euromod.eu_euromod.calibrate.descriptives¶

Statistics that describe a household sample.

All functions take a household sample and return a small table. Quantiles are quantiles of gross income over the whole sample, with survey weights; percentages are on a 0-100 scale.

Below are listed the main public classes of the euromod.eu_euromod.calibrate.descriptives module.

**Function**¶
`benefit_ratio_by_quantile`(sample, n, benefit, income)	Benefits as a share of gross income, by income quantile.
`family_composition`(sample)	How marriage and children go together in the sample.
`gini_coefficients`(sample)	Gini coefficients of gross and disposable household income.
`income_shares`(sample, n)	Share of total income held by each quantile, for gross and disposable income.
`sample_characteristics`(sample)	Size and composition of the sample.
`tax_rates_by_quantile`(sample, n, household_types)	Mean net tax rate by income quantile, for each household type.

Functions¶

euromod.eu_euromod.calibrate.descriptives.benefit_ratio_by_quantile(sample, n=10, benefit='ils_benmt', income='ils_origrepy')¶

Benefits as a share of gross income, by income quantile.

The ratio is computed for each household and then averaged, so it describes the typical household in a quantile rather than the quantile’s totals.

Parameters:

sample (pandas.DataFrame) – A household sample.
n (int) – Number of quantiles.
benefit (str) – Household total to take the ratio of; default means-tested benefits.
income (str) – Household gross income the sample was built with.

Returns:

Rows Q1..Qn and all; columns ratio (mean ratio, in percent) and weight (total survey weight, for combining quantiles).

Return type:

pandas.DataFrame

euromod.eu_euromod.calibrate.descriptives.family_composition(sample)¶

How marriage and children go together in the sample.

Returns:: Shares between 0 and 1: children_among_single and children_among_married (households with children, among those with an unmarried or a married head), single_among_children and married_among_children (the reverse).
Return type:: dict

euromod.eu_euromod.calibrate.descriptives.gini_coefficients(sample)¶

Gini coefficients of gross and disposable household income.

Returns:: gross and disposable.
Return type:: dict

euromod.eu_euromod.calibrate.descriptives.income_shares(sample, n=5)¶

Share of total income held by each quantile, for gross and disposable income.

Each column ranks households by its own income: gross shares by gross income, disposable shares by disposable income.

Parameters:

sample (pandas.DataFrame) – A household sample.
n (int) – Number of quantiles (5 for quintiles).

Returns:

Rows Q1..Qn; columns gross and disposable, in percent.

Return type:

pandas.DataFrame

euromod.eu_euromod.calibrate.descriptives.sample_characteristics(sample)¶

Size and composition of the sample.

Returns:: n (households), head_age (mean age of the head), share_married (share of households with a married head), share_children (share with at least one child), children_per_family (mean number of children among households that have any) and hh_size (mean household size). Shares are between 0 and 1. All means use survey weights.
Return type:: dict

euromod.eu_euromod.calibrate.descriptives.tax_rates_by_quantile(sample, n=10, household_types=HOUSEHOLD_TYPES)¶

Mean net tax rate by income quantile, for each household type.

The quantiles are those of the whole sample, so a column compares households of one type with all households at the same income.

Parameters:

sample (pandas.DataFrame) – A household sample.
n (int) – Number of quantiles (10 for deciles).
household_types (dict) – Conditions by name; default HOUSEHOLD_TYPES.

Returns:

Rows Q1..Qn; one column per household type, in percent.

Return type:

pandas.DataFrame