# inequity: Inequity (Inequality) Measures

## Description

gini_index() gives the normalised Gini index, bonferroni_index() implements the Bonferroni index, and devergottini_index() implements the De Vergottini index.

## Usage

gini_index(x)

bonferroni_index(x)

devergottini_index(x)


## Arguments

x

numeric vector of non-negative values

## Details

These 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. They follow the Pigou-Dalton principle (are Schur-convex): setting $$x_i = x_i - h$$ and $$x_j = x_j + h$$ with $$h > 0$$ and $$x_i - h \geq x_j + h$$ (taking from the “rich” and giving to the “poor”) decreases the inequity.

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

The normalised Gini index is given by:

$G(x_1,\dots,x_n) = \frac{ \sum_{i=1}^{n} (n-2i+1) x_{\sigma(n-i+1)} }{ (n-1) \sum_{i=1}^n x_i },$

The normalised Bonferroni index is given by:

$B(x_1,\dots,x_n) = \frac{ \sum_{i=1}^{n} (n-\sum_{j=1}^i \frac{n}{n-j+1}) x_{\sigma(n-i+1)} }{ (n-1) \sum_{i=1}^n x_i }.$

The normalised De Vergottini index is given by:

$V(x_1,\dots,x_n) = \frac{1}{\sum_{i=2}^n \frac{1}{i}} \left( \frac{ \sum_{i=1}^n \left( \sum_{j=i}^{n} \frac{1}{j}\right) x_{\sigma(n-i+1)} }{\sum_{i=1}^{n} x_i} - 1 \right).$

Here, $$\sigma$$ is an ordering permutation of $$(x_1,\dots,x_n)$$.

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

## Value

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

## Author(s)

Marek Gagolewski and other contributors

## References

Bonferroni C., Elementi di Statistica Generale, Libreria Seber, Firenze, 1930.

Gagolewski M., Bartoszuk M., Cena A., Genie: A new, fast, and outlier-resistant hierarchical clustering algorithm, Information Sciences 363, 2016, pp. 8-23. doi:10.1016/j.ins.2016.05.003

Gini C., Variabilita e Mutabilita, Tipografia di Paolo Cuppini, Bologna, 1912.

The official online manual of genieclust at https://genieclust.gagolewski.com/

Gagolewski M., genieclust: Fast and robust hierarchical clustering, SoftwareX 15:100722, 2021, doi:10.1016/j.softx.2021.100722.

## Examples

gini_index(c(2, 2, 2, 2, 2))   # no inequality
## [1] 0
gini_index(c(0, 0, 10, 0, 0))  # one has it all
## [1] 1
gini_index(c(7, 0, 3, 0, 0))   # give to the poor, take away from the rich
## [1] 0.85
gini_index(c(6, 0, 3, 1, 0))   # (a.k.a. Pigou-Dalton principle)
## [1] 0.75
bonferroni_index(c(2, 2, 2, 2, 2))
## [1] 0
bonferroni_index(c(0, 0, 10, 0, 0))
## [1] 1
bonferroni_index(c(7, 0, 3, 0, 0))
## [1] 0.90625
bonferroni_index(c(6, 0, 3, 1, 0))
## [1] 0.8333333
devergottini_index(c(2, 2, 2, 2, 2))
## [1] 0
devergottini_index(c(0, 0, 10, 0, 0))
## [1] 1
devergottini_index(c(7, 0, 3, 0, 0))
## [1] 0.7662338
devergottini_index(c(6, 0, 3, 1, 0))
## [1] 0.6493506