Slope Index of Inequality
Also known as: SII, Slope Index, Absolute Slope Index of Inequality, Kunst-Mackenbach Slope Index
The slope index of inequality (SII) is a regression-based summary measure that expresses the absolute difference in a health outcome between the bottom and the top of the socioeconomic hierarchy. Rather than comparing only the most extreme groups - which discards information and is sensitive to how categories are defined - it regresses the outcome on each group's relative position in the cumulative socioeconomic distribution and reads the inequality off the fitted line. Mackenbach and Kunst's 1997 Social Science & Medicine overview made the SII, together with its relative counterpart, the recommended pair of measures for quantifying socioeconomic inequalities in health because they use the whole population and account for group sizes. The SII is measured in the natural units of the outcome - extra deaths per 100,000, additional percentage points of disease prevalence - which makes it directly meaningful for public-health and policy audiences. Wagstaff, Paci, and van Doorslaer had earlier argued that such regression-on-rank measures, alongside the concentration index, are among the few that properly reflect the socioeconomic dimension of health. The SII has become a standard tool in health-inequality monitoring across Europe and beyond.
Key highlights
- Uses the entire population and weights groups by size, avoiding the information loss and instability of extreme-group comparisons.
- Expressed in the outcome's natural units, giving a directly interpretable measure of absolute inequality for policy.
- Estimated by familiar regression, so standard errors and confidence intervals follow immediately.
- Sensitive to changes anywhere in the social hierarchy, not just at the top and bottom.
Intuition
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How it works
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When to use it
Use the slope index of inequality when you have a health outcome measured across ordered socioeconomic groups and you want the absolute magnitude of the gradient in the outcome's natural units, using the whole population rather than just the extremes. It is the right choice for public-health reporting where the audience needs to know the real burden - excess deaths or cases attributable to social position - and for monitoring whether absolute inequality is widening even when relative inequality narrows. It pairs naturally with the relative index of inequality, and reporting both gives a complete picture. The SII is inappropriate when the socioeconomic categories are not genuinely ordinal, when you have only two crude groups (where it reduces to a simple difference), or when you care about relative rather than absolute differences. It also requires reliable population shares for each group, without which the rank scores and weighting cannot be computed.
Strengths & limitations
- Uses the entire population and weights groups by size, avoiding the information loss and instability of extreme-group comparisons.
- Expressed in the outcome's natural units, giving a directly interpretable measure of absolute inequality for policy.
- Estimated by familiar regression, so standard errors and confidence intervals follow immediately.
- Sensitive to changes anywhere in the social hierarchy, not just at the top and bottom.
- Requires a genuinely ordinal socioeconomic variable and accurate population shares for each group.
- Measures only absolute inequality; it can move in the opposite direction from relative measures and must be paired with them.
- Assumes a roughly linear gradient across rank, which may misrepresent strongly non-linear or threshold patterns.
- Being scale-dependent, it cannot be compared across outcomes measured in different units without normalization.
Common pitfalls
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Applications
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Frequently asked
How does the SII differ from simply comparing the top and bottom groups?
A top-versus-bottom comparison uses only two categories and ignores everyone in between, and its value depends heavily on how many groups you carve the population into. The SII instead places every ordered group on a 0-to-1 rank axis weighted by population size and fits a regression line, so it reflects the whole social hierarchy and the actual distribution of people. The result is more stable, uses all the data, and answers a cleaner question: the predicted absolute difference in the outcome between the very bottom and very top of the social scale.
Why report the SII and the relative index of inequality together?
The SII measures absolute inequality - the gap in the outcome's own units - while the relative index of inequality measures proportional inequality. These can diverge: as overall rates fall, the absolute gap may shrink while the relative gap grows, or vice versa. Reporting only one can mislead. Presenting both gives a complete picture of whether health inequality is improving or worsening on each scale, which matters because absolute and relative differences carry different policy meanings.
What does a particular SII value actually mean?
The SII is the fitted difference in the outcome between rank 0 (the most disadvantaged extreme) and rank 1 (the most advantaged extreme), in the outcome's natural units. So an SII of 200 deaths per 100,000 means that moving from the very bottom to the very top of the socioeconomic hierarchy is associated with 200 fewer deaths per 100,000 person-years. A value near zero indicates little absolute inequality, and the sign indicates the direction of the gradient relative to social position.
Sources
- 1.Mackenbach, J. P., & Kunst, A. E. (1997). Measuring the magnitude of socio-economic inequalities in health: an overview of available measures illustrated with two examples from Europe. Social Science & Medicine, 44(6), 757-771.
- 2.Wagstaff, A., Paci, P., & van Doorslaer, E. (1991). On the measurement of inequalities in health. Social Science & Medicine, 33(5), 545-557.
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ScholarGate. (2026, June 23). Slope Index of Inequality. ScholarGate. https://scholargate.app/social-epidemiology/slope-index-of-inequality