linarpr {vardpoor}

Linearization of at-risk-of-poverty rate

Description

Estimates the at-risk-of-poverty rate (defined as the proportion of persons with equalized disposable income below at-risk-of-poverty threshold) and computes linearized variable for variance estimation.

Usage

linarpr(
  Y,
  id = NULL,
  weight = NULL,
  Y_thres = NULL,
  wght_thres = NULL,
  sort = NULL,
  Dom = NULL,
  period = NULL,
  dataset = NULL,
  percentage = 60,
  order_quant = 50,
  var_name = "lin_arpr",
  checking = TRUE
)

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 or logical vector).

weight

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

Y_thres

Variable (for example equalized disposable income) used for computation and linearization of poverty threshold. One dimensional object convertible to one-column data.table or variable name as character, column number. Variable specified for inc is used as income_thres if income_thres is not defined.

wght_thres

Weight variable used for computation and linearization of poverty threshold. One dimensional object convertible to one-column data.table or variable name as character, column number or logical vector. Variable specified for weight is used as wght_thres if wght_thres is not defined.

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 at-risk-of-poverty threshold is done for each domain. An object convertible to data.table or variable names as character vector, column numbers as numeric vector.

period

Optional variable for survey period. If supplied, linearization of at-risk-of-poverty threshold is done for each survey period. Object convertible to data.table or variable names as character, column numbers as numeric vector.

dataset

Optional survey data object convertible to data.table.

percentage

A numeric value in range \left[ 0,100 \right] for p in the formula for at-risk-of-poverty threshold computation:

\frac{p}{100} \cdot Z_{\frac{\alpha}{100}}.

For example, to compute at-risk-of-poverty threshold equal to 60% of some income quantile, p #'should be set equal to 60.

order_quant

A numeric value in range \left[ 0,100 \right] for \alpha in the formula #'for at-risk-of-poverty threshold computation:

\frac{p}{100} \cdot Z_{\frac{\alpha}{100}}.

For example, to compute at-risk-of-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.

Details

The implementation strictly follows the Eurostat definition.

Value

A list with four objects are returned:

• quantile - a data.table containing the estimated value of the quantile used for at-risk-of-poverty threshold estimation.

• threshold - a data.table containing the estimated at-risk-of-poverty threshold.

• value - a data.table containing the estimated at-risk-of-poverty rate (in percentage).

• lin - a data.table containing the linearized variables of the at-risk-of-poverty rate (in percentage).

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, varpoord, vardcrospoor, vardchangespoor

Examples

library("data.table")
library("laeken")
data("eusilc")
dataset1 <- data.table(IDd = paste0("V", 1 : nrow(eusilc)), eusilc)
    
# Full population
d <- linarpr(Y = "eqIncome", id = "IDd",
             weight = "rb050", Dom = NULL,
             dataset = dataset1, percentage = 60,
             order_quant = 50L)
d$value
    
## Not run: 
# By domains
dd <- linarpr(Y = "eqIncome", id = "IDd",
              weight = "rb050", Dom = "db040",
              dataset = dataset1, percentage = 60,
              order_quant = 50L)
dd
## End(Not run)