Linearization of the gender pay (wage) gap.

Description

Estimation of gender pay (wage) gap and computation of linearized variables for variance estimation.

Usage

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lingpg(Y, gender = NULL, id = NULL, 
       weight = NULL, sort = NULL, Dom = NULL,
       period = NULL, dataset = NULL,
       var_name = "lin_gpg")

Arguments

Y

Study variable (for example the gross hourly earning). One dimensional object convertible to one-column data.table or variable name as character, column number.

gender

Numerical variable for gender, where 1 is for males, but 2 is for females. 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, estimation and linearization of gender pay (wage) gap 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, estimation and linearization of gender pay (wage) gap 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:

value

data.table containing the estimated gender pay (wage) gap (in percentage).

lin

data.table containing the linearized variables of the gender pay (wage) gap (in percentage) for variance estimation.

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

linqsr, lingini, varpoord , vardcrospoor, vardchangespoor

Examples

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data(ses)

dati <- data.table(ID = 1 : nrow(ses), ses)
setnames(dati, "sex", "sexf")
dati[sexf == "male", sex:= 1]
dati[sexf == "female", sex:= 2]


# Full population
gpgs1 <- lingpg(Y = "earningsHour", gender = "sex",
                id = "ID", weight = "weights",
                dataset = dati)
gpgs1$value

## Not run: 
# Domains by education
gpgs2 <- lingpg(Y = "earningsHour", gender = "sex",
                id = "ID", weight = "weights",
                Dom = "education", dataset = dati)
gpgs2$value

# Sort variable
gpgs3 <- lingpg(Y = "earningsHour", gender = "sex",
                id = "ID", weight = "weights",
                sort = "ID", Dom = "education",
                dataset = dati)
gpgs3$value

# Two survey periods
dati[, year := 2010]
dati2 <- copy(dati)
dati2[, year := 2011]
dati <- rbind(dati, dati2)
gpgs4 <- lingpg(Y = "earningsHour", gender = "sex",
                id = "ID", weight = "weights", 
                sort = "ID", Dom = "education",
                period = "year", dataset = dati)
gpgs4$value
names(gpgs4$lin)

## End(Not run)

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