DETERMINANTS OF URBAN YOUTH UNEMPLOYMENT: THE CASE OF GUDER TOWN, WESTERN SHOA ZONE, ETHIOPIA

Despite numerous interventions by government and development partners, youth unemployment has remained an intractable challenge in Ethiopia. It creates many social-economic problems in the economy of the country. This study however aimed to identifying the determinants of urban youth unemployment. The study adopted a cross sectional data of urban youth employment-unemployment. The town was purposively selected from the town of west shoa zones. The primary data was collected from 91 sample respondents through interview questionnaire from Guder town proportionally. A descriptive and econometric analysis was employed to meet the main objective of the study. The descriptive analyses results revealed that about 61.5 % of the youth are unemployed while 38.6 % are employed. Regression results from a binary logit model estimation show that sex, educational level, marital status, skill match and access to credit use of youth are found to be the significant determinants to urban youth unemployment while family prosperity and market information were statistically insignificant to urban youth unemployment in the town. The econometric results suggested the need for the government go aboard on creating jobs through identify employment opportunities and industrialization of agriculture. It also recommended that the government should facilitate formalization of familiar employment which reduces the problem of youth unemployment especially on skilled and educated youth and all secondary schools should have active partnership with employers. As well as government made to increase the availability of initial working capital, the identification of profitable (market gap) business areas and provision of practical training for urban youths to be engaged at their own business.


Introduction
Unemployment is the most challenging economic problem facing the government.It is a vital challenge for both developed and developing world (Kabaklalri et al, 2011).The ability of youth Http://www.granthaalayah.com©International Journal of Research -GRANTHAALAYAH [319] to engage in productive activities has both social and economic consequences for an economy.In developing countries, youth face not only the challenge of obtaining productive employment, but also obtaining safe and acceptable work.According to Organization for Economic Co-operation and Development (OECD.2010) estimates, there are 85 million unemployed youth around the world.
Unemployment is the condition which is characterized with the incapability of an individual to find a job of any kind.According to ILO (2012) defines unemployment is the main challenges of the modern era in both the developed and developing countries, especially youths, which the United Nations defines as, those between the ages of 15-24, are more affected by unemployment.
Young people are more vulnerable to lack experience, social networks or other qualifications that would make them difficult to find employment.In most regions youth were nearly three times more likely to be unemployed than adults (Amanuel D. 2016).
The labor force is growing with an increasing proportion of youth and employment growth is inadequate to absorb this high proportion of labor force specially the youth part in different sectors of the economy in Ethiopia (Alemnew, 2014: cited at Aynalem Sh. and Mulugeta D. (2018).The country is the highest urban unemployment rates worldwide, at about 50% of the youth labor force (Berhanu et al., 2005).
Despite the recent economic growth witnessed in Ethiopia, youth unemployment is high and rising (Martha, 2012).The problem is more severe in urban than in rural area.According to Ethiopian labor force survey report, the unemployment rate of urban youth at country level was 22.9 while it was only 3.1% in rural areas Labor Force Survey (LFS), 2013).It is special concern for Ethiopians and has a wider implication for the youth in addition to leading their life as expected to help parents and extended families (Shumet, 2011).According to a survey in 55 urban areas, unemployment was estimated at 41.3% and the incidence of youth unemployment was 45 Martha (2012) concluded that unemployment (mainly as an urban phenomenon) and underemployment continued to be serious social problems in Ethiopia despite some improvements in recent years.The problem of youth unemployment is growing rapidly in Ethiopia particularly in Guder town and unemployment results social exclusion and a sense of hopelessness on youth.This calls for a careful study of the determinants of urban youth unemployment.
Identifying the determinant factors influencing urban unemployment of youth residents should be the first step to come up with the alternative strategies to resolve the problem, even though few studies were conducted on the determinants of urban youth unemployment (Alemnew, 2014: cited in Aynalem Sh. and Mulugeta D. (2018), the results of these studies are varied depending on the specific socio-economic situation of the study area.Therefore, this study is aimed to understand the determinants and characteristic of urban youth unemployment at Guder town dwelling using the binary logistic regression model.

Study Area
The study was conducted at Guder town which is located in Oromia Regional State West Shoa Zone.It bounded by Chaliya Woreda in West, Ambo Woreda in the East, Mida kegn Woreda in North, and Tikur Inchini Woreda in South.This town has latitude and longitude 89667(8'58 N) and 377667 (37'46 E) respectively, with an elevation of 2101 meter above sea level.The town is located at 136 kilometer from capital of Addis Ababa.The town has an estimated total population of 12569 with total youth population of 1038 from two kebeles namely 01and 02 kebele Central Statically Agency (2013).

Sampling Techniques and Research Design
A cross sectional survey design was used to collect data from the respondents.Purposive sampling techniques were employed to select Guder town considering numbers of urban dwellers living there.This study is conducted at individual level, the required number of sample drawn is the total number of active labor force of youth was purposively selected from the sampling frame or the total population found in the towns.Both primary data and secondary data were collected from youth's representative respondents and different sources.The data was collected from selected respondents in the study area through interview questionnaire and key informant's discussion.

Sample Size Determination
To determine the representative sample size from the town, the study used a sample size determination formula given by Yamane (1967) Where: n is the representative sample size, N is the total youth population of the towns which is found to be the total 1038 Central Statically Agency ( 2013) and e = is level of precision defined to determine the required sample size at 95% confidence level.A total of 41 and 50 youths are from 01 and 02 kebeles town respectively.

Method of Data Analysis
Descriptive and econometric analyses were employed to meet the objective of the study.In the case of descriptive analysis tables, average and percent were employed, while the econometric analysis binary logit model was employed to identify determinants of urban youth unemployment.

Model Specification
Unemployment status of urban youths: dependent variable of the model that is dichotomies or dummy variable that take value 0 = if urban youth is employed and 1= if urban youth is unemployed.The appropriate econometric technique deal with such type of data is using binary logit and probit models and the most popular statistical techniques was used to analysis the probability of a dichotomous outcome (such as employed or unemployed) with a set of explanatory variables.Binary logistic regression model was used to identify determinants of urban youth unemployment.It is a special type of logistic regression model which is used to describe the relationship between one or more independent variables and a binary outcome variable that has only two possible values.Logistic regression is used in a wide range of applications leading to categorical dependent data analysis (Agresti, 2002).Gujarati (2004) the logistic model could be written in terms of the odds ratio and log of odds ratio, which enable one to understand the interpretation of the coefficients.In this study, the odds ratio is the ratio of the probability that the youth will be unemployed (Pi) to the probability that he/she will be employed (1-Pi).
the above formula can be rewrite as shown below for easily understanding.Taking the natural logarithm from above equation

Features of Urban Youth Unemployment
Youth unemployment by gender: The majority of the respondents are male in their sex.Of the total responses, 65.9 percent are male and 34 percent are female with average year of the sample is 24.5 years.The highest unemployment is seen for male youth 40(43.9percent) respondents but 16(17.5 percent) respondents' female youth.Using those male respondents as a base, the analysis shows that those who are female respondents have a low level unemployment rate as compared to male youth in the study area.

Youth Unemployment by Educational Status
The association between education and employment condition is crucial.It is known that education is a very important for innovation, job creation and poverty alleviation.The unemployment rate Youth unemployment by Marital status: relatively larger proportion, 53.8 percent of the youth are never married while about 46 percent of them are married with average household size is 3.2 family members.The highest unemployment rate is seen for unmarried youth 39(42.8%) of respondents but 17(18.7%) of respondents' married followed by live together.Using those unmarried as a base, the analysis shows that those who had married youth have a low level unemployment rate as compared to unmarried in the study area.

Youth Unemployment by Work Condition of Employed Youths
As regard to those employed, 10(28.5 percent) is self employed followed by private, government sector employees and unpaid family worker, 13(37%), 7(0.2%) and 5(14.3%)respectively.It is good to see the highest proportion is private organization and self employed; otherwise it is difficult for the government to provide job for all unemployed youths.
Youth unemployment by Match skill: measured with relative to mismatch and matched.From the sample respondents the unemployment youth for those skills are matched was 36(64.3%)while for those mismatch was 20(35.7%)and the respondents youth employed for those whose skills are mismatch 5 and matched was 30 respectively.

Youth Unemployment by Family Prosperity Level
measured with relative to rich and poor based on the living condition of the society at the study area.The result revealed that the unemployment rate of individuals from poor families was 38(67.8%)while for individuals from rich families was 18(32.1%)respectively.

Youth Unemployment by Access to Market Information
shows that the proportion of employment status of youth varies significantly with access to market information with regard to this, the higher 39(69.6%) of the unemployed youths always access to market information.

Youth Unemployment by Access to Credit Use
survey results show that the proportion of employment status of youth varies significantly with access to credit use with regard to this, the higher 38(67.8%) of the unemployed youths was from no access to credit use.

Youth Unemployment by Employment Status
Based on the collected data the majorities 56(61.5 percent) of the respondents are unemployed and the remaining 35(38.6 percent) respondents are employed in the study area under different working conditions.were found significant while the rest two variables (family prosperity and market information) were statistically insignificant to urban youth unemployment.

Interpretation and Discussion of Significant Model Out Come
Sex of youth: positive and significantly affect employment status of youth in the study area.The results show those males are 71.99% more likely to unemployed as compared to female youth.The study contradicts with the finding of Amanu'el D., (2016).
Educational level of youth: Education level has shown positive relationship with employment status of youth.The result reveals that as education level of respondents' increases from primary school up to certificate graduate the level of youth unemployment increased.It is found that youths who attend certificate graduate and higher education were more likely to be unemployed.The odds ratio of being unemployed increases by 23.7percent if the individual attended certificate graduate and above compared to those who are illiterate 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.

Skill Match:
The matches between the non-match skill acquired and demanded for labor market have a positive effect on youth unemployment.The result reveals that non skill match and unemployment are positively correlated.As non-match skill demand is increases, the odds ratio of being unemployed will increase by 53.8 percent if the individual's skill and the demand by the market becomes non match.This result is contradicts with the study of (Alemnew, 2014: cited in Aynalem Sh. and Mulugeta D.

(2018).
Marital Status: There is a positive association between getting unmarried and being unemployed.
The Logit model predicts that if youths are unmarried their unemployment status increases by the odds ratio of 33.5 percent compared to married.This is true if unmarried youth not it give more attention for work to improve his livelihood as compared to married youth.This is contradicts with the finding by (Krishnan 1996 /www.granthaalayah.com©International Journal of Research -GRANTHAALAYAH[322] of independent variable included into the model i  = Vector of independent variable i  = Error term (disturbance term) independent variable is zero  =Measures the change in L (logit) for a unit change in explanatory variables (X) i  =Dependent variable that take value y=o if youth is employed and y=1 if youth unemployed Table1: Summary of independent variables that may influence urban youth unemployment Variable : cited in Aynalem Sh. and Mulugeta D. (2018).Access to Credit Use: Availability of credit use is positive correlated with youth unemployment.The Logit model predicts that if youths are lack of credit use their unemployment status increases by the odds ratio of 82.5 percent compared to credit use.This is contradicts with the finding by Aynalem Sh. and Mulugeta D. (2018).

Duguma et. al., Vol.7 (Iss.8): August 2019] ISSN-2350-0530(O), ISSN-2394-3629(P) DOI: 10.5281/zenodo.3382077 Http
(Toit, 2003:03;or females and males respectively (Alemnew, 2014: cited in Aynalem Sh. and Mulugeta D. 2018).As different studies indicated unemployment is a global issue that challenging every nation.It represents the underutilization of human capital and it is very important issue that negatively affects the development of the country.The potential causes of unemployment in urban Ethiopia include increasing number of youth labor force, the rising internal migration, literacy rate, poor to modest macroeconomic performance, low level of job creation and low level of aggregate demand in the economy(Getinet, 2003; WB, 2007).Youth unemployment is the outcome of different socio-economic factors at macro and micro level(Toit, 2003:cited in Aynalem [://www.granthaalayah.com©International Journal of Research -GRANTHAALAYAH [320]

Table : 2
: Urban youth labor market characteristics by sex, education level, marital status, and access to credit use, access to market information, working condition, skill match and Family prosperity level.

Determinants of Urban Youth Unemployment Binary
logit model was selected to identify the determinants of urban youth unemployment in the study area.Before fitting the model, it was important to check existence of multi collinearity problem among explanatory variables.Variance inflated factors (VIF) was used to test existences of multi collinearity problem among variables.The calculated value of VIF was below 10, there is no serious multicollinearity problem among the explanatory variables.A total of 7 variables included into the model that may affect urban youth unemployment were considered.Among them 5 of the variables (Sex, educational level, marital status, skill match and access to credit use) Http://www.granthaalayah.com©InternationalJournal of Research -GRANTHAALAYAH[325]

Table 3 :
Logistic Regression Model result of Determinants of youth unemployment