Gini coefficient

Estimate the Gini coefficient, an inequality measure

svygini(formula, design, ...)

# S3 method for survey.design
svygini(
  formula,
  design,
  na.rm = FALSE,
  deff = FALSE,
  linearized = FALSE,
  influence = FALSE,
  ...
)

# S3 method for svyrep.design
svygini(
  formula,
  design,
  na.rm = FALSE,
  deff = FALSE,
  linearized = FALSE,
  return.replicates = FALSE,
  ...
)

# S3 method for DBIsvydesign
svygini(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

na.rm

Should cases with missing values be dropped?

deff

Return the design effect (see survey::svymean)

linearized

Should a matrix of linearized variables be returned

influence

Should a matrix of (weighted) influence functions be returned? (for compatibility with svyby)

return.replicates

Return the replicate estimates?

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

svyarpr

Author

Djalma Pessoa, Guilherme Jacob, 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)

svygini( ~eqincome , design = des_eusilc )
#>             gini     SE
#> eqincome 0.26497 0.0019

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

svygini( ~eqincome , design = des_eusilc_rep )
#>             gini    SE
#> eqincome 0.26497 0.002

if (FALSE) {

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

svygini( ~ eqincome , design = dbd_eusilc )

dbRemoveTable( conn , 'eusilc' )

dbDisconnect( conn , shutdown = TRUE )

}