Gender Pay Gap in the Western Balkans: Why Do Women Earn Less Than Men?

Ilić, Nikola

doi:10.1007/978-3-031-14706-7_4

Cited by 2 publications

(1 citation statement)

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“…Falk and Leoni (2010) claim that decreasing GPG increases the female participation ratio in the labor market. Ilić (2022), in his study, focused on the Balkan countries and confirmed that the wage gap between women and men can have adverse economic consequences, which include, for example, a decrease in the rate of employment or economic activity. This results in a significant drop in GDP per capita.…”

Section: Discussionmentioning

confidence: 90%

The Impact of Gender Inequality on GDP in EU Countries

Juhásová,

Buleca,

Tóth

et al. 2023

Central European Journal of Public Policy

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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.

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Section: Discussionmentioning

confidence: 90%