Relative median poverty gap

Estimate the difference between the at-risk-of-poverty threshold (arpt) and the median of incomes less than the arpt relative to the arpt.

svyrmpg(formula, design, ...)

# S3 method for survey.design
svyrmpg(
  formula,
  design,
  quantiles = 0.5,
  percent = 0.6,
  na.rm = FALSE,
  thresh = FALSE,
  poor_median = FALSE,
  ...
)

# S3 method for svyrep.design
svyrmpg(
  formula,
  design,
  quantiles = 0.5,
  percent = 0.6,
  na.rm = FALSE,
  thresh = FALSE,
  poor_median = FALSE,
  ...
)

# S3 method for DBIsvydesign
svyrmpg(formula, design, ...)

Arguments

formula

a formula specifying the income variable

design

a design object of class survey.design or class svyrep.design from the survey library.

...

future expansion

quantiles

income quantile, usually .5 (median)

percent

fraction of the quantile, usually .60

na.rm

Should cases with missing values be dropped?

thresh

return the poverty poverty threshold

poor_median

return the median income of poor people

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.

See also

svyarpt

Author

Djalma Pessoa and Anthony Damico

Examples

library(survey)
library(laeken)
data(eusilc) ; names( eusilc ) <- tolower( names( eusilc ) )

# linearized design
des_eusilc <- svydesign( ids = ~rb030 , strata = ~db040 ,  weights = ~rb050 , data = eusilc )
des_eusilc <- convey_prep( des_eusilc )

svyrmpg( ~eqincome , design = des_eusilc, thresh = TRUE )
#>             rmpg     SE
#> eqincome 0.18929 0.0058

# replicate-weighted design
des_eusilc_rep <- as.svrepdesign( des_eusilc , type = "bootstrap" )
des_eusilc_rep <- convey_prep( des_eusilc_rep )

svyrmpg( ~eqincome , design = des_eusilc_rep, thresh = TRUE )
#>             rmpg     SE
#> eqincome 0.18929 0.0054

if (FALSE) {

# linearized design using a variable with missings
svyrmpg( ~ py010n , design = des_eusilc )
svyrmpg( ~ py010n , design = des_eusilc , na.rm = TRUE )
# replicate-weighted design using a variable with missings
svyrmpg( ~ py010n , design = des_eusilc_rep )
svyrmpg( ~ py010n , design = des_eusilc_rep , na.rm = TRUE )

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

dbd_eusilc <-
  svydesign(
    ids = ~rb030 ,
    strata = ~db040 ,
    weights = ~rb050 ,
    data="eusilc",
    dbname=dbfile,
    dbtype="SQLite"
  )

dbd_eusilc <- convey_prep( dbd_eusilc )

svyrmpg( ~ eqincome , design = dbd_eusilc )

dbRemoveTable( conn , 'eusilc' )

dbDisconnect( conn , shutdown = TRUE )

}