Source code for ineqpy.inequality

#!/usr/bin/env python

"""Analysis of inequality.

This package provide an easy way to realize a quantitative analysis of
grouped, also make easy work with stratified data, in this module you can
find statistics and grouped indicators to this task.

Todo
----
- Rethinking this module as Class.
- https://en.wikipedia.org/wiki/Income_inequality_metrics

"""
import numpy as np
import pandas as pd
from .statistics import mean
from . import utils


__all__ = [
    "atkinson",
    "avg_tax_rate",
    "concentration",
    "gini",
    "kakwani",
    "lorenz",
    "reynolds_smolensky",
    "theil",
    "top_rest",
    "hoover",
]


[docs]def concentration(income, weights=None, data=None, sort=True): """Calculate concentration's index. This function calculate the concentration index, according to the notation used in [Jenkins1988]_ you can calculate the: C_x = 2 / x · cov(x, F_x) if x = g(x) then C_x becomes C_y when there are taxes: y = g(x) = x - t(x) Parameters ---------- income : array-like weights : array-like data : pandas.DataFrame sort : bool If true, will sort the values. Returns ------- concentration : array-like References ---------- Jenkins, S. (1988). Calculating income distribution indices from micro-data. National Tax Journal. http://doi.org/10.2307/41788716 """ # TODO complete docstring # check if DataFrame is passed, if yes then extract variables else make a # copy income, weights = utils.extract_values(data, income, weights) if weights is None: weights = utils.not_empty_weights(weights, like=income) if income.ndim == 0: income = np.array([income]) elif income.ndim == 2: income = np.squeeze(income, axis=1) if weights.ndim == 0: weights = np.array([weights]) elif weights.ndim == 2: weights = np.squeeze(weights, axis=1) # Small shortcut to avoid warnings below if income.size <= 1: return np.nan # if sort is true then sort the variables. if sort: income, weights = utils._sort_values(income, weights) # main calc f_x = np.atleast_1d(utils.normalize(weights)) F_x = f_x.cumsum(axis=0) mu = np.sum(income * f_x) cov = np.cov(income, F_x, rowvar=False, aweights=f_x)[0, 1] return 2 * cov / mu
[docs]def lorenz(income, weights=None, data=None): """Calculate Lorent's curve. In economics, the Lorenz curve is a graphical representation of the distribution of income or of wealth. It was developed by Max O. Lorenz in 1905 for representing grouped of the wealth distribution. This function compute the lorenz curve and returns a DF with two columns of axis x and y. Parameters ---------- data : pandas.DataFrame A pandas.DataFrame that contains data. income : str or 1d-array, optional Population or wights, if a DataFrame is passed then `income` should be a name of the column of DataFrame, else can pass a pandas.Series or array. weights : str or 1d-array Income, monetary variable, if a DataFrame is passed then `y`is a name of the series on this DataFrame, however, you can pass a pd.Series or np.array. Returns ------- lorenz : pandas.Dataframe Lorenz distribution in a Dataframe with two columns, labeled x and y, that corresponds to plots axis. References ---------- Lorenz curve. (2017, February 11). In Wikipedia, The Free Encyclopedia. Retrieved 14:34, May 15, 2017, from https://en.wikipedia.org/w/index.php?title=Lorenz_curve&oldid=764853675 """ if data is not None: income, weights = utils.extract_values(data, income, weights) if weights is None: weights = utils.not_empty_weights(weights, like=income) total_income = income * weights idx_sort = np.argsort(income) weights = weights[idx_sort].cumsum() / weights.sum() weights = weights.reshape(len(weights), 1) total_income = total_income[idx_sort].cumsum() / total_income.sum() total_income = total_income.reshape(len(total_income), 1) # to pandas data = np.hstack([weights, total_income]) columns = ["Equality", "Income"] index = pd.Index(weights.round(3).squeeze()) res = pd.DataFrame(data=data, columns=columns, index=index) res.index.name = "x" return res
[docs]def gini(income, weights=None, data=None, sort=True): """Calculate Gini's index. The Gini coefficient (sometimes expressed as a Gini ratio or a normalized Gini index) is a measure of statistical dispersion intended to represent the income or wealth distribution of a nation's residents, and is the most commonly used measure of grouped. It was developed by Corrado Gini. The Gini coefficient measures the grouped among values of a frequency distribution (for example, levels of income). A Gini coefficient of zero expresses perfect equality, where all values are the same (for example, where everyone has the same income). A Gini coefficient of 1 (or 100%) expresses maximal grouped among values (e.g., for a large number of people, where only one person has all the income or consumption, and all others have none, the Gini coefficient will be very nearly one). Parameters ---------- data : pandas.DataFrame DataFrame that contains the data. income : str or np.array, optional Name of the monetary variable `x` in` df` weights : str or np.array, optional Name of the series containing the weights `x` in` df` sorted : bool, optional If the DataFrame is previously ordered by the variable `x`, it's must pass True, but False by default. Returns ------- gini : float Gini Index Value. Notes ----- The calculation is done following (discrete probability distribution): G = 1 - [∑_i^n f(y_i)·(S_{i-1} + S_i)] where: - y_i = Income - S_i = ∑_{j=1}^i y_i · f(y_i) Reference --------- - Gini coefficient. (2017, May 8). In Wikipedia, The Free Encyclopedia. Retrieved 14:30, May 15, 2017, from https://en.wikipedia.org/w/index.php?title=Gini_coefficient&oldid=779424616 - Jenkins, S. (1988). Calculating income distribution indices from micro-data. National Tax Journal. http://doi.org/10.2307/41788716 TODO ---- - Implement statistical deviation calculation, VAR (GINI) """ return concentration(data=data, income=income, weights=weights, sort=sort)
[docs]def atkinson(income, weights=None, data=None, e=0.5) -> float: """Calculate atkinson index. More precisely labelled a family of income grouped measures, the theoretical range of Atkinson values is 0 to 1, with 0 being a state of equal distribution. An intuitive interpretation of this index is possible: Atkinson values can be used to calculate the proportion of total income that would be required to achieve an equal level of social welfare as at present if incomes were perfectly distributed. For example, an Atkinson index value of 0.20 suggests that we could achieve the same level of social welfare with only 1 – 0.20 = 80% of income. The theoretical range of Atkinson values is 0 to 1, with 0 being a state of equal distribution. Parameters ---------- income : array or str If `data` is none `income` must be an 1D-array, when `data` is a pd.DataFrame, you must pass the name of income variable as string. weights : array or str, optional If `data` is none `weights` must be an 1D-array, when `data` is a pd.DataFrame, you must pass the name of weights variable as string. e : int, optional Epsilon parameter interpreted by atkinson index as grouped adversion, must be between 0 and 1. data : pd.DataFrame, optional data is a pd.DataFrame that contains the variables. Returns ------- atkinson : float Reference --------- Atkinson index. (2017, March 12). In Wikipedia, The Free Encyclopedia. Retrieved 14:35, May 15, 2017, from https://en.wikipedia.org/w/index.php?title=Atkinson_index TODO ---- - Implement: CALCULATING INCOME DISTRIBUTION INDICES FROM MICRO-DATA http://www.jstor.org/stable/41788716 - The results has difference with stata, maybe have a bug. """ if (income is None) and (data is None): raise ValueError("Must pass at least one of both `income` or `df`") income, weights = utils.extract_values(data, income, weights) weights = utils.not_empty_weights(weights, income) # not-null condition income, weights = utils.not_null_condition(income, weights) # not-empty condition if len(income) == 0: return 0 # auxiliar variables: mean and distribution mu = mean(variable=income, weights=weights) f_i = np.atleast_1d(weights / sum(weights)) # density function # main calc if e == 1: return 1 - np.power(np.e, np.sum(f_i * np.log(income) - np.log(mu))) elif e >= 0 or e < 1: return 1 - np.power( np.sum(f_i * np.power(income / mu, 1 - e)), 1 / (1 - e) ) else: assert (e < 0) or (e > 1), "Not valid e value, 0 ≤ e ≤ 1" return np.nan
[docs]def kakwani(tax, income_pre_tax, weights=None, data=None): """Calculate Kakwani's index. The Kakwani (1977) index of tax progressivity is defined as twice the area between the concentration curves for taxes and pre-tax income, or equivalently, the concentration index for t(x) minus the Gini index for x, i.e. K = C(t) - G(x) = (2/t) cov [t(x), F(x)] - (2/x) cov [x, F(x)]. Parameters ---------- data : pandas.DataFrame This variable is a DataFrame that contains all data required in columns. tax_variable : array-like or str This variable represent tax payment of person, if pass array-like then data must be None, else you pass str-name column in `data`. income_pre_tax : array-like or str This variable represent income of person, if pass array-like then data must be None, else you pass str-name column in `data`. weights : array-like or str This variable represent weights of each person, if pass array-like then data must be None, else you pass str-name column in `data`. Returns ------- kakwani : float References ---------- Jenkins, S. (1988). Calculating income distribution indices from micro-data. National Tax Journal. http://doi.org/10.2307/41788716 """ # main calc c_t = concentration(data=data, income=tax, weights=weights, sort=True) g_y = concentration( data=data, income=income_pre_tax, weights=weights, sort=True ) return c_t - g_y
[docs]def reynolds_smolensky( income_pre_tax, income_post_tax, weights=None, data=None ): """Calculate Reynolds-Smolensky's index. The Reynolds-Smolensky (1977) index of the redistributive effect of taxes, which can also be interpreted as an index of progressivity (Lambert 1985), is defined as: L = Gx - Gy = [2/x]cov[x,F(x)] - [2/ybar] cov [y, F(y)]. Parameters ---------- data : pandas.DataFrame This variable is a DataFrame that contains all data required in it's columns. income_pre_tax : array-like or str This variable represent tax payment of person, if pass array-like then data must be None, else you pass str-name column in `data`. income_post_tax : array-like or str This variable represent income of person, if pass array-like then data must be None, else you pass str-name column in `data`. weights : array-like or str This variable represent weights of each person, if pass array-like then data must be None, else you pass str-name column in `data`. Returns ------- reynolds_smolensky : float References ---------- Jenkins, S. (1988). Calculating income distribution indices from micro-data. National Tax Journal. http://doi.org/10.2307/41788716 """ g_y = concentration(data=data, income=income_post_tax, weights=weights) g_x = concentration(data=data, income=income_pre_tax, weights=weights) return g_x - g_y
[docs]def theil(income, weights=None, data=None): """Calculate Theil's index. The Theil index is a statistic primarily used to measure economic grouped and other economic phenomena. It is a special case of the generalized entropy index. It can be viewed as a measure of redundancy, lack of diversity, isolation, segregation, grouped, non-randomness, and compressibility. It was proposed by econometrician Henri Theil. Parameters ---------- data : pandas.DataFrame This variable is a DataFrame that contains all data required in it's columns. income : array-like or str This variable represent tax payment of person, if pass array-like then data must be None, else you pass str-name column in `data`. weights : array-like or str This variable represent weights of each person, if pass array-like then data must be None, else you pass str-name column in `data`. Returns ------- theil : float References ---------- Theil index. (2016, December 17). In Wikipedia, The Free Encyclopedia. Retrieved 14:17, May 15, 2017, from https://en.wikipedia.org/w/index.php?title=Theil_index&oldid=755407818 """ if data is not None: income, weights = utils.extract_values(data, income, weights) else: income = income.copy() if weights is None: weights = utils.not_empty_weights(weights, like=income) else: weights = weights.copy() income, weights = utils.not_null_condition(income, weights) # variables needed mu = mean(variable=income, weights=weights) f_i = utils.normalize(weights) return np.sum((f_i * income / mu) * np.log(income / mu))
[docs]def avg_tax_rate(total_tax, total_base, weights=None, data=None): """Calculate average tax rate. This function compute the average tax rate given a base income and a total tax. Parameters ---------- total_base : str or numpy.array total_tax : str or numpy.array data : pd.DataFrame Returns ------- avg_tax_rate : float or pd.Series Is the ratio between mean the tax income and base of income. Reference --------- Panel de declarantes de IRPF 1999-2007: Metodología, estructura y variables. (2011). Panel de declarantes de IRPF 1999-2007: Metodología, estructura y variables. Documentos. """ if ( isinstance(total_base, (np.ndarray)) or not isinstance(total_base, (list)) and not isinstance(total_base, (str)) ): n_cols = total_base.shape[1] elif isinstance(total_base, list): n_cols = len(total_base) else: n_cols = 1 numerator = mean(data=data, variable=total_tax, weights=weights) denominator = mean(data=data, variable=total_base, weights=weights) # main calc res = numerator / denominator if data is not None: base_name = total_base tax_name = total_tax else: base_name = ["base" % i for i in range(n_cols)] tax_name = ["tax_%s" % i for i in range(n_cols)] names = ["_".join([t, b]) for t, b in zip(tax_name, base_name)] res = pd.Series(res, index=names) return res
[docs]def top_rest(income, weights=None, data=None, top_percentage=10.0): """Calculate the 10:90 Ratio. Calculates the quotient between the number of contributions from the top 10% of contributors divided by the number contributions made by the other 90%. The ratio is 1 if the total contributions by the top contributors are equal to the cotnributions made by the rest; less than zero if the top 10% contributes less than the rest; and greater that 1 if the top 10% contributes more than the other ninety percent. Parameters ---------- income : array-like or str This variable represent tax payment of person, if pass array-like then data must be None, else you pass str-name column in `data`. weights : array-like or str This variable represent weights of each person, if pass array-like then data must be None, else you pass str-name column in `data`. All-ones by default data : pandas.DataFrame This variable is a DataFrame that contains all data required in it's columns. top_percentage : float The richest x percent to consider. (10 percent by default) It must be a number between 0 and 100 Returns ------- ratio : float References ---------- Participation Inequality in Wikis: A Temporal Analysis Using WikiChron. Serrano, Abel & Arroyo, Javier & Hassan, Samer. (2018). DOI: 10.1145/3233391.3233536. """ if data is not None: income, weights = utils.extract_values(data, income, weights) else: income = income.copy() weights = np.ones_like(income) if weights is None else weights.copy() # Small shortcut to avoid divide by zero below if income.size <= 1: return np.nan income, weights = utils._sort_values(income, weights) # variables needed weights = utils.normalize(weights) cumw = np.cumsum(weights) ftosearch = 1 - top_percentage / 100 k = np.searchsorted(cumw, ftosearch, side='right') f_i = np.atleast_1d(income*weights) t = np.sum(f_i[k:]) r = np.sum(f_i[:k]) # Correction if k > 0: error = (ftosearch - cumw[k-1]) * income[k] t -= error r += error return t / r
[docs]def hoover(income, weights=None, data=None): """Calculate Hoover index. The Hoover index, also known as the Robin Hood index or the Schutz index, is a measure of income metrics. It is equal to the portion of the total community income that would have to be redistributed (taken from the richer half of the population and given to the poorer half) for there to be income uniformity. Formula: H = 1/2 sum_i( |xi - mu| ) / sum_i(xi) Parameters ---------- income : array-like or str This variable represent tax payment of person, if pass array-like then data must be None, else you pass str-name column in `data`. weights : array-like or str This variable represent weights of each person, if pass array-like then data must be None, else you pass str-name column in `data`. data : pandas.DataFrame This variable is a DataFrame that contains all data required in it's columns. Returns ------- hoover : float References ---------- Hoover index : https://en.wikipedia.org/wiki/Hoover_index """ if data is not None: income, weights = utils.extract_values(data, income, weights) else: income = income.copy() if weights is None: weights = utils.not_empty_weights(weights, like=income) else: weights = weights.copy() income, weights = utils.not_null_condition(income, weights) # variables needed mu = mean(variable=income, weights=weights) f_i = utils.normalize(weights) xi = f_i * income # main calc h = np.sum(abs(xi - mu)) * 0.5 / sum(xi) return h