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Testing for an economic gradient in health status using subjective data

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  • Michael Lokshin
  • Martin Ravallion

Abstract

Can self‐assessments of health reveal the true health differentials between ‘rich’ and ‘poor’? The potential sources of bias include psychological adaptation to ill‐health, socioeconomic covariates of health reporting errors and income measurement errors. We propose an estimation method to reduce the bias by isolating the component of self‐assessed health that is explicable in terms of objective health indicators and allowing for broader dimensions of economic welfare than captured by current incomes. On applying our method to survey data for Russia we find a pronounced (nonlinear) economic gradient in health status that is not evident in the raw data. This is largely attributable to the health effects of age, education and location. Copyright © 2008 John Wiley & Sons, Ltd.

Suggested Citation

  • Michael Lokshin & Martin Ravallion, 2008. "Testing for an economic gradient in health status using subjective data," Health Economics, John Wiley & Sons, Ltd., vol. 17(11), pages 1237-1259, November.
  • Handle: RePEc:wly:hlthec:v:17:y:2008:i:11:p:1237-1259
    DOI: 10.1002/hec.1318
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    File URL: https://doi.org/10.1002/hec.1318
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    3. Ravallion, Martin & Himelein, Kristen & Beegle, Kathleen, 2013. "Can subjective questions on economic welfare be trusted ? evidence for three developing countries," Policy Research Working Paper Series 6726, The World Bank.
    4. Jishnu Das & Quy-Toan Do & Jed Friedman & David McKenzie, 2008. "Mental Health Patterns and Consequences: Results from Survey Data in Five Developing Countries," World Bank Economic Review, World Bank Group, vol. 23(1), pages 31-55, August.
    5. Raj Arunachalam & Manisha Shah, 2013. "Compensated for Life: Sex Work and Disease Risk," Journal of Human Resources, University of Wisconsin Press, vol. 48(2), pages 345-369.
    6. Maria Ana Lugo & Koen Decancq, 2009. "Measuring Inequality of Well-Being with a Correlation-Sensitive Multidimensional Gini Index," Economics Series Working Papers 459, University of Oxford, Department of Economics.
    7. Ravallion, Martin, 2012. "Poor, or just feeling poor ? on using subjective data in measuring poverty," Policy Research Working Paper Series 5968, The World Bank.
    8. Christopher J. Gerry & Georgios Papadopoulos, 2015. "Sample attrition in the RLMS, 2001–10," The Economics of Transition, The European Bank for Reconstruction and Development, vol. 23(2), pages 425-468, April.
    9. Ren Mu, 2014. "Regional Disparities In Self‐Reported Health: Evidence From Chinese Older Adults," Health Economics, John Wiley & Sons, Ltd., vol. 23(5), pages 529-549, May.
    10. Kiendrebeogo,Youssouf & Ianchovichina,Elena & Kiendrebeogo,Youssouf & Ianchovichina,Elena, 2016. "Who supports violent extremism in developing countries ? analysis of attitudes based on value surveys," Policy Research Working Paper Series 7691, The World Bank.
    11. Nazim Habibov & Elvin Afandi, 2016. "Does Life Satisfaction Determine Subjective Health?," Applied Research in Quality of Life, Springer;International Society for Quality-of-Life Studies, vol. 11(2), pages 413-428, June.
    12. Kaneva, Maria & Baidin, Valerii, 2018. "Heterogeneity in reporting self-assessed health of the Russians," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 51, pages 102-125.
    13. ZHAO, Guochang, 2015. "Can money ‘buy’ schooling achievement? Evidence from 19 Chinese cities," China Economic Review, Elsevier, vol. 35(C), pages 83-104.

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