Research in Higher Education

, Volume 55, Issue 2, pp 143–165 | Cite as

Preparing Students for College and Careers: The Causal Role of Algebra II

  • Matthew N. Gaertner
  • Jeongeun Kim
  • Stephen L. DesJardins
  • Katie Larsen McClarty
Article

Abstract

In educational research and policy circles, college and career readiness is generating great interest. States are adopting various policy initiatives, such as rigorous curricular requirements, to increase students’ preparedness for life after high school. Implicit in many of these initiatives is the idea that college readiness and career readiness are essentially the same thing. This assumption has persisted, largely untested. Our paper explores this assumption in greater depth. Using two national datasets and an instrumental variables approach to mitigate selection bias, we evaluated the effects of completing Algebra II in high school on subsequent college and career outcomes (i.e., persistence and graduation as well as wages and career advancement). Results suggest Algebra II matters more for college outcomes than career outcomes and more for students completing Algebra II in the early 1990s than in the mid-2000s. Study limitations are discussed along with directions for future research, such as evaluating the opportunity cost associated with taking Algebra II for students seeking careers upon high school completion.

Keywords

High school mathematics High school course-taking Algebra II College readiness Career readiness Instrumental variable 

Supplementary material

11162_2013_9322_MOESM1_ESM.docx (52 kb)
Supplementary material 1 (DOCX 52 kb)

References

  1. Achieve (2011). State college- and career-ready high school graduation requirements. Retrieved July 1, 2012 from http://www.achieve.org/files/22_CCR_Diploma_Full_Reqs_Table-12-2011.pdf.
  2. ACT (2006). Ready for college and ready for work: Same or different? Retrieved July 1, 2012 from http://www.act.org/research/policymakers/pdf/ReadinessBrief.pdf.
  3. Adelman, C. (1999). Answers in the toolbox: Academic intensity, attendance patterns, and bachelor‘s degree attainment. Washington, DC: U.S. Department of Education.Google Scholar
  4. Adelman, C. (2006). The toolbox revisited: Paths to degree completion from high school through college. Washington, DC: U.S. Department of Education.Google Scholar
  5. Altonji, J. G. (1992). The effects of high school curriculum on education and labor market outcomes. Journal of Human Resources, 30(3), 409–438.CrossRefGoogle Scholar
  6. Altonji, J. G. (1995). The effects of high school curriculum on educational and labor market outcomes. The Journal of Human Resources, 30(3), 409–438.CrossRefGoogle Scholar
  7. Angrist, J. D., Imbens, G. W., & Rubin, D. B. (1996). Identification of causal effects using instrumental variables. Journal of the American Statistical Association, 91(434), 444–455.CrossRefGoogle Scholar
  8. Angrist, J. D., & Krueger, A. B. (2001). Instrumental variables and the search for identification: From supply and demand to natural experiments. Journal of Economic Perspectives, 15(4), 69–85.CrossRefGoogle Scholar
  9. Angrist, J. D., & Pischke, J. S. (2009). Mostly harmless econometrics. Princeton: Princeton University Press.Google Scholar
  10. Association for Career and Technical Education (2010). What is “career ready”? Retrieved July 1, 2012 from https://www.acteonline.org/uploadedFiles/Publications_and_Online_Media/files/Career_Readiness_Paper.pdf.
  11. Astin, A. W., & Oseguera, L. (2005). Pre-college and institutional influences on degree attainment. In A. Seidman (Ed.), College student retention (pp. 245–276). Westport: American Council on Education.Google Scholar
  12. Attewell, P., & Domina, T. (2008). Raising the bar: Curricular intensity and academic performance. Educational Evaluation and Policy Analysis, 30(1), 51–71.CrossRefGoogle Scholar
  13. Bailey, T. (2009). Challenge and opportunity: Rethinking the role and function of developmental education in community college. New Directions for Community College, 145, 11–30.CrossRefGoogle Scholar
  14. Balfanz, R., Bridgeland, J. M., Bruce, M., & Fox, J. H. (2012). Building a grad nation: Progress and challenge in ending the high school dropout epidemic. Washington, D.C.: Civic Enterprises.Google Scholar
  15. Baum, C. F. (2008). Instrumental variables and panel data methods in economics and finance. Proceedings from 2008 German Stata users group meeting. Berlin, Germany: STATA.Google Scholar
  16. Becker, G. S. (1965). A theory of the allocation of time. The Economic Journal, 75(299), 493–517.CrossRefGoogle Scholar
  17. Becker, G. S. (1993). Human capital: A theoretical and empirical analysis, with special reference to education (3rd ed.). Chicago: The University of Chicago Press.CrossRefGoogle Scholar
  18. Bielby, R. M., House, E., Flaster, A., & DesJardins, S. L. (2013). Instrumental variables: Conceptual issues and an application considering high school course taking. In Higher education: Handbook of theory and research (pp. 263–321). Netherlands: Springer.Google Scholar
  19. Bishop, J. (1991). Achievement, test scores and relative wages. In M. Kosters (Ed.), Workers and their wages. Washington, DC: The AEI Press, 146–181.Google Scholar
  20. Bishop, J. H., & Mane, F. (2004). The impacts of career-technical education on high school labor market success. Economics of Education Review, 23, 381–402.CrossRefGoogle Scholar
  21. Bound, J., Jaeger, D. A., & Baker, R. M. (1995). Problems with instrumental variables estimation when the correlation between the instruments and the endogenous explanatory variable is weak. Journal of the American Statistical Association, 90(430), 443–450.Google Scholar
  22. Camara, W., Echternacht, G. (2000). The SAT I and high school grades: Utility in predicting success in college (RN-10). The College Board Office of Research and Development. Retrieved September 15, 2013 from http://profesionals.collegeboard.com/profdownload/pdf/rn10_10755.pdf.
  23. Carnevale, A. P., & Desrochers, D. M. (2003). Standards for what? The economic roots of K-16 reform. Princeton: Educational Testing Service.Google Scholar
  24. Carnevale, A., & Strohl, J. (2013). Separate & unequal: How higher education reinforces the intergenerational reproduction of white racial privilege. Washington DC: Georgetown University Center on Education and the Workforce.Google Scholar
  25. Carrillo, J. D. (2003). Job assignments as a screening device. International Journal of Industrial Organization, 21, 881–905.CrossRefGoogle Scholar
  26. Casner-Lotto, J., Barrington, L., & Wright, M. (2006). Are they really ready to work? (Report BED-06-Workforce). Retrieved July 1, 2012 from http://www.conference-board.org/publications/publicationdetail.cfm?publicationid=1218.
  27. Choy, S. P. (2002). Access & persistence: Findings from 10 years of longitudinal research on students. Washington, DC: American Council on Education.Google Scholar
  28. Cohn, E., & Geske, T. G. (1990). The economics of education (3rd ed.). Oxford: Pergamon Press.Google Scholar
  29. Donath, J. (2007). Signals, cues and meaning in Signals Truth and design, MIT Press. Retrieved September 20, 2013 from http://smg.media.mit.edu/papers/Donath/SignalsTruthDesign/Signals.distribute.pdf.
  30. ENLACE Florida. (2008). Toward a college preparatory high school curriculum in Florida. Retrieved July 1, 2012 from http://www.floridacollegeaccess.org/research/Research%20Briefs/2008/college_prep_curriculum.pdf.
  31. Federman, M. (2007). State graduation requirements, high school course taking, and choosing a technical college major. The B.E. Journal of Economic Analysis & Policy, 7(1), 1–32.CrossRefGoogle Scholar
  32. Goldrick-Rab, S., Carter, D. F., & Wagner, R. W. (2007). What higher education has to say about the transition to college. Teachers College Record, 109(10), 2444–2481.Google Scholar
  33. Heckman, J. J., & Vytlacil, E. (2004). Econometric Evaluation of Social Programs. In J. J. Heckman & E. Leamer (Eds.), Handbook of econometrics (Vol. 5). Amsterdam: Elsevier.Google Scholar
  34. Horn, L. J., & Kojaku, L. K. (2001). High school academic curriculum and the persistence path through college: persistence and transfer behavior of undergraduates 3 years after entering 4-year institutions. Washington, DC: National Center for Education Statistics, Department of Education.Google Scholar
  35. Kim, J., Kim, J., DesJardins, S. L., & McCall, B. P. (2012, April). Exploring the relationship between high school math course-taking and college access and success. Paper presented at the annual meeting of the American Educational Research Association, Vancouver, Canada.Google Scholar
  36. Levine, P. B., & Zimmerman, D. J. (1995). The benefit of additional high-school math and science classes for young men and women. Journal of Business & Economic Statistics, 13(2), 137–149.Google Scholar
  37. Long, L. H. (1972). The influence of number and ages of children on residential mobility. Demography, 9(3), 371–382.CrossRefGoogle Scholar
  38. Long, B. T. (2007). The contributions of economics to the study of college access and success. Teachers College Record, 109(10), 2367–2439.Google Scholar
  39. Long, M. C., Conger, D., & Iatarola, P. (2012). Effects of high school course-taking on secondary and postsecondary success. American Educational Research Journal, 49(2), 285–322.CrossRefGoogle Scholar
  40. Loveless, T. (2013). The Algebra Imperative: Assessing Algebra in a National and International Context. Washington, DC: Brookings. Retrieved September 1, 2013 from http://www.brookings.edu/research/papers/2013/09/04-algebra-imperative-education-loveless.
  41. Mincer, J. (1958). Investment in human capital and personal income distribution. Journal of Political Economy, 66(4), 281–302.CrossRefGoogle Scholar
  42. Muller, R., & Beatty, A. (2008). The building blocks of success: Higher-level math for all students. Achieve Policy Brief.Google Scholar
  43. National Center for Education Statistics [NCES] (2013, March). Algebra I and geometry curricula: Results from the 2005 high school transcript mathematics curriculum study [NCES 2013-451]. Retrieved March 13, 2013 from http://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2013451.
  44. Perna, L. W. (2004). The key to college access: A college preparatory curriculum. In W. G. Tierney, Z. Corwin, & J. Colyar (Eds.), Preparing for college: Nine elements of effective outreach (pp. 113–134). Albany: State University of New York Press.Google Scholar
  45. Porter, S. R. (2012). Using instrumental variables properly to account for selection effects. Unpublished manuscript. Retrieved September 20, 2013 from http://www.stephenporter.org/papers/Pike_IV.pdf.
  46. Reys, B. J., Dingman, S., Nevels, N., & Teuscher, D. (2007). High school mathematics: State-level curriculum standards and graduation requirements: Center for the Study of Mathematics Curriculum.Google Scholar
  47. Rose, H., & Betts, J. R. (2001). Math matters: The links between high school curriculum, college graduation, and earnings. San Francisco: Public Policy Institute of California.Google Scholar
  48. Rose, H., & Betts, J. R. (2004). The effect of high school courses on earnings. Review of Economics and Statistics, 86(2), 497–513.CrossRefGoogle Scholar
  49. Schultz, T. W. (1961). Investment in human capital. The American Economic Review, 51(1), 1–17.Google Scholar
  50. Spence, M. (1973). Job market signaling. Quarterly Journal of Economics, 87(3), 355–374.CrossRefGoogle Scholar
  51. Spence, M. (2002). Signaling in retrospect and the information structure of markets. American Economic Review, 92(3), 434–459.CrossRefGoogle Scholar
  52. St. John, E. P., & Chung, A. S. (2006). Access to advanced math. In E. P. St. John (Ed.), Education and the public interest (pp. 135–162). Houten: Springer.CrossRefGoogle Scholar
  53. Stock, J. H., & Trebbi, F. (2003). Who invented instrumental variable regression? Journal of Economic Perspectives, 17(3), 177–194.CrossRefGoogle Scholar
  54. U.S. Department of Education, National Center for Education Statistics (2000). National Education Longitudinal Survey of 1988 (NELS), 1988/96. Washington, DCGoogle Scholar
  55. U.S. Department of Education, National Center for Education Statistics (2006). Education Longitudinal Study (ELS), 2002/06. Washington, DCGoogle Scholar
  56. Wiley, A., Wyatt, J., & Camara, W. (2010). The development of a multidimensional college readiness index. (College Board Research Report). New York: The College Board.Google Scholar
  57. Wyatt, J., Wiley, A., Camara, W., & Proestler, N. (2011). The development of an index of academic rigor for college readiness. (College Board Research Report). New York: The College Board.Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Matthew N. Gaertner
    • 1
  • Jeongeun Kim
    • 2
  • Stephen L. DesJardins
    • 2
  • Katie Larsen McClarty
    • 1
  1. 1.Center for College & Career Success PearsonAustinUSA
  2. 2.University of MichiganAnn ArborUSA

Personalised recommendations