# inequity: Inequity (Inequality) Measures¶

## Description¶

gini_index() gives the normalised Gini index and bonferroni_index() implements the Bonferroni index.

## Usage¶

gini_index(x)

bonferroni_index(x)


## Arguments¶

 x numeric vector of non-negative values

## Details¶

Both indices can be used to quantify the “inequity” of a numeric sample. They can be perceived as measures of data dispersion. For constant vectors (perfect equity), the indices yield values of 0. Vectors with all elements but one equal to 0 (perfect inequity), are assigned scores of 1. Both indices follow the Pigou-Dalton principle (are Schur-convex): setting x_i = x_i - h and x_j = x_j + h with h > 0 and x_i - h ≥q x_j + h (taking from the “rich” and giving to the “poor”) decreases the inequity.

These indices have applications in economics, amongst others. The Gini clustering algorithm uses the Gini index as a measure of the inequality of cluster sizes.

The normalised Gini index is given by:

G(x_1,…,x_n) = \frac{ ∑_{i=1}^{n-1} ∑_{j=i+1}^n |x_i-x_j| }{ (n-1) ∑_{i=1}^n x_i }.

The normalised Bonferroni index is given by:

B(x_1,…,x_n) = \frac{ ∑_{i=1}^{n} (n-∑_{j=1}^i \frac{n}{n-j+1}) x_{σ(n-i+1)} }{ (n-1) ∑_{i=1}^n x_i }.

Time complexity: O(n) for sorted (increasingly) data. Otherwise, the vector will be sorted.

In particular, for ordered inputs, it holds:

G(x_1,…,x_n) = \frac{ ∑_{i=1}^{n} (n-2i+1) x_{σ(n-i+1)} }{ (n-1) ∑_{i=1}^n x_i },

where σ is an ordering permutation of (x_1,…,x_n).

## Value¶

The value of the inequity index, a number in [0, 1].

## Examples¶

gini_index(c(2, 2, 2, 2, 2))  # no inequality
gini_index(c(0, 0, 10, 0, 0)) # one has it all
gini_index(c(7, 0, 3, 0, 0))  # give to the poor, take away from the rich
gini_index(c(6, 0, 3, 1, 0))  # (a.k.a. Pigou-Dalton principle)
bonferroni_index(c(2, 2, 2, 2, 2))
bonferroni_index(c(0, 0, 10, 0, 0))
bonferroni_index(c(7, 0, 3, 0, 0))
bonferroni_index(c(6, 0, 3, 1, 0))