vardpoor (version 0.3.6)

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")

Arguments

Y

Study variable (for example equalized disposable income). One dimensional object convertible to one-column data.frame or variable name as character, column number or logical vector with only one TRUE value (length of the vector h

id

Optional variable for unit ID codes. One dimensional object convertible to one-column data.frame or variable name as character, column number or logical vector with only one TRUE value (length of the vector has to be the same as

weight

Optional weight variable. One dimensional object convertible to one-column data.frame or variable name as character, column number or logical vector with only one TRUE value (length of the vector has to be the same as the column

sort

Optional variable to be used as tie-breaker for sorting. One dimensional object convertible to one-column data.frame or variable name as character, column number or logical vector with only one TRUE value (length of the vector ha

Dom

Optional variables used to define population domains. If supplied, linearization of the GINI is done for each domain. An object convertible to data.frame or variable names as character vector, column numbers or logical vector (length of the v

period

Optional variable for survey period. If supplied, linearization of the GINI is done for each time period. Object convertible to data.frame or variable names as character, column numbers or logical vector (length of the vector has to be the sa

dataset

Optional survey data object convertible to data.frame.

var_name

A character specifying the name of the linearized variable.

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 and 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 http://www.statcan.gc.ca/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 http://www5.statcan.gc.ca/bsolc/olc-cel/olc-cel?lang=eng&catno=12-001-X19990024882. 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 ftp://metron.sta.uniroma1.it/RePEc/articoli/2011-1-3.pdf. 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

data(eusilc)
dati <- data.frame(1:nrow(eusilc),eusilc)
colnames(dati)[1] <- "IDd"

# Full population
dat1 <- lingini2(Y="eqIncome", id="IDd", weight = "rb050",  dataset=dati)
dat1$value

# By domains
dat2 <- lingini2(Y="eqIncome", id="IDd", weight = "rb050", Dom=c("db040"), dataset=dati)
dat2$value