Income dividends and subjective survival in a Cherokee Indian cohort: a quasi-experiment.
Parvati SinghRyan BrownWilliam E CopelandE Jane CostelloTim-Allen BrucknerPublished in: Biodemography and social biology (2020)
Persons with high temporal discounting tend to value immediate gratification over future gains. Low self-reported lifespan (SRL)-an individual's assessment of a relatively short future lifespan-concentrates in low-income populations and may reflect high temporal discounting. We use casino-based cash dividends among the Eastern Band of Cherokee Indians (EBCI) as a quasi-experiment to test whether large income gains among EBCI members translate into increased SRL. We used SRL data for EBCI and White youth, aged 19 to 28, participating in two waves of the Life Time Trajectory of Youth (LTI-Y) survey from 2000 to 2010. We controlled for unobserved confounding across individuals, time, and region through a longitudinal design using a difference-in-difference analytic approach (N = 294). We conducted all analyses separately by gender and by quartile of socioeconomic status. Cash dividends correspond with a 15.23 year increase in SRL among EBCI men below the lowest socio-economic quartile at baseline relative to Whites (standard error = 5.39, p < .01). Results using other socio-economic cut-points support improved SRL among EBCI men (but not women). The large magnitude of this result among EBCI men indicates that a non-trivial cash dividend to a low-income population may confer long-term benefits on perceptions of future lifespan and, in turn, reduce temporal discounting.Abbreviations: EBCI: Eastern Band of Cherokee Indians; SES: Socioeconomic Status; LTI-Y: Life Trajectory Interview for Youth; GSMS: Great Smoky Mountains Study; SRL: Self-Reported Lifespan; SSS: Subjective Social Status.
Keyphrases
- mental health
- physical activity
- current status
- young adults
- healthcare
- middle aged
- south africa
- sleep quality
- primary care
- polycystic ovary syndrome
- cross sectional
- sensitive detection
- pregnant women
- adipose tissue
- electronic health record
- metabolic syndrome
- artificial intelligence
- machine learning
- fluorescent probe
- pregnancy outcomes
- free survival
- quantum dots