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What You Do in High School Matters: High School GPA, Educational Attainment, and Labor Market Earnings as a Young Adult

Eastern Economic Journal volume 41, pages 370–386 (2015)Cite this article

Abstract

Using abstracted grades and other data from the National Longitudinal Survey of Adolescent Health, we investigate the relationships between cumulative high school grade point average (GPA), educational attainment, and labor market earnings among a sample of young adults (ages 24–34). We estimate several models with an extensive list of control variables and high school fixed effects. Results consistently show that high school GPA is a positive and statistically significant predictor of educational attainment and earnings in adulthood. Moreover, the coefficient estimates are large and economically important for each gender. Interesting and somewhat unexpected findings emerge for race in that, after controlling for innate ability, academic performance, and other economic and demographic variables, African Americans advance further in the formal educational system than their White counterparts. Various sensitivity tests support the stability of the core findings.

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Notes

  1. Another approach to dealing with potential omitted variables bias is instrumental variables. However, it is extremely difficult to find an appropriate instrument for GPA that does not also directly influence educational attainment and/or earnings. We were unable to find an acceptable instrument in our data set and therefore do not pursue an instrumental variables approach in the present study, focusing instead on including a large number of control variables in our models that we expect will minimize the presence and effect of omitted variable bias. Cumulative GPA is calculated based on the number of years a student has course data.

  2. Respondents were asked to report how much income they received from personal earnings before taxes (i.e., wages or salaries, including tips, bonuses, and overtime pay, and income from self-employment). The question refers to personal income earned in the calendar year before the interview.

  3. We included the full sample for the earnings models, which includes both workers (positive earnings) and non-workers (zero earnings). We view these specifications as reduced-form earnings equations, which estimate the effect of high-school GPA and other variables on unconditional earnings. As this approach models the joint effect of labor force participation and earnings, it is not necessary or appropriate to consider selection effects. For non-earners, we set earnings to $1, so the natural logarithm of earnings is defined and equal to 0 in this case.

  4. As a sensitivity test, we estimate the log of personal earnings using OLS, which we discuss later in the paper.

  5. For simplicity, a person-level subscript is suppressed from the model specification.

  6. Although the sample is smaller, the results are qualitatively similar when we use the sampling weights. Significance levels change somewhat for a few of the variables (results available on request from the authors).

  7. See Table 1 for a description of the seven educational categories.

  8. Traditional earnings models typically include age-squared as well as age to allow for eventual depreciation of human capital. However, the oldest person in our sample is 34 and unlikely to be experiencing depreciating human capital.

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Acknowledgements

The authors are grateful for research assistance from Christina Gonzalez and Karina Ugarte and editorial/administrative assistance from Allison Johnson and Carmen Martinez. This research uses data from Add Health, a program project directed by Kathleen Mullan Harris and designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris at the University of North Carolina at Chapel Hill and funded by grant P01-HD31921 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperative funding from 23 other federal agencies and foundations. Special acknowledgment is due to Ronald R. Rindfuss and Barbara Entwisle for assistance in the original design. Information on how to obtain the Add Health data files is available on the Add Health website (http://www.cpc.unc.edu/addhealth). We received no direct support from grant P01-HD31921 for this analysis.

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Authors and Affiliations

  1. Department of Sociology, University of Miami, 5202 University Drive, Merrick Building, Room 121 F, Coral Gables, 33124-2030, FL, USA

    Michael T French

  2. Health Economics Research Group, Sociology Research Center, University of Miami, 5665 Ponce de Leon Blvd, Coral Gables, 33146, FL, USA

    Michael T French & Jenny F Homer

  3. Department of Sociobehavioral and Administrative Pharmacy, College of Pharmacy, Nova Southeastern University, 3200 South University Drive, Fort Lauderdale, 33328, FL, USA

    Ioana Popovici

  4. Department of Economics, University of Miami, Jenkins Bldg., Room 517D, Coral Gables, 33124-6550, FL, USA

    Philip K Robins

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  1. Michael T French
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French, M., Homer, J., Popovici, I. et al. What You Do in High School Matters: High School GPA, Educational Attainment, and Labor Market Earnings as a Young Adult. Eastern Econ J 41, 370–386 (2015). https://doi.org/10.1057/eej.2014.22

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Keywords

  • Earnings
  • educational attainment
  • high school grades
  • panel data

JEL Classifications

  • I2
  • J24
  • J31