Key Words: Unemployment, Youth, Youth Unemployment Abbreviation: MDG-Millennium Development Goal

Transcription

1 IAARD Journals eissn: International Journal of Economics And Business Management International Journal of Economics and Business Management, 2016, 2(2), Determinants of Youth Unemployment: The Case of Ambo Town, Oromia, Ethiopia Dejene Terefe Fila 1,J.Paul Mansingh 2 and Warkaw Legesse 3 1. Post-Graduate Student, Department of Rural Development & Agricultural Extension, Ambo University, Ambo, Ethiopia. 2. Professor,Department of Rural Development & Agricultural Extension, Ambo University, Ambo, Ethiopia. 3. Lecturer, Department of Rural Development & Agricultural Extension, Ambo University, Ambo, Ethiopia.... Abstract: Today, globally youth employment is a social agenda and an issue of critical importance. In Ethiopia, the labor force grows with an increasing proportion of youth; employment growth is inadequate to absorb the new entrants in the various sectors of the economy. The country has one of the highest urban unemployment rates worldwide, at about 50 per cent of the youth labor force. Thus, lack of employment opportunities for young people is among the critical development challenges facing the country. In this stand, this study was conducted to examine the Determinants of Youth Unemployment in the study area. The research was conducted in Ambo town, West Shoa Zone of Oromia region because among the towns in West Shoa zone, the unemployment rate was more in Ambo town. A sample of 140 respondents from three kebeles namely Kebele 01, 02 and 03 with high unemployment rate, was selected. The structured interview schedule was prepared to collect information on determinants of unemployment. The study was supplemented by qualitative data collected through Key Informant Interviews (KIIs), Focus Group Discussions (FGDs), and Direct Observations. Binary logistic regression was performed to assess the determinants of youth unemployment. The multivariate analysis shows that among the demographic variables, age of the respondents and migration status were significantly related to youth unemployment whereas marital status of the respondents was not significant. From the human capital variables included in the model, education and health status of the respondents were significantly related to youth unemployment, whereas participation in employment related trainings was not statistically significant. Among the economic determinants, household income, access to credit and saving services and work experience were significant. Access to job information and psycho-social factors were the two social capital variables that were significantly related to youth unemployment. As youths are more vulnerable to unemployment, efforts should be made by the government to provide credit and training so as to facilitate their entry into business and entrepreneurship. Migrants are the victims of unemployment in town. Therefore, the pushing factors of migrants should be identified to arrest the continuous drift of youth towards urban areas as this may worsen the unemployment situation in urban areas. Key Words: Unemployment, Youth, Youth Unemployment Abbreviation: MDG-Millennium Development Goal Introduction Today, globally youth employment is a social agenda and an issue of critical importance.young men and women are most eager to strike out to secure their futures and to contribute to their families, communities and societies (ILO, 2008; Abera, 2011). Globally, the number of young people is about to become the largest in history relative to the adult population. According to the ILO (2011), they constitute 47 per cent of the world s unemployed, and approximately 88 million young individuals globally are out of work. Africa is the world s youngest continent, as the proportion of youth among the region s total population is higher than in any other continent. In 2010, 70 percent of the region s population was under the age of 30, and slightly more than 20 per cent were young people between the ages of 15 to 24.The employment situation of youth in Africa particularly the Sub-Saharan Africa, is serious and challenges their livelihood (ILO, 2011). Ethiopia has witnessed rapid population growth in recent decades. The population was estimated to be about over 97 million in 2014 (CSA, 2014), making Ethiopia the second most populous country next to Nigeria in Sub Saharan Africa and 13 th in the world. The proportion of young people in the overall population has increased over the last two decades. The young cohort represented about 14 per cent of the population in 1984 and 20 per cent of the population in 2001 (Guarcello and Rosati, 2007). Today, the youth population accounted for 30 per cent of the total population and 39.6 per cent of urban population of the country (CSA, 2014). In Ethiopia, the labor force grows with an increasing proportion of youth; employment growth is inadequate to absorb the new entrants in the various sectors of the economy (Guarcello and Rosati, 2007). The country has one of the highest urban unemployment rates worldwide, at about 50 % of the youth labor force (Berhanu et al, 2005). Thus, lack of employment opportunities for young people is among the critical development challenges facing the country (Guarcello and Rosati, 2007). Oromia Regional State is one of the regions with a total population of over 34 million, having 34 per cent of the national population. Out of the total population of the region, 30 per cent were youth (CSA, 2014).Besides, the size of urban population of the region was estimated about 2.1 million in which 41.2 per cent were youth (CSA, 2010). On the other hand, employment status of youth in the region showed that youth unemployment rate was estimated about 21.2 per cent, having 14.1 per cent of males and 26.5 per cent of females in 2005 (CSA, 2006). This estimate includes both the rural and urban youth unemployment in the region. Furthermore, youth unemployment rate in urban areas of the region also found to be 18.0 per cent, with 15.5 per cent of males and the corresponding females unemployment rate was 20.4 per cent in 2007 (CSA,2010). Page No.162

2 The age range of the youth is differently determined in different states. In the Ethiopian context, it is (MYSC, 2014).Considering the existing high youth unemployment rate, recently, the government has started new initiatives to reduce the problem through fostering entrepreneurship, and by increasing youth participation in the development activities of the country (MOY, 2004).The overall goal of the National Youth Policy is to bring about the active participation of youth in the building of a democratic system and good governance as well as in the economic, social and cultural activities in an organized manner and to enable them to fairly benefit from the results (MOY, 2004).Improving the employment status of youth could lead to the achievement of Millennium Development Goals (MDGs) through identifying the factors that hindered the young people in getting employment. This rate of youth unemployment is the outcome of various social, economic and demographic factors (Hassen, 2005). A research conducted by Morris (2006) and Salvador and Killinger (2008) found that lack of experience, mismatch between their skills and the demands of labor markets, inadequate information and counseling, less access to resources and services, discrimination on the basis of age, sex, ethnicity, health, family economic status, attitudes of youth towards jobs and other factors are common barriers of youth in finding employment. These factors hindered young people in finding employment, results the depreciation of human capital and deterioration of youth employment prospects, which could lead to social exclusion (Berhanu et al., 2005; Abera, 2011).Analysis of the factors associated with youth unemployment indicated that the characteristics of individuals such as educational level, work experience, lack of employable skills, sex, health status, migration, attitudes of youth towards jobs, family economic status are associated with youth employment status (Toit, 2003). Youth in urban areas of the Oromia Regional State had limited access to employment opportunities. The rate of youth unemployment in urban areas of the region was found to be 41 per cent in 2014 (CSA, 2014). Ambo is one of the towns in Oromia Regional State with a total population of 107,980. Out of this population, 29 per cent were youth (CSA, 2014).Like other towns of the country, Ambo town also manifests the problem of youth unemployment. Youth unemployment rate was found to be 16 per cent, having 6.63 per cent male and 9.37 percent female (CSA, 2014).The facts displayed that youth population is one of the segments of the town population affected by the problem. While these general facts are clear, the specific factors affecting youth employment in the town have received little research attention. The determinants of youth unemployment in the town so far was not well assessed (Ambo Town Youth and Sport Office Final Report, 2014). In this stand, this study was conducted to examine the Determinants of Youth Unemployment in the study area. Research Methodology The research was conducted in Ambo town, West Shoa Zone of Oromia region. Ambo town was purposively selected because among the towns in West Shoa zone, the unemployment rate was more in Ambo town (Table 1). Table 1: Youth unemployment rate of some towns in West ShoaZone Name of town Youth unemployment rate (%) Ambo 29 Ginchi 25 Gudar 24 Source: West Shoa youth and sport office (2015). The study used multistage sampling. Stage 1: Ambo town was purposively selected for this study. Stage 2:The primary sampling units are kebeles. Three kebeles namely Kebele 01, 02 and 03 out of the 6 kebeles in the town are purposively selected. The purpose of selecting these three kebeles was that the unemployment rate was high when compared with other three pre-urban kebeles (Awaro Kora, Sankalle Farisi and Kisose Odoliban) (Table 2). Table 2 Unemployment rate of six kebeles of Ambo town in 2014 Kebeles Number of unemployed youths Awaro Kora, 234 Sankalle Farisi 231 Kisose Edoliban 206 Source: Ambo Town Youth and Sport Office (2014) Stage 3: For the purpose of this research, the subjects considered were youths registered with office of the Youth and Sport Affairs, Ambo, aged between years. Using simple random sampling technique, the youths were selected from the identified kebeles. The total number of unemployed youths registered with the office of the Youth and Sport Affairs, Ambo Town, Page No.163

3 from selected three kebeles viz., kebele 01, kebele 02 and kebele 03 were 2068.The sample size was determined by using the formula given by Kothari (2004). n=z 2.Npq e 2 (N-1) +z 2 p.q Where n= required sample size N= size of the population (2068) e=8% P=0.5 q=0.5 S. No. Table 3: Selected respondents from each Kebele Selected kebeles Total number of respondents Selected respondents 1 Kebele Kebele Kebele Total 3 2, Additionally, key informants such as expert of youth and sport office, expert of MSSED office, community leaders and Source: Own sketch Z= Confidence interval at 95% which is 1.96 n= (1.96) 2 x2068x0.5x0.5 (0.08) 2 x (2068-1) + (1.96) 2 x0.5x0.5 n=140 The number of respondents to be selected from each kebele was determined by probability proportion to population size method. The respondents were selected using simple random sampling technique. The details of the respondents selected from the three selected kebeles are presented in Table 3. kebele managers were included. The details are presented in Table 4. Table 4: Details of the sample size and methods of data collection (n=140) S.No. Section Sample size Methods of data collection 1 Selected respondents 140 Interview schedule Key informants (KIIs): 2 a)expert of youth and sport office b)expert of MSSED office c) Community leaders e)kebele managers 3 Focus Group Discussions(FGDs) The structured interview schedule was prepared to collect information on determinants of the respondents. The study was supplemented by qualitative data collected through Key Informant Interviews (KIIs), Focus Group Discussions (FGDs), and Direct Observations. Checklist was designed to collect the required data during FGDs and KIIs. In addition, secondary data obtained from records of administrative offices, publications, journals, books and other sources relevant to this study were also used to enrich the investigation. Multivariate analysis was performed to assess the factors that are associated with youth employment status. Since the dependent variable is dichotomous, binary logistic regression model is fitted. The logistic regression model is explained as follows: 1n {p} = β 0 + β 1 X 1 + β 2 X β i X i 1-p Where Xi s were set of independent variables: Age, migration status, marital status, educational status, participating in training programme, health status, household income, work experience, access to credit and saving Checklist 3 Checklist services, access to information and psycho-social problems.β 0 was a constant while β i s were regression coefficients. P was the probability of being unemployed and 1-p was the probability of being employed.1n[p (event)/1- p(event)] = the risk or the odds of being unemployed in the logistic regression. β i was the factor by which the odds change when the i th independent variable increased by 1 unit. If β i was positive, then Exp (β i ) would be greater than 1 which implies the odds were increasing. If βi was negative, then Exp (β i ) would be less than 1 by indicating that the odds were decreasing. When β i was zero, then Exp(β i ) equal to 1 by indicating that the odds would be the same within the categories of the i th independent variable (Nicola et al. 2006). In logistic regression, Exp (β) was the estimated multiplicative change in the odds for a unit increase in the predictor, controlling for the effect of others. The value of the relative odd ratio can be further expressed as a percentage change of the odds [Exp (β)-1*100]. Logistic regression was particularly relevant here because of the dichotomous nature of the response to the dependent variable which was Page No.164

4 unemployed or employed. The value label of the variable was 0 if a respondent was unemployed and 1 otherwise.. Description of dependent and independent variables: Table 5 Description of dependent and independent variables No Dependent variable Categorization 1 Employment status of youth If unemployed (0) and employed (1) No. Independent variables Categorization 1 Age If (0), (1) and (2) 2 Migration status If migrant (0) and not migrant (1) 3 Marital Status If unmarried (0) and married (1) 4 Educational status If illiterate (0), primary (1-8) (1),secondary (9-12) (2),diploma (3), degree and above (4) 5 Participating in job training programme If not participated at least once (0) and participated at least once (1) 6 Health status If non- healthy (0) and healthy (1) 7 Access to credit &saving services 8 Household income (in ETB) If no access to credit and saving service (0) and access to credit and saving service (1). If <500 (0), (1), (2) and >2501 (3) 9 Work Experience If not have work experience 0) and have work experience (1) 10 Access to job information If public media (0), social media(1), community leaders (2), public notice (3) 11 Psycho-social problems If no (0) and yes (1) Findings and Discussion The determinants of youth unemployment were examined using logistic regression model since the dependent variable is dichotomous. Binary logistic regression model is the multivariate statistical tool used to analyze the relationship between the dependent variable (youth employment status) and the predictor variables; namely age, migration status, marital status, educational status, participating in training programme, health status, access to job information, work experience, access to saving and credit services and psycho-social problems. The logistic regression model predicts the log odds (youth unemployed Vs employed) of the dependent variable. Multicollinearity effects Multicollinearity in logistic regression is a result of strong inter-correlation among the predictor variables (Montgomery and Peck, 1992; Garson, 2009). Prior to running the logistic regression analysis discrete explanatory variables were checked for the existence of multicollinearity and high degree of association using contingency coefficients. The results of the computation of contingency coefficients (Table 6) revealed that there was no serious problem of association among discrete variables. The contingency coefficient for the dummy variables included in the model was less than 0.75 that did not suggest Multicollinearity to be a series concern. Goodness of fit: One of the techniques used to assess the goodness of fit of a model is Hosmer and Lemeshow test. The test is used to accept or reject the alternative hypothesis the model adequately describes the data. If the significance level of the test is less than 0.05, it indicates that the alternative hypothesis is rejected and the null hypothesis which states the inadequacy of the model to describe the data is accepted. In the case of this study, the significance level of the test is found to be Thus, the alternative hypothesis which states that the model is adequate to describe the data was accepted. Application of binary logistic regression in this study is based on the dependent variable (youth unemployment) which is coded as 0 if the respondent is unemployed and a value of 1if the respondent is employed. The following explanatory variables are found to be significant in the bivariate analysis: namely age, migration status, marital status, educational status, participating in training program, health status, household income, and access to credit and saving services, work experience, social network status, access to job information, and psycho-social problems and Page No.165

5 therefore, entered in to the model. The result of logistic regression is presented in Table 7. X 1 Age X 1 1 X X 2 Migration status X X 3 Marital status Table 6.Coefficient of Contingency for Discrete Variables X 4 X 5 X 6 X 7 X 8 Access Educational Participation Health Household to Credit status in Job status income and training saving X 9 Work experience X 10 Access to Job information X 11 Psychosocial problem X X X X X X X X Source: Computed from Survey Data, Table 7.Results of Binary Logistic regression model Variables B S.E Wald df Sig. Exp (B) Age ** Migration status **.309 Marital status Educational status ** Participating in job training Health status *.370 Household income **.410 Access to credit and saving services **.377 Work experience *** Access to job information ** Psycho- social problems *.331 Note: Statistically significant at: ***P < 0.001, **P < 0.05; *P < 0.01 Source: Result of the regression analysis of own survey data (2016) 1. Age The multivariate analysis result in Table 7 shows that age affected the youth employment status in the study area. The likelihood of being unemployed for age group is higher than age groups and (reference category). The regression coefficient between age and youth unemployment is significant (P<0.05) at 5% level. Therefore, it was concluded that unemployment was more among young people. This was perhaps due to the fact that age goes with experience and knowledge which implies that fresh graduates with no experience always find it hard to access decent jobs since they lack relevant experience. Alternatively, it might be because the young people still living in their parents home and can afford longer periods of unemployment while seeking a suitable job. This result agrees with the finding of the study conducted by ILO (2006) which states that, in many economies of developing regions, young people were more than three times as likely as adults to be out of work. 2. Migration status Migration status was the other demographic factor that significantly influenced youth employment status in the study area. As indicated in Table 7, the likelihood of being unemployed for migrants was higher than non-migrants (reference category). The regression coefficient between migration and youth unemployment is significant P<0.05)at 5% level. The findings of this study thus showed consistency with the finding of other scholars (Anh et al, 2005; Todaro, 1994). It seems that non-migrants may have better opportunity for education, searching better job and other advantages, while migrants particularly from rural areas who had low level of education coupled with weak social networks and low information access could increase their risks of being unemployed. 3. Marital status As far as the marital status of youth is concerned, the unemployment was more among unmarried youth compared to those of the married youth (reference category). Page No.166

6 However, the relationship between marital status and youth employment is not statistically significant at P>0.05 (Table 7). 4. Educational status Educational level of an individual would affect his/her employment opportunity. People with higher educational level are said to be the most productive, and thus secure the best jobs and the highest salaries (Schultz, 1961). On the other hand when the necessary skills and knowledge lack, the chance of being unemployed is high. The findings of this study support this idea, having lower level of education increases the odds of unemployed. The educational level of the respondent was categorized into five groups by considering the Ethiopian school system. The respondent members who cannot read and write were categorized as illiterate and those who have completed grade 1-8 as primary level of education, and those who have completed grade 9-12 as secondary level of education those who have College diploma or TVET and degree and above. Degree and above education was used as a reference category. Thus, the result of multivariate analysis revealed for those who have attained primary and secondary level of education the odds of unemployment are found to be higher as compared to the reference category. The risk of being unemployed for other categories is higher than the reference category. And the risks of being unemployed for those who have completed primary, secondary level of education and diploma are higher respectively as compared to the reference category. The regression coefficient between education level and youth unemployment is significant (P<0.05) at 5% level (Table 7). This finding is in line with the findings of Salvador and Killinger (2008), World Bank (2009), and Okojie (2003) which stated that unemployment rate of less educated youth tends to be higher as compared to more educated youth. 5. Participating in training job programme Participating in training programme on employment opportunities is the most fundamental for getting job. Training helps the youth to become more knowledgeable and increase his skill. Lack of training may affect youth employment status. However, the result is statistically insignificant (P>0.05) (Table 7). 6. Health status The result of multivariate analysis shows that the respondents those who were not healthy were likely to be unemployed more than those whose who were healthy (reference category). The risk of unemployment among the not healthy respondent is higher than the reference category (healthy). The regression coefficient between health status and youth unemployment was significant at (P<0.01)at 10% level (Table 7). As per the study by ACEVO (2011), psychological imprint of unemployment persists into later life and that poor physical health outcome such as heart attacks later in life increases the probability of unemployment. This is illustrated by Anita (2012) that there is a strong association between unemployment and health status. From focus group discussion, it was found that youth tend to smoke and drink irresponsibly and end up in bad health as the result of their joblessness. Lack of employment leads to depression and they resort to different things to escape this emotion which ultimately damages their health. 7. Household monthly income Household income is expected to influence the employment status of youth in the study area. The result shows that, household income had significant effect on youth unemployment in Ambo. The likelihood of unemployment for those youth who lived in a household earning monthly income birr less than 500, , and per months is higher than those who lived in a household earning monthly income above 2501birr (reference category). The regression coefficient between household monthly income and youth unemployment is significant at (P<0.05) at 5% level (Table 7).The result of this study was in contradictory with the findings of World Bank (2007)which stated that unemployment was typically higher for the urban poor. A study conducted by World Bank (2008) reported that the urban economy provides opportunities for many and was the basis for growth and job creation. This finding supports the finding of this study. As Ambo is a West Shoa zone capital and has Ambo University, this would have provided opportunities for many youth to find employment irrespective of their household income status. 8. Access to credit and saving services As far as accessing credit and saving services is concerned, those respondents who did not get access to credit and saving services were likely to be unemployed as compared to those who had got. The likelihood of being unemployed for those who did not get the service is higher than those who got the service (reference category). The relationship is significant at (p < 0.05) at 5% level (Table 7).This finding confirms the finding of ILO (1991). 9. Work experience Work experience has a significant effect on the likelihood of youth employment status. The likelihood of being unemployed for those respondents who had no work experience is higher as compared to those who had work experience. The relationship is statistically highly significant at (p <0.001) at 1% level (Table 7).The result of this study confirms the findingof Foot (1986), Osterman (1980), ILO (2004),Anh et al. (2005) and Hassen (2005).Employers are usually hesitant to hire young 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 lacks work experience remain unemployed. 10. Access to job information To get employment, access of job information is the most important thing. Lack of enough information was one of the factors which made youth unemployed. The findings of this study confirm the underline 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 to those who had high information access (reference category). The relationship is statistically significant at (P <0.05)5% level (Table 7).The finding of this study confirms the findings of Granovetter (1983) and Coleman (1990). 11. Psycho-social problems The findings of this study revealed that the psychosocial problems increase the odds of unemployment. It indicated that, the relative risks of unemployment for youth who had psycho-social problems are higher as compared to those who had not (reference category). The relationship is Page No.167

7 statistically significant at (P <0.01)10% level (Table 7).The finding is similar to the finding of ACEVO (2011) which reported that the mental and physical health of youth are more important factors in the employment status of youth. The FGD participants also confirmed the experience of depression, inferiority, lower self-esteem, embarrassment among people affects the employment status. Conclusion The multivariate analysis shows that among the demographic variables, age of the respondents and migration status were significantly related to youth unemployment whereas marital status of the respondents was not significant. From the human capital variables included in the model, education and health status of the respondents were significantly related to youth unemployment, whereas participation in employment related trainings was not statistically significant. Among the economic determinants, household income, access to credit and saving services and work experience were significant. Access to job information and psycho-social factors were the two social capital variables that were significantly related to youth unemployment. In general, most of the predictor variables included in the regression analysis showed significant effect on youth unemployment in the expected direction, as it is confirmed in most of the research works also. Recommendations As youths are more vulnerable to unemployment, efforts should be made by the government to provide credit and training so as to facilitate their entry into business and entrepreneurship; improving awareness ownership; education and training (skills) enhancement. Preferring jobs only in the formal sectors particularly jobs in government offices increases the likelihood of being unemployed. Thus, strategies should be formulated to improve the awareness of youth on the importance of self-employment and any available employment by using role models and best practices. Attitudinal change through education and training by organizing awareness creation programs is the most fundamental. Having higher access of social network and information increases the chance of getting new job opportunities available in the residential areas as well as outside the area. In order to increase the social networks access and information chain; educate youth to bring change in their social communication habits using public and private media, social media and encourage them to use and access internet, mobile telephone; participate in youth related activities, visit and consult private employment agencies, friends, and relatives are suggested. Migrants are the victims of unemployment in town. Therefore, the pushing factors of migrants should be identified to arrest the continuous drift of youth towards urban areas as this may worsen the unemployment situation in urban areas. References 1. Abera, A. (2011). Demographic and socio-economic determinants of youth unemployment in Debere Birhan town, North Showa administrative zone, Amhara national regional state. Master thesis, Addis Ababa University, Addis Ababa. 2. ACEVO (2011). Youth unemployment: the crisis we cannot afford. Association of Chief Executives of Voluntary Organizations. London, UK 3. Ambo Town Youth and Sport Office Final Report (2014) 4. Anh, D. N., Le Bach Duong, N. H., & Van, N. H. (2005).Youth employment in Viet Nam: Characteristics, determinants and policy responses. International Labour Office. 5. Anita, S. (2012). Well-being of youth: impact of unemployment. Psychology Department, Volume2, No. 4, World Science Publisher, United States. 6. Berhanu, D., Abraham T. and Hannah D. (2005).Characteristics and Determinants of Youth Unemployment, Underemployment and Inadequate Employment in Ethiopia. ILO, 7. Coleman, J. C. (1990). Foundations of Social Theory. Harvard University printing Press. Cambridge, England. 8. CSA (2010). The 2007 Population and Housing Census of Ethiopia Results for Oromia Region: Part II. Statistical Report on Educational Characteristics and Economic Activity Status. Addis Ababa. 9. CSA (2014). Urban Employment /Unemployment Survey. FDRE, Central Statistics Authority.Addis Ababa, Ethiopia. 10. Garson, G. D. (2009). Multiple Regressions: Statistical Notes. North Caroline State University, North Caroline. 11. Granovetter, M. (1983).The Strength of Weak Ties: A Network Theory Revisited. Sociological Theory, Volume 1, pp ( ). 12. Guarcello, L. and Rosati, F. (2007). Child Labor and Youth Employment: Ethiopia Country Study. The World Bank, New York. 13. Hassen, K. E. (2005). Overview on the State of Global Youth Employment: Emphasis on Egypt s Case. UNDP and United Arab Emirates Municipality, Dubai. 14. ILO (1991). Meeting the Employment Challenge in Tanzania.Geneva. Kothari, C. (2004). Research Methodology: Methods and Techniques. 2nd Edition. Wishwa Prakashan, New Delhi, India. 15. ILO, (2004). Improving Prospects of Young Women and Men of Work. A guide To Youth Employment. Geneva. 16. ILO, Improving Prospects of Young Women and Men of Work. Aguide To Youth Employment. Geneva. 17. ILO, Youth Employment: Breaking Gender Barriers for Young Womenand Men. Geneva. 18. ILO, ILO Policy on Youth Employment in Cambodia. ILO Sub Regional Office for East Asia. Geneva. 19. Kothari, C.R. (2004). Research Methodology. New Age Internatonal (P) Ltd.: New Delhi. 20. Montgomery, D. and Peck A. (1992). Introduction to Linear Regression Analysis. 2nd edition. John Wiley & sons Inc., New York. 21. Morris, E. (2006). Globalization and Its Effects on Youth Employment Trends in Asia. ILO Sub Regional Office for East Asia. Geneva. 22. MOY (2004). National Youth Policy. FDRE, Ministry of youth, Sports and Culture (MYSC).Addis Ababa. Ethiopia. 23. MYSC (2004). National Youth Policy. FDRE, Ministry of youth, Sports and Culture (MYSC).Addis Ababa. Ethiopia. Page No.168

8 24. Okojie, C. (2003). Employment Creation for Youth in Africa: The Gender Dimension. Economics and Statistics University of Benin City, Nigeria. 25. Osterman, P. (1975). An Empirical Study of Labour Market Segmentation. Industrial and Labour Relations Review, vol. 28, PP ( ). 26. Salvador, R., and Killinger, N. (2008). An Analysis of Youth Unemployment in the Euro Area. Occasional Paper Series, No. 89, Frankfurt, Germany. 27. Schultz, T. Investment in Human Capital. American Economic Review, 51 (1),1-17, Todaro, M. (1994). Economic Development. Longman Group. London. 29. Toit, R. (2003).Unemployed Youth in South Africa: The Distressed Generation? Paper presented at the Minnesota International Counseling Institute, Minnesota. 30. World Bank (2008). Urban Poverty: A Global Review. Retrieved February 18, 2010, from World Bank. 31. World Bank (2009). Africa Development Indicators: Youth and Employment inafrica, Washington. Page No.169