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Estimating and Predicting Household Expenditures and Income Distributions

Author

Listed:
  • Jing Dai

    () (Universität Kassel)

  • Stefan Sperlich

    () (Université de Genève)

  • Walter Zucchini

    () (Georg-August Universität Göttingen)

Abstract

A reliable prediction of unconditional welfare distributions, like income or consumption, is essential for welfare analysis, and in particular for inequality, poverty or development studies. Where observations of expenditures or income are missing, the mean prediction based on available covariates is not just a poor estimator of the unconditional distribution; it fails to predict the required information about tails and quantiles. A new estimation method is introduced which can be combined with any mean prediction model. It is used to calculate the income distribution of a survey based on subsample information, to estimate the unconditional income distribution for the non-responding households, and to predict the household expenditures of a future panel wave. It allows for imputing welfare distributions for a census from a survey or for synthetic populations under specific scenarios. Further inference is straight-forward, including prediction of Lorenz curves, indexes like the Gini, or distribution quantiles, including confidence intervals.

Suggested Citation

  • Jing Dai & Stefan Sperlich & Walter Zucchini, 2011. "Estimating and Predicting Household Expenditures and Income Distributions," MAGKS Papers on Economics 201147, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
  • Handle: RePEc:mar:magkse:201147
    as

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    File URL: http://www.uni-marburg.de/fb02/makro/forschung/magkspapers/47-2011_dai.pdf
    File Function: First version, 2011
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    References listed on IDEAS

    as
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    1. Lucia Rizzica, "undated". "When the Cat\'s Away... The Effects of Spousal Migration on Investments on Children," Development Working Papers 361, Centro Studi Luca d'Agliano, University of Milano.

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