3. Data and methods

The empirical analyses presented in this work focus on the distribution of household income. That refers to the income earned by all members of a household, including wages, salaries, earnings from self-employment, social security benefits, pensions and retirement income, investment income, welfare payments and other sources of income. Income statistics can be expressed in various ways, depending on how such income streams and the household composition are considered in the calculation.

The main statistics on household income are as follows:

Total Income reflects the sum of all income received by all household members before any taxes or deductions are applied. This statistic is often shown as gross income and includes wages, salaries, benefits, pensions and other sources.
Net Income is the total income after deductions such as taxes, social security contributions and other mandatory payments. This shows the actual amount of money households have available to spend or save and is often referred to as disposable income.
Equivalised Income is adjusted household income that accounts for the size and composition of the household, providing a more accurate measure of economic well-being. Equivalised household income is typically calculated based on net (disposable) income using an equivalence scale (like the OECD scale), which assigns a weight to each household member (Eurostat, 2024). That allows for comparisons between households of different sizes and compositions.

All the above metrics can be shown as averages, medians or distributions across different income brackets. For example, equivalised income is often presented as median equivalised income or in income distribution analyses to reflect living standards more accurately. Regardless of whether household income values are expressed as total, net or equivalised values, official income statistics are normally expressed in national currency. That makes international comparisons difficult, not only because the units are different, but also because the purchasing power in different countries may follow different trends.

All the analyses included in this paper rely on two data strands from the National Statistical Institutes (NSIs) of the Nordic countries and self-governing territories. Those include both statistics on average equivalised (net) household income by income decile and information on socio-economic inequalities. The latter group includes information on Gini coefficients, percentile ratios and other measures of income inequality, which are generally available at an aggregated territorial level (regional or national).

To overcome the lack of inequality indicators at the local level and ensure harmonised processing of household income, the inequality metrics used in our analysis were calculated manually. The indicators were estimated based on household income by income decile at the municipal level, expressed in national currency. Missing income values for specific years were imputed using the chained equations method (van Buuren and Groothuis-Oudshoorn, 2011). For cross-regional comparisons of income data, income values were further standardised to euros in Purchasing Power Parity (PPP) standards. Annex 1 presents a detailed overview of the datasets used in this study, as well as the data preparation process.

The exploration of inequality patterns in this work relies on the estimation of density functions, Lorenz curves and various metrics of social inequality, including Gini, Atkinson, Theil, Hoover, Herfindahl, Coulter and Dalton indices on household income data. Those indicators were calculated for all units in the 2005-2022 period. The longitudinal analyses of the aforementioned inequality metrics were complemented by analyses of beta and sigma convergence (Sala-i-Martin, 1996), as well as by a club convergence analysis using the approach proposed by Phillips and Sul (2009).

Furthermore, we have combined the social and territorial dimensions by studying the contribution of inter-personal and inter-territorial inequalities to the overall inequality indices within each Nordic country and self-governing territory. That was done by applying inequality decomposition methods available in the literature (Attili, 2021; see e.g. Fields, 2003; Firpo et al., 2009; Mookherjee and Shorrocks, 1982; Panzera and Postiglione, 2020; Patil et al., 2014; Rey and Smith, 2013). Annex 2 provides additional information on the analytical methods applied in this research.