The slope index of inequality (SII) is an absolute measure of inequality that represents the difference in predicted values of an indicator between the most advantaged and most disadvantaged subgroups, obtained by fitting a regression model.
sii(
est,
subgroup_order,
pop = NULL,
weight = NULL,
psu = NULL,
strata = NULL,
fpc = NULL,
conf.level = 0.95,
linear = FALSE,
force = FALSE,
...
)The estimated SII value, corresponding estimated standard error,
and confidence interval as a data.frame.
The indicator estimate. Estimates must be available for all subgroups/individuals (unless force=TRUE).
The order of subgroups/individuals in an increasing sequence.
For disaggregated data, the number of people within each subgroup. This must be available for all subgroups.
The individual sampling weight, for individual-level data from a survey. This must be available for all individuals.
Primary sampling unit, for individual-level data from a survey.
Strata, for individual-level data from a survey.
Finite population correction, for individual-level data from a survey where sample size is large relative to population size.
Confidence level of the interval. Default is 0.95 (95%).
TRUE/FALSE statement to specify the use of a linear regression model (default is logistic regression).
TRUE/FALSE statement to force calculation with missing indicator estimate values.
Further arguments passed to or from other methods.
SII can be calculated using disaggregated data and individual-level data. Subgroups in disaggregated data are weighted according to their population share, while individuals are weighted by sample weight in the case of data from surveys.
To calculate SII, a weighted sample of the whole population is ranked from the most disadvantaged subgroup (at rank 0) to the most advantaged subgroup (at rank 1). This ranking is weighted, accounting for the proportional distribution of the population within each subgroup. The indicator of interest is then regressed against this relative rank using an appropriate regression model, and the predicted values of the indicator are calculated for the two extremes (rank 1 and rank 0). SII is calculated as the difference between the predicted values at rank 1 and rank 0 (covering the entire distribution). For more information on this inequality measure see Schlotheuber (2022) below.
The default regression model used is a generalized linear model with logit link. In logistic regression, the relationship between the indicator and the subgroup rank is not assumed to be linear and, due to the logit link, the predicted values from the regression model will be bounded between 0 and 1 (which is ideal for indicators measured as percentages). Specify Linear=TRUE to use a linear regression model, which may be more appropriate for indicators without a 0-1 or 0-100% scale.
Interpretation: SII is 0 if there is no inequality. Greater absolute values indicate higher levels of inequality. Positive values indicate that the level of the indicator is higher among advantaged subgroups, while negative values indicate that the level of the indicator is higher among disadvantaged subgroups. Note that this results in different interpretations for favourable and adverse indicators.
Type of summary measure: Complex; absolute; weighted
Applicability: Ordered dimension of inequality with more than two subgroups
Warning: The confidence intervals are approximate and might be biased.
Schlotheuber, A, Hosseinpoor, AR. Summary measures of health inequality: A review of existing measures and their application. Int J Environ Res Public Health. 2022;19(6):3697. doi:10.3390/ijerph19063697.
# example code 1
data(IndividualSample)
head(IndividualSample)
with(IndividualSample,
sii(est = sba,
subgroup_order = subgroup_order,
weight = weight,
psu = psu,
strata = strata))
# example code 2
data(OrderedSample)
head(OrderedSample)
with(OrderedSample,
sii(est = estimate,
subgroup_order = subgroup_order,
pop = population))
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