Then you can use that information to make better decisions. It’s when you’re at the peak rate of growth and nobody sees any clouds on the horizon that you have to resist the temptation to think you’ve reached a new nirvana. The charting process can help enormously. Sometimes just the fact that business is great should be enough for a CEO to say, “Let’s not plan on trees growing to the sky. Let’s watch our leading indicators, and when they start to slow, let’s take that seriously as a signal for a slowdown in business a year from now.” Trim your inventory and perhaps trim your labor, or at least stop adding even more labor and infrastructure when you’re nearing a cyclical peak. Then, when business is at a low but the leading indicators are telling you the next upturn is coming, at least prepare to start expanding again in time to take advantage of the early accelerating part of the cycle.
S+B: Are most retailers and manufacturers equipped to act that way?
ELLIS: I don’t think they are, mainly because it is such a challenge to actually observe the cycle. An article several years ago in the New York Times asked chief executives what they looked at when they were trying to forecast their markets. It was all very anecdotal. One CEO said he looked at taxi lines; if the taxi lines were long, then people had money and were ready to spend. That’s kind of interesting and even amusing, but forecasting your business should not be based on personal hunches. There should be some attempt at empirical economic analysis, beyond just reading the newspapers. Back in the 1970s, retail companies like Sears and Federated Department Stores had economists on staff. Many of these companies, especially in the age of downsizing, let that function go. They gave up on an endeavor that actually could have had great value, but not in the way traditional economists were doing it.
The very value of charting leading and lagging indicators empirically is that, if we keep the charts in front of us at all times, they don’t let us off the hook. They don’t let us revert to our intuitive assumptions or hunches. At Goldman Sachs, we used to keep the charts up-to-date and pasted in front of our desks where we had to see them on an ongoing basis. I think it’s a much more viable approach than letting data get lost in abstruse computerized statistical models.
Some economists will undoubtedly look at the intentionally simplified methodology in my book and conclude that these are the unsophisticated musings of an undereducated Wall Street analyst. But I think the methodology can empower investors, business executives, and anybody else who’s interested in the cycle of the economy. If you have a modicum of economic knowledge, a computer, and a working knowledge of Excel, then your ability to track and explore these phenomena, the causal relationships, and the sequence of events is endless.
I also strongly believe that this concept of cause-and-effect charting should be part of the way economics is taught. Today the field is dominated by the teaching of economic theory, with no numbers attached. Students need to be able to understand basic economic indicators, choose the ones relevant to the issues they care about, track them over time, and understand the sequence of cause and effect. To me, not teaching first-year economics students an accessible and pragmatic approach for mastering the numbers in today’s economy is like teaching chemistry or physics without a lab.
Imagine an economics professor assigning a student to track the cyclical outlook for a paper carton producer. The professor would say, “What would your common sense tell you to look for as a leading indicator?” It might be year-over-year growth in consumer spending for the kinds of goods, like cosmetics or toys, that are shipped in paper boxes, or, more broadly, combined consumer spending on durable and nondurable goods. That method of investigation becomes empowering. The student might uncover a useful cause-and-effect sequence, i.e., a leading indicator, and in the process learn how to use this approach later in his or her career. But even learning that there is not a predictive relationship can sometimes be valuable.