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Business and economic activity fluctuates throughout the year. Some of the differences are unique, but the majority are due to the season and the time of year.

Construction spending and home starts tend to be higher in the second and third calendar quarters than in the other two, especially in the northern portion of the United States, since outdoor building projects are easier to finish in dry, warm weather.

Clothing and gadget retail sales are higher in December due to the Christmas season. According to the economy as a whole, clothing and electronics shops could anticipate roughly one-sixth of their annual sales in December.

Grocery stores, on the other hand, have far lower monthly sales volatility. Seasonal variations in employment are also visible, especially among contract and temporary workers.

When a businessperson wants to look at a company’s sales trajectory (or an economist wants to look at the economy’s health), he or she will want to filter out these normal seasonal effects in order to understand the “true” trends. A basic and uncomplicated strategy for abstracting from seasonal affects is to compare current activity to activity at the same period the previous year

When a CEO discusses financials, he or she will compare current sales to sales from the same calendar months the previous year and the previous two years. When the CFO of a publicly traded business presents its most recent quarterly results to a group of analysts, he or she will compare revenues not just to the previous quarter, but also to the same time the previous year.

Common Distortions in the Data to Watch

When it comes to reducing seasonal effects, the “similar time” strategy isn’t reliable. The Lunar New Year, for example, is a big producer of economic activity in China since it is associated with gift-giving, entertainment, and travel. It is a moveable holiday on the solar calendar, with some years occurring in January and others in February. It’s vital to note when comparing economic activity in those two months to economic activity a year earlier when comparing economic activity in those two years.

In economic statistics, a mathematical equivalent of the “similar period” approach is used to “seasonally adjust” the data. A seasonally adjusted number for retail sales, car production, or GDP is calculated at an annual rate based on the assumption that the seasonal component of activity was running at its normal speed at the time.

As a consequence, if December clothing sales are generally twice as high as November sales, and December 2020 sales are exactly twice as high as November 2020 sales, the seasonally adjusted sales levels for November and December 2020 will be the same. December 2020 sales will be greater than November 2020 sales seasonally adjusted if December 2020 sales are more than twice as high as November 2020 sales.

Seasonal Echoes of the COVID-19 Pandemic

The COVID-19 pandemic will definitely throw the “similar period” technique of accounting for seasonal variations out of whack. Because lockdowns and shelter-in-place orders suppressed significant economic activity in the second quarter of 2020, second-quarter 2021 levels will surely be greater, but what will we learn?

Using comparable timeframes from 2019 will be one way to try to abstract from the pandemic’s distortions. Analysts with sharp eyes will be curious to observe what comparable periods companies and the media are using this year.

For example, I just read an article in the newspaper on the expanding potential for electric vehicles. Electric vehicle sales in China were six times greater in January 2021 than the previous year, according to the author, confirming the notion that the sector was about to take off. The author forgot to mention that sales decreased in January 2020 as a result of the outbreak, which began in China before spreading to Western Europe and the Western Hemisphere.

According to a recent blog post by David Lucca and Jonathan Wright of the New York Fed, the epidemic would wreak havoc on the more complicated seasonal adjustment mechanisms as well. According to Lucca and Wright, the Great Recession of 2007-2009 caused persistent seasonal echoes in seasonally adjusted data in the following years due to the significant economic upheaval.

Because seasonal adjustment processes employ a weighted average of recent comparable periods to estimate the “normal” seasonal link, a significant interruption in economic activity, such as the pandemic or the Great Recession, would provide deceptive seasonal patterns in historical data. Seasonally adjusted data for the first quarter of the year often indicated increased economic activity, which subsequently seemed to decline when seasonally adjusted data for the second quarter became available in the years after the Great Recession.

Making Inferences Will Require Extra Diligence

While statistics agencies can and have made human improvements to try to mitigate the problems that develop when a large, nonseasonal shock occurs, the seasonally adjusted series that follows may not be completely “fixed.” Lucca and Wright claim that “There are no straightforward answers to seasonal adjustment in this situation. In certain cases, the virus had a temporary and lasting influence on the economy and seasonal patterns.”

When unadjusted data is available, analysts should use it to get a sense of how the economy is performing, but bear in mind that making conclusions about “actual” behavior will be difficult for many years.

For more than 20 years, Thomas Bowne has served as the Freedonia Group’s Chief Economist. His team develops the macroeconomic measures that underpin all of Freedonia’s research, ensuring that the organization’s conclusions are consistent. He earned a bachelor’s degree in economics from Princeton University and a master’s and law degree from Stanford University.