Consider the housewife, or rather, her modern successor, the stay-at-home mom. There’sevidence that the recession may have forced her back onto the job market. Since the downturn, the labor-force participation rate for working-age men has fallen significantly. But the participation rate of women has actually ticked up. The most likely explanation is that women whose husbands have been laid off or had their income cut are going back into the workplace (at least temporarily) to help make ends meet.
Some feminists might rejoice, but to an economist, or a social conservative, this development is almost certainly a bad thing. Assuming that she and her family both wanted her to stay home, each woman who leaves for the office out of economic necessity represents a loss to the country, a loss of what economists call utility and what we may think of as net national happiness.
As she heads back to the workplace, that mother will be boosting GDP. If her husband has lost not his job but merely some income from sales commissions or a business, she will probably have to pay for child care. She may need to buy new work clothes. Money will be spent on commuting, and the family will probably shift away from homemade meals to costlier prepared foods that save time. All of these transactions further swell the national income accounts. Yet all of them also represent a decrease in life satisfaction.
These are but some of the reasons why analysts on the left and right think we need a better measure of national welfare than GDP. But if Kuznets had a hard time figuring out how to measure all the transactions in our marketplace, how much harder will it be for his multilateral successors to put a number on the things we often call priceless?
There are, broadly speaking, three ways you can try to build a “well-being” index. You can use what economists call “shadow prices,” imputing dollar values to the various things that contribute to our quality of life. But the index usually ends up being incredibly sensitive to your starting assumptions. As the economist Tyler Cowen says, “How much is a fish worth? .00000000000001 cents per fish or .0000000000000000000000000001 cents per fish? It makes a big difference!” He wryly adds, “The point of a GDP statistic is to drain away those sorts of problems. The point is not to commit you to an anti-fish position.”
The second approach is to attach weights to various indicators and use them to build a composite gauge like the Human Development Index. Unfortunately, the weights will always be somewhat arbitrary. That inherent subjectivity makes the accuracy of your index somewhat suspect, and leaves the people who created it open to accusations of bias.
Take the World Health Organization’s infamous ranking of national health systems, which in 2000 put the United States below such health-care luminaries as Oman, Colombia, and Morocco. Would you really rather get sick in Bogotá than in Berkeley? The WHO analysts heavily weighted somewhat murky estimates like equality of access, knocking us down to two places above Cuba, where antibiotics are scarce for everybody. This is so bizarre that conservatives don’t sound entirely crazy when they voice suspicions that the WHO chose its weights to produce just this result. The WHO has since stopped publishing the index.
When all else fails, of course, you can just ask people: Are you better off now than you were three years ago? Even this approach poses problems. As any marketing expert can tell you, the difference between what people say they want, and what they actually want, can be large. Their memories of years past are quite poor, and often highly selective. Survey respondents are vulnerable to all sorts of influences, from how the question is framed to a desire to impress the interviewers. That’s why polls often find substantial majorities against raising taxes and against cutting any major programs—and for reducing the deficit. Surveys also show that heterosexual men on average have many more sexual partners than heterosexual women do, which is mathematically impossible.
Besides, much of the progress in important areas of life is invisible to most people. You are indisputably better off having the option of getting a liver transplant if you should need one. But unless your skin actually starts turning yellow, you probably never think about it.
The OECD project’s daunting task is to find better ways to handle these kinds of obstacles. One possible approach is to focus on hard indicators that we can measure in a fairly standard way. But these are scarce for some aspects of life, and even when they exist, can be tricky to interpret. Life expectancy, for example, seems pretty objective. It’s a metric on which the United States does relatively poorly, causing us endless consternation. A few years ago, however, the health-care economists Robert Ohsfeldt and John Schneider recalculated the numbers after controlling for deaths from homicides and traffic accidents. Because these things tend to strike very young people, they can have an outsize impact on mortality statistics. Those deaths reflect America’s crime policy and its driving habits more than the effectiveness of its health-care system. And if you remove them from the picture, say Ohsfeldt and Schneider, America jumps to the top of the life-expectancy tables. Assuming they’re correct, does America have good health, or bad? Neither answer is the obviously right one. Such conundrums will vex analysts long into the future.
But of course, measurement problems also bedevil GDP, which is why the U.S. Bureau of Economic Analysis employs economists and statisticians rather than bookkeepers. Whatever the difficulties, the OECD project’s leaders, like their forebears during the Great Depression, are convinced of the urgent need to get a better handle on the progress our societies are making.
That sense of urgency doesn’t mean we should look for speedy results. Even though his staff had to tabulate the results without the aid of computers, Kuznets and his team produced the first national accounts more rapidly than his successors will create the newer, better set of statistics they aspire to. For all the burdens under which Kuznets labored, he had one advantage: the absence of an existing economic establishment. He did not have to convene working groups and seminars to make sure that academics around the world and the staffs of dozens of institutions felt included. Nor, once he was finished, did he have to convince governments that they should care. Giovannini describes today’s next step as developing “a framework that at least lists the domains that outline what this project should look like.” That’s several nouns away from any sort of working model.
It doesn’t help that Giovannini has left the OECD to head Italy’s statistics authority. But efforts like these have always been larger than one person. When our grandchildren face their financial Waterloo, they may have Giovannini’s brainchildren to help guide them through it. But we will have to muddle through with the legacy of Kuznets and the generations of economists who expanded and improved on his remarkable achievement.