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 tie-breakers 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 cross-sectional EU indicators based on EU-SILC; the gender pay gap. EU-SILC 131-rev/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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