| Abstract [eng] |
The main purpose of this master’s thesis is to evaluate the economic costs of gender inequality in the EU and Lithuania by identifying the primary socio-economic factors influencing the GDP growth and providing practical insights for policy formulation and further studies. This matters because gender inequality impacts macroeconomic indicators and long-term societal progress. This work consists of three parts: the analysis of scientific literature, the empirical research and its results, conclusion and recommendations. The literature analysis provides a theoretical overview of gender inequality concepts, distinguishing between sex and gender. It discusses how cultural norms, the patriarchal family model, the “motherhood penalty”, and labor market segregation, restrict women's economic participation and career opportunities. It also reviews inequality measures such as the Global Gender Gap Index (GGGI), and analyzes the economic costs of inequality, including lower productivity and the “talent tax”. After the literature analysis, the author carried out a quantitative empirical study of GDP growth determinants in 26 EU countries and Lithuania for 2006–2023. The research model applied a panel FE model, VAR model for short-run dynamics and the VECM for long-run equilibrium, complemented by correlation analysis, Granger causality tests and GDP prediction models. The analyzed gender-specific variables include GGGI, female human capital (higher education and STEM participation), labor market outcomes (employment structure and unemployment), wage and pension gaps, political and managerial representation as well as demographic variables (HDI, population, fertility) and economic ones (government budget expendenture, consumption, productivity, inflation, FDI, trade openness, physical capital). FE panel results across 26 EU countries show that female unemployment has a significant negative effect on GDP growth. At the same time, higher inequality (GII) is associated with higher growth, while female representation in parliament and part-time employment reduce growth. Granger causality indicates a delayed relationship: GGGI and the pension gap significantly predict GDP growth, while reverse causality between female full-time employment and GDP growth highlights women’s vulnerability during economic crises. The Lithuanian case analysis shows that in the short run, female-focused variables often have weak or statistically insignificant effects, frequently dominated by broader macroeconomic drivers. Short-term increases in female education and STEM participation initially correlate negatively with GDP growth, but a positive effect appears after two years. Female full-time employment and managerial representation negatively affect GDP growth. Part-time employment appears positive but is statistically unreliable. Women’s representation in parliament supports GDP growth, while women in local government is not statistically significant. In the long run, VECM results show that lower inequality (GII, GGGI), higher female higher-education and STEM participation, and narrower wage and pension gaps are positively associated with sustained GDP growth, whereas higher female unemployment and greater female managerial and political representation are negatively associated with long-run expansion. The conclusions and recommendations summarise the key theoretical insights, methodology, and empirical findings. While the results may be developed into an academic publication, they should be analysed critically due to short time series data. Future research could expand the set of variables, utilise longer time series data, and apply more complex methodological approaches. |
