Linearization of the GINI coefficient I

Description

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

Usage

1
2
3
  lingini(Y, id = NULL, weight = NULL,
          sort = NULL, Dom = NULL, period = NULL,
          dataset = NULL, var_name = "lin_gini")

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.

Value

A list with two objects are returned by the function:

value

A data.table containing the estimated Gini coefficients (in percentage) by G. Osier and Eurostat.

lin

A data.table containing the linearized variables of the Gini coefficients (in percentage) by G. Osier.

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 http://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 http://www5.statcan.gc.ca/bsolc/olc-cel/olc-cel?lang=eng&catno=12-001-X19990024882.

See Also

lingini2, linqsr, varpoord , vardcrospoor, vardchangespoor

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
data(eusilc)
dati <- data.table(IDd = 1 : nrow(eusilc), eusilc)[1 : 3,]

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

## Not run: 
# By domains
dat2 <- lingini(Y = "eqIncome", id = "IDd", weight = "rb050", Dom = c("db040"), dataset = dati)
dat2$value
## End(Not run)

Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker.