2015
DOI: 10.1007/s13524-015-0407-0
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Education and Lifetime Earnings in the United States

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 Soci… Show more

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Cited by 197 publications
(147 citation statements)
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References 48 publications
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“…In other words, we assumed that people moved in and out of poverty as they moved into and out of 10-year age-sex cohorts. This aligns with life course analysis that shows people gain income after age 18 and peak around age 65 before starting to decline again (Tamborini, Kim, and Sakamoto 2015). We acknowledge the uncertainty inherent in ACS data (Spielman, Folch, and Nagle 2014), but contend that our analysis may underestimate poverty rates (Bazuin and Fraser 2013).…”
Section: Low Education and Poverty Projectionssupporting
confidence: 83%
See 1 more Smart Citation
“…In other words, we assumed that people moved in and out of poverty as they moved into and out of 10-year age-sex cohorts. This aligns with life course analysis that shows people gain income after age 18 and peak around age 65 before starting to decline again (Tamborini, Kim, and Sakamoto 2015). We acknowledge the uncertainty inherent in ACS data (Spielman, Folch, and Nagle 2014), but contend that our analysis may underestimate poverty rates (Bazuin and Fraser 2013).…”
Section: Low Education and Poverty Projectionssupporting
confidence: 83%
“…Specifically designed for climate change research, demographic metabolism is a theoretical framework that argues that "the process of social change can be analytically captured through the process of younger cohorts replacing older ones" (Lutz 2013, 284) of population change: births, deaths, and migration. This approach creates reliable sociodemographic forecasts over decadal time scales for two key reasons: 1) many socio-demographic characteristics are either established at a young age (e.g., the proportion of people with a high school education aged 25-29 in 2015 is a good predictor of those aged 60-64 with a high school education in 2050) (Lutz and KC 2011), and 2) socio-demographic change is embedded within the age structure (e.g., life course analysis shows that earnings steadily increase after age 18, peaking around age 65, before declining through retirement) (Tamborini, Kim, and Sakamoto 2015).…”
Section: Introductionmentioning
confidence: 99%
“…Comparing to other research (e.g. Tamborini et al [18], comparing life-time earnings for US qualifications or Jeong et al [19] for returns to experience) our findings using the short-cut method are sensible and do give a reasonable idea on the relation of returns to investment for different educational pathways.…”
Section: Methodology Data Sets and Operationalizationsupporting
confidence: 66%
“…Gender is associated with both attractiveness (Hill & Lando, 1976;Hoss, Ramsey, Griffin, & Langlois, 2005) and income (Goldin, 2014;Tamborini, Kim, & Sakamoto, 2015).…”
Section: Femalementioning
confidence: 99%