Why it’s time to trust hard data

New York: The apparent conflict between hard US economic data, which looks weak, and soft data, which looks strong, adds considerable uncertainty to the outlook. Investors are confused and distracted by the conflicting information, finding it difficult to understand why the Federal Reserve might wish to hike interest rates three times — or more — this year. They shouldn’t be.

Survey-based soft data are described that way for a reason, which is that they are just a qualitative measure of how people feel or judge the outlook, but are not necessarily indicative of how they behave. Hard data quantitatively measures actual behavior or economic performance. That’s real and informative of how the economy is performing. To paraphrase George Orwell, some data are more equal than others and we should be paying far more attention to reports we know are most accurate.

Still, hard data measures are only available with lags, subject to revision and prone to measurement error, which means they can provide noisy or misleading signals regarding the economy’s performance. Within the universe of hard data, some reports are vastly more reliable and informative than others. On this basis, I’m comfortable concluding that the economy’s performance is fairly solid, aligning with the soft data, even though I readily dismiss most of those reports.

Soft data surveys opinions of people or businesses, which are mostly never revised. Once the sample is collected, the data may or may not be seasonally adjusted, but that’s pretty much it. Their main advantage is timeliness. Conceptually, they may seem to provide excellent insight into whether consumers might increase or decrease spending, since a really positive response implies higher spending. In fact, this line of logic is demonstrably false. For example, confidence surveys are always quite depressed coming out of a recession, so they never anticipate the rise in spending that always accompanies an economic recovery. Similarly, they don’t anticipate recessions well or at all. They are merely a snapshot of how people feel about the economy at each point in time and provide negligible forecasting power.

I wrote my economics PhD thesis on the effects of capital gains on household spending and I made the same obvious assumption that at least one of the several measures of consumer confidence would be helpful in the estimated equations. Yet despite my efforts, I could never find one that added even a smidgen of explanatory power to the standard consumption equation. Once disposable income, net worth and capital gains were included as explanatory variables in the equations, consumer confidence became totally irrelevant.

Remarkably, European economists rely heavily on confidence surveys to project how their economy will perform over the near term. That seems to be only because confidence survey results are available on a timelier basis than hard data reports. If hard data are not available, then one must make do with what is, even if those inferences are far more tenuous.

There’s always a critical trade-off between accuracy and timeliness. We experience large data revisions in the US, partly because our data collection agencies wish to provide the timeliest information, even if they must revise later. The first gross domestic product report for each quarter is released near the very end of the first month of the following quarter, which is fairly prompt. But then, it is revised in the second month and again in the third month. By the second revision, it is old news and not considered very relevant.