## Income and Wealth Inequality

The Household Finance and Consumption Survey (HFCS) 2020 was published on 19 May 2022. Since the first publication of 2020 estimates, the CCR has been retrospectively added to the 2018 data. As 2018 and 2020 estimates are now produced using the same methodology, comparisons can be made between them. This publication has been updated on 16 May 2023 and now includes references to 2018 estimates.

## Wealth Inequality

Open in Excel:

Lorenz Curve

Figure 8.1 plots the Lorenz curves of net household wealth for 2018 and 2020. This curve is a graphical representation of the relationship between the cumulative share of households (ranked according to the level of wealth from lowest to highest) and their cumulative share of total wealth. A perfectly equal wealth distribution would be one in which each household has the same wealth, therefore 10% of households would own 10% of cumulative wealth, 20% of households would own 20% of wealth (and so on). This scenario is depicted in Figure 8.1 by the 45o line, the line of perfect equality. The further away a curve is from the line of perfect equality the more unequal the distribution. The Lorenz curve for 2020 net household wealth is slightly closer to the line of equality than the 2018 curve, suggesting that net wealth is distributed slightly more evenly in 2020 than in 2018.

Gini Coefficient

The Gini coefficient is a statistical measure of inequality. A Gini coefficient value of zero denotes perfect equality, indicating that wealth is distributed equally amongst all households. A Gini Coefficient of 1 would denote perfect inequality where all the wealth is held by one household. The Gini coefficient for net wealth in 2020 is 0.65. The corresponding value for 2018 was 0.70.

When interpreting this figure, it is important to note that the HFCS does not capture a significant portion of household wealth in the upper percentiles (i.e. the top 1% to 2%). For example, no billionaires or households with a net wealth greater than 40 million euro were interviewed for the HFCS in 2020. We can assume that this results in an underestimation of the Gini coefficient for net household wealth.

% Cumulative Households 0 1 Perfect Equality 2018 Net Household Wealth 2020 Net Household Wealth 0 -1.1 -0.4 1 -1.1 -0.5 2 -1.2 -0.6 3 -1.2 -0.6 4 -1.2 -0.6 5 -1.2 -0.6 6 -1.2 -0.6 7 -1.3 -0.6 8 -1.3 -0.6 9 -1.3 -0.6 10 -1.3 -0.6 11 -1.3 -0.6 12 -1.2 -0.6 13 -1.2 -0.6 14 -1.2 -0.6 15 -1.2 -0.6 16 -1.2 -0.6 17 -1.2 -0.6 18 -1.2 -0.5 19 -1.2 -0.5 20 -1.2 -0.5 21 -1.1 -0.5 22 -1.1 -0.4 23 -1.1 -0.4 24 -1 -0.3 25 -1 -0.2 26 -0.9 -0.1 27 -0.9 0 28 -0.8 0.1 29 -0.7 0.2 30 -0.6 0.4 31 -0.5 0.6 32 -0.3 0.8 33 -0.1 1 34 0.1 1.3 35 0.3 1.6 36 0.5 1.9 37 0.7 2.1 38 1 2.5 39 1.3 2.8 40 1.6 3.2 41 1.9 3.6 42 2.2 4 43 2.5 4.4 44 2.9 4.8 45 3.3 5.3 46 3.7 5.7 47 4.1 6.3 48 4.5 6.7 49 5 7.3 50 5.5 7.8 51 5.9 8.4 52 6.5 9 53 7 9.6 54 7.5 10.2 55 8.1 10.9 56 8.7 11.5 57 9.3 12.2 58 10 12.9 59 10.6 13.7 60 11.3 14.4 61 12 15.2 62 12.7 16 63 13.5 16.8 64 14.3 17.6 65 15.1 18.5 66 15.9 19.3 67 16.8 20.3 68 17.7 21.3 69 18.6 22.2 70 19.5 23.3 71 20.5 24.3 72 21.5 25.4 73 22.5 26.5 74 23.5 27.7 75 24.7 28.7 76 25.7 29.9 77 27 31.2 78 28.1 32.5 79 29.5 33.9 80 30.7 35.3 81 32.1 36.7 82 33.6 38.4 83 35.2 39.9 84 36.8 41.6 85 38.3 43.4 86 40.3 45.1 87 42.1 47.1 88 44 49 89 46.2 51.3 90 48.5 53.4 91 50.8 55.8 92 53.5 58.3 93 56.4 61.2 94 59.5 64.1 95 63 67.5 96 67.2 71.4 97 71.9 75.9 98 77.6 81.5 99 87.7 91.5 100 100 100

## Income and Wealth Inequality

Open in Excel:

The Gini coefficient for gross household income is 0.43 in 2020 indicating that net wealth is distributed more unequally than gross household income. See Figure 8.2.

The Survey on Income and Living Conditions is the official source of income inequality statistics, where income inequality measures are based on equivalised disposable income, rather than on gross household income.

% Cumulative Households 0 1 Perfect Equality Gross Household Income Net Household Wealth 0 0 -0.4 1 0.1 -0.5 2 0.3 -0.6 3 0.4 -0.6 4 0.6 -0.6 5 0.8 -0.6 6 1 -0.6 7 1.2 -0.6 8 1.4 -0.6 9 1.6 -0.6 10 1.8 -0.6 11 2 -0.6 12 2.3 -0.6 13 2.5 -0.6 14 2.8 -0.6 15 3.1 -0.6 16 3.4 -0.6 17 3.7 -0.6 18 4 -0.6 19 4.4 -0.5 20 4.8 -0.5 21 5.1 -0.5 22 5.5 -0.4 23 5.9 -0.4 24 6.3 -0.3 25 6.8 -0.2 26 7.1 -0.1 27 7.6 0 28 8 0.1 29 8.5 0.2 30 9 0.4 31 9.5 0.6 32 10 0.8 33 10.5 1 34 11.1 1.3 35 11.5 1.6 36 12.2 1.9 37 12.7 2.1 38 13.3 2.5 39 13.9 2.8 40 14.5 3.2 41 15.1 3.6 42 15.8 4 43 16.4 4.4 44 17.1 4.8 45 17.8 5.3 46 18.5 5.7 47 19.2 6.3 48 19.9 6.7 49 20.7 7.3 50 21.4 7.8 51 22.1 8.4 52 23 9 53 23.7 9.6 54 24.6 10.2 55 25.4 10.9 56 26.3 11.2 57 27.1 12.2 58 28 12.9 59 28.9 13.7 60 29.9 14.4 61 30.8 15.2 62 31.8 16 63 32.8 16.8 64 33.8 17.6 65 34.8 18.5 66 36 19.3 67 37 20.3 68 38.1 21.3 69 39.2 22.2 70 40.4 23.3 71 41.5 24.3 72 42.7 25.4 73 44 26.5 74 45.2 27.7 75 46.5 28.7 76 47.8 29.9 77 49.2 31.2 78 50.6 32.5 79 51.9 33.9 80 53.3 35.3 81 54.9 36.7 82 56.3 38.4 83 57.9 39.9 84 59.5 41.6 85 61.1 43.4 86 62.9 45.1 87 64.6 47.1 88 66.3 49 89 68.3 51.3 90 70.1 53.4 91 72.3 55.8 92 74.1 58.3 93 76.5 61.2 94 78.7 64.1 95 81.5 67.5 96 83.8 71.4 97 87.2 75.9 98 90.6 81.5 99 95.4 91.5 100 100 100

### Why you can Trust the CSO

Learn about our data and confidentiality safeguards, and the steps we take to produce statistics that can be trusted by all.