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The Impact of Gender Inequality on GDP in EU Countries

Simona Juhásová
Simona Juhásová
, 
Ján Buleca
Ján Buleca
, 
Peter Tóth
Peter Tóth
 and 
Rajmund Mirdala
Rajmund Mirdala
   | Oct 14, 2023
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Published Online: Oct 14, 2023
Page range: 13 - 32
Received: May 17, 2023
Accepted: Jun 21, 2023
DOI: https://doi.org/10.2478/cejpp-2023-0011
Keywords
© 2023 Simona Juhásová et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Fig. 1:

GDP per capita for the years 2015 and 2019Source: Own based on Eurostat and EIGENote: Q1: the quadrant with the lowest EUR value of GDP per capita; Q4: the quadrant with the highest EUR value of GDP per capita
GDP per capita for the years 2015 and 2019Source: Own based on Eurostat and EIGENote: 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 2019Note: Q1: countries with low scores; Q4: countries with high scores
Gender Equality Index for the years 2015 and 2019Note: Q1: countries with low scores; Q4: countries with high scores

Fig. 3:

Gender Pay Gap for the years 2015 and 2019Source: Own based on Eurostat and EIGENote: Q1: lowest wage differences expressed in %; Q4: the highest wage differences expressed in %.
Gender Pay Gap for the years 2015 and 2019Source: Own based on Eurostat and EIGENote: 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 EIGENote: Q1: low female employment rate in %; Q4: high female employment rate in %.
Female employment rate for the years 2015 and 2019 Source: Own based on Eurostat and EIGENote: 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 EIGENote: Q1: the lower number of the female population (1000); Q4: the higher number of the female population (1000).
Population of women for the years 2015 and 2019 Source: Own based on Eurostat and EIGENote: 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 EIGENote: 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).
Graduates in tertiary education by education level for the years 2015 and 2019 Source: own based on Eurostat and EIGENote: 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).

Fig. 7:

Moran's scatter plot: GDPSource: Own based on Eurostat and EIGE
Moran's scatter plot: GDPSource: Own based on Eurostat and EIGE

Fig. 8:

Moran's scatter plot: Gender Equality IndexSource: own based on Eurostat and EIGE
Moran's scatter plot: Gender Equality IndexSource: own based on Eurostat and EIGE

Fig. 9:

Moran's scatter plot: Gender Pay GapSource: Own based on Eurostat and EIGE
Moran's scatter plot: Gender Pay GapSource: Own based on Eurostat and EIGE

Fig. 10:

Moran's scatter plot: Employment rateSource: Own based on Eurostat and EIGE
Moran's scatter plot: Employment rateSource: Own based on Eurostat and EIGE

Fig. 11:

Moran's scatter plot: Population of womenSource: Own based on Eurostat and EIGE
Moran's scatter plot: Population of womenSource: Own based on Eurostat and EIGE

Fig. 12:

Moran's scatter plot: EducationSource: Own based on Eurostat ad EIGE
Moran's scatter plot: EducationSource: Own based on Eurostat ad EIGE

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

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)

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
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