STEM Performance and Supply: Assessing the Evidence for Education Policy

Abstract

The relationship between education policy and workforce policy has long been uneasy. It is widely believed in many quarters of American society that the U.S. education system is in decline and, what’s more, that it bears significant responsibility for a wide range of social ills, including stagnant wages, increasing inequality, high unemployment, and overall economic lethargy. However, as analyzed in this paper, the preponderance of evidence suggests that the U.S. education system has produced ample supplies of students to respond to STEM labor market demand. The “pipeline” of STEM-potential students is similarly strong and expanding.

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Notes

  1. 1.

    Although it is now fashionable to look at the first decades of the post-World War II period as the golden age of U.S. education—in which education was once winning the race against increasing skill demands of technology—this nostalgia is belied by the actual accounts of the time that decried national education crises and shortages, albeit ideologically rather than empirically driven arguments that, as we will show, are repeated in today’s narratives (Ravitch 1983, 2014; Rothstein 1998).

  2. 2.

    Comprehensive, nationally representative studies of college major and occupational entry are limited to the National Center for Education Statistics longitudinal survey “Baccalaureate and Beyond,” which were for graduating cohorts in 1993 and 2008. These estimates are consistent with other data sources such as the National Science Foundation’s SESTAT on similar populations. Although the 2008 cohort entered the job market during the Great Recession of 2008, the population analyzed is restricted to those who are employed. The recession may have had an impact on occupational transition (and/or employment rates), but it does not appear to have had a large impact on these historical trends identified in other cohorts.

  3. 3.

    There is no reliable or even reasonable method for estimating non-STEM occupations that require a STEM-degreed education, but even highly speculative estimates of jobs that might need to be filled by someone with a STEM degree still show 30 to 60% more STEM graduates than employed in those jobs. Here, we use “STEM” as excluding social sciences and adjust calculations from the Census Bureau classifications that included social sciences.

  4. 4.

    See Freeman 1976 for development of the “cobweb” model of this function; see Lynn et al. 2018 for a contemporary case study.

  5. 5.

    This is another case of industry executives using a public policy platform--a Presidential Advisory Council--to give credibility to a narrative of “shortages” and supposed education failures and avoiding mention of the effect of the Great Recession of 2008 (see Lynn and Salzman, 2011).

  6. 6.

    The computer occupations, with about 3.7 to 4 million workers, comprises approximately half of the STEM workforce, or about 2 to 2.5% of the total workforce. According to projections of IT job growth by the Bureau of Labor Statistics (BLS), the total hiring demand is for, on average, 124,000 new IT workers each year, of which there is demand for about 40,000 computer science (CS) graduates, or about two-thirds the number of CS graduates each year. Despite all evidence consistently showing college graduate supply exceeding industry hiring, Congressional testimony by Microsoft’s Washington representative and counsel, Brad Smith, statements by the trade organization Code.org and echoed by the Computing Research Association, assert that college graduate supply of CS graduates is inadequate (Harsha 2014). In a notable misstatement of the BLS projections, Brad Smith testified before the Senate Committee on the Judiciary in 2013 that “The Bureau of Labor Statistics has projected approximately 122,000 new job openings each year in computing occupations requiring at least a bachelor’s degree through the end of this decade. Yet nationally, our universities are only producing approximately 51,000 bachelor’s degrees in computer science each year” (Smith 2013). In fact, as clearly stated in the BLS projections, these openings are for computer occupations at all education levels and fields of study, of which about one-third are for those with at least a bachelor’s degree in any field (Landivar, 2013). Annual computer science graduation, which has grown from 38,500 in 2011 to over 65,000 bachelor’s graduates in 2016, and from 19,000 to over 40,000 master’s degree graduates in 2011 and 2016, respectively, produces 40-50% more graduates than needed to meet demand for new IT workers. Nevertheless, these IT industry claims are used in the widely repeated and unsupported claim of a supply shortage and of the inability of our education system to keep pace (Salzman 2013; Teitelbaum 2014).

  7. 7.

    Since 1972 women have constituted between 42 and 48% of all bachelor’s-level mathematics graduates and have gradually been increasing their proportion in graduate programs. In recent years, women received 40% of the 6000 to 7000 master’s degrees in mathematics awarded each year, but only 29% of the 1700 to 1800 PhDs in mathematics.

  8. 8.

    As a hypothetical example, if two groups score 260 and 290, respectively, of which schools can affect 20% of the performance and schools of both groups achieve 15% improvement annually for 10 years, the gap would actually increase from 30 to 39 points; if the lower group’s schools improved by 20% annually and the higher (290 base score) group’s schools improved by only 15% annually, after 10 years the gap would be reduced to 8 points.

  9. 9.

    There is an extensive body of research examining these issues and we are not suggesting that schools are ineffective but, rather, expecting schools to have large impacts on education outcomes overall, or on specific differentials such as racial achievement gaps, is unsupported by the research.

  10. 10.

    The PISA organization does not appear to try to correct these misrepresentations of their findings and, in fact, often appears to promote these unsupported assertions in presentations and dissemination materials such as press releases; in particular, the rankings that are presented as meaningful ordering of performance levels fail to note that rather than a rank ordering of countries, the results should differentiate only between statistically significant differences. In other words, a “statistical tie” is, analytically, the same ranking. Using the statistically appropriate comparisons, the United States places in the second-ranked achievement group, though even that is a flawed measure, as discussed here.

  11. 11.

    As Ramirez et al. (2006:15) explain: “...much of the achievement ‘effect’ is not really causal in character. It may be, rather, that nation-states with strong prodevelopment policies, and with regimes powerful enough to enforce these, produce both more economic growth and more disciplined student-achievement levels in fields (e.g., science and mathematics) perceived to be especially development related.”

References

  1. Adams, R. J. (2003). Response to ‘Cautions on OECD’s recent educational survey (PISA)’. Oxf Rev Educ, 29(3), 377–389. https://doi.org/10.1080/03054980307445.

    Article  Google Scholar 

  2. Amsden, A. H. (2001). The rise of “the rest”: challenges to the west from late-industrializing economies. New York: Oxford University Press.

    Google Scholar 

  3. Baker, B., & Weber, M. (2016). Deconstructing the myth of American public schooling inefficiency. Washington, D.C.: Albert Shanker Institute http://www.shankerinstitute.org/resource/publicschoolinginefficiency.

    Google Scholar 

  4. Barnow, B. S., Trutko, J., & Schede, J. (2013). Occupational labor shortages. Kalamazoo, MI: Upjohn Institute for Employment Research.

    Google Scholar 

  5. Berliner, D. C., & Biddle, B. J. (1997). The manufactured crisis. myths, fraud, and the attack of America's public schools. Reading, MA: Addison-Wesley.

    Google Scholar 

  6. Bohrnstedt, G., Kitmitto, S., Ogut, B., Sherman, D., & Chan, D. (2015). School composition and the black-white achievement gap: National assessment of educational progress, 2011; NCES 2015-018. Washington, DC: National Assessment of Educational Progress (Department of Education). Retrieved from: https://statistical.proquest.com/statisticalinsight/result/pqpresultpage.previewtitle?docType=PQSI&titleUri=/content/2015/4896-34.14.xml.

  7. Cappelli, P. (2012). Why good people can't get jobs: the skills gap and what companies can do about it. Philadelphia: Wharton Digital Press.

  8. Cappelli, P. (2015). Skill gaps, skill shortages, and skill mismatches: evidence and arguments for the United States. ILR Review, 68(2), 251–290. https://doi.org/10.1177/0019793914564961.

    Article  Google Scholar 

  9. Carnoy, M., & Rothstein, R. (2015). What international test scores tell us. Society, 52(2), 122–128. https://doi.org/10.1007/s12115-015-9869-3.

    Article  Google Scholar 

  10. Douglas, D., & Attewell, P. (2017). School mathematics as gatekeeper. Sociol Q, 58(4), 648–669. https://doi.org/10.1080/00380253.2017.1354733.

    Article  Google Scholar 

  11. Douglas, D. & Salzman, H. (2018). Classification counts: Assessing postsecondary mathematics course taking. Working paper, Heldrich Center for Workforce Development, Rutgers University.

  12. Freeman, R. B. (1976). A cobweb model of the supply and starting salary of new engineers. Ind Labor Relat Rev, 29(2), 236–248.

    Article  Google Scholar 

  13. Freeman, R. B., & Salzman, H. (Eds.). (2018). U.S. engineering in a global economy. Chicago: University of Chicago Press.

    Google Scholar 

  14. Gomory, R. E., & Baumol, W. J. (2000). Global trade and conflicting national interests. Cambridge, MA: MIT Press.

    Google Scholar 

  15. Hacker, A., (2015) The math myth. New York: The New Press.

  16. Handel, M. J. (2016). What do people do at work? Journal for Labour Market Research, 49(2), 177–197. https://doi.org/10.1007/s12651-016-0213-1.

    Article  Google Scholar 

  17. Harsha, Peter (2014). About that WashPost Column on the value of a CS degree… Computing research policy blog. Retrieved from http://cra.org/govaffairs/blog/2014/09/03/.

  18. Institute of Education Sciences (2016). National center for education statistics. Digest of education statistics, 2016. Retrieved March 6, 2018, from https://nces.ed.gov/programs/digest/d16/

  19. Komatsu, H., & Rappleye, J. (2017). A new global policy regime founded on invalid statistics? Hanushek, Woessmann, PISA, and economic growth. Comp Educ, 53(2), 166–191. https://doi.org/10.1080/03050068.2017.1300008.

    Article  Google Scholar 

  20. Kuehn, D., & Salzman, H., (2018) The engineering labor market: an overview of recent trends In R. Freeman, & H. Salzman (Eds.), U.S. engineering in a global economy (2018). Chicago: University of Chicago Press.

  21. Landivar, L. C. (2013). Relationship between science and engineering education and employment in STEM occupations: American community survey reports, ACS-23. Washington, D.C.: U.S. Census Bureau.

    Google Scholar 

  22. Lazonick, W., Moss, P., Salzman, H., & Tulum, O. (2014). Skill development and sustainable prosperity: cumulative and collective careers versus skill-biased technical change Retrieved from: https://www.ineteconomics.org/research/research-papers/skill-development-and-sustainable-prosperity-cumulative-and-collective-careers-versus-skill-biased-technical-change

  23. Lowell, B. L., & Salzman, H. (2007). Into the eye of the storm: Assessing the evidence on science and engineering education, quality, and workforce demand. Washington, D.C.: The Urban Institute. https://doi.org/10.7282/T3X068P1.

    Google Scholar 

  24. Lynn, L., & Salzman, H. (2006). Collaborative advantage. Issues in Science and Technology, 22(2), 74–82. https://doi.org/10.7282/T3XP76K4.

    Article  Google Scholar 

  25. Lynn, L., & Salzman, H. (2007). The real global technology challenge. Change: The Magazine of Higher Learning, 39(4), 11–13. doi:https://doi.org/10.7282/T3JD4ZGK.

  26. Lynn, L., & Salzman, H. (2010). The globalization of technology development: implications for U.S. skills policy. In D. Finegold, M. Gatta, H. Salzman, & S. J. Schurman (Eds.), Transforming the U.S. workforce development system: lessons from research and practice (pp. 57–85). Ithaca, NY: Cornell University Press.

    Google Scholar 

  27. Lynn, L., & Salzman, H. (2011). Is the president right when he says the United States needs 10,000 engineers a year? Why not let the market decide? Manufacturing & Technology News, 18(17). https://doi.org/10.7282/T38W3FZM.

  28. Lynn, L., Salzman, H., & Kuehn, D. (2018). Dynamics of engineering labor markets: petroleum engineering demand and responsive supply. In R. Freeman, & H. Salzman (Eds.), U.S. engineering in a global economy (2018). Chicago: University of Chicago Press.

  29. Musu-Gillette, L., de Brey, C., McFarland, J., Hussar, W., Sonnenberg, W., & Wilkinson-Flicker, S. (2017). Status and trends in the education of racial and ethnic groups 2017;2017 ASI 4828-109;NCES 2017-051. Washington, D.C.: U.S. Department of Education, National Center for Education Statistics.

    Google Scholar 

  30. Naidu, S., Posner, E. A., & Weyl, E. G. (2018). Antitrust remedies for labor market power. Unpublished paper.

  31. OECD. (2004). Learning for tomorrow’s world first results from PISA 2003. Paris: OECD Publishing.

    Google Scholar 

  32. OECD. (2011). Germany: once weak international standing prompts strong nationwide reforms for rapid improvement. Lessons from PISA for the United States (pp. 201–220). Paris: OECD Publishing.

  33. Ramirez, F. O., Luo, X., Schofer, E., & Meyer, J. W. (2006). Student achievement and national economic growth. Am J Educ, 113(1), 1–29. https://doi.org/10.1086/506492.

    Article  Google Scholar 

  34. Ravitch, D. (1983). The troubled crusade: American education, 1945-1980. New York: Basic Books.

    Google Scholar 

  35. Ravitch, D. (2014). Reign of error: the hoax of the privatization movement. New York, NY: Knopf.

    Google Scholar 

  36. Reardon, S., Kalogrides, K., & Shores, K. (2017). The geography of racial/ethnic test score gaps. Stanford, CA: Center for Education Policy Analysis, Stanford University.

    Google Scholar 

  37. Rothstein, J. (2017). Inequality of educational opportunity? Schools as mediators of the intergenerational transmission of income. Washington, D.C.: The Washington Center for Equitable Growth.

    Google Scholar 

  38. Rothstein, R. (1998). The way we were? The myths and realities of America’s student achievement New York: Century Foundation Press.

  39. Rothstein, R. (2015). The racial achievement gap, segregated schools, and segregated neighborhoods: a constitutional insult. Race and Social Problems, 7(1), 21–30. https://doi.org/10.1007/s12552-014-9134-1.

    Article  Google Scholar 

  40. Salzman, H. (2013). What shortages? The real evidence about the STEM Workforce Issues in Science and Technology, (Summer 2013), 58–67. doi:https://doi.org/10.7282/T3JS9S2T

  41. Salzman, H. (2015). Impact of guestworkers on the high technology workforce. Hearing on “Immigration Reforms Needed to Protect Skilled American Workers,” submitted to the Senate Committee on the Judiciary, U.S. Senate, March 17, 2015. doi:https://doi.org/10.7282/T3ZK5JC3.

  42. Salzman, H. (2016). The impact of high-skill guestworker programs and the STEM workforce Statement for the hearing on “The Impact of High-Skilled Immigration on U.S. Workers”, submitted to the Senate Committee on the Judiciary, U.S. Senate, 25 February 2016.

  43. Salzman, H., & Douglas, D. (2018). Classification counts: Assessing postsecondary mathematics course taking. Working paper, Heldrich Center for Workforce Development, Rutgers University.

  44. Salzman, H., Kuehn, D., & Lowell, L. (2013). Guestworkers in the high-skill U.S. labor market: An analysis of supply, employment and wage trends in the IT labor market. Washington, D.C.: Economic Policy Institute, Briefing Paper #359.

  45. Salzman, H., & Lowell, B. L. (2008). Making the grade. Nature, 453(1 May 2008), 28–30. https://doi.org/10.7282/T3Q241WW.

    Article  Google Scholar 

  46. Smith, Brad (2013) “Written testimony of Brad Smith” statement submitted to the senate judiciary committee hearings on the border security, economic opportunity, and immigration modernization act of 2013, April 22, 2013. Retrieved from: https://www.judiciary.senate.gov/imo/media/doc/04-22-13BradSmithTestimony.pdf

  47. Stephan, P. E. (2012). How economics shapes science. Cambridge, MA: Harvard University Press.

    Google Scholar 

  48. Teitelbaum, M. S. (2014). Falling behind? Boom, bust, and the global race for scientific talent. Princeton, NJ: Princeton University Press.

    Google Scholar 

  49. Tucker, M. S. (2011). Standing on the shoulders of giants. Washington, D.C.: National Center on Education and the Economy.

    Google Scholar 

  50. U.S. Department of Education (2015). Institute of Education Sciences, National Center for Education Statistics, National Assessment of Educational Progress (NAEP), 2009 and 2015 Science Assessments. Retrieved from: https://www.nationsreportcard.gov/science_2015/#scores?grade=4

  51. Vanneman, A., Hamilton, L., Baldwin Anderson, J., & Rahman, T. (2009). Achievement gaps: how black and white students in public schools perform in mathematics and reading on the national assessment of educational progress: national assessment of educational progress, 2007; 2009 ASI 4896-29.9;NCES 2009455. Washington, D.C.: Institute of Education Sciences, National Center for Education Statistics.

  52. Weinberger, C. (2018). Engineering educational opportunity: Impacts of 1970s and 1980s policies to increase the share of black college graduates with majors in engineering or computer science. In R. Freeman & H. Salzman (Eds.), U.S. engineering in a global economy (2018). Chicago: University of Chicago Press.

    Google Scholar 

  53. Zhao, Y. (2009). Catching up or leading the way: American education in the age of globalization. Alexandria, VA: ASCD.

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Acknowledgments

The authors appreciate fine research assistance provided by Daniyal Rahim, contributions to this analysis and the research on math education by Daniel Douglas, suggested improvements by Greg Camilli and Uri Treisman, and support by the Sloan Foundation, and Michael Teitelbaum and Danny Goroff.

Funding

Salzman has received funding for this research from Alfred P. Sloan Foundation, Grant No. 2012-6-13 and G-2016-7310.

Benderly has received funding for this research from Alfred P. Sloan Foundation, Grant No. G-2016-7310.

Funding for this research came from the Alfred P. Sloan Foundation, Grant No. 2012-6-13 and G-2016-7310.

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Correspondence to Hal Salzman.

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Salzman, H., Lieff Benderly, B. STEM Performance and Supply: Assessing the Evidence for Education Policy. J Sci Educ Technol 28, 9–25 (2019). https://doi.org/10.1007/s10956-018-9758-9

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Keywords

  • STEM workforce
  • STEM education
  • STEM policy