gpg  R Documentation 
Estimate the gender pay (wage) gap.
gpg(
inc,
gender = NULL,
method = c("mean", "median"),
weights = NULL,
sort = NULL,
years = NULL,
breakdown = NULL,
design = NULL,
cluster = NULL,
data = NULL,
var = NULL,
alpha = 0.05,
na.rm = FALSE,
...
)
inc 
either a numeric vector giving the equivalized disposable income,
or (if 
gender 
either a factor giving the gender, or (if 
method 
a character string specifying the method to be used. Possible
values are 
weights 
optional; either a numeric vector giving the personal sample
weights, or (if 
sort 
optional; either a numeric vector giving the personal IDs to be
used as tiebreakers for sorting, or (if 
years 
optional; either a numeric vector giving the different years of
the survey, or (if 
breakdown 
optional; either a numeric vector giving different domains,
or (if 
design 
optional and only used if 
cluster 
optional and only used if 
data 
an optional 
var 
a character string specifying the type of variance estimation to
be used, or 
alpha 
numeric; if 
na.rm 
a logical indicating whether missing values should be removed. 
... 
if 
The implementation strictly follows the Eurostat definition (with default
method "mean"
and alternative method "median"
). If weights are
provided, the weighted mean or weighted median is estimated.
A list of class "gpg"
(which inherits from the class
"indicator"
) with the following components:
value 
a numeric vector containing the overall value(s). 
valueByStratum 
a 
varMethod 
a character string specifying the type of variance
estimation used, or 
var 
a numeric vector containing the variance estimate(s), or

varByStratum 
a 
ci 
a numeric vector or matrix containing the lower and upper
endpoints of the confidence interval(s), or 
ciByStratum 
a 
alpha 
a numeric value giving the significance level used for
computing the confidence interv al(s) (i.e., the confidence level is 
years 
a numeric vector containing the different years of the survey. 
strata 
a character vector containing the different domains of the breakdown. 
Matthias Templ and Alexander Haider, using code for breaking down estimation by Andreas Alfons
A. Alfons and M. Templ (2013) Estimation of Social Exclusion Indicators from Complex Surveys: The R Package laeken. Journal of Statistical Software, 54(15), 1–25. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v054.i15")}
Working group on Statistics on Income and Living Conditions (2004) Common crosssectional EU indicators based on EUSILC; the gender pay gap. EUSILC 131rev/04, Eurostat, Luxembourg.
variance
, qsr
, gini
data(ses)
# overall value with mean
gpg("earningsHour", gender = "sex", weigths = "weights",
data = ses)
# overall value with median
gpg("earningsHour", gender = "sex", weigths = "weights",
data = ses, method = "median")
# values by education with mean
gpg("earningsHour", gender = "sex", weigths = "weights",
breakdown = "education", data = ses)
# values by education with median
gpg("earningsHour", gender = "sex", weigths = "weights",
breakdown = "education", data = ses, method = "median")
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