Reducing Inequalities within and among EU Countries—Assessing the Achievement of the 2030 Agenda for Sustainable Development Targets (SDG 10)

Abstract

Reducing inequalities within and among countries is one of the main tenets of the sustainable development paradigm and has become an important pillar at the European Union level. By adopting the 2030 Agenda for Sustainable Development, EU countries have committed themselves to meet targets against which progress in reducing inequalities can be measured. Through the present research, we aim to analyze and assess the extent to which EU countries will achieve the specific SDG 10 targets. Based on data published by Eurostat for the period 2010–2020, we forecast the trends of the indicators until the year 2030, using a model based on the AAA (Holt–Winters) version of exponential smoothing (ETS), to assess the degree to which the assumed targets will be reached. For more detailed information, we used dynamic indices to analyze the dynamics of the progress achieved. The results showed that it is difficult to clearly distinguish one or more countries as part of a group of high or low performers in terms of the efforts made and the effects achieved in reducing inequalities. However, we could mention Poland as a good and very good performer on most of the indicators analyzed. As opposite examples, we can mention Bulgaria and Greece, for which more attention and involvement are needed in adopting measures to correct the negative trend forecast.

1. Introduction

Although for all the countries of the world the 2030 Agenda has represented real challenges in terms of stimulating a different way of economic and social growth in relation to the environment and its exhaustible resources, we identify today real difficulties in achieving the SDGs, carrying out activities of any kind in relation to maintaining the quality of the environment, which is, unfortunately, a constant problem.

We thus identify the presence of specific policies and strategies at the level of countries (especially developed countries) that address in a complex way the problem of environmental degradation, the problem of sustained economic growth, clean and accessible energy for all, education and health for all and the elimination of all kinds of social, economic and environmental disparities between people and between countries. Identifying and correcting the discrepancies between environmental degradation and technological progress are also goals pursued both by governments and by researchers, specialists and practitioners. This is because gaps and shocks of all kinds that occur at the level of communities, geographical and economic regions are still risk factors for which even the most advanced econometric and mathematical models cannot generate immediate corrective or preventive solutions [1,2].

Therefore, the various measures adopted by most United Nations (UN) members to achieve the Sustainable Development Goals (SDGs) included in the 2030 Agenda aim to respond primarily to challenges related to social, economic and environmental issues, establishing sets of indicators to monitor the progress or regression of each country from a sustainable growth perspective. However, the need to continuously and gradually assess the level of sustainability achieved by each country has become more and more apparent, which is why today we identify an evolution in the way of monitoring through the creation of the SDG Index, which was initially based on 77 indicators, evolving today to 99. Moreover, the scores obtained by each country as a result of monitoring through the SDG indicators have led to the grouping of countries by specific geographical areas and whose monitoring is now focused on regional analysis and forecasting indices [3,4].

We also note that the successful implementation of the 2030 Agenda depends exclusively on the approach to sustainability, human well-being, economic prosperity and environmental protection by each individual country, and each geographical region, as the SDGs are considered to be a complex, constantly interacting system that must ensure a safe and equitable global operating space for all. Moreover, it is also important to note that no SDG can be implemented and cannot act in isolation, and therefore the achievement of the targets depends solely on how synergies are harnessed and how different trade-offs are addressed [5,6,7].

Based on these general considerations, this paper takes as its starting point that sustainable development is based on the complex, multifaceted, two-way interaction between the sustainable evolution of the world and the eradication of poverty, the reduction of inequalities in their multidimensional aspects, in direct correlation with climate action in a future with temperatures at least 1.5 °C higher [8].

These fundamental connections are incorporated in the present paper by assessing the achievement of the targets proposed by the 2030 Agenda for reducing inequality at the EU Member State level. Compared to other research papers, the paper adds knowledge by analyzing how EU Member States have progressed in terms of SDG 10, while also highlighting the prospects they have in terms of reaching the targets set for the 2030s.

The present research can be seen as a dashboard that clearly highlights the thresholds achieved by each EU Member State today regarding SDG 10 targets achievement and can be a real reference that can be used to inform future strategies and programs.

We justify this research by the fact that “Reducing inequality within and between countries” (SDG 10), even though it was among the last SDGs added to the 2030 Agenda is of overwhelming importance for the world as a whole, due to the exacerbation of the phenomenon of increasing wealth disparities identified around the world, as well as the different ways in which political factors respond to these disparities. At the EU level, we unfortunately still identify disparities, even though there are largely common policies, common strategies for action and common targets. This paper may be a call for some redistribution of wealth although it is unlikely that large-scale redistribution through taxation techniques or other fiscal means will really be a feasible measure in the future.

This paper is divided into five distinct sections. Following the introduction, Section 2 presents the literature review, Section 3 describes the research methodology, Section 4 discusses the main findings of the research and Section 5 aggregates the conclusions.

2. Literature Review

The global development of society imposes a set of common rules which are derived from the major problems facing the planet and which, if dealt with in isolation, can generate far greater risks than those identified so far. Therefore, under the aegis of globalization and the new concepts of inclusive growth, and global economic development, the 2030 Agenda was created as a first universal framework for all countries of the world, with the aim of contributing mainly to the eradication of poverty but also to the implementation of sustainable goals in all aspects of human life by the year 2030. With a set of 17 concrete goals, the 2030 Agenda is today the central pillar of regional, national and local strategies and programs and by no means appears to be a mere target. We support this claim through the very multitude of government projects, initiatives of global and regional organizations and local/national public and private sector actions.

From poverty eradication to the rational use of natural resources, the 2030 Agenda encompasses and details every aspect of human life, making it a universal/global benchmark. In this broad but detailed context, the 2030 Agenda addresses one of the most important issues facing the world today: reducing inequality within and between countries. It is certainly an issue that the literature addresses in various forms, as it is an extremely difficult and complex goal with seven specific targets agreed upon by the 193 UN member states. SDG 10, therefore, is the most interesting and wide-ranging global target, addressing a wide range of inequality issues [9,10,11].

Both internationally and at the EU Member State level, the concept of the “right to development” and the reduction of inequalities imposes a number of obligations on developed countries to help the poorest. International institutions (World Bank, United Nations, etc.) therefore promote rights and can act as institutions of intervention so that by 2030 we will see a large reduction in wealth inequalities, now considered one of the fundamental problems of long-term societal sustainability [1,7].

Global and regional inequalities still affect people in the poorest countries, but the richest countries are not excluded from this situation. This is justified by the fact that inequalities between groups within the same society persist alongside inequalities between nations/countries. This is the reason for the understanding that inequalities are largely unjust and derive in large part from differences between societies and nations.

At the global level, the most plausible solution to reduce the phenomenon of inequality has focused on the implementation of global governance mechanisms that have been and continue to be contested because they cannot include aspects of understanding how each country has evolved historically as well as the position of each country in the global economic system [12,13].

Reducing existing inequalities must therefore become a priority objective of both national and international policy agendas, even if piecemeal approaches based on one-off initiatives aimed mainly at “helping the poor” may be successful. On the other hand, reducing inequality requires national policies for economic diversification, appropriate fiscal, monetary and financial strategies and social policies that target disadvantaged social groups, but which must also incorporate respect for nature as an essential principle of world sustainability in the medium and long term [14].

In this regard, recent studies [15] carried out at OECD and non-OECD levels, from the point of view of inequality and economic prosperity included health indicators by gender groups (men, women) such as life expectancy, causes of mortality and avoidable mortality, showed that significant gender differences in health showed a positive outcome for women. Relationships between gender inequalities in health and economic prosperity were also identified. Therefore, policies should focus on reducing income inequalities by gender but also in terms of avoidable mortality, such as reducing common diseases among young people.

Compared to other geographic regions of the world, within the EU Member States we identify the advantage of the people’s rights-based approach to sustainable growth; an approach that gives people the enabling context to succeed, thereby removing political and social factors, such as discrimination and repression that can prevent them from succeeding.

However, inequalities in income and wealth are increasingly evident and are even widening. This is reflected in the fact that the top 1% of the world’s population now controls up to 40% of global assets, while the poorest half of the population owns just 1% [16]. Disparities are also wide within countries, including disparities in rural/urban areas, gender disparities, ethnic minorities, migrant status and disability [17,18].

The SDGs, while including a number of explicit and implicit solutions that address inequality, are often vaguely formulated, the targets are abstract, and the issue of inequality remains a central one, requiring fundamental reform of sustainable development thinking for 2030 and beyond. Moreover, the construction of poverty eradication and development within each individual state must include values and problem-solving frameworks based on the responsiveness of all bodies involved in achieving the SDGs [19,20].

In this context, SDG 10 promotes the idea of a society in which every person enjoys the benefits of economic growth, which may be evident in some countries, particularly those that have experienced rapid growth, but often even in these situations economic growth can contribute to growing income inequality, inequality of access to opportunities by gender, religion, caste and region. Improving the social security system, introducing and expanding social insurance schemes and establishing special funds for lagging regions can therefore be solutions to key problems of inequality [21,22].

Although “reducing inequalities within and between countries” is a goal in itself, which stimulates the global community to international cooperation, the current state of the planet shows that this goal is extremely difficult and complex to achieve. SDG 10, has a series of targets that address a wide range of inequality issues, including: achieving and sustaining income growth for the top 40% of the population at a rate above the national average, promoting the social, economic and political inclusion of all, regardless of age, gender, disability, race, ethnicity, origin, religion or economic or other status; ensuring equal opportunities and reducing inequality of outcomes, including by eliminating discriminatory laws, policies and practices and promoting appropriate legislation, policies and actions to this end; adopting policies, in particular tax, wage and social protection policies and progressively achieving greater equality; improving the regulation and monitoring of global financial markets and institutions and strengthening the implementation of these regulations, ensuring greater representation of developing countries in decision-making in global international economic and financial institutions, facilitating orderly, safe, regular and responsible migration and mobility of people, including through the implementation of planned and well-managed migration policies, implementing the principle of special and differential treatment for developing countries, in particular the least developed countries, encouraging financial flows, including foreign direct investment, to countries where the need is greatest [23,24,25].

At the EU level, the situation is similar to other geographical regions, although a number of positive results are evident, at least in terms of the income gap between rich and poor. This is because in the EU, for example, S80/S20 share ratio for 2019, the income share of the richest 20% of the population was almost five times that of the poorest 20% of the population [26].

On the other hand, according to the Annual Labor Force Assessment, the COVID-19 crisis is likely to cause a deterioration in the socio-economic situation of low-income households and other marginalized groups, such as migrants and minorities. This unfavorable situation is largely due to loss of income and rising prices but also rising health expenditure, which can disproportionately affect low-income households and can have a number of long-term consequences, such as a person’s ability to save, health and children’s education. In addition, the pandemic has had negative effects on children’s mental health and exacerbated social inequality. In the same vein, the European Platform for Investment in Children (EPIC) highlights wide variations between EU Member States and suggests policy options to address the childcare gap [3,27].

Not least, inequality and poverty are closely linked, and the distribution of resources in a country directly affects both the extent and depth of poverty. Thus, in 2019, 20.9% of the EU population was at risk of poverty or social exclusion, and the urban-rural gap in the at-risk-of-poverty or social exclusion rate was 1.1 percentage points. Specifically, 21.3% of those living in cities were in this situation, compared to 22.4% in rural areas [28]. Moreover, rural areas are much more at risk of poverty because of migration and limited access to services, infrastructure and education. Rural poverty, therefore, remains at very high levels in 2019 in some European countries, such as Bulgaria and Romania, where 48.5% and 44.3% of the rural population were at risk of poverty or social exclusion, twice the EU average. On the other hand, other countries such as Austria, the Netherlands and France have much higher poverty rates in cities than in rural areas [28,29,30,31].

Equally important to mention is the fact that the concepts of poverty, inequality and further social exclusion need to be analyzed and justified from several points of view. This is because there is a complex of factors that influence the state of affairs in a country and in areas within a country, particularly those linked to the economic dimension; these are often problematic and influence the way social problems are tackled [32,33,34].

Not to be neglected in this context is also the issue of the problems and challenges caused by poverty and migration, which are not limited to a single country or region in most cases. Consequently, reducing inequalities between countries is now seen as an essential condition for resolving complex problems, which often take the form of sharing prosperity and reducing barriers of all kinds.

At the EU level, although we identify a general reduction in economic disparities, there are still differences in the North-South and West-East divides, even though inequalities between EU countries have been decreasing over the last 15 years. As an example of this, we identify the coefficient of variation of gross domestic product (GDP) per capita—expressed in purchasing power standards (PPS)—which signals that economic disparities between the Member States have narrowed since 2003, reaching 41.3% in 2019, plus an improvement in countries that joined the EU in 2004 and beyond [35].

The coefficient of variation of gross disposable household income between the Member States has decreased over time, reaching 24.9% in 2019. Northern and Western European countries with above-average GDP per capita levels had the highest gross disposable income per capita, with Eastern and Southern EU countries at the other end of the scale, with gross household income and GDP per capita levels below the EU average [36,37,38].

It is also important to note that in almost all European countries, immigrants face a higher risk of poverty than residents, which calls for a change in the approach of European policies to their impact on the well-being of immigrants. In practice, issues such as immigration policy, labor market regulation and eligibility rules for social assistance need to be considered [39,40].

Migration and social inclusion are also important topics, frequently debated in recent research, as it directly influences inequalities between people, and in this context, we highlight as an example how conflicts in unstable countries (Syria, Afghanistan, Venezuela, Colombia, etc.) have contributed to an unprecedented increase in migration in EU countries in recent years. Therefore, the integration of migrants at the level of EU Member States and beyond has been a real challenge, we would even say decisively in terms of welfare, prosperity and not least the future cohesion of society [41,42].

In terms of the current state of the number of asylum seekers in the EU, of those seeking international protection, we identify a considerable reduction after 2015, the causes being numerous, mainly related to the COVID-19 pandemic which imposed a series of restrictive movement measures, but also those related to stricter border controls.

On the other hand, the current crisis caused by the war in Ukraine should not be overlooked, which has recently led to an increase in the number of migrants, especially from neighboring countries (Poland, Romania, etc.). All these aspects could radically alter the current and future state of inequality reduction in the EU Member States, bearing in mind a potential future economic crisis generated by the oil crisis. The high number of migrants will directly affect SDG 10 specific indicators such as SDG_10_60, SDG_08_20A and SDG_08_30A. At the same time, the overlap of the crisis generated by the increase in oil and food prices and the high level of inflation will decrease the financial means of the population and, implicitly, lead to an increase in the risk of poverty and social exclusion, with direct negative effects on SDG_10_10, SDG_10_20, SDG_10_30, SDG_10_40, SDG_01_10A and SDG_01_20A indicators.

Therefore, the social inclusion of non-EU citizens is still a major challenge for all countries in the world, even if we identify procedures for monitoring and integrating people in terms of poverty, education and employment in the labor market. In addition, reducing inequality also has as its point of reference the gap between people at risk of poverty according to income, and the gap is still wide. In 2019, for example, 38.6% of non-EU citizens were at risk of poverty compared to only 15.1% of residents of the country of origin [28].

Equally significant as an example is the fact that the employment rate for non-EU 20–64-year-olds fell by 2.6% between 2019 and 2020, compared to the rates in EU countries of origin which saw a reduction of only 0.5%, further supporting the gap between the two groups. The same changes in the negative direction for migrants can also be identified in terms of leaving the education system, especially in the 18–24 age group. These changes directly affect both the income levels of these groups and their employment in the labor market in the medium and long term, as well as sustainable human development in all countries [43,44].

Reducing inequalities between people is part of the process of sustainable human development, as it is well known that people from less developed regions and countries are always economically and socially vulnerable. Moreover, this category of people is also more resistant to change, which affects the sustainable balance of society [45,46].

In the same sense, with the aim of reducing inequalities between people, at the EU level, we also note that there is a high level of convergence between countries, which is efficient, but this is only evident when wealth levels are similar and when countries invest in the long term in the efficiency of their human capital. Consequently, this strategy contributes directly to improving economic and social performance and even to attracting foreign skilled workers. Overall, this implies higher productivity, higher levels of sustainability, and consequently a balanced society in terms of quality of life for all citizens [45,47,48,49].

Another important approach to the problem of reducing inequality at the EU Member State level also considers measuring the quality of employment of minority students. Therefore, issues such as the employment rate, working conditions, pay and well-being of minority students can reflect the quality of life of this part of a country’s population. Identifying these outcomes contributes directly to creating better working conditions, meeting students’ basic needs in terms of pay and welfare, but also to incentivizing students to perform better, etc. [50,51,52,53].

Alongside all the issues identified above regarding inequalities and how they are reflected in the reality of EU Member States, we also highlight the issue of climate change, which is, unfortunately, exacerbating the imbalances between states and between people to an increasing extent. This is because rising temperatures because of greenhouse gases are generating strong negative effects, particularly in low-income countries.

This is justified by the fact that the costs of mitigating climate change may slow down the economic recovery of less developed countries and thus reduce inequalities between countries. This is also why we believe it is essential that climate change mitigation policies are properly and comprehensively formulated so that they contribute to limiting the growth of future inequalities between countries [54,55].

Taking into account all the above, we propose to assess the degree to which the SDG 10 specific indicator targets will be reached by 2030, starting from the current state of achievement of the targets, as well as the dynamics of the indicators. Based on the fact that the trend on which the indicator values started in 2010 is likely to be followed in the future, using the forecasting tools available in the future, we will assess both the ability of each EU country to reach the targets and the performance that each country could achieve. Thus, we propose the following research questions for which we will seek to provide answers through this research, thus filling part of the existing knowledge gap:

Research question 1 (RQ1)—To what extent EU countries will reach the proposed SDG 10 targets by 2030?

Research question 2 (RQ2)—Based on trend analysis and projected dynamics, is it possible to identify high performing countries or low performing countries in terms of achieving SDG 10 targets?

Inequalities between countries are high and wage inequalities between developed and developing countries are increasing. Problems such as wages in the most developed European countries compared to those in other regions of Europe or the world, income concentration and increasing economic inequalities, are not only unfair but often become unbearable for the most disadvantaged. Therefore, at least from an economic point of view and from the point of view of sustainable development policies in the 2030s, we can estimate that a significant reduction in social and economic differences between people can be achieved by supporting the migration of skilled labor in particular, together with local government action to encourage the integration and protection of disadvantaged social groups.

3. Research Methodology

In order to obtain a comprehensive and complete picture of the extent to which EU countries will be able to achieve the proposed 2030 targets for reducing inequalities within and among them described by SDG 10, we started our research with data available from Eurostat, collected from 2010 to the latest year available [56].

The guiding principle of our research was to try to assess the potential to achieve the proposed targets based on the analysis of the trend evolution from 2010–2021 and the forecast of future developments until 2030. Given that the Paris Agreement was adopted in 2015, we wanted to capture the evolution of the analyzed indicators for 5 years before this reference date, as well as the next 6 years, in order to observe the possible increases or decreases in the pace of evolution.

The research took the year 2015 as a reference for the whole analysis. With the available data for the period 2010–2021, we have forecast the future evolution of the values of the selected indicators using a model based on the AAA (Holt–Winters) version of the exponential smoothing (ETS). From the existing econometric instruments, two possible models are highlighted to be used in forecasting the time series evolution: the ARIMA model (Auto Regressive Integrated Moving Average) and the ETS model (Error Trend and Seasonality, or exponential smoothing). From the preliminary analysis of the primary data series, it can be seen that there is an important trend component, so we decided to use the ETS model for forecasting future evolution with the most relevant results.

The forecasting algorithm based on the ETS function is used with precedence for data with a significant trend or seasonality component. ETS computes as a forecast a weighted average of all observations in the input time series dataset, with exponentially decreasing weights over time, instead of constant weights (as in simple moving average models). The weights assigned to the model are constructed based on a constant parameter, known as the smoothing parameter. The exponential scale used is constructed based on the following formula [57,58,59]:

(1)$\mathrm{f}=\left\{1,\left(1-\phi \right), {\left(1-\phi \right)}^2,{\left(1-\phi \right)}^3,\dots ,\infty \right\}$

The forecasted values are considered a continuation of the timeline of historical data in the specified target time interval. The basic equations used in the ETS model are based on Holt–Winters’ multiplicative method [60], according to the following formulas:

(2)$\mathrm{for} \mathrm{level}: L_n=\alpha \frac{y_n}{S_{n-s}}+\left(1-\alpha \right)\left(L_{n-1}+B_{n-1}\right)$

(3)$\mathrm{for} \mathrm{trend}: B_n=\beta \left(L_n-L_{n-1}\right)+\left(1-\beta \right)B_{n-1}$

(4)$\mathrm{for} \mathrm{seasonality}: S_n=\gamma \frac{y_n}{L_n}+\left(1-\gamma \right)S_{n-s}$

(5)$\mathrm{for} \mathrm{forecast}: F_{n+t}=\left(L_n+B_{n}t\right)+S_{n-s+t}$

where:

• L_n = the level of the series;

• B_n = the trend;

• S_n = the seasonal component;

• s = length of seasonality (e.g., number of months or quarters in a year);

• F_n+t = the forecast for t periods ahead.

In addition, in order to gain additional information and knowledge from the analysis, we used dynamic indexes to assess the ability of Member States to reduce existing inequalities within and between EU countries in order to reach the targets of the 2030 Agenda, calculated at three key points in time, namely 2020, 2025 and 2030.

Dynamic indices are based on the analysis of homogeneous time series and calculate the ratio between the size of an economic phenomenon analyzed in two different time periods. To estimate dynamic indices [61,62,63], the ratio of the indicator under analysis at a point in time to the value of the indicator in the base period Formula (6) is used:

(6)${\mathrm{D}}_{n/0}=\frac{{\mathrm{D}}_n}{{\mathrm{D}}_0}\times 100\%$

where:

• D_n = the indicator value in a given moment of time;

• D₀ = the indicator value in a base period.

Through the dynamic indices analysis and the forecasting tools provided by the Excel 2016 software, individual dynamics indexes were calculated for each of the SDG 10 specific indicators, as well as the possibility of a convergence point between the trend of each indicator extrapolated for each EU country and the EU average values for the same indicator in the in years 2025 and 2030 (as noted in Table 1, Table 2, Table 3, Table 4, Table 5, Table 6, Table 7, Table 8, Table 9, Table 10 and Table 11, in the last column, “Int. 2025” and “Int. 2030”).

4. Results and Discussion

To assess the commitment and likelihood of EU countries to reduce existing inequalities within and among them by 2030, we used the methodological framework described above to analyze the potential degree of achievement for the SDG 10 specific indicators. The analyzed data were collected for the period 2010–2021 and the forecast horizon was between 2021 and 2030. In addition, for the analyzed timeframe, three important milestones were defined (for the years 2020, 2025 and 2030) in order to be able to observe in detail the dynamic evolution of the potential to reduce existing disparities.

In addition, through the analysis of the dynamics of evolution of the specific indicators considered as a baseline in 2015, the analysis revealed a detailed picture of the rate differential adjustment of the differences between the national level and the average value of indicators at the EU level.

A first important observation, which can be made from the outset, is that a clear concern for reducing disparities can be observed, with the data analyzed suggesting that most Member States are on a positive trend. However, there are inevitably a number of potential risks, or deviations from the proposed national targets, which have been highlighted by the research. All relevant research results were summarized, for each specific indicator, in subsequent Table 1, Table 2, Table 3, Table 4, Table 5, Table 6, Table 7, Table 8, Table 9, Table 10 and Table 11.

A first indicator specific to SDG 10 is purchasing power adjusted GDP per capita. As can be seen from the results presented in Table 1, forecasts show an upward trend by 2030 for all 27 Member States, suggesting a potential steady increase in well-being and welfare through improved economic and social conditions.

On the other hand, the same analysis also reveals that the growth rates of purchasing power adjusted GDP per capita are different between EU countries, with a group of high performers (Belgium, Denmark, Germany, France, Ireland, Luxembourg, Malta, the Netherlands, Austria, Finland and Sweden) clearly evident. Of this group, the highest growth by 2030 is expected to be in Ireland (+209% compared to 2015).

On the other hand, the same analysis also reveals that the growth rates of purchasing power adjusted GDP per capita are different among EU countries, with a group of high performers (Belgium, Denmark, Germany, France, Ireland, Luxembourg, Malta, the Netherlands, Austria, Finland and Sweden) clearly being highlighted. Of this group, the highest growth by 2030 is expected to be in Ireland (+209% compared to 2015).

At the same time, the research indicates the two European countries (Lithuania and Poland) that are expected to outpace the average growth rate of this indicator among EU countries by 2025.

Three other countries (Bulgaria, Croatia and Romania) can also be highlighted, which, despite some of the highest growth rates forecast for the indicator under analysis, will probably not exceed the EU average, given the relatively low baseline, as in 2015 they recorded some of the lowest values.

Among the countries included in the analysis, less encouraging results are recorded by Greece and Slovakia, for which relatively low growth rates of purchasing power adjusted GDP per capita are estimated and which clearly require corrective measures to be adopted as soon as possible.

Regarding the level of adjusted gross disposable income of households per capita (Table 2), the research results indicate, as in the case of the previous indicator, a general upward trend until 2030, with one exception: Greece. In the case of this country, a slight but steady decline in the values of the indicator is estimated, which may be a clear alarm signal for policymakers, who should adopt firm measures to correct this decline.

A more detailed analysis of the results shows that the same group of high-performing countries is still present, but that it is composed of a smaller number of countries than in the case of the previous indicator, namely Belgium, Denmark, Germany, France, Luxembourg, the Netherlands, Austria, Finland and Sweden. It should be noted that no data were reported for Malta.

Among the European countries with a strong performance on this indicator, Lithuania should be highlighted, for which we forecast one of the strongest developments until 2030. At the same time, some of the highest growth rates are forecast for Bulgaria and Romania, but when compared to the low baseline values of 2015, we cannot consider these countries in the group of performing countries until 2030.

In the case of SDG 10.30—Relative median at-risk-of-poverty gap—the results of the survey indicate a relative divergence between EU countries in the range analyzed. Thus, among the 27 EU countries, for 15 Member States, a downward trend of the indicator is forecast, i.e., a reduction of the existing gap, and for the remaining 12 Member States, an increase of the indicator values is estimated until 2030.

Moreover, for the group of countries for which a reduction in the relative median at-risk-of-poverty gap is expected, an unfavorable trend can be observed until 2025 (such as Denmark, Malta, Poland or Portugal), but for the year 2030, a correction of the evolution of the indicator in positive territory is forecast.

Among the EU countries for which a negative development is expected, i.e., an increase in the gap existing in the baseline period, we can mention countries with a very well-developed economic and social system, such as Germany (which is expected to develop positively by 2025), France, the Netherlands, Austria or Finland.

The widening relative median at-risk-of-poverty gap is a worrying fact, as higher income inequality implies more poverty and poverty might lead to social tensions and social exclusion (Table 3).

SDG indicator 10.41—Income distribution—is a measure of the inequality of income distribution, being calculated as the ratio of total income received by the top quintile of the population with the highest income to that received by the bottom quintile of the population with the lowest income. The analysis reflects a relatively positive situation and development for most of the countries analyzed.

However, a negative forecast of the evolution of the values of this indicator can also be observed for 11 Member States, for which the results of the research indicate an upward trend, i.e., an increase in the inequality of the population income distribution. Within the group of countries with an estimated deteriorating situation are included both economically and socially highly developed countries (Germany, Italy, Netherlands, Finland, Sweden) and less economically developed countries (Bulgaria, Latvia, Hungary, Romania).

Particularly serious is the forecast for countries with a lower degree of economic development among EU countries (such as Bulgaria or Romania), for which the forecast of an increase in the values of the income distribution indicator may have extremely damaging and long-term social effects. We draw attention to the fact that firm measures to correct the estimated trend are essential in order to be able to perceive positive effects in the near future. Even if it is obvious that the SDG 10 targets will be missed by these countries in relation to this indicator, the severity and complexity of the effects combined with other negative developments may lead, in the absence of firm and immediate measures, to increasing inequalities in the medium and long term compared to other EU countries (Table 4).

Regarding SDG 10.50—Income share of the bottom 40% of the population—we can notice a relatively similar group of EU countries, as well as a relatively similar evolution of the forecasts as in the case of the previous indicator (SDG 10.41). Analysis and estimation of the change in values of this indicator are relevant for measuring the overall change in living standards at the EU level of those with the lowest income.

The countries for which a negative trend is estimated until 2030 are those for which the disposable income is expected to decrease for the part of the population that is disadvantaged, which will inevitably lead to an amplification of existing inequalities, with negative effects on the living standards of all citizens, through the additional costs they will have to bear and the social tensions that will be increasing (Table 5).

Regarding asylum applications at the EU level, the research reveals the existence of two opposing trends and a redistribution of these applications for the period 2021–2030. We can observe a decreasing forecast of asylum applications both in economically and socially developed countries (Belgium, Denmark, Luxembourg, the Netherlands, Austria, Finland, Sweden) and in less developed countries (Bulgaria, the Baltic States, Slovakia). On the other hand, for the same horizon 2021–2030, we note an increasing forecast of asylum applications both in developed countries (Germany, France, Italy) and in countries with more modest performances (Greece, Romania, Slovenia).

A potential explanation could be that some economically more developed countries have tightened their asylum conditions and policies, while other less developed countries have been on an economic uptrend, thus becoming more attractive to asylum seekers.

It is clear that the geopolitical situation in 2022, with the war in Ukraine and the worsening economic and social conditions in all countries in the region, will induce massive changes in the evolution of asylum applications, and it is therefore particularly difficult to forecast the evolution of this indicator at the level of EU countries with any reasonable accuracy (Table 6).

The forecast of potential target values for SDG 1.10A—People at risk of poverty or social exclusion—generally suggests that there will be a positive development in the EU countries by 2030, with a reduction in the risk of poverty or social exclusion expected for most Member States.

However, there are some notable exceptions (Germany, Estonia, Spain, France, Italy, Finland) for which the research results indicate a worsening of the evolution of this indicator. Considering that most of these countries are highly developed countries, which are part of the hard core of the European Union, it follows that measures to correct the negative evolution need to be adopted as soon as possible. The increase in the proportion of people at risk of poverty or social exclusion can only lead to a worsening of the social situation in these countries and beyond, and implicitly to an increase in the associated economic costs (Table 7).

Regarding the evolution of the SDG 1.20A indicator, a mixed evolution of the projected trends until 2030 can be observed for the EU countries, which are grouped in two relatively equal groups. Thus, for about half of the EU countries an improvement in the situation is forecast, and for the other half a worsening of the existing situation.

It is important to correlate the results obtained for this indicator with the other results of the research, which will highlight some countries (such as Spain, Italy and Romania) for which it is imperative to adopt corrective measures, given the current not very positive situation, which is likely to worsen even more in the future. Germany should also be mentioned, where the results of the research again place it among the countries for which the indicator is expected to worsen (Table 8).

In terms of the evolution of the percentage of early leavers from education and training (Table 9), the research indicates a rather positive situation in the European countries analyzed, with an estimated downward trend for most countries.

However, there are a few exceptions to the positive picture, namely Bulgaria, the Czech Republic, Luxembourg, Hungary and Slovakia, where the rate of increase of this indicator is forecast until 2030. Among the countries mentioned as having an unfavorable evolution, probably the most serious situations are Bulgaria and Hungary which are, especially due to the situation recorded in the baseline period, characterized by a high percentage of early leavers from education and training (13.5% and 11.6%, respectively), tends to worsen even more until 2030.

At the opposite pole, i.e., some countries that have managed to make significant progress, we can mention Estonia, Italy, Malta or Romania, which, although they started from among the highest values of the indicator in 2015, are expected to be able to register a downward trend, correcting the existing gaps, i.e., reducing existing inequalities compared to most other EU countries.

One of the most important targets at the EU level in recent times has been to reduce the percentage of young people neither in employment nor in education and training (NEET). The results of the research reflect the effects of these sustained efforts across all EU countries in terms of both current performance and forecast values up to 2030. From the results summarized in Table 10, it can be seen that almost all Member States show significant decreases in this indicator, as well as a downward trend for the whole forecast period.

However, of the 27 EU Member States analyzed, only two countries (Denmark and Luxembourg) stand out for which the results of the analysis indicate a worsening situation in the medium and long term. If for Luxembourg a constant upward trend is estimated for the NEET percentage, in the case of Denmark it can be observed that until 2025 the forecasts indicate an upward trend but below the EU average, but until 2030 the negative trend increases, so that the forecast results become negative (Table 10).

As for the evolution of SDG 8.30A—Employment rate—which is another important indicator tracked at the EU country level, a unanimously positive evolution can be observed, i.e., an improvement of the outlook for the whole period under analysis. Thus, it proves that the aggregate efforts made at the EU level to increase the employment rate are bearing fruit as expected.

However, the results of the research reveal details of the differences in the growth rates between the countries analyzed. Thus, there are countries for which better results are forecast than in others for the 2030 horizon, as is the case in Lithuania, Hungary, Malta or Poland. At the other end of the spectrum, countries with lower growth rates can also be identified, which implies that existing differences may widen over a longer time span, as could be the case in Belgium or Italy (Table 11).

To summarize the results of the research undertaken in terms of reducing inequalities within and among EU countries, we can assess that, in general, the trend is positive. The political, economic and social efforts sustained both at the EU level and at the level of individual countries, are proving to generate positive effects both in the short term and in the medium and long term.

EU Member States have managed to improve a number of important elements of quality of life both at the level of the most lagging countries and at the level of affected social groups, as well as to reduce the gap between rich and poor by implementing key impact measures. At the same time, countries that have made significant progress can be examples of good practice for other European countries and beyond, even if further research and analysis are still needed to understand why socio-economic inequalities persist in some countries while they are increasingly reduced in others.

To answer the research questions proposed above, based on the results of the research, we can state that the answer to Research question 1 (RQ1)—To what extent EU countries will reach the proposed SDG 10 targets by 2030?—Is really difficult to provide in a transient way. As the results show, there are countries that will reach some of the targets set in the SDG 10 but are likely to miss other targets. The results of the research suggest that it is not possible to identify one or more countries that will meet all SDG 10 targets by 2030. Significant progress has been made over the period 2010–2021 in improving specific indicators, mainly due to the awareness of existing gaps and the support of all stakeholders for improved performance.

However, analyzing the results obtained, it proves difficult to maintain a high rate of progress in achieving the SDG 10 targets, especially in the current context which is marked by economic and social factors with a strong negative impact on the indicators analyzed. We can expect a potential negative correction of the trend and dynamics in the progress of SDG 10 indicators for a significant number of EU countries. Furthermore, using a different econometric tool, Szymańska [42] reached similar conclusions, stating that over the medium term, EU countries were able to make progress in reducing inequalities among them, but the income inequalities within countries still exist or have even deepened.

As regards Research question 2 (RQ2)—Based on trend analysis and projected dynamics, is it possible to identify high performing countries or low performing countries in terms of achieving SDG 10 targets?—The research results were able to highlight countries that show high potential for performance in terms of reducing inequalities within and among EU countries. Thus, putting together the results obtained for each of the indicators analyzed, we can identify Poland as one of the EU countries that is high performing in relation to the SDG 10 targets. This result is probably not accidental, as Poland has made remarkable economic and social progress over the last 15 years.

On the other hand, the second research question (RQ2) can also be answered in terms of countries for which performance is not expected to be so high by 2030. The research results suggest that Bulgaria and Greece are two of the EU countries for which there is a fairly high probability of missing most of the SDG 10 targets if targeted measures are not taken to correct the negative deviations highlighted by the present research.

Finally, a third answer in relation to RQ2 can be provided by the results of the research, namely the identification of a group of European countries (France, Italy, Germany, the Netherlands) for which there is a reasonable chance of negative deviations from the path towards achieving the SDG 10 targets. To a large extent, these negative deviations turn out to be influenced by the attractiveness of these countries for migrants, through existing economic and social standards. With the increased flow of migrants generated by the ongoing war in Eastern Europe, coupled with the impact of the negative economic factors that are currently manifesting themselves, there is a possibility that a number of indicators will be negatively influenced, leading to a decrease in the rate of progress towards the SDG 10 targets. Similar conclusions, supporting our findings, have also been published by Gavriluță et al. [64], Bieszk–Stolorz and Dmytrów [65], or Kolluru and Semenenko [66].

As our research reveals, in most European countries the inequality within and among them has been somewhat reduced in the last decade, although the last two years of the Pandemic have in some cases accentuated a certain stalling of growth at all levels, especially among the poor, thus increasing inequality for this period.

Therefore, a particular focus in the future should be directed towards the creation and implementation of composite indices that allow not only for progress made by individual countries but also for the benchmarking of EU countries according to the level of performance achieved in implementing SDG 10 and beyond. We justify this view by the fact that it is obvious that Northern European countries hold important positions in terms of SDG implementation, but often these results can be misleading because they may include statistical data at a certain point in time and progress can often be inconclusive in relation to reality.

Moreover, SDG 10 and beyond, is one of the goals subject to more frequent risks, as inequalities between people are amplified by the crises experienced by each country or region at a given time, as observed during the COVID-19 pandemic or currently in European countries in the context of the war in Ukraine. In this context of often radical change and transition to a sustainable economy, developing strategies with macro-level impact would enable companies and consequently people to overcome the obstacles they face.

Equally important is further research and analysis of the political, social, economic and environmental factors that continue to sustain inequalities in some countries. We hope that all the research results/findings will contribute to the foundation of political measures and strategies to reduce inequalities in such a way that the 2030 Agenda becomes a reality for all countries, regardless of the obstacles that have arisen in the last period, such as the pandemic COVID-19, the war in Ukraine that continues to affect the whole planet, the whole society.

5. Conclusions

The importance of reducing inequalities within and among EU countries and achieving the SDG 10 targets is considered of paramount importance by EU officials and by most central authorities in the Member States. However, most of the targets of the 17 SDGs have been set and assumed as qualitative targets, which makes it more difficult to assess the degree of achievement.

For this reason, our multidimensional research contributes to filling a knowledge gap regarding the assessment of the potential for achieving the SDG 10 targets. The results obtained reveal a series of important and relevant information regarding the forecast of the trend of European countries in terms of achieving the specific targets, as well as the rate of change of the variables analyzed.

Thus, when analyzing the results obtained, it is difficult to clearly distinguish one or more countries as part of a group of high or low performers in terms of the efforts made and the effects achieved in terms of reducing inequalities. Some countries perform better on some of the indicators, while others perform better on others. Similarly, countries are also divided in terms of lower performance.

Research results suggest that it is difficult to distinguish the countries included in the analysis according to their overall performance in achieving the SDG 10 targets. However, if we empirically summarize all the results obtained, we could mention Poland as a good and very good performer on most of the indicators analyzed. Similarly, we should draw attention to a group of countries for which the forecasts indicate more negative than positive results, we could mention Bulgaria or Greece.

Additionally, based on the results obtained, we can indicate the potential for negative deviations from the objective of reducing inequalities within and among economically and socially developed EU countries such as Germany, France, Italy, and the Netherlands. Careful monitoring may be needed to monitor the situation in the coming period, as we can expect a worsening of the economic and social context in the short and medium term, which could lead to increased inequalities in the long term.

The results of the research should also be seen in light of the limitations inherent in such an analysis. Data availability, limitations of econometric models, the economic and social effects of the COVID-19 pandemic and the war in Ukraine are some of the factors that may influence the future evolution of the indicators analyzed.

Nonetheless, we hope that through the present research we have succeeded in providing a more comprehensive perspective on the potential for reducing inequalities within and among EU countries and hope to open up new research directions and opportunities. Achieving the targets proposed by the 2030 Agenda is crucial to ensure a better and safer sustainable future for all citizens, especially in today’s extremely challenging context, with abrupt changes and a major impact on many aspects of social and economic life.

Author Contributions

Conceptualization, G.H.I. and D.F.; Methodology, G.H.I., D.F. and L.M.C.; Supervision, G.H.I., D.F. and T.M.C.; Writing—original draft, G.H.I. and D.F.; Writing—review and editing, G.H.I., D.F., T.M.C., L.M.C., M.D.O. and O.T. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Tables

Table 1

SDG 10.10—Purchasing power adjusted GDP per capita (PPS).

Countries	2015	2020	2025	2030	2020/2015	2025/2015	2030/2015	Trend	Int. 2025	Int. 2030
Belgium	33,200	35,500	43,381	48,260	1.07	1.31	1.45	UP	YES	YES
Bulgaria	13,200	16,400	22,434	27,526	1.24	1.70	2.09	UP	NO	NO
Czech Republic	24,400	27,800	32,922	37,201	1.14	1.35	1.52	UP	NO	NO
Denmark	35,300	40,300	48,211	54,810	1.14	1.37	1.55	UP	YES	YES
Germany	34,200	36,600	41,644	45,287	1.07	1.22	1.32	UP	YES	YES
Estonia	21,200	25,200	31,752	36,731	1.19	1.50	1.73	UP	NO	NO
Ireland	49,700	62,400	84,316	103,782	1.26	1.70	2.09	UP	YES	YES
Greece	19,200	18,600	21,382	22,039	0.97	1.11	1.15	UP	NO	NO
Spain	25,100	25,200	28,797	30,792	1.00	1.15	1.23	UP	NO	NO
France	29,400	31,200	35,310	38,313	1.06	1.20	1.30	UP	YES	YES
Croatia	16,500	19,200	28,963	36,496	1.16	1.76	2.21	UP	NO	NO
Italy	26,500	28,000	32,489	34,747	1.06	1.23	1.31	UP	NO	NO
Cyprus	22,900	26,400	30,570	33,141	1.15	1.33	1.45	UP	NO	NO
Latvia	17,900	21,000	26,265	30,430	1.17	1.47	1.70	UP	NO	NO
Lithuania	20,700	26,000	33,285	39,164	1.26	1.61	1.89	UP	NO	YES
Luxembourg	74,600	78,700	91,831	102,713	1.05	1.23	1.38	UP	YES	YES
Hungary	19,200	22,100	26,389	30,146	1.15	1.37	1.57	UP	NO	NO
Malta	26,900	28,900	36,234	41,566	1.07	1.35	1.55	UP	YES	YES
Netherlands	36,200	39,600	48,965	57,796	1.09	1.35	1.60	UP	YES	YES
Austria	35,900	37,200	41,434	44,453	1.04	1.15	1.24	UP	YES	YES
Poland	19,100	22,600	32,713	42,573	1.18	1.71	2.23	UP	NO	YES
Portugal	21,300	22,800	25,344	27,419	1.07	1.19	1.29	UP	NO	NO
Romania	15,500	21,400	28,294	33,745	1.38	1.83	2.18	UP	NO	NO
Slovenia	22,700	26,500	33,084	37,564	1.17	1.46	1.65	UP	NO	NO
Slovakia	21,500	20,900	22,770	23,931	0.97	1.06	1.11	UP	NO	NO
Finland	30,500	33,800	40,619	45,663	1.11	1.33	1.50	UP	YES	YES
Sweden	35,300	36,800	40,433	43,820	1.04	1.15	1.24	UP	YES	YES

Source: Eurostat, own calculations.

Table 2

SDG 10.20—Adjusted gross disposable income of households per capita (PPS).

Countries	2015	2020	2025	2030	2020/2015	2025/2015	2030/2015	Trend	Int. 2025	Int. 2030
Belgium	25,397	26,401	28,702	30,635	1.04	1.13	1.21	UP	YES	YES
Bulgaria	10,272	12,215	14,206	16,275	1.19	1.38	1.58	UP	NO	NO
Czech Republic	17,385	19,847	23,032	25,756	1.14	1.32	1.48	UP	NO	NO
Denmark	23,769	24,956	28,037	30,102	1.05	1.18	1.27	UP	YES	YES
Germany	27,653	29,539	32,950	35,651	1.07	1.19	1.29	UP	YES	YES
Estonia	15,227	17,260	20,682	23,805	1.13	1.36	1.56	UP	NO	NO
Ireland	19,975	21,965	24,423	26,220	1.10	1.22	1.31	UP	NO	NO
Greece	15,265	14,963	14,754	14,503	0.98	0.97	0.95	DOWN	NO	NO
Spain	19,198	19,183	20,875	22,216	1.00	1.09	1.16	UP	NO	NO
France	24,846	25,991	27,791	29,518	1.05	1.12	1.19	UP	YES	YES
Croatia	12,997	14,820	14,358	16,561	1.14	1.10	1.27	UP	NO	NO
Italy	21,591	22,142	22,999	23,725	1.03	1.07	1.10	UP	NO	NO
Cyprus	17,646	20,139	19,534	20,873	1.14	1.11	1.18	UP	NO	NO
Latvia	13,486	15,666	19,138	22,036	1.16	1.42	1.63	UP	NO	NO
Lithuania	16,507	20,538	24,362	28,368	1.24	1.48	1.72	UP	NO	YES
Luxembourg	33,873	34,710	37,413	39,684	1.02	1.10	1.17	UP	YES	YES
Hungary	14,004	15,736	17,969	19,863	1.12	1.28	1.42	UP	NO	NO
Malta	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A
Netherlands	25,083	26,838	28,535	30,425	1.07	1.14	1.21	UP	YES	YES
Austria	26,812	26,959	28,595	29,846	1.01	1.07	1.11	UP	YES	YES
Poland	15,253	17,430	19,573	22,043	1.14	1.28	1.45	UP	NO	NO
Portugal	17,630	18,567	20,210	21,556	1.05	1.15	1.22	UP	NO	NO
Romania	11,749	16,554	17,702	21,951	1.41	1.51	1.87	UP	NO	NO
Slovenia	17,023	19,725	21,357	23,461	1.16	1.25	1.38	UP	NO	NO
Slovakia	15,905	15,152	16,274	16,700	0.95	1.02	1.05	UP	NO	NO
Finland	24,035	24,897	27,075	28,711	1.04	1.13	1.19	UP	YES	YES
Sweden	24,721	24,614	26,087	27,500	1.00	1.06	1.11	UP	YES	YES

Source: Eurostat, own calculations.

Table 3

SDG 10.30—Relative median at-risk-of-poverty gap (cut-off point: 60% of national median equivalized disposable income).

Countries	2015	2020	2025	2030	2020/2015	2025/2015	2030/2015	Trend	Int. 2025	Int. 2030
Belgium	17.4	16.2	16.1	14.9	0.93	0.93	0.86	DOWN	YES	YES
Bulgaria	30.3	28.3	27.9	26.4	0.93	0.92	0.87	DOWN	NO	YES
Czech Republic	19.2	14.8	12.6	10.0	0.77	0.66	0.52	DOWN	YES	YES
Denmark	22.0	19.3	19.8	18.9	0.88	0.90	0.86	DOWN	YES	YES
Germany	22.0	31.5	28.7	34.5	1.43	1.30	1.57	UP	NO	NO
Estonia	21.0	21.8	18.9	18.0	1.04	0.90	0.86	DOWN	YES	YES
Ireland	18.4	14.6	13.4	11.7	0.79	0.73	0.63	DOWN	YES	YES
Greece	30.6	26.9	20.3	14.6	0.88	0.66	0.48	DOWN	YES	YES
Spain	33.8	29.4	30.1	30.2	0.87	0.89	0.89	UP	NO	NO
France	15.7	21.5	21.9	23.9	1.37	1.40	1.52	UP	YES	YES
Croatia	26.4	28.0	25.6	26.0	1.06	0.97	0.99	UP	YES	YES
Italy	29.3	31.8	34.0	37.0	1.08	1.16	1.26	UP	NO	NO
Cyprus	19.8	16.9	15.5	15.6	0.85	0.78	0.79	UP	YES	YES
Latvia	25.5	28.6	25.8	25.4	1.12	1.01	1.00	DOWN	YES	YES
Lithuania	26.0	23.2	22.0	19.5	0.89	0.85	0.75	DOWN	YES	YES
Luxembourg	17.4	17.7	21.7	24.6	1.02	1.25	1.42	UP	YES	YES
Hungary	21.8	27.9	29.8	34.7	1.28	1.37	1.59	UP	NO	NO
Malta	17.5	16.0	16.1	14.6	0.91	0.92	0.84	DOWN	YES	YES
Netherlands	16.8	17.6	18.8	20.0	1.05	1.12	1.19	UP	YES	YES
Austria	20.5	22.7	24.2	25.4	1.11	1.18	1.24	UP	YES	YES
Poland	22.3	21.0	22.6	22.4	0.94	1.01	1.01	DOWN	YES	YES
Portugal	29.0	24.4	25.0	24.7	0.84	0.86	0.85	DOWN	YES	YES
Romania	38.2	31.9	25.0	18.2	0.84	0.66	0.48	DOWN	YES	YES
Slovenia	20.3	16.9	16.5	14.7	0.83	0.81	0.73	DOWN	YES	YES
Slovakia	28.9	23.7	26.9	27.4	0.82	0.93	0.95	UP	YES	YES
Finland	13.2	13.7	14.0	14.2	1.04	1.06	1.08	UP	YES	YES
Sweden	19.9	19.9	19.5	19.3	1.00	0.98	0.97	DOWN	YES	YES

Source: Eurostat, own calculations.

Table 4

SDG 10.41—Income distribution (ratio).

Countries	2015	2020	2025	2030	2020/2015	2025/2015	2030/2015	Trend	Int. 2025	Int. 2030
Belgium	3.83	3.65	3.6	3.4	0.95	0.93	0.89	DOWN	YES	YES
Bulgaria	7.11	8.01	9.5	10.5	1.13	1.34	1.48	UP	NO	NO
Czech Republic	3.51	3.34	3.2	3.2	0.95	0.93	0.90	DOWN	YES	YES
Denmark	4.08	4.00	4.0	3.9	0.98	0.97	0.96	DOWN	YES	YES
Germany	4.80	6.47	6.1	7.2	1.35	1.28	1.51	UP	NO	NO
Estonia	6.21	5.03	4.6	4.2	0.81	0.74	0.68	DOWN	YES	YES
Ireland	4.50	4.13	3.8	3.4	0.92	0.83	0.75	DOWN	YES	YES
Greece	6.51	5.15	4.7	4.1	0.79	0.72	0.63	DOWN	YES	YES
Spain	6.87	5.77	5.0	4.2	0.84	0.73	0.62	DOWN	YES	YES
France	4.29	4.48	4.2	4.1	1.04	0.97	0.96	DOWN	YES	YES
Croatia	5.16	4.61	4.3	3.8	0.89	0.83	0.73	DOWN	YES	YES
Italy	5.84	6.21	6.5	6.8	1.06	1.11	1.16	UP	NO	NO
Cyprus	5.20	4.31	3.7	3.4	0.83	0.71	0.64	DOWN	YES	YES
Latvia	6.51	6.27	6.3	6.4	0.96	0.97	0.99	UP	NO	NO
Lithuania	7.46	6.14	6.4	6.4	0.82	0.86	0.85	DOWN	NO	NO
Luxembourg	4.26	4.99	5.8	6.3	1.17	1.35	1.49	UP	NO	NO
Hungary	4.30	4.16	4.4	4.6	0.97	1.03	1.07	UP	YES	YES
Malta	4.15	4.69	4.8	5.0	1.13	1.16	1.21	UP	YES	YES
Netherlands	3.82	4.15	4.4	4.7	1.09	1.14	1.22	UP	YES	YES
Austria	4.05	4.11	4.1	4.1	1.01	1.01	1.00	DOWN	YES	YES
Poland	4.92	4.07	2.8	1.4	0.83	0.56	0.28	DOWN	YES	YES
Portugal	6.01	4.99	4.3	3.6	0.83	0.72	0.60	DOWN	YES	YES
Romania	8.32	6.62	7.0	7.1	0.80	0.84	0.85	UP	NO	NO
Slovenia	3.60	3.32	3.1	3.0	0.92	0.87	0.83	DOWN	YES	YES
Slovakia	3.54	3.03	2.7	2.2	0.86	0.76	0.64	DOWN	YES	YES
Finland	3.56	3.72	3.7	3.7	1.04	1.03	1.03	UP	YES	YES
Sweden	4.06	4.12	4.5	4.6	1.01	1.11	1.14	UP	YES	YES

Source: Eurostat, own calculations.

Table 5

SDG 10.50—Income share of the bottom 40% of the population (%).

Countries	2015	2020	2025	2030	2020/2015	2025/2015	2030/2015	Trend	Int. 2025	Int. 2030
Belgium	23.2	23.8	23.9	24.3	1.03	1.03	1.05	UP	YES	YES
Bulgaria	17.8	16.6	14.7	13.3	0.93	0.83	0.75	DOWN	NO	NO
Czech Republic	24.8	24.9	25.1	25.1	1.00	1.01	1.01	UP	YES	YES
Denmark	23.2	23.4	23.4	23.4	1.01	1.01	1.01	UP	YES	YES
Germany	21.4	19.2	19.8	18.3	0.90	0.92	0.85	DOWN	NO	NO
Estonia	18.5	20.4	21.5	22.6	1.10	1.16	1.22	UP	YES	YES
Ireland	21.6	22.5	23.8	24.7	1.04	1.10	1.14	UP	YES	YES
Greece	18.7	20.7	21.5	22.5	1.11	1.15	1.20	UP	YES	YES
Spain	18.2	19.5	20.4	21.3	1.07	1.12	1.17	UP	NO	YES
France	22.6	22.4	23.2	23.5	0.99	1.03	1.04	UP	YES	YES
Croatia	20.3	21.8	22.2	23.1	1.07	1.09	1.14	UP	YES	YES
Italy	19.7	19.7	19.1	18.8	1.00	0.97	0.96	DOWN	NO	NO
Cyprus	20.1	22.2	24.5	26.8	1.10	1.22	1.33	UP	YES	YES
Latvia	18.1	18.8	18.5	18.8	1.04	1.02	1.04	UP	NO	NO
Lithuania	17.3	18.7	18.1	17.9	1.08	1.05	1.04	DOWN	NO	NO
Luxembourg	22.4	20.6	19.4	18.3	0.92	0.87	0.82	DOWN	NO	NO
Hungary	22.4	22.6	22.9	22.7	1.01	1.02	1.01	DOWN	YES	YES
Malta	22.3	21.6	21.4	20.9	0.97	0.96	0.94	DOWN	YES	YES
Netherlands	23.7	22.9	22.2	21.5	0.97	0.94	0.91	DOWN	YES	YES
Austria	23.1	23.2	23.4	23.6	1.00	1.01	1.02	UP	YES	YES
Poland	21.1	22.9	23.7	24.8	1.09	1.12	1.18	UP	YES	YES
Portugal	19.4	21.0	22.3	23.6	1.08	1.15	1.21	UP	YES	YES
Romania	16.8	18.3	17.9	17.8	1.09	1.06	1.06	DOWN	NO	NO
Slovenia	24.4	25.0	25.0	25.1	1.02	1.02	1.03	UP	YES	YES
Slovakia	24.8	26.1	26.9	28.2	1.05	1.09	1.14	UP	YES	YES
Finland	24.2	23.7	23.9	23.7	0.98	0.99	0.98	DOWN	YES	YES
Sweden	22.9	22.8	22.0	21.7	1.00	0.96	0.95	DOWN	YES	YES

Source: Eurostat, own calculations.

Table 6

SDG 10.60—Asylum applications (first time applicants).

Countries	2015	2020	2025	2030	2020/2015	2025/2015	2030/2015	Trend	Int. 2025	Int. 2030
Belgium	3458	1118	547	312	0.32	0.16	0.09	DOWN	NO	NO
Bulgaria	2809	499	430	180	0.18	0.15	0.06	DOWN	NO	NO
Czech Republic	117	74	119	141	0.63	1.01	1.21	UP	NO	NO
Denmark	3664	244	0	0	0.07	0	0	DOWN	NO	NO
Germany	5409	1233	3417	3716	0.23	0.63	0.69	UP	YES	YES
Estonia	171	34	0	0	0.20	0	0	DOWN	NO	NO
Ireland	695	308	814	879	0.44	1.17	1.27	UP	NO	NO
Greece	1051	3538	9699	12,379	3.37	9.23	11.78	UP	YES	YES
Spain	314	1824	2835	3947	5.81	9.03	12.57	UP	YES	YES
France	1060	1213	2257	2615	1.14	2.13	2.47	UP	YES	YES
Croatia	33	381	489	614	11.55	14.82	18.61	UP	NO	NO
Italy	1363	359	1358	1494	0.26	1.00	1.10	UP	NO	NO
Cyprus	2483	7920	12,554	16,616	3.19	5.06	6.69	UP	YES	YES
Latvia	167	76	122	115	0.46	0.73	0.69	DOWN	NO	NO
Lithuania	95	93	142	129	0.98	1.49	1.36	DOWN	NO	NO
Luxembourg	4143	2054	3236	2708	0.50	0.78	0.65	DOWN	YES	YES
Hungary	17,722	9	0	0	0.00	0.00	0.00	DOWN	NO	NO
Malta	3809	4677	6677	7760	1.23	1.75	2.04	UP	YES	YES
Netherlands	2540	783	1450	1356	0.31	0.57	0.53	DOWN	NO	NO
Austria	9893	1503	0	0	0.00	0.00	0.00	DOWN	NO	NO
Poland	270	40	0	0	0.15	0.00	0	DOWN	NO	NO
Portugal	84	87	197	238	1.04	2.35	2.84	UP	NO	NO
Romania	62	313	299	439	5.05	4.82	7.09	UP	NO	NO
Slovenia	126	1648	2050	3011	13.08	16.27	23.90	UP	NO	YES
Slovakia	50	49	7	0	0.98	0.15	0	DOWN	NO	NO
Finland	5867	261	0	0	0.04	0	0	DOWN	NO	NO
Sweden	15,931	1313	878	0	0.08	0.06	0	DOWN	NO	NO

Source: Eurostat, own calculations.

Table 7

SDG 1.10A—People at risk of poverty or social exclusion (%).

Countries	2015	2020	2025	2030	2020/2015	2025/2015	2030/2015	Trend	Int. 2025	Int. 2030
Belgium	29.7	27.0	28.2	28.2	0.91	0.95	0.95	DOWN	YES	YES
Bulgaria	31.7	23.0	14.2	3.7	0.73	0.45	0.12	DOWN	NO	NO
Czech Republic	13.8	10.6	10.0	8.4	0.77	0.72	0.61	DOWN	NO	NO
Denmark	24.3	20.3	20.9	20.2	0.84	0.86	0.83	DOWN	NO	NO
Germany	24.3	28.1	28.8	31.4	1.16	1.19	1.29	UP	YES	YES
Estonia	21.9	21.9	23.6	24.5	1.00	1.08	1.12	UP	YES	YES
Ireland	24.5	19.5	14.2	9.1	0.80	0.58	0.37	DOWN	NO	NO
Greece	33.7	26.6	15.2	3.9	0.79	0.45	0.12	DOWN	NO	NO
Spain	25.5	25.1	25.1	25.8	0.98	0.98	1.01	UP	YES	YES
France	18.5	22.1	22.2	23.0	1.19	1.20	1.24	UP	YES	YES
Croatia	20.3	19.8	14.0	11.3	0.98	0.69	0.56	DOWN	NO	NO
Italy	28.7	27.3	29.4	29.9	0.95	1.02	1.04	UP	YES	YES
Cyprus	24.9	18.4	20.4	16.1	0.74	0.82	0.65	DOWN	NO	NO
Latvia	25.6	22.7	13.2	6.4	0.89	0.51	0.25	DOWN	NO	NO
Lithuania	22.3	18.5	16.9	15.2	0.83	0.76	0.68	DOWN	NO	NO
Luxembourg	16.8	17.2	17.0	15.8	1.02	1.01	0.94	DOWN	NO	NO
Hungary	21.6	13.7	7.1	0.3	0.63	0.33	0.02	DOWN	NO	NO
Malta	23.5	19.3	17.6	15.6	0.82	0.75	0.66	DOWN	NO	NO
Netherlands	20.9	19.2	17.6	15.9	0.92	0.84	0.76	DOWN	NO	NO
Austria	24.9	25.8	25.2	24.8	1.04	1.01	1.00	DOWN	YES	YES
Poland	16.7	11.3	6.2	0.9	0.68	0.37	0.05	DOWN	NO	NO
Portugal	26.6	17.1	4.3	0	0.64	0.16	0	DOWN	NO	NO
Romania	24.1	14.1	4.8	0	0.59	0.20	0	DOWN	NO	NO
Slovenia	19.5	17.8	17.2	16.6	0.91	0.88	0.85	DOWN	NO	NO
Slovakia	16.1	8.1	5.5	1.2	0.50	0.34	0.07	DOWN	NO	NO
Finland	16.8	15.6	17.2	17.3	0.93	1.03	1.03	UP	NO	NO
Sweden	20.5	16.3	15.5	13.6	0.80	0.76	0.66	DOWN	NO	NO

Source: Eurostat, own calculations.

Table 8

SDG 1.20A—People at risk of income poverty after social transfers (cut-off point: 60% of national median equivalized disposable income, after social transfers).

Countries	2015	2020	2025	2030	2020/2015	2025/2015	2030/2015	Trend	Int. 2025	Int. 2030
Belgium	11.7	11.7	12.4	12.3	1.00	1.06	1.05	DOWN	YES	YES
Bulgaria	21.3	22.9	23.4	26.0	1.08	1.10	1.22	UP	NO	NO
Czech Republic	8.6	9.2	9.4	10.1	1.07	1.10	1.18	UP	YES	YES
Denmark	11.7	11.2	11.0	10.5	0.96	0.94	0.90	DOWN	YES	YES
Germany	16.8	16.2	16.4	16.5	0.96	0.97	0.98	UP	NO	NO
Estonia	20.6	21.0	24.5	27.4	1.02	1.19	1.33	UP	NO	NO
Ireland	15.0	12.8	12.9	12.0	0.85	0.86	0.80	DOWN	YES	YES
Greece	18.5	16.1	14.1	12.1	0.87	0.76	0.65	DOWN	YES	YES
Spain	18.4	16.9	18.0	18.2	0.92	0.98	0.99	UP	NO	NO
France	11.1	10.8	10.4	10.2	0.97	0.93	0.92	DOWN	YES	YES
Croatia	19.7	18.6	17.7	16.8	0.94	0.90	0.85	DOWN	NO	NO
Italy	17.2	17.8	18.3	18.9	1.03	1.06	1.10	UP	NO	NO
Cyprus	13.3	11.1	11.3	10.4	0.83	0.85	0.78	DOWN	YES	YES
Latvia	21.5	22.0	25.2	27.5	1.02	1.17	1.28	UP	NO	NO
Lithuania	20.7	21.1	23.2	24.6	1.02	1.12	1.19	UP	NO	NO
Luxembourg	7.8	10.4	14.2	16.1	1.33	1.82	2.06	UP	YES	NO
Hungary	13.2	12.3	12.1	12.2	0.93	0.92	0.92	UP	YES	YES
Malta	14.8	15.9	17.4	18.8	1.07	1.18	1.27	UP	NO	NO
Netherlands	10.5	12.4	15.2	17.2	1.18	1.45	1.64	UP	YES	NO
Austria	10.0	9.7	8.6	7.6	0.97	0.86	0.76	DOWN	YES	YES
Poland	16.6	15.8	14.7	14.5	0.95	0.89	0.87	DOWN	YES	YES
Portugal	18.1	15.6	16.7	16.3	0.86	0.92	0.90	DOWN	NO	NO
Romania	22.5	21.8	23.2	24.2	0.97	1.03	1.08	UP	NO	NO
Slovenia	13.4	12.2	11.8	11.3	0.91	0.88	0.85	DOWN	YES	YES
Slovakia	10.5	10.1	9.6	9.0	0.96	0.91	0.85	DOWN	YES	YES
Finland	12.6	12.0	10.2	9.5	0.95	0.81	0.76	DOWN	YES	YES
Sweden	13.5	12.7	12.5	12.3	0.94	0.92	0.91	DOWN	YES	YES

Source: Eurostat, own calculations.

Table 9

SDG 4.10A—Early leavers from education and training (%).

Countries	2015	2020	2025	2030	2020/2015	2025/2015	2030/2015	Trend	Int. 2025	Int. 2030
Belgium	9.1	7.4	4.5	2.5	0.81	0.49	0.27	DOWN	YES	YES
Bulgaria	13.5	12.8	14.0	14.2	0.95	1.03	1.05	UP	NO	NO
Czech Republic	6.1	7.4	8.2	9.5	1.21	1.34	1.56	UP	NO	NO
Denmark	7.9	9.1	8.4	8.2	1.15	1.07	1.03	DOWN	NO	NO
Germany	8.3	7.3	6.5	5.4	0.88	0.78	0.65	DOWN	YES	YES
Estonia	12.2	7.1	8.5	7.3	0.58	0.70	0.60	DOWN	NO	NO
Ireland	6.8	5.0	1.9	0	0.74	0.28	0	DOWN	YES	YES
Greece	6.9	3.0	0	0	0.43	0	0	DOWN	YES	YES
Spain	17.8	13.6	7.4	1.9	0.76	0.42	0.11	DOWN	NO	YES
France	8.8	7.6	6.2	4.5	0.86	0.71	0.51	DOWN	YES	YES
Croatia	2.8	2.1	0.9	0.4	0.75	0.33	0.16	DOWN	YES	YES
Italy	12.8	11.0	8.1	5.6	0.86	0.63	0.44	DOWN	NO	NO
Cyprus	3.2	4.8	3.3	2.1	1.50	1.03	0.65	DOWN	YES	YES
Latvia	10.0	7.4	5.9	4.0	0.74	0.59	0.40	DOWN	YES	YES
Lithuania	5.5	5.6	2.9	2.0	1.02	0.53	0.37	DOWN	YES	YES
Luxembourg	7.0	6.0	4.7	4.8	0.86	0.67	0.68	UP	YES	YES
Hungary	11.6	12.1	12.8	13.3	1.04	1.11	1.15	UP	NO	NO
Malta	16.4	11.7	7.0	3.4	0.71	0.43	0.21	DOWN	YES	YES
Netherlands	7.9	6.7	5.1	3.2	0.85	0.64	0.40	DOWN	YES	YES
Austria	5.5	5.1	4.3	3.6	0.93	0.77	0.65	DOWN	YES	YES
Poland	5.3	5.4	5.1	5.0	1.02	0.97	0.95	DOWN	YES	YES
Portugal	13.5	8.7	1.9	0	0.64	0.14	0	DOWN	YES	YES
Romania	19.1	15.6	15.0	13.5	0.82	0.78	0.70	DOWN	NO	NO
Slovenia	4.4	3.8	3.8	3.6	0.86	0.85	0.81	DOWN	YES	YES
Slovakia	6.9	7.6	9.6	11.0	1.10	1.39	1.60	UP	NO	NO
Finland	8.8	7.8	6.4	5.4	0.89	0.73	0.62	DOWN	YES	YES
Sweden	6.3	5.6	5.0	4.5	0.89	0.79	0.71	DOWN	YES	YES

Source: Eurostat, own calculations.

Table 10

SDG 8.20A—Young people neither in employment nor in education and training (NEET) (%).

Countries	2015	2020	2025	2030	2020/2015	2025/2015	2030/2015	Trend	Int. 2025	Int. 2030
Belgium	13.1	10.5	9.4	8.2	0.80	0.72	0.63	DOWN	YES	YES
Bulgaria	22.2	18.1	17.0	12.9	0.82	0.77	0.58	DOWN	NO	NO
Czech Republic	11.7	11.0	9.2	8.1	0.94	0.79	0.69	DOWN	YES	YES
Denmark	8.1	9.9	10.5	11.6	1.22	1.30	1.43	UP	YES	NO
Germany	7.0	6.4	4.7	3.3	0.91	0.66	0.47	DOWN	YES	YES
Estonia	12.5	10.7	7.7	6.2	0.86	0.62	0.49	DOWN	YES	YES
Ireland	16.3	13.4	5.5	0.2	0.82	0.34	0.01	DOWN	YES	YES
Greece	23.6	18.1	16.7	14.1	0.77	0.71	0.60	DOWN	NO	NO
Spain	17.7	15.2	14.7	11.8	0.86	0.83	0.67	DOWN	NO	NO
France	14.0	13.3	12.1	11.9	0.95	0.86	0.85	DOWN	YES	NO
Croatia	20.0	14.6	13.8	11.2	0.73	0.69	0.56	DOWN	NO	NO
Italy	24.5	22.0	22.0	21.6	0.90	0.90	0.88	DOWN	NO	NO
Cyprus	18.0	13.6	12.1	10.6	0.76	0.67	0.59	DOWN	YES	YES
Latvia	13.2	11.2	6.4	1.7	0.85	0.48	0.13	DOWN	YES	YES
Lithuania	11.8	13.1	8.8	7.0	1.11	0.74	0.59	DOWN	YES	YES
Luxembourg	5.6	6.2	7.1	7.9	1.11	1.26	1.40	UP	YES	YES
Hungary	15.0	14.7	10.7	8.3	0.98	0.71	0.55	DOWN	YES	YES
Malta	11.7	9.0	5.6	3.2	0.77	0.47	0.28	DOWN	YES	YES
Netherlands	6.1	5.2	4.8	4.3	0.85	0.78	0.70	DOWN	YES	YES
Austria	7.2	7.1	6.4	6.1	0.99	0.89	0.85	DOWN	YES	YES
Poland	14.6	12.9	12.3	11.0	0.88	0.84	0.75	DOWN	YES	NO
Portugal	12.8	10.8	8.0	5.7	0.84	0.63	0.45	DOWN	YES	YES
Romania	20.9	16.6	15.5	13.9	0.79	0.74	0.66	DOWN	NO	NO
Slovenia	11.8	8.5	9.1	8.3	0.72	0.77	0.71	DOWN	YES	YES
Slovakia	17.2	15.2	12.3	10.1	0.88	0.72	0.59	DOWN	NO	YES
Finland	12.1	10.1	9.9	9.7	0.83	0.82	0.80	DOWN	YES	YES
Sweden	6.8	6.4	4.6	3.9	0.94	0.68	0.58	DOWN	YES	YES

Source: Eurostat, own calculations.

Table 11

SDG 8.30A—Employment rate (%).

Countries	2015	2020	2025	2030	2020/2015	2025/2015	2030/2015	Trend	Int. 2025	Int. 2030
Belgium	68.5	71.5	70.6	72.6	1.04	1.03	1.06	UP	NO	NO
Bulgaria	67.2	73.4	76.4	82.4	1.09	1.14	1.23	UP	YES	YES
Czech Republic	74.8	79.6	84.4	89.2	1.06	1.13	1.19	UP	YES	YES
Denmark	76.9	78.8	79.8	82.0	1.02	1.04	1.07	UP	YES	YES
Germany	79.6	82.3	85.9	88.6	1.03	1.08	1.11	UP	YES	YES
Estonia	77.7	79.1	84.6	89.9	1.02	1.09	1.16	UP	YES	YES
Ireland	70.3	73.5	77.8	83.1	1.05	1.11	1.18	UP	YES	YES
Greece	54.9	61.6	63.0	64.6	1.12	1.15	1.18	UP	NO	NO
Spain	62.5	66.9	68.9	72.4	1.07	1.10	1.16	UP	NO	NO
France	70.8	72.6	72.0	73.7	1.03	1.02	1.04	UP	NO	NO
Croatia	60.7	66.9	69.6	73.6	1.10	1.15	1.21	UP	NO	NO
Italy	60.3	62.8	64.1	65.7	1.04	1.06	1.09	UP	NO	NO
Cyprus	67.2	75.4	73.5	75.2	1.12	1.09	1.12	UP	NO	NO
Latvia	73.9	78.0	79.4	83.7	1.06	1.07	1.13	UP	YES	YES
Lithuania	73.4	76.6	85.1	93.2	1.04	1.16	1.27	UP	YES	YES
Luxembourg	69.2	70.3	70.9	72.0	1.02	1.02	1.04	UP	NO	NO
Hungary	68.9	75.1	86.2	95.2	1.09	1.25	1.38	UP	YES	YES
Malta	68.5	76.1	85.1	93.3	1.11	1.24	1.36	UP	YES	YES
Netherlands	77.2	81.0	81.4	84.0	1.05	1.05	1.09	UP	YES	YES
Austria	75.8	77.4	79.4	80.7	1.02	1.05	1.06	UP	YES	YES
Poland	67.8	73.5	78.4	83.2	1.08	1.16	1.23	UP	YES	YES
Portugal	69.2	74.8	79.2	83.4	1.08	1.14	1.21	UP	YES	YES
Romania	66.0	70.8	74.6	78.4	1.07	1.13	1.19	UP	YES	YES
Slovenia	69.1	75.6	72.9	77.4	1.09	1.05	1.12	UP	NO	NO
Slovakia	67.6	72.5	76.8	81.6	1.07	1.14	1.21	UP	YES	YES
Finland	73.5	77.1	79.0	81.1	1.05	1.07	1.10	UP	YES	YES
Sweden	82.1	83.4	86.7	88.8	1.02	1.06	1.08	UP	YES	YES

Source: Eurostat, own calculations.

References

1. Sinha, A.; Sengupta, T.; Alvarado, R. Interplay between technological innovation and environmental quality: Formulating the SDG policies for next 11 economies. J. Clean. Prod.; 2020; 242, 118549. [DOI: https://dx.doi.org/10.1016/j.jclepro.2019.118549]

2. Pradhan, P.; Costa, L.; Rybski, D.; Lucht, W.; Kropp, J.P. A Systematic Study of Sustainable Development Goal (SDG) Interactions. Earth’s Future; 2017; 5, pp. 1169-1179. [DOI: https://dx.doi.org/10.1002/2017EF000632]

3. Diaz-Sarachaga, J.M.; Jato-Espino, D.; Castro-Fresno, D. Is the Sustainable Development Goals (SDG) index an adequate framework to measure the progress of the 2030 Agenda?. Sustain. Dev.; 2018; 26, pp. 663-671. [DOI: https://dx.doi.org/10.1002/sd.1735]

4. Allen, C.; Metternicht, G.; Wiedmann, T. Priorities for science to support national implementation of the sustainable development goals: A review of progress and gaps. Sustain. Dev.; 2021; 29, pp. 635-652. [DOI: https://dx.doi.org/10.1002/sd.2164]

5. Sachs, J.D. From millennium development goals to sustainable development goals. Lancet; 2012; 379, pp. 2206-2211. [DOI: https://dx.doi.org/10.1016/S0140-6736(12)60685-0]

6. Jakob, M.; Steckel, J.C. Implications of climate change mitigation for sustainable development. Environ. Res. Lett.; 2016; 11, 104010. [DOI: https://dx.doi.org/10.1088/1748-9326/11/10/104010]

7. Oestreich, J.E. SDG 10: Reduce inequality in and among countries. Soc. Altern.; 2018; 37, pp. 34-41.

8. Roy, J.; Tscharket, P.; Waisman, H.; Abdul Halim, S.; Antwi-Agyei, P.; Dasgupta, P.; Hayward, B.; Kanninen, M.; Liverman, D.; Okereke, C. et al. Sustainable development, poverty eradication and reducing inequalities. Global Warming of 1.5 °C: An IPCC Special Report; Masson-Delmotte, V.; Zhai, P.; Pörtner, H.O.; Roberts, D.; Skea, J.; Shukla, P.R.; Pirani, A.; Moufouma-Okia, W.; Péan, C.; Pidcock, R. et al. Cambridge University Press: Cambridge, MA, USA, 2018.

9. Holger, A. Inequality in development: The 2030 Agenda, SDG 10 and the role of redistribution. Real-World Econ. Rev.; 2020; 92, pp. 228-237.

10. Lafortune, G.; Fuller, G.; Schmidt-Traub, G.; Kroll, C. How Is Progress towards the Sustainable Development Goals Measured? Comparing Four Approaches for the EU. Sustainability; 2020; 12, 7675. [DOI: https://dx.doi.org/10.3390/su12187675]

11. United Nations. Transforming Our World: The 2030 Agenda for Sustainable Development. 2015; Available online: https://sdgs.un.org/2030agenda (accessed on 23 June 2022).

12. Acemoglu, D.; Robinson, J. Why Nations Fail: The Origins of Power, Prosperity, and Poverty; Crown: New York, NY, USA, 2012.

13. Stiglitz, J.E. The Price of Inequality: How Today’s Divide Society Endangers our Future; WW Norton & Company: New York, NY, USA, 2013.

14. Ghosh, J. Towards a Policy Framework for Reducing Inequalities. Development; 2013; 56, pp. 218-222. [DOI: https://dx.doi.org/10.1057/dev.2013.28]

15. Stefko, R.; Gavurova, B.; Ivankova, V.; Rigelsky, M. Gender Inequalities in Health and Their Effect on the Economic Prosperity Represented by the GDP of Selected Developed Countries—Empirical Study. Int. J. Environ. Res. Public Health; 2020; 17, 3555. [DOI: https://dx.doi.org/10.3390/ijerph17103555] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32438655]

16. Credit Suisse Research Institute. Global Wealth Report 2021. 2021; Available online: https://www.credit-suisse.com/media/assets/corporate/docs/about-us/research/publications/global-wealth-report-2021-en.pdf (accessed on 21 June 2022).

17. Zamora, G.; Koller, T.S.; Thomas, R.; Manandhar, M.; Lustigova, E.; Diop, A.; Magar, V. Tools and approaches to operationalize the commitment to equity, gender and human rights: Towards leaving no one behind in the Sustainable Development Goals. Glob. Health Action; 2018; 11, pp. 75-81. [DOI: https://dx.doi.org/10.1080/16549716.2018.1463657] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/29808773]

18. Hilson, G.; Hilson, A.; Siwale, A.; Maconachie, R. Female Faces in Informal ‘Spaces’: Women and Artisanal and Small-scale Mining in sub-Saharan Africa. Afr. J. Manag.; 2018; 4, pp. 306-346. [DOI: https://dx.doi.org/10.1080/23322373.2018.1516940]

19. Manioudis, M.; Meramveliotakis, G. Broad strokes towards a grand theory in the analysis of sustainable development: A return to the classical political economy. New Political Econ.; 2022; 27, pp. 1-13. [DOI: https://dx.doi.org/10.1080/13563467.2022.2038114]

20. Kluza, K.; Zioło, M.; Bąk, I.; Spoz, A. Achieving Environmental Policy Objectives through the Implementation of Sustainable Development Goals. The Case for European Union Countries. Energies; 2021; 14, 2129. [DOI: https://dx.doi.org/10.3390/en14082129]

21. Hossen, Z.; Khondker, B.H. How is India flaring in achieving SDG 10 on reduced inequality?. Sustainable Development Goals, Sustainable Development Goals Series; Hazra, S.; Bhukta, A. Springer: Cham, Switzerland, 2020.

22. Paloma, V.; Escobar-Ballesta, M.; Galván-Vega, B.; Díaz-Bautista, J.D.; Benítez, I. Determinants of Life Satisfaction of Economic Migrants Coming from Developing Countries to Countries with Very High Human Development: A Systematic Review. Appl. Res. Qual. Life; 2021; 16, pp. 435-455. [DOI: https://dx.doi.org/10.1007/s11482-020-09832-3]

23. Kuhn, H. Reducing inequality eithin and smong vountries: Tealizing SDG 10—A developmental perspective. Sustainable Development Goals and Human Rights. Interdisciplinary Studies in Human Rights; Kaltenborn, M.; Krajewski, M.; Kuhn, H. Springer: Cham, Switzerland, 2020; Volume 5.

24. Włodarczyk, B.; Szturo, M.; Ionescu, G.H.; Firoiu, D.; Pirvu, R.; Badircea, R. The impact of credit availability on small and medium companies. Entrep. Sustain. Issues; 2018; 5, pp. 565-580. [DOI: https://dx.doi.org/10.9770/jesi.2018.5.3(12)]

25. United Nations Economic and Social Council. SDG Progress Report (2022). 2022; Available online: https://sustainabledevelopment.un.org/content/documents/29858SG_SDG_Progress_Report_2022.pdf (accessed on 25 May 2022).

26. Eurostat. Income Quintile Share Ratio S80/S20 for Disposable Income by Sex and Age Group—EU-SILC survey. 2022; Available online: https://ec.europa.eu/eurostat/databrowser/view/ilc_di11/default/table?lang=en (accessed on 21 June 2022).

27. Tremblay, D.; Fortier, F.; Boucher, J.-F.; Riffon, O.; Villeneuve, C. Sustainable development goal interactions: An analysis based on the five pillars of the 2030 Agenda. Sustain. Dev.; 2020; 28, pp. 1584-1596. [DOI: https://dx.doi.org/10.1002/sd.2107]

28. Eurostat. Persons at Risk of Poverty or Social Exclusion by Degree of Urbanisation. 2022; Available online: https://ec.europa.eu/eurostat/databrowser/view/ilc_peps13n/default/table?lang=en (accessed on 21 June 2022).

29. Narayan, A.; Saavedra-Chanduvi, J.; Tiwari, S. Shared Prosperity: Links to Growth, Inequality and Inequality of Opportunity, Policy Research Working Group, no. 6649, 2013, The World Bank. Available online: https://elibrary.worldbank.org/doi/abs/10.1596/1813-9450-6649# (accessed on 25 May 2022).

30. Ionescu, G.H.; Jianu, E.; Patrichi, I.C.; Ghiocel, F.; Țenea, L.; Iancu, D. Assessment of Sustainable Development Goals (SDG) Implementation in Bulgaria and Future Developments. Sustainability; 2021; 13, 12000. [DOI: https://dx.doi.org/10.3390/su132112000]

31. Jenkins, S.P. Perspectives on Poverty in Europe. Following in Tony Atkinson’s Footsteps. Ital. Econ. J.; 2019; 6, pp. 129-155. [DOI: https://dx.doi.org/10.1007/s40797-019-00112-0]

32. Madanipour, A.; Shucksmith, M.; Talbot, H. Concepts of poverty and social exclusion in Europe. Local Econ.; 2015; 30, pp. 721-741. [DOI: https://dx.doi.org/10.1177/0269094215601634]

33. Harrison, J.L.; Montgomery, C.A.; Jeanty, P.W. A Spatial, Simultaneous Model of Social Capital and Poverty. J. Behav. Exp. Econ.; 2019; 78, pp. 183-192. [DOI: https://dx.doi.org/10.1016/j.socec.2018.09.001]

34. Băndoi, A.; Jianu, E.; Enescu, M.; Axinte, G.; Tudor, S.; Firoiu, D. The Relationship between Development of Tourism, Quality of Life and Sustainable Performance in EU Countries. Sustainability; 2020; 12, 1628. [DOI: https://dx.doi.org/10.3390/su12041628]

35. Eurostat. Reduced Inequalities in the EU: Overview and Key Trends. 2022; Available online: https://ec.europa.eu/eurostat/statistics-explained/index.php?title=SDG_10_-Reduced_inequalities#Inequalities_between_countries (accessed on 25 May 2022).

36. Eurostat. Adjusted Gross Disposable Income of Households Per Capita. 2022; Available online: https://ec.europa.eu/eurostat/databrowser/view/sdg_10_20/default/table?lang (accessed on 21 June 2022).

37. Daniel, S.; Agarwal, R.; Stewart, K.J. The Effects of Diversity in Global, Distributed Collectives: A Study of Open Source Project Success. Inf. Syst. Res.; 2013; 24, pp. 312-333. [DOI: https://dx.doi.org/10.1287/isre.1120.0435]

38. German-Soto, V.; Brock, G. Overall US and Census Region β-Convergence 1963–2015 Controlling for Spatial Effects. Comp. Econ. Stud.; 2021; 64, pp. 44-67. [DOI: https://dx.doi.org/10.1057/s41294-021-00159-y]

39. Hooijer, G.; Picot, G. European Welfare States and Migrant Poverty: The Institutional Determinants of Disadvantage. Comp. Political Stud.; 2015; 48, pp. 1879-1904. [DOI: https://dx.doi.org/10.1177/0010414015597508]

40. Bevelander, P.; Pendakur, R. The labour market integration of refugee and family reunion immigrants: A comparison of outcomes in Canada and Sweden. J. Ethn. Migr. Stud.; 2014; 40, pp. 689-709. [DOI: https://dx.doi.org/10.1080/1369183X.2013.849569]

41. ILO (International Labour Office). Decent Work: Report of the Director-General. In 87th Session, International Labour Conference, 1999, Geneva. Available online: https://www.ilo.org/public/english/standards/relm/ilc/ilc87/rep-i.htm (accessed on 25 May 2022).

42. Szymańska, A. Reducing Socioeconomic Inequalities in the European Union in the Context of the 2030 Agenda for Sustainable Development. Sustainability; 2021; 13, 7409. [DOI: https://dx.doi.org/10.3390/su13137409]

43. Eurostat. Employment Rate, by Citizenship. 2022; Available online: https://ec.europa.eu/eurostat/databrowser/view/sdg_08_30a/default/table?lang=en (accessed on 21 June 2022).

44. Biggeri, M.; Mauro, V. Towards a more ‘Sustainable’ Human Development Index: Integrating the environment and freedom. Ecol. Indic.; 2018; 91, pp. 220-231. [DOI: https://dx.doi.org/10.1016/j.ecolind.2018.03.045]

45. Blancard, S.; Hoarau, J.-F. A new sustainable human development indicator for small island developing states: A reappraisal from data envelopment analysis. Econ. Model.; 2013; 30, pp. 623-635. [DOI: https://dx.doi.org/10.1016/j.econmod.2012.10.016]

46. Erne, R.; Imboden, N. Equal pay by gender and by nationality: A comparative analysis of Switzerland’s unequal equal pay policy regimes across time. Camb. J. Econ.; 2015; 39, pp. 655-674. [DOI: https://dx.doi.org/10.1093/cje/bev003]

47. Mihi-Ramirez, A.; Melchor-Ferrer, E.; Sobieraj, J. Integration and Productivity of Labor Factor in Europe. Perspective from Nationality and the Attainment Level. Eng. Econ.; 2020; 31, pp. 18-25. [DOI: https://dx.doi.org/10.5755/j01.ee.31.1.24477]

48. Chassamboulli, A.; Palivos, T. A Search-Equilibrium Approach to the Effects of Immigration on Labor Market Outcomes. Int. Econ. Rev.; 2014; 55, pp. 111-129. [DOI: https://dx.doi.org/10.1111/iere.12043]

49. Caliendo, M.; Schmidl, R. Youth unemployment and active labor market policies in Europe. IZA J. Labor Policy; 2016; 5, pp. 1-30. [DOI: https://dx.doi.org/10.1186/s40173-016-0057-x]

50. Li, G. Analysis on Strategies to Promote Quality of Employment of Yunnan Minority Nationality College Students. Adv. Soc. Sci. Educ. Hum. Res. (ASSEHR); 2017; 80, pp. 152-154.

51. Wolff, J.; Nivorozhkin, A.; Bernhard, S. You can go your own way! The long-term effectiveness of a self-employment programme for welfare recipients in Germany. Int. J. Soc. Welf.; 2016; 25, pp. 136-148. [DOI: https://dx.doi.org/10.1111/ijsw.12176]

52. Shane, S. Why encouraging more people to become entrepreneurs is bad public policy. Small Bus. Econ.; 2009; 33, pp. 141-149. [DOI: https://dx.doi.org/10.1007/s11187-009-9215-5]

53. Ionescu, G.H.; Firoiu, D.; Tănasie, A.; Sorin, T.; Pîrvu, R.; Manta, A. Assessing the Achievement of the SDG Targets for Health and Well-Being at EU Level by 2030. Sustainability; 2020; 12, 5829. [DOI: https://dx.doi.org/10.3390/su12145829]

54. Taconet, N.; Méjean, A.; Guivarch, C. Influence of climate change impacts and mitigation costs on inequality between countries. Clim. Chang.; 2020; 160, pp. 15-34. [DOI: https://dx.doi.org/10.1007/s10584-019-02637-w]

55. Tol, R.S.J. The economic impacts of climate change. Rev. Environ. Econ. Policy; 2018; 12, pp. 4-25. [DOI: https://dx.doi.org/10.1093/reep/rex027]

56. Eurostat. SDG Indicators: Goal by Goal. 2022; Available online: https://ec.europa.eu/eurostat/web/sdi/indicators (accessed on 25 May 2022).

57. Kays, H.M.E.; Karim, A.N.M.; Daud, M.R.C.; Varela, M.L.R.; Putnik, G.D.; Machado, J.M. A Collaborative Multiplicative Holt-Winters Forecasting Approach with Dynamic Fuzzy-Level Component. Appl. Sci.; 2018; 8, 530. [DOI: https://dx.doi.org/10.3390/app8040530]

58. Akpinar, M.; Yumusak, N. Year Ahead Demand Forecast of City Natural Gas Using Seasonal Time Series Methods. Energies; 2016; 9, 727. [DOI: https://dx.doi.org/10.3390/en9090727]

59. Held, B.; Moriarty, B.; Richardson, T. Microsoft Excel Functions and Formulas; 4th ed. Mercury Learning and Information LLC: Dulles, VA, USA, 2018.

60. Makridakis, S.G.; Wheelwright, S.C.; Hyndman, R.J. Forecasting Method and Applications; 3rd ed. John Wiley & Sons: New York, NY, USA, 1998.

61. Box, G.; Jennkins, G.; Reinsel, G.; Ljung, G. Time Series Analysis: Forecasting and Control; 5th ed. John Wiley & Sons: Hoboken, NJ, USA, 2016.

62. Anghelache, C.; Manole, A. Dynamic/chronological (time) series—Theoretical presentation, structure, relationships between indices. Roman. Stat. Rev.; 2012; 10, pp. 78-87.

63. Baltac, A. Economic and Financial Analysis Based on Time Series Method. Int. J. Acad. Res. Account. Financ. Manag. Sci.; 2015; 5, pp. 77-82. [DOI: https://dx.doi.org/10.6007/IJARAFMS/v5-i3/1756]

64. Gavriluță, N.; Grecu, S.-P.; Chiriac, H.C. Sustainability and Employability in the Time of COVID-19. Youth, Education and Entrepreneurship in EU Countries. Sustainability; 2022; 14, 1589. [DOI: https://dx.doi.org/10.3390/su14031589]

65. Bieszk-Stolorz, B.; Dmytrów, K. Influence of Accession of the Visegrad Group Countries to the EU on the Situation in Their Labour Markets. Sustainability; 2020; 12, 6694. [DOI: https://dx.doi.org/10.3390/su12166694]

66. Kolluru, M.; Semenenko, T. Income inequalities in EU countries: GINI indicator analysis. Econ. Innov. Res. J.; 2021; 9, pp. 125-142. [DOI: https://dx.doi.org/10.2478/eoik-2021-0007]