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Kernel Density Estimation Basedon Grouped Data

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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.

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Bibliographic Info

Paper provided by International Monetary Fund in its series IMF Working Papers with number 08/183.

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Length: 34
Date of creation: 01 Jul 2008
Date of revision:
Handle: RePEc:imf:imfwpa:08/183

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Keywords: Income distribution; Poverty; Economic models; Data analysis; statistics; poverty line; survey; surveys; household surveys; poverty analysis; income levels; representative household; normal distribution; samples; monte carlo simulations; representative household surveys; distribution of income; standard deviation; living standards; probability; econometrics; correlation; income data; computation; mathematical statistics; household survey; mean square; living standards measurement study; sensitivity analysis; macro-models; probability density; distributional impacts; exponential family; household data; sample survey; statistical techniques; random variable; asymptotic distribution; estimation procedure; statistical method; national accounts; sample bias; impact of policies; general equilibrium models; extrapolation; outliers; poverty impact; functional form; random sample; probability function; multivariate distributions; absolute errors; distributional effects; nonparametric statistics;

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References

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Citations

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Cited by:
  1. Lakner, Christoph & Milanovic, Branko, 2013. "Global income distribution : from the fall of the Berlin Wall to the great recession," Policy Research Working Paper Series 6719, The World Bank.
  2. Camelia Minoiu & Shatakshee Dhongde, 2011. "Global Poverty Estimates," IMF Working Papers 11/234, International Monetary Fund.

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