ESTOUT: Stata module to export estimation results from estimates table

estout produces a table of regression results from one or several models for use with spreadsheets, LaTeX, HTML, or a word-processor table. eststo stores a quick copy of the active estimation results for later tabulation. esttab is a wrapper for estout. It displays a pretty looking publication-style regression table without much typing. estadd adds additional results to the e()-returns for one or several models previously fitted and stored. This package subsumes the previously circulated esto, esta, estadd, and estadd_plus. An earlier version of estout is available as estout1.

MRTAB: Stata module to compute one- and two-way tables/graphs of multiple responses

mrtab tabulates multiple responses which are held as a set of indicator variables or as a set of polytomous response variables.

WGTTEST: Stata module to test the impact of sampling weights in regression analysis

wgttest performs a test proposed by DuMouchel and Duncan (1983) to evaluate whether the weighted and unweighted estimates of a regression model are significantly different.

ALPHAWGT: Stata module to calculate Cronbach's alpha for weighted data

alphawgt computes the Cronbach's alpha statistic. It is the same than the official alpha (version 4.5.2, 09apr2002) except that fweights and aweights may be applied.

DECOMPOSE: Stata module to compute decompositions of wage differentials

Given the results from two regressions (one for each of two groups), decompose computes several decompositions of the outcome variable differential. The decompositions shows how much of the gap is due to differing endowments between the two groups, and how much is due to discrimination. Usually this is applied to wage differentials using Mincer type earnings equations.

A Stata implementation of the Blinder-Oaxaca decomposition

The counterfactual decomposition technique popularized by Blinder (1973) and Oaxaca (1973) is widely used to study mean outcome differences between groups. For example, the technique is often used to analyze wage gaps by sex or race. The present paper summarizes the technique and addresses a number of complications such as the identification of effects of categorical predictors in the detailed decomposition or the estimation of standard errors. A new Stata command called -oaxaca- is introduced and examples illustrating its usage are given.