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Article

The Effect of Digitalization on Youth Unemployment for EU Countries: Treat or Threat?

by
Oğuz Başol
1,*,
Hüseyin Sevgi
1 and
Esin Cumhur Yalçın
2
1
Department of Labor Economics and Industrial Relations, Kırklareli University, Kırklareli 39100, Turkey
2
Department of Econometrics, Kırklareli University, Kırklareli 39100, Turkey
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(14), 11080; https://doi.org/10.3390/su151411080
Submission received: 3 May 2023 / Revised: 18 June 2023 / Accepted: 12 July 2023 / Published: 15 July 2023
(This article belongs to the Special Issue Enhancing Sustainable Relationships)
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Versions Notes

Abstract

:
Unemployment affects approximately 73 million young people, representing 17.9% of the global youth workforce in 2022. Although there are several policies to prevent youth unemployment, digitalization seems to be one of the strongest. This research focuses on the impact of the Digital Economy and Society Index (DESI) on youth unemployment in 27 European countries from 2018 to 2021. For the research, the “Digital Economy and Society Index” was measured with four sub-dimensions. These were “connectivity”, “digital public services”, “human capital”, and “digital technology integration”. Additionally, “Youth Unemployment” was measured with three sub-dimensions. These included the “long-term youth unemployment rate”, “not in education, employment, or training rate”, and “youth unemployment rate”. The analyses were conducted using SmartPLS 4 and the results showed that the DESI reduces youth unemployment each year. According to the findings, the indicators of DESI can be an effective strategy for combating youth unemployment in European countries.

1. Introduction

In recent years, many countries have faced various labor market problems. One of the most important of these problems is undoubtedly youth unemployment. Rapid changes in labor markets in parallel with technological development, the lack of education, domestic responsibilities, illness, discouragement, and the inability to create new jobs are among the main causes of youth unemployment. However, if the youth unemployment problem is not solved in the short or medium term, it will turn into a permanent and much more difficult problem for the economy. Youth unemployment becomes long-term unemployment after 12 months and has a major impact on the economy, society, and on the psyche of individuals. Although many measures are being taken to prevent youth unemployment, the current picture shows that the problem is due to a structural problem that goes beyond the level of development of countries. Therefore, regardless of the country’s level of development, the problem of youth unemployment is directly related to the policies implemented by the country.
Today, the stages of economic and social digitalization in the world give hope for a solution to the problem of youth unemployment, because, in many other countries, the digitalization of the economy offers new opportunities to solve youth unemployment. In this context, one of the most important discussions in the literature is the measurement of digitalization, as it is also worth noting that there is no single meaning for measuring digitalization in the literature. However, some of the most popular tools for measuring digitalization are the “Enabling Digitalization Index—EDI” developed by Euler Hermes [1], the “Digital Adoption Index—DAI” developed by the World Bank [2], “The Digitalization Index—DiGiX” developed by BBVA Research [3], and the “Digital Economy and Society Index—DESI” developed by the European Commission [4]. EDI measures the ability of countries to help digital companies to thrive and traditional businesses to harness the digital dividend [1]. DAI is a worldwide index that measures countries’ digital adoption across three dimensions of the economy, people, government, and business [2]. DiGiX assesses the factors, agents’ behavior, and institutions that enable a country to fully leverage information and communication technologies (ICTs) for increased competitiveness and wellbeing [3]. In this study, we preferred DESI for measuring digitalization, as it not only provides a clear overview of the data collected over the last six years but also measures digitalization in economic and social terms. Additionally, the Digital Economy and Society Index (DESI) helps to identify these opportunities and the degree of digitalization within the European Union countries.
The debate about the impact of digitalization policies on youth unemployment is largely ongoing. Some authors argue that digitalization will reduce youth unemployment [5,6,7,8,9,10,11,12,13], while others claim that digitalization increases youth unemployment [10,14,15]. The motivation for preparing this research is primarily based on two reasons: Firstly, the impact of digitalization on youth unemployment continues to be debated in the literature. Researchers, on the other hand, try to highlight studies that support their ideas. This study aims to provide a framework for presenting data-based evidence from the researchers’ own echo chambers. Secondly, DESI data and SmartPLS were not used in a literature review to measure the impact of digitalization on youth unemployment. Therefore, there is a wide gap in the literature, which this study aims to fill. This research focuses on the following questions: “How did the DESI index impact youth unemployment in EU countries between 2018 and 2021?” In this context, the next part of the study describes the Digital Economy and Society Index (DESI), the third part describes the concept of youth unemployment and digitalization, the fourth part the method and data, and, finally, there is a discussion part.

2. The Digital Economy and Society Index

The Digital Economy and Society Index (DESI) is an integrated index created to measure the digital competitiveness of European Union countries and monitor their overall digital performance. The main objective of DESI is to identify the areas in each European Union country that need investment in regard to the economic and social digitalization process. For this reason, DESI measures countries’ digital performance with four basic sub-dimensions and almost 30 indicators [16].
These four dimensions are connectivity, human capital, digital infrastructure, the integration of digital technology, and digital public services, and they are not isolated from each other but are directly related. These dimensions consist of more than one sub-dimension and indicator [17].
The connectivity sub-dimension focuses on the demand and supply aspects of fixed and mobile broadband Internet. Fixed broadband considers the availability of general and ultrafast broadband, fast broadband, and very high-capacity fixed networks. Mobile broadband includes 4G coverage, mobile broadband usage, and 5G readiness. These dimensions also take into account the prices offered to consumers, as connectivity is recognized as a social right in the EU [18].
The human capital sub-dimension focuses on the impact of digital transformation in the economy and society on the labor market and workforce. In this context, the digital skills required in the workplace have become more advanced, and companies and institutions expect most of their employees to have these skills. As reliance on the Internet and digital technology increases, the workforce must keep pace with evolving skill requirements [19].
The integration of digital technology and digital infrastructure sub-dimensions has a structural approach toward digital and social transformation. This approach has two main elements in terms of the labor market and especially youth unemployment. The first is to increase employment by providing structural opportunities for new jobs created by new technologies. The second is to support new youth entrepreneurship in the digital transformation process. To this end, these two sub-dimensions consist of indicators such as broadband coverage, fixed broadband adoption, mobile broadband adoption, digital intensity index, cloud computing, big data, and artificial intelligence [20,21].
The sub-dimension of digital public services is aimed at the digitalization of public services and the introduction or improvement of e-government solutions. The EU strives to modernize and improve public administration processes to make them more user-friendly, citizen-centric, and interoperable. The objective is to promote access to and the use of digital public services by individuals and businesses. This dimension measures both the demand and the supply of digital public services and open data [22].

3. Youth Unemployment and Digitalization

The ILO and OECD define “youth” as people aged 15–24 [23,24] and the European Commission [25] describes “youth” as people aged 15–29 years. The minimum age for young people is generally based on the minimum age to leave school and start working, which can vary from country to country. In this context, young unemployed people are described as those who are unemployed, available for work, and who have actively looked for a job in the past four weeks. The youth unemployment rate is the percentage of unemployed youth in the youth labor force, and youth unemployment is widely recognized as a major policy issue for several countries, regardless of their level of development [24].
According to the ILO’s Global Employment Trends for Youth, between 2019 and 2020, youth aged 15–24 years experienced a much higher percentage of job loss than adults (defined as those aged 25 and over). Many of them have dropped out of the labor market or have not re-entered it at all due to enormous difficulties in finding and securing employment, while many governments have imposed lockdowns, which has caused employers to suffer massive revenue losses due to plant closures. The global youth employment deficit was 8.2% in 2020, while the corresponding adult deficit was less than half. The potential labor force, which includes young people who are not in the labor force but have weak attachments to the labor market, also increased by 7 million [23].
Youth unemployment can arise for unique reasons, alongside broader labor market issues. In recent years, the rapid transformation of labor markets with technological developments, the change in the way jobs are filled, and the emergence of new jobs are among the main factors affecting youth unemployment. On the other hand, individual reasons, such as opportunities for young people to benefit from education, worries about the future, and market expectations also fuel youth unemployment [26,27].
The main risk of youth unemployment occurs when long-term unemployment becomes a permanent problem. If young people have been unemployed and have been actively looking for work for at least one year, their unemployment turns into long-term unemployment [28]. Long-term unemployment is a serious and dramatic problem for all economies, regardless of their level of development [29]. It profoundly affects the economy, society, and individuals [30,31,32,33,34].
Another fact is that young people’s school-to-work transition is becoming more complex, and time-consuming, and it increases the likelihood of young people leaving the labor market, which makes it difficult to monitor their situation with traditional labor market indicators. The concept of youth unemployment is insufficient to explain the vulnerable situation of young people in transitioning to the labor market; therefore, alternative concepts for those young people who have left both work and education and are at high social risk need to be proposed in the literature. In this sense, the concept of NEET (youth not employed, educated, or trained) fills this gap [35].
Broader than youth unemployment, NEET is a more comprehensive account of youth labor market entry. NEETs include disaffected young workers who are economically inactive due to disability or household chores but exclude young people from education/training, thus preventing young people from entering the labor market. The concept of NEET is related to its perceived potential to address a wide range of youth vulnerabilities, touching on issues such as unemployment, early school dropouts, and labor market discouragement [36]. According to the ILO, NEETs and youth unemployment are common names for the same major problem. It is also seen as inevitable that improving youth employment will be different in low- and middle-income countries than in high-income countries. However, with the right investments in digital technologies, many young people will be able to enter the labor market. In the ILO’s Global Employment Trends for Youth report, widespread broadband coverage will create 24 million new jobs worldwide by 2030. About 6.4 millions of these new jobs will be for young people [23]. Given the self-renewing rate of technological developments, increased investments in digital technology and engaging young workers in new technologies will be effective in solving youth unemployment in the coming years [37].
The problem of youth unemployment is not so much isolated from the ongoing transformations of society and the economy as it is a multidimensional problem. Therefore, digitalization, which has initiated the transformation of the social and economic fabric in recent years, also bodes well for youth unemployment. In this context, digitalization is one of the main drivers of technological change and new opportunities, providing unprecedented access to knowledge [38,39,40].
As digitalization leads to radical changes in the structure of labor markets, the literature assumes that it will lead to a decline in employment [41,42,43,44]. Despite these views, it is widely believed that new technologies will create new jobs and increase employment, especially in the medium to long term [10,11]. In fact, with the intensive use of digital technologies in recent years, new jobs and new labor markets have emerged that did not exist before. The digitalization of the economy and society has created new jobs while increasing worker productivity. Many new jobs that have emerged on the axis of new technologies have started to create new fields of employment, especially for young people, and have played a key role in solving youth unemployment [45,46,47,48].
Fossen and Sorgner [49] categorize digitalization as either destructive or transformative. Destructive digitalization, also referred to as computerization or automatization, is the automation of certain tasks and operations that substitute machines for workers. Contrarily, transformative digitalization describes the circumstances in which ICT tools enable worker contact and digital technologies raise labor productivity [50,51]. Today, a transformative framework is emerging around the relationship between technology and youth unemployment in labor markets that are rapidly going digital. In order to address the issue of youth unemployment, it is crucial to improve young people’s access to digital technologies.
Digital technology plays an important role in creating innovative professionals and solving the problem of youth unemployment. Many technological developments based on information and communication technologies (ICT), such as the Internet of things, artificial intelligence, and cloud technology, are reshaping the labor market and strengthening the position of young people in the labor market [5]. Research by Rocheska et al. [6] argues that changes in labor markets due to the development of ICT offer numerous job opportunities for young people. Today, digital technologies support young people to have access to a wider job market. In their study, Myovella et al. [52] concluded that digitalization contributes positively to economic growth regardless of the level of development of a country. Metu et al. [7] examined the role of ICT development in addressing youth unemployment in Sub-Saharan African countries, and the findings show that the development and use of ICT reduce youth unemployment in the region. And, other research shows that digital transformation helps to reduce unemployment by providing employment opportunities in each sector [8,9,53]. Furthermore, the emerging platform economy increases the communication possibilities of digital technologies and helps young people to overcome the traditional boundaries of the labor market. Thus, traditional concepts of work and workplace are eroded, and young people can enter the labor market regardless of time and place. Moreover, the speed and flexibility afforded by digitalization facilitate various youth business ventures and pave the way for youth entrepreneurship [54,55]. Therefore, digitalization can be seen as a strategy to combat youth unemployment. Based on the abundant literature, the following hypothesis is drawn:
H1. 
A higher Digital Economy and Society Index reduced youth unemployment in EU countries over the period 2018–2021.

4. Method and Data

4.1. Partial Least Squares Structural Equation Modeling (PLS-SEM)

Partial least squares structural equation modeling (PLS-SEM) is a second-generation multivariate data analysis method commonly used in social science research because it can test theoretically supported nonlinear and additive causal models. PLS is a soft modeling approach for SEM, which makes no assumptions about the data distribution and is advantageous for small sample sizes [56,57]. It is possible to use PLS path modeling with highly skewed data [58]. The steps of the PLS-SEM algorithm are summarized as follows [59]:
Step 1: When applying the PLS algorithm, the distribution of the indicator variables is first standardized so that the mean is 0 and the standard deviation is 1. Thus, the latent variables (factors) in PLS are also standardized since they are obtained from linear combinations of indicator variables. As a result, both the measurement coefficients (external model) and the structural path coefficients (internal model) take values between −1 and 1.
Step 2: The indicator variables are used to create the X and Y component scores. An iterative process with four steps is used for this purpose: (a) Scores are assigned to the latent variables based on equally weighted indicator scores. (b) To maximize the coefficient of determination R2 of each independent latent variable, structural (internal) paths are assigned to link the latent variables based on regression. That is, the component values estimated at a given iteration are used to calculate the structural path weights. Using regression (with successive iterations), the structural weights are adjusted to maximize the correlation strength of successive pairs of X and Y component values, thereby maximizing the covariance of each X value with the Y variable. In this way, the explained variance in the dependent component is maximized. (c) Structural weights are used to adjust the estimates of the latent variable values. (d) Measurement weights (external weights) are estimated to link latent variables to indicator variables. These four iterative steps end when the (external) measurement weights of the indicator variables no longer change significantly. The indicator variable weights in the final iteration form the basis for calculating the final estimates of the latent variable scores. The latent variable scores, in turn, are used as the basis for OLS regressions to calculate the structural (internal) weights in the model. The (external) measurement weights linking the latent variables to the indicator variables are estimated.

4.2. Data

Table 1 shows the variables used in the analysis, their definitions, and the sources from which the data were obtained. The sample size is 27 (n = 27) and the study data cover the period 2018–2021. The selected 27 European countries in the model are Austria, Belgium, Bulgaria, Croatia, Cyprus, Czechia, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, the Netherlands, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, and Sweden.
Accordingly, we measured the Digital Economy Society Index using 4 sub-dimensions. These are “connectivity”, “digital public services”, “human capital”, and “digital technology integration”. As a second factor, we measured youth unemployment using 3 sub-dimensions. These are “long-term youth unemployment rate”, “not in employment, education or training rate” and “youth unemployment rate”. Table 2 contains descriptive statistics for the years 2018–2021 in regard to indicators.
Table 2 shows that the average values of CO, DPS, HC, and IDT, which are indicators of DESI based on the years in EU countries, have increased. When DESI was analyzed by year, Romania had the lowest index value in 2018, 2019, 2020, and 2021, while Finland had the highest index value in 2018, 2019, 2020, and Denmark had the highest DESI index value in 2021. When the values of the LYU were analyzed, the lowest value was observed in the Netherlands, except for in 2019. In 2019, Sweden had the lowest LYU value. The highest LYU value was observed in Greece. When analyzing NEET, the lowest value was observed in the Netherlands and the highest value in Italy across all years. When the YU values were analyzed by year, Czechia had the lowest YU value across all years, while Greece had the highest value.

5. Results

For SmartPLS 4.0, indicators and models need to be checked for reliability, validity, multicollinearity, the effect size of the model (f2), and the prediction power of the model (Q2) and R2. All criteria are seen in Table 3.
Table 4 shows the factors, indicators, outer loadings, composite reliability, and Cronbach’s alpha results of the models. According to the results, the outer loads of the indicators in the model are higher than 0.60, and the composite reliability and Cronbach’s Alpha values of the variables are higher than 0.70. Accordingly, the indicators and factors in the models are reliable.
Table 5 shows the validity values in the predicted model. Accordingly, it was observed that the average variance extracted (AVE) is greater than 0.50 and that the square root of the average variance extracted is higher than the correlation values. Accordingly, the models are valid.
A reliable and valid model should also be checked for multicollinearity. Accordingly, it was determined that the correlations between indicators are below approximately 0.80 and the variance inflation factor (VIF) is lower than 10. As a result, there are no multicollinearity problems in the models, as can be seen in Table 6.
Table 7 shows the results of the predicted models. According to this, all paths in the predicted models are significant at a 95% confidence level (t > 1.96). The Digital Economy and Society Index (DESI) has a statistically significant effect on youth unemployment across all years. Accordingly, the DESI decreased youth unemployment in the 27 EU countries in 2018 (β = −0.641; t: 9.143), 2019 (β = −0.652; t: 6.329), 2020 (β = −0.643; t: 8.775), and 2021 (β = −0.679; t: 7.236). Furthermore, between 41.1% and 46.1% of the change in youth unemployment is explained by the DESI, which is low (R2 < 0.50). Moreover, the effect size of the predicted models is high (0.35 < f2), but the prediction powers of the models are low (0.25 > Q2). These results indicate that the claimed hypothesis (H1) is confirmed (see figures in Appendix A).

6. Conclusions and Discussion

This research focuses on the effect of the Digital Economy and Society Index (DESI) on youth unemployment in 27 European countries over the period 2018–2021. The analyses were conducted via SmartPLS 4, and the results show that the Digital Economy and Society Index has a significant effect on youth unemployment in the EU countries for the years 2018 to 2021.
According to the results, it can be argued that digitalization can be a powerful strategy to fight youth unemployment, which is supported by numerous studies. For example, research by Solutions for Youth Employment [69] showed that digitalization creates new job opportunities for society [70] and also for young people. Similarly, research by Azmuk [5] concluded that digital technology plays an important role in solving the problem of youth unemployment. Research by Rocheska et al. [6] argued that the development of ICT in Southeastern European countries presupposes numerous job opportunities for young people, and that digitalization offers important potential for new job opportunities. Metu et al. [7] examined the role of ICT development in ending youth unemployment in sub-Saharan countries, and the results showed that ICT development reduces youth unemployment in the region. The study by Vaishya and Yeoward [8] showed that digital transformation helps to reduce unemployment by providing different jobs in each sector. Klier et al. [9] showed that IT can be a strategy to fight youth unemployment. Marra [13] examined in depth the digital education partnerships developed by the University of Naples with global technology and advanced manufacturing companies. As a result of the study, digital education partnerships increase human capital and reduce youth unemployment. Enciso-Santocildes et al. [12] showed that digital content education in Spain increases digital competitiveness and facilitates employment. The study conducted by Rodriguez and Franco [71] in Portugal concluded that improving participants’ skills in digital entrepreneurship and digital information systems can be a solution to youth unemployment. In addition, Gruber [72] found that SMEs tend to employ young people based on their aptitude for digital technologies. Teixeira [73] examined the Industry 4.0 policies of European countries and found that the digital agenda promotes growth and employment. He stated that supporting the ICT sector, especially in Greece, would boost the digital economy and employment. The same study also showed that EU programs are planning steps to promote youth employment in the context of economic and digital transformation.
Some studies claim the opposite, for example, that technological change leads to job losses and/or conversions, and can affect inequalities between workers and countries [10,41,42,43,44]. Dube et al. [14] examined the relationship between ICT market size and unemployment rates for the period of 1990–2013 in Turkey. The results show that changes in youth unemployment rates are positively correlated with growth in domestic IT production. Young people in low- and middle-income countries are most at risk of automation and are negatively impacted by technological changes, for example, when working in occupations with an increased risk of automation, which can lead to unemployment [15]. In addition, ILO [15] gives several guidelines at the macro, meso and micro levels. At the macro level, macroeconomic and sectoral policies are needed to support investments in the digital sector in order to foster innovation and create jobs in new sectors. For the meso level, the renewal of educational and professional programs is necessary to take into account in order to be in tune with labor market trends and needs (for example, courses that include digital and soft skills, and educational programs that strongly focus on updated digital developments). At the micro level, young people should be encouraged to engage in technology entrepreneurship [15].

6.1. Practical Implications

The link between DESI and youth unemployment clearly illustrates how new technologies affect labor markets. Any expenditure to support the technological transition in economic and social terms will help to reduce youth unemployment and improve the long-term sustainability of labor markets. In this sense, the improvements in digital infrastructures and the under-sizing of digital public services should be seen as tools for public investment. They could be considered in the context of social assistance schemes and included in investment plans to achieve a fair distribution of income in the labor market and reduce long-term unemployment.
Education policy has a direct influence on the growth of the human capital dimension, a crucial phase of digital transformation. In this regard, the modernization of educational infrastructure and curriculum with new technologies is crucial for the development of educators and the transmission of new knowledge to the younger generation. To break down the physical and chronological barriers that hinder education, formal education reform and adaptation to new communication technologies are essential. For the future of youth unemployment in the labor market, a new strategy of education as public investment in the context of new technologies is vital.
DESI, helping the private sector with digital integration, will have a significant impact. Adopting strategies that accelerate the digital transformation of companies can be a crucial tool to reduce youth unemployment. In this context, the joint efforts of the public and commercial sectors, and the integration of digital technology into social life will make it easy for young people to access new developments such as big data, cloud computing, and artificial intelligence research. By adapting quickly to new technologies, younger workers will be able to enter the job market easily.

6.2. Limitations

This research has some limitations, the first of which is the method, as it was conducted via the SmartPLS 4 program. The second limitation is the selected data. The three different data (e.g., youth unemployment rate) were taken from the EUROSTAT youth database and the DESI data were taken from the European Commission database; thus, it should be borne in mind that with broader indicators the results may change. The third limitation is the date. The model was predicted for the years between 2018 and 2021 (the latest common data), and different years may result in different datasets. Finally, the last limitation is the selected countries, i.e., only the selected European countries are included in the analysis; thus, the results may vary for other countries.

6.3. Future Research Directions

Also, we have some suggestions for future studies. Future researchers may widen the model, years, countries, and techniques, further examining the effects of digitalization by classifying countries by their level of development. In addition, looking at the big picture such as by examining BRICS or OECD could enrich the literature. Finally, the use of EDI, DAI, or DiGiX indexes instead of the DESI could also contribute to the literature.

Author Contributions

Conceptualization, O.B., H.S. and E.C.Y.; methodology, O.B. and E.C.Y.; software, O.B. and E.C.Y.; validation, O.B. and E.C.Y.; resources, H.S.; writing—original draft preparation, O.B., H.S. and E.C.Y.; writing—review and editing, O.B., H.S. and E.C.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

This research used the public datasets of Eurostat and European Commission.

Acknowledgments

We would like to acknowledge Burcu Taşkın for proofreading our manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

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Figure A1. Results for predicted model in the year of 2018.
Figure A1. Results for predicted model in the year of 2018.
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Figure A2. Results for predicted model in the year of 2019.
Figure A2. Results for predicted model in the year of 2019.
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Figure A3. Results for predicted model in the year of 2020.
Figure A3. Results for predicted model in the year of 2020.
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Figure A4. Results for predicted model in the year of 2021.
Figure A4. Results for predicted model in the year of 2021.
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Table
Table 1. Factors and indicators used in analysis.
Table 1. Factors and indicators used in analysis.
FactorIndicatorDescriptionSource
Digital Economy and Society Index (DESI)Connectivity
(CO)		DESI Connectivity Dimension calculated as the weighted average of the five sub-dimensions: 1a Fixed Broadband Take-Up (25%), 1b Fixed Broadband Coverage (25%), 1c Mobile Broadband (35%), and 1d Broadband Price Index (15%)European Commission
Digital Public Services
(DPS)		DESI Digital Public Services Dimension calculated by taking the score for 5a e-GovernmentEuropean Commission
Human Capital
(HC)		DESI Human Capital Dimension calculated as the weighted average of the two sub-dimensions: 2a Internet User Skills (50%) and 2b Advanced Skills and Development (50%)European Commission
Integration of Digital Technology
(IDT)		DESI Integration of Digital Technology Dimension calculated as the weighted average of the two sub-dimensions: 4a Business Digitization (60%) and 4b e-Commerce (40%)European Commission
Youth Unemployment (15–29 age) (%)
(YUN)		Long-term Youth Unemployment Rate (%)
(LYU)		The long-term youth unemployment rate of people aged 15–29 unemployed for 12 months or longerEUROSTAT
Not in Employment, Education or Training Rate (%)
(NEET)		The indicator measures the share of the population aged 15 to 29 who are not employed and not involved in education or training. The numerator of the indicator refers to persons who meet the following two conditions: (a) they are not employed and (b) they have not received any education or trainingEUROSTAT
Youth Unemployment Rate (%)
(YU)		The youth unemployment rate of unemployed young people in the labor force aged 15–29EUROSTAT
Table
Table 2. Descriptive statistics of indicators.
Table 2. Descriptive statistics of indicators.
StatisticsDESIYUN
CODPSHCIDTLYUNEETYU
2021Mean12.7817.1312.119.753.4012.0312.56
Std Dev.2.184.082.412.703.304.165.83
Min9.435.378.185.120.505.505.20
Max18.5122.9417.7814.8714.9023.1028.40
2020Mean10.9515.8311.978.863.0812.7412.96
Std Dev.1.853.962.382.453.363.966.12
Min6.914.507.914.830.705.705.30
Max13.7121.4917.1313.9016.9023.3029.80
2019Mean9.6514.7311.748.283.0211.6110.93
Std Dev.1.623.832.322.263.553.915.96
Min5.933.687.564.530.605.703.70
Max12.4220.2016.5012.6917.1022.2028.90
2018Mean8.86613.84211.5877.6293.54812.03311.73
Std Dev.1.663.742.332.064.114.246.76
Min5.103.187.504.220.705.704.40
Max11.6219.1416.7811.7719.6023.4032.30
Table
Table 3. Reliability, validity, and multicollinearity criteria.
Table 3. Reliability, validity, and multicollinearity criteria.
CriteriaRecommended Criteria ValuesSource
ReliabilityOuter Loadings > 0.60Hulland, 1999 [60]
Composite Reliability > 0.70Hulland, 1999 [60]
Cronbach’s Alpha > 0.70Hair et al., 2010 [61]
ValidityAverage Variance Extracted (AVE) > 0.50Bagozzi & Yi, 1988 [62]
The Square Root of the AVE > Correlation valuesFornell & Larcker, 1981 [63]
MulticollinearityCorrelations between the Independent Variables < 0.80Preacher & Hayes, 2004 [64]
The Variance Inflation Factor (VIF) < 10Hair et al., 2014a [65]
Effect Size of the Model0.02 < f2 low; 0.15 < f2 medium; 0.35 < f2 highHair et al., 2014b [66]
Prediction Power of the Model0.25 > Q2 low; 0.25 < Q2 < 0.50 medium; 0.50 < f2 highGeisser, 1974 & Stone, 1974 [67]
R-squared0.25 < R2 < 0.50 low; 0.50 < R2 < 0.75 medium; 0.75< R2 highHair et al., 2011 [68]
Table
Table 4. Reliability results of the models.
Table 4. Reliability results of the models.
YearsFactorsIndicatorsOuter Loadings (OL)Composite Reliability (CR)Cronbach’s Alpha (CA)
2018DESICO0.6720.8970.843
DPS0.875
HC0.913
IDT0.835
YUNLYU0.9380.9170.944
NEET0.933
YU0.893
2019DESICO0.6550.9020.851
DPS0.888
HC0.920
IDT0.861
YUNLYU0.9220.9150.882
NEET0.932
YU0.794
2020DESICO0.6980.9090.862
DPS0.854
HC0.929
IDT0.883
YUNLYU0.8980.9040.864
NEET0.930
YU0.778
2021DESICO0.7670.9200.883
DPS0.846
HC0.937
IDT0.889
YUNLYU0.9130.9000.863
NEET0.923
YU0.755
Table
Table 5. Validity results of the models.
Table 5. Validity results of the models.
YearsFactorsAVEDESIYUN
2018DESI0.6870.829 a−0.641 b,**
YUN0.850−0.641 b,**0.922 a
2019DESI0.7010.838 a−0.652 b,**
YUN0.783−0.652 b,**0.885 a
2020DESI0.7150.846 a−0.643 b,**
YUN0.759−0.643 b,**0.871 a
2021DESI0.7430.862 a−0.679 b,**
YUN0.753−0.679 b,**0.867 a
a Square root of AVE; b Correlations value; ** p < 0.01.
Table
Table 6. Multicollinearity results of the models.
Table 6. Multicollinearity results of the models.
YearsFactorsIndicatorsVIFCorrelations
2018 CODPSHC
DESICO1.289-
DPS2.5590.556 **-
HC4.5460.399 *0.766 **-
IDT3.5600.3480.7030.776 **
LYUNEETYU
YUNLYU4.962-
NEET2.5430.779 **-
YU4.1530.671 **0.646 **-
CODPSHC
2019DESICO1.325-
DPS2.6410.561 **-
HC4.4580.386 *0.770 **-
IDT3.6090.3800.752 **0.811 **
LYUNEETYU
YUNLYU4.564-
NEET2.1410.778 **-
YU3.2330.561 **0.439 *-
CODPSHC
2020DESICO1.326-
DPS2.4250.509 **-
HC5.1560.482 *0.803 **-
IDT4.0410.390 *0.706 **0.841 **
LYUNEETYU
YUNLYU3.025-
NEET1.9190.767 **-
YU2.4520.553 **0.501 **-
CODPSHC
2021DESICO1.645-
DPS2.3090.566 **-
HC4.4820.672 **0.792 **-
IDT3.6000.460 *0.744 **0.803 **
LYUNEETYU
YUNLYU5.513-
NEET2.0710.725 **-
YU3.7450.752 **0.446 *-
*p < 0.05, ** p < 0.01.
Table
Table 7. Predicted models’ results.
Table 7. Predicted models’ results.
YearsPathCoefficientt-Valuef2Q2R2
2018DESI→YUN−0.6419.1430.6970.2760.411
2019DESI→YUN−0.6526.3290.7400.2170.425
2020DESI→YUN−0.6438.7750.7040.2070.413
2021DESI→YUN−0.6797.2360.8550.2400.461
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Başol, O.; Sevgi, H.; Yalçın, E.C. The Effect of Digitalization on Youth Unemployment for EU Countries: Treat or Threat? Sustainability 2023, 15, 11080. https://doi.org/10.3390/su151411080

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Başol O, Sevgi H, Yalçın EC. The Effect of Digitalization on Youth Unemployment for EU Countries: Treat or Threat? Sustainability. 2023; 15(14):11080. https://doi.org/10.3390/su151411080

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Başol, Oğuz, Hüseyin Sevgi, and Esin Cumhur Yalçın. 2023. "The Effect of Digitalization on Youth Unemployment for EU Countries: Treat or Threat?" Sustainability 15, no. 14: 11080. https://doi.org/10.3390/su151411080

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