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Prediction and determination of household permanent income

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  • Abul Naga, Ramses H.
  • Burgess, Robin

Abstract

This paper is about the determination and prediction of permanent income in household data. Standard static welfare indicators (e.g. per capita expenditure and income) are imperfect in this respect as they typically contain a high transitory component. The framework we employ is consistent with the permanent income hypothesis but is supplemented with a causes equation where unobservable permanent income is explicitly modelled as a function of causal variables which play a key part in its determination. Simultaneous estimation of the model allows us to compare how well different standard static welfare indicators identify permanent income but more importantly enables us to predict permanent income using information contained both in the causal variables and in the standard static welfare indicators. The paper is closed by an application of the methodology to household data from the rural sectors of two Chinese provinces.

Suggested Citation

  • Abul Naga, Ramses H. & Burgess, Robin, 1997. "Prediction and determination of household permanent income," LSE Research Online Documents on Economics 2143, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:2143
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    File URL: http://eprints.lse.ac.uk/2143/
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    References listed on IDEAS

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    1. Lillard, Lee A & Willis, Robert J, 1978. "Dynamic Aspects of Earning Mobility," Econometrica, Econometric Society, vol. 46(5), pages 985-1012, September.
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    6. Milton Friedman & Simon Kuznets, 1945. "Income from Independent Professional Practice," NBER Books, National Bureau of Economic Research, Inc, number frie54-1, June.
    7. Glewwe, Paul, 1991. "Investigating the determinants of household welfare in Cote d'Ivoire," Journal of Development Economics, Elsevier, vol. 35(2), pages 307-337, April.
    8. Goldberger, Arthur S, 1972. "Maximum-Likelihood Estimation of Regressions Containing Unobservable Independent Variables," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 13(1), pages 1-15, February.
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    Cited by:

    1. Ramses H. Abul Naga, 2005. "Social Welfare Orderings: A Life‐Cycle Perspective," Economica, London School of Economics and Political Science, vol. 72(287), pages 497-514, August.
    2. repec:bla:econom:v:72:y:2005:i:3:p:497-514 is not listed on IDEAS
    3. Gary Fields & Paul Cichello & Samuel Freije & Marta Menéndez & David Newhouse, 2003. "For Richer or for Poorer? Evidence from Indonesia, South Africa, Spain, and Venezuela," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 1(1), pages 67-99, April.
    4. Nicoletta Rosati, 2006. "A nonparametric analysis of welfare and the economic shocks," CeMMAP working papers CWP22/06, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. Ramsès H. Abul Naga & Enrico Bolzani, 2006. "Poverty and Permanent Income: A Methodology for Cross-Section Data," Annals of Economics and Statistics, GENES, issue 81, pages 195-223.
    6. Riccardo Massari, 2005. "A Measure of Welfare Based on Permanent Income Hypothesis: An Application on Italian Households Budgets," Giornale degli Economisti, GDE (Giornale degli Economisti e Annali di Economia), Bocconi University, vol. 64(1), pages 55-92, September.

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    More about this item

    Keywords

    Permanent income; prediction; determination; living standards; rural China.;
    All these keywords.

    JEL classification:

    • I30 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - General
    • O12 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Microeconomic Analyses of Economic Development
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models

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