What it’s all worth, in work-life cash

A Census Bureau research report estimates lifetime earnings by education, race/ethnicity and gender.

The report, by the Bureau’s Tiffany Julian and Robert Kominski, uses national data from the American Community Survey to create “synthetic work-life estimates” of earnings.

The method takes earnings information from one time period — in this case the years 2006-2008 combined (before the recession) — and calculates how much money people would make if they lived through their whole work lives (40 years, from age 25 to 64) during that period. Demographers use the same method to estimate life expectancy. It’s a way of using the most current period to project an image of the future in today’s shape. It’s a better look at the future, for most purposes, than looking back at the lives of people who are wrapping it up today.

Here is a figure they made, using earnings from people working full-time and year round:

That is for people working full-time and year-round at their jobs. That is not reasonable, of course, if people take time out of the labor force, or out of full-time work. So this understates the earnings gaps, especially by gender, since women take more time out of the labor force than men, on average.

They also reported the projected lifetime earnings for all workers — including those working only part-time or part of the year. The figure above showed a ratio of 4.7-to-1 from top to bottom, whereas the all-worker data has a ratio of 5.6-to-1 from White male professional-degree holders to Latina high school graduates.

I turned their all-worker table into this graph with men and women color coded:

This is not a real prediction, just a projection of the present into the future. But the scale is good for the imagination — the gap from top to bottom is 3.65 million dollars in 2008 terms.

Note that in addition to employer discrimination, these gaps reflects the full range of influences on people’s earnings, including sorting into occupations, part-time work, lost tenure and experience from time out of the labor force, and regional variation (which is one reason Asian workers show up high – many live in expensive cities like San Francisco and Honolulu).