We find that poverty and inequality is better estimated when the data is generated from unimodal distributions than when it is drawn from multimodal distributions. To identify biases associated with this method of estimating the two Lorenz curve functional forms, we analyze unit data from several household surveys and a wide range of theoretical distributions. The methods are implemented using the computational tool POVCAL, developed and distributed by the World Bank.
In this study we assess the performance of two functional forms for the Lorenz curve proposed by Kakwani (1980) and Villasenor and Arnold (1989). The estimation of poverty and inequality often requires the use of grouped data as complete household surveys are neither always available to researchers nor easy to analyze.
0 kommentar(er)
