Harmonizing the Interplay Between SDG 3 and SDG 10 in the Context of Income Inequality: Evidence from the EU and Ukraine

Abstract

This paper investigates how Sustainable Development Goals SDG 3 (Health and Well-being) and SDG 10 (Reducing Inequality) interacted during the period 2009–2021 within the context of income disparities in the European Union and Ukraine. The central assumption is that lowering income inequality improves overall population health. The research proposes a conceptual model with four main elements: classifying countries according to their Gini index along with their performance on SDG 3 and SDG 10; analyzing how income inequality and progress on SDG 10 influence health outcomes (SDG 3); categorizing countries based on the strength of links between inequality measures and well-being indicators; and interpreting these results in the context of Ukraine’s European integration aspirations. Methodologically, cluster analysis, correlation and regression models, and semantic differentiation are applied. The findings show that a reduction in income inequality positively affects health and well-being. Nonetheless, Ukraine continues to face considerable structural and institutional hurdles. From a governance standpoint, the study highlights the need for cohesive policies that integrate economic, health, and social dimensions. Effective public management should coordinate national reforms to match EU healthcare and social policy standards. Strengthening institutions, ensuring fair access to healthcare services, and adopting inclusive policy instruments remain crucial to advancing both SDG 3 and SDG 10 targets, as well as supporting Ukraine’s broader integration with the European Union.

1. Introduction

Achieving a balance between economic equality and public well-being is a crucial aspect of sustainable development, as reflected in the interconnection between SDG 3 (Good Health and Well-being) and SDG 10 (Reduced Inequalities). The European Union has made significant progress in reducing income disparities through inclusive social policies, universal healthcare, and targeted economic reforms. However, the complex relationship between these goals requires a deeper understanding of how income distribution affects health outcomes across different socio-economic contexts. Ukraine’s strategic goal of European integration entails the alignment of its socio-economic policies with EU standards, particularly in the context of achieving the Sustainable Development Goals (SDGs). One of the key challenges in this process remains reducing economic inequality (SDG 10) and ensuring a higher level of well-being for the population (SDG 3). The relationship between these indicators remains ambiguous, as economic, social, and political factors may influence their dynamics differently across countries.

In light of Ukraine’s integration process into the European Union, the study of mechanisms for overcoming inequality becomes particularly relevant. Many EU countries demonstrate a positive correlation between reducing economic inequality and improving the health of the population. At the same time, some countries are faced with the fact that reducing income inequality does not always directly affect the improvement in well-being indicators. This suggests the need for a comprehensive approach to analysing the factors that shape the relationship between SDG 3 and SDG 10 [1,2,3,4,5,6,7].

This paper includes a literature review outlining the main approaches to the analysis of inequality and well-being; a methodological section describing the data sources and analytical methods employed; research findings detailing the relationships between SDG 3, SDG 10, and the Gini coefficient; and conclusions offering practical recommendations for aligning Ukraine’s socio-economic policy with EU integration objectives.

Given the strategic importance of Ukraine’s path toward European integration, the findings of this study may be of value not only to the academic community but also to policymakers seeking to adapt inequality-reduction mechanisms in accordance with European sustainable development standards.

The study’s results, which focus on harmonizing the interaction between SDG 3 (Health and Well-being) and SDG 10 (Reduced Inequality) in the context of Ukraine and EU countries, carry important practical implications for public governance. They can be applied at the macro level, for example, in shaping national socio-economic policy and adapting Ukrainian institutions to EU standards, as well as at the meso and micro levels, such as in the development of regional governance strategies, the administration of social programs, and the optimization of healthcare management. Such applications can help enhance decision-making effectiveness in areas related to human capital, public health, and social equity.

2. Literature Review

A review of the literature on the issues addressed in this work shows that, to achieve the Sustainable Development Goals, particularly reducing inequality (SDG 10), it is essential to consider both inter- and intra-country interactions. While synergies between goals often outweigh trade-offs, income inequalities within countries persist or even deepen [1,2,3,4,6,7].

The interrelationship between the Sustainable Development Goals—especially SDG 3 (Good Health and Well-being) and SDG 10 (Reduced Inequality)—is attracting increasing attention from researchers and international organizations. Among the relevant publications, it is worth highlighting authors who have examined the interplay between SDG 3 and SDG 10 using shared analytical approaches and conceptual frameworks. An important question arises: how can these goals be harmonized in the face of substantial economic, social, and political disparities among countries? Warchold, Pradhan, and Kropp (2020) analyze synergies among different SDGs and argue that interactions between them tend to have a positive overall effect, outweighing the potential trade-offs [1]. The authors apply regional and socio-economic analysis to assess the influence of income levels, demographic characteristics, and public policy on progress toward sustainable development. They conclude that linear relationships between goals tend to dominate over nonlinear ones, implying that countries with higher income levels tend to experience more predictable reductions in inequality and improvements in public health.

On the other hand, a study by Nae, Florescu, and Bălășoiu (2024) shows that in post-communist countries, globalization, labor market development, and effective public administration can contribute to reducing income inequality [2]. The researchers consider the interaction of these factors in the context of policy reforms, noting that structural changes in the economies of these countries affect social mobility, access to quality health services, and ultimately the achievement of SDGs 3 and 10.

One of the key obstacles in implementing SDG 10 is the accurate measurement of inequality, which directly influences the effectiveness of public policy. Fukuda-Parr (2019) examines the political dimensions of selecting indicators to monitor progress toward SDG 10 and concludes that existing metrics often fail to capture crucial aspects of inequality [3]. The author highlights that economic indicators commonly used by international organizations to assess inequality have significant limitations. For instance, the Gini coefficient does not always reflect real socio-economic disparities, as it overlooks factors such as the shadow economy and unequal access to social goods like healthcare and education.

The issue of financial decentralization and regional disparities in access to services is addressed in a study analyzing the impact of fiscal decentralization on reducing social inequality in Ukraine [4]. The author finds that decentralization has the greatest impact on poverty reduction, thereby supporting the implementation of SDG 10. However, the study also warns of potential risks stemming from uneven resource distribution across regions, which may lead to imbalances in access to healthcare and education. Digitalization plays a crucial role in reducing inequality. Shpak et al. (2022) prove that the level of digital literacy largely determines the possibilities of social mobility and access to high-quality health services [5]. They emphasize that the digital divide remains one of the key problems for developing countries, including Ukraine.

In the context of our research, particular attention is paid to analyzing the European experience and best practices in achieving SDG 10 and SDG 3, with a focus on assessing regional disparities. The authors [6] examine the progress of EU countries in the field of reducing inequality and conclude that considerable variation among countries stems from differences in economic structures, social policy approaches, and historical conditions. For example, Poland shows positive dynamics due to proactive state support for the middle class, whereas Bulgaria and Greece exhibit more uneven outcomes due to financial system instability and high unemployment. Szymańska (2021) offers a more detailed analysis of income inequality in EU countries for the period of 2010–2019 [7]. She observes that while income inequality between the EU countries is gradually narrowing, internal inequality within countries tends to persist or even worsen. The study underscores the role of social policies, demographic changes, and social protection spending in the process of income equalization. In particular, Poland’s experience in the healthcare sector offers valuable insights for the application of best practices. Khozhylo et al. (2023) analyze the impact of the COVID-19 pandemic on the Polish healthcare system and emphasize the importance of universal access to healthcare services [8]. The researchers note that the effective management of medical resources allowed Poland to largely mitigate the negative consequences of the pandemic, which may be a useful experience for Ukraine.

Several European countries, particularly Sweden and Denmark, demonstrate leading performance in the implementation of the SDGs, especially in achieving SDG 9, which focuses on infrastructure development, sustainable industrialization, and innovation. This allows ensuring economic sustainability, which has an impact on reducing social inequality. For example, Sweden and Denmark are at the forefront of SDG 9 implementation, while countries in eastern and south-western Europe report lower indicators. Nevertheless, the period from 2013 to 2019 shows a notable increase in SDG 9 progress in these regions [9]. EU member states have also made significant advances toward SDG 7, which pertains to energy efficiency and renewable energy. Malta has demonstrated the most substantial progress, while Sweden, Denmark, Estonia, and Austria are nearing full achievement of this goal [10].

Khajepasha and Gkartzios (2024) analyze regional convergence in the EU, highlighting the slowdown of this process due to income inequality [11]. Income inequality remains a serious challenge even in advanced economies. The overall progress of the EU countries in implementing the Sustainable Development Goals can be assessed relative to the European average. On average, EU countries have achieved 58% of the progress toward reaching the best results in implementing the SDGs. Assessments of national indicators through indices provide a more accurate picture of the progress [12]. Biekš et al. (2022) study sustainable economic development in the EU countries and prove that the ecological footprint has a significant impact on economic growth, reaching 31% [13]. Research of Kussul et al. (2019) demonstrates that high-resolution satellite data and deep learning methods significantly improve the ability to estimate SDG indicators [14]. This enables governments and international organizations to obtain objective data on regional socio-economic indicators, which is important for combating inequality, taking into account environmental factors in the process of economic planning, and combating social inequality.

Circular economy practices and climate change mitigation strategies represent essential dimensions of sustainable development. Countries with higher efficiency in implementing circular economy models tend to have lower population density, which helps reduce environmental degradation. However, increases in material footprint and CO₂ emissions may hinder progress toward achieving SDG 13 (Climate Action) [15]. In the case of Ukraine, its capacity to adapt to changes associated with the European Green Deal and the pursuit of the SDGs is evaluated through an assessment of its current achievements and readiness for transformation. Despite numerous challenges, Ukraine has shown some progress in implementing sustainable development, although there is still some distance to go before these goals are fully achieved [16]. The EU’s post-Euromaidan policy toward Ukraine emphasizes the securitization of development, the link between security and state-building [17]. This points to the importance of integrating economic reforms with overall development strategies.

Investment also plays a pivotal role in reducing inequality. Yuldashev et al. [18] have shown that foreign direct investment significantly reduces inequality in Asian countries, especially when accompanied by growth in human capital. This finding offers a valuable reference point for Ukraine, where investment in human capital remains critically important. Situm et al. [19] analyze healthcare financing in Austria and Ukraine, suggesting alternative financing instruments that can help reduce inequalities in access to healthcare. This is especially relevant for Ukraine, which continues to face substantial challenges in financing its healthcare system. Dvulit et al. [20] highlight the importance of an integrated approach to sustainable development planning. The authors stress the interconnectedness of social, economic, and environmental dimensions, which is vital for achieving both SDG 3 (Health and Well-being) and SDG 10 (Reduced Inequality).

Some scholarship also emphasizes the role of institutional capacity, welfare systems, and political structures in mediating the impact of inequality on health outcomes [21]. The uneven implementation of SDGs across countries often reflects global power dynamics, where resource allocation and policy priorities are shaped by external donor frameworks, limiting local agency and adaptation. Some scholarship has raised critical questions about the universal applicability of the SDGs, particularly in relation to postcolonial and post-socialist contexts. Decolonial theorists argue that global development frameworks, including the SDGs, often reflect Eurocentric assumptions, prioritizing technocratic metrics and standardized targets over locally defined needs, cultural contexts, and historical trajectories [22,23]. These frameworks risk perpetuating structural inequalities by reinforcing dependency on external donors and international benchmarks, rather than enabling endogenous, community-driven development [24]. Moreover, as Fanon [25] and other postcolonial scholars have emphasized, formal equality metrics may obscure deeper issues of epistemic injustice, systemic exclusion, and power asymmetries that shape health, education, and well-being outcomes. Integrating decolonial perspectives thus offers a vital lens for critically evaluating how SDG implementation may unintentionally reproduce global hierarchies and marginalize alternative visions of sustainability and justice.

Also, some researches emphasize that the impact of inequality extends beyond health and economic dimensions, influencing political stability, civic engagement, and nationalism. Cederman et al. [26] highlight how horizontal inequalities can exacerbate domestic instability and conflict, especially in transition societies. Solt [27] demonstrates that income inequality is associated with lower political engagement and higher support for nationalist movements, which poses a significant challenge for countries undergoing European integration. Considering these perspectives allows a more comprehensive understanding of how inequality may indirectly affect well-being through institutional trust and societal cohesion—factors that are particularly relevant for Ukraine and EU enlargement dynamics.

Recent research also highlights the role of labor rights in addressing inequality: Bagwell et al. [28] show that stronger union protections are significantly associated with reduced levels of economic inequality, making labor policy an important complementary mechanism in achieving SDG 10. Overall, the literature review demonstrates that the harmonization of SDG 3 and SDG 10 is achievable only under conditions of effective strategic planning, fiscal decentralization, digital transformation, and the adaptation of international practices. Harmonizing the interaction between SDG 3 (Good Health and Well-being) and SDG 10 (Reduced Inequality) is critical for ensuring equitable and sustainable development. Advancements in reducing inequality can positively impact population health outcomes, particularly by ensuring equitable access to healthcare services and education. It is therefore crucial that policies and strategies targeting SDG 3 also address the underlying social and economic determinants of income inequality. Achieving harmony between SDG 3 and SDG 10 requires a comprehensive approach that takes into account economic, social, and environmental aspects. EU countries such as Sweden and Denmark offer valuable examples, demonstrating how the development of infrastructure, innovation, and sustainable economic development can contribute to achieving health goals and reducing social inequalities. Ukraine, while facing numerous challenges, has the potential to align these goals through its ongoing adaptation to the European Green Deal framework.

Further research is needed to identify the most effective mechanisms for aligning SDG 3 and SDG 10 in Ukraine, considering the country’s socio-economic specifics and integration into the EU. A deeper understanding of these interconnections will help develop evidence-based policies that support sustainable and inclusive growth in line with European standards.

The purpose of the study is to assess the impact of income inequality on the level of well-being of the population through a cross-country analysis of the EU countries and Ukraine in 2009–2021, testing the hypothesis that reducing income inequality has a positive impact on the level of well-being of people.

Objectives of the research:

• To analyze the dynamics of income inequality (as measured by the Gini coefficient) and the level of achievement of SDG 3 and SDG 10 in the EU countries and Ukraine during the period 2009–2021;
• To determine the presence and nature of the relationship between the level of income inequality and the achievement of SDG 3 based on correlation and regression analysis;
• To identify clusters of countries based on the similarity of relationships between SDG 3, SDG 10, and the Gini coefficient by employing cluster analysis techniques;
• To conduct analytical grouping of countries taking into account the characteristics of their socio-economic models and the level of impact of income inequality on well-being indicators;
• To test the hypothesis of a positive impact of reducing income inequality on the level of well-being of the population by identifying countries where this trend is most pronounced;
• To formulate practical recommendations for Ukraine, taking into account the experience of the EU, aimed at adapting socio-economic policy in the context of European integration and achieving SDG 10 and SDG 3.

The study applies correlation analysis, regression analysis, cluster analysis, and analytical grouping methods to identify key patterns in the relationship between income inequality and the level of well-being of the population.

3. Materials and Methods

This study utilizes data from the Sustainable Development Report (SDR) [29] and selected indicators related to the 17 Sustainable Development Goals (SDGs), with a specific focus on SDG 3 (Health and Well-being) and SDG 10 (Reduced Inequality) for the period 2009–2021. These data are collected by international organizations and published at the global level. They are important for monitoring progress toward the goals aimed at improving the health and well-being of populations, as well as reducing inequality in the world.

The statistical sources of data for this research include the following:

• World Bank: it provides data on economic, social, and environmental issues, e.g., on poverty, gender equality, economic growth, health, and education;
• World Health Organization (WHO): it provides global data on health, diseases, life expectancy, and quality of health services, particularly for Goal 3: Good Health and Well-Being;
• EUROSTAT database [30].

Data for the goals mentioned above are published annually and reflect each country’s progress in achieving sustainable development. The methodology for calculating and measuring data in the Sustainable Development Report depends on each specific SDG and the corresponding indicators. The research utilizes Big Data techniques to process and analyze an extensive dataset from the Sustainable Development Report, available on Kaggle [31], and the World Bank Group [32], specifically from the Poverty and Inequality Platform [33]. The World Bank compiles these data based on primary household surveys conducted by the government statistical agencies and the World Bank country departments. To ensure data consistency across countries and time periods, minor gaps were addressed using harmonization techniques and trend-based assumptions derived from secondary sources. Observations with major data omissions were excluded from the regression and clustering analyses.

For high-income economies, data on inequality primarily come from the Luxembourg Income Study (LIS) database, one of the leading international sources of harmonized microdata on income, inequality, and poverty. LIS facilitates cross-country comparisons by providing standardized datasets, as many high-income governments submit their national statistics through this platform. Consequently, LIS indicators are widely used to ensure an accurate representation of socioeconomic inequality in these economies. The dataset is licensed under CC BY-4.0 [34].

A dataset covering the 27 European Union countries and Ukraine was used to analyze the impact of socio-economic inequality on the level of well-being of the population (SDG 3). The data include information on income level, regional affiliation, as well as special notes related to national currencies and their conversion into the euro.

The countries included in the analysis belong to the Europe and Central Asia region according to the World Bank classification, with the exception of Malta, which is classified as the Middle East and North Africa region. Most countries have a high-income status, while Ukraine is classified as an upper-middle-income country.

The metadata features include historical aspects of the conversion of national currencies into the euro, which is important for correct accounting for economic indicators in the long term. For example, for countries that joined the Eurozone after 1999 (Croatia, Cyprus, Estonia, Latvia, Lithuania, Malta, Slovakia, and Slovenia), the corresponding official conversion rates at the time of accession were taken into account. This allows for correct economic comparisons between countries with different currency histories. It is important to note that for Romania, the World Bank used alternative currency conversion rates due to the existence of multiple exchange rates in certain periods. This may affect the assessment of income levels and economic inequality in the country, which must be taken into account when interpreting the results.

The obtained metadata allow for a more detailed study of the relationship between the level of economic inequality (SDG 10), the Gini coefficient (used as a measure of income inequality, it is a key indicator for assessing income inequality and is used to monitor progress in achieving SDG 10 “Reduced Inequality”), and the general level of well-being of the population (SDG 3), taking into account the specifics of national economies and social policies. The Gini index is used to measure income inequality in a society. It is one of the indicators for assessing progress toward Sustainable Development Goal 10 “Reduced inequality” [35]. However, it is important to acknowledge that both the composite SDG indexes and the Gini coefficient have limitations. SDG scores may fail to capture intra-country disparities among subpopulations, while the Gini coefficient does not account for informal income or non-monetary dimensions of inequality, which is particularly relevant in countries with significant shadow economies.

To achieve the stated aim of the study, we formulated a hypothesis that reducing income inequality has a positive impact on people’s well-being. This hypothesis was developed in the context of the creation of relevant key blocks of concept research: (1) the clustering of the EU countries and Ukraine based on the Gini coefficient, SDG 10, SDG 3, and study of the obtained distributions by the degree of achievement of the relevant sustainable development goals; (2) the modeling results of SDG 3 for the population of the EU and Ukraine based on SDG 10 achievement and income inequality (Gini coefficient); (3) the grouping of countries by the value of the correlation coefficient between SDG 10, SDG 3, and the Gini coefficient; and (4) the semantic interpretation of the proposed model constructions and grouping results from the position of the Ukraine’s European integration prospects.

To create such a concept, multidimensional methods were used, in particular, cluster analysis for grouping countries by similar characteristics, a comparison of results for different regions, and the systematization of data to identify key trends, elements of the semantic differential technique, correlation-regression analysis, and analytical clustering. These methods made it possible to identify the patterns and connections between reducing inequality and improving health and well-being, confirming the hypothesis put forward through the application of the key blocks of the proposed concept.

4. Results

In the current global context of sustainable development, one of the key global challenges is income inequality, which significantly affects the economic, social, and political stability of societies. Reducing inequality is the main goal of Sustainable Development Goal 10 (SDG 10), which aims to reduce social and economic barriers that impede equal access to opportunities. Achieving this goal requires a comprehensive approach, including income redistribution policies, ensuring equal access to education and health care, and creating conditions for inclusive economic growth. To assess progress in reducing inequality, the dynamics of SDG 10 in the countries of the European Union and Ukraine were analyzed.

As Ukraine continues its European integration process, it is actively working to align its socio-economic reforms with EU standards. In recent years, the country has undergone significant changes in social protection policies, but the level of economic inequality remains a significant challenge. Ukraine’s success in achieving SDG 10 depends on the further implementation of structural reforms aimed at improving social protection, reducing income disparities and expanding opportunities for all social groups. In this regard, it is necessary to take into account not only economic, but also demographic and political factors that may affect the dynamics of this indicator.

An important aspect of studying SDG 10 is the analysis of the dynamics of this indicator over time. Changes in the level of inequality can be caused by both internal reforms and external economic factors, such as global financial crises, the COVID-19 pandemic or the war. To assess the effectiveness of inequality reduction policies, we analyzed how SDG 10 indicators changed in the EU countries and Ukraine in 2009–2021, taking into account possible crisis phenomena and the response of governments to them.

The analysis of relevant statistical data allowed us to identify key trends and patterns in the level of economic inequality in the EU and Ukraine. The results of the calculations, which reflect the dynamics of achieving SDG 10 in different countries from 2009 to 2021, are presented in Figure 1.

As can be seen, most developed countries (e.g., the Czech Republic, Finland, Germany, and the Netherlands) had consistently high indicators, close to 100%. The Central and Eastern European countries (Bulgaria, Romania, Latvia) show lower indicators, meaning greater socio-economic inequality. Bulgaria and Romania had a significant drop in indicators after 2015. During the period of 2009–2021, Ukraine’s SDG 10 indicator remained stable at 100%, except for a slight decrease in 2017–2019 (99.6%, 99.5% and 98.7%). This may indicate some structural problems in income equality policies or reflect specificities of the calculation methodology. Bulgaria shows the largest decline (from 79.6 in 2009 to 51.0 in 2021). Estonia and Lithuania also show fluctuations. Western Europe generally demonstrates stability, while Eastern Europe has strong differentiation between countries, which may indicate the impact of socio-economic reforms. Ukraine demonstrates a stable SDG 10 indicator, but a deeper analysis is needed to determine whether this is a real reflection of income equality or a peculiarity of the indicator’s calculation.

Figure 2 shows the corresponding dynamics of the Gini coefficient for the studied countries over the specified period. The Gini coefficient is an important indicator for assessing the level of inequality in income distribution in countries, which is directly related to achieving Sustainable Development Goal 10 (SDG 10), which involves reducing social and economic inequality both within and between countries.

The dynamics of the Gini coefficient, presented in Figure 2, show that there is stability in the indicators in Western Europe. Western European countries (Austria, Germany, Belgium) demonstrate a consistently low level of inequality. In Belgium, the Gini coefficient decreased from 28.6 (2009) to 26.6 (2021), which indicates an effective social policy. A greater variation in this indicator is characteristic of Germany. Thus, the Gini coefficient for the studied period demonstrates a slightly higher level of inequality, with fluctuations in the range from 30.2 to 32.4. Austria has stable values of the Gini coefficient within 29.7–31.5, which indicates a moderate level of income inequality. In the Baltic countries (Latvia, Lithuania, Estonia), higher inequality is observed, but it is gradually decreasing. Thus, in Lithuania, the Gini coefficient decreased from 37.4 (2014) to 35.1 (2021), which may indicate positive changes in economic policy.

Among the 28 countries studied, significant inequality is evident in countries in the Balkans and Southern Europe. Bulgaria and Romania have the highest inequality rates. Bulgaria peaked in 2016 (40.6), although by 2021 the rate had fallen to 39.0.

The dynamics of income differentiation in Ukraine are consistently low compared to other EU countries, with fluctuations within 24.0–26.6. This indicates reduced income inequality compared to most European countries. At the same time, it is worth noting the increase in the coefficient in Ukraine in certain years (for example, from 24.0 in 2014 to 26.6 in 2019), which may indicate certain economic and social challenges, in particular in conditions of war and economic instability. In Ukraine, the Gini index is officially used as an indicator for monitoring the achievement of SDG 10.

In particular, the Resolution of the Cabinet of Ministers of Ukraine “Some Issues of Ensuring the Achievement of Sustainable Development Goals in Ukraine” dated 29 December 2024, №1190-r states that the Gini index is one of the indicators for assessing income inequality. It declares the need for income inequality in Ukraine by 2030. It is important to note that this Resolution was adopted almost at the end of the third year of Russia’s full-scale war against Ukraine, which indicates the extreme importance of reducing income inequality [36].

According to the data of the State Statistics Service of Ukraine, the concentration coefficient (Gini index) for monetary income of the population is published as part of the survey of household living conditions [37].

Ukraine demonstrates consistency in its Gini coefficient over the period under review, from 25.3 in 2009 to 25.6 in 2021, with minor fluctuations in 2019 and 2020. This indicates a gradual reduction in economic inequality in Ukraine. The lack of significant fluctuations may indicate the effectiveness of social and economic reforms [36], but it is also an indicator that Ukraine still needs further efforts to significantly reduce inequality. In particular, it is positive that in 2020, when many countries were facing the negative economic consequences of the pandemic, Ukraine maintained a stable level of inequality, which may indicate successful support for the poor through government programs.

The decline in the Gini coefficient in Ukraine in recent years may be partly due to the reforms and integration processes taking place in the country within the framework of European integration. Such processes can contribute to the reduction in economic inequality through the harmonization of economic and social standards with European countries, investment in infrastructure, access to European markets, and common development funds. Balanced growth and the reduction in economic inequality are important for achieving these goals both at the EU level and in Ukraine. Analysis of these trends in the context of European integration processes in Ukraine emphasizes the importance of further economic integration and the implementation of policies aimed at reducing inequality and improving access to basic services for all segments of the population.

Countries with low inequality (Gini coefficients of 25–30, such as Sweden, Denmark, the Czech Republic, and Slovenia) demonstrate stable and sustainable economic growth with increased social well-being of citizens. These countries have implemented effective policies to combat poverty, foster equitable distribution of wealth, and build social protection, which contribute to the implementation of SDG 10.

Countries with high levels of inequality (Gini coefficients of 35 and above, such as Bulgaria, Latvia, and Romania) have more pronounced economic inequality, which creates additional difficulties in achieving social justice and equal opportunities for all citizens. This indicates the need for serious reforms to ensure equal access to resources and reduce the social gap.

Ukraine’s integration into the EU opens up new opportunities for improving national policies toward achieving SDG 10. The European Union has high standards of social equality and justice, which include combating poverty, supporting equal opportunities for all, and ensuring social rights. Ukraine, moving closer to the EU standards, can improve its economic and social institutions, including the taxation system, social assistance, and educational programs, which will help reduce economic inequality.

However, the integration process may also increase the demands on Ukraine to adapt national laws and practices to European norms, which may require greater efforts to equalize the socio-economic situation against the backdrop of high competition in European markets.

An important conclusion should be drawn from the synchronized assessment of the dynamics of SDG 10 and the Gini coefficient: a decrease in the Gini coefficient will lead to an increase in SDG 10. This is confirmed by the comparison results. Countries with a consistently low Gini coefficient (less than 30) have high performance in achieving SDG 10 (e.g., Sweden (25.8 in 2021), Denmark (27.4 in 2021), the Czech Republic (25.1 in 2021), and Slovenia (27.2 in 2021)). These countries are actively reducing social inequality and ensuring equal opportunities for all segments of the population. Countries with a high Gini coefficient (more than 35), such as Bulgaria (41.0 in 2021), Romania (35.8 in 2021), and Latvia (34.9 in 2021), have significant difficulties in achieving SDG 10 due to large social and economic gaps between different population groups. For Ukraine, with a Gini coefficient of 25.6 in 2021, there is a chance for further improvement, but it is necessary to take into account the specifics of the country’s development, which depends on deep reforms and integration into the European economic and social space.

SDG 10 (reducing inequality) and SDG 3 (well-being) are closely linked, since income inequality can lead to social and economic barriers. Thus, it is critical to examine their mutual influence to confirm the hypothesis that reducing income inequality has a positive impact on people’s well-being.

Therefore, clarifying the relationship between SDG 10 and SDG 3 becomes particularly important in the context of our further research. Since socio-economic inequality directly affects the well-being of the population, it is important to consider how trends in reducing inequality are reflected in the achievement of SDG 3. In particular, an analysis of the dynamics of SDG 3 in the EU countries and Ukraine will allow identifying key factors that contribute to increasing the level of well-being of the population and the formation of effective development strategies.

Figure 3 shows the dynamics of achieving SDG 3 in the EU and Ukraine from 2009 to 2021.

As the results of the analysis of the dynamics of SDG 3 in the EU countries show, for most of them, there is a stable or gradual increase in the SDG 3 indicator during the period under study. In particular, Austria, Belgium, the Netherlands, and Luxembourg show their high level and stability (approximately 92–96) throughout the period. Finland (95.4 in 2021) and Sweden (96.9 in 2021) demonstrate consistently high well-being indicators, which indicate high social equality for citizens. Bulgaria, Latvia, Romania, and Lithuania have lower indicators, although positive dynamics are noted during the period, indicating an improvement in the level of well-being (Bulgaria increased its indicator from 72.5 to 79.3). Thus, in general, the EU is characterized by a stable trend toward improving the well-being indicators of the population, which may be associated with a high level of social standards, investments in healthcare, and effective policies in this area.

In Ukraine, the SDG 3 score in 2009 was 66.0 and gradually increased over the following years, reaching 76.6 in 2021. This indicates a positive trend, but at the same time, these indicators in Ukraine remain significantly lower than in most EU countries, although they have a higher rate of decline than other studied countries. In 2014, there was a decrease in the indicator (65.3), which could be associated with economic difficulties and political instability due to the annexation of Crimea and the beginning of Russia’s military invasion of eastern Ukraine. Despite critical challenges, Ukraine was able to ensure a gradual increase in SDG 3 since 2015. In 2020, in the conditions of the COVID-19 pandemic, the indicator increased to 75.7, which may indicate a growing level of attention to health in the context of global threats. However, this growth was much less noticeable compared to the EU countries, indicating the need for additional efforts in the field of medicine and social services in Ukraine.

The interaction between SDG 3 and SDG 10 is essential for achieving the Sustainable Development Goals. The analysis of the dynamics of SDG 3 and SDG 10 for the EU countries and Ukraine revealed differences in the pace of progress and the degree of achievement of these goals. Some countries demonstrate a stable improvement in indicators, while others face challenges, in particular in the field of social equality and well-being. At the same time, the level of economic inequality, reflected through the Gini coefficient, remains an important factor affecting the socio-economic development of states. For a deeper understanding of the divergence of individual countries and the identification of their common trends, hierarchical cluster analysis was applied. Such studies involve the use of the potential of the semantic differential method [38].

This approach allowed us to group countries by the level of similarity of their indicators, which contributed to the identification of regional features and potential areas of equalization policy. The construction of hierarchical dendrograms based on the Gini coefficient, SDG 3, and SDG 10 indicators for the period 2009–2021 allowed us to visualize the grouping of countries and identify patterns of their development. The figures (Figure 4, Figure 5 and Figure 6) present the results of cluster analysis, which allowed us to better assess the similarities and differences between countries in the context of social equality and well-being of the population.

The analysis of clustering results on the Gini coefficient (2009–2021) demonstrates the division of the studied set of countries into three groups (

Table 1. Distribution of selected countries by Gini coefficient in 2009–2021.

Cluster	Characteristics of the Cluster	Countries	Mean
0	High Inequality	Greece, Italy, Portugal, Latvia, Spain, Romania, Lithuania, Bulgaria	Ranges from 34.5 to 38.0
1	Low Inequality	Slovenia, Ukraine, Slovak Republic, Czechia, Finland, Belgium, Denmark, Sweden	Ranges from 24.9 to 28.7
2	Moderate Inequality	Malta, Hungary, Austria, Netherlands, Croatia, Poland, Germany, Ireland, Estonia, France, Luxembourg, Cyprus	Ranges from 29.6 to 32.9

Source: authors’ analysis based on World Bank data [32].

).

Countries from cluster 0 exhibit higher income inequality, with Bulgaria having the highest Gini coefficient. This cluster includes both Southern and Eastern European countries, reflecting structural economic challenges and disparities. Ukraine belongs to Cluster 1, with countries that have low income inequality, including Nordic and Central European nations. This suggests that Ukraine’s income distribution aligns with the EU standards in this cluster, which is a positive indicator for its integration. Countries in Cluster 2 (a mix of Western and Eastern European nations) have moderate income inequality. This cluster represents a middle ground in terms of economic equality.

The analysis of the results of clustering by SDG 10 (2009–2021) demonstrates the division of the studied set of countries into three groups (

Table 2. Distribution of selected countries by the degree of SDG 10 achievement in 2009–2021.

Cluster	Characteristics of the Cluster	Countries	Ranges
0	Lower indicators	Bulgaria, Cyprus, Spain, Greece, Italy, Lithuania, Latvia, Portugal, Romania	60.33–79.42
1	Average indicators	Austria, Germany, Estonia, France, Croatia, Hungary, Ireland, Luxembourg, Malta, Netherlands, Poland, Sweden	82.56–96.07
2	The highest indicators	Belgium, Czech Republic, Denmark, Finland, Slovakia, Slovenia, Ukraine	98.68–100.0

Source: authors’ analysis based on World Bank data [32].

).

Based on the conducted cluster analysis of SDG 10 for EU countries and Ukraine, the following conclusions were made. Cluster 0 included 9 countries with an average SDG 10 score (74.57), mainly countries of the Southern and Eastern Europe. Cluster 1 consists of a set of 12 countries with an average SDG 10 score (89.65), including both “old” and “new” EU members. Ukraine and the seven most developed countries in the context of SDG 10, with an average score of 99.58, formed Cluster 2. It should be noted that Ukraine has a higher degree of achievement of SDG 10 (99.82) than the EU average, with a +13.0-point advantage in favor of Ukraine. Such an achievement is a positive signal from the point of view of European integration processes for Ukraine and the EU.

Overall, there is an interesting geographical pattern, with a certain geographical distribution, with Southern European countries tending to perform lower. In terms of economic performance, the analyzed countries are distributed between clusters 1 and 2, suggesting that economic development is not the only determinant of SDG 10.

The analysis of the results of clustering SDG 3 (2009–2021) demonstrates the division of the studied set of countries into three groups, which is presented in

Table 3. Distribution of selected countries by the degree of SDG 3 achievement in 2009–2021.

Cluster	Characteristics of the Cluster	Countries	Ranges
0	Lower indicators	Ukraine, Bulgaria, Romania, Latvia, Hungary, Lithuania	70.04–81.78
1	Average indicators	Poland, Croatia, Estonia, Slovakia, Greece, Czech Republic, Portugal, Malta, Cyprus, Slovenia	83.69–90.88
2	The highest indicators	Austria, Belgium, Italy, France, Germany, Ireland, Spain, Denmark, Finland, Luxembourg, Netherlands, Sweden	91.70–95.91

Source: authors’ analysis based on World Bank data [32].

.

As can be seen from Table 3, Cluster 0 consists of 6 countries, including Ukraine, with an average SDG 3 achievement rate (78.14). Moreover, this indicator for 13 years from 2009 to 2021 demonstrated the lowest value among all of the analyzed countries and amounted to 70.04, which is 18.91 points less than the EU average. In our opinion, Ukraine’s inclusion in the lowest segment of clustering for SDG 3 while simultaneously being at the top of countries in terms of SDG 10 achievement rate indicates that, at first glance, it is Ukraine that violates the hypothesis that the lower the income inequality, the higher the well-being of the population. However, this can be explained by the existence of a shadow economy, an imperfect methodology for collecting and processing statistical information (many activities or results of the economy are not included in the calculation of gross domestic product, which indirectly affects the quality of medical services and other social determinants used in SDG 3 calculation). Almost 36% of the studied set of population of countries fell into Cluster 1 with an average SDG 3 achievement rate (87.98). Cluster 2 included 42% of 28 countries with an average SDG 3 achievement rate (93.58).

So, there is also an interesting geographical pattern—Cluster 0 mainly comprises Eastern European countries that joined the EU after 2004. From an economic perspective, countries with a higher level of economic development (Western and Northern Europe) demonstrate better indicators of achieving SDG 3. Almost all countries from the first wave of the EU enlargement belong to Cluster 2 with the highest indicators.

Analysis of the distribution of countries by Gini coefficient (Table 1) and level of achievement of SDG 10 (Table 2) and SDG 3 (Table 3) in 2009–2021 allowed us to diagnose the existence of relationships between the indicated features. The obtained results show the presence of certain trends in the EU countries and Ukraine, which indicates the need for further modeling of these relationships and determining the necessary quantitative metrics for their assessment.

In order to better understand the impact of income inequality (Gini coefficient) and overall inequality (SDG 10) on the achievement of SDG 3 in the EU countries and Ukraine in 2009–2021, a modeling of these relationships was conducted (

Table 4. Modelling Results of SDG 3 for the Population of the EU and Ukraine Based on SDG 10 Achievement and Income Inequality (Gini Coefficient) in 2009–2021.

Country	SDG 3 for the Population of the EU and Ukraine Based on SDG 10 Achievement				SDG 3 for the Population of the EU and Ukraine Based on Income Inequality (Gini Coefficient)
	R2	R	Fisher’s Test	Student’s t-Test	R2	R	Fisher’s Test	Student’s t-Test
Austria	0.5613	0.7492	14.08	3.75	0.394	−0.6277	7.15	−2.67
Belgium	0.7926	0.8903	42.03	6.48	0.6789	−0.8239	23.26	−4.82
Bulgaria	0.8596	−0.9271	67.34	−8.21	0.8048	0.8971	45.35	6.73
Cyprus	0.3974	0.6304	7.25	2.69	0.3991	−0.6317	7.31	−2.70
Czech Republic	0.0965	0.3107	1.18	1.08	0.3594	−0.5995	6.17	−2.48
Germany	0.6175	−0.7858	17.76	−4.21	0.7222	0.8499	28.60	5.35
Denmark	0.7197	−0.8484	28.25	−5.31	0.3437	0.5863	5.76	2.40
Spain	0.1665	0.4081	2.20	1.48	0.0639	−0.2527	0.75	−0.87
Estonia	0.6508	0.8067	20.50	4.53	0.1591	−0.3989	2.08	−1.44
Finland	0.3944	−0.6281	7.17	−2.68	0.0204	−0.143 0	0.23	−0.48
France	0.3041	0.5515	4.81	2.19	0.4123	−0.6421	7.72	−2.78
Greece	0.683	0.8264	23.70	4.87	0.4879	−0.6985	10.48	−3.24
Croatia	0.643	0.8019	19.81	4.45	0.5875	−0.7665	15.67	−3.96
Hungary	0.3141	−0.5604	5.04	−2.24	0.0532	0.2307	0.62	0.79
Ireland	0.5299	0.7279	12.40	3.52	0.4965	−0.7046	10.85	−3.29
Italy	0.2337	−0.4834	3.35	−1.83	0.0722	0.2687	0.86	0.93
Lithuania	0.1323	−0.3637	1.68	−1.30	0.1231	0.3509	1.54	1.24
Luxembourg	0.3587	−0.5989	6.15	−2.48	0.3456	0.5879	5.81	2.41
Latvia	0.0068	0.0827	0.08	0.28	0.1862	−0.4315	2.52	−1.59
Malta	0.0233	−0.1525	0.26	−0.51	0.017	0.1306	0.19	0.44
Netherlands	0.314	−0.5603	5.03	−2.24	0.0249	−0.1578	0.28	−0.53
Poland	0.7695	0.8772	36.73	6.06	0.5698	−0.7548	14.57	−3.82
Portugal	0.7247	0.8513	28.95	5.38	0.462	−0.6797	9.45	−3.07
Romania	0.0213	0.1461	0.24	0.49	0.3382	−0.5815	5.62	−2.37
Slovak Rep.	0.1252	0.3538	1.57	1.25	0.6388	−0.7993	19.46	−4.41
Slovenia	0.0000	0.0000	0.00	0.00	0.3094	−0.5563	4.93	−2.22
Sweden	0.5982	−0.7735	16.38	−4.05	0.6154	0.7845	17.60	4.20
Ukraine	0.2238	−0.4731	3.17	−1.78	0.4569	0.6759	9.25	3.04

Source: authors’ analysis based on Kaggle [31] and World Bank data [32].

). The obtained results allow us to assess the significance of the factors determining the level of achievement of SDG 3 and identify the key determinants that contribute to it.

Based on the calculations (Table 4), it was found that any form of inequality negatively affects the well-being of the population. This applies to both income inequality and the overall level of inequality assessed through SDG 10. Since income inequality is an integral part of the SDG 10 calculation methodology, along with social and political inequality, the study carried out a separate analysis of its dynamics based on the Gini coefficient for selected countries. This approach made it possible to more clearly demonstrate the impact of economic inequality on the overall level of well-being of the population. Detailed analysis allowed us to identify 18 countries that demonstrate a negative relationship between income inequality (Gini coefficient) and well-being (SDG 3). This means that in these countries, the increase in income inequality negatively affects the level of well-being of the population. The countries with a positive relationship between income inequality and SDG 3, at the same time, have a negative relationship between SDG 3 and SDG 10. This suggests that formal policies to reduce inequality (SDG 10) do not always contribute to improving well-being (SDG 3) and sometimes even have the opposite effect.

Drawing on the calculations of the analytical grouping of countries according to their interconnections, the following conclusions were made. The first grouping involves division into two groups, as follows:

• Countries with a negative relationship between income inequality (Gini coefficient) and SDG 3. This group includes 18 countries that demonstrate a trend in which increasing differentiation in income distribution leads to a decrease in the level of well-being of the population. The group consists of Austria, Belgium, Cyprus, Czechia, Spain, Estonia, Finland, France, Greece, Croatia, Ireland, Latvia, the Netherlands, Poland, Portugal, Romania, the Slovak Republic, and Slovenia;
• Countries with a positive relationship between income inequality and SDG 3, as well as a negative relationship between SDG 3 and SDG 10. This group includes 10 countries that are characterized by the simultaneous existence of both of these relationships. The group consists of Bulgaria, Germany, Denmark, Hungary, Italy, Lithuania, Luxembourg, Malta, Sweden, and Ukraine.

Given the ambiguity of the results presented in Table 4, a further detailed grouping of countries into two main categories within the analytical grouping was carried out. It takes into account the value of the correlation coefficient between the level of achievement of SDG 10 and SDG 3, as well as between the Gini coefficient of income inequality and SDG 3. The results are presented in

Table 5. Grouping of countries by the value of the correlation coefficient between SDG 10, SDG 3, and the Gini coefficient.

Group No.	Characteristics of the Group	Countries	Notes
1	High and moderate positive impact of SDG 10 and significant/moderate negative impact of income inequality	Austria, Belgium, Cyprus, Croatia, Estonia, France, Greece, Ireland, Poland, Portugal	The strong correlation between reducing inequality and improving well-being; the inverse relationship between income inequality and SDG 3 indicates an increase in well-being due to reduced income differentiation.
2	Minor positive impact of SDG 10 and minor negative impact of income inequality	Czech Republic, Latvia, Romania, Spain	Countries where inequality policies are less pronounced, but the overall situation is not worsened by income inequality.
3	Significant negative impact of SDG 10 and significant positive impact of income inequality	Bulgaria, Germany, Denmark, Luxembourg, Sweden	High social standards but sometimes, reducing inequality does not contribute to improving well-being, and income inequality partially drives better SDG 3 indicators.
4	Moderate negative impact of SDG 10 and slight positive impact of income inequality	Hungary, Italy	Income inequality has little impact on SDG 3, but rising income inequality may even improve the situation slightly.
5	Small positive impact of SDG 10 and large/moderate negative impact of income inequality	Slovakia, Slovenia	Reducing inequality contributes to achieving SDG 3; judging by the results obtained, significant attention is paid to addressing income inequality in these countries.
6	Negative impact of SDG 10 and slight positive impact of income inequality	Lithuania, Malta	These countries are characterized by little attention to the impact of inequality on the well-being of the population.
7	Inverse relationship between SDG 10 and SDG 3 and Gini coefficient	Finland, Netherlands	A unique situation: reducing inequality can lead to a deterioration in the well-being of the population. There is a slight inverse relationship between income inequality and the level of well-being, that is, under conditions of reducing income inequality, the level of well-being can increase slightly.
8	Ukraine has indicators closest to group 3	Ukraine	Special case: indicators are closer to Group 3, but Ukraine forms a separate cluster position.

Source: authors’ analysis based on Kaggle [31] and World Bank data [32].

.

A strong correlation between SDG 10 and SDG 3 is observed in countries in Group 1, which indicates the effectiveness of policies aimed at reducing inequality. Groups 3 and 7 demonstrate an unexpected trend—reducing inequality does not always improve the well-being of the population, and in some cases even the opposite. Ukraine has a unique situation that requires additional analysis, since it does not fit into standard patterns, but is closest to Group 3. The impact of income inequality on SDG 3 is ambiguous—in some countries, inequality stimulates the improvement in well-being indicators, and in others, on the contrary, it worsens them. Policies to reduce inequality should be adapted to the specifics of each country, taking into account not only economic but also social and cultural aspects.

However, some countries displayed inverse or counterintuitive patterns that merit further attention. In Ukraine, relatively low income inequality, as measured by the Gini coefficient, does not align with strong performance in SDG 3. This may be explained by a combination of factors such as the extensive shadow economy, uneven access to healthcare services, and underreporting or data limitations. In Germany, on the other hand, a comprehensive welfare system and redistributive policies may mitigate measurable inequality, while structural disparities in health outcomes across regions or socioeconomic groups persist. These cases highlight the importance of considering institutional, political, and cultural variables—such as public healthcare investment, governance quality, and trust in institutions—as potential mediators in the inequality–health nexus.

5. Discussion and Conclusions

The results of our study confirm the hypothesis put forward by implementing relevant key blocks of the research concept that reducing income inequality has a positive effect on the level of well-being of the population. Through the conducted correlation and regression analysis, it was found that reducing the level of income inequality contributes to increasing the level of well-being of the population, i.e., achieving SDG 3.

The cluster analysis revealed the patterns in the distribution of EU countries and Ukraine by the level of economic inequality (SDG 10) and the achievement of SDG 3. Visualization of grouping based on the Gini coefficient and sustainable development indicators demonstrated the presence of regional features: countries in Southern and Eastern Europe have higher income inequality and lower indicators of population well-being, while countries in Northern and Central Europe demonstrate a more even distribution of income and better well-being results. Ukraine, despite relatively low income inequality, fell into the group of countries with the lowest indicators of SDG 3, which indicates relevant structural problems.

The identified patterns confirm the relationship between economic inequality and the level of well-being of the population but also indicate the existence of additional factors that influence this relationship. In particular, for Ukraine, the shadow economy, social inequality, as well as methodological features of statistical data collection play an important role. Ukraine’s high position in achieving SDG 10 does not correlate with the high level of SDG 3, which indicates the need for a comprehensive approach to the formation of social equality policies.

The implementation of the following blocks of the concept has demonstrated that the level of income inequality in Ukraine, although somewhat lower compared to other countries, remains insufficient to significantly improve well-being indicators. High social inequality remains a significant problem for Ukraine.

In view of the European integration processes, Ukraine can significantly benefit from the application of the best European practices in the field of health care and social policy. To this end, it is necessary to continue the implementation of European standards, in particular within the framework of the health care system and access to social services. Reforms should focus on increasing the efficiency of health services, ensuring equal access to them in all regions, and reducing social inequality.

To overcome the existing barriers to achieving SDG 3 in Ukraine, steps such as strengthening health financing, increasing investment in infrastructure, and developing health insurance programs targeting the most vulnerable groups are critically important. The experience of EU countries, in particular Poland and the Czech Republic, can be useful for Ukraine, as these countries have demonstrated positive changes in the health sector after joining the EU. However, to fully integrate and achieve SDG 3, Ukraine still needs to address many social and economic challenges, including raising the level of income of the population.

The calculations carried out within the framework of the proposed concept confirmed that inequality in any form has a negative impact on the well-being of the population. The study of the relationship between income inequality and the level of well-being of the population in Ukraine and the EU countries yielded important results. Our calculations, based on data on the Gini coefficient and well-being indicators, indicate that a more even distribution of income contributes to the achievement of SDG 3. However, despite the positive results for some countries, the dynamics of SDG 3 indicators in Ukraine revealed significant lags behind most EU countries, which indicates the need for further reforms. It should also be noted that income inequality indicators in Ukraine remain relatively low (Gini coefficient 24.6–26.6 in 2009–2021). However, despite gradual positive changes, Ukraine still faces many barriers, such as insufficient funding of medical institutions and unequal access to services in different regions. However, low income levels and limited funding for social policies do not allow these positive changes to be fully realized. Our study also examined the important role of Ukraine’s European integration processes in achieving SDG 3.

This study confirms the initial hypothesis that reducing income inequality generally has a positive impact on population well-being (SDG 3); however, the strength and direction of this relationship vary across countries. Correlation and regression analysis revealed that in 18 out of 28 countries, a significant negative correlation exists between the Gini coefficient and SDG 3 indicators, for example, Belgium (R = –0.8239) and Poland (R = –0.7548), suggesting that lower income inequality is consistently associated with improved health outcomes. Conversely, Ukraine, despite its relatively low income inequality (Gini = 25.6 in 2021), demonstrates one of the lowest SDG 3 scores (76.6), which is 16 points below the EU average. This discrepancy points to structural and institutional barriers that inhibit the translation of formal equality into actual well-being.

Based on the proposed concept, directions for achieving SDG 3 for Ukraine have been developed based on the policy of equalization and the prospects of European integration, as follows:

• Harmonization of the healthcare system with EU standards through the development of a roadmap for reforming the healthcare system in accordance with EU standards; implementation of European treatment protocols and medical standards; and strengthening the healthcare system through the use of best practices from countries represented in Cluster 2;
• Improving access to medical services by increasing funding for primary health care and preventive medicine; introducing telemedicine and mobile medical teams in rural and remote areas; and simplifying access to specialized specialists and quality medicines;
• Development of health insurance and financing using the experience of Poland, the Czech Republic, and Slovakia in introducing mandatory health insurance; optimizing the financing of hospitals and medical institutions according to European models; and a transparent system of co-financing and public–private partnership in healthcare;
• Investments in talent management in the field of medicine, in particular in advanced training programs for doctors and medical staff according to European standards; expanding opportunities for internships and exchange of experience with the EU countries; and introducing mechanisms for retaining medical staff (salary increases, housing programs, etc.);
• Using European integration as a catalyst for change through the implementation of the successful experience of Cluster 1 countries to implement reforms; preparation of infrastructure projects in the medical sector with the involvement of EU funds; and advocacy for reforms at the international level to obtain additional assistance and funding.

Ukraine has the potential to significantly improve SDG 3 indicators, and this requires a combination of reforms, investments, and international cooperation. Thus, the results of the study can be used to improve policies to reduce inequality and improve the health of the population both in Ukraine and in the EU countries.

The results obtained can be effectively used to support decision-making in the field of public administration when planning reforms aimed at reducing inequality and improving the quality of life of the population. In particular, a model assessment of the impact of inequality on well-being allows public managers to adjust social programs, develop more targeted medical interventions, and implement indicative planning based on the indicators of the SDG. For managers of social projects and heads of health care institutions, these results are the basis for assessing the effectiveness of management decisions and developing innovative mechanisms for improving access to services. In addition, at the corporate level (micro level), the research recommendations can be applied to the formation of corporate social responsibility (CSR) policies, assessing the impact of business on the social environment, and developing ESG strategies (E—environment, S—social responsibility, G—governance), which are the basis for ratings and non-financial assessments of enterprises, the state, and other organizations. Thus, the results contribute to the formation of management decisions focused on achieving the Sustainable Development Goals, integrating the principles of equality and inclusion at all levels of management— from the state to the organizational level.

Further research can focus on a deeper analysis of the factors influencing the discrepancies between SDG 3 and SDG 10, in particular, the impact of the level of financing for medicine, social protection, and economic mobility. This will contribute to the development of effective strategies to harmonize economic development and social well-being.