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LMI Myth Busting Series
LMI Myth Busting: Seasonal Adjustment
Myth:
To show the impact seasonal events like winter weather has on employment levels, it is best to use seasonally adjusted data.
Busted:
Actually, just the opposite is true. The process of seasonally adjusting data removes the impacts of seasonal events like winter weather.
Over the course of a year, employment levels undergo sharp fluctuations due to such seasonal events as changes in weather, harvest, major holidays and the opening and closing of schools. Because these seasonal events follow a more or less regular pattern each year, their influence on statistical trends can be eliminated by adjusting the statistics from month to month. Seasonal adjustments make it easier to observe underlying trends that are more truly economic—such as cyclical trends and other nonseasonal movements in a data series.
The graph below illustrates the impact of seasonal adjustment, using monthly South Dakota labor force estimates for the last 12 months. The blue line shows actual labor force levels each month. As you can see, the size of the labor force peaked in July and was at its lowest point in January 2019. A January low is typical for South Dakota, related in part to the coldest weather. For example, cold January temperatures hamper many types of construction work. January also commonly brings employment declines in retail trade following the holiday shopping season when retailers are at full staff.
