Kernel Density Estimation Basedon Grouped Data: The Case of Poverty Assessment

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International Monetary Fund, Jul 1, 2008 - Business & Economics - 34 pages
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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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Contents

I Motivation
3
II The Data Structure and the Bias of the Estimator
6
III The Bandwidth and Kernels Considered
9
IV Monte Carlo Study
11
V Country Studies
16
VI Global Poverty
17
VII Conclusions
19
References
21
Appendix
26
Appendix Tables
27
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