# inequality¶

Implements inequality and segregation measures such as Gini, Lorenz Curve

quantecon.inequality.gini_coefficient[source]

Implements the Gini inequality index

Parameters: y : array_like(float) Array of income/wealth for each individual. Ordered or unordered is fine Gini index: float The gini index describing the inequality of the array of income/wealth

References

https://en.wikipedia.org/wiki/Gini_coefficient

quantecon.inequality.lorenz_curve[source]

Calculates the Lorenz Curve, a graphical representation of the distribution of income or wealth.

It returns the cumulative share of people (x-axis) and the cumulative share of income earned.

Parameters: y : array_like(float or int, ndim=1) Array of income/wealth for each individual. Unordered or ordered is fine. cum_people : array_like(float, ndim=1) Cumulative share of people for each person index (i/n) cum_income : array_like(float, ndim=1) Cumulative share of income for each person index

References

Examples

>>> a_val, n = 3, 10_000
>>> y = np.random.pareto(a_val, size=n)
>>> f_vals, l_vals = lorenz(y)

quantecon.inequality.rank_size(data, c=1.0)[source]

Generate rank-size data corresponding to distribution data.

Parameters: data : array_like the set of observations c : int or float restrict plot to top (c x 100)% of the distribution rank_data : array_like(float, ndim=1) Location in the population when sorted from smallest to largest size_data : array_like(float, ndim=1) Size data for top (c x 100)% of the observations

Examples

>>> y = np.exp(np.random.randn(1000))  # simulate data
>>> rank_data, size_data = rank_size(y, c=0.85)

quantecon.inequality.shorrocks_index(A)[source]

Implements Shorrocks mobility index

Parameters: A : array_like(float) Square matrix with transition probabilities (mobility matrix) of dimension m Shorrocks index: float The Shorrocks mobility index calculated as $s(A) = \frac{m - \sum_j a_{jj} }{m - 1} \in (0, 1)$ An index equal to 0 indicates complete immobility.

References

  Wealth distribution and social mobility in the US: A quantitative approach (Benhabib, Bisin, Luo, 2017). https://www.econ.nyu.edu/user/bisina/RevisionAugust.pdf