# Lorenz curve

Not to be confused with Lorentzian distribution.

In economics, the Lorenz curve is a graphical representation of the cumulative distribution function of the empirical probability distribution of wealth, and was developed by Max O. Lorenz in 1905 for representing inequality of the wealth distribution.

The curve is a graph showing the proportion of the distribution assumed by the bottom y% of the values, although this is not rigorously true for a finite population (see below). It is often used to represent income distribution, where it shows for the bottom x% of households, what percentage y% of the total income they have. The percentage of households is plotted on the x-axis, the percentage of income on the y-axis. It can also be used to show distribution of assets. In such use, many economists consider it to be a measure of social inequality.

The concept is useful in describing inequality among the size of individuals in ecology,[1] and in studies of biodiversity, where cumulative proportion of species is plotted against cumulative proportion of individuals.[2] It is also useful in business modeling: e.g., in consumer finance, to measure the actual delinquency Y% of the X% of people with worst predicted risk scores.

A typical Lorenz curve

## Explanation

Points on the Lorenz curve represent statements like "the bottom 20% of all households have 10% of the total income." (see Pareto principle).

A perfectly equal income distribution would be one in which every person has the same income. In this case, the bottom "N"% of society would always have "N"% of the income. This can be depicted by the straight line y = x; called the "line of perfect equality."

By contrast, a perfectly unequal distribution would be one in which one person has all the income and everyone else has none. In that case, the curve would be at "y" = 0 for all "x" < 100%, and "y" = 100% when "x" = 100%. This curve is called the "line of perfect inequality."

The Gini coefficient is the area between the line of perfect equality and the observed Lorenz curve, as a percentage of the area between the line of perfect equality and the line of perfect inequality. The higher the coefficient, the more unequal the distribution is. In the diagram on the right, this is given by the ratio A/(A+B), where A and B are the indicated areas.

## Definition and calculation

The Lorenz curve can usually be represented by a function L(F), where F, the cumulative portion of the population, is represented by the horizontal axis, and L, the cumulative portion of the total wealth or income, is represented by the vertical axis.

For a population of size n, with a sequence of values yi, i = 1 to n, that are indexed in non-decreasing order ( yiyi+1), the Lorenz curve is the continuous piecewise linear function connecting the points ( Fi, Li ), i = 0 to n, where F0 = 0, L0 = 0, and for i = 1 to n:

$F_i = i/n\,$
$S_i = \Sigma_{j=1}^i \; y_j\,$
$L_i = S_i / S_n \,$

Note that the statement that the Lorenz curve gives the portion of the wealth or income held by a given portion of the population is only strictly true at the points defined above, but not at the points on the line segments between these points. For instance, in a population of 10 households, it doesn't make sense to say that 45% of them earn a certain portion of the total. If the population is modeled as a continuum then this subtlety disappears.

For a discrete probability function f(y), let yi, i = 1 to n, be the points with non-zero probabilities indexed in increasing order ( yi < yi+1). The Lorenz curve is the continuous piecewise linear function connecting the points ( Fi, Li ), i = 0 to n, where F0 = 0, L0 = 0, and for i = 1 to n:

$F_i = \Sigma_{j=1}^i \; f(y_j)\,$
$S_i = \Sigma_{j=1}^i \; f(y_j)\,y_j\,$
$L_i = S_i / S_n \,$

For a probability density function f(x) with the cumulative distribution function F(x), the Lorenz curve L(F(x)) is given by:

$L(F(x))=\frac{\int_{-\infty}^{x} t\,f(t)\,dt}{\int_{-\infty}^\infty t\,f(t)\,dt} =\frac{\int_{-\infty}^{x} t\,f(t)\,dt}{\mu}$

where $\mu$ denotes the average.

For a cumulative distribution function F(x) with inverse x(F), the Lorenz curve L(F) is given by:

$L(F)=\frac{\int_0^F x(F_1)\,dF_1}{\int_0^1 x(F_1)\,dF_1}$

The inverse x(F) may not exist because the cumulative distribution function has intervals of constant values. However, the previous formula can still apply by generalizing the definition of x(F):

x(F1) = inf {y : F(y) ≥ F1}

For an example of a Lorenz curve, see Pareto distribution.

## Properties

A practical example of a Lorenz curve: the Lorenz curves of Denmark, Hungary, and Namibia

A Lorenz curve always starts at (0,0) and ends at (1,1).

The Lorenz curve is not defined if the mean of the probability distribution is zero or infinite.

The Lorenz curve for a probability distribution is a continuous function. However, Lorenz curves representing discontinuous functions can be constructed as the limit of Lorenz curves of probability distributions, the line of perfect inequality being an example.

The information in a Lorenz curve may be summarized by the Gini coefficient and the Lorenz asymmetry coefficient.[1]

The Lorenz curve cannot rise above the line of perfect equality. If the variable being measured cannot take negative values, the Lorenz curve:

Note however that a Lorenz curve for net worth would start out by going negative due to the fact that some people have a negative net worth because of debt.

The Lorenz curve is invariant under positive scaling. If X is a random variable, for any positive number c the random variable c X has the same Lorenz curve as X.

The Lorenz curve is flipped twice, once about F = 0.5 and once about L = 0.5, by negation. If X is a random variable with Lorenz curve LX(F), then −X has the Lorenz curve:

L X = 1 − L X (1 − F)

The Lorenz curve is changed by translations so that the equality gap F − L(F) changes in proportion to the ratio of the original and translated means. If X is a random variable with a Lorenz curve L X (F) and mean μ X , then for any constant c ≠ −μ X , X + c has a Lorenz curve defined by:

$F - L_{X+c}(F) = \frac{\mu_X}{\mu_X + c} ( F - L_X(F))\,$

For a cumulative distribution function F(x) with mean μ and (generalized) inverse x(F), then for any F with 0 < F < 1 :

• If the Lorenz curve is differentiable:
$\frac{d L(F)}{d F} = \frac{x(F)}{\mu}$
• If the Lorenz curve is twice differentiable, then the probability density function f(x) exists at that point and:
$\frac{d^2 L(F)}{d F^2} = \frac{1}{\mu\,f(x(F))}\,$
• If L(F) is continuously differentiable, then the tangent of L(F) is parallel to the line of perfect equality at the point F(μ). This is also the point at which the equality gap F − L(F), the vertical distance between the Lorenz curve and the line of perfect equality, is greatest. The size of the gap is equal to half of the relative mean deviation:
$F(\mu) - L(F(\mu)) = \frac{\text{mean deviation}}{2\,\mu}$