This presentation describes a Stata implementation of a space-filling location selection algorithm. The objective is to select a subset from a discrete list of locations so that the spatial coverage of the locations by the selected subset is optimized according to a geometric criterion. Such an...

This talk presents a simple graphical device for visualization of patterns of income mobility. The device uses color palettes to picture information contained in transition matrices created from a fine partition of the marginal distributions. The talk explains how these graphs can be constructed...

The talk illustrates a user-written command that extends the official kdensity to estimate density functions by the kernel method. The extensions are of two types. Firstly, the new command allows the use of an 'adaptive kernel' approach with varying, rather than fixed, bandwidths. Secondly,...

A set of Stata routines to help analysis of `income mobility' are presented and illustrated. Income mobility is taken here as the pattern of income change from one time period to another within an income distribution. Multiple approaches have been advocated to assess the magnitude of income...

This presentation illustrates Stata’s implementation of the repeated half-sample bootstrap proposed by Saigo et al. (2001, Survey Methodology). This resampling scheme is easy to implement and is appropriate for complex survey designs, even with small stratum sizes. The user-written...

I discuss estimation of poverty measures from household survey data in Stata and show how to derive analytic standard errors that take into account survey design features. Where needed, standard errors are adjusted for the estimation of the poverty line as a fraction of the mean or median...

Distributive analysis typically consists in estimating summary measures capturing aspects of the distribution of sample points beyond central tendency. Stochastic dominance analysis is also central for comparisons of distributions. Unfortunately, data contamination, and extreme data more...

Extreme data are known to be highly influential when measuring income inequality from microdata. Similarly, Lorenz curves and dominance criteria are sensitive to data contamination in the tails of the distribution. In this presentation, I intend to introduce a set of user-written packages that...

This short talk describes the module -dsginideco- which decomposes the change in income inequality between two time periods into two components, one representing the progressivity (pro-poorness) of income growth, and the other representing reranking. Inequality is measured using the generalized...