Microsoft Word - 2-Diriba (90-103).docx 90 IJIBEC Determinants of Graduate Youth Unemployment (A Case Study in West Shoa Zone, Ethiopia) 1. Diriba Ayele Gebisa 2. Negash Geleta Etana Corresponding Email: dirayele@gmail.com Ambo University Ethiopia Keywords: Unemployment; Graduate Youth; Binary Logistic Regression; Odds Ratio DOI https://doi.org/10.28918/ijibec.v3i2. 1573 JEL: A10, M50 Published : 10 Nov 2019 Abstract Today, youth unemployment is a common agenda and a critical issue of all countries; particularly in developing countries. In Ethiopia, lack of employment opportunities for educated young people is a critical development challenges facing the country. The objective of this study was to identify the determinants of graduate youth unemployment. To answer the research objective data was collected by a structured questionnaire from 312 samples of respondents, wherein a snow ball sampling technique was used and data was analysed using mixed research design, descriptive and casual design; where a binary logistic regression model used to examine the relationship between dependent and independent variables. The result indicated that amongst the nine variables of the determinants of graduate youth unemployment; education, number of graduates, work experience, career advice, market information, family income, aspire to the low-income job, and education quality, all were significantly affected unemployment rate except entrepreneurial ability. As a result, based on these findings, it can be recommended that there is a critical need for government, NGOs and all other stakeholders to work on these determinants to reduce graduate unemployment. International Journal of Islamic Business and Economics Available at http://e-journal.iainpekalongan.ac.id/index.php/IJIBEC ISSN 2599-3216 E-ISSN 2615-420X Vol 3 No 2 2019 91 International Journal of Islamic Business and Economics (IJIBEC), 3(2) December 2019, 90-103 1. Introduction The All countries can face particular macro-economic problems at different times. Among the fundamental macro-economic problems the recently rising and threatening the whole nation is the problem of youth unemployment, particularly graduated youth unemployment. Youth unemployment is a fundamental problem facing and challenging the social, political and economic activities of all countries. According to Msigwa and Kipesha, (2013) an active and qualified youth has the potential to change the social and economic development of the country if they are well managed and involved in all important sectors of the economic activities of the country. However, youth those have ample potential to influence and change the socio- economic activities of counties becoming a major policy challenge for all nations in the world due to unemployment problem. Unemployment is becoming sources of complex and significant crises such as rising crime rates and violence, dependence on family, low self-esteem, poor social adaptation, depression, and loss of confidence that devastate and hamper the socio economic development of the country (Kabaklarli et al, 2011). As ILO (2001) defined, unemployment is a condition of being without work but actively seeking available job at the prevailing wage level in the given period of time; while graduate unemployment is a type of unemployment among people holding academic degrees (Saptakee, 2001). Graduate unemployment is caused by countless of factors some of which involve a difference between aspirations, skills, and self-concept of graduates and employment opportunities available to them (Sampson, 1992). Several studies have conducted various studies in different parts of the world and investigated different factors and their impacts on youth unemployment. According the investigations made, the study done by Assad & Levison (2013), Baah-boateng (2016) on reasons of employment inadequacy for youth identified low rate of job creation and increasing environmental threats as major factors for youth unemployment. Muhammedhussen (2016) show that youth unemployment’s dream to create their own job is constrained highly by shortage of finance and lack of work place. Similarly, the result of Msigwa & Kipesha (2013), Bayrak & Tatli (2014), Baldry (2015), Ndyali (2016), Nyarko, Baah-boateng, & Nketiah-amponsah (2015) identified gender, location, education, skills, and marital status as important factors for youth employment. Further, Kakwagh & Agnes (2010) and Asmare & Mulatie (2014) also revealed that 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 were the primary factors for high youth unemployment rate. The 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). However, recently the country has been started to register an encouraging economic growth and significant reductions in poverty (Woldehanna, Hoddinott, and Dercon, 2008). In the same way, the Economist (January 6, 2011) reported that Ethiopia had ranked as the fifth fastest growing economy in the world through the periods 2001-2010 and forecasted to grow at 8.1% during 2011-2015. In spite of the recently started promising economic growth still the rate of underemployment and unemployment remained high and becoming the serious socio-economic problems of the country (Bimal & Kayak, 2014). Some of the limited studies that addressed the employment challenges in Ethiopia made by (WB, 2007; Nzinga & Tsegay, 2012; and Yohannes & Missaye, 2014). 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 International Journal of Islamic Business and Economics (IJIBEC), 3(2) December 2019, 90-103 92 absence of career advice, aspires to low income jobs and work experience. Hence, the purpose of this study was to investigate determinants of graduate unemployment. 2. Research Method This This research used a mixed research design means descriptive and casual design. The descriptive design employed using descriptive statistics such as frequency mean and cross tab and casual design employed binary logistic regression to investigate the impact of the various independent variables under the considerations on the dependent variables. For this study, primary data was 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. 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 size calculated using Yemane Taro formula (1967) by using 95 % confidence level & 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. This study used a binary logistic regression model. The particular model was chosen by the researchers’ based on the behavior of the dependent variable; where the dependent variable is categorical variable with two groups (employed & unemployed which valued as 1 & 0 respectively). The researchers had chosen the cumulative logistic regression model for its easiness and more significant interpretation of odds ratio; even though the logit model yield similar parameter estimates (Gujarati, 2004). Therefore, the binary logistic regression model of a dichotomous dependent variable which is takes either 1 or 0 value depending on Y is used. The probability that the outcome is present (probability of success) will be given by: We obtain 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: Where = (β0, β1, β2… βk) are the model parameters and = (X1, X2… Xk) are explanatory variables. The above equations give suitable representations of the success probability, odds, & 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. Finally employed model was:* GUS=β0+β1NG+β2EL+β3Eship+Β4CA+β5ALJ+β6WE+β7FI+ β8JI +β9QE Where: 93 International Journal of Islamic Business and Economics (IJIBEC), 3(2) December 2019, 90-103  GUS=Graduate Unemployment Status  Β0=the intercept,  ß1-9 is the coefficient to be estimated.  NG= number of graduate  EL=Education level  Ership= Entrepreneurship  AC= Career advise  ALJ= Aspire to low income job  WE=work experience  FI= Family income  JI= Job information  QE=Quality of education Data Analysis: Data was analyzed using descriptive statistics cross tab and binary logit regression using SPSS V.20.. 3. Results and Discussions The descriptive statistics of the demographic characters of the respondents and the distributions of the major variables under considerations were as follows: Table 1. Respondents’ Demographic Characteristics Item Category Frequency % Gender Male 167 53.50% Female 145 46.50% Total 312 100% Level of Education Level 1-4 210 67.31% BA 102 32.69% Total 312 100% Graduate Unemployment Status Employed 76 24.36% Unemployed 236 75.64% Total 312 100% Source: Computed from surveyed data 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 shown on the same table 1, were 210(67.31%) of them were academically from level 1-4 and the rest 102(32.69%) of them were held BA degree. The results show that out of the total respondents, the majority about 67.31% of the graduated unemployed were from level 4. Table 1 also display the distribution of the status of graduate unemployment, from Table 1, 76(24.36%) of the respondents were employed and 236(75.64%) of them were unemployed. This shows that the majority, three-fourths, of the graduated youth unable to be employed. 3.1. Descriptive Analysis Of Factors Influencing Graduate Youth Unemployment Table 2. Gender and education distribution by Graduate Unemployment Status International Journal of Islamic Business and Economics (IJIBEC), 3(2) December 2019, 90-103 94 Category Graduate Unemployment Status Total X2 sig Employed Unemployed Gender Male 48(15.38%) 119(38.15%) 167(53.53%) 3.747 0.053 Female 28(8.97%) 117(37.5%) 145(46.47%) Total 76(24.36%) 236(75.64%) 312(100.0%) Education Level level 1-4 24(7.69%) 186(59.62%) 210(67.31%) 58.288 0.000 BA degree 52(16.67%) 50(16.02%) 102(32.69%) Total 76(24.36%) 236(75.64%) 312(100%) Source: Computed from survey data Table 2 presents gender and education distribution by graduate unemployment status. This objective was to investigate whether there is a relationship between gender and status of graduate unemployment; level of education and graduate unemployment status a cross tab between the variables done on Table 2 above. The cross tabulation indicate that males were more employed 15.38 %, compared to females 8.97% graduated and also 38.15% % of the males graduated were unemployed compared to 37.5 % of the females graduated. This shows the existence of an association between gender difference and unemployment status. Also the Pearson Chi-square shows the existence of a major variance between graduated males and females status in unemployment, where the p-value is 0.053 at the p < .05 level. This is the fact that males are less restricted by family and culture to go freely where they like compared to female in actively searching for opportunities. Similarly, the cross tab made between level of education and unemployment status also show that only 7.69% of respondents from level 1-4 of educational status employed compared to 16.67% of respondents employed in BA degree, or 59.62% of the respondents within level 1-4 education status were unemployed compared to 16.02% of the respondents with education status of BA degree holder. Also, the chi-square test further revealed that there is a statistically strong relationship between education level and unemployment condition at 5% probability level. The findings showed the inverse and significant relationship between the levels of education. This means that unemployment status means as education level increases unemployment decreases. Table 3. Number of graduate and work experience distribution by graduate unemployment Dependent Variable Numbers of graduate in the market Total Test Small large X2 Sig Graduate Unemployment Status Employed 28 48 76 25.545 0.000 % 8.97% 15.38% 24.35% Unemployed 27 209 236 % 8.66% 66.99% 75.65% Total Freq. 55 257 312 % 17.63% 82.37% 100% Dependent Variable Work Experience Needed Total X2 Sig Low High Graduate Employed 38 38 76 95 International Journal of Islamic Business and Economics (IJIBEC), 3(2) December 2019, 90-103 Source: Computed From Survey Data Table 3 indicate numbers of graduates in the market, which is about 82.37% of the respondents indicated that a large number of graduate youth in the market to find jobs. Only 17.63% of respondents indicated that low in number this may be in case of fields. The result of the cross tab also shows that out of 82.37%of the respondents were unemployed as a result the large number of graduate which constitute around 66.99% compared to small 8.66%. As indicated in Table 3 the chi-square test also shows the significant statistical relationship between work experience needed and unemployment status. Among the key factors that responsible for the unemployment of graduated youth one of the major variables is absence of work experience. As shown in Table 3, 63.46% of the respondents indicated that different job vacancy announced at different times needed high work experience rather than a fresh graduate with zero experience. Only 24.36% of respondents indicated 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 compared to 24.36% of those employed with high work experience requirement. The chi-square test also shows the significant statistical relationship between work experience needed and unemployment status. Table 4.Career advice and job information distribution by graduate unemployment Dependent Variable Career advice provision. Total Test Low high X2 Sig Graduate Unemployment Status Employed 23 53 76 15.152 0.000 % 7.37% 16.99% 24.36% Unemployed 132 104 236 % 42.31% 33.33% 75.64% Total Freq. 155 157 312 % 49.68% 50.32% 100% Dependent Variable Job information available Total X2 Sig Low high Graduate Unemployment Status Employed 41 35 76 0.395 0.530 % 13.14% 11.22% 24.36% Unemployed 137 99 236 % 43.91% 31.73% 75.64% Total Freq. 178 134 312 % 57.05% 42.95% 100% Source: Computed From Survey Data Unemployment Status % 12.18% 12.18% 24.36% 35.852 0.000 Unemployed 38 198 236 % 12.18% 63.46% 75.64% Total Freq. 76 236 312 % 24.36% 75.64% 100% International Journal of Islamic Business and Economics (IJIBEC), 3(2) December 2019, 90-103 96 Career advice is important especially for unemployed graduate youth. Provision of a good career advice for unemployed graduate youth energizes to search better job, rather than merely being 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, that among the respondents, 49.3% of them replied that they acquired low career advice about 42.31% unemployed and the remaining 50.32% replied that they got high career advice, which is 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 are employed with low career advice, or only 43.91% of graduated youth are 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 career advice and graduate youth unemployment level. Labor market information plays a significant role in maintaining the efficiency of the labor market. Labor market information is scarce, and is not available to all job seekers. Improved accessibility to information more requires the availability of facilities as transportation, availability of newspaper where the job to be announced and internet facility. For graduated youth from poor families and those living in the remote rural area further from better facilities getting job information is unusual. Table 4 shows; that the availability of labor market information also influences the level of graduated youth unemployment. In Table 4, 57.05% of the respondents replied that there was low availability of labor market information and 42.95% of them replied there was highly available market information for graduated youth. Then we can conclude that as job information was available unemployment is decreasing. 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. Table 5. Family income and aspire to job distribution by graduate unemployment Dependent Variable Family income/economic performance Total Test Low Income Medium income X2 Sig. Graduate Unemployment Status Employed 19 57 76 43.052 0.000 % 6.09% 18.27% 24.36% Unemployed 160 76 236 % 51.28% 24.36% 75.64% Total Freq. 179 133 312 % 57.37% 42.63% 100% Dependent Variable Aspire to join low salary/income jobs. Total X2 Sig. low interest high interest Graduate Unemployment Status Employed 29 47 76 89.656 0.000 % 9.29% 15.07% 24.36% Unemployed 213 23 236 % 68.27% 7.37% 75.64% Total Freq 242 70 312 % 77.56% 22.44% 100% Source: Computed From Survey Data 97 International Journal of Islamic Business and Economics (IJIBEC), 3(2) December 2019, 90-103 Table 5 indicates that there is a strong correlation between the employments of graduated youth and their family economic/income level. Unemployment and family’s level of income have a marked two ways relationship, where household income is higher unemployment rates lower and the vice-versa. Table 5 display, 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, the frequency distributions the cross tab between family economic performance and the unemployment status of the graduated youth. The result show that, 18.27% of employed graduated youth were from the family of medium economic performance compared to 6.09% of employed graduated youth from low-income earner, or only 68.27% unemployed from the family of low-income earner compared to 24.36% of unemployed graduated youth from the family of medium economic performer. The variables are statistically significant at 5% level of significance. Employment status 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% was unemployed while the remaining 22.44% % had a high aspiration to join low salary jobs and from this about 7.37% was unemployed and the left 15.07% employed. This result shows that the relationship is statistically significant at 5% level. Table 6. Entrepreneurship and education quality distribution by graduate unemployment Variables Employed Unemployed T-Value Value Sig. value N Mean SD N Mean SD 2.66 0.167 Entrepreneurship 76 3.1 0.31 236 2.98 0.34976 Quality of education 76 2.89 0.325 236 2.31 0.432 10.73 0.001* Source: Computed From Survey Data * is significant at less than 5% probability level. SD = standard deviation 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 to high education coverage regardless that the education quality created poor education quality that made graduated student incompetent relative to the requirements of the labor market (Guarcell & Rosita, 2009). Table 6 shows; the mean of 2.89 of response of employed graduated youth education quality was more than the mean.2.31, the 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. The other variable that influences the graduated youth unemployment is their entrepreneurship ability. Table 6 shows, that the mean response of employed graduated youth entrepreneurship ability is 3.1 compared to 2.98 mean response of unemployed graduated youth entrepreneurship ability. The t-value, 0.167, is not statistically sufficiently significant at 5% level of significance. Therefore, one can generalize that entrepreneurial ability of graduated International Journal of Islamic Business and Economics (IJIBEC), 3(2) December 2019, 90-103 98 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. 3.2. Binary Logistic Regression Results As mentioned in the methodology section logic model was chosen to identify the causes of graduate unemployment in the target area of study. The correlation of the independent variable was conducted so as to test whether the problem of co-linearity exists or not. Thus, it was found that there was no significant multi-co-linearity issue between the examined explanatory variables. The log likelihood ratio also tested and presented as follows. 3.3. Model Summary Table 7. Model Summary -2 Log likelihood Cox & Snell R Square Nagelkerke R Square 131.753 .497 .742 Source: Analysis Result 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 shows that there is no major error left over in the model. The model summary provides some approximation of R2 statistic in logistic regression. Cox and Snell R2 or Nagelkerke R2 is an analogous statistic in logistic regression to the coefficient of determination R2in linear regression, but not close analogue. Cox and Snell’s R2 attempts to imitate multiple R2 based on likelihood. In this study, Cox and Snell R2 indicate that 49.7% of the deviation in the dependent variable was explained by the explanatory variables. Nagelkerke R2 in the Table 7 is 0.742 that shows 74.2% of the variability in the dependent variable using of graduate unemployment method was explained by the explanatory variables. 3.4. Multy-Co-Linearity Table 8. Multi-co-linearity Model Co linearity Statistics Tolerance VIF EDL .893 1.120 NG .895 1.117 WE .888 1.126 CA .951 1.052 LMI .973 1.028 FI .915 1.093 ATLJ .864 1.158 ERSHIP .954 1.049 QE .810 1.234 Dependent Variable: Graduate unemployment status Source: Analysis result 99 International Journal of Islamic Business and Economics (IJIBEC), 3(2) December 2019, 90-103 Abbreviations  EDL=Educational level  NG=Number of graduate  WE=Work Experience  CA=Career Advice  LMI=Labor Market information  FI=Family Income  ATLJ Aspire to low job/income  ERSHIP=Entrepreneurship  QE=Quality of education Multi-co-linearity might push up standard errors; but until there is not any perfect multi- co-linearity there is no bias if regression estimates will be used. For the purpose of the problem of perfect multi-co-linearity among the variables, variance inflated factors (VIF) are estimated and displayed in the above Table 8. For this particular study, variance inflation factor for all variables tested and resulted below 10; showing that there is no signal of co- linearity that does influence the main variable of interest in a model. Therefore, it can be concluded that multi-co- linearity is not a problem in the data. Baum (2006) explained that near co-linearity that doesn't affect the key variable of concern in a model cannot be a difficult issue and can be ignored. 3.5. Binary Logistic Result Table 9 Binary Logistic Results Variables B S.E. Wald Sig. Exp(B) EDL 3.106 .674 21.263 .000* .045 NG 1.153 .674 2.921 .087** 3.167 WE 2.223 .673 10.901 .001** 9.234 CA -1.529 .583 6.874 .009* .217 LMI -.934 .560 2.788 .095** .393 FI -2.680 .643 17.377 .000* .069 ATLJ -4.098 .750 29.850 .000* .017 ERSHIP -.061 .839 .005 .942 1.063 QE -4.162 .812 26.298 .000* .016 Source: Analysis result * =Significant at 0%, **= Significant at 5%, ***= Significant at 10% Table 9 presents the calculated logistic model. The logistic regression coefficients, sig, Wald, and odds ratio for each of the independent variables are shown in Table 9. The “sig” column conveys the significance (or p-value) of all variables whereas β values show the trend of the association of a specific independent variable to the dependent variable. On the other side, Exp (β) column denotes 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 are statistically significant, except entrepreneurship. As a result, the study reveals that education level and graduate youth unemployment have a positive relationship. The odds ratio of being unemployment rises by .045 as a unit individual graduate from higher education and gets a certificate increase. This may be because of individuals’ job preference; the presence of high competition in the government sectors and the slow growth of the private sector as compared to the number of youth graduates per year. The results confirm the study of (Nganwa, Assefa, & Mbaka, 2015) that shows having an education certificate did not guarantee employment. Likewise, the reason why unemployment rates is higher for educated young is absence of resources to support full-time job search in International Journal of Islamic Business and Economics (IJIBEC), 3(2) December 2019, 90-103 100 Ethiopia like many other developing countries unlike the situations in Latin American countries (Godfrey, 2003). Similarly, Nebil, Gezahegn & Hayatet (2010) revealed that low level of education is a reason for unemployment in Ethiopia. Also the result of study made by Broussar & Tekelesilassie (2012) show that youth with grater education were less likely to be unemployed. This shows that the labor demand has not been able to accommodate in terms of number and diversified graduating students with different skills. The number of graduates and unemployment has a positive relationship from Table 9, which means that as the number of the graduates increases in the selected area, unemployment also increases. The results indicate statistically significant relationship at 10% level of significance. For this variable, the odds ratio is 3.167. This implies that a 1 percent increase in the number of graduates the unemployment increase by 3.167 times. Table 9, further provided that if all other variables are being fixed, the odds of a graduate youth with low work experience to be unemployed were approximately 9.23 times higher than those of youth having work experience. This implies that a graduate youth who has a relatively low work experience is more likely to be unemployed, compared to those with adequate job experience. The result is statistically significant at (p <0.001) at 1% level; and the result confirms the conclusions of Duong & Vanet (2005) and Hassen (2005). The study has also shown the existence of inverse relationship between career advice and unemployment. The odd ratio was .217. This implies that as a 1% decrease in career advice result to 21.7% increase of 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 .393. This implies that as access to job information decrease by 1% the probability of graduate unemployment is increased by 39.3%. The result shows that lack of access to job information significantly affect youth unemployment status. Regarding the relationship between family income and youth unemployment status, the results show that individuals from low-income families are more unemployed compared to those from high-income families and the results are statistically significant at a 1% level of significance. The odds ratio of unemployed graduate youth decreases by .069 if their families are high economic performance. The result is because of the fact that graduated youth from high and middle income families are in better position to search jobs from everywhere available without finance limitation or able to start their own business with minimum initial capitals. The result confirm the finding of (Amanuel, 2016) that show graduated youth whose family is poor are more likely to be unemployed related to those from better income family. Aspire to the job and graduate unemployment has a negative relationship and the odds ratio was .017. This implies that as the interest too low income decreased by 1% the probability of graduate unemployment increase by 1.7 %. The result goes with the finding of Serneels (2004) that stated people prefer earning wage informal employment in public or private sector than self-employment due to the fear of hard work and risk of self-employment. As revealed in Table 9 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 entrepreneurship ability increase by 1% the probability of unemployment may be decreased by 1.063 as odd ratio reports. This may be in case of any graduated individuals not needing to have entrepreneurial ability, but they can create job opportunities for graduates. According to UN Habitat (2003) people are driven to make their own jobs because of the low probability of getting jobs in the labor market. 101 International Journal of Islamic Business and Economics (IJIBEC), 3(2) December 2019, 90-103 Table 9 also display the statistically significant (p = 0.00 < 0.05) and inverse relationship between quality of education and graduate youth unemployment. Similarly, the odds of well- trained graduate youth being unemployed were .016 times lower than those of less quality. The results support the findings of Gebeyaw (2011) that concluded the inverse relationship between training and unemployment. The implication of this result is that, training is one of the important strategies for decreasing youth unemployment. 4. Conclusion Based The aim of the study was to investigate the determinants of graduate youth unemployment in West Shoa Zone, Ethiopia. The study used mixed research design, descriptive and casual design; a binary logistic regression model used to examine the relationship between dependent and independent variables. The conclusions from the results of the descriptive analysis show that the existence of close association at 5% level of significance between gender difference and unemployment status, the inverse and significant relationship between the levels of education and unemployment, significant and positive statistical relationship between work experience needed and unemployment status, and significant relationship between career advices and graduate youth unemployment level; whereas insignificant statistical relationship between the labor market information and graduated unemployment status; entrepreneurial ability of graduated youth and graduated youth unemployment status at 5% level of significance. On the other hand, before the analysis of the binary logistic regression, the researchers checked the existence of the problem of multi co-linearity and proved that there was no significant multi-co-linearity issue between the explanatory variables under investigations. Therefore, the results of binary logistic regression show that out of the nine explanatory variables, eight variables including number of graduate, job information and work experience are significantly related to unemployment rate at 5% level of significance; whereas education level, career advice, aspire to low income job, family income, and quality of education are statistically significant at 0% level of significance; however entrepreneurial ability is not significantly related to unemployment rate at 5% level of significance. Generally, the results of this research, reinforce the earliest well-known education and knowledge theory introduced by Ibnu Khaldun in Islamic Economics regarding labor. Ibnu Khaldun stressed the professionalism, skills and specialization that must be possessed by workers to get the right position. 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