This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons. org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Copyright © 2021 The Author(s). Published by Vilnius Gediminas Technical University *Corresponding author. E-mail: tokarww@ukr.net Business, Management and Economics Engineering ISSN: 2669-2481 / eISSN: 2669-249X 2021 Volume 19 Issue 2: 373–388 https://doi.org/10.3846/bmee.2021.15382 CLUSTER ANALYSIS OF THE EUROPEAN UNION GENDER EQUALITY AND ECONOMIC DEVELOPMENT Oksana VINSKA 1, Volodymyr TOKAR 2* 1Department of European Economy and Business, Faculty of International Economics and Management, Kyiv National Economic University named after Vadym Hetman, Kyiv, Ukraine 2Department of Software Engineering and Cybersecurity, Faculty of Information Technology, Kyiv National University of Trade and Economics, Kyiv, Ukraine Received 31 July 2021; accepted 28 September 2021 Abstract. Purpose – The article aims at discovering classes and clusters of EU member-states considering their levels of economic development and gender equality to foster the enhance- ment of EU cohesion policy. Research methodology – The methodology includes the grouping by two parameters, economic development and gender equality, and the cluster analysis, the “far neighbor principle”, agglom- erative hierarchical classification algorithm and the usual Euclidean distance as the distance between objects. Findings – There are no gender equality laggards among EU member-states. More developed countries belong to gender equality leaders, while there are two gender equality leaders and one gender equality adopter among transition countries. The group of less developed countries consists of six gender equality leaders and seven gender equality adopters. Research limitations – The results of cluster analysis may be impacted by off-shore activity of Ireland and Luxembourg. Practical implications – The EU supranational bodies can use our results to develop more ef- ficient cohesion policy tools to ensure the adherence to the principle of gender equality. Originality/Value  – The study is a pioneer one in determining nine classes and five clusters of EU member-states considering their levels of economic development and gender equality, as well as in introducing three types of countries depending on their level of gender equality, namely gender equality leaders, adopters, and laggards. Keywords: cluster analysis, economic development, EU member-states, gender equality, GDP per capita. JEL Classification: C38, D63, J16. http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/licenses/by/4.0/ https://doi.org/10.3846/bmee.2021.15382 https://orcid.org/0000-0002-4360-0933 https://orcid.org/0000-0002-1879-5855 374 O. Vinska, V. Tokar. Cluster analysis of the European Union gender equality and economic development Introduction Ensuring equality between men and women is the EU fundamental value aiming at fos- tering socioeconomic progress for benefits of all members of the society. As stated in the Treaty on the Functioning of the European Union, integrated European nations strive to eliminate inequalities (European Union, 2021) making the EU one of the global leaders in female economic, social and political emancipation. This unique regional block demonstrates a significant progress in unlocking women’s potential. Promoting gender equality in Europe goes back to the Treaty of Rome in 1957, which ensured the principle of equal pay for male and female workers for equal work or work of equal value. However, only the adoption of “Strategy for equality between women and men 2010–2015”, “European Pact for gender equality 2011–2020”, and “Strategic Engagement for gender equality 2016–2019” gave the additional momentum for ensuring gender equality in EU member-states (Jacquot, 2020), but the progress is not homogeneous among them and there are a lot of institutional efforts ahead to reach the universal emancipation of European women. The EU doesn’t have a specialized separate gender equality policy, but it follows the Gender Equality Strategy 2020–2025 aimed at building the Union of Equality till 2025. The above-mentioned strategy has an integrative approach consisting of intersectionality and gender mainstreaming. The gender equality will be introduced to all spheres of interior and exterior life, common actions and policies of EU member-states and partner countries. Therefore, there is a high demand for applied research on issues related to gender equality in terms of determining specialized approach to each country considering its social, economic and cultural peculiarities. Our hypothesis is that there is a definite interplay between levels of gender equality and economic development of EU member-states. The novelty of our research resides on the combination of cluster analysis and grouping with developing appro- priate classifying terminology for countries of interest. Thus, the article aims at discovering classes and clusters of EU member-states considering their levels of economic development and gender equality to foster the enhancement of EU cohesion policy. 1. Literature review There is a wide range of publications on gender equality and economic development. For instance, Altuzarra et  al. (2021) display the effects of gender gaps in terms of education, labor market and institutional representation on economic growth. They analyze 105 de- veloping countries in 1990–2017 leaving without attention European peculiarities useful for implementing efficient tailor-made policies in this region. Mitra et  al. (2015) explored the impact of gender equality on economic growth, namely the role of equality of economic opportunities and equality in economic and political outcomes for economic development. These authors found that economic growth of developed countries result from equality in outcomes, while developing ones mostly depend on gender equality in opportunity. How- ever, some developed countries demonstrate different tendencies, for instance, Bulgaria and Luxembourg proving the necessity for detailed investigation of EU member-states. Kabeer and Natali (2013) shed light on asymmetrical inter-influence of gender equal- ity and economic growth showing that gender equality in education and employment Business, Management and Economics Engineering, 2021, 19(2): 373–388 375 substantially fosters economic growth, but economic growth by itself doesn’t lead to fast elimination of gender inequality in healthcare, human rights and well-being. It means that governments must focus on gender policy and efficient redistribution tools to ensure equal benefits for women and men. However, such a shift from the meritocratic principle of gender equal opportunities to gender equal results may cause social and political tensions. Applying panel data analysis Rodríguez (2017) demonstrated that the growing female participation in labor markets has no or little effect on economic development of five Latin American countries implicating low productivity of female labor force probably due to com- paratively low-productivity sectors of economy or limited amount of countries analyzed. Similarly, Baerlocher et  al. (2021) did not discover any evidence of economic growth due to high productivity of women engaged in labor markets. Using the linear model based on neoclassical production function, these authors suggested that GDP grew only thanks to a greater parcel of working population resulting in a moderate increase of living standards. The limitations of this model include the underestimation of higher employment rates of males compared with female ones, as well as gender gap in education and training. Minasyan et  al. (2019) pointed out that there is a positive correlation between gender equality in education, especially initial one, and economic growth. However, Borgonovi and Han (2021) highlighted the problem of higher fear of failure among female students compared with male ones in developed countries preventing them from selection of certain highly-qualified and accordingly payed occupations that probably partially explains gender pay gaps and the lack of women in STEM. Rai et  al. (2019) emphasized the importance of including female second shifts at home, namely, house chores and childcare duties ignored by GDP, into UN Social Development Goal 8 focusing on productive employment and decent jobs, but economic theory is still in the process of developing adequate methodology for calculating economic value of male and female reproductive inputs that could be to some extent substituted by the government. For example, employing policy modeling and its gender impact, Ilkkraracan et al. (2021) discov- ered that Early Childhood Education and Care program in Turkey generated over million new jobs (females took 57% of them), decreased female unpaid time in households decreased by one third, and, therefore, resulted in improved living standards of women in general and the ones with small children in particular. The overall positive impact for gender equality was substantial, however, high budget expenses involved make such programs unbearable for many developing countries at least in the nearest future. Barth et  al. (2021) used Decennial Census of the United States and the Longitudinal Employer Household Dynamics (LEHD) data to show how the gender earnings gap changes considering career advancement within one company and gains changing employers for college educated workers and for those without a degree. They stated that career advance- ment within one company is the main driver of gender pay gap mainly caused by marriages. Therefore, regardless of education, a personal choice of creating a family undermines female careers and leads to the growing gender pay gap. Kennedy et al. (2017) proved that there is an interplay between economic prosperity and decreasing the gender wage inequality. Using data for labor productivity and wage gap for 1986–2013 in Australia, they discovered that the reduction of gender pay gap by 10% results 376 O. Vinska, V. Tokar. Cluster analysis of the European Union gender equality and economic development in 3% increase in per capita output emphasizing the economic efficiency of eliminating gen- der pay gap. Shehu et al. (2017) observed that that poor economic conditions hit women harder than men, e. g., US companies tend to provide lower compensations for women compared to men during harsh economic conditions with the exception of CEOs treated equally regardless of gender. Moreover, Brzezinski (2021) demonstrated that pandemics affected women more than men causing the increase in gender inequality, especially in terms of employment rates. Finally, Kovalenko and Töpfer (2021), using US data for 1979–2019 and structural vector auto-regression model to analyze the cyclical dynamics and gender pay gap, showed that gender pay gaps are diminished by bargaining power in the short-run period and technology shocks in the medium-run one. However, they found a link between the decrease in gender pay gap and increase in unemployment rates. It is worth mentioning, that innovations and technology shifts could also increase the gender pay gap. For example, Aksoy et  al. (2021) found out that robotization in analyzed 20 European countries enlarges earnings of employees of both genders, however it increases the gender pay gap due to the gender composition of the workforce, namely men accounting for the lion’s share of employment in STEM. Analyzing the efficiency of introducing student loans in Chile to fight labor market gen- der disproportions, Didier (2021) argued that this funding tool increased female labor market participation and lowered gender pay gap, but intensified the glass ceiling raising the actuality of further research of expediency of implementing gender quotas to shatter the glass ceiling. Morsy (2020) used the World Bank Global Findex database for 141 countries considering ownership structure of banks and variables on gender gaps such as gender gap in education, income, and labor market participation to disclose conditions of limiting the access to finance for women. The worst situation is observed in countries where there is an insignificant share of foreign banks, prevalence of state-owned banks, non-transparent credit information and huge gender gaps in education. Dheer et  al. (2019) stressed the importance of female business contribution to the eco- nomic growth, but noticed that women are less likely to create a new business, because of social and cultural peculiarities regarding understanding of the essence of masculinity and femininity, as well as traditional institutions preventing from equalizing opportunities. 2. Methodology Considering findings presented in the publication of Vinska and Tokar (2017) focused on gender gaps in economic opportunities and participation in the EU, we would like to determine groups of EU member-states in terms of levels of gender equality and economic development using the GDP per capita at current market prices in euros in 2016–2020, as well as the level of gender equality evaluated by the Gender Equality Index developed by the European Institute for Gender Equality [EIGE]. The EIGE fosters the pursuing the gender mainstreaming. It arranges gender equality training, assesses policies and actions via prism of their gender impact, advances institutional Business, Management and Economics Engineering, 2021, 19(2): 373–388 377 transformation towards gender inclusiveness, provides guidance on gender equality in aca- demia and research (helping to develop gender equality plans in research organizations), as- sists the European and national Parliaments in making their procedures gender sensitive and makes sure that gender budgeting is one of the priorities in management of the EU funds. Its significant expertise was used to develop the Gender Equality Index, which is an important tool for countries’ bench-marking on their levels of gender equality. The EIGE provides researchers with valuable statistical data for conducting investigations and enables policy makers to use research findings for fine-tuning measures on ensuring gender equal- ity. Moreover, European Union intends to launch annual monitoring of gender equality based on the data withdrawn from the Gender Equality Index. The Gender Equality Index consists of six core main domains each in its turn including several indicators (European Institute for Gender Equality, 2021): 1. work – participation, segregation and quality of work; 2. money – financial resources, economic situation; 3. knowledge – attainment and participation, segregation; 4. time – care activities, social activities; 5. power – political, economic, social; 6. health – status, behavior, access. Firstly, for further elaboration of recommendations aimed at tackling the gender gaps problem in Europe, we use the EU NUTS classification of more developed, transition and less developed regions (Eurostat, 2021) and adapt it to determine groups of the EU mem- ber-states considering the level of gender equality assessed by the Gender Equality Index: – gender equality leaders – countries with the level of gender equality more than 90% of the EU average; – gender equality adopters – countries with the level of gender equality ranging from 75% to 90% of the EU average; – gender equality laggards – countries with the level of gender equality less than 75% of the EU average. Secondly, we apply the “far neighbor principle” of cluster analysis and the Microsoft Ex- cel data mining add-in for conducting computations. We use the agglomerative hierarchical classification algorithm. We will take the usual Euclidean distance as the distance between objects: ( ) ( )2i, j i,l j,ip x = x x−∑ , (1) where: l – attributes; k – number of attributes. The combination of two above-mentioned methods provides new opportunities for the EU member-states and supranational policy-makers to implement the Union of equality by 2035 via developing more tailor-made programs. Moreover, it is appropriate to expand this approach to the global context including advance and developing nations all over the world applying similar indices’ databases for calculations, for instance, the Global Gender Gap Index. 378 O. Vinska, V. Tokar. Cluster analysis of the European Union gender equality and economic development 3. Results Cluster analysis Table  1 indicates that the average growth of GDP per capita at current market prices in EU member-states equaled 11.6% in 2016–2020. Ireland showed the maximum relative in- crease – 29.1%, while Greece, Italy, Spain, and Sweden experienced decline. Table  1. GDP per Capita in EU Member-states at current market prices in 2016–2020, in euros (own calculations based on source: Eurostat, 2021) Period 2016 2017 2019 2020 Average Change, % Ranking Austria 40920 41990 44780 42300 42498 3.4 6 Belgium 37960 39130 41460 39110 39415 3.0 9 Bulgaria 6820 7400 8780 8750 7938 28.3 27 Croatia 11170 11920 13340 12170 12150 9.0 25 Cyprus 22230 23410 25270 23400 23578 5.3 14 Czech Republic 16790 18330 21140 20120 19095 19.8 18 Denmark 49420 51140 53370 53600 51883 8.5 3 Estonia 16670 18130 21220 20440 19115 22.6 17 Finland 39580 41080 43510 42940 41778 8.5 7 France 33430 34250 36140 34040 34465 1.8 10 Germany 38070 39440 41510 40120 39785 5.4 8 Greece 16170 16470 17110 15490 16310 –4.2 19 Hungary 11830 12960 14950 13940 13420 17.8 23 Ireland 57020 62550 72260 73590 66355 29.1 2 Italy 28210 28940 29980 27780 28728 –1.5 11 Latvia 12940 13890 15900 15430 14540 19.2 22 Lithuania 13560 14950 17470 17510 15873 29.1 21 Luxembourg 93930 95170 102200 101640 98235 8.2 1 Malta 23190 25010 26920 24630 24938 6.2 12 Netherlands 41590 43090 46880 45870 44358 10.3 5 Poland 11110 12170 13900 13640 12705 22.8 24 Portugal 18060 19020 20800 19660 19385 8.9 16 Romania 8630 9580 11510 11290 10253 30.8 26 Slovakia 14920 15540 17220 16770 16113 12.4 20 Slovenia 19590 20820 23170 22010 21398 12.4 15 Spain 23980 24970 26430 23690 24768 –1.2 13 Sweden 46990 47730 46390 45850 46740 –2.4 4 Average 27955 29225 31615 30584 29845 11.6 X Business, Management and Economics Engineering, 2021, 19(2): 373–388 379 Table  2 shows that the level of gender equality in EU member-states increased by 4.7 points in 2010–2018. Table  2. Levels of Gender Equality in EU Member-states in 2010–2018 (own calculations based on source: European Institute for Gender Equality, 2021) Period 2010 2012 2015 2017 2018 Average Change Ranking Austria 58.7 61.3 63.3 65.3 66.5 63.0 7.8 12 Belgium 69.3 70.2 70.5 71.1 71.4 70.5 2.1 6 Bulgaria 55 56.9 58 58.8 59.6 57.7 4.6 16 Croatia 52.3 52.6 53.1 55.6 57.9 54.3 5.6 22 Cyprus 49 50.6 55.1 56.3 56.9 53.6 7.9 23 Czech Republic 55.6 56.7 53.6 55.7 56.2 55.6 0.6 20 Denmark 75.2 75.6 76.8 77.5 77.4 76.5 2.2 2 Estonia 53.4 53.5 56.7 59.8 60.7 56.8 7.3 18 Finland 73.1 74.4 73 73.4 74.7 73.7 1.6 3 France 67.5 68.9 72.6 74.6 75.1 71.7 7.6 5 Germany 62.6 64.9 65.5 66.9 67.5 65.5 4.9 11 Greece 48.6 50.1 50 51.2 52.2 50.4 3.6 27 Hungary 52.4 51.8 50.8 51.9 53 52.0 0.6 26 Ireland 65.4 67.7 69.5 71.3 72.2 69.2 6.8 7 Italy 53.3 56.5 62.1 63 63.5 59.7 10.2 13 Latvia 55.2 56.2 57.9 59.7 60.8 58.0 5.6 15 Lithuania 54.9 54.2 56.8 55.5 56.3 55.5 1.4 21 Luxembourg 61.2 65.9 69 69.2 70.3 67.1 9.1 9 Malta 54.4 57.8 60.1 62.5 63.4 59.6 9.0 14 Netherlands 74 74 72.9 72.1 74.1 73.4 0.1 4 Poland 55.5 56.9 56.8 55.2 55.8 56.0 0.3 19 Portugal 53.7 54.4 56 59.9 61.3 57.1 7.6 17 Romania 50.8 51.2 52.4 54.5 54.4 52.7 3.6 25 Slovakia 53 52.4 52.4 54.1 55.5 53.5 2.5 24 Slovenia 62.7 66.1 68.4 68.3 67.7 66.6 5.0 10 Spain 66.4 67.4 68.3 70.1 72 68.8 5.6 8 Sweden 80.1 79.7 82.6 83.6 83.8 82.0 3.7 1 Average 59.8 61.0 62.4 63.6 64.5 62.2 4.7 X Applying by analogy the NUTS approach (Eurostat, 2021) we receive the following groups of EU member-states considering GDP per capita and levels of gender equality (Table 3). 380 O. Vinska, V. Tokar. Cluster analysis of the European Union gender equality and economic development Table 3. Groups and classes of EU member-states considering GDP per capita at current market prices in 2016–2020 and applying NUTS classification approach (own calculations based on source: Eurostat, 2021) EU member- state Ratio of GDP per capita to the EU average one, % Type ratio of a level of gender equality to the EU average one, % Type Class Austria 142 more developed country 101 gender equality leader 1 Belgium 132 more developed country 113 gender equality leader 1 Bulgaria 27 less developed country 93 gender equality leader 7 Croatia 41 less developed country 87 gender equality adopter 8 Cyprus 79 transition country 86 gender equality adopter 5 Czech Republic 64 less developed country 89 gender equality adopter 8 Denmark 174 more developed country 123 gender equality leader 1 Estonia 64 less developed country 91 gender equality leader 7 Finland 140 more developed country 118 gender equality leader 1 France 115 more developed country 115 gender equality leader 1 Germany 133 more developed country 105 gender equality leader 1 Greece 55 less developed country 81 gender equality adopter 8 Hungary 45 less developed country 84 gender equality adopter 8 Ireland 222 more developed country 111 gender equality leader 1 Italy 96 more developed country 96 gender equality leader 1 Latvia 49 less developed country 93 gender equality leader 7 Lithuania 53 less developed country 89 gender equality adopter 8 Luxembourg 329 more developed country 108 gender equality leader 1 Malta 84 transition country 96 gender equality leader 4 Netherlands 149 more developed country 118 gender equality leader 1 Poland 43 less developed country 90 gender equality leader 7 Portugal 65 less developed country 92 gender equality leader 7 Business, Management and Economics Engineering, 2021, 19(2): 373–388 381 EU member- state Ratio of GDP per capita to the EU average one, % Type ratio of a level of gender equality to the EU average one, % Type Class Romania 34 less developed country 85 gender equality adopter 8 Slovakia 54 less developed country 86 gender equality adopter 8 Slovenia 72 less developed country 107 gender equality leader 7 Spain 83 transition country 111 gender equality leader 4 Sweden 157 more developed country 132 gender equality leader 1 Table 4 contains 9 potential classes of EU member-states depending on combination of their types of economic development (GDP per capita at current market prices) and level of gender equality. Table 4. Classes of EU member-states considering GDP per capita at current market prices and levels of gender equality (own elaboration) Class number and title GDP per capita Gender Equality Amount of EU member-states 1 More developed countries Gender equality leaders 11 2 Gender equality adopters 0 3 Gender equality laggards 0 4 Transition countries Gender equality leaders 2 5 Gender equality adopters 1 6 Gender equality laggards 0 7 Less developed countries Gender equality leaders 6 8 Gender equality adopters 7 9 Gender equality laggards 0 However, there are only 5 classes determined according to our calculations. Moreover, there are no gender equality laggards among EU member-states. More developed countries be- long to the group of gender equality leaders. There are 2 gender equality leaders and 1 gender equality adopter among transition countries. Finally, we see 6 gender equality leaders and 7 gender equality adopters among less developed countries. Therefore, we strongly recommend to introduce the special gender prism for the EU funding allocated to Croatia, Czech Repub- lic, Greece, Hungary, Lithuania, Romania, and Slovakia. Considering 5 classes found by our calculations, we apply the cluster analysis using formula (1) and receive: ( ) ( ) ( ) 2 2 1,2 63.02 70.05 42498 39415 3083.01p x = + =− −∑ ; End of Table 3 382 O. Vinska, V. Tokar. Cluster analysis of the European Union gender equality and economic development ( ) ( ) ( ) 2 2 1,3 63.02 57.66 42498 7938 34560p x = + =− −∑ ; ( ) ( ) ( ) 2 2 1,4 63.02 54.3 42498 12150 30348p x = + =− −∑ . We put the received data into a table (distance matrix). From the distance matrix it fol- lows that objects 6 and 8 are closest (P6,8  = 20.04) and combined into one cluster. When forming a new matrix of distances, we select the largest value from the values of objects No. 6 and No. 8. As a result, we have 26 clusters: S (1), S (2), S (3), S (4), S (5), S (6,8), S (7), S (9), S ( 10), S (11), S (12), S (13), S (14), S (15), S (16), S (17), S (18), S (19), S (20), S (21), S (22), S (23), S (24), S (25), S (26), S (27). Repeating the same procedures several times, finally, we receive the following five clusters (Table 5). Table 5. Clusters of EU Member-states based on Levels of Gender Equality and Economic Development (own calculations) Clusters 1, 9, 2, 11, 20, 27, 10 3, 23, 4, 21, 13, 16, 12, 24, 17 5, 19, 26, 15, 6, 8, 22, 25 7, 14 18 1, 9, 2, 11, 20, 27, 10 0 38802.008 27645.013 31890 63770 3, 23, 4, 21, 13, 16, 12, 24, 17 38802.008 0 20790 58417.001 90297 5, 19, 26, 15, 6, 8, 22, 25 27645.013 20790 0 47260.002 79140.001 7, 14 31890 58417.001 47260.002 0 46352.001 18 63770 90297 79140.001 46352.001 0 Table  6 shows that Cluster 1 consists of 9 countries, namely Bulgaria, Croatia, Greece, Hungary, Latvia, Lithuania, Poland, Romania, and Slovakia. The levels of gender equality are within 50.4–58.0 with the average value equaling 54.4. GDPs per capita are within 7938– 16310 euros with the average value equaling 13255.8 euros. Table 6. Cluster 1 of EU Member-states (own calculations) EU member-states Gender Equality GDP per Capita Bulgaria 57.7 7938.0 Croatia 54.3 12150.0 Greece 50.4 16310.0 Hungary 52.0 13420.0 Latvia 58.0 14540.0 Lithuania 55.5 15873.0 Poland 56.0 12705.0 Romania 52.7 10253.0 Slovakia 53.5 16113.0 Average 54.4 13255.8 Minimum 50.4 7938.0 Maximum 58.0 16310.0 Business, Management and Economics Engineering, 2021, 19(2): 373–388 383 Table  7 shows that Cluster 2 consists of 8 countries, namely Cyprus, Czech Republic, Estonia, Italy, Malta, Portugal, Slovenia, and Spain. The levels of gender equality are within 53.6–68.8 with the average value equaling 59.7. GDPs per capita are within 19095–28728 euros with the average value equaling 22625.6 euros. Table 7. Cluster 2 of EU Member-states (own calculations) EU member-states Gender Equality GDP per Capita Cyprus 53.6 23578.0 Czech Republic 55.6 19095.0 Estonia 56.8 19115.0 Italy 59.7 28728.0 Malta 59.6 24938.0 Portugal 57.1 19385.0 Slovenia 66.6 21398.0 Spain 68.8 24768.0 Average 59.7 22625.6 Minimum 53.6 19095.0 Maximum 68.8 28728.0 Table  8 shows that Cluster 3 consists of 7 countries, namely Austria, Belgium, Fin- land, France, Germany, Netherlands, and Sweden. The levels of gender equality are within 63.0–82.0 with the average value equaling 71.4. GDPs per capita are within 34465–46740 euros with the average value equaling 41291.3 euros. Table 8. Cluster 3 of EU Member-states (own calculations) EU member-states Gender Equality GDP per Capita Austria 63.0 42498.0 Belgium 70.5 39415.0 Finland 73.7 41778.0 France 71.7 34465.0 Germany 65.5 39785.0 Netherlands 73.4 44358.0 Sweden 82.0 46740.0 Average 71.4 41291.3 Minimum 63.0 34465.0 Maximum 82.0 46740.0 Table  9 shows that Cluster 4 consists of 2 countries, namely Denmark and Ireland. The levels of gender equality are within 69.2–76.5 with the average value equaling 72.9. GDPs per capita are within 51883–66355 euros with the average value equaling 59119.0 euros. 384 O. Vinska, V. Tokar. Cluster analysis of the European Union gender equality and economic development Table 9. Cluster 4 of EU Member-states (own calculations) EU member-states Gender Equality GDP per Capita Denmark 76.5 51883.0 Ireland 69.2 66355.0 Average 72.9 59119.0 Minimum 69.2 51883.0 Maximum 76.5 66355.0 Table 10 shows that Cluster 5 consists of Luxembourg only with the level of gender equal- ity – 67.1, and GDP per capita equaling 98235.0 euros. Table 10. Cluster 5 of EU Member-states (own calculations) EU member-states Gender Equality GDP per Capita Luxembourg 67.1 98235.0 Table 11 indicates that there are substantial discrepancies in the results of grouping of EU member-states applying the cluster analysis and comparison with the average values of GDP per capita and gender equality due to the abnormally high levels of GDP per capita of some countries sometimes referred to as the inner off-shores within the EU, namely, Ireland and Luxembourg. It is especially obvious, when there are no large differences in gender equality levels, while GPDs per capita differ to the large extent. Table  11. Clusters and classes of EU member-states considering GDP per capita and level of gender equality (own elaboration) # Cluster Class 1 Bulgaria, Croatia, Greece, Hungary, Latvia, Lithuania, Poland, Romania, and Slovakia Less developed countries – gender equality adopters: Croatia, Czech Republic, Greece, Hungary, Lithuania, Romania, and Slovakia 2 Cyprus, Czech Republic, Estonia, Italy, Malta, Portugal, Slovenia, and Spain Less developed countries – gender equality leaders: Bulgaria, Estonia, Latvia, Poland, Portugal, and Slovenia 3 Austria, Belgium, Finland, France, Germany, Netherlands, and Sweden Transition countries – gender equality adopters: Cyprus 4 Denmark and Ireland Transition countries – gender equality leaders: Malta, Spain 5 Luxembourg More developed countries – gender equality leaders: Austria, Belgium, Denmark, Finland, France, Germany, Ireland, Italy, Luxembourg, Netherlands, and Sweden We think that less developed countries which are gender equality adopters should fall under the special gender lens while receiving funds from the EU budget. European institu- tions should pay particular attention to the issue of gender equality fostering the economic Business, Management and Economics Engineering, 2021, 19(2): 373–388 385 progress of theses EU member-states. The improperly tailored financial support under the cohesion policy may result in widening the gender gap contradicting the aim of the EU to build a Union of Equality by 2025. For example, Cyprus despite of economic progress does not distribute achieved benefits following the principle of gender equality. 4. Discussion Our results somewhat support the findings of Altuzarra et al. (2021) when it comes to influ- ence of gender gaps in terms of education, labor market and institutional representation on economic growth. However, even though we have used a more comprehensive index it covers only the EU member-states. Analogically, considering that we have used the index containing wage and education dimensions, our conclusions are in harmony with the ones of Minasyan et al. (2019) highlighting a positive correlation between gender equality in education and eco- nomic growth, as well as, Kennedy et al. (2017) pointing out an interplay between economic prosperity and decreasing the gender wage inequality. We also agree with Mitra et al. (2015) that economic growth of developed countries result from equality in outcomes. However, our research has not included developing countries, therefore, we can neither prove nor reject the idea that developing countries mostly depend on gender equality in opportunity. Our find- ings concord with Kabeer and Natali (2013) showing that economic growth by itself doesn’t lead to fast elimination of gender inequality in healthcare, human rights and well-being. The first limitation of our research is connected with inner EU off-shore zones, namely Ireland and Luxembourg. Their distorted indicators of GDP per capita influence the cluster analysis. However, in our grouping both countries fall into the group of more developed gender equality leaders, therefore, we can state that our methodology has weakened this limitation. Moreover, the ongoing process of global corporate tax harmonization will lessen, if not eradicate, this limitation in the future. The second limitation is caused by the nature of the Gender Equality Index used for calculations in our research. As any other index containing both quantitative and qualita- tive data, the Gender Equality Index is not totally free from subjective influence of experts’ opinions. However, the strict procedures for selecting experts within EIGE minimize any potential bias, therefore, our results adhere to strict standards of reliability. Conclusions Combining the levels of economic development (more developed countries, transition coun- tries, and less developed countries) and gender equality (gender equality leaders, gender equality adopters, and gender equality laggards), we introduce nine potential classes of EU member-states, five of them were found in the EU according to the results of our calculations, namely: eight less developed countries – gender equality adopters (Croatia, Czech Republic, Greece, Hungary, Lithuania, Romania, and Slovakia), six less developed countries  – gen- der equality leaders (Bulgaria, Estonia, Latvia, Poland, Portugal, and Slovenia), one transi- tion country – gender equality adopter (Cyprus), two transition countries – gender equality leaders (Malta and Spain), and eleven more developed countries  – gender equality leaders 386 O. Vinska, V. Tokar. Cluster analysis of the European Union gender equality and economic development (Austria, Belgium, Denmark, Finland, France, Germany, Ireland, Italy, Luxembourg, Neth- erlands, and Sweden). All more developed countries are gender equality leaders supporting women’s emancipation. There are no gender equality laggards proving that the Cohesion policy has a positive impact on gender equality. Five clusters significantly differ from the above-mentioned classes due to the striking discrepancies in economic development of EU member-states, including the inner off-shores, Ireland and Luxembourg. Therefore, there is a need for fine-tuning European policies. There should be a special gender prism applied to the EU funding allocated to less developed countries – gender equality adopters (Croatia, Czech Republic, Greece, Hungary, Lithuania, Romania, and Slovakia) to ensure their gender sensitivity, while financial support for Cyprus should focus on combination of its economic progress and distribution of benefits following the principle of gender equality. We think that the strengthening of gender budgeting and suspension of EU financing in case of missing targets on female emancipation will result in improved levels of gender equality in EU member-states. The applied dimensions of designed methodology include the assessing progress of less developed EU member states in their advancement towards gender equality and economic prosperity. The prospects for further pieces of research include the wide range of analytical investiga- tions of EU and global structural analysis of groups and clusters considering levels of gender equality in various social and economic sectors for elaborating applied measures to improve the status quo. The EU and global economic systems move towards a new technological edge with the dominance of science, technology and innovations. Thus, we suppose that the retrospective approach can be turned into perspective one if to concentrate on female empowerment in STEM. Author contributions Oksana Vinska and Volodymyr Tokar conceived the study and were responsible for the de- sign and development of the data analysis. Volodymyr Tokar was responsible for data collec- tion and analysis. Oksana Vinska and Volodymyr Tokar were responsible for data interpreta- tion. Oksana Vinska and Volodymyr Tokar wrote the first draft of the article. 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