Experts in Economics & Statistics

A consistent choice for industry leading companies
and the nation's preeminent law firms for over 40 years.

Welch Consulting Employment Index Falls in May

on 03 June 2016

The Welch Consulting Employment Index is continuing its retreat from the level it hit in March. While the index has fallen to 99.1 in May from the recent peak of 99.5 in March, it currently remains above the 2015 year-end value of 99.0.    

The Welch Index measures full-time equivalent employment after adjustment for population growth and the aging of the workforce. The Index value of 99.1 indicates that adjusted full-time equivalent employment is 0.9% below its level in the base year of 2004.  

Over the past 12 months the index has risen from 98.5 to 99.1. 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 0.6% 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 98.6 in November 2015 to 99.1 currently – an increase of 0.5%. This rate of change is in line with the overall trend for the last 3 years of about 1.0% increase per year.

When broken out by gender, men saw a decline in employment in May, while women had a small increase. The index for men fell 0.2 points in May, from 97.8 to 97.6, while the index for women rose from 101.0 to 101.1, reversing April’s decline. For the past 12 months the index for men has risen 0.5 points and the index for women has risen 0.8 points.

may2016 welch index

may2016 welch index gender

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.