The Impact of Gender Inequality on GDP in EU Countries
In recent years, gender inequality has been considered the main characteristic of insufficient gross domestic product (GDP) growth. This paper discusses the evolution of GDP per capita in 21 countries of the European Union between 2015 and 2019. Using panel regression, we investigated the change in...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Sciendo
2023-10-01
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| Series: | Central European Journal of Public Policy |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/cejpp-2023-0011 |
| _version_ | 1797340439899013120 |
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| author | Juhásová Simona Buleca Ján Tóth Peter Mirdala Rajmund |
| author_facet | Juhásová Simona Buleca Ján Tóth Peter Mirdala Rajmund |
| author_sort | Juhásová Simona |
| collection | DOAJ |
| description | In recent years, gender inequality has been considered the main characteristic of insufficient gross domestic product (GDP) growth. This paper discusses the evolution of GDP per capita in 21 countries of the European Union between 2015 and 2019. Using panel regression, we investigated the change in GDP per capita through five variables. The analysis results showed that female employment rate is the most statistically significant and positive variable on GDP. Gender Equality Index also appeared to be an essential variable. The second part of our analysis consisted of an explanatory spatial data analysis of all variables to examine the spatial dimension of the variables. To explain spatial econometrics, we used selected methods, namely, choropleth maps, Local Indicators of Spatial Association (LISA) cluster analysis, Moran‘s scatter plots, and Moran‘s I statistics. Based on the visualization of choropleth maps, GDP per capita did not change during the observed period, even though the values of the explanatory variables changed. For GDP per capita, the same applies in the case of LISA cluster analysis. At the end of the monitored period, the countries were included in the same cluster as at the beginning. When plotting Moran‘s scatter plot, it was found that GDP per capita did not tend to have positive or negative spatial autocorrelation or no spatial autocorrelation. Moran‘s I statistic showed that GDP per capita values were not randomly dispersed; they were grouped according to a specific formula into clusters. |
| first_indexed | 2024-03-08T10:03:02Z |
| format | Article |
| id | doaj.art-b3a5c4f13d884d00bb553e9a0a41541e |
| institution | Directory Open Access Journal |
| issn | 1802-4866 |
| language | English |
| last_indexed | 2024-03-08T10:03:02Z |
| publishDate | 2023-10-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Central European Journal of Public Policy |
| spelling | doaj.art-b3a5c4f13d884d00bb553e9a0a41541e2024-01-29T08:53:23ZengSciendoCentral European Journal of Public Policy1802-48662023-10-01172133210.2478/cejpp-2023-0011The Impact of Gender Inequality on GDP in EU CountriesJuhásová Simona0Buleca Ján1Tóth Peter2Mirdala Rajmund3Technical University of Košice, Faculty of Economics, Department of Finance, SlovakiaTechnical University of Košice, Faculty of Economics, Department of Finance, SlovakiaTechnical University of Košice, Faculty of Economics, Department of Finance, SlovakiaTechnical University of Košice, Faculty of Economics, Department of Economics, SlovakiaIn recent years, gender inequality has been considered the main characteristic of insufficient gross domestic product (GDP) growth. This paper discusses the evolution of GDP per capita in 21 countries of the European Union between 2015 and 2019. Using panel regression, we investigated the change in GDP per capita through five variables. The analysis results showed that female employment rate is the most statistically significant and positive variable on GDP. Gender Equality Index also appeared to be an essential variable. The second part of our analysis consisted of an explanatory spatial data analysis of all variables to examine the spatial dimension of the variables. To explain spatial econometrics, we used selected methods, namely, choropleth maps, Local Indicators of Spatial Association (LISA) cluster analysis, Moran‘s scatter plots, and Moran‘s I statistics. Based on the visualization of choropleth maps, GDP per capita did not change during the observed period, even though the values of the explanatory variables changed. For GDP per capita, the same applies in the case of LISA cluster analysis. At the end of the monitored period, the countries were included in the same cluster as at the beginning. When plotting Moran‘s scatter plot, it was found that GDP per capita did not tend to have positive or negative spatial autocorrelation or no spatial autocorrelation. Moran‘s I statistic showed that GDP per capita values were not randomly dispersed; they were grouped according to a specific formula into clusters.https://doi.org/10.2478/cejpp-2023-0011gender inequalitygender policyspatial econometricseuropean unionj16 |
| spellingShingle | Juhásová Simona Buleca Ján Tóth Peter Mirdala Rajmund The Impact of Gender Inequality on GDP in EU Countries Central European Journal of Public Policy gender inequality gender policy spatial econometrics european union j16 |
| title | The Impact of Gender Inequality on GDP in EU Countries |
| title_full | The Impact of Gender Inequality on GDP in EU Countries |
| title_fullStr | The Impact of Gender Inequality on GDP in EU Countries |
| title_full_unstemmed | The Impact of Gender Inequality on GDP in EU Countries |
| title_short | The Impact of Gender Inequality on GDP in EU Countries |
| title_sort | impact of gender inequality on gdp in eu countries |
| topic | gender inequality gender policy spatial econometrics european union j16 |
| url | https://doi.org/10.2478/cejpp-2023-0011 |
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