By Tyler Cowen February 21, 2014

‘May my children grow up in a world where no one knows who the central banker is” is a wise saying. One also can hope for a world where arguments about measuring GDP (gross domestic product, the sum total of the goods and services produced within a nation) or the inflation rate are rare. In good economic times, we tend to take reported economic numbers for granted, but more recently, conspiracy theories have run wild. It is sometimes claimed that “real GDP” or “true inflation” is much higher or lower than what is officially proclaimed. For instance, both Ron Paul and Sen. Tom Coburn have mistakenly charged that inflation is actually running at or above 8 percent a year, which would mean Social Security benefits are not indexed upward enough and real GDP is plummeting, both implausible conclusions.

Fortunately, the popular economics book sector has come to the rescue with two new and useful entries on what our economic statistics mean and why we should (mostly) trust them. This topic is no longer for wonks only.

If you are going to read only one book on GDP, Diane Coyle’s “GDP: A Brief but Affectionate History” should be it. More important, you should read a book on GDP, as many of the political debates of our time revolve around this concept. Can we afford our current path of entitlement spending? Was the Obama fiscal stimulus worth it? When will China overtake the United States as the world’s largest economy?

The answers all depend on GDP. In 140 pages of snappy text, Coyle lays out what GDP numbers measure, what roles they play in economic policymaking and forecasting, and how GDP numbers can sometimes mislead us, albeit not in the way many current critics suggest.

If you need to be convinced of the importance of good GDP numbers, look to sub-Saharan Africa. We don’t know how well a lot of those nations are really doing. In 2010, as Coyle notes, Ghana made its GDP go up 60 percent overnight just by changing its measurement conventions. That’s not a good sign for the accuracy of either the old or the new numbers. One reason the United States responded better to the recent financial crisis than to the crash of 1929 was that 21st-century policymakers had better and more timely numbers about the true health of the economy.

‘GDP: A Brief but Affectionate History’ by Diane Coyle (Princeton Univ.)

The main problem with GDP is that in popular discourse and in the financial press, it tends to displace other indicators of how the economy is doing, much as the Dow and the S&P 500 tend to crowd out other indicators of the stock market. I, for one, would welcome the advent of a new economic statistic — call it “GDP revised.” Coyle notes, “We might move toward a different approach in time.”

In my vision of such a revision, at least one version of revised GDP would no longer count spending on defense or domestic security, because each of those is geared toward avoiding destruction rather than providing enjoyable goods and services; as an intermediate good, the value of security will be picked up in any case by the production of the other goods and services it enables. I also would not count education, another intermediate good; we spend more and more on it, and our GDP measures are valuing how much we spend and not how much we learn. Let’s also consider that perhaps one-third or more of U.S. health-care spending is wasteful, and chop that off, too. With such a number in hand, the narrative of recent U.S. economic history probably would look less promising. It might help explain, for instance, why the income for the median or typical household has risen only slightly since 1973.

Yet markets are developing new innovations whose benefits probably are undervalued by the GDP concept. This is the potential revision to GDP that commands the most attention from Coyle. For instance, consumers attach great value to Facebook, Google and Wikipedia, all of which are absolutely free to their users and do not enter directly into GDP calculations. I would go further yet, noting that the modern world also better matches plans and goals. Perhaps you can meet your ideal spouse on Match.com or at least pick up cheaper collectibles, better suited to your taste, on eBay. Who makes mistaken purchases of music these days, when you can hear a lot of the songs in advance online? Just about everything is reviewed online, which helps us spend with greater effectiveness. These gains are not well-represented by the older methods of calculating GDP.

I sometimes call myself a happiness optimist but a revenue pessimist. Think of a world where life feels a lot better than what the economic numbers are suggesting but your ability to pay the bills is not improving.

Zachary Karabell’s “The Leading Indicators” I found somewhat less useful than Coyle’s book. It covers more ground, but the wonk in me believes that some of the topics included — say, the unemployment rate, the inflation rate and “Gross National Happiness” — warrant an entire book to themselves. The content is good, but at the end one feels one has learned a variety of points rather than gained a comprehensive understanding. It’s the kind of expansive writing that works well for sweeping historical narratives but less so for these nuts-and-bolts topics. Nonetheless, like “GDP,” it demystifies a lot of current debates, explains its subject matter clearly and shows that the major published macroeconomic statistics are neither nonsense nor conspiracy. Most people could read this book with enjoyment and profit.

Karabell devotes more time to the topic of happiness than does Coyle, but here I wished for a more systematic look at attempts to measure societal happiness, as done by Ed Diener, Angus Deaton, Daniel Kahneman, Betsey Stevenson, Justin Wolfers and other social scientists. “Time use” studies can ask people how happy they are in a given moment, and polls and questionnaires can ask people about their overall life satisfaction. Wealth seems to matter more when we measure overall happiness rather than the short-term variety. Perhaps higher earners are more harried and stressed from moment to moment, but over time they have a greater chance of achieving status and their lifetime goals, and thus their reflective judgments about their lives are more positive. Most likely the happiness concept isn’t so simple after all, which is one reason the more statistically precise concept of GDP has shown such staying power, in spite of its limitations.

I do not agree with Karabell’s claim that “Bhutan is now routinely described as one of the happiest nations in the world.” The prime minister of Bhutan, Tshering Tobgay, has moved away from talk of “Gross National Happiness,” perhaps because he has realized that his country has relatively little of it. Most of the population is engaged in subsistence farming and has only a minimal chance of performing rewarding or creative labor. The prime minister instead wishes to focus on concrete goals such as “a motorized rototiller for every village and a utility vehicle for each district.” For all the talk of being content with less, external debt has soared to 90 percent of GDP. If anything, Bhutan may show that measures of GDP get at happiness more clearly than does focusing on happiness more directly. Just look at where immigrants wish to move — it is almost always wealthier countries.

As the age of big data comes upon us, it remains to be seen what we will do with all those numbers. The danger is that we will measure imperfect concepts such as GDP and inflation with ever-increasing accuracy rather than reexamine what such measures are good for. The big gains from economic statistics may instead come from teaching the public some of the most basic truths about these concepts. For instance, most Americans still confuse inflation, as it refers to a proportionally higher nominal level of prices and wages, with a lower standard of living. When estimating what is the rate of price inflation, people also seem to overweight gasoline prices and other frequent, everyday purchases and underrate a lot of the items that are becoming cheaper. Thus, even though we live in a time when inflation rates are relatively low, and many economists think they should be somewhat higher, the public perceives inflation as already intolerably high.

Coyle points out that the word “statistics” has the same etymological root as “state,” and thus it is no surprise that an increasing mistrust of the state has led to an increasing mistrust and misunderstanding of economic statistics. These two books will help put a dent in that problem.

Tyler Cowen is a professor of economics at George Mason University.