{smcl}
{* 27apr2011}{...}
{hi:help oaxaca}
{hline}
{title:Title}
{pstd}{hi:oaxaca} {hline 2} Blinder-Oaxaca decomposition of outcome differentials
{title:Syntax}
{p 8 15 2}
{cmd:oaxaca} {depvar} [{it:indepvars}] {ifin} {weight}
{cmd:,} {opt by(groupvar)}
[ {help oaxaca##opt:{it:options}} ]
where {it:indepvars} is {it:term} [{it:term} {it:...}]
with {it:term} as {varlist}
or {cmd:(}[{help oaxaca##subsume:{it:name}}{cmd::}] {varlist}{cmd:)}
and {varlist} may contain {cmdab:n:ormalize(}{help oaxaca##norm:{it:spec}}{cmd:)}
{synoptset 25 tabbed}{...}
{marker opt}{synopthdr:options}
{synoptline}
{syntab :Main}
{synopt :{opt by(groupvar)}}specifies the groups; {cmd:by()} is required
{p_end}
{synopt :{opt swap}}swap groups
{p_end}
{synopt :{opt lin:ear}}linear decomposition; the default
{p_end}
{synopt :{opt logit}}logit decomposition
{p_end}
{synopt :{opt probit}}probit decomposition
{p_end}
{synopt :{cmdab:nod:etail}}suppress detailed decomposition
{p_end}
{synopt :{opt a:djust(varlist)}}adjustment for selection variables
{p_end}
{syntab :Decomposition type}
{synopt :{cmdab:three:fold}[{cmd:(}{cmdab:r:everse}{cmd:)}]}three-fold
decomposition; the default
{p_end}
{synopt :{opt w:eight(# [# ...])}}two-fold decomposition using specified weights
{p_end}
{synopt :{cmdab:p:ooled}[{cmd:(}{it:{help oaxaca##mopts:model_opts}}{cmd:)}]}two-fold
decomposition using pooled model including {it:groupvar}
{p_end}
{synopt :{cmdab:o:mega}[{cmd:(}{it:{help oaxaca##mopts:model_opts}}{cmd:)}]}two-fold
decomposition using pooled model excluding {it:groupvar}
{p_end}
{synopt :{opt ref:erence(name)}}two-fold decomposition using stored model
{p_end}
{synopt :{opt split}}split unexplained part of two-fold decomposition
{p_end}
{syntab :SE/SVY}
{synopt :{cmd:svy}[{cmd:(}{it:{help oaxaca##svy:svyspec}}{cmd:)}]}survey data estimation
{p_end}
{synopt :{opth vce(vcetype)}}{it:vcetype} may be may be {opt analytic},
{opt r:obust}, {opt cl:uster}{space 1}{it:clustvar}, {opt boot:strap},
or {opt jack:knife}
{p_end}
{synopt :{opt cl:uster(varname)}}adjust standard errors for intragroup correlation
(Stata 9)
{p_end}
{synopt :{cmdab:fix:ed}[{cmd:(}{it:varlist}{cmd:)}]}assume non-stochastic regressors
{p_end}
{synopt : {cmd:suest}[{cmd:(}{it:name}{cmd:)}] | {cmd:nosuest}}do/do not use
{helpb suest} to obtain joint variance matrix
{p_end}
{synopt :{opt nose}}suppress computation of standard errors
{p_end}
{syntab :Models}
{synopt :{cmd:model1(}{it:{help oaxaca##mopts:model_opts}}{cmd:)}}estimation
details for the Group 1 model
{p_end}
{synopt :{cmd:model2(}{it:{help oaxaca##mopts:model_opts}}{cmd:)}}estimation
details for the Group 2 model
{p_end}
{synopt :{opt noi:sily}}display model estimation output
{p_end}
{synopt :{opt relax}}do no stop on dropped coefficients/zero variances
{p_end}
{synopt :{it:estopts}}options passed through to all models
{p_end}
{syntab :X-Values (linear decomposition only)}
{synopt :{cmd:x1(}{it:{help oaxaca##x1x2:names_and_values}}{cmd:)}}provide custom
X-values for Group 1
{p_end}
{synopt :{cmd:x2(}{it:{help oaxaca##x1x2:names_and_values}}{cmd:)}}provide custom
X-values for Group 2
{p_end}
{syntab :Reporting}
{synopt :{opt xb}}display table with coefficients and means
{p_end}
{synopt :{opt l:evel(#)}}set confidence level; default is {cmd:level(95)}
{p_end}
{synopt :{opt eform}}report exponentiated results
{p_end}
{synopt :{opt nole:gend}}suppress legend
{p_end}
{synoptline}
{p 4 6 2}
{cmd:bootstrap}, {cmd:by}, {cmd:jackknife}, {cmd:statsby}, and
{cmd:xi} are allowed; see {help prefix}.
{p_end}
{p 4 6 2}
Weights are not allowed with the {helpb bootstrap} prefix.
{p_end}
{p 4 6 2}
{cmd:aweight}s are not allowed with the {helpb jackknife} prefix.
{p_end}
{p 4 6 2}
{cmd:vce()}, {cmd:cluster()}, and weights are not allowed with the {cmd:svy}
option.
{p_end}
{p 4 6 2}
{cmd:fweight}s, {cmd:aweight}s, {cmd:pweight}s, and {cmd:iweight} are allowed;
see {help weight}; {cmd:aweight}s are not allowed with {cmd:logit} or
{cmd:probit}
{p_end}
{title:Description}
{pstd} {cmd:oaxaca} computes the so-called Blinder-Oaxaca decomposition,
which is often used to analyze wage gaps by sex or race. {it:depvar} is the
outcome variable of interest (e.g. log wages) and {it:indepvars} are
predictors (e.g. education, work experience, etc.). {it:groupvar}
identifies the groups to be compared. The standard errors of the
decomposition components are computed using the delta method and take into
account the variability induced by stochastic regressors. For methods and
formulas see Jann (2008).
{pstd} {cmd:oaxaca} also supports the non-linear decomposition
for binary dependent variables proposed by Yun (2004). See the
{cmd:logit} and {cmd:probit} options. An alternative non-linear decomposition
for binary dependent variables, suggested by Fairlie (2005), is available as
{helpb fairlie} from the SSC Archive (see
{net "describe fairlie, from(http://fmwww.bc.edu/repec/bocode/f/)":ssc describe fairlie}).
{pstd} {cmd:oaxaca} typed without arguments replays the last
results, optionally applying {cmd:xb}, {cmd:level()}, {cmd:eform}, or
{cmd:nolegend}.
{marker subsume}
{title:Subsume results for sets of variables}
{pstd}Decomposition results can be aggregated for subsets of variables
using syntax
{it:...} {cmd:(}[{it:name}{cmd::}] {varlist}{cmd:)} {it:...}
{pstd} where {it:name} provides a label for the subset (the name of the first
variable in the subset is used as label if {it:name} is omitted). For example,
you could type
{com}. oaxaca lnwage educ (expten: exper tenure), by(female){txt}
{pstd} to subsume the contributions of {cmd:exper} and {cmd:tenure}. Apart
from variable names, also {cmd:_cons} and {cmd:_offset} can be specified as
part of a subset.
{marker norm}
{title:Normalization of categorical variables}
{pstd} For categorical regressors, the detailed decomposition results
depend on the choice of the (omitted) base category. A solution is to
compute the decomposition based on "normalized" effects, i.e. effects that
are expressed as deviation contrasts from the grand mean (Yun 2005). To
"normalize" the effects for a set of indicator variables representing a
categorical variable include the indicator variables in the list of
regressors using syntax
{it:...} {opt n:ormalize(spec)} {it:...}
{pstd}where {it:spec} usually simply is the list of indicator variables.
Note that an indicator variable has to be supplied for every category
(including the base category). For example, you could type
{com}. tabulate isco, generate(isco) nofreq
. oaxaca lnwage educ exper normalize(isco1-isco9), by(female){txt}
{pstd}The {cmd:tablate, generate()} command is a convenient way to generate
a set of indicator variables from a categorical variable (such as the 9
major group ISCO-88 job classification). The base category to be omitted
from model estimation can be designated using the {cmd:b.} operator, but this
should not affect the decomposition results. For example, you could type
{com:... normalize(married b.single divorced) ...}
{pstd}The first variable is taken if no base category is marked.
{pstd}Note that {cmd:normalize()} is allowed within subsumed variable
sets. For example, you could type
{com:... (family: kids6 normalize(married b.single divorced)) ...}
{pstd}Normalization can also be applied to interactions between a
categorical variable and a continuous variable. In this case, type
{cmd:#} followed by the name of the continuous variable at the end
in {cmd:normalize()}. Because usually you would also want to normalize
the main effects you should supply two {cmd:normalize()} statements, one
for the main effects and one for the interactions. Example: Suppose
{cmd:d1}, {cmd:d2}, and {cmd:d3} are indicator variables representing a
categorical variable and {cmd:d1x}, {cmd:d2x}, and {cmd:d3x} are
interactions of these indicators with a continuous variable {cmd:x}. You
could then type
{com:... normalize(d1 d2 d3) normalize(d1x d2x d3x # x) ...}
{title:Options}
{dlgtab:Main}
{phang} {opt by(groupvar)} specifies the {it:groupvar} that defines the two
groups that are to be compared. {cmd:by()} is required.
{phang} {opt swap} reverses the order of the groups.{p_end}
{phang} {opt linear} causes the standard linear decomposition to be
computed. This is the default. The estimation command for the
group models defaults to {helpb regress}.
{phang} {opt logit} causes the non-linear decomposition for a binary
dependent variable to be computed using the weighting method described
by Yun (2004). The estimation command for the
group models defaults to {helpb logit}.
{phang} {opt probit} causes the non-linear decomposition for a binary
dependent variable to be computed using the weighting method described
by Yun (2004). The estimation command for the
group models defaults to {helpb probit}.
{pstd} Only one of {opt linear}, {opt logit}, or {opt probit} is allowed.
{phang} {opt nodetail} suppresses the detailed results and
only computes the overall decomposition.
{phang} {opt adjust(varlist)} causes the group differential to be adjusted
by the contribution of the specified variables before computing the
decomposition. This is useful, for example, if the specified variables are
selection terms. Note that {cmd:adjust()} is not needed if {helpb heckman}
is used to estimate the models. {cmd:_offset} is allowed in {cmd:adjust()}.
{dlgtab:Decomposition type}
{phang} {cmd:threefold}[{cmd:(}{cmdab:reverse}{cmd:)}] computes the
three-fold decomposition. This is the default. The decomposition is
expressed from the viewpoint of Group 2. Specify {cmdab:threefold(reverse)}
to express the decomposition from the viewpoint of Group 1.
{phang} {opt weight(# [# ...])} computes the two-fold decomposition where
{it:#} [{it:# ...}] are the weights given to Group 1 relative to Group 2 in
determining the reference coefficients (weights are recycled if there are
more coefficients than weights). For example, {cmd:weight(1)} uses the
Group 1 coefficients as the reference coefficients, {cmd:weight(0)} uses
the Group 2 coefficients.
{phang} {cmd:pooled}[{cmd:(}{it:{help oaxaca##mopts:model_opts}}{cmd:)}]
computes the two-fold decomposition using the coefficients from a pooled
model over both groups as the reference coefficients. {it:groupvar} is
included in the pooled model as an additional control variable. Estimation
details may be specified in parentheses; see the
{helpb oaxaca##mopts:model1()} option below.
{phang} {opt omega}[{cmd:(}{it:{help oaxaca##mopts:model_opts}}{cmd:)}]
computes the two-fold decomposition using the coefficients from a pooled
model over both groups as the reference coefficients (without including
{it:groupvar} as a control variable). Estimation details may be specified
in parentheses; see the {helpb oaxaca##mopts:model1()} option below.
{phang} {opt reference(name)} computes the two-fold decomposition using the
coefficients from a stored model. {it:name} is the name under which the
model was stored; see {helpb estimates store}. It is suggested not to
combine {cmd:reference()} with {cmd:vce(bootstrap)} or {cmd:vce(jackknife)}.
{phang} {opt split} causes the "unexplained" component in the two-fold
decomposition to be split into a part related to Group 1 and a part related
to Group 2.
{pstd}Only one of {cmd:threefold}, {cmd:weight()}, {cmd:pooled},
{cmd:omega}, and {cmd:reference()} is allowed.
{dlgtab:X-Values}
{marker x1x2}
{phang} {opt x1(names_and_values)} and {opt x2(names_and_values)} provide
custom values for specific predictors to be used for Group 1 and Group 2 in
the decomposition (only allowed with linear decomposition). The default is
to use the group means of the predictors. The syntax for
{it:names_and_values} is
{p 12 16 2}{it:varname} [{cmd:=}] {it:value} [[{cmd:,}] {it:varname} [{cmd:=}] {it:value} {it:...} ]
{pmore}Example: {cmd:x1(educ 12 exp 30)}
{p_end}
{dlgtab:SE/SVY}
{marker svy}
{phang} {cmd:svy}[{cmd:(}[{it:vcetype}] [{cmd:,} {it:svy_options}]{cmd:)}]
executes {cmd:oaxaca} while accounting for the survey settings identified
by {helpb svyset} (this is essentially equivalent to applying the
{helpb svy} prefix command, although the {helpb svy} prefix is not allowed with
{cmd:oaxaca} due to some technical issues). {it:vcetype} and
{it:svy_options} are as described in help {helpb svy}.
{phang} {opt vce(vcetype)} specifies the type of standard errors
reported. {it:vcetype} may be may be {opt analytic} (the default),
{opt robust}, {opt cluster}{space 1}{it:clustvar}, {opt bootstrap},
or {opt jackknife}; see {help vce_option:{bf:[R]}{space 1}{it:vce_option}}.
{phang}
{opt cluster(varname)}
adjusts standard errors for intragroup correlation; this is Stata 9 syntax for
{cmd:vce(cluster}{space 1}{it:clustvar}{cmd:)}.
{phang} {cmd:fixed}[{cmd:(}{it:varlist}{cmd:)}] identifies fixed regressors
(all if specified without argument; an example for fixed regressors are
experimental factors). The default is to treat regressors as
stochastic. Stochastic regressors inflate the standard errors of the
decomposition components.
{phang} {cmd:suest}[{cmd:(}{it:name}{cmd:)}] enforces using {helpb suest} to
obtain the covariances between the models/groups. {cmd:suest} is implied by
{cmd:pooled}, {cmd:omega}, {cmd:reference()}, {cmd:svy},
{cmd:vce(cluster)}, and {cmd:cluster()}. Specify {cmd:suest(}{it:name}{cmd:)}
to save {helpb suest}'s estimation results under {it:name} using
{helpb estimates store}. {cmd:nosuest} prevents applying {helpb suest}
(this may cause biased standard errors).
{phang} {opt nose} suppresses the computation of standard errors.
{dlgtab:Model estimation}
{marker mopts}
{phang}
{cmd:model1(}{it:model_opts}{cmd:)} and {cmd:model2(}{it:model_opts}{cmd:)}
specify the estimation details for the two group models. The syntax for
{it:model_opts} is
{p 12 16 2}[{it:{help estimation_commands:estcom}}] [{cmd:,}
{opt sto:re(name)} {opt add:rhs(spec)} {it:estcom_options} ]
{pmore}where {it:estcom} is the estimation command to be used and
{it:estcom_options} are options allowed by {it:estcom}. {opt store(name)}
saves the model's estimation results under {it:name} using
{helpb estimates store}. {opt addrhs(spec)} adds {it:spec} to the "right-hand
side" of the model. For example, use {cmd:addrhs()} to add extra variables
to the model. Examples:
{cmd:model1(heckman, select(}{it:varlist_s}{cmd:) twostep)}
{cmd:model1(ivregress 2sls, addrhs((}{it:varlist2}{cmd:=}{it:varlist_iv}{cmd:)))}
{pmore}Note that {cmd:oaxaca} uses the first equation if a model contains
multiple equations. Furthermore, coefficients that only occur in one of the
models are assumed zero in the other model. It is required, however, that
the associated variables contain non-missing values for all observations in
both groups.
{phang}
{opt noisily} displays the models' estimation output.
{phang} {opt relax} causes {cmd:oaxaca} to continue its computations even
if coefficients are dropped from the models (e.g. due to collinearity) or
if some coefficients have zero variances. The default is to return error
in such a situation.
{phang}
{it:estopts} are common options to be passed through to the models.
{dlgtab:Reporting}
{phang}
{opt xb} displays a table containing the regression coefficients
and predictor values on which the decomposition is based.
{phang}
{opt level(#)} specifies the confidence level, as a percentage, for confidence
intervals. The default is {cmd:level(95)} or as set by {helpb set level}.
{phang} {opt eform} specifies that the results be displayed in
exponentiated form.
{phang} {opt nolegend} suppresses the legend about the sets of independent
variables.
{title:Examples}
{com}. {stata "use http://fmwww.bc.edu/RePEc/bocode/o/oaxaca.dta"}
. {stata oaxaca lnwage educ exper tenure, by(female)}
. {stata oaxaca lnwage educ exper tenure, by(female) weight(1)}
. {stata oaxaca lnwage educ exper tenure, by(female) pooled}
. {stata svyset [pw=wt]}
. {stata oaxaca lnwage educ exper tenure, by(female) pooled svy}
. {stata oaxaca lnwage educ exper tenure, by(female) pooled vce(bootstrap)}
. {stata tabulate isco, nofreq generate(isco)}{txt}
. {stata "oaxaca lnwage educ exper tenure normalize(isco?), by(female) pooled"}
. {stata "use http://fmwww.bc.edu/RePEc/bocode/h/homecomp.dta, clear"}
. {stata "oaxaca homecomp female age (educ:hsgrad somecol college) (marstat:married prevmar) if white==1|black==1, by(black) logit pooled":oaxaca homecomp female age (educ:hsgrad somecol college)}
{stata "oaxaca homecomp female age (educ:hsgrad somecol college) (marstat:married prevmar) if white==1|black==1, by(black) logit pooled":(marstat:married prevmar) if white==1|black==1, by(black)}
{stata "oaxaca homecomp female age (educ:hsgrad somecol college) (marstat:married prevmar) if white==1|black==1, by(black) logit pooled":logit pooled}
{txt}
{title:Saved Results}
{synoptset 15 tabbed}{...}
{p2col 5 15 19 2: Scalars}{p_end}
{synopt:{cmd:e(N)}}number of observations
{p_end}
{synopt:{cmd:e(N_1)}}number of observations in Group 1
{p_end}
{synopt:{cmd:e(N_2)}}number of observations in Group 2
{p_end}
{synopt:{cmd:e(N_clust)}}number of clusters
{p_end}
{synoptset 15 tabbed}{...}
{p2col 5 15 19 2: Macros}{p_end}
{synopt:{cmd:e(cmd)}}{cmd:oaxaca}
{p_end}
{synopt:{cmd:e(title)}}{cmd:Blinder-Oaxaca decomposition}
{p_end}
{synopt:{cmd:e(by)}}name group variable
{p_end}
{synopt:{cmd:e(group_1)}}value of group variable for Group 1
{p_end}
{synopt:{cmd:e(group_2)}}value of group variable for Group 2
{p_end}
{synopt:{cmd:e(depvar)}}name of dependent variable
{p_end}
{synopt:{cmd:e(model)}}{cmd:linear}, {cmd:logit}, or {cmd:probit}
{p_end}
{synopt:{cmd:e(threefold)}}{cmd:threefold}, {cmd:threefold(reverse)}, or
empty
{p_end}
{synopt:{cmd:e(weights)}}weights specified by {cmd:weight()} or
empty
{p_end}
{synopt:{cmd:e(refcoefs)}}{cmd:pooled}, {cmd:omega}, name of
reference model, or empty
{p_end}
{synopt:{cmd:e(legend)}}definitions of regressor sets
{p_end}
{synopt:{cmd:e(normalized)}}normalized indicator sets
{p_end}
{synopt:{cmd:e(adjust)}}names of adjustment variables
{p_end}
{synopt:{cmd:e(fixed)}}{cmd:fixed}, {cmd:fixed(}{it:varlist}{cmd:)}, or
empty
{p_end}
{synopt:{cmd:e(suest)}}{cmd:suest} or empty
{p_end}
{synopt:{cmd:e(wtype)}}weight type
{p_end}
{synopt:{cmd:e(wexp)}}weight expression
{p_end}
{synopt:{cmd:e(clustvar)}}name of cluster variable
{p_end}
{synopt:{cmd:e(vce)}}{it:vcetype} specified in {cmd:vce()}
{p_end}
{synopt:{cmd:e(vcetype)}}title used to label Std. Err.
{p_end}
{synopt:{cmd:e(properties)}}{cmd:b V}
{p_end}
{synoptset 15 tabbed}{...}
{p2col 5 15 19 2: Matrices}{p_end}
{synopt:{cmd:e(b)}}decomposition results
{p_end}
{synopt:{cmd:e(V)}}variance-covariance matrix of decomposition results
{p_end}
{synopt:{cmd:e(b0)}}vector containing coefficients and X-values
{p_end}
{synopt:{cmd:e(V0)}}variance-covariance matrix of {cmd:e(b0)}
{p_end}
{synoptset 15 tabbed}{...}
{p2col 5 15 19 2: Functions}{p_end}
{synopt:{cmd:e(sample)}}marks estimation sample{p_end}
{p2colreset}{...}
{title:References}
{phang}
Fairlie, Robert W. (2005). An extension of the Blinder-Oaxaca decomposition
technique to logit and probit models. Journal of Economic and Social
Measurement 30: 305-316.
{phang} Jann, Ben (2008). The Blinder-Oaxaca decomposition for
linear regression models. The Stata Journal 8(4): 453-479. [Working
paper version available from: {browse "http://ideas.repec.org/p/ets/wpaper/5.html"}]
{phang} Yun, Myeong-Su (2004). Decomposing differences in the
first moment. Economics Letters 82: 275-280.
{phang} Yun, Myeong-Su (2005). A Simple Solution to the Identification
Problem in Detailed Wage Decompositions. Economic Inquiry 43: 766-772.
{title:Author}
{p 4 4 2}Ben Jann, Institute of Sociology, University of Bern, jann@soz.unibe.ch
{title:Also see}
{p 4 13 2}
Online: help for {helpb regress}, {helpb logit}, {helpb probit},
{helpb heckman}, {helpb suest}, {helpb svyset}; {helpb fairlie} (if installed)