Poverty and inequality are often estimated from grouped data as complete household
surveys are neither always available to researchers nor easy to analyze. In this study we assess
the performance of functional forms proposed by Kakwani (1980a) and Villasenor and Arnold
(1989) to estimate the Lorenz curve from grouped data. The methods are implemented using the
computational tools POVCAL and SimSIP, developed and distributed by the World Bank. To
identify biases associated with these methods, we use unit data from several household surveys
and theoretical distributions. We find that poverty and inequality are better estimated when the
true distribution is unimodal than multimodal. For unimodal distributions, biases associated with
poverty measures are rarely larger than one percentage point. For data from multi-peaked or
heavily skewed distributions, the biases are likely to be higher and of unknown sign.