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Education, Gender, and State-Level Gradients in the Health of Older Indians: Evidence from Biomarker Data

Author

Listed:
  • Jinkook Lee

    (Rand Corporation)

  • Mark E. McGovern

    (Harvard Center for Population and Development Studies)

  • David E. Bloom

    (Harvard T. H. Chan School of Public Health)

  • P. Arokiasamy

    (International Institute for Population Studies)

  • Arun Risbud

    (National AIDS Research Institute, Pune (NARI))

  • Jennifer O’Brien
  • Varsha Kale
  • Perry Hu

    (Division of Geriatrics, UCLA School of Medicine)

Abstract

This paper examines health disparities in biomarkers among a representative sample of Indians aged 45 and older, using data from the pilot round of the Longitudinal Aging Study in India (LASI). Hemoglobin level, a marker for anemia, is lower for respondents with no schooling (0.7 g/dL less in the adjusted model) compared to those with some formal education. There are also substantial state and education gradients in underweight and overweight. The oldest old have higher levels of C-reactive protein (CRP) (1.1 mg/L greater than those aged 45-54), an indicator of inflammation and a risk factor for cardiovascular disease, as do those with greater body-mass index (an additional 1.2 mg/L for those who are obese compared to those who are of normal weight). We find no evidence of educational or gender differences in CRP, but respondents living in rural areas have CRP levels that are 0.8 mg/L lower than urban areas. We also find state-level disparities, with Kerala residents exhibiting the lowest CRP levels (1.96 mg/L compared to 3.28 mg/L in Rajasthan, the state with the highest CRP). We use the Blinder-Oaxaca decomposition approach to explain group-level differences, and find that state-level gradients in CRP are mainly due to heterogeneity in the association of the observed characteristics of respondents with CRP, as opposed to differences in the distribution of endowments across the sampled state populations. JEL Codes: I12, I14, D30, O15

Suggested Citation

  • Jinkook Lee & Mark E. McGovern & David E. Bloom & P. Arokiasamy & Arun Risbud & Jennifer O’Brien & Varsha Kale & Perry Hu, 2016. "Education, Gender, and State-Level Gradients in the Health of Older Indians: Evidence from Biomarker Data," PGDA Working Papers 12115, Program on the Global Demography of Aging.
  • Handle: RePEc:gdm:wpaper:12115
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    Cited by:

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    2. Böckerman, Petri & Bryson, Alex & Viinikainen, Jutta & Hakulinen, Christian & Pulkki-Råback, Laura & Raitakari, Olli & Pehkonen, Jaakko, 2017. "Biomarkers and long-term labour market outcomes: The case of creatine," Journal of Economic Behavior & Organization, Elsevier, vol. 142(C), pages 259-274.
    3. Aurino, Elisabetta, 2017. "Do boys eat better than girls in India? Longitudinal evidence on dietary diversity and food consumption disparities among children and adolescents," Economics & Human Biology, Elsevier, vol. 25(C), pages 99-111.
    4. Peifeng Hu & Serena Wang & Jinkook Lee, 2017. "Socioeconomic gradients of cardiovascular risk factors in China and India: results from the China health and retirement longitudinal study and longitudinal aging study in India," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 62(7), pages 763-773, September.
    5. Schott, Whitney & Aurino, Elisabetta & Penny, Mary E. & Behrman, Jere R., 2019. "The double burden of malnutrition among youth: Trajectories and inequalities in four emerging economies," Economics & Human Biology, Elsevier, vol. 34(C), pages 80-91.
    6. Baten, Joerg, 2017. "Economics, human biology and inequality: A review of “puzzles” and recent contributions from a Deatonian perspective," Economics & Human Biology, Elsevier, vol. 25(C), pages 3-8.

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    More about this item

    Keywords

    Biomarkers; Health Disparities; Gender Differences; Blinder-Oaxaca Decomposition; Aging;
    All these keywords.

    JEL classification:

    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior
    • I14 - Health, Education, and Welfare - - Health - - - Health and Inequality
    • D30 - Microeconomics - - Distribution - - - General
    • O15 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Economic Development: Human Resources; Human Development; Income Distribution; Migration

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