Birth and Fortune Revisited: A Cohort Analysis of Underemployment, 1974–2004

Abstract

The cohort is a key concept in the study of social demography and social change. The enduring influence of cohort membership can arise from history-based and/or size-based effects. The most prominent proponent of size-based cohort effects is Easterlin (Birth and fortune: The impact of numbers on personal welfare, 1980) who argues that individuals hailing from unusually large cohorts will experience adverse labor market conditions relative to the members of the smaller cohorts that bracket them. Drawing on data from the March Current Population Survey for the period spanning 1974–2004, we examine the influence of relative cohort size on underemployment. The results provide modest support for the Easterlin thesis, showing the odds of underemployment to be greatest among members of relatively large cohorts, net of other significant predictors. The results also show that the impact of relative cohort size differs by educational level, suggesting that adverse economic conditions produced by large cohort size can be offset by broader changes in the labor market and other social institutions.

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Notes

  1. 1.

    We began the analysis with data from 1974 for a number of reasons. Prior to 1974 Hispanic ethnicity was not recorded by the CPS and we wanted to be able to account for effects associated with this important population group. Further, by starting the analysis in the mid-1970s we are able to exclude the effects associated with the Vietnam War, including the military draft and educational draft deferments.

  2. 2.

    Race/ethnicity has been shown to have an important impact on underemployment, with whites suffering far lower odds of being underemployed compared to their black and Hispanic counterparts (see for example, Slack and Jensen 2002). Beginning in 2003, the CPS allowed individuals to identify themselves as members of more than one racial group. In order to create racial/ethnic group definitions that were consistent over the entire period examined and that allowed sufficient sample sizes for robust race/ethnic group comparisons, the analysis was restricted to those identifying as non-Hispanic white only, non-Hispanic black only, and Hispanic only.

  3. 3.

    RCS can be operationalized as a “variable” measure (for example, see O’Brien et al. 1999), which allows the RCS value to change as a cohort ages over time. For example, when the cohort was aged 18–20, RCS would be calculated as the percentage of the population 18–62 that was 18–20; when the cohort was aged 21–23, RCS would be calculated as the percentage of the population 18–62 that was 21–23; and so on. In this analysis we use the “fixed” measure of RCS because it provides a measure of the relative numbers of young adults upon labor market entry, and thus is most consistent with Easterlin’s focus on the adverse effects of large cohort membership on “the economic fortunes of young workers—their earnings, unemployment experience, and rate of advance up the career ladder” (1980: p. 4).

  4. 4.

    A series of sensitivity analyses were conducted to test for the robustness of this relationship. Specifically, two additional models using 3-year age and period groups were run, one using “pre-boom,” “baby boom,” and “post-boom” cohort dummy variables, and the other using a GFR-based measure of RCS. Yet another model was run using 5-year age, period, and RCS definitions. These models yielded similar substantive results to those presented in Table 2 and are reported in the Appendix.

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Acknowledgments

This research was conducted with support from the Council on Research, Louisiana State University, and the United States Department of Agriculture (NRICGP 98-35401-6157). Infrastructural support was provided by the Population Research Institute, The Pennsylvania State University, which has core support from the National Institute on Child Health and Human Development (1 R24 HD1025, 2 R24 HD041025-06). The authors are responsible for any substantive or analytic errors.

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Correspondence to Tim Slack.

Appendix

Appendix

Table A1 Logistic regression models of underemployment with 5-year age, period, and cohort groups
Table A2 Logistic regression models of underemployment with cohort dummy variables
Table A3 Logistic regression models of underemployment with GFR-based RCS measures

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Slack, T., Jensen, L. Birth and Fortune Revisited: A Cohort Analysis of Underemployment, 1974–2004. Popul Res Policy Rev 27, 729–749 (2008). https://doi.org/10.1007/s11113-008-9091-8

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Keywords

  • Age-period-cohort analysis
  • Baby boom
  • Cohort size
  • Underemployment