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Kernel Density Estimation Based on Grouped Data; The Case of Poverty Assessment

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  • Camelia Minoiu
  • Sanjay Reddy

Abstract

We analyze the performance of kernel density methods applied to grouped data to estimate poverty (as applied in Sala-i-Martin, 2006, QJE). Using Monte Carlo simulations and household surveys, we find that the technique gives rise to biases in poverty estimates, the sign and magnitude of which vary with the bandwidth, the kernel, the number of datapoints, and across poverty lines. Depending on the chosen bandwidth, the $1/day poverty rate in 2000 varies by a factor of 1.8, while the $2/day headcount in 2000 varies by 287 million people. Our findings challenge the validity and robustness of poverty estimates derived through kernel density estimation on grouped data.

Suggested Citation

  • Camelia Minoiu & Sanjay Reddy, 2008. "Kernel Density Estimation Based on Grouped Data; The Case of Poverty Assessment," IMF Working Papers 08/183, International Monetary Fund.
  • Handle: RePEc:imf:imfwpa:08/183
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    Cited by:

    1. Christoph Lakner & Branko Milanovic, 2016. "Global Income Distribution: From the Fall of the Berlin Wall to the Great Recession," World Bank Economic Review, World Bank Group, vol. 30(2), pages 203-232.
    2. Dhongde, Shatakshee & Minoiu, Camelia, 2013. "Global Poverty Estimates: A Sensitivity Analysis," World Development, Elsevier, vol. 44(C), pages 1-13.

    More about this item

    Keywords

    Economic models; Data analysis; Poverty; Income distribution; kernel density estimation; grouped data; statistics; poverty line; survey; surveys; household surveys;

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