Hoover index (Robin Hood index) — iq

Computes the Hoover index, also known as the Robin Hood index or the Schutz coefficient. It equals the maximum proportion of total income that would need to be redistributed to achieve perfect equality, or equivalently, half the mean absolute deviation divided by the mean.

Usage

iq_hoover(
  x,
  weights = NULL,
  na.rm = FALSE,
  ci = FALSE,
  R = 1000L,
  level = 0.95,
  negatives = c("error", "keep")
)

Arguments

x: Numeric vector of incomes.
weights: Optional numeric vector of survey weights.
na.rm: Logical. Remove NA values? Default FALSE.
ci: Logical. Compute bootstrap confidence intervals? Default FALSE.
R: Integer. Number of bootstrap replicates. Default 1000.
level: Numeric. Confidence level. Default 0.95.
negatives: Character. "error" (default) aborts on negatives; "keep" permits them.

Value

An S3 object of class "iq_hoover" with elements:

value: Numeric. The Hoover index (0 to 1 with non-negative input).
n: Integer. Number of observations.
se, ci_lower, ci_upper, level: Bootstrap CI fields, NULL unless ci = TRUE.

Examples

d <- iq_sample_data("income")
iq_hoover(d$income)
#> 
#> ── Hoover Index ────────────────────────────────────────────────────────────────
#> • Value: 0.3126
#> • Observations: 1000

# With bootstrap CIs
iq_hoover(d$income, ci = TRUE, R = 200)
#> 
#> ── Hoover Index ────────────────────────────────────────────────────────────────
#> • Value: 0.3126
#> • Observations: 1000
#> • Bootstrap 95% CI: [0.2944, 0.3322]

# Perfect equality
iq_hoover(rep(100, 50))
#> 
#> ── Hoover Index ────────────────────────────────────────────────────────────────
#> • Value: 0
#> • Observations: 50