The “Soft” Data Gets Softer

I worked in Europe for two weeks earlier this month. Among the regional treats I enjoy most are pretzels, which local bakeries take to a high level. There is a divide, however, between those who enjoy soft pretzels and those who prefer hard pretzels.

A divide has recently developed between soft and hard economic data. At a time when conditions are changing rapidly, understanding the difference between the two is terribly important.

In the vernacular of economists, “soft” data is generated by surveys. It gauges impressions about current and future conditions. Examples include measures of consumer and business confidence. The construction of soft data is not uniform: some series are indexed relative to a baseline date, while others are diffusion indices. (A common diffusion index takes the difference between positive and negative responses to a survey.)

“Hard” data results from more rigorous accounting for things like spending and output. Sampling and assumptions are commonly used to assemble these statistics, but they are considered to be more objective than softer metrics.

Sentiment surveys ask about prospects going forward, in an attempt to be forward-looking. Hard data is retrospective. Economists can use histories of hard data to model what might happen in the future: this is the bedrock of forecasting. But during times of rapid change, history may not be a reliable guide to what lies ahead.
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The current environment may fall into that latter category. Tariffs are on their way to levels that we have not seen in a century, depressing growth and placing upward pressure on the price level. It is too early to see direct impacts on quantities like gross domestic product (GDP). And so soft data is getting additional attention.