The statistics that drive our big-picture economic thinking — GDP, unemployment figures, and inflation rates, among others — have come to be regarded as nearly sacrosanct. Investors, policymakers, and everyday consumers rely on them to make decisions trivial and earthshaking alike, often measured in trillions of dollars. Zachary Karabell, in his book The Leading Indicators: A Short History of the Numbers That Rule Our World, traces the history of these numbers and questions how useful they actually are.
Octavian Report: The Leading Indicators examines the history, uses, and misuses of major economic statistics. How did you choose this topic?
Zachary Karabell: We’ve come to live in this world that is intimately shaped by a very limited set of numbers, and I’m always curious about how we come to live in the world that we’re in. And one thing that becomes apparent when you start asking, “How did this happen? How did it happen that we have defined our national success largely by reference to one number, namely GDP, gross domestic product. How did that happen?” -- this wasn’t always the case. Lincoln didn’t get up and say, “Gross domestic product even in spite of the Civil War has increased and therefore I’m a good president.” And Franklin Roosevelt barely was able to say that.
The question is: do these numbers depict what is going on in the world around us with any degree of accuracy? When you start looking at the background and the history and then at the world we live in today, it becomes clear that while GDP and unemployment and trade figures and inflation numbers do provide some insight into how these things work systemically, they are a product of the moment in time when they were created. They were all invented in the 1930s and the 1940s and they were all invented largely to help the United States and Great Britain deal with the Great Depression and with World War II. That’s it. That was the world those numbers were designed to help those policymakers navigate.
The result is we’re really good at measuring mid-20th-century industrial nation-states, because that was the nature of economies then. We don’t live in that world anymore, but we use these numbers ever more in the world we are living in to navigate.
OR: Are they in your view generally directionally correct and accurate?
Karabell: These numbers measure what they measure, and they actually do a good job doing that. The problem is that what they measure and what’s going on in the world around us don’t line up so well. Take unemployment: is the unemployment number directionally good at gauging whether employment writ large in any particular country is getting worse or better?
The answer is at best provisionally yes. As so many have been pointing out over the past couple of years, you have this somewhat unprecedented but definitely unfamiliar phenomenon of the unemployment rate in the United States going down along with the number of people who are actually working decreasing, the labor force participation rate.
As much as it would be great to answer the question of “Do these tell us something directionally that’s helpful,” the minute you start scratching the surface and you look at what goes into these numbers, that becomes a harder and harder question to answer conclusively.
OR: What do you make of the fact that people are investing or trading trillions of dollars based on these numbers?
Karabell: Using these numbers as trading hooks, as theses for forward action of any of the financial instruments that people trade I think is unbelievably questionable. It’s probably one of the reasons that most funds and most people who do this have seen results less than what they would have expected to produce for their investors or themselves.
There’s no way of really gaming out a GDP report as a direct one-on-one connection to earnings. GE or Honeywell or any number of large industrial companies are exposed 50 percent, 60 percent, and 70 percent to global trends rather than to U.S. trends. Does the U.S. really get to be the determinate direction of whether or not that company’s going to make its earnings? Does it matter to Amazon? GDP could be going down one percent and Amazon could get 10 percent more revenue just because they’re stealing a share, because they can afford thinner margins.
The idea that you can easily transform these economic statistics into viable trading theses, I think, is showing at best the law of diminishing returns and at worst leading you astray.
OR: The statistics themselves are revised over decades, correct? Including GDP?
Karabell: Right. And many of them are revised in subsequent months. The thing that provokes the trade is the initial release. Revisions, which happen frequently because almost all data when it comes out is provisional, more information comes in, better calculations, better adjustments -- very rarely does the trade then get triggered by the subsequent revisions.
But there’s a market need and a kind of a public need for instant information, so we release GDP as quickly as possible. But the change disappears. There’s never a headline saying “Jobs -- from two months ago, it was spectacularly wrong.” It’s always “X number of jobs was created this month.” And that is part of the media game of it. “They were created this month. They weren’t created this month.” They were a statistical adjustment or creation based on a limited number of sampling and a lot of math. That’s fine. That’s how statistics work. But you wouldn’t know that’s how statistics work given the public debate.
OR: I think you do a great job in The Leading Indicators, going back to the example of William the Conqueror's Domesday Book, of giving examples of how flawed the statistics themselves are. Real policy decisions are made based on them; could you talk about your analysis of how the U.S.-China trade deficit is calculated?
Karabell: The whole relationship between the United States and China has unfolded really since China joined the WTO in 2001. It’s probably the primary bilateral economic relationship in the world. And most of the understanding of that relationship unfolds via these numbers, right? It’s an abstract statistical relationship based on a flow of goods and services.
The problem is, the way we measure that flow stems from the only way we know how to measure that flow. GDP and trade figures assume that you either make stuff within the boundaries of your country and then you sell it to your own citizens and then it boosts domestic GDP, or you make stuff and you sell it to another country, in which case it boosts your GDP, or you buy stuff from somewhere else, in which case you’ve sent your money to someone else. That sounds very facile, but that’s the entire framework of our current trade statistics.
Zachary Karabell is Head of Global Strategy at Envestnet. His most recent book is The Leading Indicators: A Short History of the Numbers That Rule Our World.