Introduction Europe has begun to recover from the global financial crisis, but the recovery is slow and tentative. Investment and output remain well below pre-crisis levels. Unemployment is still unacceptably high, with the decrease of the capacity of economies to grow, skills atrophy and talent outmigration. Particularly, youth unemployment is a serious problem.
In most southern countries, youth unemployment is almost three times more sensitive to growth than adult unemployment during the crisis. Although the mix varies across these countries, they had a combination of sharp increases in unemployment during the crisis, together with persistently high levels of unemployment, In addition, youth unemployment is of high societal relevance, more and more people and governments value the contribution of this research study to society. The Juncker Commission has set up a plan of investments of €315 billion over 2015–2017, expected to bring significant support to the areas of Europe with the highest job losses. Employment generation is further targeted by other strategies, such as Europe 2020, aimed at producing inclusive economic growth with ‘a strong emphasis on job creation and poverty reduction’ (European Commission, 2010). The European Union (EU) is undertaking an effort to counterbalance the effect of the crisis on unemployment by trying to get people back into work. However, concerns 2 EU in global economy 545136 remain about the distribution of unequal, high unemployment rate in Europe, especially in Southern European countries.
Despite the increasing policy and support focus on unemployment in southern Europe, there is relatively little analysis of the nature and drivers of this phenomenon, particularly youth unemployment in southern Europe, Quantitative macro-studies such as this research paper are scant. Most research tends to focus on total unemployment in Europe. This paper tries to fill this gap by documenting the main trends in youth unemployment before and after the crisis in southern European countries. It attempts to distill the main underlying reasons for the large increases in youth unemployment and outlines elements of a comprehensive strategy to address the problem, and possible solutions. The research question?what generates youth unemployment in Southern Europe from 1990 to 2016? In order to explore the possible reasons and to get possible solutions at country levels, this paper is structured as follows: Section two presents research design. Section three reviews the literature background of this study, after which section four describes the data and method employed?empirical analysis and discussion. And then a conclusion is the final section of this research paper.
3 EU in global economy 545136 Research design In this research the databases are Eurostat, Worldbank-data and Oecd-data. And the statistical tool applied in the research is Statistical Package for Social Science (SPSS). The data of this research covers 18 southern European countries, and the sample includes: Andorra, France, Monaco, Portugal, Spain, Italy, Albania, Bosnia and Herzegovina, Bulgaria, Croatia, Greece, Kosovo, Macedonia, Montenegro, Romania, Serbia, Slovenia, Turkey. These countries are the intersection of the available countries in all datasets being used. There are some criteria for sample selection, obtaining a sample of observations corresponding to 18 countries, from total 21 southern European countries.
If the data of a country about 7 variables from 1990 to 2016 is missing 7 variables, the country will be deleted. As a result, Gibraltar, San Marino, and Vatican City were deleted. The research is based on annual data from 1990 to 2016, although the actual sample size varies depending on data availability. The stylized facts go and before 1990 if data are available, but the full analysis could not be extended before 1990 at this stage given data gaps.
This is also the reason of data selection. About the variables description, the empirical model has tested at a country level for the associations between independent variables (possible reasons) 4 EU in global economy 545136 and the dependent variable, namely youth unemployment, measured by long-term youth unemployment rate, which is the number of unemployed as a ratio to the labor force. The share of the labor force not employed or involved in further education or training over 6 months, is considered the most accurate measure of youth unemployment. And youth refers to individuals aged 15–24 years (Luedemann, E., 2014).
About the independent variables that cannot be directly collected from the databases they are measured by the following variables: Marco-economy, measured by GDP per capita; Government quality, measured by by LMP expenditure (% GDP) and youth temporary employment (% of dependent employment); Education, measured by the percentage of education level (0-2) population, the percentage of education level (3-4) population, the percentage of education level (5-8) population. In order to examine the relationship between independent variables and youth unemployment and to get possible reasons of youth unemployment across southern European countries, there is a set of testable hypotheses: Table 1 5 EU in global economy 545136 H1-1 There is a positive relationship H1-2 There is a negative relationship between GDP per capita and between GDP per capita and long-term youth unemployment rate in long-term youth unemployment rate in Southern European countries from Southern European countries from 1990 to 2016. 1990 to 2016. H2-1 There is a positive relationship H2-2 There is a negative relationship between LMP expenditure and between LMP expenditure and long-term youth unemployment rate in long-term youth unemployment rate in Southern European countries from Southern European countries from 1990 to 2016. 1990 to 2016.
H3-1 There is a positive relationship H3-2 There is a negative relationship between youth temporary between youth temporary employment and long-term youth employment and long-term youth unemployment rate in Southern unemployment rate in Southern European countries from 1990 to European countries from 1990 to 2016. 2016. H4-1 There is a positive relationship H4-2 There is a negative relationship between the percentage of education between the percentage of education level (0-2) population and long-term level (0-2) population and long-term youth unemployment rate in Southern youth unemployment rate in Southern European countries from 1990 to European countries from 1990 to 2016. 2016. H5-1 There is a positive relationship H5-2 There is a negative relationship between the percentage of education between the percentage of education level (3-4) population and long-term level (3-4) population and long-term youth unemployment rate in Southern youth unemployment rate in Southern European countries from 1990 to European countries from 1990 to 2016. 2016.
6 EU in global economy 545136 H6-1 There is a positive relationship H6-2 There is a negative relationship between the percentage of education between the percentage of education level (5-8) population and long-term level (5-8) population and long-term youth unemployment rate in Southern youth unemployment rate in Southern European countries from 1990 to European countries from 1990 to 2016. 2016. Literature review The previous publication of several studies on youth unemployment indicated the interest of the scientific community in this topic. Specifically, most macro-econometric studies have found significant positive effects on aggregate unemployment of spending on ALMPs, especially on training (OECD, 2006, Chapter 6 and 7). However, micro-econometric evaluations of ALMPs find that the effectiveness of programs varies, and that programs that seem similar at first glance can yield very different outcomes. Micro-econometric studies also show that ALMPs that specifically target young people are not very effective regardless of the type of the program, i.e., they have a lower probability of yielding positive results.
However, in this research, the results suggested that LMP expenditure is negative associated with long-tern youth unemployment rate, and the coefficient for LMP expenditure is not significant. 7 EU in global economy 545136 In addition, studies by Cahuc, P., Carcillo, S., Rinne, U. and Zimmermann, K.F. (2013) found that the high levels of youth unemployment could be explained by both the output gap and labor market factors.
Of particular relevance are labor costs (measured by the tax wedge and minimum wages relative to the median wage), especially for low-skilled labor; the opportunity cost of working (measured by unemployment benefits); and spending on active labor market policies (ALMPs). Insufficient vocational training and pervasive labor market duality also affect youth unemployment rates. In this research there are three perspectives to analyze youth unemployment, namely from marco-economy perspective, government quality perspective, and educational perspective. Empirical analysis and discussion This section presents the results of the various empirical analyses that have been performed.
In order to determine whether and to what degree a relationship exists between two or more quantifiable variables, a correlation coefficient is used to express the degree of relationship. Model Summary and ANOVA are used to check the regression and significance of all variables in the research. Insert Table 6 Correlations around here 8 EU in global economy 545136 The results of this research paper suggest GDP per capita, LMP expenditure, youth temporary employment, education level (0-2) population and education level (5-8) population are negative associated with long-tern youth unemployment rate.
However, and the coefficient for LMP expenditure and youth temporary employment are not significant, as shown in Table 6. Education level (3-4) population is significantly positive associated with long-tern youth unemployment rate. Specifically, an increase in GDP per capita is associated with lower youth unemployment rates by around 0.28 percentage points. An increase in education level (0-2) population corresponds to lower youth unemployment by around 0.
27 percentage points, but a similarly defined increase in education level (3-4) population has a significant positive correlation with youth unemployment, with around 0.39 percentage points. So according to the statistical results, hypotheses H1-2, H2-2, H3-2, H4-2, H5-1 and H6-2 are accepted, and the rest of them are rejected. This paper examines the factors driving youth unemployment in Southern Europe and the analysis finds that the youth unemployment reasons is multi-faceted. 9 EU in global economy 545136 More positive macro-economy, lower youth unemployment A macro-economy is a natural reference point for discussions about unemployment problem. The lack of economic growth plays an important role in explaining the upsurge in youth unemployment during the crisis. According to Cataldo, M.D.
(2015), the sharp decline in economic activity—the largest such decline since the great depression—can on average explain about 50 percent of the increase in youth unemployment during the crisis. In most southern countries, youth unemployment is almost three times more sensitive to growth than adult unemployment. This is possibly due to the concentration of youth employment in cyclically sensitive sectors of the economy, such as construction, and the generally more fragile employment conditions of younger workers, such as temporary and part-time contracts, which make them more susceptible to the effects of the recession. For example, a first category, which includes the hardest-hit countries such as Greece, Spain, and Italy, experienced large increases in youth unemployment after the crisis.
But they also had relatively high—i.e., above average—youth unemployment rates to begin with. In these cases, the crisis appears to have exacerbated an existing unemployment problem.
In addition, Ireland and Cyprus also experienced large increases in youth unemployment after the crisis, but from relatively low pre-crisis levels. In such cases, the crisis seems 10 EU in global economy 545136 to have been the main driving force behind the current high unemployment rates. However, for other countries like Belgium, France, Finland and Sweden, with above average pre-crisis unemployment rates, but they had small increases since the crisis. In these countries, youth unemployment largely existed before the crisis.
Thus, any analysis of youth unemployment in southern Europe would also need to address other potential reasons. Higher government quality, lower youth unemployment More Spending on Active Labor Market Policies, will lower youth unemployment. Spending on ALMPs varies widely across countries, and some countries would increase spending in this area after the crisis. Given dramatic increases in unemployment during the crisis, ALMP funds have had to be distributed across greater numbers of the unemployed. Especially, training is associated with significant reductions in youth unemployment rates. The program “Youth on the Move,” a European “Youth Guarantee” should be implemented that enables every EU citizen aged between 15 and 24 years to claim the right for employment, vocational training, or participation in a training program. This proposal was inspired by similar approaches in a number of EU countries (e.
g., Austria, the Netherlands, 11 EU in global economy 545136 Sweden, and Finland). The European Parliament joined this proposal and called for its legal implementation in January 2013 (Eichhorst et al., 2013b). However, EU labor market policy would face the huge challenge to provide every young person with (regular or subsidized) work, training, or an internship within four months after graduating or entering unemployment. The “Youth Guarantee” would thus force government authorities in many countries to cooperate more closely with public and private employment services, schools, universities, vocational training providers, employers and unions. In addition, our analysis finds a higher share of temporary employment is related to lower unemployment rates for youth. Young workers are more likely to be employed on temporary contracts than adult workers.
The disparity between the adults and youth in this regard is particularly large in Spain, Italy and Portugal, which have had some of the largest increases in youth unemployment. For example, Spain restricted temporary employment contracts and this policy of government led to its high youth unemployment. So the attitudes from governments on temporary contracts can directly impact youth employment problem. For example, Ireland boosted the temporary employment rate during the recession in order to reduce youth unemployment.
And in Austria, Germany and Switzerland these countries have a long tradition of dual apprenticeship systems. In these countries, temporary employment of young people is thus not necessarily synonymous with job insecurity, but 12 EU in global economy 545136 rather part of their vocational education. So a higher share of temporary employment is related to lower unemployment rates for youth. Higher education, lower youth unemployment; however more vocational training, lower youth unemployment Educational attainment may have a large impact on employability (OECD, 2013).
The share of workers in the population with low education has been declining steadily across all countries in Europe. There are three levels of education in this research, namely the primary level (0-2) of education, the secondary level (3-4) of education, and the tertiary level (5-8) of education. Generally low-skilled youth unemployment tends to be concentrated among those with primary levels of education and individuals with lower education levels have had worse employment outcomes. While in this research, young individuals with secondary education levels increased youth unemployment. That means the majority of employed youth have primary level or tertiary level of education.
Because the levels of formal education may not provide a complete picture of the skills of the young unemployed. Vocational training and apprenticeships are important forms of teaching their skills, for example, Germany’s dual vocational training system. Such systems also exist in a number of other European countries, such as Austria and Switzerland. So low education may 13 EU in global economy 545136 be less of an obstacle for youth employment, perhaps because young workers are more amenable to training. And the labor market gradually compensates for inequalities resulting from different initial education levels by allowing non-graduates to acquire professional skills on-the-job (Richter, 2014). Conclusion This research paper examines the factors driving youth unemployment in Southern Europe and the analysis finds that the youth unemployment reasons is multi-faceted. And there is no single solution to the youth unemployment problem. Strong sustainable economic growth will be crucial, given the high sensitivity of youth unemployment to growth; in addition policies need to be comprehensive, country-specific, and focused on reviving economic growth and advancing labor market reforms.
Active labor market policies are programs that intervene in the market to address unemployment. International experience indicates that there is no “one size fits all” model of successful ALMPs. Policymakers are increasingly focused on tackling youth unemployment. Policies to resolve this issue have been formulated at both the European Union and national levels. The most notable examples are the Youth Guarantee Scheme and the Youth Employment Initiative, aimed at providing European Union funds to support 14 EU in global economy 545136 ALMPs for young people not in education, employment or training in regions with high youth unemployment.
What’s more, labor market reforms also include lowering labor costs by reducing the tax wedge and reconsidering minimum wage policies (which largely affect youth employment) to increase labor demand; reforming unemployment benefits to better incentivize the transition from inactivity to work; improving skill levels and work-related training; and promoting cost-effective ALMPs. It is necessary to make different solutions and policies according to national conditions, and to take into account different national situations and the implementation of these policies.