Associations of (un)observed factors and gender inequality in European countries: Evidence from spatial panel data model

This study aims to explore gender inequality using spatial panel data models and take a step forward in adopting such a comprehensive approach to identify and estimate the factors driving gender inequality in European countries. The analysis utilizes panel data covers 41 European countries spanning the period from 1990 to 2020. By employing spatial panel data models, the study accounts gender inequality by mainly considering unobserved effects and various other factors, including social, economic, human rights, and environmental degradation. The findings reveal that spatial effects play a significant role in gender inequality, as evidenced by the Gender Inequality Index heatmap and the Moran's I spatial autocorrelation test. Among the panel data regression models estimate to spatial effects, the FE-SAC model is determined to be the most efficient and consistent based on various model selection criteria. The main results show that life expectancy at birth, expected years of schooling, and democracy index have decreasing effects on gender inequality, whereas CO2 emissions per capita have increasing effects. Ultimately, the study concludes that gender equality is linked to unobserved effects such as the same culture, beliefs, and values which are main reasons of spatial effects.

Automated summary

This study aims to explore gender inequality in European countries using spatial panel data models, which take into account unobserved effects and various factors. The analysis is based on panel data covering 41 European countries from 1990 to 2020. The findings indicate that spatial effects play a significant role in gender inequality, and the FE-SAC model is determined to be the most efficient and consistent among the panel data regression models. The main results show that factors such as life expectancy, education, democracy, and CO2 emissions have varying effects on gender inequality.