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How Much of Observed Economic Mobility is Measurement Error? IV Methods to Reduce Measurement Error Bias, with an Application to Vietnam

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  • Paul Glewwe

Abstract

Research on economic growth and inequality inevitably raises issues concerning economic mobility because the relationship between long-run inequality and short-run inequality is mediated by income mobility; for a given level of short-run inequality, greater mobility implies lower long-run inequality. But empirical measures of both inequality and mobility tend to be biased upward due to measurement error in income and expenditure data collected from household surveys. This paper examines how to reduce or remove this bias using instrumental variable methods, and provides conditions that instrumental variables must satisfy to provide consistent estimates. This approach is applied to panel data from Vietnam. The results imply that at least 15 percent, and perhaps as much as 42 percent, of measured mobility is upward bias due to measurement error. The results also suggest that measurement error accounts for at least 12 percent of measured inequality. Copyright 2012, Oxford University Press.

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  • Paul Glewwe, 2012. "How Much of Observed Economic Mobility is Measurement Error? IV Methods to Reduce Measurement Error Bias, with an Application to Vietnam," World Bank Economic Review, World Bank Group, vol. 26(2), pages 236-264.
  • Handle: RePEc:oup:wbecrv:v:26:y:2012:i:2:p:236-264
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    File URL: http://hdl.handle.net/10.1093/wber/lhr040
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    Cited by:

    1. Dang,Hai-Anh H. & Lanjouw,Peter F. & Swinkels,Robertus A & Dang,Hai-Anh H. & Lanjouw,Peter F. & Swinkels,Robertus A, 2014. "Who remained in poverty, who moved up, and who fell down ? an investigation of poverty dynamics in Senegal in the late 2000s," Policy Research Working Paper Series 7141, The World Bank.
    2. Dwayne Benjamin & Loren Brandt & Brian McCaig, 2017. "Growth with equity: income inequality in Vietnam, 2002–14," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 15(1), pages 25-46, March.
    3. Jolliffe,Dean Mitchell & Dang,Hai-Anh H. & Carletto,Calogero & Dang,Hai-Anh H. & Jolliffe,Dean Mitchell & Carletto,Calogero, 2017. "Data gaps, data incomparability, and data imputation : a review of poverty measurement methods for data-scarce environments," Policy Research Working Paper Series 8282, The World Bank.

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