Linearization of the gender pay (wage) gap

Estimate the difference between the average gross hourly earnings of men and women expressed as a percentage of the average gross hourly earnings of men.

svygpg(formula, design, ...)

# S3 method for survey.design
svygpg(formula, design, sex, na.rm = FALSE, ...)

# S3 method for svyrep.design
svygpg(formula, design, sex, na.rm = FALSE, ...)

# S3 method for DBIsvydesign
svygpg(formula, design, sex, ...)

Arguments

formula: a formula specifying the gross hourly earnings variable
design: a design object of class survey.design or class svyrep.design from the survey library.
...: future expansion
sex: formula with a factor with labels 'male' and 'female'
na.rm: Should cases with missing values be dropped?

Value

Object of class "cvystat", which are vectors with a "var" attribute giving the variance and a "statistic" attribute giving the name of the statistic.

Details

you must run the convey_prep function on your survey design object immediately after creating it with the svydesign or svrepdesign function.

References

Guillaume Osier (2009). Variance estimation for complex indicators of poverty and inequality. Journal of the European Survey Research Association, Vol.3, No.3, pp. 167-195, ISSN 1864-3361, URL https://ojs.ub.uni-konstanz.de/srm/article/view/369.

Jean-Claude Deville (1999). Variance estimation for complex statistics and estimators: linearization and residual techniques. Survey Methodology, 25, 193-203, URL https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X19990024882.

Author

Djalma Pessoa and Anthony Damico

Examples

library(laeken)
library(survey)
data(ses)
names( ses ) <- gsub( "size" , "size_" , tolower( names( ses ) ) )
des_ses <- svydesign(id=~1, weights=~weights, data=ses)
des_ses <- convey_prep(des_ses)

# linearized design
svygpg(~earningshour, des_ses, ~sex)
#>                  gpg     SE
#> earningshour 0.24137 0.0104
# replicate-weighted design
des_ses_rep <-  as.svrepdesign( des_ses , type = "bootstrap" )
des_ses_rep <- convey_prep(des_ses_rep)

svygpg(~earningshour, des_ses_rep, ~sex)
#>                  gpg     SE
#> earningshour 0.24137 0.0107

if (FALSE) {

# database-backed design
library(RSQLite)
library(DBI)
dbfile <- tempfile()
conn <- dbConnect( RSQLite::SQLite() , dbfile )
dbWriteTable( conn , 'ses' , ses )

dbd_ses <- svydesign(id=~1, weights=~weights, data="ses", dbname=dbfile, dbtype="SQLite")
dbd_ses <- convey_prep( dbd_ses )

svygpg(formula=~earningshour, design=dbd_ses, sex= ~sex)

dbRemoveTable( conn , 'ses' )

}