This section discusses the literature which has investigated those characteristics that influence individual labour market status. These characteristics, among other factors, include individual personal characteristics (such as age, gender, education, ethnicity, etc.) and characteristics at the household level (e.g. the presence of children or spouse, type of housing, etc.). Other macro-level variables, such as the rate of growth of GDP or the unemployment rate, are usually also included into the analysis as proxies for the labour market or shocks to the economy. In most cases, it is found that the labour market status of young people is more vulnerable to any kind of economic shocks as compared to that of the adult labour market (Bell and Blanchflower, 2010b, 2011a, 2011b; Marcus and Gavrilovic, 2010). Hence, young people are more likely to experience multiple spells of being in-and-out of the NEET state.
In the case of the youth labour market studies, the discussions have been mostly centred on issues relating to the transition from school to the labour market, also known as the school-to-work transition, as well as the determinants of youth unemployment (Freeman and Wise, 1982; Dolton et al., 1994; Marks and Fleming, 1998; Lassibille et al., 2001; Ryan, 2001; Andrews et al., 2002; Bradley and Nguyen, 2004; Andrews and Bradley, 1997; Caroleo and Pastore, 2007; Quintini et al., 2007; Choudhry et al., 2012; Bell and Blanchflower, 2011a, 2011b; Lucchino et al., 2012). Most of these studies have moved from a cross-sectional analysis into a more advanced longitudinal investigation.
In much of the previous literature, it is argued that the main reason for unemployment among youths is that these young people have lower levels of human capital and therefore lower productivity compared to their adult counterparts (Caroleo and Pastore, 2007; Bell and Blanchflower, 2011a, 2011b; Gregg and Wadsworth, 2011; Choudhry et al., 2012; Jenkins and Taylor, 2012). Caroleo et al. (2007) further argue that despite the increasing educational attainment, young people still lack two components of human capital, i.e. generic and job-specific work experience, which they refer to as the “youth experience gap” problem. For this reason, young people would move in and out of employment in search of the best job-worker match. As a consequence, employers prefer adult workers to youth workers. This implies that the employment probability for adults is higher than that for youths.
Bell et al. (2011b, pp. 242) state several reasons why youth unemployment rate may be higher than that of adult. The first reason comes from the internal labour market side, which relates to the lack of general and firm-specific skills owned by the young workers compared to those owned by the adult workers. Secondly, in the external labour market, young workers may be less efficient in job searching activities compared to adults since they are likely to have fewer networking opportunities and less experience in finding a job. Lastly, on the supply side, they argue that youths are less likely to have significant financial commitments than their elders, which may then restrict the job search activities of these young people.
Lower levels of human capital and a lack of work experience are no doubt significant aspects of youth unemployment. However, there are other factors that play a significant role in influencing youth unemployment. A study by D’lppolito (2011) compares the labour market outcomes in Denmark and Italy, where the author argues that labour market regulations, the strength of the economy, and the proportion of young people in the population are among significant determinants of youth unemployment. One of the main findings in this study is that the growth in real GDP significantly reduces the youth unemployment rate in both Danish and Italian youth labour markets.
Other studies show that personal characteristics such as age, gender, ethnicity, and education also significantly influence the likelihood of unemployment among youths. Harris (1996), using the Australian Longitudinal Survey (1985-1988), finds that personal characteristics such as age, education, and financial commitments have a positive impact on the employment probability. The probability of employment increases with age and the financial commitment of buying a house exerts a significant positive effect on employment prospects, particularly for males. The author further finds that females are less likely to supply their labour if they have children. Other factors such as, place of residence, marital status, and household type are also found to be important determinants of unemployment duration and incidence of unemployment. In this study, however, there is little evidence of a racial group disadvantage, although it is found that people with disabilities are disadvantaged in the workplace. Harris (1996, pp. 127) suggests that a policy directed towards education will have direct implications for reducing unemployment levels as well as the length of unemployment spells.
A study by Marks et al. (1998), again for Australian youths, aims to analyse the factors influencing youth unemployment by utilizing panel data from four cohorts of Australian young people born in the years between 1961 and 1975. They find that age is an important variable with respect to the incidence of unemployment, where older young people are less likely to be unemployed than their younger counterparts. They also find that after controlling for school achievement, the school qualifications such as degrees, apprenticeships and TAFE certificates become less significant. Based on this result, the authors argue that increasing post-school participation would initially reduce unemployment, but for those with poor skills in literacy and numeracy this reduction in unemployment would not be sustainable in the long-run. In terms of gender, they find that the unemployment incidence for men is not significantly different to women. Yet, after taking into account labour market experience, the result shows that men are more likely to become unemployed than women. Parental background (i.e. parental occupation) also influences the probability of unemployment but the effect from this variable become smaller once school factors and qualifications are taken into consideration.
As discussed previously, in the case of youth labour market, the concern is mostly on the number of young people who are in NEET and not in NEET. Bynner and Parsons (2002) analyse the impacts of earlier educational achievement and circumstances of young people (over the ages 16-18) on the probability of entering into and exiting from NEET status in their later lives (outcomes at the age of 21). They use longitudinal data from the 1979 British Birth Cohort Study (BCS70). They find that capital in the home, represented by a lack of parental interest in a child’s education (for girls at age 10) and parent’s not reading to child (for boys at age 5) play a significant role in predicting NEET. Moreover, for boys, living in the inner-city also has a significant role in predicting NEET, whereas for girls family poverty also matters. These variables remain significant even after taking into account the highest educational attainment achieved at the age of 16. Another study concerning NEET is conducted by Pemberton (2008) in the case of young people in Greater Merseyside, UK. The author highlights the importance of intergenerational factors and youth disaffection as the main predictors of NEET status.
The impact of economic recession on youth unemployment has also been discussed in much literature (see Demidova and Signorelli, 2010; Marcus and
Gavrilovic, 2010; Scarpetta et al., 2010; Choudhry et al., 2012; Kelly et al., 2013). Kelly et al. (2013), for example, looks at the transition in the labour market for Irish youths, taking into account the impact of the last Great Recession in 2009. Utilizing the longitudinal household survey dataset, they analyse the impacts of socioeconomic and demographic characteristics on the probability of young people transitioning from unemployment status to employment in 2006 and 2011, representing the period before the recession and the recovery period after the recession, respectively. This study finds that the rate of transition to employment for unemployed youths fell dramatically between 2006 and 2011. Moreover, they argue that this fall is not due to changes in the composition or the characteristics of the unemployed group but to changes in the external environment, where education and nationality factors have become more important for finding a job in Ireland (Kelly et al., 2013, pp. 16).
Bell et al. (2011a), using the 2009 Eurobarometer studies, examines the individual characteristics associated with having lost a job during the recession. They find that males, people aged 15 – 24, and immigrants are those who are more likely to have lost their jobs during recession. Another cross-country study by Choudhry et al. (2012) using a large sample of more than 70 countries (including the OECD and developing countries) for the period 1980-2005 finds that financial crises significantly increase the youth unemployment rate and the impacts from crises on the youth unemployment rate are higher than their impacts on the overall unemployment rate.23 They further investigate the persistence of these effects and find that the adverse effects of crises on the youth unemployment rate are high in the second and third year after the financial crisis and disappear after five years of the financial crisis.
Another study by Dietrich (2013), utilizing the European Union Labour Force Survey from 2001 to 2010, finds that the youth unemployment rate responds directly to the business cycle, as measured by the GDP growth and lagged GDP growth. In this case, a decrease in GDP leads to a significant increase in the youth unemployment rate within countries. Moreover, this study also investigates the effects of macro variables on the youth unemployment ratio (YPUER), defined as the unemployed share of the total youth (15-24) population, and NEET ratio. Again, it is found that YPUER is
23 The authors define crises as the sum of systemic banking crises and non-systemic banking crises.
significantly influenced by GDP growth and other labour market variables. In contrary, the impact of macro variables on the NEET ratio is less significant. It is argued that this finding is consistent with the assumption that NEET consists of more heterogeneous groups of people, including those opting for sabbaticals, voluntary leisure time, or taking over family care (Dietrich, 2013, pp. 314).
A more recent study by Bell and Blanchflower (2015) for Greece reveals that it is the 25-29 year olds who were hit hardest by the latest 2008 recession in that they failed to make a successful transition from school-to-work. Hence, they emphasize the importance of considering those within this age group when analysing youth unemployment since the proportion of this group who are NEETs, at least in Greece during the 2008 recession, is very high and their unemployment even outnumbers those aged 15-19.
In the case of the UK labour market, Jenkins and Taylor (2012) utilize the 18 waves of BHPS data and the first wave of Understanding Society survey for individuals aged between 15-69 years old to analyse the relationship between non- employment rates and age. The probit regression models are used, where the probability of being non-employed is estimated separately for each survey year and gender, controlling for other variables such as education, housing tenure, government region, health and marital status, household type, access to car, and presence of children. Although not specifically focusing on young people, this study finds strong evidence that young people, both men and women, have been hit particularly hard by the Great Recession, especially for those with no qualifications. This study also finds that the impacts of the recent Great Recession are stronger than that of the early 1990s recession. Moreover, the authors show that the non-employment trends for those aged 15-22 years old began to rise in the mid-2000s (prior to the Great Recession period), which is much earlier than for other age groups, and continued to increase until the end of 2009. They argue that the increase in non-employment rates prior to the Great Recession is due to increasing participation in post-compulsory education while the latter increase in the late 2000s reflects the impacts of the recent recession.
Other studies which examine the labour market for the older age groups are conducted, among others, by Bruce et al. (2000) and Cappellari et al. (2005). Bruce et al. (2000) analyses the labour market transitions between wage employment, self-
employment, and retirement for older workers in the United States by concentrating on the determinants of self-employment among older workers. They argue that self- employment is an important labour market activity for older workers. Their findings suggest that the effects from credit market imperfections seem to be more apparent than the impacts from employer-provided health insurance as determinants of self- employment transitions.
A study by Cappellari et al. (2005) estimates the static probabilities of being in labour market states at a given point in time, in the case of older men and women aged 50 to the State Pension Age in the UK. Utilizing the UK Labour Force Survey (LFS) 1993-1994, they first find that both men and women become more likely to be inactive and less likely to be employed as they get older. They argue that for these older workers, as they get older they will be more likely to retire and, thus, drop out of the labour force. Having better qualifications is associated with a lower probability of being employed and also becoming inactive. This result is explained by the decision on early retirement. In this case, better qualified individuals are more likely to be able to afford early retirement while individuals with lower qualifications need to remain active in the labour market.
Furthermore, Cappellari et al. (2005) also finds significant household characteristics as determinants of the probability of being in a labour market state at a given point in time. They find that the presence of dependent children and a partner who is also employed increases the probability of being employed and lowers the probability of being inactive. Regarding this finding, the authors argue that family responsibilities and the employment of one’s partner would encourage employment for the other. Another possible explanation suggested by the authors is that this is due to assortative mating. Specifically, couples are formed from those with similar characteristics, which in this case is their attitude towards employment. In terms of the household tenure, those who were paying off a mortgage were less likely to be inactive and were more likely to be employed than those who owned their own house, suggesting the need to stay employed for those who still need financial resources to repay their mortgage. In addition, this study also finds evidence of regional effects, in particular for men. In this case, living in the north of the UK (including Wales) is associated with a higher probability of being inactive and a lower probability of being
employed, as compared to living in central London, while the reverse is true for those living in the south.
Overall, previous studies have found that personal characteristics, such as age and education, and household characteristics (e.g. the presence of children or spouse) are among the most statistically significant factors that influence the probability of unemployment, particularly for young people. In most of these studies, however, the analysis of the state of the labour market for young people is discussed separately from those of older age groups. Moreover, previous studies regarding the impact of recessions have shown that young people have been hit particularly hard by the economic downturns compared to their adult counterparts, although only a few studies have examined the impact of other periods, such as before or after a recession. Our study will try to fill this gap in the literature by not only investigating the determinants of NEET for both youths and adults, but we will also include estimations for different business cycle periods in our models.