There is no unique definition of youth; each and every country has its own definition which varies over time. Most of these definitions depend on cultural, institutional and socio- political issues. Global governance institutions such as the United Nations (UN) and the International Labour Organisation (ILO) conventionally consider individuals under the age group of 15-24 as youths. In Mauritius, Statistics Mauritius defines youth as persons aged from 15 to 24 years. The category of youth is commonly defined by chronological age. Legally, the minimum age of youth varies for different purposes marriage, criminal responsibility, voting right, access to alcoholic beverages, consent to medical treatment, and military service to mention a few. Curtain (2001) defined youth as an economic and social concept referring to a separate stage in the lifecycle between childhood & adulthood.
2.1.2 Definition Of Youth Unemployment
Unemployment can be defined as the difference between labour supply and labour demand. Moreover, the ILO defines unemployment as ‘unemployed as those people who have not worked more than one hour during the short reference period but who are available for and actively seeking work’ (O’Higgins, 1997). As per the ILO Convention No. 138, the minimum age for employment is 15 years but variation exists in the youth definition for statistical purpose.
The UN and ILO define youth unemployment as the share of the labour force ages 15-24 inclusive without work but available for and seeking employment. But in December 2006, there was an amendment to the labour Act in Mauritius, whereby it was decided that the minimum legal working age for citizens of Mauritius is 16. Following this Employment Rights Act 2008, the labour force estimates for Mauritius are based on population aged 16 years and above. With reference to this Act, this dissertation will focus on youth within the age bracket of 16 till 24.
2.1.3 Models Of Youth Unemployment
There are four models which helping determining the factors affecting youth unemployment. These groups of factors form a temporal sequence with social background most removed in time from the measurement of unemployment incident followed by school factors, qualifications, and prior employment experience being the most influence on unemployment incidence as shown below.
Figure 2.1: Models of Youth Unemployment Incidence
Source: Marks, G. N., and Fleming, N., 1998. Factors influencing youth unemployment in Australia: 1980-1994, LSAY Research Report. Longitudinal surveys of Australian youth research report, number 7. Page 22
Model 1 specifies social background factors as influences on youth unemployment. Total effects of age, gender, parental occupational status, location and ethnicity on being unemployed for three months or more in a given year are isolated. On the other hands, model 2 adds school factors namely achievement in literacy and numeracy. The effects for the social background factors are direct effects net of school factors. Model 3 adds educational qualifications. The final model adds employment experience. This model produces the direct effects of qualifications, school and social background factors net employment experience.
2.1.4 Causes Of Youth Unemployment
The main causes of youth unemployment which have been widely studied in the economic literature can be classified in two groups: they can be analysed from a macroeconomic or microeconomic point of view. These causes contribute to the increase in youth unemployment.
Performance of Economy
Following the former approach, the determinants of youth unemployment more often quoted are aggregate demand, youth wages, and the size of the youth labour force and the lack of skills among youth (O’Higgins, 2001) Indeed, unemployment of young people seems to be more sensitive to changes in aggregate demand than adult unemployment, as young people are more likely than older workers to leave their jobs voluntary and to do so, albeit to a lesser extent, during a recession. On the demand side, it is likely that the first reaction of firms to a recession is to stop recruitment, and this affects young people more strongly. Furthermore, when firms start redundancy procedures, it is cheaper for them to fire young workers rather than older workers. Turning to the argument of wages, the evidence seems to suggest that, in industrialised countries (Blanchflower, 1999) the young are not being priced out of jobs by wages that are too high.
Labour Market Regulations
Moreover, the Employment Protection Legislation (EPL) and other interventions, such as the introduction of minimum wages, have been recurrently criticised to affect youth unemployment. The enforcement of the minimum wage can limit the attractiveness of taking in young workers without any prior work experience. Several studies argued that countries with strict EPL usually present also high youth unemployment, however there is no clear indication of this correlation (ILO, 2006). The effect of minimum wages on youth employment is often found to be significant (Neumark and Wascher, 1999).
Labour Market Mismatch
The microeconomic theory puts forward other explanations to youth unemployment, however, not specific to the young generation. The theory of human capital (Schultz, 1961, Becker, 1964, Mincer, 1974) differentiates the individuals by their schooling and training investment and accounts for some of the differences in productivities between young people and more generally between cohorts. Young people with low education and experience will go through more difficulties to find employment (Giret, 2001).
Another possible cause of high youth unemployment and low quality employment – low entrance wages, bad-quality jobs, and diffusion of non-standard labour contracts – has been found in the mismatch between the knowledge acquired through formal education and the skills required by the labour market.
Information and signaling failures
Under economic perspective, youth unemployment is caused by an imbalance between supply and demand. In most circumstances the demand for young workers is lower than its supply. A potential justification is the mismatch between the youths’ education and the skills required to obtain a certain occupation. Young people are often unsophisticated and typically lack information about the compensation and job categories that their education and limited experience can fetch. Thus, if young job seekers sense that opportunities open to them are, and will remain, limited until they have acquired more experience and more training, then they will be better equipped to assess their marketability. Employers cannot find the right competences and working experience within the youth labor force (ILO, Tackling Youth Employment Challenges, 2011).
Individual Supply Factors
Behavioral and personal attributes of young individuals have a role in their employment prospects. Economists consider individual decisions with regard to acquiring intangible forms of capital such as those related to a person’s education or skills and health is important in determining their outcomes in the labor market (Becker, 1975). These decisions are conceptualized within the human capital theory which has at its core the rational, utility-maximizing individual who is constantly engaged in making cost-benefit decisions with regard to investment in his own education or training (Becker, 1975). The theory makes a distinction between different forms of human capital; those that are acquired through formal education and those which may be specific to a job and are acquired through training. The cost of getting higher levels of education or training involves the opportunity cost of not being engaged in work and thus includes the lost income from not working throughout the period of education or training.
While the human capital theorists recognized the influence of family on individuals’ capabilities and thus outcomes, sociologists theorized the importance of the impact of family relationships on youth outcomes in their own right (Coleman, 1988). His theory is originally applied to the case of high school drop outs and is thus explicating the family background and social capital in relation to educational attainment, this study applies the theory to employment outcomes of youth. Family background is measured in terms of parent education or employment impacts youth outcomes. Family background may interfere with youth outcomes in three different ways, through human capital, financial capital and social capital. Coleman (1988) stated that the presence of human capital of parents such as high levels of education can potentially help in achieving better cognitive skills, but when the relationships are missing, for example, when the parents are too busy to attend to their children, parent education may not have any impact.
The proposition of young people in the population was often referred to as an indirect determinant of youth unemployment. According to Freeman’s analysis an increase in the relative number of youngsters has a positive impact on the youth unemployment rate (Freeman & Wise, 1982). Location problems can also account for mismatches of supply and demand for skills. There is a high cost associated with out- of- town job search and employment.
2.1.5 Effect Of Youth Unemployment
For every young person, a job offering decent work is an important step in completing the transition to adulthood, a milestone towards independence and self-reliance. However, growing and persistent youth unemployment has a negative impact on social development. Long-term unemployment is especially harmful.
Youth unemployment can generate frustration and low self-esteem, and can lead to increased vulnerability among some young people to drugs, disease and crime. Goldsmith, Veum and Darity (1996, 1997) found that being jobless injures self-esteem, and fosters feelings of externality and helplessness among youths. Moreover, they also found evidence that the psychological imprint of joblessness persists. The unemployed also appear to have a higher propensity to commit suicide. Further, unemployment can reduce the life expectancy of workers.
Youth unemployment can also lead to the marginalization and exclusion of young people. There is evidence that unemployment can expose youth to greater risks of lower future wages, repeated periods of unemployment, longer unemployment spells as adults, and income poverty.
In addition, young people with disabilities continue to face enormous challenges in the labour market. In some countries, ethnicity, particularly among young migrants, is a factor in their social exclusion and marginalization.
2.1.6 Duration Of Youth Unemployment
The main variable of interest is the duration of unemployment is stochastic and denoted by T.
The cumulative distribution function of T is given by:
where t denotes realization of T, and S(t)=1-F(t) is the survivor function of T.
This hazard function is a very popular way of analyzing duration data for several reasons. This model can handle censored durations, variables that change over time and allow examination of duration dependence (Ham and Rea, 1987). In the empirical literature, T is taken as a continuous random variable (Grogan and van den Berg, 2001) for convenience. However, in practice monthly data is used for T. The theoretical developments of the hazard function and the associated likelihood function with the grouped duration data are provided by Prentice and Gloeckler (1978), Kiefer (1988) and Sueyoshi (1995).
An alternative specification about the hazard rate following Sueyoshi (1995) is the Proportional Hazard Model (PHM). In this model for each group interval, a Type-I extreme value random variable is used. The result is a traditional proportional hazard specification which is separable in time and the vector of covariates. The derivatives of the log hazards with respect to the covariates are independent of time.
Among the other alternatives, there are log logistic interval hazards and log-normal interval hazards model. In the non-proportional hazard specifications logistic cumulative and standard normal distributions, respectively is considered. Then the likelihood function for the log-logistic model is the same as model that for a standard binary-logit regression model (Jenkins, 1995). In both cases the derivatives of the log-hazards with respect to the covariates are weighted by a time-dependent term. This term depends on elapsed duration and the hazard level in the log-logistic model and on the covariates values, the coefficient estimates, and time in the log-normal model. The details of the various specifications can be found in Kiefer (1988) and Sueyoshi (1995).
2.1.7 Impact Of Youth Unemployment
A spell of unemployment creates a direct loss of income for an individual and the economy as well in terms of lost productivity. According to Bell and Blanchflower (2009):
“Unemployment while young, especially of long duration, causes permanent scars rather than temporary blemishes. For the young a spell of unemployment does not end with that spell; it raises the probability of being unemployed in later years and has a wage penalty. These effects are much larger than for older people.”
Impact on future employment prospects
A period of unemployment can lead to a depreciation of skills, forgone work experience and signal low productivity to potential employers (OECD 2010). As a result of these factors, unemployment can influence an individual’s future employment prospects.
In addition, there is some empirical evidence that early-career unemployment is linked with repeated incidence of unemployment. Burgess et al. (1999) found evidence of this effect among low-skilled individuals. Further research by Gregg (2001) concluded that “an extra three months’ youth unemployment (pre-23) for men leads to an extra one and a third months out of work (around 1 month of which is unemployment) between 28 and 33. The effect for women is about half this, even when inactivity as well as unemployment is considered”.
Impact on future earnings
There has been substantial research looking at the extent to which unemployment harms an individual in terms of future earnings. The OECD (2010) outlines that most studies find that, on average, early youth unemployment has serious negative effects on incomes. Gregg and Tominey (2004) found that youth unemployment does indeed impose a wage scar upon individuals, in the magnitude of 12% to 15% at age 42. However, this penalty is lower, at 8% to 10%, if individuals avoid repeat incidence of unemployment.
Impact on well-being and health
Research by the Prince’s Trust (2012) found that the emotional health of young people can suffer if they are unemployed, compared to those who are in work or education. Bell and Blanchflower (2010) outline that:
“There is a wealth of literature showing that unemployment is a stressful life event that directly makes reduces individual well-being. This applies not just to the unemployed, but also may affect the employed due to increased anxiety over job security. Unemployment increases susceptibility to malnutrition, illness, mental stress, and loss of self-esteem, and increases the risk of depression. The unemployed also appear to be at higher risk of committing suicide, and of poor physical health outcomes later in life”.
Youth unemployment can also have adverse social impacts. Fougère et al. (2009) undertook a research in France and concluded that increases in youth unemployment induce increases in crime, specifically, burglaries, thefts and drug offences. Bell and Blanchflower (2010) highlight that unemployment is often part of the cycle where involvement in crime reduces subsequent employment prospects which in turn increase the probability of participating in crime. Moreover, ILO (2011) also notes that discontented youth have also engaged in protests in other countries, notably Greece, Italy and the United Kingdom, with at times violent outcomes.
Impact on the public purse
Youth unemployment results in a range of both short-term and long-term costs not only for the individuals concerned, but also for their families and dependants, for wider society and for the public purse.
Studies on youth unemployment in OECD Countries
Bell and Blanchflower (2011) analyses the impact of the increase of youth unemployment in Europe and UK since the start of the Great Recession using the most recently available micro-data files across countries on a comparable basis controlling for personal characteristics. These are mostly based on survey responses by individuals and on company level survey in Europe. They find evidence that the least educated person were badly affected. This study shows evidence that youth unemployment has increased drastically Spain, Greece and Ireland, while youth unemployment has remained relatively low in Austria, Denmark, Germany and the Netherlands. The duration of youth unemployment proved to have harmful impacts on a number of outcomes namely happiness, job satisfaction, wages and health for many years later. People are adversely affected in terms of reduced wellbeing by increases in unemployment.
Breen (2005) seeks to explain the variation in youth unemployment on the basis of institutional and economic differences using data from 27 OECD countries from late 1990s. He shows that youth unemployment is high in the regulated labour markets in which employers are restricted in their freedom to dismiss unsuitable workers compared to adult unemployment. But youth unemployment tends to be low in the liberal labour markets and also in countries in which the educational system sends very clear signals about job seekers’ abilities and skills. Demography factors have an important role in increasing youth unemployment. He also shows that institutional factors interact with market forces to shape levels of youth unemployment in OECD countries. They reinforce the view that systems of vocational training which teach specific skills and incorporate a strong work- based element provide a preventative to youth unemployment by offsetting the negative effects of extensive worker protection against dismissals.
Wooden (1996) examines the trends of part time employment and identify the key characteristics of employment among young people in Australia. Australian secondary data on employment were used. The author divides the youth labour market into a number of segments according to age, sex, study status and hours of work and quantifying those segments. He shows that the unemployment rate declines in successively older youth cohorts. The most readily apparent explanation for the inverse relationship with age is that older young people are more qualifies and thus more employable. He further shows that young men generally show higher levels of unemployment than young women, i.e. 12.4% of man aged 15 to 19 was unemployed compared to 10.8% of young women.
Chapman and Smith (1992) examine the influences on the duration of unemployment. They use the Australian longitudinal Survey and showed that males had a mean unemployment duration of 37 weeks compared to 25 weeks for females. Furthermore, they find on average rural people were unemployed for an average of 56 weeks compared to 24 weeks for city residents. Those who had completed year 12 or beyond experienced 6 weeks less time unemployed than those who had not completed secondary school. University graduates typically show lower levels of unemployment. Prior experience of unemployment does substantially increase the length of time unemployed. They also find that there is no difference in the probability of being unemployed between Australian born youths and those born in non- English speaking countries.
Studies on youth unemployment in Developing Countries
In a recent study, Omotosho, Idowu, Esere, and Arewah (2009) try to explore the problems and counselling needs of unemployed youths in Nigeria. A multi-staged sampling technique was used to select 1,750 unemployed youths from the six geo-political zones of the country including Abuja, the Federal Capital Territory. The result shows that both male and female unemployed youths face same problems in Nigeria. Youth unemployment leads to depression, low self-esteem, frustration and a number of other negative consequences. In addition, one of the major causes of unemployment is lack of productive and marketable skills. Many of the school leavers in Nigeria are not adequately prepared to fit into the production sector of the economy and cannot provide services that can generate sustainable income.
Mlatsheni and Rospabe (2002) try to determine the factors affecting youth unemployment in South Africa among different population groups, notably race groups and genders using the multinomial logit. A sample of 15 453 observations were used from the October Household Survey (OHS) 1999. The independent variables are age, racial, gender employment, education, experience, marital status, number of children in the households, family background and location. The results show that Africans, Indians and Coloured young people have a lower access to employment than White young people. Being 15 to 24 years old (compared to 24 to 30 years old) decreases the probability of finding a job by 30 percent, meaning that youth unemployment crisis is stronger among the youngest people. Having unemployed members in the family decreases the probability of being employed suggesting that the precariousness of the household renders the job search efforts of the family’s members costly. Further, they observe that living in an urban area significantly reduces the young’s access to employment. Education has been found to play a major role in the probability of finding a job. The whole society may suffer from a high level of youth unemployment as there is a link between youth joblessness and serious social problems such as drug abuse, vandalism and crime.
Studies on youth unemployment in Emerging Countries
Ahmad and Azim (2010) analyze the youth labour market participation in Pakistan using micro data of Labour Force Survey (2006-07) of 32744 households. Results of Logistic Regression analysis suggest that age, sex, marital status, migration, training, location, education level and characteristics of household have significant impact on employment probabilities of youth in Pakistan. This study shows that a significant number of youth starts their career early which can be costly for productivity and earnings later in life. These young people face higher unemployment rate at the start of their career which gradually decreases with increase in age. It was also noted that there does exist a significant difference between male and female youth labour market outcomes in different regions of the country. Unemployment rate among female youth is much higher than that of male youth in all regions of country. An important finding of the study is about the youth in Balochistan which are more willing to work but least likely to get employment as compared to youth in other provinces. It has also been concluded that youth is a diverse social group with different characteristics and attitudes about work in different regions of Pakistan.
Akhtar and Lubna (2005) explore both the macro and micro dimensions of youth unemployment in Pakistan. Household micro level data from the latest Labour Force survey (2003-04) was used, with a sample of 25 690 youth. A binomial probit model was used. The independent variables are age, gender, education, region, head of households, parent education level, household size, and household head occupational status. The results shows that youth possessing vocational and technical training have a higher probability of being unemployed. Youth unemployment appears to be an urban phenomenon as compared to rural areas in Pakistan. Probability of youth unemployment decreases in case of large family size both in rural and urban areas. Further it was pointed out that occupation of head of household is an important determinant of youth unemployment. Further, they results suggest that the probability of youth unemployment chances decrease in case of large family size and increases if the head of household is employed in informal sector.
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