The usual inequality measures for one variable, in one tibble, so a report does not rest on whichever index came to hand. Weight by population for inequality between people rather than between countries.
Usage
inequality(
x,
weights = NULL,
measures = c("gini", "theil_t", "theil_l", "atkinson", "cv", "palma", "p90_p10"),
epsilon = 1
)Arguments
- x
A numeric vector, such as GDP per capita.
- weights
Optional non-negative weights (population), the same length as
xor length 1.- measures
Any of
"gini","theil_t","theil_l"(the mean log deviation),"atkinson","cv"(the coefficient of variation),"palma"(the top 10%'s share over the bottom 40%'s) and"p90_p10"(the 90th percentile over the 10th). All by default.- epsilon
The Atkinson index's inequality aversion (default
1): larger values weigh the bottom of the distribution more.
Value
A tibble of measure and value, one row per measure, every one
computed on the same values: the finite, positive values of x with a
non-missing weight. The Theil and Atkinson indices need positive values,
so a zero or negative value is dropped from all of them, with a warning
(class countryatlas_nonpositive_dropped). A measure that is undefined
on the data (too few values) is NA.
Which inequality
Milanovic (2005) separates three concepts. Concept 1 is inequality between
countries as units, each counting once: weights = NULL. Concept 2 weights
each country by its population but still gives everyone their country's
mean: weights = population. Concept 3, inequality between all the
world's people, needs each country's internal distribution, which a
country-level table does not have, so it is out of scope here; concept 2
understates it by exactly the within-country inequality it cannot see.
References
Atkinson, A. B. (1970). On the measurement of inequality. Journal of Economic Theory 2(3), 244-263. doi:10.1016/0022-0531(70)90039-6
Milanovic, B. (2005). Worlds Apart: Measuring International and Global Inequality. Princeton University Press.
Palma, J. G. (2011). Homogeneous middles vs. heterogeneous tails, and the end of the "inverted-U". Development and Change 42(1), 87-153. doi:10.1111/j.1467-7660.2011.01694.x
Examples
snap <- countryatlas::world_snapshot$countries
inequality(snap$gdp_per_capita) # concept 1
#> # A tibble: 7 × 2
#> measure value
#> <chr> <dbl>
#> 1 gini 0.638
#> 2 theil_t 0.746
#> 3 theil_l 0.916
#> 4 atkinson 0.600
#> 5 cv 1.54
#> 6 palma 9.36
#> 7 p90_p10 43.7
inequality(snap$gdp_per_capita, weights = snap$population) # concept 2
#> # A tibble: 7 × 2
#> measure value
#> <chr> <dbl>
#> 1 gini 0.612
#> 2 theil_t 0.686
#> 3 theil_l 0.774
#> 4 atkinson 0.539
#> 5 cv 1.40
#> 6 palma 7.70
#> 7 p90_p10 32.7