Woe betide any politician who is willing to advocate a drop in growth in the interests of some greater cause, be it social or environmental. In the US the idea of sacrificing growth by, say, taxing gasoline or carbon more heavily as a measure against global warming would be politically unthinkable. Indeed, Donald Trump’s decision to quit the Paris accord on climate change in the name of growth won strong support among sections of the American public. When Kevin Rudd, Australia’s former prime minister, tried to introduce a carbon emissions trading scheme, his bill was defeated on the grounds that it would raise business costs and damage the economy. He was unceremoniously drummed out of office.10
Adding drugs and prostitution to British national income helps draw out more clearly these questions about what we are measuring and the sort of society we are endorsing. If we take the exercise to its logical conclusion, should we not, for example, also count hit men and protection rackets as part of our national economy? If a hit man takes a fee and performs a service, doesn’t that meet Eurostat’s definition of something that should be counted: a monetized transaction between willing parties?
Shouldn’t we also count trade in stolen goods? Well, we do. As Sanjiv Mahajan, an expert on national accounts, explains, there is a distinction between the initial act of theft and the sale of stolen goods. If I steal your Ferrari that is an involuntary transaction, which does not appear in national income. But if I then sell your Ferrari and go out and “buy caviar and a bottle of claret at Fortnum & Mason” with the proceeds, that will turn up as retail sales, thus boosting the economy. “I wouldn’t want to see a headline in the Sun newspaper saying, MORE THEFT CONTRIBUTES TO THE ECONOMY,” says Mahajan. “But it does in a way because you’ve got money without producing anything. You’ve used the same good twice.”
Mahajan is aware of how arbitrary, even illogical, this way of thinking might seem. But national income, he says, has never pretended to be a moral measure, nor a proxy for well-being. “If you want to increase GDP, you should raise value-added tax, increase use of illegal drugs and prostitution and have a war,” he offers. “Sounds like a right happy time, doesn’t it?”
* * *
—
On the outskirts of the Welsh cathedral city of Newport, not far from the River Usk, is a windswept business park. There sits a drab squat building of brick and glass. It is the sort of structure that gives modern architecture a bad name, just the type of place where you might expect rows of statisticians to be laboring away over rows of statistics. Outside on the lawn an off-white sign, held aloft by two metal posts, completes the picture. The notice reads OFFICE FOR NATIONAL STATISTICS, followed by some words in Welsh, SWYDDFA YSTADEGAU GWLADOL, which presumably mean, “Beware: statisticians at work.”
In 2007 the Office for National Statistics, ONS for short, moved almost lock, stock, and barrel from London to this part of south Wales. Virtually all of the London-based staff quit rather than make the move to Newport. It’s not an episode the Welsh Tourist Board likes to brag about.
Compiling Britain’s statistics can be a thankless task. In spite of the expertise and diligence of those who have built careers at the ONS, a survey in the Financial Times found that only 10 percent of Britons thought the figures it produced were accurate. Most believed data were manipulated for political purposes.11 Still, woe betide the compilers of Britain’s national accounts if these figures—apparently trusted by no one—are late. In June 2010 the statistics office delayed release of national income data after admitting it had discovered potential errors in the numbers. The two-week postponement caused ripples in the markets, which speculated about possible revisions to growth data already released. The update, when it came, did indeed show that the recession had been deeper than thought, with the economy having shrunk from peak to trough by 6.4 percent rather than 6.2 percent as previously stated.
The ONS has not only had to move location; it has also been subject to budget cuts of millions of pounds. That has obliged it to trim the sample size of its surveys and to contemplate abandoning some series of statistics altogether. The government even threatened to scrap the next census, the foundation of many other data sets, on the grounds that it cost too much. The 2011 census set Britain back a hefty £480 million.12 Collecting good statistics is expensive. It has not always been a political priority, as Keynes pointed out more than sixty years ago.
For the 650 or so people working on national income in Newport the end of each quarter is like a starting gun. They have only twenty-five days to produce their first estimate, a tall order given that it can take up to three years for all the relevant information to come in. The first published release, therefore, is a rough-and-ready estimate which is gradually refined as more data become available. For each quarter, the ONS publishes estimates after 25 days, 55 days, and 85 days, by which time 90 percent of the relevant data are available.13 Statistics agencies around the world work to slightly different schedules, but in broad terms their methodology is the same.
There are three recipes for GDP. Although each uses different ingredients, in theory they should end up tasting exactly the same. In practice, because of the dizzying array of data and assumptions that goes into each method, they often turn out quite different. That leaves national accountants having to reconcile the three sets of numbers by weeding out dodgy-looking outliers.
Before we get on to the three recipes, let’s start with a definition. The Office for National Statistics—whose motto is the wonderfully pithy and entirely laudable “Better statistics, better decisions”—says GDP is “the value of goods and services produced during a given period.” That makes it sound awfully simple and begs the question of why it took hundreds of years to come up with.
Of GDP’s three little words, the first is “gross,” which simply means a number with nothing subtracted.14 Kuznets had also considered net national product, which would have removed various things, including wear and tear on the machinery used to produce finished goods. Next, “domestic” means in the home country. That makes it distinct from gross national product, which includes everything produced by a country’s companies whether at home or abroad. In the age of the multinational, this distinction matters. Finally comes “product,” which means everything produced, both goods and services.
The three recipes are known as the expenditure, income, and production methods.15 They measure what is spent, what is earned, and what is made. An economy should only produce what is bought (once imports and exports are taken into account), and people can only spend what they earn. That’s why, in theory, the three measures should come out the same.
The production method is the sum of everything produced by factories and farmers, hairdressers and patisseries. Working out the value of production is not straightforward, as it is easy to double-count. Take the example of a bakery.16 You can’t simply add up the value of the doughnuts, loaves, croissants, and doughnuts—did I mention doughnuts?—in order to arrive at the right number. That is because you’d be counting in these goods items that you have tallied up earlier. You’d have counted the flour when you were totting up the output of the miller. And you’d have counted the wheat that went to make the flour when you were adding up the output of wheat farmers.
So when it comes to working out the contribution of bread to an economy, you’re actually trying to count what’s known as the “value-added,” the additional value that has been created in the process of turning flour—as well as butter, electricity, labor, and rent—into a loaf of crusty farmhouse or German pumpernickel. You have to subtract the value of all the intermediate goods that go into making the finished product. The production formula is deceptively simple: the value of all goods and services produced over a given period minus the value of intermediate goods.
Next comes the expenditure method, which calculates something economists sometimes refer to as “aggregate demand.” That is everything “spent,” whether by households, b
usinesses, or government. Because we’re calculating domestic product, we need to add in exports, since these were made at home, and subtract imports, since these were made abroad. The formula for this recipe is: consumer spending plus government spending and investment plus business investment plus exports less imports. It is, perhaps, the best-known recipe in the economic cookbook.17 The final recipe is the income approach, which measures all the income earned in an economy, mostly in wages, profits, dividends, rent, and taxes. When it comes to measuring our economy, we are what we earn.
As in the US, Europe, and many other countries, most of the numbers on which the ONS relies come from sample surveys. They are not a full reckoning of every transaction made in the economy. “There’s no computer in the sky counting up all the receipts,” says Umair Haque, an author who has criticized our economic measures. “It’s a very crude survey and so we shouldn’t treat it as sacrosanct.”18
To take a mundane example, the ONS cannot know every time I pop down to the shop to buy a packet of Fig Newtons or a toilet plunger. Information on the former comes from a variety of sources: from the biscuit company, which should know roughly how many packets it produced; from supermarkets and shopkeepers, who should know roughly how many they have sold; and from households, who should know exactly how delicious Fig Newtons are. But the ONS cannot ask every household in the land how many Fig Newtons and toilet plungers they bought last week. “Oh, and while we’re at it, did you perchance purchase anything else?” Instead it relies on sample surveys. An important one is the Living Costs and Food Survey. An interviewer sent by the ONS conducts an initial face-to-face interview and then leaves a diary in which each person in the family, including children, records their expenditure over a week or more. Each year about 5,000 people in Britain fill out such forms, from a population of about 65 million.
Businesses are more intensively sampled. Each month the ONS sends out 45,000 surveys to UK companies of all description. Just as Kuznets did, statisticians categorize businesses by sector and subsector, so that information from one can be scaled up to form a representative picture of the whole. Guidance is provided by something called the International Standard Industrial Classification (Revision 4), which is compiled by the United Nations. If you’re a nerd, it can make fascinating reading. In its more than 290 pages every conceivable business is classified, from, to take two random examples, “fishing cruise” companies to “manufacture of luggage, handbags and the like, saddlery and harness.” Each category is further subdivided into dozens of items. Then the results are scaled up to represent the sector as a whole. Think of it as an exit poll. Not all people leaving the polling station are asked how they voted, but a large enough sample is collected to present a fairly reliable picture.
The ONS is also trying to harvest more information from what statisticians call “administrative data.” This is information collected by the government for non-statistical purposes in the day-to-day course of running the country. Examples might include driving licenses, registration of births or deaths, customs clearances, tax records, and so on. These provide rich pickings for statisticians because they often cover the entire population and contain real data as opposed to estimates derived from surveys. For a cash-strapped agency, administrative data have another advantage: they have already been collected and come free of charge. In 2015 the ONS announced plans to take data directly from Her Majesty’s Revenue and Customs VAT returns. It estimated that using this data could halve the amount of surveys it would need to send out in future.
Once data start to come in, the statistical work begins. Different numbers go into each of the three formulas outlined above. Then, all three estimates must be reconciled using something called a “supply and use table,” which is really a set of matrixes in which different results can be compared.
Finally, numbers have to be adjusted for seasonality and for inflation. It’s not much use reporting that car sales went up dramatically in one month if people always buy lots of cars at one particular time of year. Far better to smooth out the numbers by adjusting for seasonal factors. Otherwise, just imagine the headlines in January: SLUMP IN CHRISTMAS TREE SALES, ECONOMY ON ROCKS.
Inflation is harder, and even more important, to account for. Growth is normally adjusted for inflation. It would be misleading to say that the economy had expanded by 15 percent if 14 percentage points of that increase were accounted for by price rises. People are more interested in the “real” rate of growth. Statisticians either compare volumes of production (rather than value) or apply a deflator, which discounts the effect of inflation.19 Now all we need is to wait for Abramsky and Drew to finish counting all those drugs and prostitutes, and, hey presto, there’s your GDP.
3
THE GOOD, THE BAD, AND THE INVISIBLE
In the summer of 2012 Janice S, a sixty-four-year-old former sales assistant living near Stamford in Connecticut, felt pains in her chest.1 She was driven four miles by ambulance to a hospital, where she underwent three hours of tests and had some fleeting encounters with a doctor. Eventually she was told she had nothing more than indigestion and was sent home. That was the good part. The bad part was the bill: $995 for the ambulance, $3,000 for doctors, and $17,000 for the hospital—altogether $21,000 for a routine screening.2 Heartburn has never been so expensive.
What could have possibly cost so much? Among the hospital’s charges were three “troponin I” tests for $199.50 each. A troponin test measures the levels of certain proteins in the blood associated with heart attacks. The hospital charged patients using a “chargemaster,” a price list that seemed to bear no relation to cost—or reality. When hospital administrators were asked about the chargemaster they grew nervous and changed the subject.
If Medicare, the US government insurance scheme, had covered the cost of the troponin test, it would have paid the hospital $13.94 for each one rather than the $199.50 Janice was charged. Because Janice was out of work she was not insured. Nor was she covered by Medicare, which starts at age sixty-five. Janice was also charged $157.61 for a complete blood count test. Medicare would have reimbursed the hospital $11.02 for the same procedure. Other huge markups included the charge for a simple acetaminophen tablet—a generic version of Tylenol—the price of which had been inflated by 10,000 percent. According to Stamford Hospital’s filings, its total expenses on lab work like Janice’s over a twelve-month period were $27.5 million, while its total charges were $293.2 million, not a bad little earner for what is officially a nonprofit organization.
Each year the US spends around 17 percent of GDP on health care.3 That is almost twice the amount spent in most advanced countries. The UK puts 9 percent of GDP into health care, Japan 10.2 percent, and France, which has a world-class health system, 11.5 percent. Singapore, which also has an exceptionally good health service, spends just 4.9 percent, less than a third of the US. Each and every week Americans lay out more than $55 billion on medical expenditure, close to what it cost to clean up after Hurricane Sandy in 2012.
You’d have thought that all this money would produce spectacular results, but you’d be wrong. Health outcomes in the US are no better than in most developed countries, and in some cases considerably worse. The US comes in at number 31 in the life-expectancy league tables, just below Costa Rica.4 Its average life expectancy of 79.3 years for both sexes compares with 83.7 for Japan, the best-placed country. In other words, the Japanese spend half as much and live four years longer.
The US is likely to fall farther behind. By 2030 women in South Korea are likely to have a life expectancy of nearly ninety-one, according to a recent study published in The Lancet, which put the achievement down to universal health care, good childhood nutrition, and the rapid take-up of new medical technology. By contrast, the study found, the US was expected to have the lowest life expectancy of any rich country by 2030.5 In infant mortality, which measures the number of infants who die before the age of one, the US doesn’t fare
any better. In 2015 it came 57th in the world, just behind Bosnia and Herzegovina, with 5.72 deaths per thousand live births. Monaco was best at 1.82.
Defenders of the US health care system dispute these numbers. Such raw data, they say, fail to account for differences between countries in diet, ethnicity, levels of inequality, and social problems such as drug abuse. They also don’t take into account the very high levels of violent deaths in the US, especially from shootings. Infant mortality numbers may not be comparable, these people argue, because they are measured differently in different countries. Some of those objections may be valid, although the lobbying of the US health industry means one should take such objections with a huge dose of sodium (presumably marked up 10,000 percent).6
You’d need to be drawing a pretty sizable check from Big Pharma to argue that Americans are getting good value for their health care dollar. So what exactly accounts for the inflated costs? The health industry’s formidable lobbying machine ensures that laws in Washington are made with the interests of health care providers in mind.7 As a result the patient sometimes comes out second best. The pharmaceutical and health-care-product industries, combined with groups representing doctors, hospitals, nursing homes, and insurers, spent $5.36 billion between 1998 and 2012 on lobbying, according to Steven Brill, who has researched the US health care industry extensively. That compared with $1.53 billion spent by the defense and aerospace industries and $1.3 billion spent by oil and gas interests over the same period. In other words, the “health-care-industrial complex spends more than three times what the military-industrial complex spends in Washington.”8
The Growth Delusion Page 4