The Impact of Gender Inequality on GDP in EU Countries

Many unique gender equality measures have been developed worldwide and across various areas. Gender contents have been added to many gender-blind measures, and gender mainstreaming is presented as crucial for successful policy achievements. Therefore, public policymakers expect an improvement in the gender situation at the global level. The European Union (EU) activities and mechanisms in the field of public policy serve to create a unique European identity based on the diversity of national and cultural traditions. In the process of the creation of gender policy, public policy must reflect and take into account gender dynamics in the policy formulation processes, which can significantly influence the success of adoption and subsequent outputs of specific measures.

Gender equality is an important moment of the public policy of the EU and its member countries. The issue of gender equality applies to various areas of public policy. The indisputable advantage of public policy is that it can be implemented as a response to current developments in the country. Among the essential priorities of the public policy of almost every country nowadays is the solution to gender inequality, which, like any other economic or social factor, affects the country's economic growth. Despite the initial rejection of the impact of gender inequality on the financial situation in the country, the application of this issue is firmly anchored in the field of public policy, through which it offers a solution to the current problem. The EU public policy was shaped by other goals of gender equality policy, which eventually transformed into all EU policies in the international context. The goal was to incorporate the perspective of gender equality into mainstream politics.

To demonstrate the success of gender equality policy measures, reviewing them after they have been adopted is essential. For example, this policy can be built on a combination of different public policy instruments, a process of inclusive empowerment, or gender transformation as the final result. Implementing proper gender and political measures is crucial for successful political outcomes. The political process is complex, but engaging in gender politics can bring positive results in many areas (Engeli & Mazur, 2018).

In recent years, women and men have become increasingly alike regarding education (EDU), earnings, and employment. These changes impact the country's economic inequality, especially if partners within the household have similar levels of education and income. Increased equality within the household can create favorable as well as unfavorable situations. The results show that higher similarity between partners does not increase inequality to a relevant degree. The influx of women into employment contributes to reducing inequality between households. The increase in inequality is caused by increased polarization between low- and high-income families in the spread of income within family types (Grotti & Scherer, 2016).

Decades of continuous progress in reducing gender inequality in developed and developing countries appear to be only apparent as 2000 saw stagnation and regression in many countries in various areas of gender inequality. This decline is most visible in the labor market. The solution to gender inequality is to put together effective policies (Klasen, 2020). Gender mainstreaming is a commonly discussed field nowadays. It plays a role in many aspects of human life related to employment, social inclusion, and social protection policies (European Commission, 2019). Whatever aspects of human life are considered, the profession plays its role. Also, when aiming at the young population (PEP), youth work is a critical perspective (Council of Europe, 2019). However, this is important for more than just the young PEP; the policies also focus on the entire PEP (National Commission for the Promotion of Equality, 2008).

Many authors state a strong correlation between gender attitudes and the economic situation of each country. To avert negative consequences, it is essential to correctly determine which determinants affect them (Elveren et al., 2022).

The essential goal of the paper is to map the situation of core indicators, deliver quantitative analysis, and formulate policy recommendations in the EU member countries. The selected socioeconomic indicators demonstrate the explored field's statistical significance. The paper offers an overview of gender inequality through the selected socioeconomic determinants regarding the female aspect. Firstly, the theoretical background is offered to demonstrate recent research in the examined field. Secondly, the investigated data is described along with reasoning for the selected observed variables and the employed methodology. Thirdly, the analytical outcomes are shown, and subsequently, the discussion demonstrates the obtained findings from interpreting the desired outcomes and the policymaking process.

The EU is committed to advancing gender equality and ensuring everyone has equal opportunities and rights. The EU has implemented various gender policy instruments to promote gender equality across member states. These instruments include directives, regulations, and recommendations for equal pay, work–life balance, and gender-based violence prevention. The EU also funds programs and initiatives supporting gender equality goals. Also, the European Institute for Gender Equality (EIGE) serves as a resource center for information and research on gender equality issues.

The EU policy aimed at eliminating gender inequality has made significant progress in recent years. The main tools for applying gender equality are legal regulations, including a gender perspective in all policies, and various measures to support women. The EU fulfills its commitment to achieving gender equality through multiple strategies. Within the framework of the strategy for gender equality for the years 2020–2025, it adopted binding measures to ensure the transparency of remuneration. As part of this strategy, the European Commission also adopted a new draft of the EU directive on combating violence against women and domestic violence. It started implementing a campaign to combat gender stereotypes (European Commission, 2023).

The EU is constantly creating anti-discrimination policies aimed at addressing multiple inequalities. However, not all of them achieve the desired results. Some mechanisms of the established legislation may cause resistance to introducing such an anti-discrimination directive due to institutionalization. It is also important to realize that each country may react differently to the specified demands, and not all will positively affect equality (Lombardo & Giorgio, 2013).

Gender inequality and its impact on the economic performance of countries were researched by many authors with different purposes and conclusions. Various economic outcomes are influenced by trust, individualism, gender attitudes, human capital, or economic development, and the factors mentioned positively impact financial results (Dutta & Sobel, 2022). Despite various political initiatives to empower women, gender inequality remains (Mukorera, 2020). Examining the impact of perceptions of economic inequality with conspiracy thinking confirms that this thinking can reinforce it. This is because economic inequality is perceived as a form of crisis that affects the entire social structure of the society, which also increases the perception of anomie within the collective (Jetten et al., 2022).

In export-oriented economies, where women provide most of the labor force in the export sector, gender inequality contributes to relatively lower wages for women. Gross domestic product (GDP) growth positively affects gender wage inequality (Seguino, 2000). Gender inequality decreases as countries grow through different mechanisms. With the help of the development process, it is possible to explain the relationship between GDP and gender inequality, while not forgetting other specific factors of each society. For example, in some vulnerable countries, cultural norms that exacerbate gender inequality by favoring men persist (Jayachandran, 2015). Gender differences in many countries are unfair and reduce the country's economic performance. It is difficult to determine the effects of gender differences on GDP and to calculate how much GDP would be more significant if gender differences were eliminated. Results from different countries confirm that reducing the gender gap in EDU leads to higher economic performance in countries (Klasen, 2018). Reducing gender inequality contributes to social justice and human development. It also positively impacts various institutions’ productivity, reducing GDP growth and poverty (Verdier-Chouchane, 2016). Globalization and gender equality significantly impact the economic growth of individual countries. The study shows that the ratio of women to men in the labor force positively affects economic growth (Farooq et al., 2020).

The impact of gender inequalities is seen in many sectors of the economies of the EU member countries. A proper policymaking process brings benefits for all the touched actors. For instance, the forestry sector aims at the rural policy. Female employment is considered necessary in every field and as well as area. Information communication technologies represent the sector where hiring women could be more obvious, even in the geographic part of the world where the female PEP does not have such a representation as in developed countries. Female gender inclusion has become a crucial point in the national policy to mitigate differentiation and enhance the development of particular economies (Asongu & Odhiambo, 2020). A supranational perspective, as seen in the EU, is missing in other parts of the world. As it is seen, this raises plenty of issues related to gender inequality. The developing countries demonstrate a lower consideration of diminishing this differentiation. Therefore, it is necessary to point to the international policies created by the international institutions that would play a role in supranational regulations. For instance, higher EDU in Kenya has problems with gender inequality. Ocean science is discussed to be one of the most touched fields.

Moreover, evaluating the performed national policies would bring an overview of the explored field to suggest further steps for enhancing the investigated field. The female PEP forms a minority in higher EDU in Kenya However, some study programs demonstrate its dominance also. It is the desired outcome of the focused national policy (Ojwala et al., 2022).

There is a close link between educational inequality and economic inequality (Blanden et al., 2023). When examining women's EDU in low- and middle-income countries, the factors that can eliminate gender inequalities in these countries are primarily sought. The effectiveness of national policies in promoting EDU and the overall progress achieved by individual countries are examined (Bennell, 2023). Gender equality and EDU are positively linked to economic growth (Minasyan et al., 2019). EDU is critical in increasing household income and wealth (Vo & Ho, 2022). EDU can have both direct and indirect effects on GDP per capita. The results indicate that an increase in the Gender Equality Index (GEI) in primary, secondary, and tertiary EDU increases GDP per capita.

In the same way, reducing gender inequalities in the educational attainment of different age groups positively affects GDP per capita. Therefore, it is in the interest of countries to implement policies that reduce gender inequality for sustainable economic growth (Assoumou-ella, 2019). Women‘s EDU positively benefits the economy of the state and society. In contrast, women‘s EDU level and its increase can affect the next generations‘ EDU level. A long-term relationship exists between gender inequality in EDU and a country‘s economic growth (Yumusak et al., 2013). Political institutions, culture, or even religion can be behind gender inequality in EDU. However, the study results indicate that even if political institutions have some influence, this influence is insignificant. On the contrary, culture and religion play a primary role in the emergence of gender inequality in EDU (Cooray & Potrafke, 2011).

Gender inequality is more pronounced in developing countries and arises due to insufficient measures in public administration and social and religious institutions. A study shows that gender equality and religious tensions influence gender inequality in the neighboring countries. Increasing economic growth and reducing religious tension in a given country promote gender equality and positively impact neighboring countries (Iqbal et al., 2022). The market drives economic development, women‘s employment opportunities, and gender inequality in households (Matthews & Nee, 2000). The development of the female labor force positively impacts income inequality (Heathcote et al., 2017). The most important drivers of increasing female labor force participation are changes in preferences and growth in women‘s relative wages, and changes in marriage patterns have a similarly significant impact.

Since the beginning of the 21st century, trends in income inequality have undergone a significant shift, whether in developed or developing economies. Since then, it has started to have a downward tendency. Every country‘s challenge is reducing inequality despite unexpected situations and market developments (Anderson, 2022). The results show that economic growth improves subjective well-being in the long run when income inequality decreases (Mikucka, 2017). The main factors contributing to gender inequality are the increasing inequality in working hours and the growing covariance between working hours and wages (Beckmannshagen & Schrőder, 2022). The difference in the remuneration between women and men, the difference in participation in employment, and also women‘s earnings are the problems in all countries (Anastasiou et al., 2015). Political support for basic income also affects gender inequality. Interest in the primary salary decreases with the increasing level of job qualifications of job seekers (Day, 2022). Examining wage inequality within a French firm showed that this difference relates to the firm itself and not to sectors or occupations. The cause of gender inequality is the automation of production (Domini et al., 2022).

One of the ways to allocate labor resources more efficiently, improve the economic performance of border regions, and also to reduce territorial and economic inequality is cross-border commuting. Cross-border commuting results from push and pull factors that affect employees differently. However, they generally react in the expected way to wages, job availability, unemployment, distance, and language. However, results that assess age, gender, EDU, and the occupational sector show that the reduction in inequality is minimal and may even be increasing (Edzes et al., 2022).

In managing inequalities, it is essential to analyze, among other factors, the role of financial development and the development of financial technologies (Frost et al., 2022). Technological change, the primary driver of long-term economic growth, can be positively related to women‘s political empowerment, and this relationship also applies vice versa. Women‘s political empowerment positively affects technological changes (Dahlum et al., 2022).

The panel data consists of annual observations from 2015 to 2019 for 21 countries of the EU, all available in the Eurostat and EIGE databases. The European Commission carries them out. The following 21 countries of the EU are distinguished – Austria, Belgium, Bulgaria, Czech Republic, Denmark, Estonia, Finland, France, Germany, Hungary, Italy, Latvia, Lithuania, Luxembourg, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, and Sweden. It is to be noted that six states were omitted from the analysis because of missing data.

A total of six variables are investigated in the analysis. Gross domestic product (GDP) represents the explained variable. The remaining five variables perform as the explanatory variables, and these are the Gender Equality Index (GEI), the Gender Pay Gap (GPG), the Employment Rate (ER), the Population (PEP), and the graduates in tertiary by educational level (women per 100 men) (EDU). We draw data on GPG, ER, PEP, and EDU according to females. The GDP variable is calculated as the ratio of real GDP to the average PEP of a particular year. The GEI variable is a composite metric using three dimensions: reproductive health, empowerment of women, and labor market. The GPG variable means unadjusted GPG. It represents the difference between male and female paid employees‘ average gross hourly earnings as a percentage of male paid employees‘ average gross hourly earnings. The ER variable represents the ER of women. It is calculated by dividing the number of women between the age of 20 and 64 in employment by the total female PEP of the same age group. The PEP variable includes all main labor market characteristics, such as the total PEP, employment, ER, full-time and part-time employment, and activity rates. The EDU variable covers indicators and statistics of the crucial aspects of EDU.

The research aims to test the state of gender inequality in selected EU countries in terms of GDP influenced by various indicators. Our research consists of two parts. In the first part, we use panel data regression analysis to explore the data set. We start with a model using longitudinal data represented by the following regression scheme: (1) GDPit∼β0it+β1itGEIit+β2itGPGit+β3itERit+β4itPEPit+β5itEDUit+uit {\rm{GD}}{{\rm{P}}_{{\rm{it}}}}\~{\beta _{0{\rm{it}}}} + {\beta _{1{\rm{it}}}}{\rm{GE}}{{\rm{I}}_{{\rm{it}}}} + {\beta _{2{\rm{it}}}}{\rm{GP}}{{\rm{G}}_{{\rm{it}}}} + {\beta _{3{\rm{it}}}}{\rm{E}}{{\rm{R}}_{{\rm{it}}}} + {\beta _{4{\rm{it}}}}{\rm{PE}}{{\rm{P}}_{{\rm{it}}}} + {\beta _{5{\rm{it}}}}{\rm{ED}}{{\rm{U}}_{{\rm{it}}}} + {{\rm{u}}_{{\rm{it}}}} To test the stationarity of our data set, we use augmented Dickey–Fuller (ADF) test (Dickey & Fuller, 1979). The F test is applied to obtain the overall significance of our linear regression model. The model is tested for potential errors. Autocorrelation is explored through the Durbin–Watson test. Heterogeneity is explored through the Breusch–Pagan test. The normal distribution of data is explored through the Jarque Bera test, and multicollinearity is tested using the Variance Inflation Factor (VIF). To differentiate between fixed effects and random effect models in panel analysis, we perform the Hausman test (Croissant & Millo, 2018).

Spatial econometrics is used to test the spatial dimension of the variables. Firstly, choropleth maps are created. A choropleth map is a thematic map where geopolitical areas are colored-shaded or patterned concerning a value. Visualization patterns depend on data distribution, sizes, classifications, and boundaries between classes (MacEachren, 1982; Tennekes, 2018). Secondly, to show the presence of spatial autocorrelation, we carried out Local Indicators of Spatial Association (LISA) cluster analysis. LISA analysis represents the extent of significant spatial clustering of similar values around this observation, with the LISA sum for all observations proportional to the global spatial association indicator (Anselin, 1995). The cluster analysis can reveal the inner relationships between the explored variables (Lipták et al., 2015; Gavurova et al., 2019). Below, Moran‘s scatter plots are shown.

The Moran scatter plot consists of a plot with the original variable on the x-axis and the spatially lagged variable on the y-axis. An essential aspect of visualization in the Moran scatter plot is the classification into four quadrants. Positive spatial autocorrelation corresponds to the upper-right and lower-left quadrants. Negative spatial autocorrelation corresponds to the upper-left and lower-right quadrants. It means that here we can find similar values at neighboring locations. Such a view demonstrates the territorial links between the examined objects (Gavurova et al., 2016).

On the contrary, we can find different values in these two quadrants at neighboring locations (Anselin, 1996). The last step in the second part of our analysis is a measure of spatial autocorrelation through Moran‘s I statistics. It is calculated as a randomly positioned test of the null hypothesis. Rejection of the null hypothesis increases the comprehension of the data set and its spatial structure. It measures the location of a spatially mean value and spatially weighted neighboring value of a variable. If I is greater than 0, we reject the null hypothesis. Positive spatial autocorrelation means the value of a variable in one location depends positively on the value of the same variable in neighboring locations (Anselin, 1995).

According to the mentioned methodologies and observed data, the following research questions are formulated:

Is there any link between GDP and selected gender-related indicators?

Gender inequality has received much attention in the past decades. A distinct feature of such discussion is women's employment, educational inequality, or wage gap in GDP results (e.g., Klasen & Lamanna, 2009; Falk & Leoni, 2010). Many studies confirm the mutual causality between gender inequality and the economic performance of countries. Economic progress empowers women, and thus promotes equality for both sexes.

On the other hand, there are developing countries that, based on gender equality, use human capital more effectively, and there is significant economic progress (Ghost & Ramanayake, 2020). Gender inequality is a phenomenon that is often the result of insufficient measures on the part of public institutions. Due to the influence of globalization, unequal status can also be transferred to neighboring countries. In the same way, increasing economic growth, which is supported by gender equality, can have a positive effect on neighboring countries due to globalization. EDU level only affects outputs within the home country and does not affect neighboring countries (Iqbal et al., 2022).

The result of the LISA cluster analysis brings an exciting conclusion about GDP per capita. At the beginning and end of the observed period, all countries belonged to the same clusters, even though the explanatory variables moved within the clusters over time. The clustering process is based on the factors that have arisen previously from the processed research. In their study, Klasen and Lamanna (2009) claim that gender inequalities in EDU and employment considerably reduce economic growth because such gender inequality reduces human capital in the society. It is also aimed at the EU policies focused on the equality of sexes. Such outcomes serve policymakers to prepare suitable regulations in this field to mitigate the differentiation in each field of the sexes inequality. The EU member countries’ national policies should reflect these regulations’ supranational aims. Hence, individual countries modify their national and regional policies to offer as many potential benefits as possible.

When monitoring GEI, we observe that the northern and eastern EU countries maintained their position in the monitored period. However, there are also countries where the change occurred, for example, Spain, which moved from the original high–low cluster to the high–high cluster. It means that it was positively influenced by the neighboring outcome values, improving the score and moving toward a gender-balanced country. Austria also moved within the clusters, but remained in the position of an outlier, that is, without positive spatial autocorrelation. According to EIGE (2019), with 70.1 out of 100 points, Spain was the ninth in the EU, and its score was even 2.7 points higher than the score for the whole EU. Since 2005, it has progressed toward gender equality faster than other EU countries. Austria has 65.3 out of 100 and ranks 13 in the EU ranking according to achieved GEI score and its score is 2.1 points lower than the score for the EU.

When examining GPG, there was a change in the classification into individual clusters in several cases. The total number of countries included in a different cluster at the end of the observation is seven, representing up to one-third of the examined countries. The GPG shows many countries that are not spatially autocorrelated with each other. Based on this, the level of wages of women in a given country does not affect neighboring countries. 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. A GPG can also be understood very specifically. Various fields are not directly associated with such an issue.

Nevertheless, undoubted ties can be found. Orthopedics as a medical specialization offers an interesting perspective as women earn significantly less than men here. The suggested fee code should prepare a compromise to make the wages more similar. It needs to be a proper policy proposal, but as it makes a consensus between two or more sides, this would be perceived as a guideline or a protocol for practice (Halim et al., 2023). Gender wage inequality is lowest in public institutions, followed by public and private enterprises. These findings can significantly contribute to understanding and addressing gender inequality in earnings, primarily through policies to promote gender equality (He & Wu, 2017).

As for the ER variable, there was also a movement of countries between clusters in most cases. During the observed period, ER of women was mainly maintained by the Nordic countries (Sweden, Finland, Denmark, Estonia, Latvia), which also belong to the cluster with a high spatial autocorrelation. The biggest jump was recorded for Slovakia, which moved from the original cluster four to cluster one, but was always in the cluster with a positive spatial autocorrelation. Women hold high management positions much more rarely than men. It is an obvious fact. Nonetheless, it is not related to their responsibility, performance, or labor productivity. This fact is understood as a historical phenomenon. Usually, there are no rules or policies to diminish this state. On the one hand, it is not required in private enterprises. On the other hand, the state, regional, or local authorities are not in a situation where such a policy would play a role. National gender-related policies are associated with differences in corporate governance outcomes. The gender-related topics represent the critical inputs to organizational and individual selection (Thams et al., 2018).

Participation of women in employment still varies widely within the EU, but female employment is increasing rapidly in Europe. A key factor for improving employment and earnings is women's increasing EDU. This fact leads to overall higher efficiency and support of economic growth (Gudrun, 2007). The participation of women in the labor market is improving due to the policy. The proof is, for example, the family policy focused on the labor market, the positive effect of which has been manifested in recent years. However, it is again essential to correctly set and verify the given measures because sometimes, alongside effective policies, some have the opposite effect and strengthen the existing gender inequality in the labor market (Seo, 2023). GDP per capita, labor force participation, EDU, and PEP share are among the primary sources where gender inequality can be identified. The regression model results show a positive relationship between GDP per capita and female labor force participation (Acar & Dogruel, 2012). Constraints to female labor force participation can result in lower economic growth (Alonso et al., 2019). Getting the policy right is very important.

For example, the EU's pension policy appears gender neutral, but ultimately harms most women. Its goal was to reduce the state's responsibility and increase individual responsibility, but this reform favors male work models, thereby disadvantaging women and their work engagement (Earles, 2013). The EU also has a range of equal opportunity policies and higher EDU programs (Bourabain & Verhaeghe, 2022). Female employment also plays a substantial role in mitigating a pay gap. Increasing EDU brings a new aspect to this area. It is reflected in the focus of the national and regional policies and at the international level. The EU pays attention to many measures that describe the position of women in society. One of the incomes of the female PEP should be bound to the benefits of employing women. Altogether, this brings many perspectives to be investigated in further research.

The discussed indicators possess a very close relation to the policymaking processes. It is indisputable that the gender inequality index, GPG, ER, PEP, and the tertiary EDU graduates influence many policy fields of each country's economy. Without proper analytical processing, the policymaking process would lack a statistical basement with fundamental backing. Hence, a deep investigation should be carried out before constructing a national policy related to a field of the national economy. Generally, a pay gap introduces a pay system distortion. It causes more unwanted points followingly. For instance, women are subsequently paid lower pensions at retirement age. Although they can retire earlier than men, it is not enough to make the arisen gap cleared away. The People's Republic of China retirement policy includes strict restrictions for potential pensioners. One of the main issues is the industry heterogeneity. Several factors cause this. For instance, an aspect of the explored ones is the ownership of the enterprises (Liu & Xu, 2023).

Governments should apply a holistic approach to gender inequality in their policies and legislation and, in this way, regulate the state of equality (Ovadia, 2022). Examining political support for basic income from the point of view of income inequality indicates that the correct setting of income taxation is significant, which affects the effects of gender inequality (Day, 2022). Disclosure of gender equality policies and their impact is essential for subsequent evaluation of such interventions (Escamilla-Solano et al., 2022). Researches show that countries with a more favorable status of gender equality have developed better approaches in the field of gender policy, especially at the level of institutions (Reidl et al., 2020). All these aspects can be considered in the political–economic models of the particular countries (Tkáčová et al., 2018).

A policymaking process at every authority level, from national through regional to local, is based on many inputs. It is not possible to simulate all these inputs. Hence, policymakers not only consider the obtained findings from the processed studies, but also concentrate on the ways of their achievement. From a perspective of gender inequality, the policy at a local level does not have such ruling power as a higher-positioned policy. This is a naked fact that should be considered when analyzing the policymaking process. Therefore, it is important to push on policymakers at the highest national level to make a national policy as practical as possible, and thus, it becomes a crucial point in each process related to the explored field, that is, the gender inequality issue. Besides the policy construction recommendations, the outcomes are also interesting for academia. The values of the statistical outcomes demonstrate the statistical significance of the applied gender-related indicators, and hence, these numbers show the strength of the employed methodology in the explored field.

All the particular policies are aimed at the main points of the gender inequality field. These aspects represent the key areas to which the policymaking authorities should pay attention to diminish the differences for all the indicators related to the discussed topic – from a pay gap issue through female employment to tertiary EDU of the female PEP. It is necessary to set the national policies of the EU member states, so that these crucial perspectives come to the forefront. Therefore, this should be performed in all the analytical models associated with the discussed fields. Successively, the desired outcomes are brought by the appropriate aim of the ratified EU policies and the other norms for mitigating gender inequalities.

Gender inequality is a fundamental and multidimensional problem not only for the EU countries, but for the whole world. The biggest problem is that countries strive for fast economic growth when equal opportunities are forgotten, and gender inequality deepens in all areas, ultimately harming economic development. Understanding the determinants of gender differences in the economic field and subsequently dealing with their impacts is a matter of interest for policymakers who try to establish a balance through various measures. Tracking gender differences, especially in economic areas, is important when explaining economic results. Reducing gender inequality is a long-term political commitment of the EU. The situation is gradually improving, although public policies are expected to have a more substantial impact. However, inequalities persist in almost all areas where there is a reduction in economic opportunities for women, ultimately affecting the global economy.

Regarding the limitation of the analysis, the data comes from the Eurostat and EIGE databases. These offer a large amount of data, but the observed data before 2015 are unavailable. Hence, such a shorter period is explored. Nevertheless, this does not lower the quality of the analytical outcome as the other authors applied the time series very similarly. The explained variable through regression analysis is in the form of the most applied macroeconomic indicator, that is, GDP. This is selected by inspiration from the other referenced studies in the paper, as many of them examined it as the explanatory variable. Hence, this indicator has been considered from the other perspective to bring a novel approach to deal with the socioeconomic and sociodemographic indicators in the field of gender inequality.