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The Impact of Gender Inequality on GDP in EU Countries Cover

The Impact of Gender Inequality on GDP in EU Countries

Open Access
|Oct 2023

Figures & Tables

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

Country20152019
GDPGEIGPGERPEPEDUGDPGEIGPGERPEPEDU
Belgium114334114334
France112214112414
Germany112124112224
Italy134324133324
Luxembourg114334114334
Denmark111134111134
Portugal432334434234
Spain423414414314
Austria241232221234
Finland112143112143
Sweden113141113141
Czechia441131441132
Estonia441141441141
Hungary444241442243
Latvia441141441141
Lithuania444241442241
Poland443411443311
Slovakia441441441141
Slovenia424442423242
Bulgaria442442442441
Romania444443443343

Moran's I statistics

Year/variableGDPGEIGPGERPEPEDU
20150.4398 (0.0029)0.6058 (0.0006)0.0676 (0.2805)0.2252 (0.0806)0.0249 (0.3415)0.5838 (0.0007)
20170.4420 (0.0029)0.5816 (0.0316)0.0760 (0.2673)0.1243 (0.1857)0.0223 (0.3463)0.4948 (0.0033)
20190.4451 (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

ModelFEMREMPooled
variableEstimatesignificance (p–value)Estimatesignificance (p–value)Estimatesignificance (p–value)
GEI103.8315*(0.0768)134.9978*(0.0438)767.4512***(0.0002)
GPG−35.1447−(0.6136)−51.2419−(0.5193)−828.2484**(0.0077)
ER312.0235***(<0.0001)290.4494***(<0.0001)598.8017*(0.0456)
PEP0.2875−(0.6866)0.1763−(0.7297)−0.8835*(0.0174)
EDU−8.6735−(0.6576)−18.6857−(0.4093)−389.8658***(<0.0001)
Panel diagnosticsCollinearity diagnostics
Test p-valueVIF testGEI 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
Language: English
Page range: 13 - 32
Submitted on: May 17, 2023
Accepted on: Jun 21, 2023
Published on: Oct 14, 2023
Published by: University of Matej Bel in Banska Bystrica, Faculty of Economics
In partnership with: Paradigm Publishing Services
Publication frequency: 2 issues per year

© 2023 Simona Juhásová, Ján Buleca, Peter Tóth, Rajmund Mirdala, published by University of Matej Bel in Banska Bystrica, Faculty of Economics
This work is licensed under the Creative Commons Attribution 4.0 License.