Effect of Gender Differences and Other Factors on Remuneration of Employees in EU Countries
, y | 24 jul 2021
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Publicado en línea: 24 jul 2021
Páginas: 92 - 108
DOI: https://doi.org/10.2478/danb-2021-0007
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© 2021 Irena Antošová, Naďa Hazuchová, Jana Stávková, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.
Figure 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 |
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 | ||
| Sum of Squares | df | Mean Square | F | Sig. | |
| Regression | 179923994323776 | 15 | 11994932954918 | 186379 | 0.000 |
| Residue | 66423709628580 | 1032103 | 64357631 | ||
| Total | 246347703952357 | 1032118 | |||
Dependence of the income quintile and job sector
| Value | df | P-value | |
|---|---|---|---|
| Pearson Chi-Square | 19,395,930 | 44 | 0.000 |
| 152,605,405 |
Dependence of choice of job sector and gender
| Value | df | P-value | |
|---|---|---|---|
| Pearson Chi-Square | 25,314,552 | 11 | 0.000 |
| 152,605,405 |
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% |
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 | ||||
| Sum of Squares | df | Mean Square | F | Sig. | |||
| Regression | 143388514070823440 | 12 | 11949042839235286 | 37805381 | 0.000 | ||
| Residue | 88774983921376272 | 280873721 | 316067248 | ||||
| Total | 232163497992199712 | 280873733 | |||||
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% |
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% |
Dependence of the income quintile and gender
| Value | df | P-value | |
|---|---|---|---|
| Pearson Chi-Square | 2,591,547 | 4 | 0.000 |
| 153,705,678 |