Partial Identification of Poverty Measures with Contaminated and Corrupted Data
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This paper applies a partial identication 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.