vardpoor (version 0.20.1)

lingini2: Linearization of the Gini coefficient II

Description

Estimate the Gini coefficient, which is a measure for inequality, and its linearization.

Usage

lingini2(
  Y,
  id = NULL,
  weight = NULL,
  sort = NULL,
  Dom = NULL,
  period = NULL,
  dataset = NULL,
  var_name = "lin_gini2",
  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.

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 Gini 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 Gini 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.

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.

Value

A list with two objects are returned by the function:

• value - a data.table containing the estimated Gini coefficients (in percentage) by Langel and Tille (2012) and Eurostat.

• lin - a data.table containing the linearized variables of the Gini coefficients (in percentage) by Langel and Tille (2012).

References

Eric Graf, Yves Tille, Variance Estimation Using Linearization for Poverty and Social Exclusion Indicators, Survey Methodology, June 2014 61 Vol. 40, No. 1, pp. 61-79, Statistics Canada, Catalogue no. 12-001-X, URL https://www150.statcan.gc.ca/n1/pub/12-001-x/12-001-x2014001-eng.pdf 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. Matti Langel, Yves Tille, Corrado Gini, a pioneer in balanced sampling and inequality theory. Metron - International Journal of Statistics, 2011, vol. LXIX, n. 1, pp. 45-65, URL http://dx.doi.org/10.1007/BF03263549. 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.

See Also

lingini, linqsr, varpoord, vardcrospoor, vardchangespoor

Examples

# NOT RUN {
library("data.table")
library("laeken")
data("eusilc")
dataset1 <- data.table(IDd = paste0("V", 1 : nrow(eusilc)), eusilc)
    
# Full population
dat1 <- lingini2(Y = "eqIncome", id = "IDd",
                 weight = "rb050",  dataset = dataset1)
dat1$value
    
# }
# NOT RUN {
# By domains
dat2 <- lingini2(Y = "eqIncome", id = "IDd",
                 weight = "rb050", Dom = c("db040"),
                 dataset = dataset1)
dat2$value
# }
# NOT RUN {

# }