Methodological Notes

Population Coverage

All surveyed households and their members are included in our estimates of Gini and Atkinson coefficients, percentile ratios, and poverty lines. Poverty lines are calculated based on the total population. Those lines are then used to calculate poverty rates among subgroups (children and the elderly). Thus, when calculating poverty rates, the subgroups vary, but the poverty lines remain constant within any given dataset.

Income Concept

All Key Figures use the LIS data on disposable household income. For more information on the components of income included in this measure, see METIS

Bottom- and Top-Coding

Although LIS does not apply bottom- or top-coding to the microdatasets themselves, we bottom-and top-code income when creating the Key Figures. Before equivalisation, top and bottom coding is applied by setting boundaries for extreme values of log transformed Disposable Household Income: at the top Q3 plus 3 times the interquartile range (Q3-Q1), and at the bottom Q1 minus 3 times the interquartile range.

Equivalence Scale

Throughout the Key Figures, we use equivalised income. For the Inequality and Poverty Key Figures, equivalised income is equal to unadjusted household income (DHI) divided by the square root of the number of household members (NHHMEM)
(Equivalised Income = DHI/√NHHMEM). All members of a given household have the same equivalent income, regardless of age, gender, or relationship to the household head.

Percentile Methodology

Beginning with the Winter data release of December 2025, LIS adopts Definition 4 from Hyndman & Fan (1996) for the computation of the Key Figures and all percentile-based estimates (median, percentile ratios, poverty lines based on the median, decile shares, etc.). Under this definition, a percentile is obtained by locating its position in the cumulative distribution and linearly interpolating between the two adjacent ordered values, producing smooth and stable estimates.

We are adopting this method because it:

  • works consistently across R and Stata,
  • relies on cumulative weights, which ensures stability when computing shares or averages within distributional groups. Such estimates can be highly sensitive on small subsamples and when many repeated values or ties are observed in the microdata.

As a result, previously published figures have been revised to align with this unified approach. Even indicators not directly based on percentiles may be affected, as an interquartile rule is applied to cap top and bottom coded disposable household incomes in every dataset. Only 36 statistics showed a difference larger than 1 % between old and new estimate (=0.23 % of all concerned statistics). Nevertheless, even with a harmonized methodology, minor precision differences across software environments may still occur, but these remain rare.

To ensure consistent replication, users can already apply this methodology in LISSY:

  • R users may rely on the ‘lissyrtools’ package, which implements Definition 4 by default, when using the relevant functions;
  • Stata users may use the ‘percentils’ command developed by Philippe Van Kerm, which is pre-installed and ready to use in LISSY.

Reference scripts illustrating these procedures are available in the Replication Code section of the LIS website here.

Weighting

We use person-level adjusted weights when generating income indicators for the total population (HWGT*NHHMEM) . When computing the poverty rate among children, we construct a child weight by multiplying the household weight by the number of household members under the age of eighteen (HWGT*NHHMEM17). When computing the rate of poverty among the elderly, an elderly weight is constructed using the number of household members aged 65 and older ((HWGT*NHHMEM65).

Missing Values and Zero Incomes

All households where disposable income (DHI) is missing are excluded.

Treatment of Currency

Note that, in the Key Figures, median and mean equivalised income are expressed in the units of national currency that are in use today.