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August 2006, Vol. 129, No.8
Conducting the Mass Layoff Statistics program: response and findings
Sharon P. Brown and Patrick Carey
The devastation caused by Hurricane Katrina created several data collection and analytical challenges for the Bureau of Labor Statistics’ (BLS) Mass Layoff Statistics (MLS) program. The sheer number of mass layoffs based on initial claims filings for unemployment insurance (UI) against establishments led to acute workload problems in affected States. There was a need to ensure that mass layoffs directly or indirectly related to Hurricane Katrina were consistently identified by analysts working on the MLS program in all States. Another challenge was understanding and resolving apparent contradictions between data on potential layoff events based solely on administrative data and responses from the employers themselves. While the MLS program has undertaken special data collection efforts in the past, nothing matched this collection in terms of immediacy, magnitude, and geographic concentration. BLS also requested special interim reporting of Hurricane Katrina-related layoff activity in order to verify that procedures were adequately being followed and to provide important and relevant information quickly. The success in meeting these challenges was grounded in the ongoing collaboration that exists between BLS and the State agencies in this program. This article details the efforts made to identify and track layoff activity related to Hurricane Katrina and provides some of the resulting information.1
This excerpt is from an article published in the August 2006 issue of the Monthly Labor Review. The full text of the article is available in Adobe Acrobat's Portable Document Format (PDF). See How to view a PDF file for more information.
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1 Hurricane Rita hit Louisiana and Texas on September 24, 2005, only 26 days after Hurricane Katrina hit on August 29. Because of the difficulties in separating Katrina-related and Rita-related layoffs, the mass layoff data in this article include some Rita-generated cases.
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