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Kernel density estimation on grouped data: the case of poverty assessment

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  • Camelia Minoiu

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  • Sanjay Reddy

    ()

Abstract

Grouped data have been widely used to analyze the global income distribution because individual records from nationally representative household surveys are often unavailable. In this paper we evaluate the performance of nonparametric density smoothing techniques, in particular kernel density estimation, in estimating poverty from grouped data. Using Monte Carlo simulations, we show that kernel density estimation gives rise to nontrivial biases in estimated poverty levels that depend on the bandwidth, kernel, poverty indicator, size of the dataset, and data generating process. Furthermore, the empirical bias in the poverty headcount ratio critically depends on the poverty line. We also undertake a sensitivity analysis of global poverty estimates to changes in the bandwidth and show that they vary widely with it. A comparison of kernel density estimation with parametric estimation of the Lorenz curve, also applied to grouped data, suggests that the latter fares better and should be the preferred approach. Copyright Springer Science+Business Media, LLC. 2014

Suggested Citation

  • Camelia Minoiu & Sanjay Reddy, 2014. "Kernel density estimation on grouped data: the case of poverty assessment," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 12(2), pages 163-189, June.
  • Handle: RePEc:kap:jecinq:v:12:y:2014:i:2:p:163-189
    DOI: 10.1007/s10888-012-9220-9
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Jirasavetakul,La-Bhus Fah & Lakner,Christoph, 2016. "The distribution of consumption expenditure in Sub-Saharan Africa : the inequality among all Africans," Policy Research Working Paper Series 7557, The World Bank.
    2. Saari, M. Yusof & Rahman, M. Affan Abdul & Hassan, Azman & Habibullah, Muzafar Shah, 2016. "Estimating the impact of minimum wages on poverty across ethnic groups in Malaysia," Economic Modelling, Elsevier, vol. 54(C), pages 490-502.

    More about this item

    Keywords

    Kernel density estimation; Lorenz curve; Grouped data; Income distribution; Global poverty; I32; D31; C14; C15;

    JEL classification:

    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty
    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General

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