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.

JMPIERCE: Stata module to perform Juhn-Murphy-Pierce decomposition

jmpierce computes the decomposition of differences between two outcome distributions introduced by Juhn, Murphy and Pierce (1993). Examples are the decomposition of changes in the income distribution over time, the decomposition of male-female wage differentials, or the decomposition of wage inequality differences between countries. This routine was previously circulated as jmp.

SMITHWELCH: Stata module to compute trend decomposition of outcome differentials

smithwelch computes decompositions of differences in mean outcome differentials. Smith and Welch (1989) used such decomposition techniques in their analysis of the change in the black-white wage differential over time. An alternative application would be the decomposition of country differences in the male-female wage gap.

JMPIERCE2: Stata module to compute trend decomposition of outcome differentials

jmpierce2 computes the decomposition of differences in mean outcome differentials proposed by Juhn, Murphy and Pierce (1991). An example is the decomposition of the change of the black-white or the male-female wage differential over time or the decomposition of differences in the male-female wage differential between countries. This routine was previously circulated as jmp2.

FAIRLIE: Stata module to generate nonlinear decomposition of binary outcome differentials

fairlie computes the nonlinear decomposition of binary outcome differentials proposed by Fairlie (1999, 2003).