The Impact of Gender Inequality on GDP in EU Countries
Bibliographic Data
| ID | 16852836 |
|---|---|
| Authors | Simona Juhásová (0000-0003-2788-8006, Technical University of Košice), Ján Buleca (0000-0002-6613-2167, Technical University of Košice), Peter Tóth (0000-0002-5010-9456, Technical University of Košice), Rajmund Mirdala (0000-0002-9949-3049, Technical University of Košice) |
| Year | 2023 |
| Volume | 17 |
| Issue | 2 |
| Pages | 13-32 |
| Publication date | 2023-12-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Central European Journal of Public Policy (JOURNAL) |
| Journal identifiers | ISSN: 1802-4866 • E-ISSN: 1802-4866 |
| Publisher | Walter de Gruyter GmbH (PUBLISHER • DE) |
| DOI | 10.2478/cejpp-2023-0011 |
| OpenAlex | W4387639357 |
| Language | EN |
| Citations received | 1 |
| References cited | 68 |
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
Econometrics · Economic growth · Economics · Gross domestic product · Inequality · Panel data · Per capita · Population · Regression analysis · Scatter plot · Spatial analysis · Statistic · Statistics · Variables · Demography · Fiscal Policy and Economic Growth · Mathematics · Regional Economics and Spatial Analysis · Spatial and Panel Data Analysis
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| Unique citing works | 1 |
|---|---|
| Citations per year | 0.5 |
| Citation span | 2024 - 2024 (1) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 1 |