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Alabama Layoffs — May 2020

Employers in Alabama submitted 3 WARN Act notices in May 2020, putting at risk an estimated 1,321 workers — up substantially from April and up 1248% versus May 2019. The average filing covered 440 workers, with 2 closures among the notices.

3
Notices Filed
1,321
Workers Affected
440
Avg per Notice
2
Closures

Industry Breakdown

Industry breakdown for Alabama
IndustryNoticesWorkers
Manufacturing1633

The Manufacturing sector emerged as the hardest-hit sector with 633 workers across 1 notice.

Geographic Hotspots

Top counties by layoff notices
CountyNoticesWorkers
Jefferson1633
Etowah1592
Lee196

Jefferson absorbed the greatest share of layoffs, accounting for 48% of all affected workers with 633 workers across 1 notices.

Top cities by layoff notices
CityNoticesWorkers
Fairfield1633
Gadsden1592
Auburn196

Layoff Type Analysis

Layoff types breakdown
TypeNoticesWorkers
Closure2688
Layoff1633

The high proportion of closures (52% of affected workers) suggests structural shifts rather than temporary cutbacks in Alabama's labor market.

Largest Layoffs

Largest layoff notices
CompanyCityWorkersTypeDate
U.S. Steel Tubular ProductsFairfield633Layoff
GoodyearGadsden592Closure
Weidplas North AmericaAuburn96Closure

Topping the list was U.S. Steel Tubular Products at its Fairfield facility, reporting 633 affected workers. Goodyear followed with 592 workers.

Trend & Outlook

This marks the third consecutive month of rising layoff activity.

These figures highlight mounting pressure on the Alabama labor market, with activity running above both recent and year-ago benchmarks. The Manufacturing sector warrants close attention heading into the next period.

This analysis is based on official WARN Act filings reported by Alabama. The Worker Adjustment and Retraining Notification (WARN) Act requires employers with 100+ employees to provide 60-day advance notice of mass layoffs and plant closings. Data is updated daily by WARN Firehose. View all Alabama WARN notices, browse layoffs by state, or download the full dataset.

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