The Welch Consulting Employment Index continued its advance to recent highs in November, moving from 101.9 to 102.1. This is a level not seen since March, 2008. The index for women climbed strongly this month, while the index for men held steady.

The Welch Index measures full-time equivalent employment after adjustment for population growth and the aging of the workforce. An Index value of 100.0 indicates that adjusted full-time equivalent employment is the same as its level in the base year of 2004.

Over the past 12 months the index has risen from 100.9 to 102.1. The increase in the Index over the past year means that full-time equivalent employment has been growing at a faster rate than the adult population.  Full-time equivalent employment increased 1.2% faster than the adult population over the past year (after making adjustments for the aging of the U.S. adult population). Looking back at the most recent 6 months, the index rose from 101.6 in May to 102.1 currently. The rate of change over the past year is on pace with the overall trend for the last 3 years of an increase of about 1.1% per year. The most recent six months show a trend on pace with the past year and the past three years.

In November the indices for women and men diverged. The index for women fell from 105.3 to 105.1. This is still up from 103.5 a year ago. The index for men rose from 99.3 to 99.6. The index for men is up 0.8 points from its year ago value of 98.8. Over the last three years the index for women is up 4.3 points while the index for men is up 2.6 points.

Technical Note: Full-time equivalent employment equals full-time employment plus one half of part-time employment from the BLS household survey (the Current Population Survey). The data reported for a given month is generally from the calendar week that contains the 12th day of the month. 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.