vardpoor (version 0.20.1)

linpoormed: Linearization of the median income of individuals below the At Risk of Poverty Threshold

Description

Estimation of the median income of individuals below At Risk of Poverty Threshold and computation of linearized variable for variance estimation. The At Risk of Poverty Threshold is estimated for the whole population always. The median income is estimated for the whole population or for each domain.

Usage

linpoormed(
  Y,
  id = NULL,
  weight = NULL,
  sort = NULL,
  Dom = NULL,
  period = NULL,
  dataset = NULL,
  percentage = 60,
  order_quant = 50,
  var_name = "lin_poormed",
  checking = TRUE
)

Value

A list with two objects are returned by the function:

• value - a data.table containing the estimated median income of individuals below the At Risk of Poverty Threshold.

• lin - a data.table containing the linearized variables of the median income below the At Risk of Poverty Threshold.

Arguments

Y

Study variable (for example equalized disposable income). One dimensional object convertible to one-column data.table or variable name as character, column number.

id

Optional variable for unit ID codes. One dimensional object convertible to one-column data.table or variable name as character, column number.

weight

Optional weight variable. One dimensional object convertible to one-column data.table or variable name as character, column number.

sort

Optional variable to be used as tie-breaker for sorting. One dimensional object convertible to one-column data.table or variable name as character, column number.

Dom

Optional variables used to define population domains. If supplied, linearization of the median income of persons below a poverty threshold is done for each domain. An object convertible to data.table or variable names as character vector, column numbers.

period

Optional variable for survey period. If supplied, linearization of the median income of persons below a poverty threshold is done for each time period. Object convertible to data.table or variable names as character, column numbers.

dataset

Optional survey data object convertible to data.table.

percentage

A numeric value in range \([0,100]\) for \(p\) in the formula for poverty threshold computation: $$\frac{p}{100} \cdot Z_{\frac{\alpha}{100}}.$$ For example, to compute poverty threshold equal to 60% of some income quantile, \(p\) should be set equal to 60.

order_quant

A numeric value in range \([0,100]\) for \(\alpha\) in the formula for poverty threshold computation: $$\frac{p}{100} \cdot Z_{\frac{\alpha}{100}}.$$. For example, to compute poverty threshold equal to some percentage of median income, \(\alpha\) should be set equal to 50.

var_name

A character specifying the name of the linearized variable.

checking

Optional variable if this variable is TRUE, then function checks data preparation errors, otherwise not checked. This variable by default is TRUE.

References

Working group on Statistics on Income and Living Conditions (2004) Common cross-sectional EU indicators based on EU-SILC; the gender pay gap. EU-SILC 131-rev/04, Eurostat.
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/pub/12-001-x/1999002/article/4882-eng.pdf.

See Also

linarpt, linrmpg, varpoord, vardcrospoor, vardchangespoor

Examples

library("laeken")
library("data.table")
data("eusilc")
dataset1 <- data.table(IDd = paste0("V", 1 : nrow(eusilc)), eusilc)
 
# Full population
d <- linpoormed(Y = "eqIncome", id = "IDd",
                weight = "rb050", Dom = NULL,
                dataset = dataset1, percentage = 60,
                order_quant = 50L)
  
if (FALSE) {
# Domains by location of houshold
dd <- linpoormed(Y = "eqIncome", id = "IDd",
                 weight = "rb050", Dom = "db040",
                 dataset = dataset1, percentage = 60,
                 order_quant = 50L)
dd}