The Determinants of Youth Unemployment: A Panel Data Analysis of OECD Countries.

AuthorBayrak, Riza
  1. Introduction

    As the boundaries of globalization have become uncertain, the international movement of labor and (un)employment have emerged as key global issues. Kokocak (2015) argues that globalization and information-based structural changes in the economy have positioned the competent and well-equipped person, and thereby the concept of human capital, at the center of the economy. Hence, human capital, which encompasses a well-trained and qualified workforce and includes factors such as knowledge, skills, ability, and level of education, has come to the forefront as a development strategy. Labor is therefore not just a production factor, it is also a strategic factor that determines plans and helps the development of other production factors. Exclusion of labor from the production system therefore results in serious social costs (Bell and Blanchflower, 2010) in addition to the costs of welfare-reduction (Murat and Sahin, 2011, p. 60).

    Data for several countries show that the youth unemployment rate is approximately two to four times the adult unemployment rate (Torun and Anca, 2011, p. 170; Sayin, 2012, p. 35; Marelli and Vakulenko, 2014, p. 3). This can lead to an increase in risky and anti-social behaviors among the young such as social alienation, suicide, high alcohol and cigarette consumption, and a tendency to engage in collective crime. This is extremely wasteful as young people are creative, dynamic and open to new ideas (Casson, 1979, p. 3; Morrell et al., 1998; Brewer, 2004, p. 13; Savci, 2007, p. 97; Coenjaerts et al., 2009, p. 120; Stuckler et al., 2009; Adak, 2010, p. 110-113; Sayin, 2011, p. 43; TSI, 2014, p. 26). The social and economic structure may, therefore, have undesirable negative effects.

    In terms of age range, there are several different definitions of "youth" in the research literature. For example, young people are defined as those aged between 16-24 in the United States and the United Kingdom whereas The International Labor Office (ILO) and European countries define the young labor force as consisting of people aged between 15-24 years. The Eurostat (The European Union Statistical Institute), on the other hand, describes the youth labor force as workers aged between 15-29 (TSI, 2015:3). In contrast, the OECD defines young unemployed people as those aged between 15-24 who do not work but are available for employment and have taken active steps to find work in the last four weeks. (OECD, 2016).

    Table 2 shows the youth unemployment rates in selected countries for the period 2005-2015.

    A worldwide review of youth unemployment rates, the overall number of unemployed young people and labor force participation rates (see Table 1), suggest that the youth unemployment rate is 13%, the number of young unemployed people totals approximately 71 million, and 46% of young people participate in the labor force. In regional terms, the highest youth unemployment rate (30.6%) can be found in Arab countries and the highest number of unemployed young people, 13.8 million, can be found in South Asia. Labor force participation is lowest in Arab countries where it stands at just 30.4% (ILO, 2016, pp. 6-16).

    Over the last decade, the youth unemployment rate in OECD countries has fluctuated between 11-13% (average 13%) for the period 2005-2008 and between 15%-17% (average 16%) for the period 2009-2015 (Table 2). In the EU-28, the youth unemployment rate fluctuated between 16-19% (average 17%) for the period 2005-2008 and between 20%-24% (average 22%) for the period 2009-2015. Therefore, youth unemployment in the EU-28 was approximately 4% higher than OECD countries from 2005-2008 and 6% higher from 2009-2015.

    Taking into consideration the Global Financial Crisis and the Great Recession, the average rate was approximately 13% for the period 2005-2008 and approximately 16% for the period 2009-2015. Therefore, after the 2008 financial crisis, the youth unemployment rate rose significantly over the ensuing years (Figure 1). This is supported by other findings in the research literature (Scarpetta et al., 2010; O'Higgins, 2012; Choudhry et al., 2013; Bruno et al., 2017). It is therefore reasonable to conclude that the youth unemployment rate was greatly affected by the global financial crisis.

  2. Determinants of Youth Unemployment: Literature Review

    Okun's law states that, as the economy grows, the rate of employment will increase. Many researchers have therefore examined the effects of GDP on employment (e.g., Lee, 2000; Solow, 2000; Akhtar and Shahnaz, 2005; IMF, 2010; Kabaklarli et al., 2011; Bartolucci et al., 2011; Choudhry et al., 2013; Wajid and Kalil, 2013; Bayar, 2014; Abbas, 2014; Arslan and Zaman, 2014; Gunaydin and Cetin, 2015; Bruno et al., 2017). A growing economy will therefore create employment (Logeay, 2001; Kreishan, 2011). Conversely, in periods of recession, the younger population are negatively affected by the decrease in demand (Bell and Blanchflower, 2011). This relationship is clearly shown in Figure 1. Additionally, in the light of Okun's Law, several researchers (e.g., Clark and Summers, 1982; Hess et al., 1994, O'Higgins, 1997) have argued that economic development is one of the main determinants of youth unemployment. Moreover, many research studies (Caporale, 2014:9; Bayrak and Tatli, 2016; Bruno et al., 2017) have concluded that GDP is a determinant of youth unemployment due to the negative relationship that clearly exists between GDP and youth unemployment. In this study, the annual GDP growth rate was therefore included as one of the determinants of youth unemployment.

    The Philips Curve shows that there is a negative relationship between inflation and unemployment. Thus, a change in unemployment within an economy has a predictable effect on price inflation. The inverse relationship between unemployment and inflation can be depicted as a downward sloping, concave curve, with inflation on the Y-axis and unemployment on the X-axis. Increasing inflation decreases unemployment and vice versa (Friedman, 1977, p. 455). Several researchers (Kabaklarli et al., 2011; Maqbool et al., 2013, Arslan and Zaman, 2014) have examined the effects of inflation on employment using the Philips Curve. The consumer price index (CPI), which reflects inflation, was therefore included in this study as the second variable.

    Ulgener (1991) asserts that factors of production affect economic growth both in terms of quantitative value and in terms of efficiency and productivity. If productivity increases, the subsequent increase in GDP growth will exceed that of total input. Thus, productivity is one of the most important drivers of economic development, social progress and higher living standards (Prokopenko, 2001, p.7). An increase in labor productivity may lead to a short-term decrease in labor demand. In the long term, however, increasing productivity will help create new job opportunities (Uzay, 2005, p. 61). Many researchers have examined the effects of labor productivity on employment (e.g., Linzert, 2001; Tripier, 2002; Saygili et al., 2001; Lentz and Mortensen, 2004; Pissarides and Vallanti, 2004, Pazarlioglu and Cevik, 2007; Ladu, 2005; Hall et al., 2008; Bocean et al., 2008; Korkmaz, 2010; Kabaklarli et al., 2011; Turkyilmaz and Ozer, 2008; Parisi et al., 2015). Labor productivity was therefore included in the analysis as the third variable affecting youth unemployment.

    According to classical theory, saving money is the main determinant of economic growth. Savings are equal to investments, and all savings turn into investments without any leakage (Marshall, 1920, p. 558). The Harrod-Domar model explains growth in terms of the level of savings, underpinning the claim that savings are a key element of economic growth (Meade, 1962, p. 8). Neoclassical Theory states that savings are equal to investments, and investments are therefore not independent of savings (Akyuz, 2009, p. 383). Tapsin (2011), for example, explored the causal relationship between savings, growth, and employment. The rate of saving has therefore been included in the analysis as one of the variables determining youth unemployment.

    The youth unemployment problem has been considered from the perspective of supply and demand as well. On the one hand, youth unemployment is therefore a consequence of inadequate demand due to economic stagnation and periods of recession (Murat, 1995; Togan, 2008; Scarpetta et al., 2010; Torun and Arica, 2011). Minimum wage practices (Ghellap, 1998) or sided wage policies applied to young people (O'Higgins, 1997), along with employment protection legislation (EPL) practices (O'Higgins, 2012), increase youth unemployment by reducing the demand for a young labor force. Conversely, those who focus on the supply-side attribute a lack of quality in the young workforce as the main cause of youth unemployment (Icli, 2001; Muller, 2005). Furthermore, young people with low human capital and few skills, i.e. an education and level of quality unable to meet market requirements, are more likely to experience long-term unemployment, and perhaps social exclusion, than young people with higher levels of human capital and superior skills (Icli, 2001; Muller, 2005; OECD, 2005).

    There are also other institutional variables and policies that serve as determinants of youth unemployment. For example, Nickell et al. (2005) argue that the unemployment benefit system, the system of wage determination, employment protection legislation (EPL), labor taxes, and barriers to labor mobility are all institutional variables that affect unemployment. In this context, effectively designed active labor market policies (ALMPs) can reduce unemployment by improving the efficiency of the job-matching process and by enhancing the work experience and skills of those who participate in them (Brandt et al., 2005). The key role that active labor market policies (ALMP) play in reducing unemployment has been empirically confirmed in...

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