Tab. 1:
Results of panel regression models
| Model | FEM | REM | Pooled | |||
|---|---|---|---|---|---|---|
| variable | Estimate | significance (p–value) | Estimate | significance (p–value) | Estimate | significance (p–value) |
| GEI | 103.8315 | *(0.0768) | 134.9978 | *(0.0438) | 767.4512 | ***(0.0002) |
| GPG | −35.1447 | −(0.6136) | −51.2419 | −(0.5193) | −828.2484 | **(0.0077) |
| ER | 312.0235 | ***(<0.0001) | 290.4494 | ***(<0.0001) | 598.8017 | *(0.0456) |
| PEP | 0.2875 | −(0.6866) | 0.1763 | −(0.7297) | −0.8835 | *(0.0174) |
| EDU | −8.6735 | −(0.6576) | −18.6857 | −(0.4093) | −389.8658 | ***(<0.0001) |
| Panel diagnostics | Collinearity diagnostics | |||||
| Test | p-value | VIF test | GEI | 1.8806 | ||
| F-test | <2.22e-16 | GPG | 1.2296 | |||
| Jarque–Bera | 0.5462 | ER | 1.9123 | |||
| Durbin–Watson | 0.0002 | PEP | 1.3320 | |||
| Breusch–Pagan | 0,1870 | EDU | 1.1288 | |||
Fig. 1:
GDP per capita for the years 2015 and 2019
Source: Own based on Eurostat and EIGE
Note: Q1: the quadrant with the lowest EUR value of GDP per capita; Q4: the quadrant with the highest EUR value of GDP per capita
Fig. 2:
Gender Equality Index for the years 2015 and 2019
Note: Q1: countries with low scores; Q4: countries with high scores
Fig. 3:
Gender Pay Gap for the years 2015 and 2019
Source: Own based on Eurostat and EIGE
Note: Q1: lowest wage differences expressed in %; Q4: the highest wage differences expressed in %.
Fig. 4:
Female employment rate for the years 2015 and 2019
Source: Own based on Eurostat and EIGE
Note: Q1: low female employment rate in %; Q4: high female employment rate in %.
Fig. 5:
Population of women for the years 2015 and 2019
Source: Own based on Eurostat and EIGE
Note: Q1: the lower number of the female population (1000); Q4: the higher number of the female population (1000).
Fig. 6:
Graduates in tertiary education by education level for the years 2015 and 2019
Source: own based on Eurostat and EIGE
Note: Q1: the lowest number of women with tertiary education (women per 100 men); Q4: the highest number of women with tertiary EDU (women per 100 men).
Tab. 2:
Results of LISA cluster analysis
| Country | 2015 | 2019 | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| GDP | GEI | GPG | ER | PEP | EDU | GDP | GEI | GPG | ER | PEP | EDU | |
| Belgium | 1 | 1 | 4 | 3 | 3 | 4 | 1 | 1 | 4 | 3 | 3 | 4 |
| France | 1 | 1 | 2 | 2 | 1 | 4 | 1 | 1 | 2 | 4 | 1 | 4 |
| Germany | 1 | 1 | 2 | 1 | 2 | 4 | 1 | 1 | 2 | 2 | 2 | 4 |
| Italy | 1 | 3 | 4 | 3 | 2 | 4 | 1 | 3 | 3 | 3 | 2 | 4 |
| Luxembourg | 1 | 1 | 4 | 3 | 3 | 4 | 1 | 1 | 4 | 3 | 3 | 4 |
| Denmark | 1 | 1 | 1 | 1 | 3 | 4 | 1 | 1 | 1 | 1 | 3 | 4 |
| Portugal | 4 | 3 | 2 | 3 | 3 | 4 | 4 | 3 | 4 | 2 | 3 | 4 |
| Spain | 4 | 2 | 3 | 4 | 1 | 4 | 4 | 1 | 4 | 3 | 1 | 4 |
| Austria | 2 | 4 | 1 | 2 | 3 | 2 | 2 | 2 | 1 | 2 | 3 | 4 |
| Finland | 1 | 1 | 2 | 1 | 4 | 3 | 1 | 1 | 2 | 1 | 4 | 3 |
| Sweden | 1 | 1 | 3 | 1 | 4 | 1 | 1 | 1 | 3 | 1 | 4 | 1 |
| Czechia | 4 | 4 | 1 | 1 | 3 | 1 | 4 | 4 | 1 | 1 | 3 | 2 |
| Estonia | 4 | 4 | 1 | 1 | 4 | 1 | 4 | 4 | 1 | 1 | 4 | 1 |
| Hungary | 4 | 4 | 4 | 2 | 4 | 1 | 4 | 4 | 2 | 2 | 4 | 3 |
| Latvia | 4 | 4 | 1 | 1 | 4 | 1 | 4 | 4 | 1 | 1 | 4 | 1 |
| Lithuania | 4 | 4 | 4 | 2 | 4 | 1 | 4 | 4 | 2 | 2 | 4 | 1 |
| Poland | 4 | 4 | 3 | 4 | 1 | 1 | 4 | 4 | 3 | 3 | 1 | 1 |
| Slovakia | 4 | 4 | 1 | 4 | 4 | 1 | 4 | 4 | 1 | 1 | 4 | 1 |
| Slovenia | 4 | 2 | 4 | 4 | 4 | 2 | 4 | 2 | 3 | 2 | 4 | 2 |
| Bulgaria | 4 | 4 | 2 | 4 | 4 | 2 | 4 | 4 | 2 | 4 | 4 | 1 |
| Romania | 4 | 4 | 4 | 4 | 4 | 3 | 4 | 4 | 3 | 3 | 4 | 3 |
[i] Source: Own elaboration by the authors
[ii] Note: 1 – high–high area; 2 – high–low area; 3 – low–high area; 4 – low–low area; 2015: GDP: 1 – (25,860–82,820); 2 – (36,140); 4 – (5700–23,090); GEI: 1 – (64.9–79.7); 2 – (66.1–67.4); 3 – (54.4–56.5); 4 – (51.2–61.3); GPG: 1 – (15.1–26.7); 2 – (15.5–21.8); 3 – (7.3–14.1); 4 – (4.7–14.2); ER: 1 – (66.4–77.6); 2 – (66.2–72.2); 3 – (50.5–65); 4 – (50.3–64.6); PEP: 1 – (6688.6–11,693); 2 – (8895.8–16,588.9); 3 – (106.1–2037.1); 4 – (272.1–3326.1); EDU: 1 – (153.32–193.7); 2 – (123.92–156.38); 3 – (139.85–149.66); 4 – (100.6–147); 2019: GDP: 1 – (27,230–83,590); 2 – (38,090); 4 – (6630–25,180); GEI: 1 – (66.9–83.6); 2 – (65.3–68.3); 3 – (59.9–63); 4 – (51.9–59.8); GPG: 1 – (14–21.7); 2 – (13.3–19.2); 3 – (3.3–11.8); 4 – (1.3–10.9); ER: 1 – (71.7–78.9); 2 – (72.1–77.4); 3 – (53.9–68.1); 4 – (69.4–70.2); PEP: 1 – (6730.7–11,937.4); 2 – (9231–17,398.1); 3 – (122.9–2174.3); 4 – (278.2–3387.7); EDU: 1 – (151.02–192.75); 2 – (150.04–157.57); 3 – (144.12–144.88); 4 – (98.24–144).
Fig. 7:
Moran's scatter plot: GDP
Source: Own based on Eurostat and EIGE
Fig. 8:
Moran's scatter plot: Gender Equality Index
Source: own based on Eurostat and EIGE
Fig. 9:
Moran's scatter plot: Gender Pay Gap
Source: Own based on Eurostat and EIGE
Fig. 10:
Moran's scatter plot: Employment rate
Source: Own based on Eurostat and EIGE
Fig. 11:
Moran's scatter plot: Population of women
Source: Own based on Eurostat and EIGE
Fig. 12:
Moran's scatter plot: Education
Source: Own based on Eurostat ad EIGE
Tab. 3:
Moran's I statistics
| Year/variable | GDP | GEI | GPG | ER | PEP | EDU |
|---|---|---|---|---|---|---|
| 2015 | 0.4398 (0.0029) | 0.6058 (0.0006) | 0.0676 (0.2805) | 0.2252 (0.0806) | 0.0249 (0.3415) | 0.5838 (0.0007) |
| 2017 | 0.4420 (0.0029) | 0.5816 (0.0316) | 0.0760 (0.2673) | 0.1243 (0.1857) | 0.0223 (0.3463) | 0.4948 (0.0033) |
| 2019 | 0.4451 (0.0029) | 0.6035 (0.0006) | 0.0462 (0.3184) | 0.0940 (0.2277) | 0.0262 (0.3385) | 0.5125 (0.0023) |
[i] Source: Own elaboration by the authors based on Eurostat and EIGE
[ii] Note: p-values are in parentheses; significance level α = 0.05.