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Welch Consulting Employment Index Advances in April

Posted by Super User
on 15 May 2017

The Welch Consulting Employment Index climbed to 100.7 in April, up from 100.5 in March. This is the highest value for the Index since September 2008.

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 99.2 to 100.7. 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 1.5% 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 99.6 in October 2016 to 100.7 currently – an increase of 1.1%. This rate of change over the past year is on pace with the overall trend for the last 3 years of about 1.1% increase per year, but in the last six months the rate of change has been above trend.

The change in the index for men was stronger in March than that for women, reversing the pattern seen in March. The index for women ticked down from 103.2 to 103.1. The index for men rose from 98.4 to 98.7. For the past 12 months the index for men has risen 0.9 points and the index for women has risen 2.2 points.

 

Welch Employment Index April 2017

Welch Employment Index by Gender April 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.