Wyoming Unemployment Rate Increases to 5.0% in May
Wyoming’s seasonally adjusted1 unemployment rate increased from 4.5% in April to 5.0% in May. The last time our seasonally adjusted unemployment rate was at this level was June 1999. The state’s over-the-year job growth rate continued to decline, falling from -0.9% in April to -1.6% in May. In contrast, the U.S. unemployment rate stood at 9.4% in May and U.S. job growth was -4.0%.
April to May’s employment increase of 6,100 jobs, or 2.1% was somewhat smaller than the normal seasonal increase for May (approximately 7,500 jobs). Employment decreased in natural resources & mining (-1,000 jobs, or -3.7%) and other services (-100 jobs, or -0.8%). Seasonal job gains were seen in construction (1,700 jobs, or 7.0%), retail trade (500 jobs, or 1.6%), professional & business services (700 jobs, or 4.0%), leisure & hospitality (2,400 jobs, or 7.7%), and government (including public schools, colleges, & hospitals; 1,500 jobs, or 2.1%).
On the other hand, over the year, Wyoming employment fell by 4,900 jobs, or 1.6%. The largest job losses occurred in natural resources & mining (-2,500 jobs, or -8.7%) and construction (-2,400 jobs, or -8.5%). Smaller job losses were seen in professional & business services (-700 jobs, or -3.7%), leisure & hospitality (-700 jobs, or -2.1%), retail trade (-500 jobs, or 1.6%), and other services (-200 jobs, or -1.6%). Job gains were reported in four sectors: government (1,400 jobs, or 2.0%), educational & health services (700 jobs, or 2.9%), wholesale trade (100 jobs, or 1.1%), and transportation & utilities (100 jobs, or 0.7%).
Most county unemployment rates increased slightly from April to May. Big Horn County posted the highest unemployment rate (7.6%), followed by Lincoln (6.6%), Teton (6.4%), and Fremont (6.4%) counties. The lowest unemployment rates were found in Albany (3.1%), Sublette (3.6%), Campbell (4.1%), and Goshen (4.1%) counties.
1Seasonal adjustment is a statistical procedure to remove the impact of normal regularly recurring events (such as weather, major holidays, and the opening and closing of schools) from economic time series in order to obtain a better understanding of changes in economic conditions from month to month.