also see: mvdcmpgroup
help mvdcmp

Title

mvdcmp -- multivariate decomposition for nonlinear response models

Syntax

mvdcmp groupvar [, options ] : estimation_command depvar [indepvars] [weight] [, options]

where

groupvar specifies a binary variable coded 0 or 1 identifying the two groups;

estimation_command (see help estcom) should begin with
regress, svyregress, logit, svylogit, probit, svyprobit, poisson, svypoisson, nbreg, svynbreg, cloglog, or svycloglog;

options

reverse reverse decomposition by swapping groups

norm(varlist1|varlist2) identify dummy variable sets and apply deviation contrast normalization

scale(real) multiply coefficients and standard errors by a scaling factor [default scale(1)]

norm() requires that all dummy variables corresponding to each level of a factor be specified. It is most useful for factors having more than two levels.
For example, suppose that a set of dummy variables a1, a2, and a3 corresponds to a 3-level factor,
then norm(a1-a3) will implement the anova-type normalization. With more than one factor, dummy
variable sets should be separated by |. For example, suppose that dummy variables b1, b2, b2, and b4 correspond to a
4-level factor to be normalized along with the 3-level factor above, then norm(a1-a3|b1-b4)
will implement a normalization of both factors.

Models

regress linear regression model

svyregress linear regression model for complex survey design (see svy and svyset)

logit logit model

svylogit logit model for complex survey design

probit probit model

svyprobit probit model for complex survey design

poisson Poisson regression model

svypoisson Poisson regression model for complex survey design

nbreg negative binomial regression model

svynbreg negative binomial regression model for complex survey design

cloglog complementary log-log regression model

svycloglog complementary log-log regression model for complex survey design

fweights, pweights, aweights, and iweights are allowed
when supported by the model (see weight) in addition to the following model options:

options

cluster(varname) (see cluster)

robust (see robust)

offset(varname) (see offset) Applies to poisson, nbreg, svypoisson, and svynbreg only.

Note that logit, svylogit, probit, and svyprobit allow fractional responses (see fracreg). Use the robust option when fitting a fractional response using a logit or probit model.

Description

mvdcmp computes a multivariate decomposition for a variety of models,
and is often used to analyze differentials by race, sex, or time. estimation_command is the model of interest,
depvar is the outcome variable and indepvars are predictors. groupvar identifies the groups to be compared.

Examples

logit regression decomposition

mvdcmp blk: logit devnt pctsmom nfamtran medu inc1000 nosibs magebir

negative binomial regression decomposition with offset term and options reverse and scale

mvdcmp consprot, scale(100) reverse : nbreg nabort medu adjinc south urban profam books, offset(lognpreg)

Saved Results

mvdcmp saves the following in e():

Scalars

e(N) number of observations

e(scale) value of scale if used

Macros

e(cvarlist) complete list of coefficient names for ease of post-processing

e(depvar) names of dependent variable

e(indvar) names of independent variables

e(high) name of high-outcome group

e(low) name of low-outcome group

Matrices

e(b) estimates

e(V) variance/covariance matrix of estimates

References

Jann, B. (2008). "The Blinder-Oaxaca Decomposition for Linear Regression Models." The Stata Journal, 8: 453-479.

Powers, Daniel A., Hirotoshi Yoshioka, and Myeong-Su Yun. (2011). “mvdcmp: Multivariate Decomposition for Nonlinear Response Models.” The Stata Journal, 11: 556-576.

Yun, M-S. 2004. "Decomposing Differences in the First Moment." Economics Letters, 82: 275-280.

Yun, M-S. 2005. "Hypothesis Tests when Decomposing Differences in the First Moment." Journal of Economic and Social Measurement, 30: 305-319.

Yun, M-S. 2005. "A Simple Solution to the Identification Problem in Detailed Wage Decompositions." Economic Inquiry, 43: 766-772.

Authors

Daniel A. Powers, University of Texas at Austin, dpowers@austin.utexas.edu

Hirotoshi Yoshioka, University of Texas at Austin, hiro12@prc.utexas.edu

Myeong-Su Yun, Inha University, msyun@inha.ac.kr

Also see

Online help for oaxaca, fairlie,
devcon, regress, logit, probit, poisson, nbreg, cloglog and svy.