Poverty measures that are widely used generate inconsistency and incoherence because of their failure to be adequately grounded in a conception of what ultimately matters and why. Obscurantism in relation to underlying values is the ultimate source of the observed difficulties. Only poverty...

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...

Kernel density estimation (KDE) has been prominently used to measure poverty from grouped data (Sala-i-Martin, 2006, QJE). In this paper we analyze the performance of this method. Using Monte Carlo simulations for plausible income distributions and unit data from several household surveys, we...

We review The Great Indian Poverty Debate edited by Angus Deaton and Valerie Kozel. The volume has great value as a survey of the complex issues involved in estimating poverty in India, which have recently been the subject of substantial controversy. However, the volume has notable omissions....

Grouped data have been widely used to analyze the global income distribution because individual records from nationally representative household surveys are often unavailable. In this paper we evaluate the performance of nonparametric density smoothing techniques, in particular kernel density...