Local Area Unemployment Statistics

Seasonal Adjustment

Over the year, the size of the Nation's labor force, the levels of employment and unemployment, and other measures of labor market activity undergo sharp fluctuations due to seasonal events including changes in weather, harvests, 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. These adjustments make it easier to observe the cyclical, long term trend and other nonseasonal movements in the series. In evaluating changes in a seasonally adjusted series, it is important to note that seasonal adjustment is an approximation and initial adjustment must be based on experience.

Beginning in 1992, BLS introduced publication of seasonally adjusted labor force data for the 50 States, the District of Columbia, and Puerto Rico. Beginning in 1996, seasonal adjustment was extended to estimates for the Los Angeles-Long Beach metropolitan area and New York City. In 1998, seasonally adjusted data for census regions and divisions were first published.

With the introduction of the LAUS Redesign in 2005, seasonal adjustment occured within the model estimation process through the removal of the seasonal component. This modeling approach is used in developing labor force estimates for Census divisions, States, the Los Angeles-Long Beach-Glendale metropolitan division, and New York City. It was extended to six substate areas in 2005. One of these areas was the New Orleans-Metarie-Kenner, LA metropolitan area, which was subsequently removed due to various technical problems following Hurricane Katrina. The remaining five areas are:

In 2010, a smoothed-seasonally adjusted (SSA) series was introduced to reduce the number of spurious turning points in the former estimates. The estimates are smoothed using the Henderson Trend Filter (H13) that suppresses irregular variation in real time. The H13 uses a filtering procedure, based on moving averages, to remove the irregular fluctuations from the seasonally-adjusted series, leaving the trend. Symmetric moving averages are used to smooth the historical series while asymmetrical averages are used in real time. This new approach addresses longstanding issues related to end-of-year revision and enhances the analytical utility of the estimates.

For more information on Smoothed-Seasonal Adjustment, see Smoothed-Seasonally-Adjusted Estimates (SSA) Questions and Answers.

Last Modified Date: March 03, 2010

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