Crisis and Youth Inactivity: Central and Eastern Europe during the Financial Crisis of 2008 and the COVID-19 Outbreak of 2020 ^†

Abstract

This paper analyzes eleven Central and Eastern European countries after the financial crisis of 2008 and the COVID-19 pandemic of 2020. It investigates the heterogeneity in the labor market among the selected countries based on the youth inactivity, secondary education attainment, and income share of the bottom fifty percent of the population. A hierarchical cluster analysis with Ward’s method and k-means clustering generated diverse cluster solutions. A comparative analysis of the four-cluster solutions for 2008 and 2020 showed multiple changes in the cluster composition. The joint groupings of geographically and historically close countries, such as the Baltics, the former Czechoslovakia, and the former Yugoslav republics of Croatia and Slovenia, were identified for 2008. Lithuania emerged as a singleton in 2020. The youth inactivity, educational levels, and income inequality reveal the status of the youth in Central and Eastern Europe during these crises.

1. Introduction

Financial and economic crises usually expose hidden social problems. In addition to an overall decline in the economic activity and the growth in unemployment or inflation, problems that are emphasized in the academic literature, other challenges often emerge. A cohort of young people, Not in Employment, Education, or Training (NEET), always persists in times of economic boom and recession. The Organization for Economic Co-operation and Development (OECD) warns that the majority of the NEET population is made up of people who do not want to work [1]. The NEET population is diverse: many who do not work do so for health reasons, are pursuing alternative careers, or are discouraged [2]. Income inequality raises the ubiquitous phenomenon of social polarization.

Unemployment, educational deficiency, and income inequality motivate a closer examination of the inactive youth in Central and Eastern Europe (CEE). This paper explores the similarities and differences among eleven countries in the CEE region in 2008, the first year of the global financial crisis, and in 2020, when the COVID-19 pandemic broke out.

2. Data and Methods

Our methodological approach relies on cluster analysis of labor-related data in eleven CEE countries (Bulgaria, Croatia, Estonia, Czechia, Hungary, Latvia, Lithuania, Poland, Romania, Slovakia, and Slovenia) at two separate points in time, 2008 and 2020.

Applying cluster analysis, similar objects are grouped into homogeneous, mutually heterogeneous groups. Since cluster analysis sorts similar objects into clusters, it is required to apply similarity measures to see how similar these objects are. In this case, the distance measure is used as a similarity measure, and the most commonly used distance measures are Euclidean distance and squared Euclidean distance. There are different methods of cluster analysis, and they are divided into hierarchical and non-hierarchical methods. Hierarchical cluster analysis begins with each object in a separate cluster until all objects are finally found in one cluster. It is important to note that, once a cluster is formed, it is not separated, but other objects are added to that cluster, and the number of clusters is determined based on the dendrogram. Ward’s method with squared Euclidean distances is the most commonly used hierarchical cluster analysis method. For non-hierarchical cluster analysis, the k-means method is most often used, and it is necessary to know the number of clusters before conducting the analysis. The number of clusters found by hierarchical cluster analysis and dendrogram can be used in non-hierarchical analysis [3]. Therefore, both methods have disadvantages that are reduced by applying both methods.

We combined hierarchical and non-hierarchical approaches: Ward’s method and k-means method. For determining the final number of clusters, we applied commonly used stopping rules such as the Caliński–Harabasz Pseudo F and the Duda–Hart Je(2)/Je(1) indices with pseudo t-squared values [4,5]. In order to preserve some level of complexity, we picked the final number of clusters based on both stopping rules and the visual inspection of dendrograms.

The analysis was conducted for two years: 2008 and 2020. The year 2008 marked the beginning of the global financial crisis, and the COVID-19 crisis began in 2020. We analyzed the following three variables: the NEET (Not in Employment, Education, or Training) inactive rate, which is one pole of the disaggregated NEET rate; secondary educational attainment; and the income share of the bottom 50 percent of the population. Two cluster analyses were performed for 2008 and 2020 on the eleven European Union member-states in Central and Eastern Europe. In both cases, we conducted hierarchical and non-hierarchical cluster analyses.

3. Results

For 2008, we first applied hierarchical clustering analysis. The dendrogram obtained by Ward’s method with squared Euclidean distances shown in Figure 1 indicates a few possible clustering solutions: from two to four clusters. Based on the cluster analysis stopping rules, we chose a solution with four clusters:

• Bulgaria, Estonia, Latvia, and Lithuania;
• Czechia and Slovakia;
• Croatia and Slovenia;
• Hungary, Poland, and Romania.

Second, we performed a non-hierarchical clustering analysis with the k-means method. We obtained the same four clusters as those reported via hierarchical clustering by Ward’s method.

For 2020, we also began with hierarchical clustering. The dendrogram obtained by Ward’s method with the squared Euclidean distances shown in Figure 2 also indicates solutions from two to four clusters. We ultimately chose a solution with four clusters:

• Bulgaria, Poland, and Romania;
• Estonia, Latvia, and Slovenia;
• Czechia, Croatia, Hungary, and Slovakia;
• Lithuania.

We again performed non-hierarchical clustering with the k-means method. Again, k-means clustering delivered the same cluster structure as hierarchical clustering with Ward’s method.

4. Discussion

Between 2008 and 2020, the European Union (EU) introduced several programs to address the dynamic challenges faced by youth in a changing economic landscape. The EU placed a particular focus on combating the rise in youth inactivity and unemployment following the global financial crisis and the outbreak of the COVID-19 pandemic. The largest launch of financial support occurred in 2013 when the EU approved the new European Social Fund framework [6]. The EU implemented the Youth Employment Initiative [7] and Youth Guarantee program [8] with targeted funding for the period from 2014 to 2020. These eleven CEE countries, as EU members, developed and used national programs and policies targeting the youth labor market.

The justifications for the four-cluster solutions can be found in both the geographical proximity of the countries and the policy similarities for 2008, the EU initiatives for the period in between, and the national and regional challenges in the wake of the COVID-19 shock in 2020. Interestingly, all the CEE countries experienced a decrease in secondary education attainment between 2008 and 2020. The income share of the bottom 50 percent of the population was the lowest in Lithuania (only 7.5 percent). By this measure, Lithuania experienced the greatest degree of income inequality. Bulgaria, Czechia, Slovakia, Hungary, and Romania exhibited the highest rates of youth inactivity, while Slovenia reported the opposite.

5. Conclusions

Two snapshots of Central and Eastern Europe show an interesting change in the cluster composition from 2008 to 2020. The global financial crisis yielded clusters that are defined by political and territorial ties, such as the grouping of former Czechoslovakia, the grouping of the two former Yugoslav countries, Croatia and Slovenia, and the Baltic countries.

The COVID-19 pandemic delivered a different set of clusters. The comparative analysis revealed more details. Croatia and Hungary joined the two halves of former Czechoslovakia, while Lithuania stood out relative to its fellow Baltic countries. Instead, Estonia and Latvia were joined by Slovenia. Lithuania is the country that faced the highest income inequality in the entire CEE region in 2020. That distinction may justify the placement of Lithuania in a cluster of its own.

Inactive youth are a heterogeneous category within the total NEET population. Young individuals do not participate in the labor market for many different reasons. In future research studies, it would be useful to analyze the heterogeneity of the NEET-inactive population in Central and Eastern Europe. CEE countries face challenges common to the region as well as challenges unique to each country. Active labor market policies, along with the implementation of educational and social reforms, are intended to integrate young people into the labor market. Youth Guarantee and Reinforced Youth Guarantee represent merely the first steps in the fight against the long-term exclusion of young people from the labor market.