1 Q3 22.6 -6.2 3.1 Jan 3.0 10.3 Dec§‡‡ 1.4 -7.9 2.9 -84.0 705 14.8

Colombia -3.5 Q4 26.5 -7.0 1.6 Jan 2.5 13.4 Dec§ -3.6 -8.8 5.2 -36.0 3,577 -4.6

Mexico -4.5 Q4 13.0 -8.3 3.5 Jan 3.4 4.4 Dec 2.6 -2.8 5.7 -80.0 20.5 -6.5

Peru -1.7 Q4 37.9 -12.0 2.7 Jan 1.8 13.1 Jan§ 1.0 -8.0 4.2 31.0 3.65 -6.6

Egypt 0.7 Q3 na 3.6 4.4 Jan 5.1 7.2 Q4§ -3.6 -8.5 na nil 15.7 -0.7

Israel -1.3 Q4 6.3 -3.6 -0.4 Jan -0.6 4.5 Jan 3.9 -11.8 1.1 49.0 3.26 4.9

Saudi Arabia -4.1 2020 na -4.2 5.7 Jan 3.4 8.5 Q3 -3.7 -10.6 na nil 3.75 nil

South Africa -6.0 Q3 66.1 -7.1 3.2 Jan 3.2 32.5 Q4§ 1.1 -14.5 8.8 -8.0 14.6 3.6

Source: Haver Analytics. *% change on previous quarter, annual rate. †The Economist Intelligence Unit estimate/forecast. §Not seasonally adjusted. ‡New series. **Year ending June. ††Latest 3 months. ‡‡3-month moving average. §§5-year yield. †††Dollar-denominated bonds.

Markets

% change on: % change on:

Index one Dec 31st index one Dec 31st

In local currency Feb 24th week 2019 Feb 24th week 2019

United States S&P 500 3,925.4 -0.2 21.5 United States NAScomp 13,598.0 -2.6 51.5 China Shanghai Comp 3,564.1 -2.5 16.9 China Shenzhen Comp 2,347.3 -4.6 36.2 Japan Nikkei 225 29,671.7 -2.0 25.4

Japan Topix 1,903.1 -3.0 10.6

Britain FTSE 100 6,659.0 -0.8 -11.7 Canada S&P TSX 18,484.5 0.6 8.3 Euro area EURO STOXX 50 3,706.0 0.2 -1.0 France CAC 40 5,798.0 0.6 -3.0

Germany DAX* 13,976.0 0.5 5.5

Italy FTSE/MIB 23,098.2 -0.3 -1.7

Netherlands AEX 664.5 -2.4 9.9

Spain IBEX 35 8,269.6 1.8 -13.4

Poland WIG 57,596.3 -2.4 -0.4

Russia RTS, $ terms 1,445.9 -1.1 -6.7 Switzerland SMI 10,727.7 -0.8 1.0

Turkey BIST 1,483.0 -3.7 29.6

Australia All Ord. 7,049.4 -1.5 3.6 Hong Kong Hang Seng 29,718.2 -4.4 5.4

India BSE 50,781.7 -1.8 23.1

Indonesia IDX 6,251.1 0.4 -0.8

Malaysia KLSE 1,557.6 -2.4 -2.0

Pakistan KSE 45,362.6 -3.0 11.4

Singapore STI 2,924.6 0.1 -9.3

South Korea KOSPI 2,995.0 -4.4 36.3 Taiwan TWI 16,212.5 -0.9 35.1

Thailand SET 1,491.1 -1.6 -5.6

Argentina MERV 49,606.7 -5.3 19.0

Brazil BVSP 115,667.8 -3.9 nil

Mexico IPC 45,151.4 0.2 3.7

Egypt EGX 30 11,435.4 0.2 -18.1

Israel TA-125 1,615.3 -3.0 -0.1

Saudi Arabia Tadawul 9,115.8 0.3 8.7 South Africa JSE AS 66,200.8 -1.4 16.0 World, dev'd MSCI 2,802.4 -0.5 18.8 Emerging markets MSCI 1,376.8 -4.7 23.5

US corporate bonds, spread over Treasuries

Dec 31st

Basis points latest 2019

Investment grade 127 141

High-yield 376 449

Sources: Refinitiv Datastream; Standard & Poor's Global Fixed Income Research. *Total return index.

Commodities

The Economist commodity-price index % change on 2015=100 Feb 16th Feb 23rd* month year

Dollar Index

All Items 163.6 193.8 22.4 76.0 Food 127.8 129.2 3.8 34.2 Industrials

All 197.1 254.1 33.7 106.6 Non-food agriculturals 144.6 363.8 175.6 266.2 Metals 212.6 221.6 6.9 70.4 Sterling Index

All items 179.3 209.7 19.2 62.4 Euro Index

All items 149.7 176.8 22.4 57.3 Gold

$ per oz 1,810.5 1,805.4 -2.6 9.1 Brent

$ per barrel 63.5 65.5 17.0 17.4 Sources: Bloomberg; CME Group; Cotlook; Refinitiv Datastream;

Fastmarkets; FT; ICCO; ICO; ISO; Live Rice Index; LME; NZ Wool Services; Thompson Lloyd & Ewart; Urner Barry; WSJ. *Provisional.

For more countries and additional data, visit Economist.com/indicators

The Economist February 27th 2021

Graphic detail Covid-19 mutations

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