EU Labor Market Inequalities and Sustainable Development Goals

Abstract

Reducing inequalities for EU citizens and promoting upward convergence is one of the priorities on the agenda of the European Commission and, certainly, inequality will be a very important public policy issue for years to come. Through this research we aim to investigate EU labor market inequalities, reflected by the specific indicators proposed for Goal 8 assumed by the 2030 Agenda for Sustainable Development, based on cluster analysis for all the 27 Member States. The research results showed encouraging results from the perspective of convergence in the EU labor market, but also revealed a number of analyzed variable effects that manifested regional inequalities that were generated in the medium and long term. Based on the observations made, we want to provide information for policy-makers, business practitioners, and academics so as to constitute solid ground for identifying good practices and proposing to implement policies aimed at reducing existing inequalities and supporting sustainable development.

1. Introduction

The United Nations 2030 Agenda for Sustainable Development promotes and provides a comprehensive picture of how society and the economy are working to provide a better future, to reduce threats to the planet’s sustainability [1].

Conceived as an action plan, Agenda 2030 includes a set of 17 sustainable development goals, grouped around 5 key pillars—the planet, people, prosperity, global peace, partnership—thus creating a conceptual and legal framework for production and sustainable consumption, natural resource management, eradicating poverty in all aspects, promoting people’s potential and equality, ensuring a prosperous and comfortable life for people, economic, social, and technological progress in harmony with nature, encouraging a peaceful, non-violent society, and strengthening partnerships through global solidarity [2,3].

Achieving the Sustainable Development Goals (SDGs), as set out in the 2030 Agenda document, are increasingly evident in the actions of all countries of the world, but also in the processes of monitoring and measuring progress, thus enabling relevant decisions to be taken, improving the expected results, and, consequently, stimulating all the factors involved in sustainable actions for the environment and society.

On the other hand, as some researchers suggest, inequality among people has been increasing in many parts of the EU since the 1990s, as low-income Europeans have been falling behind in the labor market, which makes this fall quite likely to manifest itself in the long run, with an impact on economic growth and the balance of the labor market [4,5]. These aspects are much more important for the countries located in south-eastern Europe and the Mediterranean region [4,6,7,8].

All these considerations are thus central elements of the long-term existence of society, which is why we consider it particularly important to analyze how people relate to all these global actions but also how they contribute to their own development and prosperity through their own actions, to eliminate imbalances of any kind [9,10,11,12,13].

To investigate the EU labor market inequalities, we decided to explore the data provided by Eurostat, for SDG 8 specific indicators. Using hierarchical cluster analysis, we aimed to assess the main factors quantified by the analyzed indicators that divide or unite the EU countries in clusters based on the inequalities or, respectively, the performances in the field. Specifically, the paper is based on identifying how the implementation of SDG 8 targets is reflected in the results of specific economic indicators at the level of European states.

Based on these observations, this paper focuses on one of the most important issues identified globally, and at the level of EU Member States, on how SDG 8, through the proposed targets (of which there are 10), contributes to reducing existing inequalities and imbalances in the labor market. With direct reference to “sustained, inclusive and sustainable growth, full and productive employment and decent work for all”, we can say that SDG 8 reflects how the most important resource of economies is sustainably used, developed, and sustained in the direction reducing inequalities of any kind, unfortunately present in certain labor markets [14].

In order to highlight the way in which the SDG 8 targets, through the 10 strategic pillars (specific targets), represent or not immeasurable values of reducing the existing inequalities on the labor market by developing sustainable business, we present below a series of defining points of view—the basis of the foundation of our research.

At the same time, we mention that “promoting sustainable, inclusive and sustainable growth, full and productive employment and decent work for all” can be approached from two perspectives, namely, by supporting sustainable business on the one hand and the rights of people to a decent job on the other hand.

We therefore point out that although full employment and decent work are components of SDG 8, Agenda 2030, through the measures imposed, targets the economy as a whole and can sometimes present, in certain situations, a real obstacle to achieving the goal, reducing inequalities and imbalances of any kind.

From this point of view, there are still many questions or ambiguities regarding the fact that, while ensuring human rights and full employment, decent work, in a business-oriented context of economic growth, is an obligation of states, and unfortunately, these are often just benefits of economic growth. Therefore, this is also the reason why ambiguities of SDG 8 and Agenda 2030 can sometimes be identified; ambiguities that often maintain existing inequalities in the labor market, both in the EU market and in other regions of the world [15].

The results of this research will bring a better understanding of the economic forces acting on the labor market at the EU level and also will give us the opportunity to realistically assess the progress towards the targets assumed by the European Commission, but also the national barriers that stand in the way of achieving the objectives. Links between tourism development, quality of life, and sustainable performance contribute to the community’s efforts to identify the best solutions and models of sustainable development.

This paper is divided into five sections, starting with an introduction in Section 1, and followed by a literature review in Section 2. Section 3 includes information on the sample and model, followed by results and discussion in Section 4. The last section (Section 5) unveils the main conclusions of the research.

2. Literature Review

As we mentioned above, the existing inequalities in the labor market can be addressed through the SDGs assumed by the 2030 Agenda, analyzing the specific indicators proposed for SDG 8. SDG 8 focuses mainly on the importance of the right to work for all, and productive and sustainable growth. There are unfortunately still some significant differences in the labor market, mainly generated by the measures. Economic growth, with a focus on GDP and its per capita growth at the level of certain regions/countries, unfortunately largely excludes the promise of SDG 8 for inclusive, sustainable, and decent work for all [16].

Therefore, from the point of view of “maintaining economic growth per capita according to the national context and, in particular, the growth of gross domestic product of at least 7% per year in the least developed countries” (8.1), we note that that the argument that human society can separate economic growth—defined as an increase in gross domestic product (GDP)—from increasing environmental impact can be extremely attractive. However, there are views that underline the fact that the development of a growth-oriented policy is misleading because we see that GDP is increasingly seen as an unfavorable factor for increasing the well-being of society. Therefore, we emphasize that GDP growth is a questionable societal goal, because society can sustainably improve well-being, including the well-being of its natural assets, but only by eliminating GDP growth as an objective in favor of more comprehensive measures of society’s well-being [17].

In the same vein, there are a number of other arguments regarding the implementation of the single strategy for sustainable business development, which underline the idea that this can often be very risky. These views are supported by three main arguments, namely: growth as progress, growth to avoid economic instability, and growth to compensate for unemployment due to increased labor productivity [15,18].

On the other hand, we also highlight the fact that longer growth periods—periods of growth per capita—are frequently associated with greater equality in income distribution, even when monitoring a number of other determining factors. Furthermore, in the long run, avoiding excessive labor market inequalities and sustaining economic growth can be two sides of the same coin [19].

“Achieving higher levels of economic productivity through diversification, technological modernization and innovation, including by focusing on high value-added and labor-intensive sectors” is another main target of SDG 8 and as content emphasizes, can be a long-term way to reduce inequalities in the labor market. This goal is mainly supported by the idea that the benefits of knowledge seem quite selective and concentrated in a relatively small number of regions, and the benefits of innovation appear to be ubiquitous and beneficial in any country, even in regions with a low endowment of scientific knowledge [20,21,22].

There are also a number of arguments regarding the effects of technological and non-technological innovations on firms’ productivity, employment, and the existence of possible complementarities between these different forms of innovation, which lead us to the idea that innovation can significantly reduce inequalities in the labor market, implicitly in terms of the level of training and attitude of employees towards change and evolution [23,24].

In fact, changes in work organization, including re-engineering, team development, incentive wages, and employee involvement, were a significant component of the evolution in productivity growth in the 1990s, which is why we support the idea that innovations in the workplace largely generate increased productivity with obvious implications for the potential to reduce inequalities in the labor market [25,26].

Starting from another objective of SDG 8, namely “promoting development-oriented policies that support productive activities, create decent jobs, entrepreneurship, creativity and innovation and that encourage the formalization and growth of micro, small and medium-sized enterprises, including through access to financial services”, we identify a series of relevant points of view, in the sense that the idea is supported according to which entrepreneurs are the ones who identify opportunities, creating value through employees, and being the key to regional development [27].

In this context, we emphasize that what is essential is human resources, with a major role in decision-making, in defining the character of the place, authenticity, commitment, and common goals, which leads us to the conclusion that, yes, entrepreneurship, and as a consequence, initiative and innovation, contribute directly to the elimination of many inequalities in the labor market.

Moreover, not to be neglected is the fact that the long-term relationship between the two components—entrepreneurship and people—generates during the business cycle multiple implications on local and regional entrepreneurial policies and the employment and development of people [28,29].

On the other hand, even in innovation-based economies, only 1–2% of the workforce starts a business in a given year. However, entrepreneurs, especially innovative entrepreneurs, are vital for the competitiveness of the economy, especially the sustainable economy. Moreover, it is clear that the success of entrepreneurship is achieved only if the business environment is receptive to innovation, to which are added policy-makers, and which must take into account potential job losses. These losses can occur in the medium term through “creative destruction”, because, unfortunately, entrepreneurs often aim exclusively at profit [30,31].

In support of these views, another specific target of SDG 8 “is to gradually improve the efficient use of global resources in terms of consumption and production by 2030 and to strive to separate economic growth from environmental degradation, in line with the 10-year framework of the Sustainable Consumption and Production Programs”, which highlights how a country’s economy, in this case the regional economy, stimulates sustainable production, by focusing on entrepreneurship, full employment, and ensuring full freedoms while ensuring increasing the potential for innovation [32].

The available research also highlights how the above objectives have important implications for future economic opportunities in a highly resource-efficient global economy, thus contributing to human development and the achievement of sustainable development goals through sustainable action [33,34].

In fact, we also support the idea that sustainable consumption and production should play an important role in formulating and implementing the Sustainable Development Goals (SDGs), but also how this could be achieved, as it is unanimously acknowledged that unsustainable models of consumption and production were the main cause of environmental damage. In fact, the transition to the production and consumption of sustainable products has required and requires fundamental changes in the way society works and the way we live our lives.

Such a full-scale transition will take a long time, with multiple and complex actions being essential to transform society, people, their well-being, and, consequently, the way each country’s labor market and economy work, and the nature of human occupation [35,36].

Along with the above, SDG 8 should, through another specific target, “substantially reduce the percentage of young people who are not professionally employed and do not follow any educational or training program”, an aspect that can greatly contribute to reducing labor market imbalances. Thus, in the studies and strategies identified to stimulate employment but also to create jobs for young people, we identify valuable points of view on entrepreneurship, which is increasingly accepted, and which can be a strategy and priority for job creation and improving the living conditions but also the economic independence of young people [37].

Unfortunately, the problem of unemployment, which is faced by both educated young people and less qualified young people, has become increasingly important, especially in many developing economies. Therefore, we believe that there are still constraints that prevent young people looking for work, on the one hand, but there is also an urgent need to direct young people to self-employment and entrepreneurship through vocational and entrepreneurial training programs, as a possible short-term intervention strategy [38].

In the same sense of the existence of inequalities on the labor market, we also identify aspects related to forced labor, this being the newest frontier of the global effort to eliminate human trafficking and exploitation. This is, moreover, the new paradigm that all states of the world face in terms of international human rights [39,40].

Although the global economy is facing such negative situations, we are identifying increased actions to regulate international labor markets in order to eliminate forced labor, thus forcing companies to assess the real potential of the market and real employment opportunities. All these measures are also the responsibility of governments, companies, and employers, for all actions taken from the point of view of people and to end modern slavery and trafficking in human beings, and to ensure the prohibition and elimination of the worst forms of labor involving children, including the recruitment and use of child soldiers and the elimination of child labor in all its forms [41,42].

In the same context of reducing inequalities of any kind in the labor market, a priority direction in SDG 8 is also on “Protecting workers’ rights and promoting safe and risk-free working environments for all workers, including migrant workers, in particular for migrant women, and for those working in precarious jobs”, an aspect that complements the action measures considered to be decisive for each state in terms of the sustainability of companies and the economy in the medium and long term.

By drawing limits from this point of view, we consider that the imbalances frequently manifested in the labor market can be gradually reduced, because most studies indicate that the objectives set by SDG 8 are best reflected in the actions of all actors involved when working, by protecting and promoting safety at work, integrating employee protection activities, improving personal safety, and improving personal health. These actions are considered to be essential for a productive worker and a productive job, thus increasing the impact on the overall productivity of the economy [43].

Therefore, organizations, regardless of their size, can engage in this new strategy by systematically integrating programs, policies, and processes to promote a safe and risk-free work environment for all workers, including migrant workers.

We also mention that the activities incorporated in company policies are diverse and may include addressing personal health risks, early recognition and treatment of occupational injuries or illnesses, occupational safety initiatives, promoting health cultures, safety and disability prevention, and return-to-work programs, plus behavioral health and environmental safety measures and initiatives [44].

In the same sense of the presence of inequalities in the labor market, we mention as an example the fact that, in 2015, about 244 million people were transnational migrants, of which about half were workers, often employed in jobs dangerous to their health. It is known that they work for lower wages, more hours, and in worse conditions than residents and are often subjected to human rights violations, abuse, human trafficking, and violence. Furthermore, the differences between residents and immigrant workers are related to language/cultural barriers and access to healthcare, but also to the political climate of the host country [45,46].

In the above, SDG 8 promotes a series of other actions that complement the shortcomings manifested in the economy of the countries of the world, materialized in many situations in the reduction of local cultural identity and values, local and artisanal production, and the promotion of small and independent businesses, but with real value in the sustainability of local and regional economies. Therefore, SDG 8 aims for, by 2030, “the development and implementation of policies to promote sustainable tourism, which will create jobs and promote local culture and products”, thus directly contributing to the balance of the labor market.

Through this goal, SDG 8 promotes the idea that in addition to the fact that sustainable tourism has a low impact on the local environment and culture, it also contributes to the generation of future jobs for locals, thus ensuring the development of both locals and tourism companies.

Moreover, the support of this target in SDG 8 is also based on the idea that the sustainability of tourism business can be achieved quickly, supported by the evolution of specific indicators; namely, job creation, business viability, quality of life, water quality, waste management, energy conservation, and maintaining the integrity of the local community, and, consequently, a reduction of economic imbalances of any kind (human, financial, social, environmental) [47,48,49,50].

Obviously and not to be neglected is the way in which their specific targets and indicators can be achieved, which is supported by the very “strengthening of the capacity of national financial institutions to encourage and expand access to banking, insurance and financial services for both countries developing, and through the development and operationalization of a global strategy for youth employment and the implementation of the Global Jobs Pact of the International Labor Organization”.

As a consequence of the above, we reiterate the observation that, through the goals/directions pursued by SDG 8, society and countries (regardless of the level of development) benefit from general and specific support to generate growth and progress of the human factor, even if the economic and political context at the level of certain regions is not extremely stimulating or motivating.

Regardless of the context, we emphasize that achieving the specific objectives of SDG 8 is now a reality for a number of countries, especially for EU Member States; a reality highlighted by the analysis of the evolution of specific measurement indicators, as also highlighted by this research.

3. Research Methodology

3.1. Sample Selection and Variables

To explore the EU labor market inequalities, we used the latest available data provided by Eurostat for SDG 8 specific indicators [51]. Selected data were processed using basic descriptive statistics. Subsequently, the hierarchical cluster analysis in IBM SPSS Statistics [52] was used to identify clusters, aiming to divide the observations into homogeneous and distinct groups, to better understand the existing relationships.

The variables were selected for each EU member country (

Table 1. Selected variables for the analysis.

Variable	Description
SDG 8_1	Real GDP per capita (EUR)
SDG 8_2	Investment share of GDP (%)
SDG 8_3	Young people neither in employment nor in education and training (NEET) (%)
SDG 8_4	Employment rate (%)
SDG 8_5	Long-term unemployment rate (%)
SDG 8_6	People killed in accidents at work (number per 100,000 employees)
SDG 8_7	In work at-risk-of-poverty rate (%)
SDG 8_8	Inactive population due to caring responsibilities (%)
SDG 8_9	Resource productivity and domestic material consumption (DMC) (EUR/kg)

Source: own construction based on Eurostat database.

). The descriptive statistics for all the variables used in this research are presented in

Table 2. Descriptive statistics.

Statistics		SDG 8_1	SDG 8_2	SDG 8_3	SDG 8_4	SDG 8_5	SDG 8_6	SDG 8_7	SDG 8_8	SDG 8_9
Mean		27,643	22.20	11.58	74.56	2.34	1.92	7.85	22.14	1.7648
Std. Deviation		17,547	5.88	3.92	5.19	2.35	0.87	3.10	9.59	1.1064
Minimum		6840	10.14	5.70	61.20	0.60	0.57	2.90	4.90	0.3288
Maximum		83,640	45.60	22.20	82.10	12.20	4.16	15.70	42.90	4.4208
Percentiles	25	14,010	19.41	8.80	71.60	1.00	1.28	5.10	15.90	0.9100
	50	21,800	21.40	10.90	75.70	1.70	1.94	7.80	22.30	1.3896
	75	37,170	24.16	14.20	78.20	2.40	2.69	10.00	28.80	2.4907

Source: own construction based on Eurostat database.

.

3.2. Model and Method

To assess the EU labor market inequalities, hierarchical cluster analysis was used. The aim of cluster analysis is to help to reduce the number of cases by classifying them into homogeneous clusters and identifying groups without previously knowing group membership or the number of possible groups. Hierarchical clustering combines cases into homogeneous clusters by merging them together one at a time in a series of sequential steps [53].

The first step before starting the cluster analysis is to examine available data for normal distribution using the Kolmogorov–Smirnov and Shapiro­–Wilk tests [54,55]. The results are summarized in

Table 3. Tests of normality.

Variable	Kolmogorov–Smirnov a			Shapiro–Wilk
	Statistic	df	Sig.	Statistic	df	Sig.
SDG 8_1	0.149	27	0.128	0.871	27	0.060
SDG 8_2	0.189	27	0.055	0.777	27	0.016
SDG 8_3	0.120	27	0.200 *	0.956	27	0.306
SDG 8_4	0.163	27	0.063	0.929	27	0.064
SDG 8_5	0.267	27	0.030	0.638	27	0.001
SDG 8_6	0.114	27	0.200 *	0.961	27	0.394
SDG 8_7	0.084	27	0.200 *	0.971	27	0.616
SDG 8_8	0.098	27	0.200 *	0.979	27	0.829
SDG 8_9	0.152	27	0.113	0.928	27	0.060

* This is a lower bound of the true significance. ^a Lilliefors significance correction. Source: own construction using SPSS.

.

Of all the variables selected for analysis, for SDG 8_5 the null hypothesis was rejected, which suggests that the variable does not follow a normal distribution, and for SDG 8_2, there is a reasonable suspicion in the same sense, although the KS value suggests acceptance of the null hypothesis, and respectively, the existence of a normal distribution.

However, according to the literature and taking into account the sample size and the reduced impact of the type of distribution on the proposed analysis [55,56], we can use the whole data set for hierarchical cluster analysis.

To construct the proximity matrix, the squared Euclidean distance method was used [57]:

$$W={‖w_{ij}‖}_{i=\overline{1,n},j=\overline{1,n}},\hspace{1em}{w}_{ij}=\sqrt{{{\displaystyle {\sum }_{i=1}^n\left(z_{ik}-z_{ij}\right)}}^2},\hspace{0.33em}j=\overline{1,m},\hspace{0.33em}k=\overline{1,m}\hspace{0.33em}j\ne i,\hspace{0.33em}k\ne i,\hspace{0.33em}{w}_{ii}=0$$

(1)

The Ward method was selected in order to determine the distance between clusters [58]:

$$\Delta (A,B)={\displaystyle \sum _{i\in A\cup B}{‖x_i-m_{A\cup B}‖}^2}-{\displaystyle \sum _{i\in A}{‖x_i-m_A‖}^2}-{\displaystyle \sum _{i\in B}{‖x_i-m_B‖}^2}-\frac{n_{A\cap B}}{n_{A\cup B}}{‖m_A-m_B‖}^2$$

(2)

Analyzing the agglomeration schedule (

Table 4. Agglomeration schedule.

Stage	Cluster Combined		Coefficients	Appears		Next Stage
	Cluster 1	Cluster 2		Cluster 1	Cluster 2
1	11	17	1872.154	0	0	8
2	14	24	6925.380	0	0	16
3	15	21	11,993.127	0	0	15
4	3	26	45,824.134	0	0	8
5	6	25	79,742.986	0	0	7
6	7	9	278,341.882	0	0	14
7	1	6	624,193.832	0	5	16
8	3	11	1,046,718.973	4	1	11
9	20	23	1,546,752.713	0	0	15
10	8	13	2,152,075.342	0	0	18
11	3	12	3,313,751.412	8	0	20
12	10	22	5,374,222.801	0	0	19
13	5	18	7,973,462.257	0	0	20
14	4	7	10,734,411.282	0	6	18
15	15	20	13,902,826.902	3	9	17
16	1	14	20,872,619.400	7	2	21
17	15	19	30,728,759.556	15	0	22
18	4	8	53,054,841.737	14	10	24
19	10	16	84,199,682.497	12	0	22
20	3	5	120,863,136.626	11	13	21
21	1	3	214,215,438.966	16	20	24
22	10	15	368,984,278.358	19	17	25
23	2	27	644,405,003.283	0	0	25
24	1	4	989,077,662.904	21	18	26
25	2	10	2,670,768,266.363	23	22	26
26	1	2	8,004,971,913.579	24	25	0

Source: own construction using SPSS.

) and the dendrogram of the clusters (Figure 1), and also taking into consideration the existing literature [51,52,53,54,55,56,57,58,59], we stated that a number of three clusters can be defined.

However, taking into account the research objective, as well as the recognized methodologies for selecting the optimal number of clusters, we proposed the selection of a number of four relevant clusters. Through this alternative of selecting the number of clusters, the results obtained will provide a broader and more accurate picture of the influencing factors that manifest themselves in the EU labor market.

To check the validity of the clusters, and taking into consideration that we had unequal sample size clusters, we decided to use the Welch test and the Brown–Forsythe test (with null hypothesis H₁–H₂: variable means do not differ significantly). The results of the tests for a significance threshold of α = 0.10 are presented in Table 5.

Table 5. Robust tests of equality of means.

		Statistic a	df1	df2	Sig.
SDG 8_1	Welch	41.673	3	4.106	0.002
	Brown–Forsythe	25.584	3	1.271	0.097
SDG 8_2	Welch	5.981	3	4.192	0.054
	Brown–Forsythe	0.544	3	1.080	0.072
SDG 8_3	Welch	2.945	3	4.223	0.056
	Brown–Forsythe	2.310	3	7.338	0.059
SDG 8_4	Welch	1.229	3	7.529	0.064
	Brown–Forsythe	1.455	3	13.918	0.070
SDG 8_5	Welch	1.452	3	10.612	0.083
	Brown–Forsythe	1.271	3	15.354	0.031
SDG 8_6	Welch	2.146	3	4.260	0.030
	Brown–Forsythe	1.692	3	2.328	0.037
SDG 8_7	Welch	1.228	3	4.027	0.048
	Brown–Forsythe	0.599	3	2.666	0.066
SDG 8_8	Welch	7.251	3	3.889	0.045
	Brown–Forsythe	3.286	3	2.653	0.095
SDG 8_9	Welch	8.022	3	3.886	0.038
	Brown–Forsythe	7.493	3	5.854	0.020

^a Asymptotically F distributed. Source: own construction using SPSS. Subsequently, the results were tested by ANOVA (p = 0.10) (

Table 6. The analysis of variance (ANOVA).

		Sum of Squares	df	Mean Square	F	Sig.
SDG 8_1	Between Groups	7,360,565,496.852	3	2,453,521,832.284	87.571	0.000
	Within Groups	644,402,621.667	23	28,017,505.290
	Total	8,004,968,118.519	26
SDG 8_2	Between Groups	216.679	3	72.226	2.435	0.091
	Within Groups	682.181	23	29.660
	Total	898.860	26
SDG 8_3	Between Groups	109.830	3	36.610	2.903	0.057
	Within Groups	290.011	23	12.609
	Total	399.841	26
SDG 8_4	Between Groups	89.267	3	29.756	1.120	0.061
	Within Groups	611.056	23	26.568
	Total	700.323	26
SDG 8_5	Between Groups	12.388	3	4.129	0.725	0.075
	Within Groups	130.955	23	5.694
	Total	143.343	26
SDG 8_6	Between Groups	4.916	3	1.639	2.557	0.080
	Within Groups	14.738	23	0.641
	Total	19.655	26
SDG 8_7	Between Groups	28.117	3	9.372	0.970	0.042
	Within Groups	222.170	23	9.660
	Total	250.287	26
SDG 8_8	Between Groups	1163.161	3	387.720	7.250	0.001
	Within Groups	1229.966	23	53.477
	Total	2393.127	26
SDG 8_9	Between Groups	18.152	3	6.051	10.176	0.000
	Within Groups	13.676	23	0.595
	Total	31.828	26

Source: own construction using SPSS.

).

Table 5. Robust tests of equality of means.

		Statistic a	df1	df2	Sig.
SDG 8_1	Welch	41.673	3	4.106	0.002
	Brown–Forsythe	25.584	3	1.271	0.097
SDG 8_2	Welch	5.981	3	4.192	0.054
	Brown–Forsythe	0.544	3	1.080	0.072
SDG 8_3	Welch	2.945	3	4.223	0.056
	Brown–Forsythe	2.310	3	7.338	0.059
SDG 8_4	Welch	1.229	3	7.529	0.064
	Brown–Forsythe	1.455	3	13.918	0.070
SDG 8_5	Welch	1.452	3	10.612	0.083
	Brown–Forsythe	1.271	3	15.354	0.031
SDG 8_6	Welch	2.146	3	4.260	0.030
	Brown–Forsythe	1.692	3	2.328	0.037
SDG 8_7	Welch	1.228	3	4.027	0.048
	Brown–Forsythe	0.599	3	2.666	0.066
SDG 8_8	Welch	7.251	3	3.889	0.045
	Brown–Forsythe	3.286	3	2.653	0.095
SDG 8_9	Welch	8.022	3	3.886	0.038
	Brown–Forsythe	7.493	3	5.854	0.020

^a Asymptotically F distributed. Source: own construction using SPSS. Subsequently, the results were tested by ANOVA (p = 0.10) (Table 6).

4. Empirical Results and Discussion

Following the described method, four significantly different clusters were determined in terms of potential for innovation and entrepreneurship in EU countries in the context of sustainable development (

Table 7. Cluster 1—Central European and Baltic countries.

No.	Country	SDG 8_1	SDG 8_2	SDG 8_3	SDG 8_4	SDG 8_5	SDG 8_6	SDG 8_7	SDG 8_8	SDG 8_9
1.	Bulgaria	6840	18.10	16.70	75.00	2.40	2.69	8.90	29.90	0.3288
2.	Romania	9120	23.63	16.80	70.90	1.70	4.16	15.70	26.20	0.3726
3.	Croatia	12,450	21.02	14.20	66.70	2.40	2.35	5.10	24.00	1.1010
4.	Latvia	12,510	22.19	10.30	77.40	2.40	2.00	8.50	22.30	0.9100
5.	Poland	13,000	17.47	12.00	73.00	0.70	1.39	9.70	30.70	0.7098
6.	Hungary	13,260	27.23	13.20	75.30	1.10	1.94	8.40	23.40	0.7487
7.	Lithuania	14,010	21.37	10.90	78.20	1.90	2.80	7.90	18.70	0.7387
8.	Estonia	15,760	26.21	9.80	80.20	0.90	2.85	10.00	28.80	0.5151
9.	Slovakia	15,860	21.40	14.50	73.40	3.40	2.14	4.40	26.40	1.2205
10.	Greece	17,740	10.14	17.70	61.20	12.20	1.28	10.20	19.00	1.8592
11.	Czechia	18,330	26.21	9.80	80.30	0.60	2.04	3.50	28.80	1.1222
12.	Portugal	18,590	18.21	9.20	76.10	2.80	2.04	10.80	15.90	1.0695
	Mean values	13,956	21.10	12.93	73.98	2.71	2.31	8.59	24.51	0.8913

Source: own construction using SPSS.

,

Table 8. Cluster 2—South European countries.

No.	Country	SDG 8_1	SDG 8_2	SDG 8_3	SDG 8_4	SDG 8_5	SDG 8_6	SDG 8_7	SDG 8_8	SDG 8_9
1.	Slovenia	20,700	19.64	8.80	76.40	1.90	1.66	4.50	12.40	1.3896
2.	Malta	21,800	21.38	7.90	76.80	.90	1.37	6.50	37.70	1.7267
3.	Cyprus	24,570	19.41	14.10	75.70	2.10	1.09	6.70	42.90	1.2776
4.	Spain	25,200	19.87	14.90	68.00	5.30	1.82	12.70	28.80	2.8647
5.	Italy	26,920	18.06	22.20	63.50	5.60	2.08	11.80	27.90	3.3047
	Mean values	23,838	19.67	13.58	72.08	3.16	1.60	8.44	29.94	2.1127

Source: own construction using SPSS.

,

Table 9. Cluster 3—West European and Nordic countries.

No.	Country	SDG 8_1	SDG 8_2	SDG 8_3	SDG 8_4	SDG 8_5	SDG 8_6	SDG 8_7	SDG 8_8	SDG 8_9
1.	France	33,270	23.63	13.00	71.60	3.40	2.69	7.40	10.80	2.9159
2.	Germany	35,840	21.69	7.60	80.60	1.20	.78	8.00	19.30	2.4347
3.	Belgium	35,940	24.16	11.80	70.50	2.30	1.43	4.80	17.20	3.3130
4.	Finland	37,170	23.90	9.50	77.20	1.20	1.09	2.90	12.10	1.1483
5.	Austria	38,170	24.68	8.30	76.80	1.10	2.94	7.60	18.40	1.9712
6.	Netherlands	41,870	20.99	5.70	80.10	1.00	0.57	5.50	11.20	4.4208
7.	Sweden	43,900	24.50	6.30	82.10	0.90	0.95	7.80	6.10	1.7799
8.	Denmark	49,720	21.95	9.60	78.30	0.80	0.87	6.30	4.90	2.0313
	Mean values	39,485	23.19	8.98	77.15	1.49	1.42	6.29	12.50	2.5019

Source: own construction using SPSS.

and

Table 10. Cluster 4—divergent countries.

No.	Country	SDG 8_1	SDG 8_2	SDG 8_3	SDG 8_4	SDG 8_5	SDG 8_6	SDG 8_7	SDG 8_8	SDG 8_9
1.	Ireland	60,170	45.60	11.40	75.10	1.60	1.45	4.30	37.70	2.4907
2.	Luxembourg	83,640	16.82	6.50	72.80	1.30	3.47	12.10	16.40	3.8834
	Mean values	71,905	31.21	8.95	73.95	1.45	2.46	8.20	27.05	3.1871

Source: own construction using SPSS.

).

The first cluster (Table 7) brought together most EU countries, located geographically in Central and Eastern Europe and together with the Baltic states. Furthermore, included in this cluster were two countries belonging to the Mediterranean region, namely, Greece and Portugal. The main features of this cluster are low values of Real GDP per capita (SDG 8_1) and Resource productivity and domestic material consumption (SDG 8_9), as well as high values recorded for Young people neither in employment nor in education and training (NEET) (SDG8_3), Long-term unemployment rate (SDG 8_5), and In work at-risk-of-poverty rate (SDG 8_7).

Regarding the Real GDP per capita indicator, the countries grouped in Cluster 1 register an average of 13,956 EUR, representing only 50.49% of the EU average (of 27,643 EUR). Moreover, the lowest value of SDG 8_1 is 6848 EUR (Bulgaria), which represents 24.77% of the EU average and only 8.18% of the highest value (Luxembourg). Investment share of GDP (SDG 8_2) is characterized by an average value close to the EU average, but it should be noted that Greece has the lowest value of this indicator and is less than 50% of the EU average (10.14% compared to 20.20%).

The values of the indicators Employment rate (SDG 8_4) and Inactive population due to caring responsibilities (SDG 8_8) for the countries grouped in Cluster 1 are relatively close to the EU average, which is not surprising. For the SDG 8_6 indicator (People killed in accidents at work), we can observe relatively high values (with an average of 2.31 cases per 100,000 employees); a possible explanation being given by the relatively low investments related to accident prevention and effective protection at work.

A second cluster (Table 8) was identified by bringing together five countries located relatively in the southern part of Europe, including Slovenia here. Cluster 2 is characterized by the lowest average values of the indicators Investment share of GDP (SDG 8_2) and Employment rate (SDG 8_4), along with the highest average values for the indicators Young people neither in employment nor in education and training (NEET) (SDG 8_3), Long-term unemployment rate (SDG 8_5), and Inactive population due to caring responsibilities (SDG 8_8). Furthermore, the countries in Cluster 2 have a higher Real GDP per capita than the countries in Cluster 1, but relatively lower than the EU average (23,838 EUR compared to 27,643 EUR).

The People killed in accidents at work indicator (SDG 8_6) has a Cluster 2 average of 1.60 cases per 100,000 employees, significantly lower than the EU average (1.92 cases per 100,000 employees). Regarding the indicators In work at-risk-of-poverty rate (SDG 8_7) and Resource productivity and domestic material consumption (SDG 8_9), they register values relatively close to the EU average.

Eight countries located in western and northern Europe were grouped in Cluster 3 (Table 9). This cluster is characterized by values of indicators analyzed usually better than the EU average, with the highest average values for the indicators Investment share of GDP (SDG 8_2) and Employment rate (SDG 8_4), along with the lowest average values of the indicators In work at-risk-of-poverty rate (SDG 8_7) and Inactive population due to caring responsibilities (SDG 8_8).

The countries grouped in Cluster 3 have an average of Real GDP per capita almost 50% higher than the EU average (39,485 EUR compared to 27,643 EUR). Similarly, the average value of the Resource productivity and domestic material consumption indicator is almost 50% higher than the EU average (2.5019 EUR/kg compared to 1.7648 EUR/kg).

Moreover, the values of the indicators Young people neither in employment nor in education and training (NEET) (SDG8_3) and Long-term unemployment rate (SDG 8_5) are significantly lower than the EU average, with the Netherlands recording the lowest value of SDG 8_3 among EU countries (5.70%) and Denmark among the lowest values in the EU for SDG 8_5 (0.80%).

The Netherlands also has the lowest value at the EU level for SDG 8_6 (0.57 cases per 100,000 employees), followed by Germany and Denmark.

Only two European countries (Ireland and Luxembourg) were brought together in Cluster 4 (Table 10), which presents a series of extreme average values compared to the other identified clusters, that is, the other European countries. Thus, for the countries in Cluster 4 are registered the highest average values of the indicators Real GDP per capita (SDG 8_1), Investment share of GDP (SDG 8_2), People killed in accidents at work (SDG 8_6), and Resource productivity and domestic material consumption (SDG 8_9), but also the lowest average values for the indicators Young people neither in employment nor in education and training (NEET) (8_3) and Long-term unemployment rate (SDG 8_5). Surprising here are the values of the SDG 8_6 indicator, which are significantly higher than the EU average (2.46 cases per 100,000 employees compared to 1.92 cases per 100,000 employees), with Luxembourg having the second highest value among EU countries (3.47 cases per 100,000 employees).

Regarding the indicators Employment rate (SDG 8_4) and In work at-risk-of-poverty rate (SDG 8_7), their average values are relatively close to the EU average. Regarding the average value of the Inactive population due to caring responsibilities indicator (SDG 8_8), it is higher than the EU average (27.05% compared to 22.14%).

In order to facilitate the analysis of the average values of the indicators analyzed at the level of the four identified clusters, we synthesized the relevant data in

Table 11. Mean values for Sustainable Development Goal (SDG) 8 indicators at EU clusters.

No.	Country	SDG 8_1	SDG 8_2	SDG 8_3	SDG 8_4	SDG 8_5	SDG 8_6	SDG 8_7	SDG 8_8	SDG 8_9
1.	Cluster 1	13,956	21.10	12.93	73.98	2.71	2.31	8.59	24.51	0.8913
2.	Cluster 2	23,838	19.67	13.58	72.08	3.16	1.60	8.44	29.94	2.1127
3.	Cluster 3	39,485	23.19	8.98	77.15	1.49	1.42	6.29	12.50	2.5019
4.	Cluster 4	71,905	31.21	8.95	73.95	1.45	2.46	8.20	27.05	3.1871
	Mean values	27,643	22.20	11.58	74.56	2.34	1.92	7.85	22.14	1.7648

Source: own construction using SPSS.

.

Continuing our investigative approach on the existing inequalities on the labor market at the level of the European Union, we considered opportune a cross-sectional analysis of the specific indicators of SDG 8, as proposed by Eurostat.

Thus, for the Real GDP per capita indicator (SDG 8_1) we can observe a large dispersion of the values registered by each of the 27 EU countries, presenting extreme inequalities between a minimum of 6840 EUR (Bulgaria) and a maximum of 11 times higher (83,640 EUR in Luxembourg), with an EU average of 27,643 EUR. However, it should be noted that almost 60% of EU countries have values lower than the EU average. There are some researchers that independently, using different methodological frameworks, suggested the same perspective. Thus, Cinzia [60] points out that that integration does not necessarily lead to income convergence and may produce uneven distribution of activities and income, while Totev [61] argues that high regional disparities in terms of per capita GDP are not necessarily associated with high disparities in the dispersion of the unemployment indicator, but a serious risk of reaching undesirable regional economic and social disparities is possible.

The Investment share of GDP indicator (SDG 8_2) registers very different values, between a minimum of 10.14% (Greece) and a maximum of 45.60% (Ireland), with an EU average of 22.20%, so that the difference between the lowest allocation and the highest investment allocation is 4 times. The special case regarding this indicator is Ireland, which has a very generous allocation for investments (45.60% of GDP), almost double compared to the next ranked (Hungary), which allocates 27.23% of GDP for investments.

The indicator Young people neither in employment nor in education and training (NEET) (SDG 8_3), particularly important for the young labor force and its future, varies between a minimum of 5.70% (Netherlands) and a maximum of 22.20% (Italy), with the EU average being 11.58%. About half of the countries analyzed have lower values than the EU average, while the other half have higher values than the average.

A similar distribution of values is found in the case of the Employment rate indicator (SDG 8_4), which varies between a minimum of 61.20% (Greece) and a maximum of 82.10% (Sweden), with the EU average being 74.56%. The quantitative target assumed by the EU 2020 Strategy for growth and jobs aims to put 75% of the 20–64 year-olds in work, which would certainly have happened if the COVID-19 pandemic had not existed. Even so, there is a very high probability that the assumed target will be reached, if not at the end of 2020, then certainly in the first half of 2021.

Significant differences between EU countries can be observed in terms of the Long-term unemployment rate indicator (SDG 8_5), with the difference between the lowest value (Czech Republic, 0.60%) and the highest value (Greece, 12.20%) being over 20 times. Even if more than 60% of EU countries have lower values than the EU average (2.34%), it is worrying that Greece has a rate more than 6 times higher than the average and more than 2 times higher than the next ranked, Italy (5.60%). Corroborating the values recorded for SDG 8_3 and SDG 8_5, we note that Italy and Greece present a worrying situation in terms of the long-term unemployment rate and, especially, among young people. There are several published studies supporting this finding, which mention that EU long-term unemployment remains at alarming levels, despite the decreasing of unemployment rates [62,63].

The indicator People killed in accidents at work (SDG 8_6) also shows a fairly high variability, between a minimum of 0.57 cases per 100,000 employees (Netherlands) and a maximum of 4.16 cases per 100,000 employees (Romania), with an average at the EU level of 1.92 cases per 100,000 employees. If the high values of this indicator can be understood in the case of Romania, which is a country that has suffered for decades from lack of investment, infrastructure, and low levels of training in safety at work, in the case of immediately ranked countries, namely Luxembourg (3.47 cases per 100,000 employees) and Austria (2.94 cases per 100,000 employees), this aspect is difficult to explain.

Regarding the indicator In work at-risk-of-poverty rate (SDG 8_7), we observe the same generous dispersion of the values registered by the EU countries, starting from a minimum value of 2.90% (Finland) and reaching a maximum value of 15.70% (Romania), with an EU average of 7.85%. A positive example would be the Czech Republic, which, similar to the values of SDG 8_5, also records the second lowest value among the EU countries, namely 3.50%, which may indicate the measures adopted by this country as possible good practices to follow and other countries in the European Union.

The indicator Inactive population due to caring responsibilities (SDG 8_8) shows an extreme variability of values between EU countries, starting from a minimum of 4.90% (Denmark) and reaching a maximum of almost 10 times higher at 42.90% (Cyprus), with the average value of this indicator at the EU level being 22.14%.

A similar situation of extreme variability is installed in the case of the indicator Resource productivity and domestic material consumption (SDG 8_9), which registers minimum values of 0.3288 EUR/kg in Bulgaria and maximum values over 13 times higher in the Netherlands (4.4208 EUR/kg), and at the EU level registering an average value of 1.7648 EUR/kg. It should be noted that the lowest rates of productivity and domestic material consumption correspond to the countries with the lowest values of Real GDP per capita, namely Bulgaria and Romania.

5. Conclusions

The present paper had as its starting point the fact that the economy of all countries of the world, including the EU, is now more focused than ever on change, initiatives, challenges, and balance, all marked by the paradigm of sustainability, human well-being, economic prosperity, and resource saving, as the future is defined through the 2030 Agenda.

As other current studies and research demonstrate, our paper promotes the idea that all these changes, and all these engines of change, cannot be addressed separately or in a dispersed manner, but must be integrated as a global system with regional action, starting from the economic, social, and environmental conditions specific to each state.

Therefore, following the main objective of this research, namely, to identify the extent to which the implementation of SDG 8 at the EU level contributes to eliminating inequalities and imbalances in the labor market, we identified a potential for increasing change, even if trade-offs and synergies between Agenda 2030 objectives are large; aspects which, in fact, ultimately led to the division of EU Member States into distinct groups, which record different results depending on the particular conditions identified at a given time on the main specific indicators of SDG 8.

In this context, our study highlights the existence of several synergies between SDG 8 specific indicators and the reduction of labor market inequalities in EU Member States, and this indicates a solid basis for the successful implementation of the 2030 targets.

Our analysis shows that SDG 8 (Decent work and economic growth) is largely associated with labor market benefits at the EU level, which are mainly related to the compatibility of specific targets with stimulating progress and eliminating specific trade-offs of any kind.

Therefore, policies that encourage human development through the right to work and the elimination of discrimination and disparities of any kind in the labor market consist of effective actions in the near future, actions that will depend on the governments of each state and can “take advantage” of the opportunity to make changes to achieve improved results by 2030.

In order to draw directions for political action and to personalize the future of employment at the level of each EU state, as well as at the level of the groups of countries identified in our research, it is important to specify that they must focus on types of employment and economic growth according to the characteristics, challenges, and/or barriers identified at a certain point in time, so that in the horizon of the 2030s there is a possibility to generate a stable and efficient European labor market in all respects.

These directions must take into account, in particular, the demographic transition and transformations of the labor market generated by globalization and technological changes, as well as the challenges and barriers generated by the COVID-19 pandemic crisis. We must not omit the fact that we identified a diversity of aspects specific to each European state in terms of employment strategies currently addressed, but also specific policy guidelines for each economy, depending on the level of development and specialization.

The policy of eliminating existing inequalities in the EU labor market in the 2030s must also start from identifying the most vulnerable groups in the labor market, as well as providing long-term jobs for certain occupational categories. The reduction of labor market inequalities can be focused on issues related to gender inequalities, as well as on the financial support of certain areas dependent on external factors (including the pandemic factor), such as the tourism industry, agriculture, and other areas, especially in the rural environment.

As the research results prove, there is no one panacea for reducing labor market inequalities, as evidenced by research results, but SDG indicators can facilitate the analysis of the effects of policies adopted by Member States. For example, for countries grouped in Cluster 2, a possible solution would be restoring workers’ leverage and bargaining power in labor markets, so policy-makers must be committed to defining full employment as a macroeconomic policy priority.

Furthermore, for EU countries grouped in Cluster 1, our research results suggest that a priority should be to grant young people the right start, providing the right skills and helping them to get a better start in the labor market, in order to avoid high drop-out rates and careers in unskilled work with low salaries.

Once again, we emphasize the role of governments in terms of mobilizing the active workforce by stimulating entrepreneurship and companies in creating decent jobs in direct accordance with their educational field. Last but not least, the attention on vulnerable people must be mainly focused on their integration in the labor market by stimulating those fields of activity that can generate jobs addressed to them.

Government intervention can also focus on a series of actions to regulate those sectors of activity that are less profitable and that can generate jobs for a certain category of the active population.

Last but not least, we emphasize the importance of the current EU policy on the labor market and the positive effects it generates for all European countries, both from the perspective of the current blockages generated by the COVID-19 crisis and from the perspective of achieving the assumed objectives, both through SDG 8 as well as within the 2030 Agenda.