Figure 1
Percentage difference between men’s and women’s gross income in the EU in 2019
Source: own processing of EU-SILC microdata
Table 1
Average gross employment income in EU countries in Euros monthly
| Average men’s income | Average women’s income | Gender differences | Average men’s income | Average women’s income | Gender differences | ||
|---|---|---|---|---|---|---|---|
| LU | 5841 | 4892 | 949 | ES | 2053 | 1726 | 327 |
| DK | 5036 | 4140 | 896 | LV | 1236 | 966 | 270 |
| FI | 3903 | 3076 | 827 | MT | 2035 | 1781 | 254 |
| IE | 4282 | 3472 | 810 | EE | 1381 | 1139 | 242 |
| UK | 3452 | 2651 | 801 | PT | 1430 | 1200 | 230 |
| AT | 3779 | 3041 | 738 | LT | 1008 | 790 | 218 |
| NL | 4265 | 3571 | 694 | SK | 1010 | 795 | 215 |
| DE | 3630 | 3009 | 621 | HR | 1100 | 894 | 206 |
| FR | 3062 | 2513 | 549 | SI | 1863 | 1660 | 203 |
| BE | 3945 | 3425 | 520 | EL | 1451 | 1254 | 197 |
| CY | 2085 | 1577 | 508 | PL | 1033 | 837 | 196 |
| IT | 2399 | 1982 | 417 | HU | 795 | 664 | 131 |
| SE | 3447 | 3033 | 414 | BG | 613 | 516 | 97 |
| CZ | 1378 | 1001 | 377 | RO | 805 | 757 | 48 |
Table 2
Income quintiles (Q) according to gender representation
| Share of men in the quintile concerned | Share of women in the quintile concerned | Share of the male population in quintiles | Share of the female population in quintiles | Share of population in quintiles | |
|---|---|---|---|---|---|
| Q 1 | 52% | 48% | 27% | 35% | 30% |
| Q 2 | 57% | 43% | 23% | 25% | 24% |
| Q 3 | 59% | 41% | 25% | 24% | 25% |
| Q 4 | 65% | 35% | 12% | 9% | 11% |
| Q 5 | 74% | 26% | 13% | 7% | 10% |
| Total | 59% | 41% | 100% | 100% | 100% |
Table 3
Dependence of the income quintile and gender
| Value | df | P-value | |
|---|---|---|---|
| Pearson Chi-Square | 2,591,547 | 4 | 0.000 |
| N of Valid Cases | 153,705,678 |
Table 4
Factors affecting employment income in the EU
| R | R Square | Adjusted R Square | Std. Error of the Estimate | ||||
|---|---|---|---|---|---|---|---|
| 0.79 | 0.62 | 0.62 | 17778 | ||||
| ANOVA | |||||||
| Sum of Squares | df | Mean Square | F | Sig. | |||
| Regression | 143388514070823440 | 12 | 11949042839235286 | 37805381 | 0.000 | ||
| Residue | 88774983921376272 | 280873721 | 316067248 | ||||
| Total | 232163497992199712 | 280873733 | |||||
| Parameter estimates | |||||||
| Independent variables | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | |
| Gender | B | Std. Error | Beta | ||
| −2862.2 | 2.2 | −0.16 | 1322.2 | 0.000 | |
| Education | 2624.3 | 0.9 | 0.32 | 2946,0 | 0.000 |
| Economic activity | 1338.4 | 1.1 | 0.15 | 1208.3 | 0.000 |
| Age | 0.1 | 0.0 | 0.08 | 1445.6 | 0.000 |
| Household structure | 808.3 | 0.3 | 0.13 | 2493.7 | 0.000 |
| Job sector | −759.4 | 0.4 | −0.21 | 2086.2 | 0.000 |
| Number of full-time of months work | 2720.7 | 0.4 | 0.78 | 7106.9 | 0.000 |
| Number of part-time of months work | 1749.7 | 0.5 | 0.23 | 3644.2 | 0.000 |
| Number in unemployment of months | 465.6 | 0.5 | 0.04 | 922.5 | 0.000 |
| Type of employment contract | −1998.6 | 3.0 | −0.08 | −672.7 | 0.000 |
| Management position | −3732.6 | 2.6 | −0.236 | 1443.8 | 0.000 |
| Health | 679.9 | 1.3 | 0.057 | 540.7 | 0.000 |
Table 5
Factors affecting the difference in employment income between men and women
| R | R Square | Adjusted R Square | Std. Error of the Estimate | ||
|---|---|---|---|---|---|
| 0.86 | 0.73 | 0.73 | 8002 | ||
| ANOVA | |||||
| Sum of Squares | df | Mean Square | F | Sig. | |
| Regression | 179923994323776 | 15 | 11994932954918 | 186379 | 0.000 |
| Residue | 66423709628580 | 1032103 | 64357631 | ||
| Total | 246347703952357 | 1032118 | |||
| Parameter estimates | |||||
| Independent variables | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | |
| Employment Absolute Income | B | Std. Error | Beta | ||
| −0.6 | 0.0 | −1.04 | −825.1 | 0.000 | |
| Health | 482.3 | 11.4 | 0.06 | 42.3 | 0.000 |
| Education | −8.6 | 0.1 | −0.25 | −142.2 | 0.000 |
| Job sector | 99.4 | 0.4 | 0.27 | 233.6 | 0.000 |
| Number of months of full-time work | −117.7 | 3.7 | −0.08 | −31.9 | 0.000 |
| Number of part-time of months work | 227.8 | 4.3 | 0.04 | 52.9 | 0.000 |
| Number in unemployment of months | 38.2 | 6.0 | 0.00 | 6.3 | 0.000 |
| Reason than 30 for hours working a week less | 43.4 | 6.0 | 0.01 | 7.3 | 0.000 |
| Type of employment contract | 2995.0 | 23.1 | 0.23 | 129.9 | 0.000 |
| Management position | 1331.4 | 20.9 | 0.16 | 63.7 | 0.000 |
| Job change in the last year | 6634.3 | 31.2 | 0.85 | 212.7 | 0.000 |
| Age | −79.7 | 0.8 | −0.24 | −106.0 | 0.000 |
| Disposable household income | 0.1 | 0.0 | 0.32 | 276.2 | 0.000 |
| Household structure | 10.0 | 3.3 | 0.01 | 3.1 | 0.002 |
| Household at risk of poverty | 6818.5 | 32.8 | 0.13 | 207.8 | 0.000 |
Table 6
Dependence of choice of job sector and gender
| Value | df | P-value | |
|---|---|---|---|
| Pearson Chi-Square | 25,314,552 | 11 | 0.000 |
| N of Valid Cases | 152,605,405 |
Table 7
Representation of men and women in industries and gender differences in income
| Classified according to ISCO | Share of men in the job sector | Share of women in the job sector | Differences between men’s and women’s income |
|---|---|---|---|
| Legislators and Managers | 66.4% | 33.6% | 27% |
| Science and Technology | 65.9% | 34.1% | 22% |
| Healthcare | 27.6% | 72.4% | 28% |
| Training and Education | 29.8% | 70.2% | 26% |
| Public Administration | 49.9% | 50.1% | 34% |
| Information Technology | 83.0% | 17.0% | 23% |
| Law, Culture and Sport | 44.3% | 55.7% | 17% |
| Administration | 40.4% | 59.6% | 35% |
| Services and Retail | 40.0% | 60.0% | 45% |
| Agriculture, Forestry and Fishing | 78.0% | 22.0% | 67% |
| Craftsmen and Blue-collar Workers | 85.2% | 14.8% | 57% |
Table 8
Income quintiles according to job sector
| Income quintile | 1 | 2 | 3 | 4 | 5 |
|---|---|---|---|---|---|
| Job sector | |||||
| Legislators and Managers | 26.6% | 16.1% | 16.0% | 10.6% | 30.7% |
| Science and Technology | 20.7% | 17.2% | 20.2% | 19.0% | 23.0% |
| Healthcare | 32.3% | 20.7% | 22.9% | 13.8% | 10.3% |
| Training and Education | 30.9% | 20.9% | 22.8% | 14.6% | 10.8% |
| Public Administration | 34.0% | 18.9% | 20.6% | 11.0% | 15.4% |
| Information Technology | 35.1% | 14.6% | 14.4% | 14.4% | 21.5% |
| Law, Culture and Sport | 40.0% | 20.1% | 20.5% | 9.2% | 10.1% |
| Administration | 31.6% | 27.0% | 23.7% | 10.7% | 7.1% |
| Services and Retail | 39.1% | 28.9% | 24.6% | 4.9% | 2.4% |
| Agriculture, Forestry and Fishing | 27.5% | 27.4% | 41.4% | 2.8% | 0.9% |
| Craftsmen and Blue-collar Workers | 26.0% | 28.4% | 32.9% | 8.8% | 3.9% |
| Total | 30.1% | 23.6% | 24.8% | 10.8% | 10.8% |