Wyoming Unemployment Rate Increases to 6.6% in August
The Research & Planning Section of the Wyoming Department of Employment has reported that the state’s seasonally adjusted1 unemployment rate increased from 6.5% in July to 6.6% in August (not a statistically significant increase). It remained much lower than the national unemployment rate of 9.7%. Over-the-year job losses in Wyoming (-3.6%) were less severe than U.S. job losses (-4.4%).
Over-the-year Wyoming employment decreased by 11,000 jobs, or 3.6%. The largest job losses were seen in natural resources & mining (including oil & gas; -5,300 jobs, or -17.5%) and construction (-4,600 jobs, or -14.9%). Other sectors with job losses included leisure & hospitality (-1,100 jobs, or -2.8%), professional & business services (-900 jobs, or -4.5%), retail trade (-400 jobs, or -1.2%), and financial activities (-400 jobs, or -3.4%). Employment was unchanged from a year earlier in transportation & utilities and information. Job gains occurred in government (including public schools, colleges, and hospitals; 1,300 jobs, or 2.0%), educational & health services (500 jobs, or 2.0%), and wholesale trade (200 jobs, or 2.2%).
From July to August, employment decreased by 2,200 jobs, or 0.7%. This decrease is not consistent with historical seasonal patterns. On average, employment tends to increase slightly from July to August. Job losses were seen in natural resources & mining (including oil & gas; -600 jobs, or -2.4%), leisure & hospitality (-600 jobs, or -1.5%), and government (-1,100 jobs, or -1.6%). The construction sector added 300 jobs, or 1.2%.
Across Wyoming’s 23 counties, all unemployment rates remained higher than
year-ago levels. The highest unemployment rates were found in Big Horn
and Fremont counties (both 7.7%) followed by Natrona and Uinta counties
(both 6.9%). Albany County posted the lowest unemployment rate (4.1%) followed
by Teton (4.2%) and Sublette (4.4%) counties.
1 Seasonal 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.