# Mean log deviation

In statistics and econometrics, the mean log deviation (MLD) is a measure of income inequality. The MLD is zero when everyone has the same income, and takes larger positive values as incomes become more unequal, especially at the high end.

## Definition

The MLD of household income has been defined as

$\mathrm {MLD} ={\frac {1}{N}}\sum _{i=1}^{N}\ln {\frac {\overline {x}}{x_{i}}}$ where N is the number of households, $x_{i}$ is the income of household i, and ${\overline {x}}$ is the mean of $x_{i}$ . Naturally the same formula can be used for positive variables other than income and for units of observation other than households.

Equivalent definitions are

$\mathrm {MLD} ={\frac {1}{N}}\sum _{i=1}^{N}(\ln {\overline {x}}-\ln x_{i})=\ln {\overline {x}}-{\overline {\ln x}}$ where ${\overline {\ln x}}$ is the mean of ln(x). The last definition shows that MLD is nonnegative, since $\ln {\overline {x}}\geq {\overline {\ln x}}$ by Jensen's inequality.

MLD has been called "the standard deviation of ln(x)", (SDL) but this is not correct. The SDL is

$\mathrm {SDL} ={\sqrt {{\frac {1}{N}}\sum _{i=1}^{N}(\ln x_{i}-{\overline {\ln x}})^{2}}}$ and this is not equal to the MLD. For example, for the standard lognormal distribution, MLD = 1/2 but SDL = 1.

## Related statistics

The MLD is a special case of the generalized entropy index. Specifically, the MLD is the generalized entropy index with α=0.