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Welch Consulting Employment Index Takes a Step Back in August

The Welch Consulting Employment Index pulled back in August, falling two-tenths of a point to 100.5. The index has been bouncing up and down in a range between 100.4 and 100.7 since March of this year.

The Welch Index measures full-time equivalent employment after adjustment for population growth and the aging of the workforce. The 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 99.6 to 100.5. 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 0.9% 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 increased from 100.2 in February to 100.5 currently – an increase of 0.3%. The rate of change over the past year is on pace with the overall trend for the last 3 years of about 1.0% increase per year, while growth has slowed in the last 6 months.

The indices for both men and women declined this month. The index for men fell from 98.4 to 98.3. The index for women fell 0.4 points, from 103.6 to 103.2, reversing its July increase. For the past 12 months the index for men has risen 0.5 points and the index for women has risen 1.4 points.


Welch Employment Index August 2017

Welch Employment Index by Gender August 2017

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.