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Health Outcomes and Socio-economic Status Among the Elderly in China: Evidence from the CHARLS Pilot

Abstract

We are concerned in this paper with measuring health outcomes among the elderly in Zhejiang and Gansu provinces, China, and examining the relationships between different dimensions of health status and measures of socio-economic status (SES). We are CHARLS pilot data to document health conditions, using a very rich set of health indicators that include both self-reported measures and biomarkers. We also examine correlations between these health outcomes and two important indicators of socio-economic status (SES): education and log of per capita expenditure (log pce), our preferred measure of household resources. In general education tends to be positively correlated with better health outcomes, as it is in other countries. However, unmeasured community influences turn out to be highly important, much more so than one usually finds in other countries. While it is not yet clear which aspects of communities matter and why they matter, we set up an agenda for future research on this topic. We also find a large degree of under-diagnosis of hypertension, a major health problems that afflicts the aged. This implies that the current health system is not well prepared to address the rapid aging of the Chinese population, at least not in Gansu and Zhejiang.

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Notes

  1. 1.

    Overweight is defined using World Health Organization standards of having a BMI 25 or above.

  2. 2.

    See http://charls.ccer.edu.cn.

  3. 3.

    The contact and interview rate was 86% of those households that were chosen to be sampled. This is much better than HRS-type surveys in the US and Europe, which now tend to be in the 60 or even 50% range, and compares favorably with other surveys done in Asia.

  4. 4.

    Spouses who are under 45 years old are dropped from this analysis.

  5. 5.

    Here we use the sample weights allowing for household non-response using local community dummies to predict household nonresponse. We do not incorporate non-response for the biomarkers in the weights. The results using weights that do so using inverse probability weights (IPW) are similar in nature. Incorporating non-response with IPW requires an assumption of selection on observables, which is very strong and unlikely to be met. Using more standard selection methods is best using exclusion restrictions, which we do not plausibly have.

  6. 6.

    Heights were measured using a lightweight SECA aluminum height board, the SECA 214 portable stadiometer. Weights were measured using a portable digital scale, the Beaver Tech HTS7270. Blood pressure was taken with a digital meter, the Omron HEM 712c meter.

  7. 7.

    A linear spline allows different slopes to the left and right of the knot point with the two lines being joined at the knot point. The first coefficient reported is the slope to the left of the knot point and the second coefficient is the change in the slope between the two knot points.

  8. 8.

    Cohort effects would arise because younger birth cohorts have more schooling and also faced better health conditions when they were babies and in the fetus, compared to older cohorts. There is an accumulation of evidence now that better health conditions when young are associated with better health in old age (for instance Barker 1994; Gluckman and Hanson 2005; Strauss and Thomas 2008, for an economist’s perspective).

  9. 9.

    We tried a specification with interactions between level of schooling dummies and age to help get at causality (see Witoelar et al. 2009, for such an exercise for Indonesia), but except for IADLs, these were generally not significant.

  10. 10.

    In an expanded working paper (Strauss et al. 2010) we add a specification using dummies for province and rural residence within province. Clearly this is a coarse definition of location.

  11. 11.

    In a few cases there was no variation in the dependent variable among observations within the PSU. In these cases we refined the PSU to be a more aggregate area, usually the county or city.

  12. 12.

    Since sample size differs depending on the health measure used, we compute means of the independent variables for the ADL sample, which is the largest, at 1,250 men and 1,225 women. For each dependent variable we use the actual regression sample; see the regression tables for sample sizes for each. Sample sizes will differ because of missing observations. For the biomarkers, such as blood pressure, some 30% of the sample were not measured. This can be because the elder person was too frail or a younger-aged person was working and not able to be measured. As noted above, this corresponds with the experience of the HRS and related surveys.

  13. 13.

    The sources are the Matlab Health and Socioeconomic Survey (1996); the China Health and Nutrition Survey (1991); the Indonesia Family Life Survey (2000); the South African Demographic Health Survey (1998); the Mexican Family Life Survey (2002); and the NHANES3 (National Health and Nutrition Examination Survey III) (1988–1994).

  14. 14.

    We use standard levels of significance to mean a probability value of .05 or less.

  15. 15.

    With community fixed effects, testing the joint significance of the community dummies is not straightforward. Because there are in our case few observations per cluster, we cannot cluster the standard errors after estimation using community fixed effects and use an F-test to test for the joint significance of clusters (Wooldridge, personal communication). To test the community dummies we reestimate the model with community dummies and robust standard errors, without clustering, and do the F-test.

  16. 16.

    It could be that the significance of the community dummy variables represents the impact of province and rural/urban location. This is not the case for our results. When we stratify the sample by the two provinces and run separate regressions for each province using community fixed effects within each province, the community dummies are still generally significant. Results are available upon request.

  17. 17.

    Because of the way the questionnaire is designed, those who report taking medicines are a subset of those who report a positive doctor diagnosis.

  18. 18.

    Our physical activities and ADL assessments include: walking for 100 m; stooping, kneeling, crouching; extending arms above shoulder level; lifting weights like a heavy bag of groceries; picking up a small coin from a table; climbing several flights of stairs without help, standing from a sitting position without help, dressing without help; bathing or showering; cutting food and eating; going to the bathroom without help (including sitting down and getting up); controlling urination and defecation; and getting into and out of bed. The IADL assessments are having difficulties with doing household chores; preparing hot meals; shopping for groceries; managing money; making phone calls (if they have a phone); and taking medications.

  19. 19.

    The answers for CES-D are on a four-scale metric, from rarely, to some days (1–2 days), to occasionally (3–4 days) to most of the time (5–7 days). We score these answers in the way suggested by the US National Institute of Mental Health researcher (Lenore Radloff) who created the CES-D, using numbers from 0 for rarely to 3 for most of the time, for negative questions such as do you feel sad. For positive questions such as do you feel happy, the scoring is reversed from 0 for most of the time to 3 for rarely.

  20. 20.

    Only 2% of women report that they currently smoke.

  21. 21.

    Here smoking is defined as smoking cigarettes or cigars. Current smoking prevalence for older men in CHARLS is quite close to rates of 55% reported from the China Health and Nutrition Survey (CHNS), for other provinces; see Kenkel et al. (2009). The CHNS rates are for all adult ages.

References

  1. Banks, J., Marmot, M., Oldfield, Z., & Smith, J. P. (2006). Disease and disadvantage in the United States and in England. Journal of the American Medical Association, 295(17), 2037–2045.

    Article  Google Scholar 

  2. Banks, J., Muriel, A., &Smith, J. P. (2009). Disease prevalence, incidence and determinants of mortality in the United States and England, manuscript, Department of Economics, University College London.

  3. Barker, D. (1994). Mothers, babies and health in later life. London: BMJ Publishing Group.

    Google Scholar 

  4. Barker, D. (1997). Maternal nutrition, fetal nutrition and diseases in later life. Nutrition, 13(9), 807–813.

    Article  Google Scholar 

  5. Cutler, D., & Lleras-Muney, A. (2010). Understanding differences in health behaviors by Education. Journal of Health Economics, 29(1), 1–28.

    Google Scholar 

  6. Das, J., Quy-Toan, D., Friedman, J., McKenzie, D., & Scott, K. (2007). Mental health and poverty in developing countries. Social Science and Medicine, 65(3), 467–480.

    Article  Google Scholar 

  7. Deaton, A. (1997). The analysis of household surveys: A microeconometric approach. Baltimore: Johns Hopkins University Press.

  8. Friedman, J., & Thomas, D. (2008). Psychological health, before, during and after an economic crisis: results from Indonesia 1993–2000. World Bank Economic Review, 23(1), 57–76.

    Article  Google Scholar 

  9. Fuchs, V. (1982). Time preference and health: an exploratory study. In V. Fuchs (Ed.), Economic aspects of health. Chicago: National Bureau of Economic Research.

  10. Gluckman, P., & Hanson, M. (2005). The fetal matrix: Evolution, development and disease. Cambridge.

  11. Goldman, D., & Smith, J. P. (2002). Can patient self-management help explain the SES health gradient? Proceedings of the National Academy of Sciences, 99(16), 10929–11093.

    Article  Google Scholar 

  12. Hoddinott, J., Maluccio, J., Behrman, J., Flores, R., & Martorell, R. (2008). Effect of a nutrition intervention during early childhood on economic productivity in Guatemalan adults. Lancet, 371, 411–416.

    Article  Google Scholar 

  13. Hossain, S. (1997). Tackling health transition in China, Policy Research Working Paper No. 1813, Washington DC: World Bank.

  14. Kenkel, D., Lillard, D., & Liu, F. (2009). An analysis of life-course smoking behavior in China. Health Economics, 18, S147–S156.

    Article  Google Scholar 

  15. Kinsella, K., & He, W. (2009). An aging world: 2008, US Census Bureau, International Population Reports, PS95/09-1. Washington DC: US Government Printing Office.

    Google Scholar 

  16. Lance, P., Akin, J., Dow, W., & Loh, C. (2004). Is cigarette smoking in poorer Nations sensitive to price? Evidence from Russia and China. Journal of Health Economics, 23, 173–189.

    Article  Google Scholar 

  17. Lee, N. (2009). Measurement error and its impact on estimates of income and consumption dynamics, mimeo. Department of Economics: Chinese University of Hong Kong.

    Google Scholar 

  18. Lee, J., & Smith, J. P. (2008). Explanations for education gradients in depression—The case of Korea, mimeo, RAND Corporation, Santa Monica, CA.

  19. Lleras-Muney, A. (2005). The relationship between education and adult mortality in the US. Review of Economic Studies, 72(1), 189–221.

    Article  Google Scholar 

  20. Lopez, A., Mathers, C., Ezzati, M., Jamison, D., & Murray, C. (2006). Measuring the global burden of disease and risk factors, 1990–2001. In A. Lopez, C. Mathers, M. Ezzati, D. Jamison, & C. Murray (Eds.), Global burden of disease and risk factors. Oxford: Oxford University Press.

    Chapter  Google Scholar 

  21. Luo, Z. (2003). Socioeconomic determinants of body mass index of adults Chinese in 1990s, presented at the Northeast Consortium Development Conference (NEUDC), October 2003, mimeo, Department of Epidemiology, Michigan State University.

  22. Maluccio, J., Hoddinott, J., Behrman, J., Martorell, R., Quisumbing, A., & Stein, A. (2009). The impact of improving nutrition during early childhood on education among Guatemalan adults. Economic Journal, 119(537), 734–763.

    Article  Google Scholar 

  23. Marmot, M. G. (1999). Multi-level approaches to understanding social determinants. In L. Berkman & I. Kawachi (Eds.), Social epidemiology (pp. 349–367). Oxford: Oxford University Press.

    Google Scholar 

  24. McArdle, J., Fisher, G., & Kadlec, K. (2007). Latent variable analysis of age trends in tests of cognitive ability in the Health and Retirement Survey, 1992–2004. Psychology and Aging, 22(3), 525–545.

    Article  Google Scholar 

  25. McArdle, J., Smith, J. P., & Willis, R. (2009). Cognition and economic outcomes in the Health and Retirement Survey, manuscript, RAND Corporation, Santa Monica, CA.

  26. Parker, S., Teruel, G., & Rubalcava L. (2010). Perceptions and knowledge of underlying health conditions in Mexico, paper presented at the Population Association of America Annual Meetings, Dallas, Texas, 2010.

  27. Patel, V., & Kleinman, A. (2003). Poverty and common mental disorders in developing countries. Bulletin of the World Health Organization, 81(8), 609–615.

    Google Scholar 

  28. Popkin, B. (1999). Urbanization, lifestyle changes and the nutrition transition. World Development, 27(11), 1905–1916.

    Article  Google Scholar 

  29. Popkin, B. (2002). The shift in stages of the nutrition transition in the developing world differs from past experiences. Public Health Nutrition, 5(1A), 205–214.

    Article  Google Scholar 

  30. Popkin, B., Ge, K., Zhai, F., Guo, X., Ma, H., & Zohoori, N. (1993). The nutrition transition in China: a cross-sectional analysis. European Journal of Clinical Nutrition, 47, 333–346.

    Google Scholar 

  31. Popkin, B., Paeratakul, S., Zhai, F., & Ge, K. (1995a). A review of dietary and environmental correlates of obesity with emphasis on developing countries. Obesity Research, 3(S2), S145–S153.

    Google Scholar 

  32. Popkin, B., Paeratakul, S., Zhai, F., & Ge, K. (1995b). Dietary and environmental correlates of obesity in a population study of China. Obesity Research, 3(S2), S135–S143.

    Google Scholar 

  33. Schultz, T. P. (1984). Studying the impact of household economic and community variables on child mortality. In W. H. Mosely, & L. Chen (Eds.), Child survival: Strategies for research, population and development review, 10(Supplement):215–236.

  34. Seeman, T., Burt Singer, J., Rowe, R. H., & McEwen, B. (1997). Price of adaption-allostatic load and its health consequences. Archives of Internal Medicine, 157(19), 2259–2268.

    Article  Google Scholar 

  35. Smith, J. P. (1999). Healthy bodies and thick wallets: the dual relation between health and economic status. Journal of Economic Perspectives, 13(2), 145–167.

    Article  Google Scholar 

  36. Smith, J. P. (2009). The impact of childhood health on adult labor market outcomes. Review of Economics and Statistics, 91(3), 478–489.

    Article  Google Scholar 

  37. Strauss, J. (1986). Does better nutrition raise farm productivity? Journal of Political Economy, 94(2), 297–320.

    Article  Google Scholar 

  38. Strauss, J. (1993). The impact of improved nutrition on labor productivity and human resource development: An economic perspective. In P. Pinstrup-Andersen (Ed.), The political economy of food and nutrition policies. Baltimore: Johns Hopkins.

    Google Scholar 

  39. Strauss, J., & Thomas, D. (1995). Human resources: Empirical modeling of household and family decisions. In J. R. Behrman & T. N. Srinivasan (Eds.), Handbook of development economics (Vol. 3A). Amsterdam: North Holland.

    Google Scholar 

  40. Strauss, J., & Thomas, D. (1998). Health, nutrition and economic development. Journal of Economic Literature, 36(3), 766–817.

    Google Scholar 

  41. Strauss, J., & Thomas, D. (2008). Health over the life course. In T. P. Schultz & J. Strauss (Eds.), Handbook of development economics (Vol. 4). Amsterdam: North Holland.

    Google Scholar 

  42. Strauss, J., Lei, X., Park, A., Shen, Y., Smith, J. P., Yang, Z., et al. (2010). Health outcomes and socio-economic status among the elderly in China: Evidence from the CHARLS pilot, RAND Labor and Population Working Paper No. WR-774.

  43. Thomas, D. (2010). Health and socio-economic status: The importance of causal pathways. In J. Y. Lin & B. Pleskovic (Eds.), Annual World Bank Conference on Development Economics, Global 2009 (pp. 355–384). Washington DC: World Bank.

    Google Scholar 

  44. Thomas, D., & Strauss, J. (1997). Health and wages: evidence for men and women in urban Brazil. Journal of Econometrics, 77(1), 159–186.

    Article  Google Scholar 

  45. Thomas, D., Strauss, J., & Henriques, M.-H. (1991). How does mother’s education affect child height? Journal of Human Resources, 26(2), 183–211.

    Article  Google Scholar 

  46. UNICEF. (2009). China statistics, www.unicef.org/infobycountry/china_statistics.html.

  47. Waaler, H. (1984). Height, weight and mortality: the Norwegian experience. Acta Medica Scaninavica, 215(S679)), 1–56.

    Google Scholar 

  48. Wagstaff, A., Yip, W., Lindelow, M., & Hsiao, W. (2009). China’s health system and its reform: a review of recent studies. Health Economics, 18, S7–S23.

    Article  Google Scholar 

  49. Wilkenson, R. G. (1996). Unhealthy societies: The afflictions of inequality. London: Routledge.

    Book  Google Scholar 

  50. Witoelar, F., Strauss, J., & Sikoki, B. (2009). Socioeconomic success and health in later life: Evidence from the Indonesia family life survey, mimeo, University of Southern California.

  51. Wooldridge, J. (2002). Econometric analysis of cross-section and panel data. Cambridge: MIT Press.

  52. World Health Organization. (2009). China, WHO Country Health Information Profiles.

  53. Zhao, Y., Strauss, J., Park, A., & Sun, Y. (2009). China health and retirement longitudinal study user’s guide, China Center for Economic Research, Peking University.

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Correspondence to John Strauss.

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Comments from Richard Suzman and David Weir are greatly appreciated, as are suggestions from two referees and the Editors. Earlier versions were presented at the annual meetings of the Population Association of America, April 2009, Detroit, the World Congress of the International Association of Gerontology and Geriatrics, July 2009, Paris, and the 2nd International Conference on Health and Retirement in China, July 2009, Beijing. This research was supported by grants from the National Institute of Aging, the China National Natural Science Foundation and the World Bank, China.

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Strauss, J., Lei, X., Park, A. et al. Health Outcomes and Socio-economic Status Among the Elderly in China: Evidence from the CHARLS Pilot. Population Ageing 3, 111–142 (2010). https://doi.org/10.1007/s12062-011-9033-9

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Keywords

  • Health status
  • Health-SES correlations
  • Chinese elderly