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
poverty measures increase by 15 to 30 percent.