Partial Identification of Poverty Measures with Contaminated and Corrupted Data

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Abstract
This paper applies a partial identi cation approach to poverty measurement when data errors are non-classical in the sense that it is not assumed that the error is statistically independent of the outcome of interest, and the error distribution has a mass point at zero. This paper shows that it is possible to find non-parametric bounds for the class of additively separable poverty measures. A methodology to draw statistical inference on partially identified parameters is extended and applied to the setting of poverty measurement. The methodology developed in this essay is applied to the estimation of poverty treatment effects of an anti-poverty program in the presence of contaminated data.
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WP 2006-07 January 2006
JEL Classification Codes: C14; I32
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2006-01-01
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Charles H. Dyson School of Applied Economics and Management, Cornell University
Keywords
Poverty Measurement; Partial Identi cation; Contamination Model; Statistical Inference; Nonparametric bounds; Poverty Comparisons
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