Determination of the Convergence of Turkey and European Union Countries in Terms of Youth Labor Indicators by Cluster Analysis

Abstract

The aim of this study, which was conducted on the basis of the convergence hypothesis, is to reveal the convergence problems of Turkey towards the European Union based on the basic indicators of the youth labor market. For this purpose, a large gender-disaggregated data set has been constructed with 29 observation units consisting of the European Union Average, European Union Countries, and Turkey, using the basic indicators of the youth labor market that point to the future, within Eurostat and Ilostat data. The clustering method, which is one of the advanced statistical techniques, was preferred to determine which countries are similar to each other and which are different from each other within the data set. In this study, where non-hierarchical and hierarchical clustering methods were used together, it was concluded that Turkey diverges from the developed countries of the European Union, such as Denmark, Germany, and the Netherlands, and is similar to countries such as the EU (27), Bulgaria, Czechia, and Italy. Along with this result, this study also reveals remarkable gender differences in the indicators for young men and young women in the youth labor market in Turkey, and that Turkey’s main convergence problem towards the European Union is realized in NEET rates. In this context, this study is completed with suggestions for various policy measures to address convergence problems, such as NEET, unemployment of young women, and low labor force participation rates of young women in Turkey, within the scope of sustainable development goals such as quality education and gender equality.

1. Introduction

Among the issues that countries prioritize within the scope of their sustainable development goals are the integration of young people into the labor market, economic growth, and the improvement of social welfare [1,2,3]. It is well known that Turkey also attaches importance to these policies within its sustainable development goals. At the same time, the long-standing process of full membership between the EU countries and Turkey brings with it the need for convergence in labor markets and social welfare programs [4].

The COVID-19 pandemic, the Russia–Ukraine war, strategic issues in the Middle East, and the economic crises that have arisen in recent years have had a negative impact on young people’s participation in the labor market [5,6]. Undoubtedly, these negative effects are manifesting themselves as critical new risks in NEET (young people not in education, employment, or training) rates and gender-based employment [7]. In addition, with developments in artificial intelligence and technology, young people are moving away from standard job descriptions and seeking remote work, flexible employment, artificial intelligence engineering, and employment in digital media. This highlights the need to redesign employment policies for young people around the world [8]. Digitalization has accelerated global interaction, causing countries to become more similar to each other in political, policy, and cultural terms, just as employment policies for young people need to be revised. This phenomenon is referred to in the literature as the convergence hypothesis.

The convergence hypothesis is an approach that suggests that countries tend to become more similar to each other over time in terms of variables such as economic growth, education, income, and labor market indicators [9]. Solow (1956) and Swan’s (1956) neo-classical growth models have been strengthened and developed by endogenous growth and human capital theories [10,11]. It is argued that this situation will increase the participation of young people in education and employment, which will increase countries’ economic growth as well as their similarities with each other [10,11,12,13]. Within this paradigm, the literature on sustainable development has also frequently discussed the situation of young people in the labor market. The literature states that young people should not be evaluated solely on the basis of economic indicators, but that gender equality, education, and social integration are also highly effective in the inclusion of young people in the labor market [1]. This is because high NEET rates among young women in particular, in terms of gender, and relatively low labor force participation rates are seen as critical factors threatening countries’ long-term economic growth [14]. The employment of young people is seen as the most important focus point for ageing societies. In this context, from an individual perspective, the inclusion of young people in the labor force is seen as a key factor in their transition to adulthood, while from a social perspective, it is not only a matter of concern for the welfare of young people, but also a factor that affects the overall economic development of countries [15,16,17]. Within this framework, the aim of this study is to identify the similarities and differences between Turkey and EU countries in terms of indicators related to youth labor markets, based on the convergence hypothesis.

In this study, within the framework of the convergence hypothesis, the youth labor market indicators of 27 EU member countries and Turkey have been analyzed in terms of gender discrimination (labor force participation, unemployment, NEET, and employment rates), which is one of the key issues of sustainable development. The study’s original contribution to the literature lies in its comprehensive presentation of Turkey’s position among EU countries using hierarchical and k-means clustering methods with up-to-date data (Eurostat and Ilostat). In this sense, the analysis of the youth labor market conducted in this study aims to fill a gap in the literature by providing a comprehensive perspective on the similarities between countries, not only from an economic standpoint but also by shedding light on their long-term labor markets.

Based on the above, the main hypothesis to be tested in this study is as follows: “Turkey’s youth labor force indicators differ statistically from those of EU countries, but are similar to those of Southern and Eastern EU countries.” In this context, the aim of this study is to identify which EU country or countries Turkey, as a candidate country for the EU, resembles or differs from in terms of youth labor force indicators.

2. Theoretical Background

Since the beginning of the modern era, employment has been an important issue that has been focused on in order to ensure the efficient continuation of production. In line with countries’ sustainable development goals, the continuity of employment and the improvement of its conditions have become a key area of interest for policymakers [18]. In capitalist societies in particular, individuals are generally evaluated according to their success in material, cultural, or personal development [19]. From this perspective, being part of the workforce has become one of the most important factors in social integration. According to Weller, employment facilitates networking by providing opportunities for interpersonal development and enabling people to participate in collective actions [19]. From this perspective, being employed facilitates social acceptance for individuals.

This study, conducted with consideration of Turkey’s status as a candidate country for European Union membership, which it has been striving to achieve for nearly half a century, is based on the convergence thesis, which is one of the growth theories within labor market indicators. According to the convergence hypothesis, as summarized in Solow and Swan’s neo-classical growth model, developing countries tend to grow faster than developed countries, which in turn leads to a reduction in income inequality over time, enabling developing countries to eventually catch up with developed countries in the long term [20,21].

Considering Turkey’s efforts to become a candidate for EU membership for half a century, it appears that there have been various political turning points in the convergence between Turkey and the EU. Within this framework, the EU’s relations with Turkey, which went beyond economic union with the Maastricht Treaty, reached the candidate status level at the Helsinki Summit in 1999, and the candidacy process continued on the condition that Turkey fulfil certain obligations within the framework of candidate country status. Within this paradigm, Turkey is expected and required to fulfil various obligations such as democracy, the rule of law, and a stable institutional structure within the scope of the Maastricht and Copenhagen criteria. On the other hand, with the Lisbon and Europe 2020 strategies, the EU expects candidate countries to align with various targets in the areas of employment, education, social inclusion, R&D, climate, and environment, within the framework of sustainable and inclusive growth [22]. Within the scope of the convergence hypothesis, it can be seen that convergence problems have become an important research topic in the context of the objectives of balancing economic development levels, in other words, reducing differentiation within the union, since the implementation of European Union accession and harmonization policies [23]. At the core of the convergence hypothesis, the level of economic development is undoubtedly the labor market, which lies at the intersection of economic and social life. In this context, unemployment, employment, informality, workplace characteristics, sectoral distribution, and education levels are crucial indicators for countries’ labor markets. Additionally, the structure of the young labor market, which plays a significant role in the dynamics of economic development, is directly influenced by these characteristics of countries [24].

Research into the current state of the young labor market, which is considered a benchmark for a country’s future economic development, has been the subject of numerous studies as it is seen as important in the context of convergence issues [1,17,25,26]. Within this framework, analyses of the labor market are often conducted with a focus on the concepts of unemployment and employment. In particular, theoretical models constructed around differences in unemployment rates are generally discussed in terms of the balance or imbalance between labor supply and demand. Factors that contribute to the balance between labor supply and demand include the demographic characteristics of young people, the characteristics of the labor market, and the stock of human capital [27]. Blanchard and Katz emphasize in their studies that regions have different average unemployment rates for equilibrium approaches and that asymmetric shocks temporarily separate regions from each other. However, they state that the effects of these shocks are not permanent and that regions eventually converge [26]. Contrary to this view, Marston states that the role of trade unions, social security policies implemented by governments, high unemployment insurance rates, local opportunities, and climate conditions play a negative role in the self-correction of regional imbalances [28]. Similarly, authors who advocate the disequilibrium approach believe that labor market adjustment problems play a role in maintaining regional disparities [25,29]. With a similar approach to regional differences, Fujita and Krugman (2004) refer to a concept they call “new economic geography.” They argue that spatial differences in economic growth and unemployment are caused by innovation differences between developed and underdeveloped regions and the migration of skilled labor [30]. Iammarino et al. (2018) state that the broad wave of economic development has weak spillover mechanisms for spreading prosperity to other regions and ensuring income convergence [31]. In the middle of the twentieth century, diffusion mechanisms were stronger due to the need for diffusion to allow the technologies developed at the beginning of the century to mature. During this process, it can be said that there was a long transition period that benefited less developed regions and triggered convergence between regions. In today’s economies, however, it is argued that the structural situation described above can only be weakly offset by the diffusion of knowledge, entrepreneurship, and labor migration. As a result, due to the failure of economic development to enhance spatial and social mechanisms, interregional inequality has increased and become entrenched in the current EU [31]. The convergence hypothesis states that income, production levels, and labor market indicators in countries will become similar to one another due to the influence of global integration. For this reason, endogenous growth models emphasize that human capital models, such as the level of education, labor force participation, the effectiveness of the young population, and innovation capacity, are very important for growth. In other words, endogenous growth theories consider technology, investment in human capital, and labor market equilibrium as the fundamental variables for countries’ convergence and the convergence theory [32,33]. A similar approach can also be observed in the sustainable development literature. Indeed, it emphasizes that labor market indicators are not only related to the economic level but also directly linked to sustainability principles such as education and social integration. For this reason, conditions that could disrupt the balance of the labor market, such as unemployment, NEET, gender-based disparities, and declining labor force participation, are seen as factors that could slow economic growth in the long term. It is anticipated that this situation will influence countries’ sustainable development policies in the long term [34].

The EU places great emphasis on young people within its enlargement, sustainable development, and cohesion policies. Strengthening the youth labor force, increasing employment, and reducing the number of NEETs are considered policy measures directly related to the EU’s sustainable development goals [35]. When examining the convergence relationship between Turkey and EU countries, issues in the youth labor market, high NEET rates, declining participation of young women in the labor force, and gender-based issues emerge as factors that weaken the convergence process and hinder sustainable development goals [36]. The importance of this study is clearly demonstrated by this situation.

Within the framework of the theoretical background mentioned above, this study is expected to highlight an important issue. Indeed, considering Turkey’s status as a candidate country for EU membership, its membership in the Council of Europe, and the EU-focused nature of its customs union and trade axis based on geopolitical proximity, Turkey’s convergence with the EU has emerged as a prominent issue on the agenda of both Turkey and Europe and is gaining increasing importance. However, differences within the EU’s social, political, and economic structure, the troubled structure of the Middle East, political struggles such as the Ukraine–Russia War, as well as global financial crises and pandemics are disrupting this process and providing an important ground for research. Building on this, this study aims to analyze the similarities and differences between Turkey and EU countries in terms of the youth labor market from a sustainable development perspective.

3. Literature Review

A comprehensive cluster analysis based on statistics of youth unemployment between Turkey and European Union countries requires an in-depth examination of various studies focusing on the employment of young individuals, particularly those categorized as NEET (Not in Employment, Education, or Training) and those employed in the informal sector. The findings from these analyses will significantly contribute to understanding the complex dynamics of youth employment, identifying the socio-economic factors influencing these statistics, and clarifying the areas where countries converge and diverge.

The issue of youth employment in Turkey is of great interest due to high unemployment rates and the prevalence of NEET status among young people aged 15–34. The report titled “NEET in Rural Areas of Turkey 2009–2019” presents a ten-year analysis of NEET statistics. The findings highlighted in the report emphasize that the proportion of young people in the total population is decreasing. However, the findings on unemployment and NEET status in the report raise concerns about young people’s future employment opportunities and economic participation [37]. Therefore, it is highly beneficial to analyze the situation of the young labor force through NEETs during the European Union membership process. Indeed, the labor market in Turkey faces certain challenges in terms of the young population, particularly in terms of unemployment and NEET (Not in Education, Employment, or Training) rates.

The OECD reports that the youth unemployment rate in Turkey has fluctuated around 25% in recent years and has become a persistent issue [38]. The NEET rate, which is a critical indicator of youth participation in the labor market, is another cause for concern. Research conducted in this context shows that approximately 24% of young people in Turkey fall into the NEET category. In other words, it is understood that a significant portion of the country’s young population is unable to realize their potential in the labor force [39].

Emma and Gregg emphasize the importance of the socio-economic consequences of this trend in their work, as it could have long-term effects by reducing future employability and wage expectations due to a long-term disengagement from both the education system and the labor market [40]. This situation is further complicated by gender inequalities, with young women disproportionately represented among NEETs due to cultural norms and limited access to labor market opportunities [41]. NEET is not seen as a problem specific to Turkey, but is also recognized as an issue in EU countries. This is also evident in studies conducted on EU countries. Indeed, according to Eurostat data, youth unemployment rates in Northern European countries such as Germany, Denmark, and the Netherlands are relatively lower compared to Southern European countries like Spain, Greece, and Italy. Alongside youth unemployment, one of the major challenges in Southern European countries is the high NEET rates, similar to those observed in Turkey [42].

According to Stefano, Anne, and Thomas, the lack of vocational training programs combined with the rigid structure of the labor market has created an unfavorable environment for young workers in Greece [43].

In their study examining the NEET status among Turkish youth, Yiğit, Çakmak, and Çakmak emphasize the socio-economic determinants affecting NEET status. They state that family education and employment are important factors contributing to NEET status, alongside other factors. The findings of the study highlight that parents with higher education levels are associated with a lower likelihood of their children being classified as NEET. On the other hand, the study reveals that compulsory education is an important factor in reducing NEET risks, particularly among men and married or divorced women, thereby demonstrating the mutual interaction between education and family dynamics in shaping young people’s employment trajectories [44].

According to Gök Bayrak and Çalışır, various factors contribute to the challenges Turkey faces in integrating young people into the labor market. First, despite significant reforms in the country’s education system, there are still shortcomings in aligning educational outcomes with the needs of the labor market. Despite efforts to expand vocational education and training programs, there remains a mismatch between the skills provided by the education system and those demanded by employers, which disrupts the education–employment pattern [45].

In another study in the literature on education, Jespersen, Munch, and Skipper [46] concluded that education programs in Denmark had a positive impact on employment and earnings. In contrast, Fredriksson and Johansson [47] found that job creation and education programs reduced the long-term sustainability of employment opportunities.

Carling and Richardson [48], in their work conducted in Sweden, stated that employment subsidies and training provided by businesses were more effective, while Sianesi [49] stated that employment incentives were the most effective program.

According to Ryan, Germany’s success in managing youth unemployment stems from its strong two-tier education system, which combines classroom education with on-the-job training through apprenticeships [50]. In their work, Eichhorst, Holger, and Ulf found that countries’ emphasis on vocational education has resulted in a smooth transition from school to work for many young people and significantly reduced the time spent unemployed or inactive after graduation [51]. Both studies show that labor market harmonization, which has been effectively implemented in the Netherlands and Denmark, plays an important role in ensuring that young workers receive education that meets employers’ expectations [50,51].

Kurtsal examined regional inequalities using quantitative methods in his study. He found that youth unemployment rates were higher in regions with low levels of education and limited job search resources. The negative correlation between education levels and youth unemployment rates reinforces the view that educational opportunities are vital to solving the youth unemployment crisis in Turkey. The study also emphasizes the necessity of targeted interventions to address the various challenges faced by young people in different regions [52].

The ILO points to the rigidity of the labor market and education and skills mismatches as obstacles to youth employment in Turkey. These obstacles contribute to a labor market environment in which young people are disproportionately affected by economic downturns, as seen during the COVID-19 pandemic, which has further exacerbated existing vulnerabilities in the labor market [53].

According to Bradley et al., the prevalence of temporary employment contracts and flexible working arrangements condemns a large number of young workers to precarious employment conditions [25]. The study finds that this situation further increases the vulnerability of young workers to economic shocks [25].

The phenomenon of informal employment is another critical dimension of youth employment [54]. Görmüş’s study sheds light on the prevalence of informal employment among young people and its consequences. Informal employment is characterized by the absence of job security, low wages, and the lack of social rights, and disproportionately affects young men compared to young women. Based on data from the 2021 Turkey Household Labor Force Survey, the study emphasizes that higher education levels are associated with an increased likelihood of young individuals being employed in low-wage jobs in the informal sector and argues that educational attainment does not necessarily translate into better employment outcomes [54].

Within the European Union, Southern European countries face more serious problems in the labor market than Northern countries. For example, Greece’s youth unemployment and NEET rates have remained high since the country’s economic crisis. Within this paradigm, there are differences in the characteristics of the youth labor market among European Union member countries. According to Eurostat data, youth unemployment rates in Northern European countries such as Germany, Denmark, and the Netherlands are relatively lower compared to Southern European countries like Spain, Greece, and Italy. In addition to youth unemployment, one of the major challenges faced by Southern European countries is high NEET rates, similar to those observed in Turkey [42].

The similarities and differences between EU countries can largely be attributed to differences in economic and labor market policies and education systems. In this context, the literature shows that cluster analysis is effectively used to examine labor market statistics in various contexts.

The current literature emphasizes that the youth labor market should not be analyzed solely in terms of unemployment and NEET rates, but that gender, education quality, active and passive labor market policies, technological transformation, labor flexibility, and post-pandemic changes in work dynamics should also be taken into account. Within the context of sustainable development goals, the participation of young women in the labor force, inclusive education, and regional policy tools are increasingly highlighted in the literature. In particular, the effects of external shocks such as the COVID-19 pandemic and the war in Ukraine on youth employment and NEET rates have become an important agenda item in new-generation research.

In the European Union literature, youth unemployment and NEET rates are mostly analyzed in terms of North/South and East/West divisions. In high-performing countries, the education–employment link is strong, and active labor market policies are effectively implemented; in low-performing countries, structural unemployment, the mismatch between the education system and the labor market, and gender inequality are the main problems. In particular, dual education and vocational training programs for young people in Germany, the Netherlands, and Scandinavian countries are presented as success stories; in Southern and Eastern European countries and Turkey, the need for structural reforms is emphasized in the literature.

Krpan, Kedžo, and Žmuk (2023) used a hierarchical clustering approach in their study. According to the results of the study, Belgium, Cyprus, Denmark, France, Ireland, Latvia, Lithuania, Luxembourg, and the Netherlands are the countries with the highest average percentage of young adults with tertiary education in 2021 [55]. On the other hand, Belgium, Bulgaria, Hungary, Ireland, Lithuania, Malta, Poland, and Romania were identified as the countries where higher education provides the highest average employment and income benefits for young adults. According to the researchers, as the level of education increases, unemployment rates decrease, and employment rates and income levels increase [55].

Bradley et al.’s (2020) study examines regional differences in youth unemployment and NEET rates in European Union member countries using a clustering analysis method [25]. The study found that the spatial segregation of youth unemployment and NEET rates stems from regional differences influenced by labor market conditions. According to the study’s findings, youth unemployment and NEET rates in Spain, Italy, and the United Kingdom vary depending on economic cycles, labor force quality, and competition in the labor market. In particular, the study concluded that the effects of economic crises on youth unemployment are more pronounced in Spain and Italy [25].

In their 2024 study, Kusawanto et al. analyzed unemployment data in the Jakarta region by age group using the K-means clustering method. According to their findings, the data were divided into three clusters based on age groups, revealing that the unemployment rate was highest among young people and that this group faced difficulties entering the labor market [56]. In Pavelka and Löster’s study on cluster analysis, the authors used cluster analysis to categorize member states according to their youth unemployment trends in the labor market following the economic crisis in the European Union, examining countries in terms of youth unemployment, unemployment rates, and long-term unemployment rates [57].

Paşnicu et al. identified clusters with similar labor market characteristics using labor market indicators from European Union countries. According to the findings of the study, European Union countries were divided into four clusters. The highest-performing cluster was identified as Sweden, the Netherlands, Austria, Germany, Denmark, and the United Kingdom. The lowest performing cluster was found to be Southern European countries, similar to other studies. Hierarchical clustering analysis revealed that the labor market performances of European Union countries are quite different from one another and that these differences stem from structural and political factors, among other findings of the study. Researchers have found that young people, referred to as a vulnerable group in Southern and Eastern European countries, face difficulties in entering the labor market and have lower employment opportunities compared to Western and Northern European countries [58].

Babucea et al. conducted a clustering analysis study in Romania in 2009, at the onset of the economic crisis, to examine the geographical distribution of unemployment rates and to understand the similarities and regional differences in registered unemployment across different counties in Romania. In this study, hierarchical clustering analysis and the Squared Euclidean distance calculation metric were employed for the analysis. The findings revealed that Romania’s counties could be divided into three main clusters based on unemployment rates, and significant regional differences in unemployment rates were observed during the economic crisis [59].

It is clearly seen that regional comparative analyses in the literature seem to focus on unemployment, employment, and NEET among young people. Accordingly, studies should address the problem of youth unemployment by considering both its individual and social dimensions. There are significant differences in youth unemployment rates between countries and regions, and these differences are directly influenced by labor market policies, economic structures, and crises. In this context, it is understood from the studies in the literature that regional and national policies for the integration of young people into the labor market should be made compatible with each other, vulnerable groups should be supported, and more targeted strategies should be implemented in the fight against unemployment. However, there are only a limited number of studies that analyze the situation of the youth labor market in Turkey through clustering analysis, comparing similarities and differences with EU countries. This situation highlights an important gap in the literature. Therefore, this study aims to fill this gap in the literature. The examination of similarities and differences between Turkey and EU countries in terms of youth labor market policies using current data and statistical methods, particularly a comprehensive assessment of gender-based NEET and employment indicators, represents the innovative aspect of this study.

4. Materials and Methods

4.1. Descriptive Statistics

This study aims to reveal the similarities and differences between Turkey, a candidate country for EU membership, and EU countries in terms of youth labor force indicators.

Table 1. Indicators of the youth labor market in EU countries and Turkey.

		Youth Labor Force Participation Rates (2024)			Youth Unemployment Rates (2024)			NEET (15–24) (2024)			Youth Employment Rates (2024)
	Countries	Male	Female	Total	Male	Female	Total	Male	Female	Total	Male	Female	Total
1	EU (27)	43	37.5	40.3	14.9	14.0	14.5	9.4	9.1	9.2	37.3	33.0	35.2
2	Belgium	32.3	30.5	31.5	17.6	14.4	16.1	7.6	5.7	6.7	27.1	25.9	26.5
3	Bulgaria	26.3	19.2	22.9	12.8	11.1	12.1	11.3	11.6	11.4	21.2	16.2	18.8
4	Czechia	51.9	24.2	32.6	7.9	8.8	8.3	4.4	8.1	6.3	29.2	21.8	25.5
5	Denmark	63.7	64.2	64	11.8	11.3	11.5	7.3	7.0	7.1	55.9	58.2	57.0
6	Germany	56.7	51.8	54.3	6.5	5.2	5.9	7.2	7.7	7.5	52.8	48.7	50.8
7	Estonia	42.8	45.4	44.1	18.4	16.4	17.3	12.0	7.2	9.6	33.4	38.7	36.1
8	Ireland	54.6	54.6	54.6	10.7	10.6	10.7	6.8	6.1	6.5	47.9	48.5	48.2
9	Greece	26.6	22.9	24.8	24.4	29.4	26.7	11.8	11.3	11.6	20.2	16.3	18.3
10	Spain	34.8	30.9	32.9	28.8	28.5	28.7	10.7	9.1	9.9	25.0	22.1	23.6
11	France	44.6	40.9	42.8	18.2	16.1	17.2	11.5	9.4	10.5	36.7	33.6	35.2
12	Croatia	36.4	24.2	30.5	17.2	21.8	19.0	10.1	9.6	9.8	30.7	19.5	25.3
13	Italy	31	21.5	26.4	21.1	25.2	22.7	13.0	12.3	12.7	24.3	16.2	20.4
14	Cyprus	45.2	42	43.6	19.1	14.7	16.9	11.0	12.8	11.9	36.5	36.4	36.5
15	Latvia	37.8	31.5	34.7	14.3	10.0	12.3	8.0	6.3	7.2	31.7	29.5	30.6
16	Lithuania	34.5	33.4	34	17.2	10.2	13.8	14.5	12.6	13.5	29.8	31.9	30.8
17	Luxembourg	37	33	35	15.6	22.7	18.8	7.2	10.7	8.9	32.0	25.8	29.0
18	Hungary	35.4	26.8	31.2	13.4	12.0	12.8	8.6	11.0	9.8	30.7	23.9	27.4
19	Malta	55.2	53.1	54.2	11.6	6.6	9.1	8.1	8.3	8.2	50.5	49.0	49.8
20	Netherlands	70.8	72.1	71.4	8.3	8.2	8.2	3.1	3.5	3.3	76.8	76.2	76.5
21	Austria	61.9	56.5	59.3	10.2	10.7	10.4	8.6	8.8	8.7	56.3	49.8	53.1
22	Poland	36.5	27.6	32.1	11.6	11.1	11.4	7.1	6.7	6.9	31.9	25.3	28.7
23	Portugal	37.2	33.6	35.5	20.7	19.8	20.3	8.5	7.2	7.9	29.5	26.8	28.2
24	Romania	30.3	19.5	25	21.5	22.2	21.8	13.6	19.6	16.5	23.4	13.7	18.7
25	Slovenia	40.2	32.2	36.3	10.6	8.7	9.9	7.1	7.5	7.3	36.3	28.4	32.6
26	Slovakia	33.7	21.3	27.6	22.1	16.0	19.8	10.1	7.6	8.9	25.4	17.8	21.7
27	Finland	53.5	56.4	54.9	16.9	15.4	16.2	8.3	7.1	7.7	43.9	47.9	45.9
28	Sweden	59.1	58.2	58.7	22.5	21.6	22.1	5.1	5.1	5.1	44.8	44.8	44.8
29	Turkey	56.1	31.3	44.1	14.3	23.4	17.5	15.6	29.7	22.4	49.2	25.3	37.6

Source: Eurostat and Ilostat data compiled by the authors.

below shows the variables used in the clustering analysis for EU countries and Turkey. The variables presented in Table 1 reflect data from 2024. In this context, indicators such as youth unemployment rates, NEET (young people not in education, employment, or training) rates, and youth employment rates were selected using Eurostat and ILO statistics.

As can be seen from Table 1, it is possible to state that there are significant differences between European Union countries and Turkey in terms of youth labor force participation rates, youth unemployment rates, NEET rates, and employment rates. Women in Turkey, and especially young women, are disadvantaged in terms of access to education and face difficulties in participating in the labor force. This situation necessitates structural reforms to align with European Union countries in terms of gender equality and youth employment. At the same time, the fact that NEET rates are quite high compared to European Union countries indicates that there are problems in the full socio-economic integration of the young population in Turkey, as can be seen from the data in Table 1. In European Union countries, there is a more balanced distribution in terms of NEET, but high youth unemployment rates are observed in Southern European countries such as Spain. In this regard, it would not be incorrect to state that there are similarities between Turkey and European Union countries. This study, which aims to present interpretations based on these data using statistical methods, will contribute significantly to the literature on the youth labor market by identifying similarities and differences between Turkey and European Union countries using cluster analysis.

4.2. Cluster Analysis

According to Abonyi and Feil, Cluster analysis is a method that aims to identify internal groups of unlabeled data [60]. Cluster analysis is one of the multivariate statistical methods and is an advanced statistical method used to classify data within a group based on similarities and differences between groups [61]. Techniques related to clustering analysis are divided into two groups in the literature: hierarchical and non-hierarchical [62]. The hierarchical clustering method is divided into two categories. These are called agglomerative and divisive methods. The agglomerative hierarchical clustering method is one of the most commonly used techniques in the literature for these two categories. In this method, all observations are first grouped into a single cluster, and then outlier observations are separated from the cluster to form new clusters. The following methods are used to form clusters in this method: “Single Linkage Method, Complete Linkage Method, Average Linkage Method, Ward’s Method, and Central Method” [62].

In hierarchical clustering methods, the “number of clusters” is determined using criteria such as distance coefficients, dendrograms, and agglomeration coefficients [63]. Non-hierarchical clustering methods are divided into three categories. These are sequential start, parallel start, and average split techniques. Among these, the most commonly used technique is “K-means” [63]. In non-hierarchical clustering methods, the number of clusters is determined based on the researcher’s experience and knowledge of the literature. In non-hierarchical clustering techniques, repeating the process and ensuring that the convergence criteria are between “0 and 1” makes the results more reliable. Despite this advantage, interpreting the method can be more difficult than other methods [63].

Cluster analysis is, in summary, an important technique widely used in social sciences for classifying data, which can be performed using hierarchical or non-hierarchical approaches. Hierarchical and non-hierarchical techniques each have their own advantages and disadvantages. In both methods, the researcher’s experience and the nature of the data set are important in determining which method to use in the analysis.

Based on the information provided regarding the clustering analysis method, the data set used in the study includes four main variables: youth unemployment rates, NEET (young people not in education, employment, or training) rates, youth labor force participation rates, and youth employment rates, all broken down by gender. The European Union (27 countries) and Turkey, along with 29 other observation units, were compared using cluster analysis.

In the literature on cluster analysis, it is stated that the number of observations required for the sample size should be 3–4 times greater than the number of variables. The data set used in this study easily meets this requirement stated in the literature. Although the variables used in the data set are categorical, Z-scores were calculated to ensure standardization. The Z-score method minimizes the effects of scale and unit differences in variables, making them comparable. This method contributes to the normalization of data by transforming the means of variables to zero and their standard deviations to one [64,65]. After standardizing the Z-scores, the analysis phase of the study was initiated. The analyses were performed using the SPSS 25 software package. In the analysis section, Ward’s method was chosen from hierarchical clustering methods as it was appropriate for the content of the data set. This choice was made because we wanted to keep the variance value within the clusters at a minimum level [61,66]. When applying Ward’s method, the “Euclidean square” distance is generally preferred, while the number of clusters is determined based on the researcher’s experience and knowledge of the subject [66]. In addition, the Silhouette Index and Davies–Bouldin Index performance measures were used to determine the number of clusters. Based on both indices and the literature review, it was decided that the data set should be grouped into two clusters. After determining the number of clusters, comments were made according to the cluster groups. In the final part of the analysis, the K-means method was used to identify differences between clusters in the context of labor market indicators. ANOVA analysis was conducted to identify differences between clusters. Following these results, the study was completed by presenting various conclusions that explain the quantitative findings with qualitative predictions through scatter plots, providing a holistic perspective on all the results.

5. Findings

As the first stage of the analysis, there are three methods for determining the number of clusters: distance coefficients, dendrograms, and clustering table coefficients. Since Ward’s method was preferred among the hierarchical clustering methods in this study, square Euclidean values should be calculated as the distance measure [66].

$$d_{ij}=\sqrt[2]{{\sum }_{k=1}^p{\left(x_{ik}-x_{jk}\right)}^2}$$

(1)

The variable p in the equation represents the Euclidean distance between observations “i” and “j”. ${\mathit{x}}_{\mathit{i}\mathit{k}}$ represents the k variable value of observation “i”. “$\mathit{p}$” represents the number of variables [66].

$$d_{ij}={\sum }_{k=1}^p{\left(x_{ik}-x_{jk}\right)}^2$$

(2)

Equation (2) is the Euclidean square distance measure. By applying the data set to the formula shown in this equation, the values in

Table 2. Square Euclidean distance matrix of EU countries and Turkey according to basic youth labor force indicators.

COUNTRY	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24	25	26	27	28	29
1: EU (27)	0.00	4.36	11.04	10.22	18.78	14.62	2.35	9.14	23.28	21.22	1.45	5.59	15.88	2.45	2.26	6.63	4.11	2.88	9.45	55.28	13.00	3.91	4.59	23.46	3.34	8.54	6.27	16.34	39.55
2: Belgium	4.36	0.00	8.40	10.24	34.96	28.37	7.27	19.60	17.18	16.41	6.99	4.15	13.46	9.65	1.87	11.81	3.77	3.29	22.20	76.21	29.13	2.59	2.20	23.99	5.15	3.34	15.51	22.13	59.80
3: Bulgaria	11.04	8.40	0.00	15.21	55.88	40.26	16.10	36.29	19.15	27.00	13.68	7.27	11.26	14.75	8.43	6.76	11.37	3.52	35.05	109.38	43.62	7.68	12.70	13.74	10.61	7.38	33.39	50.51	45.25
4: Czech Rep.	10.22	10.24	15.21	0.00	31.70	18.00	20.55	16.56	44.21	44.98	18.26	16.89	34.61	20.09	5.71	23.44	13.71	7.15	18.31	64.38	24.53	3.67	17.21	43.44	3.34	19.26	21.99	33.15	65.94
5: Denmark	18.78	34.96	55.88	31.70	0.00	5.11	20.17	2.89	74.68	62.22	21.19	42.01	63.66	21.37	25.85	37.80	32.60	33.57	3.38	11.63	1.48	29.83	33.98	75.75	22.79	49.50	5.60	13.75	65.87
6: Germany	14.62	28.37	40.26	18.00	5.11	0.00	20.81	2.45	74.19	67.66	20.58	37.07	60.01	20.91	17.18	30.17	29.59	23.00	1.47	19.80	2.72	18.79	32.94	69.65	12.16	43.70	10.39	25.47	64.26
7: Estonia	2.35	7.27	16.10	20.55	20.17	20.81	0.00	12.51	20.20	16.18	0.60	7.23	14.35	2.05	6.74	6.52	6.88	8.24	13.00	59.59	15.70	10.33	4.70	23.18	10.13	9.43	5.55	14.22	40.44
8: Ireland	9.14	19.60	36.29	16.56	2.89	2.45	12.51	0.00	57.78	49.39	13.15	27.36	47.67	14.27	12.31	26.06	19.89	18.70	1.15	20.73	2.11	14.65	21.36	59.71	9.87	32.95	3.36	12.68	62.21
9: Greece	23.28	17.18	19.15	44.21	74.68	74.19	20.20	57.78	0.00	2.95	18.58	7.49	1.86	20.41	26.94	20.55	12.32	20.81	60.59	136.99	65.42	28.58	10.33	7.44	35.51	7.86	40.53	42.77	46.98
10: Spain	21.22	16.41	27.00	44.98	62.22	67.66	16.18	49.39	2.95	0.00	15.50	9.20	6.01	17.44	26.13	23.30	12.06	23.43	52.92	117.92	56.48	29.52	7.67	14.51	35.30	8.53	30.96	28.23	51.91
11: France	1.45	6.99	13.68	18.26	21.19	20.58	0.60	13.15	18.58	15.50	0.00	5.41	12.13	0.78	6.18	5.12	5.34	6.14	12.97	61.62	15.51	9.22	4.27	18.98	8.94	8.09	6.42	15.44	33.61
12: Croatia	5.59	4.15	7.27	16.89	42.01	37.07	7.23	27.36	7.49	9.20	5.41	0.00	3.86	7.42	7.31	8.88	1.92	4.40	29.22	90.63	33.53	7.81	2.22	10.94	11.14	2.24	19.37	26.23	39.12
13: Italy	15.88	13.46	11.26	34.61	63.66	60.01	14.35	47.67	1.86	6.01	12.13	3.86	0.00	13.41	19.73	11.43	9.15	13.20	48.49	123.90	53.04	21.11	8.36	3.61	26.30	5.26	33.78	41.03	34.88
14: Cyprus	2.45	9.65	14.75	20.09	21.37	20.91	2.05	14.27	20.41	17.44	0.78	7.42	13.41	0.00	8.25	4.68	6.71	6.91	12.91	62.15	15.46	11.57	6.46	17.40	10.62	10.33	7.24	17.14	27.67
15: Latvia	2.26	1.87	8.43	5.71	25.85	17.18	6.74	12.31	26.94	26.13	6.18	7.31	19.73	8.25	0.00	9.91	6.25	1.99	13.33	62.25	19.75	0.63	5.85	29.16	1.06	7.66	12.04	22.70	56.04
16: Lithuania	6.63	11.81	6.76	23.44	37.80	30.17	6.52	26.06	20.55	23.30	5.12	8.88	11.43	4.68	9.91	0.00	12.54	6.32	22.52	87.14	27.99	12.66	11.72	12.72	12.81	9.92	19.96	37.45	29.16
17: Luxembourg	4.41	3.77	11.37	13.71	32.60	29.59	6.88	19.89	12.32	12.06	5.34	1.92	9.15	6.71	6.25	12.54	0.00	4.53	22.89	74.72	26.38	6.49	1.98	17.11	8.75	5.75	13.56	18.39	41.61
18: Hungary	2.88	3.29	3.52	7.15	33.57	23.00	8.24	18.70	20.81	23.43	6.14	4.40	13.20	6.91	1.99	6.32	4.53	0.00	18.57	76.24	24.83	1.87	6.26	18.25	2.87	6.06	17.05	29.76	41.97
19: Malta	9.45	22.20	35.05	18.31	3.38	1.47	13.00	1.15	60.59	52.92	12.97	29.22	48.49	12.91	13.33	22.52	22.89	18.57	0.00	22.40	1.34	16.21	24.25	57.45	10.49	34.31	4.83	17.49	55.17
20: Netherlands	55.28	76.21	109.38	64.38	11.63	19.80	59.59	20.73	136.99	117.92	61.62	90.63	123.90	62.15	62.25	87.14	74.72	76.24	22.40	0.00	18.31	66.30	77.45	141.61	55.35	100.65	30.69	36.69	120.11
21: Austria	13.00	29.13	43.62	24.53	1.48	2.72	15.70	2.11	65.42	56.48	15.51	33.53	53.04	15.46	19.75	27.99	26.38	24.83	1.34	18.31	0.00	22.83	28.85	62.39	16.32	41.29	5.13	16.53	52.03
22: Poland	3.91	2.59	7.68	3.67	29.83	18.79	10.33	14.65	28.58	29.52	9.22	7.81	21.11	11.57	0.63	12.66	6.49	1.87	16.21	66.30	22.83	0.00	7.68	30.52	0.81	9.07	15.94	27.09	58.32
23: Portugal	4.59	2.20	12.70	17.21	33.98	32.94	4.70	21.36	10.33	7.67	4.27	2.22	8.36	6.46	5.85	11.72	1.98	6.26	24.25	77.45	28.85	7.68	0.00	18.33	10.47	2.62	12.83	15.93	49.69
24: Romania	23.46	23.99	13.74	43.44	75.75	69.65	23.18	59.71	7.44	14.51	18.98	10.94	3.61	17.40	29.16	12.72	17.11	18.25	57.45	141.61	62.39	30.52	18.33	0.00	35.00	12.87	45.07	56.52	23.44
25: Slovenia	3.34	5.15	10.61	3.34	22.79	12.16	10.13	9.87	35.51	35.30	8.94	11.14	26.30	10.62	1.06	12.81	8.75	2.87	10.49	55.35	16.32	0.81	10.47	35.00	0.00	13.24	12.78	25.39	55.80
26: Slovakia	8.54	3.34	7.38	19.26	49.50	43.70	9.43	32.95	7.86	8.53	8.09	2.24	5.26	10.33	7.66	9.92	5.75	6.06	34.31	100.65	41.29	9.07	2.62	12.87	13.24	0.00	23.84	29.83	50.93
27: Finland	6.27	15.51	33.39	21.99	5.60	10.39	5.55	3.36	40.53	30.96	6.42	19.37	33.78	7.24	12.04	19.96	13.56	17.05	4.83	30.69	5.13	15.94	12.83	45.07	12.78	23.84	0.00	5.27	51.21
28: Sweden	16.34	22.13	50.51	33.15	13.75	25.47	14.22	12.68	42.77	28.23	15.44	26.23	41.03	17.14	22.70	37.45	18.39	29.76	17.49	36.69	16.53	27.09	15.93	56.52	25.39	29.83	5.27	0.00	69.35
29: Türkiye	39.55	59.80	45.25	65.94	65.87	64.26	40.44	62.21	46.98	51.91	33.61	39.12	34.88	27.67	56.04	29.16	41.61	41.97	55.17	120.11	52.03	58.32	49.69	23.44	55.80	50.93	51.21	69.35	0.00

were obtained.

In Table 2, it can be seen that countries with the shortest distances in the Euclidean distance matrix are more similar to each other in terms of the young labor market. Accordingly, the basic labor force indicators of European Union countries and Turkey are closer to countries such as Romania, Cyprus, Lithuania, and Italy than to other European Union countries in terms of Euclidean distance. Therefore, it would not be incorrect to state that Turkey’s young labor market is similar to that of Eastern and Southern European countries. On the other hand, the fact that the square Euclidean distance is the farthest from countries like Sweden and Denmark indicates that Turkey diverges from Northern European countries in this regard. In general, it is possible to say that the reduction in geographical distances between countries leads to similar characteristics emerging in their labor markets. If a general comment is made regarding Turkey’s Euclidean square values, it can be understood from Table 2 that Turkey cannot be directly included in the clusters formed by the central countries of the European Union. Although it is geographically close to Eastern European countries, the long distances to other European Union countries differentiate Turkey in terms of its young labor market.

Another method used to determine the number of clusters is the dendrogram method. Based on the young labor force data set of European Union countries and Turkey, a dendrogram was prepared using threshold values of 5–10–15–20 and visualized as follows. The dendrogram method, created using the Ward linkage method, statistically rescales the distances between countries to reveal similarities and differences between clusters. The dendrogram shown in Figure 1 was created to make the similarities and differences between European Union countries and Turkey more clearly visible.

The dendrogram shows clusters of countries that are similar to each other within the 0–20 threshold values. In this analysis, as the similarity between countries increases, they are closer to 0 (zero), and as the similarity between countries decreases, they are farther apart, such as 20 units [67].

Figure 1 shows a dendrogram depicting the combined distances between countries within the 0–20 threshold values. When the threshold value reaches 20, it is understood that a single cluster encompasses all countries. In this case, the most efficient level for the data set to be used in the study must be selected. Upon closer examination of the figure, it is observed that when the threshold value is set to five distances, smaller and more numerous clusters are formed. While selecting smaller distances may allow for a more stringent definition of similarities between countries, the fact that countries form their own clusters is not an ideal situation for cluster analysis [63]. In line with these explanations, cluster analyses with two, three, four, and five clusters were performed within the threshold value range of 5–20, and the cluster proximity of each observation unit, the square Euclidean distance, and the cluster membership table were determined. In addition, the Silhouette Index was used to select the cluster pairs to be merged in the process of creating the clustering hierarchy [68]. In the two, three, four, and five-cluster analyses conducted within this scope, it was observed that the two-cluster situation exhibited a more consistent structure compared to other cluster numbers, with a Silhouette Index score of 0.415. Although the Silhouette score indicates an average performance, as shown in Figure 2, the most consistent result is obtained with a two-cluster separation.

On the other hand, the study did not limit itself to the Silhouette index but also used the Davies–Bouldin (DB) index. The DB index was calculated to be 0.843. This indicates that the most appropriate clustering for the data set is a two-cluster system according to both the Silhouette and DB clustering performance criteria. The performance scores in question indicate a reasonably acceptable situation in terms of the clusters being sufficiently compact and well-separated from one another [69,70,71].

Table 3. Cluster memberships.

Country Numbers	Countries	Included in	Distance
5	Danimarka	1	0.847
6	Almanya	1	1.914
8	İrlanda	1	0.953
19	Malta	1	1.321
20	Hollanda	1	3.907
21	Avusturya	1	1.090
27	Finlandiya	1	1.843
28	İsveç	1	3.350
1	AB (27)	2	1.552
2	Belçika	2	1.818
3	Bulgaristan	2	2.438
4	Çekya	2	3.727
7	Estonya	2	2.035
9	Yunanistan	2	3.423
10	İspanya	2	3.537
11	Fransa	2	1.586
12	Hırvatistan	2	1.069
13	İtalya	2	2.520
14	Kıbrıs	2	1.859
15	Letonya	2	2.088
16	Litvanya	2	2.186
17	Lüksemburg	2	1.478
18	Macaristan	2	1.441
22	Polonya	2	2.366
23	Portekiz	2	1.525
24	Romanya	2	3.461
25	Slovenya	2	2.714
26	Slovakya	2	1.716
29	Türkiye	2	5.959

below shows which countries belong to which cluster in the two-cluster structure decided upon in the calculations.

Table 3 shows which countries are integrated into which cluster. Distance indicates the distance of countries from the cluster center. According to this, 8 of the 29 observation units included in the study (Denmark, Germany, Ireland, Malta, the Netherlands, Austria, Finland, and Sweden) are included in the first cluster. The remaining countries, including Turkey, are part of the second cluster. This situation can be attributed to the socio-economic differences between the northern and southern countries of the European Union, as mentioned in the theoretical section. Similarly, Turkey’s position as the country furthest from the center among the countries in the second group can be interpreted as indicating that it shares certain similarities with the European Union countries in terms of the dynamics of its young labor market, but does not fully resemble them. Indeed, within the second cluster, the countries furthest from the center after Turkey are Romania (3.461), Spain (3.537), and Greece (4.423). In this sense, it can be stated that these countries, which are distinct from European Union countries, face similar young labor market issues as Turkey. This situation is also similar to the findings of Paşniçu et al.’s study in terms of geographical similarity [58].

Table 4. Initial and final cluster centers and Euclidean distance.

Variables	Initial Cluster Centers		Final Cluster Centers
	Cluster 1	Cluster 2	Cluster 1	Cluster 2
Youth Labour Force Male	2.24	−1.11	1.30	−0.49
Youth Labour Force Female	2.33	−1.24	1.39	−0.53
Youth Labour Force Total	2.35	−1.20	1.40	−0.53
Youth Unemployment Male	−1.41	1.05	−0.66	0.25
Youth Unemployment Female	−1.09	1.03	−0.63	0.24
Youth Unemployment Total	−1.31	1.10	−0.68	0.26
NEET Male	−2.07	1.48	−0.82	0.31
NEET Female	−1.23	2.02	−0.59	0.22
NEET Total	−1.67	1.93	−0.72	0.28
Youth Employment Male	3.07	−1.04	1.29	−0.49
Youth Employment Female	2.92	−1.29	1.35	−0.52
Youth Employment Total	3.05	−1.19	1.34	−0.51

shows the initial values with which the data were analyzed prior to the clustering analysis. According to this, the data were divided into two clusters. The initial averages for each variable in the observations are given. For example, the male youth unemployment rate was set at −1.41 in the first cluster and 1.05 in the second cluster. This shows the degree to which the clusters are separated from each other. The center distances shown indicate the distances from the cluster centers to the observation units, while the final cluster center values represent the arithmetic means of the variables across the clusters. In this context, when the clustering analysis is completed, it is appropriate to state that the countries in the first cluster are generally more positive in the labor market than those in the second cluster, based on the final average values of each cluster. For example, when examining the variable values related to the labor force participation rates of young people, the value is 1.40 in the first cluster and drops to −0.53 in the second cluster. On the other hand, when unemployment rates and NEET rates are evaluated, it has been statistically determined that the values of the countries in the second cluster are higher. This situation can undoubtedly be attributed to imbalances in the labor market. The central values of the final clusters summarize the profiles of the clusters and help to understand the differences between the two clusters more clearly. Indeed, this result reflects a similar situation in the literature, showing that northern countries have more balanced youth labor market dynamics compared to southern countries [25,27,31,59,72].

The results of the inter-cluster analysis are very valuable for social scientists. The statistical significance values of these results are also of great importance from a scientific point of view. For this reason, a normality test was first applied to the data to determine whether the differences between clusters were statistically significant. Consequently, the ANOVA test [73], a parametric test, was applied to identify whether there were statistically significant differences between the variables. The results of the ANOVA test are presented in

Table 5. ANOVA test results for labor indicators.

Variables	Cluster	N	Mean	Std. Error	F	Sig
Youth Labour Force Male	1	8	1.30	0.17	53.59	0.00
	2	21	−0.49	0.14
Youth Labour Force Female	1	8	1.39	0.16	88.59	0.00
	2	21	−0.53	0.11
Youth Labour Force Total	1	8	1.40	0.16	90.29	0.00
	2	21	−0.53	0.11
Youth Unemployment Male	1	8	−0.66	0.34	5.67	0.02
	2	21	0.25	0.20
Youth Unemployment Female	1	8	−0.63	0.28	5.08	0.03
	2	21	0.24	0.21
Youth Unemployment Total	1	8	−0.68	0.32	5.93	0.02
	2	21	0.26	0.20
NEET Male	1	8	−0.82	0.22	9.65	0.00
	2	21	0.31	0.21
NEET Female	1	8	−0.59	0.12	4.26	0.05
	2	21	0.22	0.24
NEET Total	1	8	−0.72	0.17	7.00	0.01
	2	21	0.28	0.22
Youth Employment Male	1	8	1.29	0.28	50.87	0.00
	2	21	−0.49	0.11
Youth Employment Female	1	8	1.35	0.24	69.85	0.00
	2	21	−0.52	0.10
Youth Employment Total	1	8	1.34	0.26	67.21	0.00
	2	21	−0.51	0.10

below.

When Table 5 is examined, it is determined that there are significant differences between the clusters formed according to the variables of the youth labor market based on the results of the ANOVA test. Accordingly, it has been concluded that there are statistically significant differences between the first and second clusters in all indicators related to the labor force participation rates, unemployment rates, NEET rates, and employment rates of young people. When examining the variables and subgroups related to young people’s labor force participation, unemployment, NEET, and employment rates, it is observed that there is a statistically significant difference between the values of countries in the first cluster and those in the second cluster. This situation can be interpreted as indicating that the labor market dynamics implemented by countries in the first cluster are better than those in the second cluster. In this regard, it would not be incorrect to state that the differences between the two clusters are significant and that the two clusters are clearly distinct. When evaluating Turkey, it is of interest to determine whether there is a notable divergence between Turkey and European Union countries in terms of youth unemployment and youth employment rates. To examine this situation closely, a scatter plot showing the total youth employment and unemployment rates in the observation units is presented in Figure 3.

When examined closely with a focus on Turkey, Figure 3 shows that youth employment rates are above the EU (27) average but below the average of countries such as the Netherlands, Austria, Denmark, and Ireland. On the other hand, although Turkey lags behind the EU (27) average in terms of youth unemployment rates, it would not be inaccurate to state that it is in a better position than many European Union countries, such as Spain, Italy, and Greece. However, within a broader framework, it should not be overlooked that Turkey, which is in the second group, clearly differs from EU countries in the first group in terms of youth labor market variables.

According to NEET rates, which indicate that young people are excluded from education and employment, Turkey and European Union countries are observed to diverge from one another. The NEET situation, which is one of the most significant issues in the labor market alongside unemployment, indicates that young people in Turkey face more negative outcomes in terms of employment or education compared to their peers in European Union countries. To examine this situation in detail, a scatter plot showing the gender breakdown of NEET rates among women and men is presented in Figure 4.

As shown in Figure 4, Turkey stands out completely from other European countries in terms of both female and male NEET rates, occupying a significantly separate negative position. This situation not only highlights that the fundamental issue in Turkey’s youth labor market is the NEET problem but also suggests that the NEET issue will be a key challenge for Turkey’s future convergence with the EU. Additionally, to conduct a comprehensive analysis, Figure 5 presents a scatter plot matrix incorporating all relevant indicators.

As can be seen in Figure 5, although Turkey is in the second group, its labor market differs significantly from many European Union countries in terms of its internal dynamics. Gender differences are particularly striking in Turkey’s young labor market. Accordingly, the most dramatic differences are seen in labor force participation rates, unemployment rates, NEET rates, and finally, employment rates. In this context, when compared to the EU, labor force participation among young men is above the EU average, while labor force participation among young women is below the EU average. No significant difference is observed in the unemployment rate among young men, but a significant difference is observed in the unemployment rate among young women. The most dramatic finding is that the NEET rate is high among both men and women. Based on all these observations and statistical results, it is evident that Turkey’s youth labor market is significantly diverging from EU countries in terms of education and employment integration, and that the most fundamental convergence issue in the future will occur within the NEET framework.

6. Discussion

The clustering analysis conducted has divided European Union countries and Turkey into two clusters in the context of youth labor market variables. Countries included in the first cluster have better labor market dynamics than those in the second cluster. The results of the clustering analysis indicate that the similarities and differences between the two groups of countries in terms of socio-economic characteristics are statistically supported in line with the literature [25,58].

As can be seen in the “Cluster Table,” Turkey is in the second cluster. Although there are other European Union countries similar to this cluster, it would not be wrong to say that Greece is the closest similar country [58]. When looking at all countries in the data set, this situation essentially reveals that the labor market dynamics of northern European countries are more balanced than those of southern European countries. Indeed, Turkey’s distance from the center of the second group, along with countries like Greece, Romania, and Spain, places these countries as the farthest from the center after Turkey. This situation generally reveals that, when looking at all countries in the data set, Northern European countries have more balanced labor market dynamics than Southern European countries [25,27,31,59,72]. Indeed, Turkey’s distance from the center of the second cluster, along with countries such as Greece, Romania, and Spain, places them as the countries furthest from the center after Turkey.

It is believed that one of the main reasons for this negative picture during Turkey’s candidacy process was the irregularities in the stability of the education–employment pattern. Indeed, Ryan’s 2001 study also concluded that the education model in developed economies more clearly defines the education–employment pattern [50]. This situation is seen as the most important reason for the low youth unemployment rates in countries such as Germany, which are included in the group of northern countries in the data set used in the study [51]. Similar to Gökbayrak’s study, this study also shows that in countries in the second group, such as Turkey, the weakness of the education–employment pattern and the mismatch between the labor market and education result in higher youth unemployment rates [45]. Another important reason is the need for structural reforms in labor markets in line with new skills. In this case, one of the common problems of the countries in the second group can be expressed as low rates of women’s participation in employment and high rates of informal employment. Countries in this group, and particularly Turkey, must implement policies to increase women’s labor force participation rates and carry out labor market activities aimed at reducing informality as part of their economic convergence process with the European Union [24]. In general, it is understood that Turkey is similar to countries such as Romania, Greece, and Spain in terms of young labor market indicators. The underlying causes of this similarity include economic fragility and the impact of crises, as well as issues such as the lack of alignment between education and employment, the structure of the labor market, the ineffective operation of the flexible labor market system, the problem of informal employment, and cultural and geographical similarities.

7. Conclusions

The European Union, established to maintain and improve the welfare levels of countries in Europe, has had bilateral relations with Turkey since the 1950s. The process of joining the European Union is one of the highest priority areas for developing countries such as Turkey, in line with the sustainable development goals.

On the other hand, Turkey’s geographical proximity and socio-economic similarities to the European Union have also been effective in bringing Turkey closer to the European Union countries over the years. Full membership in the European Union requires Turkey to resemble European Union member countries in certain respects.

Indeed, certain socio-economic criteria have been established for this convergence, and full compliance with these criteria has been demanded. It is observed that Turkey has made intensive efforts to comply with these criteria and to achieve rapid development in line with the sustainable development goals. However, it is understood that these efforts have sometimes been hampered by certain problems experienced on a global scale. Therefore, it is important to know the extent to which Turkey’s policies targeting young people in the labor market are compatible with those of European Union countries in line with the sustainable development goals. Based on this point, the purpose of this study is to reveal which European Union country or countries Turkey resembles or differs from in terms of young labor force indicators through cluster analysis.

According to the study’s findings, Turkey is in a more extreme position, with more pronounced differences from EU countries, and its labor market is more problematic compared to those of these countries. Additionally, when examining the average values of the second cluster in which Turkey is located:

• Youth Unemployment Rate: In the group that Turkey belongs to, the average youth unemployment rate for men is 37.79%, and for women it is 29.97%. However, it has been determined that youth unemployment rates in Turkey, especially for women, are quite high compared to European Union countries. While the female unemployment rate in Turkey stands at a high 23.4%, the EU average is significantly below this level.
• NEET (Young People Not in Education, Employment, or Training): Turkey stands out in terms of its negative NEET rate. While the average NEET rate for the second group is 10.15%, Turkey’s rate is approximately twice as high at 22.4%. This indicates that the proportion of Turkey’s young population not in education or employment is twice as high as the average for European Union countries.
• Temporary Employment Rate: Turkey’s temporary employment rate among young people is also high, indicating that Turkey’s young workforce is at greater risk in terms of job insecurity when compared to European Union countries.
• Dendrogram graph: Turkey is seen to be in a group with France, Cyprus, Estonia, Hungary, and Poland. However, when the distances in the dendrogram are examined, Turkey’s distance from these countries is calculated to be higher. Therefore, this situation shows that Turkey has a different profile among European Union countries in terms of the young labor market. Although Turkey is grouped with European Union countries that have medium to low performance in the dendrogram, it has been concluded that Turkey’s labor market structure is structurally very different from those of these countries.

Turkey can be said to be at a disadvantage when compared to European Union countries in terms of young labor market data. Considering this disadvantageous situation in the labor market in terms of the density of the young population in Turkey, it is clear that lessons should be learned from the policies of countries in Group 1, such as Germany and Denmark. At this point, establishing a system similar to the “Youth Guarantee” system mentioned in Caliendo and Schmidl’s 2016 study could have positive effects on Turkey’s young labor market [74]. The system aims to facilitate young people’s adaptation to the labor market. However, the economic challenges such a system would create within the budget and the differences in the social welfare state structures between Turkey and Denmark make it clear that the system cannot be directly implemented in Turkey [74]. As stated in Eichhorst et al.’s 2015 study, adapting the dual vocational education system, which is jointly run by employers and the state, seems to be more appropriate for solving problems in the labor market, especially among young people [75]. At this point, the most important issue to consider is not the direct adaptation of the application to Turkey, but the restructuring of the policy in accordance with the requirements of Turkey’s labor market structure. Compared to the European Union averages, youth unemployment rates in Turkey are particularly high among women, and the labor force participation rates of young people are also observed to be quite low. At the same time, a significant portion of Turkey’s young population is classified as NEET. The high rate of NEET should be considered one of the most important social policy issues that Turkey needs to address, given its long-term consequences. Indeed, adapting this system may not be effective in directly reducing NEET rates. This is because the high level of socio-economic inequality in Turkey has been observed to reduce young people’s interest in vocational education [76,77]. At the same time, the problem of informal employment in developing countries such as Turkey also hinders the effective functioning of active labor market policies. Therefore, in order to increase the success of these policies, it is necessary to reduce informality first. In other words, rather than directly adopting the policies of the first-tier countries that demonstrate good examples, it is necessary to implement these policies alongside complementary and supportive policy measures [78].

When evaluated within the general scope of the research, Turkey shows similarities with European Union countries, but also reveals certain differences. Turkey’s process of harmonization with the European Union reveals a number of problems, particularly under the theme of sustainability. Among the sustainability themes, the most significant issue is the lack of gender equality in the sub-themes of quality education and employment, which are essential for achieving the education–employment pattern. This situation is expected to lead to complex sustainability issues in the future, including health, quality of life, and poverty problems among young people in Turkey. Future studies using both cross-sectional and longitudinal methods will enable panel data analysis, which will provide a more detailed picture of the research due to the availability of data sets covering many countries. This will allow for a more detailed examination of the problems in the youth labor market, which is one of the most important labor issues for countries, and will be effective in the development of more sustainable policies specific to each country.