The Welch Consulting Employment Index for May came in at 98.5, a notable increase over 98.2 in April. The strong employment situation seen in the Welch Index is also visible in the BLS reports of non-farm payrolls increasing by 280,000 jobs.

The Welch Index for women jumped to 100.3 in May, its highest level since February 2009. The Index for men is also on the rise, at 97.0, a level it has been hovering around since February of this year. The last time the Index for men was above 97.0 was in November 2008.  While men  have seen stronger gains in full-time equivalent employment over the past year than women have (with the men’s Index rising 1.3 from 95.7 to 97.0 and the women’s Index rising 0.9 from 99.4 to 100.3), women’s employment is above the baseline level of 100 set in 2004, while men’s employment is still below that level. The employment of men had taken a harder hit in the recession and is still recovering.

The Welch Index measures full-time equivalent employment, adjusting for population growth and the aging of the workforce.  The increase in the Index from 98.2 in April to 98.5 in May means that full-time equivalent employment grew at a faster rate than the adult population in the past month.  The Welch Index is about 1.2% higher than it was one year ago.  Full-time equivalent employment has been increasing about 1.1% faster than adult population growth over the past two years (after making adjustments for the aging of the U.S. adult population).  The Welch Index of 98.5 indicates that full-time equivalent employment is 1.5% below its level in the base year of 2004, after adjusting for population growth and changes in the age distribution of the labor force

Technical Note: Full-time equivalent employment equals full-time employment plus one half of part-time employment from the BLS household survey.  The Welch index adjusts for the changing age distribution of the population by fixing the age distribution of adults to the distribution in the base year of 2004.  Seasonal effects for the share of workers employed in part-time jobs are removed in a regression framework using monthly indicator variables.