Measure income and wealth inequality in R. Gini coefficients with confidence intervals, Theil indices, Atkinson indices, Palma ratios, Lorenz curves, poverty measures, tax progressivity, and more. All functions accept optional survey weights, and every measure now exposes bootstrap confidence intervals via ci = TRUE.

This release responds to feedback from Frank Cowell and Emmanuel Flachaire on the v0.1.0 release.

• Bootstrap CIs on every measure. iq_theil, iq_atkinson, iq_sgini, iq_palma, iq_hoover, iq_kolm, iq_percentile_ratio, iq_polarisation, iq_shares, iq_concentration, iq_kakwani, and iq_poverty all gain ci, R, and level arguments. iq_compare() runs a single bootstrap loop and propagates CIs to every row.
• Negative values supported. Gini, S-Gini, top shares, Palma, Hoover, Wolfson, Kakwani, and iq_compare() accept negatives = "keep" for distributions containing negatives (wealth, post-tax income).
• Raffinetti et al. (2017) normalised Gini. iq_gini(x, negatives = "keep", normalised = TRUE) rescales the index back into [0, 1] when negatives are present.
• Wagstaff correction added to iq_concentration alongside the existing Erreygers correction.
• iq_kakwani bug fix. The post-tax Gini no longer takes the absolute value of negative net incomes, so the Reynolds-Smolensky index is honest under high tax burdens.

# install.packages("devtools")
devtools::install_github("charlescoverdale/inequality")

The main CRAN package for inequality measurement (ineq) was last updated in 2014. It computes basic Gini and Theil indices but has no support for survey weights, no confidence intervals, no subgroup decomposition, no poverty measures, no Palma ratio, and no tax progressivity analysis.

inequality fills these gaps. You bring income or wealth data from any source (household surveys, tax records, administrative data, simulations) and the package handles measurement, decomposition, and comparison. Every function accepts optional weights, returns a clean S3 object with a print method, and cites the underlying academic paper.

library(inequality)

# Built-in sample data: 1000 synthetic incomes
d <- iq_sample_data("income")

# How unequal is this distribution?
iq_gini(d$income)
#> -- Gini Coefficient --
#> * Gini: 0.43
#> * Observations: 1000

# With confidence intervals
iq_gini(d$income, ci = TRUE)
#> -- Gini Coefficient --
#> * Gini: 0.43
#> * Observations: 1000
#> * Bootstrap 95% CI: [0.4085, 0.453]

iq_compare(d$income)
#> -- Inequality Comparison (n = 1000) --
#> * Gini              0.4300
#> * Theil T (GE1)     0.3307
#> * Theil L (GE0)     0.3241
#> * Atkinson (e=0.5)  0.1506
#> * Atkinson (e=1.0)  0.2768
#> * Palma ratio       2.1668
#> * Hoover            0.3126
#> * P90/P10           7.83
#> * P80/P20           3.92

iq_shares(d$income)
#> -- Income Shares --
#> * Bottom 50%: 21.2% of income (50% of population)
#> * P50-P90:    47.1% of income (40% of population)
#> * P90-P99:    24.0% of income (9% of population)
#> * Top 1%:      6.6% of income (1% of population)

d <- iq_sample_data("grouped")
iq_decompose(d$income, d$group)
#> -- Between-Within Decomposition (Theil T (GE(1))) --
#> * Total: 0.463
#> * Between: 0.116 (25.1%)
#> * Within: 0.347 (74.9%)

d <- iq_sample_data("income")
iq_poverty(d$income, line = 20000)
#> -- Poverty Measures (line = 20000) --
#> * Headcount (FGT0): 18.2%
#> * Poverty gap (FGT1): 0.0521
#> * Severity (FGT2): 0.0198
#> * Sen index: 0.0912
#> * Watts index: 0.0634

pre_tax <- d$income
tax <- pre_tax * (0.10 + 0.15 * (pre_tax / max(pre_tax)))
iq_kakwani(pre_tax, tax)
#> -- Fiscal Progressivity (Progressive) --
#> * Kakwani index: 0.1832
#> * Reynolds-Smolensky index: 0.0421

# Lorenz curve
lc <- iq_lorenz(d$income)
plot(lc)

# Growth incidence curve
panel <- iq_sample_data("panel")
gic <- iq_growth_incidence(panel$income_t0, panel$income_t1)
plot(gic)

Report bugs or request features at GitHub Issues.

inequality, Gini, Theil, Atkinson, Kolm, Lorenz, Palma, Kakwani, poverty, FGT, decomposition, income distribution, wealth, survey weights, economics, progressivity