svyfgt: FGT measure of poverty

In convey: Income Concentration Analysis with Complex Survey Samples

FGT measure of poverty

Description

Estimate the FGT measure.

Usage

svyfgt(formula, design, ...)

## S3 method for class 'survey.design'
svyfgt(
  formula,
  design,
  g,
  type_thresh = "abs",
  abs_thresh = NULL,
  percent = 0.6,
  quantiles = 0.5,
  na.rm = FALSE,
  thresh = FALSE,
  deff = FALSE,
  linearized = FALSE,
  influence = FALSE,
  ...
)

## S3 method for class 'svyrep.design'
svyfgt(
  formula,
  design,
  g,
  type_thresh = "abs",
  abs_thresh = NULL,
  percent = 0.6,
  quantiles = 0.5,
  na.rm = FALSE,
  thresh = FALSE,
  deff = FALSE,
  linearized = FALSE,
  return.replicates = FALSE,
  ...
)

## S3 method for class 'DBIsvydesign'
svyfgt(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.
...	passed to svyarpr and svyarpt
g	If g=0 estimates the headcount ratio; If g=1 estimates the average normalised poverty gap, and if g=2 estimates the average squared normalised poverty gap
type_thresh	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.
abs_thresh	poverty threshold value if type_thresh is "abs"
percent	the multiple of the the quantile or mean used in the poverty threshold definition
quantiles	the quantile used used in the poverty threshold definition
na.rm	Should cases with missing values be dropped?
thresh	return the poverty threshold value
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). Not implemented yet for linearized designs.
return.replicates	Return the replicate estimates?

Details

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

The FGT poverty measures have three special cases. When g = 0, the FGT measure is the headcount poverty rate, assigning the same "poverty-weight" to all persons below the poverty line. When g = 1, it becomes the poverty gap ratio, a measure which accounts for the intensity of income shortfall among the poor. When g = 2. it becomes the squared poverty gap ratio, a measure that also accounts for inequality of poverty intesity across the poor. The g is a poverty sensitivity parameter, adding more weight to people with greater income shortfalls as it increases.

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.

Author(s)

Djalma Pessoa, Anthony Damico, and Guilherme Jacob

References

James Foster, Joel Greer and Erik Thorbecke (1984). A class of decomposable poverty measures. Econometrica, Vol.52, No.3, pp. 761-766.

Y.G. Berger and C. J. Skinner (2003), Variance estimation for a low income proportion. Journal of the Royal Statistical Society: Series C (Applied Statistics), Vol. 52, No. 4, pp. 457-468. DOI \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/1467-9876.00417")}

Buhong Zheng (2001). Statistical inference for poverty measures with relative poverty lines. Journal of Econometrics, Vol. 101, pp. 337-356.

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

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 )

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

# headcount ratio, poverty threshold fixed
svyfgt(~eqincome, des_eusilc, g=0,  abs_thresh=10000)
# poverty gap index, poverty threshold fixed
svyfgt(~eqincome, des_eusilc, g=1,  abs_thresh=10000)
# headcount ratio, poverty threshold equal to arpt
svyfgt(~eqincome, des_eusilc, g=0, type_thresh= "relq" , thresh = TRUE)
# poverty gap index, poverty threshold equal to arpt
svyfgt(~eqincome, des_eusilc, g=1, type_thresh= "relq", thresh = TRUE)
# headcount ratio, poverty threshold equal to .6 times the mean
svyfgt(~eqincome, des_eusilc, g=0, type_thresh= "relm", thresh = TRUE)
# poverty gap index, poverty threshold equal to 0.6 times the mean
svyfgt(~eqincome, des_eusilc, g=1, type_thresh= "relm" , thresh = TRUE)

#  using svrep.design:
# headcount ratio, poverty threshold fixed
svyfgt(~eqincome, des_eusilc_rep, g=0,  abs_thresh=10000)
# poverty gap index, poverty threshold fixed
svyfgt(~eqincome, des_eusilc, g=1,  abs_thresh=10000)
# headcount ratio, poverty threshold equal to arpt
svyfgt(~eqincome, des_eusilc_rep, g=0, type_thresh= "relq" , thresh = TRUE)
# poverty gap index, poverty threshold equal to arpt
svyfgt(~eqincome, des_eusilc, g=1, type_thresh= "relq", thresh = TRUE)
# headcount ratio, poverty threshold equal to .6 times the mean
svyfgt(~eqincome, des_eusilc_rep, g=0, type_thresh= "relm" , thresh = TRUE)
# poverty gap index, poverty threshold equal to 0.6 times the mean
svyfgt(~eqincome, des_eusilc_rep, g=1, type_thresh= "relm", thresh = TRUE)

## Not run: 

# 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 )

# headcount ratio, poverty threshold fixed
svyfgt(~eqincome, dbd_eusilc, g=0, abs_thresh=10000)
# poverty gap index, poverty threshold fixed
svyfgt(~eqincome, dbd_eusilc, g=1, abs_thresh=10000)
# headcount ratio, poverty threshold equal to arpt
svyfgt(~eqincome, dbd_eusilc, g=0, type_thresh= "relq", thresh = TRUE)
# poverty gap index, poverty threshold equal to arpt
svyfgt(~eqincome, dbd_eusilc, g=1, type_thresh= "relq")
# headcount ratio, poverty threshold equal to .6 times the mean
svyfgt(~eqincome, dbd_eusilc, g=0, type_thresh= "relm")
# poverty gap index, poverty threshold equal to 0.6 times the mean
svyfgt(~eqincome, dbd_eusilc, g=1, type_thresh= "relm")

dbRemoveTable( conn , 'eusilc' )

dbDisconnect( conn , shutdown = TRUE )


## End(Not run)

convey documentation built on Oct. 16, 2024, 5:10 p.m.