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Kernel Density Estimation Based on Grouped Data: The Case of Poverty Assessment
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Author/Editor: |
Minoiu, Camelia | Reddy, Sanjay |
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July 1, 2008 |
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Electronic Access: |
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Disclaimer: This Working Paper should not be reported as representing the views of the IMF. The views expressed in this Working Paper are those of the author(s) and do not necessarily represent those of the IMF or IMF policy. Working Papers describe research in progress by the author(s) and are published to elicit comments and to further debate.
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Summary: 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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Series: |
Working Paper No. 08/183 |
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Subject(s): |
Poverty | Economic models | Income distribution | Data analysis |
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Author's keyword(s): |
kernel density estimation
| income distribution
| grouped data
| poverty
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Published: |
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July 1, 2008 |
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Format: |
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Paper |
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Stock No: |
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WPIEA2008183 |
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Pages: |
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34 |
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Price: |
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US$18.00 (Academic Rate: US$18.00 )
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