The U.S. labor market has been changing, and FRED can help us reveal some patterns. The graph traces four major occupational groups: nonroutine cognitive (green line), nonroutine manual (purple line), routine cognitive (blue line), and routine manual (red line). Managers and computer scientists, for example, fall into the nonroutine cognitive category since their jobs require mental skills and adapting to the project at hand. Manufacturing and transportation-related jobs fall in the routine manual category.
The trend is clear: Middle-skill occupations such as manufacturing and production are declining, and both high-skill and low-skill occupations such as managers and professionals on one end and personal care services on the other are growing. Another way to describe the trend is that routine jobs are decreasing and nonroutine jobs are increasing. Economists refer to this process as “job polarization,” which is driven by both automation and offshoring. Automation reduces routine jobs because repetitive tasks are easily performed by machines or computers. Offshoring reduces low- and mid-skilled labor-intensive jobs because these tasks may be performed more cheaply or efficiently in foreign countries.
How this graph was created: Search in the “Release” view on the FRED home page for the “Employment Situation” report, choose the first link to view the data from the Current Population Survey, and select Table A-13. In the first subsection (Monthly, Employed), select these four series and add them to the graph: Management, professional, and related occupations; Service occupations; Sales and office occupations; and Production, transportation and material moving occupations. For this last series, use the “Add Data Series” feature to add the employment levels for Construction and extraction occupations and Installation, maintenance, and repair occupations. Then edit the group of series with the “Create your own data transformation” option and type a+b+c in the formula box.
Suggested by Maximiliano Dvorkin and Hannah Shell
