vardpoor (version 0.2.0.8.1)

linpoormed: Linearization of the median income below the at-risk-of-poverty gap

Description

Estimate the relative median at-risk-of-poverty gap, which is defined as the relative difference between the median equivalized disposable income of persons below the at-risk-of-poverty threshold and the at-risk-of-poverty threshold itself (expressed as a percentage of the at-risk-of-poverty threshold) and its linearization.

Usage

linpoormed(inc, id, weight=NULL, sort=NULL, Dom=NULL,
              period=NULL, dataset = NULL, percentage=60,
              order_quant=50, na.rm=FALSE, var_name="lin_poormed")

Arguments

inc

either a numeric vector, 1 column data.frame, matrix, data.table giving the equivalized disposable income, or (if dataset is not NULL) a character string, an integer or a logical vector (length is the same as 'dataset

id

optional; either 1 column data.frame, matrix, data.table with column names giving the personal IDs, or (if dataset is not NULL) a character string, an integer or a logical vector (length is the same as 'dataset' colum

weight

optional; either a numeric vector, 1 column data.frame, matrix, data.table giving the personal sample weights, or (if dataset is not NULL) a character string, an integer or a logical vector (length is the same as 'dat

sort

optional; either a numeric vector, 1 column data.frame, matrix, data.table giving the personal IDs to be used as tie-breakers for sorting, or (if dataset is not NULL) a character string, an integer or a logical vector

Dom

optional; either a data.frame, matrix, data.table matrix with column names giving different domains, or (if dataset is not NULL) character strings, integers or a logical vectors (length is the same as 'dataset' column

period

optional; either a data.frame, matrix, data.table with column names giving different periods, or (if dataset is not NULL) character strings, integers or a logical vectors (length is the same as 'dataset' column coun

dataset

an optional; name of the individual dataset data.frame.

percentage

a numeric value in $[0,100]$ giving the percentage of the income quantile to be used for the at-risk-of-poverty threshold (see linarpt).

order_quant

a numeric value in $[0,100]$ giving the order of the income quintale (in percentage) to be used for the at-risk-of-poverty threshold (see linarpt).

na.rm

a logical indicating whether missing values should be removed.

var_name

a character string specifying the name of the linearized variable.

Value

• The function returns two values:
• value
  a data.frame containing the estimate(s) the gini coeffients (in percentage) by domain, or (if Dom is NULL) totals, using Eurostat definition definition.
• lin
  a data.frame containing the values of linearized variables of the GINI coefficient (in precentage) by domains or (if Dom is NULL) totals.

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. Deville, J. C. (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

linarpt, varpoord, var_srs

Examples

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

d<-linpoormed("eqIncome", id="IDd", weight = "rb050", Dom = NULL,
         dataset = dati, percentage = 60, order_quant=50, na.rm = FALSE)

dd<-linpoormed("eqIncome", id="IDd", weight = "rb050", Dom = "db040",
         dataset = dati, percentage = 60, order_quant=50, na.rm = FALSE)