svywatts {convey}

Watts measure of poverty

Description

Estimate the Watts measure for the cases: alpha=0 headcount ratio and alpha=1 poverty gap index.

Usage

svywatts(formula, design, ...)

## S3 method for class '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 class '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 class 'DBIsvydesign'
svywatts(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

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

thresh

return the poverty threshold value

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). 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.

For the svywatts and svywattsdec functions, zeroes and negative numbers in the analysis domain cause an error because of the logarithm function in the definition of this poverty measure. However, zeroes and negative values in the full survey design that are outside of the domain of analysis are valid to calculate the poverty threshold because zeroes and negatives are not a problem for computing quantiles (used when type_thresh = "relq") or means (used when type_thresh = "relm") . Missing values are treated differently. NA values anywhere in the full survey design (not only the subset, or the domain of analysis) will cause these quantiles and means to return NA results. To ignore NA values throughout, set na.rm = TRUE.

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)

Guilherme Jacob, Djalma Pessoa, and Anthony Damico

References

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, DOI doi:10.1080/02664763.2010.515674.

Anthony B. Atkinson (1987). On the measurement of poverty. Econometrica, Vol.55, No.4, (Jul., 1987), pp. 749-764, DOI doi:10.2307/1911028.

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.

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 )

# 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)
# poverty threshold equal to arpt
svywatts(~eqincome, des_eusilc , type_thresh= "relq", thresh = TRUE)
# poverty threshold equal to 0.6 times the mean
svywatts(~eqincome, des_eusilc , type_thresh= "relm" , thresh = TRUE)
# using svrep.design:
# poverty threshold fixed
svywatts(~eqincome, des_eusilc_rep  ,  abs_thresh=10000)
# poverty threshold equal to arpt
svywatts(~eqincome, des_eusilc_rep  , type_thresh= "relq", thresh = TRUE)
# poverty threshold equal to 0.6 times the mean
svywatts(~eqincome, des_eusilc_rep  , 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 )

# 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 )


## End(Not run)