Concurrent seasonal adjustment uses the most recent raw data in calculating seasonal adjustment factors, in contrast with methods where factors are estimated periodically and projected forward. The Current Employment Statistics - State and Area (CES-SA) program at the U.S. Bureau of Labor Statistics uses a unique two-step seasonal adjustment approach where the benchmarked universe data are adjusted separately from the survey-based data due to different seasonal patterns exhibited by data from the two sources. A history of survey-based estimates are used each January to provide projected factors for the coming year. Switching to a concurrent adjustment method was examined and is being considered. The concurrent adjustment method yielded factors that were more accurate (evidenced by smaller revisions.) Employment data were less volatile month-to-month under the concurrent method, and also more closely matched universe data were less volatile month-to-month under the concurrent method, and also more closely matched universe data, which are considered to be a more accurate gauge of economic reality. Some of the improvement was due to better estimation of a regression effect that is used to adjust for the varying number of weeks between survey reference periods.