Determinants of graduate youth unemployment: A case study in West Shoa Zone, Ethiopia

Today youth unemployment is a common agenda and a critical issue of all country. Particularly, it is the serious problem in developing country. In Ethiopia, lack of employment opportunities for educated young people is the critical development challenges facing the country. This study was conducted to identify the determinants of graduate youth unemployment. To answer the research objective, data were collected by a structured questionnaire from 312 respondents selected using snow ball sampling technique and analyzed using binary logistic regression. The result indicated that all the nine variables of education, such as number of graduates, work experience, career advice, market information, family income, aspire to the low-income job, education quality, and entrepreneurship, significantly affect unemployment rate except entrepreneurship. As a result, based on these findings, it can be recommended that there is a critical need for government to work on these determinants to reduce graduate unemployment.


INTRODUCTION
Youth unemployment is a new concept of global problems, threatening whole humanity including educated and illiterate mass especially youth to cope up the magnitude of the problem in their respective locality.Energetic, courageous, and qualified youth can make changes to social economic development if they are well utilized and managed (Msigwa and Kipesha, 2013).However, unemployment among young people has become a major policy challenge for all nations in the world.Unemployment results in substantial crises from psychological, social and economic perspectives; some of them are: increasing crime rates and violence, dependence on family, low self-esteem, poor social adaptation, depression, and loss of confidence (Kabaklarli et al., 2011).Graduate unemployment is a kind of unemployment amongst people with academic degrees (Saptakee, 2001).ILO (2001) defines unemployment as situation of being out of work or 'without work', that is, were not in paid employment or self-employment as specified by the international definition, 'currently available for work', that is, were available for paid employment or self-employment during the reference period; and 'seeking work', that is, had taken specific steps in a specified recent period to seek paid employment or self-employment Graduate unemployment is caused by countless of factors some of which include a mismatch between aspirations, skills, and self-concept of graduates and employment opportunities available to them (Sampson, 1992).Regarding the factors and impacts of youth unemployment, various scholars have conducted several studies in different parts of the world.According to Assad and Levison (2013), employment inadequacy for youth showed high rates because of low-job creation and increasing environmental threats.Msigwa and Kipesha (2013) found that gender, geographical location, education, skills, and marital status are all important factors that explain youth employment status.Kakwagh and Agnes (2010) found increasing population growth, a high degree of geographical mobility, lack of employable skills, and low participation of youth in decision-making processes and the perception of policy makers.Developing countries are more victimized than others and Ethiopia has its own long history of unemployment than any other countries.Ethiopia is a poor agrarian country with per capita income of USD 350 (World Bank, 2011).Recently, however, the country has been achieving promising economic growth.According to The Economist (January 6, 2011), the country had the 5th fastest growing economy in the world during the periods of 2001-2010 at an average annual GDP growth rate of 8.4% and the 3rd with a forecast of 8.1% during the periods of 2011-2015.Despite such improvements, unemployment is high and is one of the socio-economic problems in the country.This shows that the economy cannot provide adequate jobs to the growing population in both rural and urban areas.Unemployment and underemployment continue to be serious social problems in Ethiopia despite some improvements in recent years (Bimal, 2014).There are few studies that address the employment challenges in Ethiopia.Most of the studies give a narrow view of the labor market-few studies tend to concentrate on the incidence of unemployment in specific categories, such as urban youth unemployment (Serneels, 2004;WB, 2007).Then based on the reviewed literature, some of the common predictors that influence graduate youth unemployment were: the number of graduated youth, family economic performance, levels of education, entrepreneurship skills, access to job information, quality of education, the absence of career advice, aspires to low income jobs and work experience.Hence, the purpose of this study was to assess the determinants of graduate unemployment.

RESEARCH DESIGN
This research used a mixed research designs such as descriptive and casual designs.The descriptive design employed descriptive statistics such as frequency mean and cross tab, and casual design employed binary logistic regression to determine the impact of the independent variables on the dependent variables.

Data sources, type and collection instruments
For this study, primary data were collected from unemployed and employed graduated youths based on the data at the end of 2008 E.C and used the questionnaire as data collection instrument.

Study population, sample size and sampling technique
The populations of the study were 11,595 unemployed graduated youth registered in Zonal Labor and Social Affairs Office of West Shoa Zone at the end of 2008 E.C. From this population, 346 sample sizes were calculated using Yemane Taro formula (1967) by using 95 % confidence level and 0.05 precision levels and the sampling techniques used were systematic and snowball sampling system.But from the total sample calculated, only data collected from 312 were edited and analyzed.

Model
This study used a binary logistic regression model.The binary logistic regression model was selected due to the nature of the dependent variable, if the dependent variable was categorical variable with only two categories (employed and unemployed which were valued as 1 and 0, respectively).The logit model yield similar parameter estimates, but the cumulative logistic regression model is preferred because of its comparative mathematical simplicity and more meaningful interpretation of odds ratio (Gujarati, 2004).Hence, the binary choice logistic regression model that assumes a dichotomous dependent variable which takes either 1 or 0 value depending on Y is used.The probability that the outcome is present (probability of success) is given as: We obtained the odds of success as: In logistic regression analysis, it is assumed that the explanatory variables affect the response through a suitable transformation of the probability of the success.The transformed variable, denoted by logit (π) is the log-odds and is related to the explanatory variables as: = (β0, β1, β2… βk) are the model parameters and = (X1, X2… Xk) with, are explanatory variables.The above equations give suitable representations of the success probability, odds, and log-odds.Indeed, these representations facilitate interpretations of parameter estimates.The parameter refers to the effect of Xi on the log odds that Y = 1, controlling the other X's in the model.

Data analysis
Data were analyzed using descriptive statistics cross tab and binary logit regression using SPSS V.20.

RESULT AND DISCUSSION
This part of the study deals with the descriptive statistics of the demographic characters of the respondents and the distributions of the major variables under considerations.
As shown in the above table, out of the total respondents, 167(53.5%) of them were male and the remaining 145(46.50%) of them were female.The gender ratio of the respondents was fairly equally represented in the sample.Similarly, the distribution of level of education of the respondents is shown in Table 1.Thus 210(67.31%) of them were academically from level 1-4 and the rest 102(32.69%) of them were holders of BA degree.This result shows that out of the total respondents, the majority (67.31%) of the graduated unemployed were from level 4. Table 1 also shows the distribution of the status of graduate unemployment.From the table, 76(24.36%) of the respondents were employed and 236(75.64%) of them were unemployed.This shows that majority, three-fourths, of the graduated youths were not employed.

Descriptive analysis of factors influencing graduate youth unemployment
Table 2 shows gender and education distribution based on graduate unemployment status.To investigate whether or not there is a relationship between gender and graduate unemployment status, and level of education and graduate unemployment status, a cross tab between the variables is made as shown in the Table 2.The cross tab made indicates that males were more employed (15.38%), as compared with females (8.97%) graduats, and also 38.15%% of the males graduates were unemployed as compared with 37.5% of the females graduates.This shows the existence of an association between gender difference and unemployment status.
The Pearson Chi-square also shows the existence of a significant difference between graduated males and females in unemployment status, where the p-value is 0.053 at the p < 0.05 level.This is the fact that males are free and confident as compared with female in actively searching for job to be employed.Similarly, the cross tab made between level of education and unemployment status also shows that only 7.69% of respondents from level 1-4 of educational status were employed as compared with 16.67% of respondents employed with BA degree, or 59.62% of the respondents within level 1-4 education status were unemployed as compared with 16.02% of the respondents with education status of BA degree holder.Also, the chi-square test revealed that there is a statistically strong association between education level and unemployment status at 5% probability level.The findings showed the inverse and significant relationship between the level of education and unemployment status, as the education level increases, unemployment decreases.

Numbers of graduate in the market:
As shown in Table 3, about 82.37% of the respondents indicated that a large number of graduate youths are in the market in search for jobs.Only 17.63% of respondents said that the lower number may be due to fields.The result of the cross tab also shows that 82.37% of the respondents were unemployed as a result the large number of graduates which constitute around 66.99% as compared with small number (8.66%).As shown in Table 3, the chi-square test also shows the significant statistical relationship between work experience needed and unemployment status.

Lack of work experience:
Lack of work experience is one among the major factors that are responsible for unemployment of graduated youths.As shown in Table 3, 63.46% of the respondents indicated that different job vacancy announced at different times need high work experience rather than a fresh graduate with zero experience.Only 24.36% of respondents said that the job vacancy needs low work experience.The result of the cross tab also shows that 75.64% of the respondents were unemployed as a result of the lack of high work experience as compared with 24.36% of those employed with high work experience.The chi-square test also shows the significant statistical relationship between work experience needed and unemployment status.
Career advice: Career advice is important especially for unemployed graduate youths.Provision of a good career advice for unemployed graduate youths energizes to search better job rather than merely employed in any organization or motivate them to start their own job freely rather than searching to be employed in private or public organization.As Table 4 shows, among the respondents, 49.3% of them replied that they acquired low career advice, about 42.31%    unemployed and the remaining 50.32% replied they got high career advice still about 33.33% unemployed.
Similarly, the cross tab shows that 16.99% of graduated youth with high career advice employed compared to 7.37% of graduated youth employed with low career advice, or only 43.91% of graduated youth employed with low career advice compared to 31.73% of graduated youth unemployed with high career advice.The chi-square test also shows the significant relationship between the two variables.
Labor market information: Labor market information plays an important role in providing the efficiency of the labor market.Labor market information is scarce and moreover is not available to all job seekers.Better access to information further requires the availability of facilities as transportation, availability of newspaper where the job is to be announced and internet facility.For poorer graduated youths and those living further from remote centers, getting these facilities are rare.As shown in Table 4, the availability of labor market information also influences the level of graduated youth unemployment.From the Table, 57.05% of the respondents replied that there was low availability of labor market information and 42.95% of them replied that there was highly available market information for graduated youths.Then we can conclude that as job information is available, unemployment is decreases.But the chi-square test is 0.53, which means there is no statistically significant relationship between the labor market information and graduated unemployment status.
Family income/ economic performance: It is obvious that there is a strong relationship between the employments of graduated youth and their family economic/income level.Unemployment and household income have an evident bi-directional relation.
Unemployment rates lower the higher household income and vice-versa.As shown in Table 5, 57.37 % of the respondents were from the family of low economic performance and the remaining 42.63% were from medium family economic performance.Besides, for the frequency distributions, the cross tab between family economic performance and the unemployment status of the graduated youth showed that 18.27% of employed graduated youth were from the family of medium economic performance as compared with 6.09% of employed graduated youth from low-income earner, or only 68.27% unemployed from the family of low-income earner as compared with 24.36% of unemployed graduated youths from the family of medium economic performer.Thus, this is statistically significant difference at 5% level.

The aspiration to join low salary/income jobs:
Employment status was also influenced by aspires of graduate to join low salary/income jobs.As indicated in Table 5, 77.56% of the respondents had a low aspiration to join low salary jobs; from this, about 68.27% were unemployed while the remaining 22.44% % had a high aspiration to join low salary jobs, of which about 7.37% were unemployed and the remaining 15.07% were employed.This result is statistically significant at 5% level.

Quality of education:
Poor quality of education and graduation of students without acquiring enough knowledge and understanding the contents and objective of the curriculum designed and lack of desired theoretical and practical knowledge, created high unemployment problem in Ethiopia (British Council, 2014).Similarly, the emphasis is given by Ethiopian government on high education coverage regardless of the poor education quality that made graduated student incompetent relative to the requirements of the labor market (Guarcell and Rosita, 2009).As shown in Table 6, the mean (2.89) response of employed graduated youth education quality was more than the mean (2.31) response of unemployed graduated youth education quality.The result showed that the mean score of the employed graduated youth are positive and significance at less than 5% level of significance.Therefore, it can be concluded that low quality of education leads to high unemployment status.

Entrepreneurship ability:
The other variable that can influence the graduated youths unemployment is their entrepreneurship ability.As Table 6 shows, the mean response of employed graduated youth entrepreneurship ability is 3.1 as compared with 2.98 mean response of unemployed graduated youth entrepreneurship ability.The t-value, 0.167, is not significant enough at 5% level of significance.Therefore, it can be concluded that entrepreneurship ability of graduated youth is not significantly related to graduated youth unemployment status.This means that whether the graduated unemployed youth have enough entrepreneurship skills or not, it does not matter to be employed or not.

Binary logistic regression results
As mentioned in the methodology logit model was selected  to identify the determinants of graduate unemployment in the study area.The correlation of the independent variable conducted so as to test whether the problem of co-linearity exists or not.Accordingly, it is found that there was no serious multi-co-linearity problem among the explanatory variables.The log likelihood ratio is also tested and presented hereafter.

Model summary
In general, -2Log Likelihood (-2LL) is a measure of badness of-fit, illustrating error remaining in the model after accounting for all independent variables.The -2LL of 131.753 indicates that there is no significant error remaining in the model.The model summary provides some approximation of R 2 statistic in logistic regression.Cox and Snell R2 or Nagelkerke R 2 is an analogous statistic in logistic regression to the coefficient of determination R 2 in linear regression, but not close analog.Cox and Snell's R 2 attempts to imitate multiple R 2 based on likelihood.In this study, Cox and Snell R 2 indicated that 49.7% of the variation in the dependent variable was explained by the explanatory variables.The Nagelkerke R 2 in Table 7 is 0.742, which indicates that 74.2% of the variability in the dependent variable, use of graduate unemployment method, is explained by the explanatory variables.

Multi-co-linearity
Multi-co-linearity may inflate standard errors.However, as long as there is no perfect Multi-co-linearity, the regression estimates will not be biased.To check whether perfect multi-co-linearity is a problem, variance inflated factors (VIF) are calculated and presented in Table 8.If the highest variance inflation factor is greater than 10, there is evidence of co-linearity.However, near co-linearity that does not influence the main variable of interest in a model may not be a big problem and can be ignored (Baum, 2006).As can be noted from the table, there is no VIF because the VIF of all other explanatory variables is less than 10, it can be concluded that Multi-co-linearity is not a problem in the data.

Binary logistic results
Table 9 shows the results of the estimated logistic model of the determinants of graduate youth unemployment in the Case of Selected Towns of West Shoa Zone.The logistic regression coefficients, sig, Wald test, and odds ratio for each of the predictors are presented in this table.The "sig" column shows the significance (or p-value) of each of the variables while β values explain the direction of the relationship of the particular independent variable, with the dependent variable.On the other hand, Exp (β) column represents the odds ratio.Using 0.05 and 0.1 level of significance as a standard for the test of statistical significance, the coefficients of all the variables, with the exception of entrepreneurship was found to be statistically insignificant.
As a result, the study showed that education and graduate youth unemployment have a positive relationship.The odds ratio of being unemployment increases by 0.045 if the individual graduates from higher education and get a certificate.This may be due to job preference by educated individuals, the existence of high competition in government sectors and slow growth of the private sector as compared with the numbers graduating per year.This result is in line with those of Nganwa et al. (2015) who showed that having an education certificate did not guarantee employment.Another justification for why unemployment rates tend to be higher among the more educated young is that there is unavailability of resources to support full-time job search in Ethiopia like many other developing countries and unlike the situations in Latin American countries (Godfrey, 2003).As Nebil et al. (2010) demonstrated in their study, low level of education is a cause of unemployment.Similarly, Broussar and Tekelesilassie (2012) indicated that educational attainment and unemployment have positive relationships.Their conclusion is that youths with higher education were less likely to be unemployed in 1999 than they were in 2011.This suggests that labor demand has been unable to keep rapidity with the increases in educational attainment, particularly with jobs which demand highly skilled labor.
The number of graduate and unemployment has a positive relationship.This means that as the number of the graduate increases in the selected area, unemployment also increases and statistically significant at 10% level.For this variable, the odds ratio is 3.167.This implies a 1% increase in the number of graduates and unemployment increases by 3.167 times.Then increasing the number of graduates is another factor affecting the scarcity of job opportunities.Size of the labor force has been increasing while the demand for labor has a slower growth rate than the growing population (Nebil et al., 2010).Mismatch of education and training skills with the requirements of the labor market is another important reason for the high level of unemployment.
From the table, if all other variables are held constant, the odds of a graduated youths with low work experience being unemployed were about 9.234 times higher than those youths with work experience.This means that a graduated youth who has low work experience is less likely to be unemployed, as compared with youths with job experience.The results are in agreement with studies, and the findings of the relationship is statistically significant at (p <0.001) at 1% level.The result of this study confirms the finding of Foot (1986), Osterman (1980), ILO (2004), Anh et al. (2005) and Hassen (2005).Employers are usually hesitant to hire educated people who have little or no practical work experience since the costs to retrain and/or upgrade skills of young workers are often too high.As a result, youths who lack work experience remain unemployed.Our results also provide evidence to support the view held by many that prior work experience is likely to positively impact on the probability of unemployed youth finding employment.In conclusion, as the vacancy needs high work experience, the probability of unemployment is high.
The study also showed that there was a negative relationship between career advice and unemployment.The odd ratio was 0.217.This implies that a 1% decrease in career advice result in 21.7% increase in graduate youth unemployment.As a result, the relationship between access to job information and graduate youth unemployment was negative and statistically significant at 10% level.The odds ratio was 0.393.Then this implies that as access to job information decrease by 1%, the probability of graduate unemployment is increased by 39.3%.The findings of this study confirm the underlined statement that lack of access to job information increases the odds of unemployment.It indicated that the relative risks of unemployment for youth who had low information access from various employment sources are higher as compared with those who had high information access.
In the case of family income, the result indicated that individuals from low-income families are less unemployed than those from high-income families and statistically significant.The odds ratio of unemployed graduate youth decreases by 0.069 if their families are high economic performance.This is because families from medium/highincome families may have a better situation for searching for jobs and they can easily get initial capital to start their own business.Similarly, youth's occupational status in Ethiopia significantly differs with respect to family wealth index.In this regard, those whose family is poor are more likely unemployed as compared with those whose family are medium and above (Amanuel, 2016).
Aspire to the job and graduate unemployment has a negative relationship and the odds ratio was 0.017.This implies that as the interest to low income decreased by 1%, the probability of graduate unemployment increased by 1.7 %, this is caused by graduated youth aspiring to get 'white collar' jobs (hence do not value 'blue collar' jobs).According to Serneels (2007), public sector and formal private sector employment are considered 'good jobs' due to their high wages, while self-employment, casual and cooperative employment are considered to be 'bad jobs'.As shown in Table 8, there was a negative relationship between entrepreneurship skill and unemployment.But it is not significant since the p-value is greater than 0.06(0.942>0.05).This implies that as the entrepreneurship ability increases by 1%, the probability of unemployment may be decreased by 1.063 in terms of odd ratio.This may be in case of graduated individuals not having entrepreneurship ability but can create job opportunity for graduates.According to UN Habitat (2003 ) cited in Nebil et al. (2010), most people are motivated to start their own businesses and create their own employees because of the chances of finding jobs in the labor market where the availability of employment opportunities are so limited.However, starting a small business without the capacity to sustain it or cope with other related challenges is not a possible solution to the problem of unemployment.
The study also showed that the quality of education and graduate youth unemployment has a negative relationship and is statistically significant (p = 0.00 < 0.05).This result is in agreement with the prior expectation and indicates that the high level of graduated youth unemployment in the selected area may emanate from lack of quality education.The sample odds of well trained graduated youth being unemployed were 0.016 times lower than those of less quality.The results are in line with the findings of Gebeyaw(2011) in a study conducted in Addis Ababa, they found training to have a negative impact on unemployment and was statistically significant.The implication of this result is that training could be an important strategy to reduce youth unemployment in the selected areas.

CONCLUSIONS AND RECOMMENDATIONS
The aim of the study was to investigate the determinants of graduate youth unemployment in West Shoa Zone Selected Towns and suggest the way forward for justifying the unemployment challenge.The study used a binary logistic regression model to examine the relationship between dependent and independent variables.The results showed that out of nine explanatory variables tested except entrepreneurship, the remaining were statistically significant.Based on the findings, the recommendations forwarded are that the government must strengthen the laws and policies which will enable the graduate youth to acquire the quality of education, reduce work experience needed for the different job vacancy, develop systems on how to distribute job-related information and also provide career advice to graduate youth unemployment.Thus government and private sectors can use this output as the solution to reduce the graduate unemployment in the selected areas.

Table 1 :
Demographic characteristics of the respondents.

Table 2 :
Gender and education distribution by graduate unemployment status.
Source: Computed from survey of 2017.

Table 3 :
Number of graduate and work experience distribution by graduate unemployment.
Source: Computed from survey of 2017.

Table 4 :
Career advice and job information distribution by graduate unemployment.
Source: Computed from survey of 2017.

Table 5 :
Family income and aspire to job distribution by graduate unemployment.
Source: Computed from survey of 2017.

Table 6 :
Entrepreneurship and education quality distribution by graduate unemployment.
Source: Computed from survey of 2017.* is significant at less than 5% probability level.SD = standard deviation.
Source: Analysis result.