The objective of measuring poverty is usually to make comparisons over time or
between two or more groups. Common statistical inference methods are used to
determine whether an apparent difference in measured poverty is statistically significant.
Studies of relative poverty have long recognized that when the poverty line is calculated
from sample survey data, both the variance of the poverty line and the variance of the
welfare metric contribute to the variance of the poverty estimate. In contrast, studies
using absolute poverty lines have ignored the poverty line variance, even when the
poverty lines are estimated from sample survey data. Including the poverty line variance
could either reduce or increase the precision of poverty estimates, depending on the
specific characteristics of the data. This paper presents a general procedure for
estimating the standard error of poverty measures when the poverty line is estimated from
survey data. Based on bootstrap methods, the approach can be used for a wide range of
poverty measures and methods for estimating poverty lines. The method is applied to
recent household survey data from Mozambique. When the sampling variance of the
poverty line is taken into account, the estimated standard errors of Foster-Greer-
Thorbecke and Watts poverty measures increase by 15 to 30 percent at the national level,
with considerable variability at lower levels of aggregation.
