Estimate the Watts measure for the cases: alpha=0 headcount ratio and alpha=1 poverty gap index.
svywatts(formula, design, ...)
# S3 method for survey.design
svywatts(
formula,
design,
type_thresh = "abs",
abs_thresh = NULL,
percent = 0.6,
quantiles = 0.5,
thresh = FALSE,
na.rm = FALSE,
deff = FALSE,
linearized = FALSE,
influence = FALSE,
...
)
# S3 method for svyrep.design
svywatts(
formula,
design,
type_thresh = "abs",
abs_thresh = NULL,
percent = 0.6,
quantiles = 0.5,
thresh = FALSE,
na.rm = FALSE,
deff = FALSE,
linearized = FALSE,
return.replicates = FALSE,
...
)
# S3 method for DBIsvydesign
svywatts(formula, design, ...)a formula specifying the income variable
a design object of class survey.design or class svyrep.design from the survey library.
passed to svyarpr and svyarpt
type of poverty threshold. If "abs" the threshold is fixed and given the value of abs_thresh; if "relq" it is given by percent times the quantile; if "relm" it is percent times the mean.
poverty threshold value if type_thresh is "abs"
the multiple of the the quantile or mean used in the poverty threshold definition
the quantile used used in the poverty threshold definition
return the poverty threshold value
Should cases with missing values be dropped?
Return the design effect (see survey::svymean)
Should a matrix of linearized variables be returned
Should a matrix of (weighted) influence functions be returned? (for compatibility with svyby). Not implemented yet for linearized designs.
Return the replicate estimates?
Object of class "cvystat", which are vectors with a "var" attribute giving the variance and a "statistic" attribute giving the name of the statistic.
you must run the convey_prep function on your survey design object immediately after creating it with the svydesign or svrepdesign function.
Harold W. Watts (1968). An economic definition of poverty. Institute For Research on Poverty Discussion Papers, n.5. University of Wisconsin. URL https://www.irp.wisc.edu/publications/dps/pdfs/dp568.pdf.
Buhong Zheng (2001). Statistical inference for poverty measures with relative poverty lines. Journal of Econometrics, Vol. 101, pp. 337-356.
Vijay Verma and Gianni Betti (2011). Taylor linearization sampling errors and design effects for poverty measures and other complex statistics. Journal Of Applied Statistics, Vol.38, No.8, pp. 1549-1576, URL https://dx.doi.org/10.1080/02664763.2010.515674.
Anthony B. Atkinson (1987). On the measurement of poverty. Econometrica, Vol.55, No.4, (Jul., 1987), pp. 749-764, URL https://www.jstor.org/stable/1911028.
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.
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 )
# replicate-weighted design
des_eusilc_rep <- as.svrepdesign( des_eusilc , type = "bootstrap" )
des_eusilc_rep <- convey_prep( des_eusilc_rep )
# filter positive incomes
des_eusilc <- subset( des_eusilc , eqincome > 0 )
des_eusilc_rep <- subset( des_eusilc_rep , eqincome > 0 )
# poverty threshold fixed
svywatts(~eqincome, des_eusilc , abs_thresh=10000)
#> watts SE
#> eqincome 0.051744 0.0023
# poverty threshold equal to arpt
svywatts(~eqincome, des_eusilc , type_thresh= "relq", thresh = TRUE)
#> watts SE
#> eqincome 0.062404 0.0024
# poverty threshold equal to 0.6 times the mean
svywatts(~eqincome, des_eusilc , type_thresh= "relm" , thresh = TRUE)
#> watts SE
#> eqincome 0.078038 0.0024
# using svrep.design:
# poverty threshold fixed
svywatts(~eqincome, des_eusilc_rep , abs_thresh=10000)
#> watts SE
#> eqincome 0.051744 0.002
# poverty threshold equal to arpt
svywatts(~eqincome, des_eusilc_rep , type_thresh= "relq", thresh = TRUE)
#> watts SE
#> eqincome 0.062404 0.0021
# poverty threshold equal to 0.6 times the mean
svywatts(~eqincome, des_eusilc_rep , type_thresh= "relm" , thresh = TRUE)
#> watts SE
#> eqincome 0.078038 0.002
if (FALSE) {
# 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 )
# filter positive incomes
dbd_eusilc <- subset( dbd_eusilc , eqincome > 0 )
# poverty threshold fixed
svywatts(~eqincome, dbd_eusilc , abs_thresh=10000)
# poverty threshold equal to arpt
svywatts(~eqincome, dbd_eusilc , type_thresh= "relq", thresh = TRUE)
# poverty threshold equal to 0.6 times the mean
svywatts(~eqincome, dbd_eusilc , type_thresh= "relm" , thresh = TRUE)
dbRemoveTable( conn , 'eusilc' )
dbDisconnect( conn , shutdown = TRUE )
}