Interesting set of reasons for "mismatch"

Credit: Stenard and Sauermann

Via a tweet from @biochembelle, an interesting measurement by Stenard and Sauermann (PDF) of why scientists and engineers end up in positions where their work is different than their highest degree using NSF SESTAT data*:

Our measure of educational mismatch is based on the following survey question: “To what extent was your work on your principal job related to your highest degree?” Respondents answered using a 3-point scale ranging from “closely related” to “somewhat related” and “not related.” 11% of the sample report that their job is not related to their highest degree (henceforth called a “mismatch”), while 31% report that the job is somewhat related, and 58% of respondents indicate that their job is closely related to their education. 

Respondents who reported a mismatch were asked about the primary reason: “What was the most important reason for working in an area outside the field of your highest degree?”. Response options included “pay, promotion opportunities,” “working conditions (e.g., hours, equipment, working environment),” “job location,” “change in career or professional interests,” “family-related reasons (e.g., children, spouse’s job moved),” “job in highest degree field not available,” and “some other reason.” 

As you can see from the graph, "pay and promotion" and "change in career interests" were the highest, but I found it interesting and informative that "job in highest degree field not available" was as high as it was. Would be fascinating to get breakdown of fields of study and the like. These folks were measured sometime between 2003 and 2008; bet you the "job in highest degree field not available" are higher now.

*From the paper, the strictures of the graph above:

SESTAT is based on regular surveys conducted by NSF and is constructed to represent the general population of scientists and engineers in the U.S who have at least a Bachelor’s degree. 

First, we exclude individuals who were not in the labor force during the observation period as well as those who were unemployed. We exclude individuals under the age of 22 and over the age of 65 to account for potentially different dynamics among those still in training or close to retirement. We limit our sample to full-time employees (defined as working at least 30 hours per week and 30 weeks per year) working in the U.S. and in a for-profit organization; we exclude individuals who are employed in academia, government, or non-profit organizations, as these sectors are likely characterized by different labor market dynamics. 

Since we focus on educational mismatches among scientists and engineers, we also exclude individuals whose highest degrees are outside of science and engineering, as well as individuals who have a professional degree (e.g., JD, MBA, or MD). Finally, since we are concerned with transitions to entrepreneurship between periods, we exclude individuals who were observed only once. Our final sample includes 25,530 unique individuals, 15,801 of which were observed in all three time periods and 9,729 of which were observed in 2 time periods, for a total of 66,861 person-year observations.