scv.2d {ineq.2d}

Function performing two-dimensional decomposition of the squared coefficient of variation (SCV)

Description

The function performs two-dimensional decomposition of the squared coefficient of variation according to Garcia-Penalosa & Orgiazzi (2013). That is, the coefficient can be decomposed by some feature that members of the studied population possess (e.g., sex, education, age) and their income source at the same time.

Usage

scv.2d(
  data,
  total,
  feature = NULL,
  sources = NULL,
  weights = NULL,
  perc = FALSE
)

Arguments

data

Data frame containing income data. Must contain at least one column with numeric values.

total

String specifying the name of the column containing data on total income.

feature

String specifying the name of the column containing information about the feature used for inequality decomposition. If left blank, total income is not decomposed by feature.

sources

Vector containing strings specifying the names of the columns with data on income sources, the sum of which must be equal to total income. If left blank, or the same value as in "total" is specified, then total income is not decomposed by income source.

weights

String specifying the name of the column containing population weights.

perc

If set to TRUE, then the function returns percentage shares of every inequality component in overall inequality. Set to FALSE by default.

Value

Data frame containing values of components of SCV.

Columns of the data frame represent values of the feature used for decomposition. There can be inequality within groups formed by this feature and between them - there are twice as much columns as values of the given feature. Whether a column contains a value of within or between-group inequality is indicated by ".W" and ".B" suffixes respectively.

Every row of the data frame represents an income source.

Thus, every value in this data frame is the contribution of inequality in income earned from i-th source by members of j-th population cohort to overall income inequality.

Remember that overall SCV, which is the sum of all values in the data frame, is always positive. However, some components of the coefficient can have negative contribution to inequality.

If all members of the studied population earn the same income, SCV normally must be equal to zero. But, scv.2d calculates "alpha," which is the absolute contribution of the given income source to overall inequality. Its calculation is impossible for identical incomes because the formula involves division by variance of income, which is zero in this case. Thus, the function will return the NaN value.

References

Garcia-Penalosa, C., & Orgiazzi, E. (2013). Factor Components of Inequality: A Cross-Country Study. Review of Income and Wealth, 59(4), 689-727.

Examples

# Load the test data set.
data("us16")

# No decomposition, just SCV of total income.
result <- scv.2d(us16, "hitotal", weights = "hpopwgt")

# Decomposition of income inequality by gender.
result <- scv.2d(us16, "hitotal", "sex", "hitotal", "hpopwgt")

# Decomposition of income inequality by gender and income source.
result <- scv.2d(us16, "hitotal", "sex", c("hilabour", "hicapital",
"hitransfer"), "hpopwgt")