help forgdecomp-------------------------------------------------------------------------------

Decomposition of outcome differentials after nonlinear models

Syntax

gdecompgroupvar[,options]:estimation_commandwhere

groupvarspecifies a binary (numeric) variable identifying the two groups;

estimation_command(see help estcom) should begin with thelogit,logistit,logistit,probit,poisson, ornbreg;

optionsaredxweight(high|low)reverseeformlevel(#)noheadernocoefdummies(varlist_1[\varlist_2..])

Description

gdecompimplements a generalized Blinder-Oaxaca decomposition which applies to categorical and count outcomes (and parallel to this, to nonlinear regression models). First,estimation_commandis estimated in the two groups ofgroupvar. Then the observed difference in the dependent variable ofestimation_commandbetween the groups defined bygroupvaris decomposed into three parts: (1) a part due to differences in endowments (labeled by E), and (2) a part due to differences inmarginal effectsand finally (3) a part due to difference in baseline predictions or constants (labeled by U). See theMethods and formulassection below.Typed without arguments,

gdecompreplays the estimation results.gdecompshares all features of estimation commands; see help estcom for details.Before using

gdecomp, please install the latest version of margeff. (The latest version is 2.0.1, dated 15 Septermber 2006). See other packages carrying out Blinder-Oaxaca decompositions at the bottom of this help file.

Options

dxweight(high|low)affects the calculation of the endowment effect. Ifdxweight(high)is specified then differences in endowments are evaluated at the high-outcome regression line. Ifdxweight(low)is specified then differences in endowments are weighted with the marginal effects from the low-outome group. The default isdxweight(high).

reversetellsgdecompthat the group with the lower average of the outcome variable should be treated as the high-outcome group. By default,gdecompdefines the low-outcome group to be the group with the largest observed mean of the outcome variable. The default behavior generalizes the idea that average earnings are higher in the high-outcome group. Thereverseoption makes sense and should be used only if high value of the outcome variable indicate outcomes that are "negatively" valued (or, outcomes decreasing subjective utility). Do not use this option if large categories of the outcome variable record high salaries or being in the labor force; use this option if large categories of the outcome variable record being unemployed.

eformtellsgdecompthat the dependent variable is the natural logarithm of the outcome variable, so that correct marginal effects (changes in the exponential of the linear prediction) can be calculated. This option is useful if the dependent variable is the logarithm of wage.Warning:with this option, you do not request the results to be displayed in exponentiated form.

level(#)specifies the confidence level, in percent terms, for the confidence intervals of the computed statistics; see help level.

noheadersuppresses the display of overall and variable-level decomposition results.

nocoefsuppresses the display of the decomposition results for the variables, and forcesgdecompto display the E, C and U components (without respective standard errors). .

dummies(varlist_1[\varlist_2... ])modifies the calculation of marginal effects for dummy variables. Here,varlist_1[\varlist_2... ] are lists of dummy variables, where all dummies of a list indicate different categories of the same underlying categorical variable. Letxvarbe a categorical variables withK+1 (K>1) categories. In this case, not xvar, butKdummies - say, D1, ..., DK - are included in the regression model. The estimated marginal effects for theseKdummies may be misleading (see an example in the help file margeff). The correct result is obtained if one specifies thedummies(D*)option.

Methods and FormulasLet y1 and y0 be the means of the dependent variable Y in the high-outcome and the low-outcome groups, respectively (thus y1>y0). Let

x1 andx0 the row vectors of the means of the explanatory variables X1,...,Xk, andm1 andm0 the column vectors of the marginal effects in groups 1 and 0, anda1 anda0 the baseline predictions in groups 1 and 0.If the

dxweight(high|low)option is omitted ordxweight(high)is specified, then the raw differential y1-y2 is approximated asy1-y0 = (

x1-x0)m1 +x0(m1-m0) +a1-a0If, however, the

dxweight(low)option is specified, then the raw differential y1-y2 is approximated asy1-y0 = (

x1-x0)m0 +x0(m1-m0) +a1-a0Whatever method is chosen, the first part on the right-hand side is the endowments effect (E), and the second part on the right-hand side is the coefficient effect (C), and the third part is the difference due to differences in "constants" (unexplained part, U).

AuthorTamás Bartus, Corvinus University, Budapest, tamas.bartus@uni-corvinus.hu

Also seeOn-line: help for gdecomp decompose oaxaca if installed