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

Author

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  • Ms. 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

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

    1. Dhongde, Shatakshee & Minoiu, Camelia, 2013. "Global Poverty Estimates: A Sensitivity Analysis," World Development, Elsevier, vol. 44(C), pages 1-13.
    2. Christoph Lakner & Branko Milanovic, 2016. "Global Income Distribution: From the Fall of the Berlin Wall to the Great Recession," The World Bank Economic Review, World Bank, vol. 30(2), pages 203-232.
    3. Sami Bibi & AbdelRahmen El Lahga, 2010. "Generating Reliable Data to Perform Distributional Analysis in the Arab Region," Working Papers 561, Economic Research Forum, revised 10 Jan 2010.
    4. Walter, Paul & Weimer, Katja, 2018. "Estimating poverty and inequality indicators using interval censored income data from the German microcensus," Discussion Papers 2018/10, Free University Berlin, School of Business & Economics.
    5. 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.
    6. Francesco Vercelli, 2019. "The evolution of inequality and social cohesion in Europe: 1957-2017," Questioni di Economia e Finanza (Occasional Papers) 526, Bank of Italy, Economic Research and International Relations Area.
    7. John Paolo Rosales Rivera, 2022. "A nonparametric approach to understanding poverty in the Philippines: Evidence from the Family Income and Expenditure Survey," Poverty & Public Policy, John Wiley & Sons, vol. 14(3), pages 242-267, September.

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