shapowen: Stata module to provide a generic Shapley-Owen value calculator (2025).
shapowen is a generic Shapley-Owen value calculator. It is designed as a generic prefix command to allow for a broad spectrum of implementations of Shapley value decompositions.
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Install: ssc install shapowen
exbsample: Stata module to compute exchangeably weighted (or Bayesian) bootstraps (2022).
exbsample generates bootstrap replication weights for implementation of exchangeably weighted bootstrap schemes, also known as the Bayesian bootstrap. It can be used as an alternative to bsample. Exchangeably weighted bootstrap schemes (or weighted, or exchangeable bootstraps) are alternatives to the traditional non-parametric (paired) bootstrap. Standard bootstrap replications involve generating bootstrap samples of size N by drawing with replacement from the original data. Such a bootstrap resample can be seen as a frequency weighted version of the original data, with integer weights representing the number of times each observation is drawn in a resample. (See the weight option of Stata's bootstrap drawing command bsample.) Exchangeably weighted bootstrap schemes can be seen as extensions of this representation: bootstrap resamples are created by generating replication weights directly from appropriate distribution functions. See Praestgaard and Wellner (1993) for details. This technique is also known as the Bayesian bootstrap (Rubin, 1981).
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Install: ssc install exbsample
bipolar: Stata module to calculate four measures of income bi-polarization (2020).
bipolar calculates four measures of income bi-polarization.
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Install: ssc install bipolar
sgini: Generalized Gini coefficients (with factor decompositions) (2020).
sgini is a light-weight package to calculate generalized Gini and Concentration coefficients. It also allows factor decomposition (income source). As a by-product, the companion commands sginicorr computes (generalized) Gini correlations and fracrank generates fractional rank variables.
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Install: ssc install sgini
_grndraw: Stata module for random number generation from the GB2, Singh-Maddala, Dagum, Fisk and Pareto distributions (2017, updated 2025).
_grndraw is an egen function for random number generation from the GB2, Singh-Maddala, Dagum, Fisk and Pareto distributions. Simulation relies on standard inverse transform sampling methods.
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Install: ssc install _grndraw
wdiscrim: Stata module to compute earnings discrimination statistics (2014, updated 2021).
wdiscrim calculates 'distributionally-sensitive' summary measures of earnings discrimination from a sample of actual and counterfactual wage predictions.
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Install: ssc install wdiscrim
mdepriv: Stata module to compute synthetic indicators of multiple deprivation (2014, updated 2020).
mdepriv calculates basic synthetic scores of multiple deprivation. The synthetic scores are weighted sums of individual unidimensional items taking values on the [0,1] range. Several alternative weighting rules are available.
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Install: ssc install mdepriv
rhsbsample: Stata module for repeated half-sample bootstrap sampling (2013).
rhsbsample is a variant of the official bsample command. rhsbsample draws bootstrap samples from the data in memory using the repeated half-sample bootstrap algorithm proposed in Saigo, Shao and Sitter (Survey Methodology, 2001). This bootstrap resampling scheme is suitable for bootstrapping complex survey data.
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Install: ssc install rhsbsample
dsginideco: Stata module to compute decomposition of inequality change into pro-poor growth and mobility components (2009, updated 2017).
dsginideco implements the decomposition of changes in (generalized) Gini coefficients into progressivity/pro-poor growth and reranking components proposed in Jenkins and Van Kerm, Oxford Economic Papers, 2006. See the online manual (http://medim.ceps.lu/stata/dsginideco_v1.pdf) for details on methods, option descriptions and usage examples.
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Install: ssc install dsginideco
paretofit: Stata module to fit a Type 1 Pareto distribution (2007, updated 2015).
paretofit fits by ML a Pareto (Type I) distribution to sample observations on a random variable. Unit record data are assumed (rather than grouped data).
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Install: ssc install paretofit
progres: Stata module to measure distributive effects of an income tax (2007).
progres is a module for assessing the distributive effects of an income tax. progres takes unit-record data on pre-tax and post-tax income and computes several classic measures of (net) redistributive effects, progressivity, vertical equity and reranking (horizontal inequity). All indices are derived from (generalized) Gini coefficients of inequality and (generalized) concentration coefficients.
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Install: ssc install progres
akdensity: Stata module to perform adaptive kernel density estimation (2005, updated 2010).
akdensity estimates density functions by the kernel method with varying bandwidth (a.k.a. adaptive kernel density estimation). Pointwise variability bands are also provided. The package is an update for the Stata Journal article st0037 which appeared in SJ-3-2 (2003).
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Install: ssc install akdensity
dummieslab: Stata module to convert categorical variable to dummy variables using label names (2004, updated 2010).
dummieslab generates a set of dummy variables from a categorical variable. One dummy variable is created for each level of the original variable. Names for the dummy variables are derived from the value labels of the categorical variable.
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Install: ssc install dummieslab
glcurve: Stata module to derive generalised Lorenz curve ordinates (2004, updated 2008).
Given variable varname, call it x with c.d.f. F(x), glcurve either draws its Generalised Lorenz curve or generates two new variables containing the Generalised Lorenz ordinates for x, i.e. GL(p) at each p = F(x) (or both). For a population ordered in ascending order of x, a graph of GL(p) against p plots the cumulative total of x divided by population size, against cumulative population share. GL(1) = mean(x). glcurve can also be used to derive many other related concepts such as concentration curves and 'Three Is of Poverty' (TIP) curves, with appropriate definition of varname, order of cumulation (set with sortvar option), and normalisation (e.g. by the mean of varname). For example, the 'ordinary' Lorenz curve L(p) = GL(p)/mean(x). Users of this routine for Stata 5 or 6 should use glcurve5; users of Stata 7 should use glcurve7.
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Install: ssc install glcurve
_gclsort: Stata module to sort a single variable via egen (2002).
clsort is an extension to generate function. The generated variable is obtained by sorting a variable provided as argument (either in increasing order or based on a specified key variable). Unlike sort and gsort, this leaves the rest of the data unaffected.
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Install: ssc install _gclsort
inequal7: Stata module to compute measures of inequality (2001, updated 2007).
inequal7 computes a series of standard inequality measures for income distributions. It is a revised and upgraded version of inequal published by Edward Whitehouse in STB-23. inequal7 also supersedes the previously released inequal2 with output format now in compliance with Stata 7 standards. The inequality measures computed are: the "relative mean deviation", the "coefficient of variation", the "standard deviation of logs", the"Gini index", the "Mehran index", the "Piesch index", the "Kakwani index", "Theil entropy index", the "mean log deviation" and the "generalised entropy measure" with sensitivity parameters -1 and 2.
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Install: ssc install inequal7
poverty: Stata module to calculate poverty measures (1999).
poverty computes a series of poverty measures based on the (income) distribution described by varname. The poverty measures that can be computed by poverty are: headcount ratio, aggregate poverty gap, poverty gap ratio, income gap ratio, Watts index, Sen index, Takayama index, Thon index, and three indices( Foster, Greer and Thorbecke; Clark et al.; and Kakwani) for various parameter values. The poverty line is either directly specified by the user or computed relative to the median of varname. This is version 2.0.0 of the software.
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Install: ssc install poverty
coranal: Stata module to perform simple correspondence analysis (1998).
The command coranal produces numerical results as well as graphical outputs for simple correspondence analyses. The computation algorithm draws most largely on Blasius and Greenacre, eds., Correspondence Analysis in the Social Sciences - Recent Developments and Applications, 1994.
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Install: ssc install coranal
mca: Stata module to perform multiple correspondence analysis (1998).
The command mca produces numerical results as well as graphical representations for multiple correspondence analyses (MCA). mca actually conducts an adjusted simple correspondence analysis on the Burt matrix constructed with varlist (i.e. matrix of frequency counts resulting from all two-way cross-tabulations of the variables in varlist including the cross-tabulations of each variable with itself). It can be shown that the total inertia of the Burt matrix is high due to the fitting of the diagonal sub-matrices. Consequently, a simple correspondence analysis applied to this matrix usually results in maps of apparently poor quality. As a remedy, if not otherwise specified (see Options), mca adjusts the obtained principal inertias (eigenvalues) following a method suggested by Benzecri and presented in Greenacre (1984).
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Install: ssc install mca