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Education and Lifetime Earnings in the United States

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Demography

Abstract

Differences in lifetime earnings by educational attainment have been of great research and policy interest. Although a large literature examines earnings differences by educational attainment, research on lifetime earnings—which refers to total accumulated earnings from entry into the labor market until retirement—remains limited because of the paucity of adequate data. Using data that match respondents in the Survey of Income and Program Participation to their longitudinal tax earnings as recorded by the Social Security Administration, we estimate the 50-year work career effects of education on lifetime earnings for men and women. By overcoming the purely synthetic cohort approach, our results provide a more realistic appraisal of actual patterns of lifetime earnings. Detailed estimates are provided for gross lifetime earnings by education; net lifetime earnings after controlling for covariates associated with the probability of obtaining a bachelor’s degree; and the net present 50-year lifetime value of education at age 20. In addition, we provide estimates that include individuals with zero earnings and disability. We also assess the adequacy of the purely synthetic cohort approach, which uses age differences in earnings observed in cross-sectional surveys to approximate lifetime earnings. Overall, our results confirm the persistent positive effects of higher education on earnings over different stages of the work career and over a lifetime, but also reveal notably smaller net effects on lifetime earnings compared with previously reported estimates. We discuss the implications of these and other findings.

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Notes

  1. Specifically, we estimated a logistic regression on the likelihood of an administrative match across a range of characteristics, including age, age-squared, education, race/ethnicity, family income, and marital history. Using the results of that regression, we multiply the inverse of the match probability given the characteristics by SIPP Wave 2 person weights.

  2. Those born in 1960 and 1961, for example, are excluded from the sample because their age-20 earnings (i.e., earnings in year 1980 and 1981) go beyond the year for which data are available.

  3. To check whether our results are sensitive to this restriction, we varied the number of years of positive earnings to one, three, and four, finding basically the same results.

  4. Some will argue that marriage and children are endogenous with earnings, so they should not be included as covariates. To address this concern, we additionally estimated the net 50-year lifetime earnings without controlling for the effects of marriage and children (results not shown). The difference between these estimates and the estimates reported in this article are small. This may be because the effects of marriage and childbearing are mostly associated with level of education rather than operating within the same level of education.

  5. AP courses were not officially operational until 1955, but 62 % of men of the oldest cohort who attended college claim to have taken an AP course. Given that our analysis involves cohorts over several decades during which the educational system was evolving, various measurement issues may be associated with these control variables.

  6. Earnings growth rates are estimated by regressing log annual earnings on time, separately by educational groups.

  7. In computing net lifetime earnings, we set all covariates except education equal to the mean of the entire sample.

  8. For both genders, AP courses, college preparatory classes, and type of high schools explain the virtually all the reduction in the return to college education. For example, the 98 % of the reduction in the return to BA compared with HSG is attributable to these three variables. Other covariates cancel each other out more or less when included in addition to these three variables.

  9. We used propensity score matching techniques (PSM) to estimate cumulative earnings by education as a robustness check. We do not use PSM as our primary method because it yields results that are essentially the same as those based on median quantile regressions. When correctly specified, quantile regression is more efficient than PSM.

  10. We omit room and board from this calculation because persons who do not attend college also need to pay those costs.

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Acknowledgments

The findings and conclusions presented in this article are those of the authors and do not represent the views or opinions of the U.S. Social Security Administration or any agency of the federal government. The administrative data used in this article are restricted-use and undergo disclosure review before their release. For researchers with access to these data, our programs are available upon request. We thank the Editor and the anonymous reviewers of Demography for helpful comments. Thanks also to Gayle Reznik, Patrick Purcell, Kevin Whitman, and Natalie Lu for comments. All remaining errors are our own. This research was supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development of the National Institute of Health (Grant No. 1R03HD073464) and Spencer Foundation (Grant No. 201400077).

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Authors and Affiliations

  1. Office of Retirement Policy, U.S. Social Security Administration, 500 E. Street, SW, 9th floor, Washington, DC, 20254, USA

    Christopher R. Tamborini

  2. Department of Sociology, University of Kansas, 1415 Jayhawk Boulevard, Room 716, Lawrence, KS, 66045, USA

    ChangHwan Kim

  3. Department of Sociology, Texas A&M University, 311 Academic Building, 4351 TAMU, College Station, TX, 77843, USA

    Arthur Sakamoto

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  1. Christopher R. Tamborini
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Correspondence to ChangHwan Kim.

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Tamborini, C.R., Kim, C. & Sakamoto, A. Education and Lifetime Earnings in the United States. Demography 52, 1383–1407 (2015). https://doi.org/10.1007/s13524-015-0407-0

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