Youth unemployment has been one of the most challenging problems in countries with large young and rapidly growing populations. By taking into account the challenges of unemployment, the major objective of this study was to identify factors affecting urban youth unemployment in Ethiopia in the case of Bale-Robe town, bale zone. Both primary data and secondary data were collected. Primary data was collected using a structured questionnaire. Probability sampling methods were applied to select 384 sample respondents from the town. Descriptive and econometric models were used for data analysis. Both logistic and probit regression models were fitted and compared. Based on the goodness of fit test result by AIC and BIC, probit regression was used in this research. The result of this study revealed that social capital variables, educational attainment of the youth, being single, mother’s education, work experience and family income in etb (000) were among the significant variables affecting the status of unemployment at less than 5% significance level. However, being a migrant has a positive effect on the probability of being unemployed. Based on the findings of the study, government and all concerned bodies must expand higher education centres which helps youths to acquire technical skill to create their own jobs, provision of basic education for youth parents, and create conducive environment to get more job opportunities are the areas that must be taken into consideration.                

Unemployment is a problem for both developed and developing countries. However, the impact and intensity might differ from place to place. In Ethiopia, coupled with population growth and increased poverty, it has a significant impact on growth and development at large. It causes a waste of economic resources such as the productive labour force and affects the long run growth potential of an economy. According to the Ethiopian Youth Policy, youth comprise persons aged between 15 and 29 years. Youth unemployment rate is high, at 11.8%. Youth unemployment rate is higher in urban (22.9%) than rural areas (8.1%). In urban areas, youth female unemployment is disproportionately high, at 28.4%, compared with youth males (11.6%) [1]. While the problem of unemployment is usually treated as a macroeconomic topic (because unemployment relates to aggregate production), economists recognize that the decisions made by individual workers on how long to search for jobs and the way specific labour markets encourage or impede hiring are also critical in determining the unemployment rate. The problem of unemployment is high in urban Ethiopia than in rural Ethiopia. Found that both the incidence and duration of youth unemployment is higher in urban Ethiopia [2]. According to report, the rate of youth unemployment in urban areas in 2021 is 23.1 percent and for rural areas, it was about 12 percent [3]. According to during the COVID-19, unemployment rose from 3.5 percent in February to a peak of 14.7 percent in April [4]. This extraordinarily steep increase in unemployment makes the COVID recession the deepest economic contraction since the Great Depression. Unemployment has many adverse effects, including increased depression and other mental health problems, increased crime rates, overall lower economic productivity and consumption, lower rates of volunteerism, and erosion of skills. Showed that the probability of being unemployed is high among young females, youths who are educated up to post-secondary level, vocationally trained youths, and youths with high levels of language and digital literacy [5]. Results showed that the youths' struggle to acquire work emanating among others from inadequate skills sets and education levels whilst lacking the needed labour market information. According to between 2006 and 2011, the prevalence of urban youth unemployment was high as compared to the total unemployment rate in Ethiopia [6]. Regarding the determinants of urban youth, different scholar found mixed results. Using descriptive and cross tabulation analysis, found that youth unemployment is highly related with regional location, sex, marital status and education [7]. Applied logistic regression model analysis and showed that saving and supports from different organizations are important variables that reduces the probability of being unemployed [8]. However, showed that educational level of the youth, access to information, income of parents and economic factor of the parents were significant variables that negatively affect the probability of being unemployed [9]. Because of its enormous economic and social costs, policymakers try to limit the amount of unemployment in an economy. In the Oromia region, 24.6 percent of urban youths and 6.9 percent of rural youths were reported as unemployed. Therefore, the major objective of this study was to identify major factors affecting urban youth unemployment in Ethiopia in the case of Bale-Robe town, Bale zone, Oromia, Ethiopia.

Definition of unemployment

Employment is the number of people who have a job. Unemployment is the number of people who do not have a job but are looking for one. According to a person is unemployed if she/he does not have a job, has actively looked for work in the prior four weeks, and is currently available for work. In Ethiopian context labour market situation, relaxed and defined unemployed as persons who had no work but were available for work. They may be either seeking work or not seeking /discouraged job seekers. Discouraged job seekers are those unemployed who want a job but are not taking any active steps to search for work because they think a job is not available in the labour market.

Theories of unemployment

There are two important relations among output, unemployment, and inflation. The first, called Okun’s law is a relation between output growth and the change in unemployment: High output growth typically leads to a decrease in the unemployment rate. The second, called the Phillips curve, is a relation between unemployment and inflation: A lower unemployment rate typically leads to a higher inflation rate.

Classical theories

The classical theory of employment is based on the assumption of flexibility of wages, interest and prices. This means that wage rate, interest rate and price level change in their respective markets according to the forces of demand and supply. Changes in these variables automatically adjust the economic system in such a way as to ensure full employment.

Efficiency Wage Theory

According to this theory, firms operate more efficiently if wages are above the equilibrium level. Therefore, it may be profitable for firms to keep wages high even in the presence of a surplus of labour. High wages make workers more eager to keep their jobs and thus motivate them to put forward their best effort. If the wage were at the level that balanced supply and demand, workers would have less reason to work hard because if they were fired, they could quickly find new jobs at the same wage. Therefore, firms may raise wages above the equilibrium level to provide an incentive for workers not to shirk their responsibilities.

Keynesian theory

According to Keynes, the volume of employment in a country depends on the level of effective demand of the people for goods and services. Unemployment is attributed to the deficiency of effective demand. It is to be kept in mind that Keynes’ theory is a short run theory when population, labour force, technology, etc., do not change. Once Keynes remarked that since “in the long run we are all dead”, it is of no use to present a long run theory. In view of this, one can argue that the volume of employment depends on the level of national income/output.

Description of the study area

The study was conducted in Robe town, Bale Zone, Oromia Region at a distance of 430 KM from Addis Ababa. The town was founded in 1930. Robe town is capital of Bale Zone. Robe town is found in to the south east of the Addis Ababa. The town has three kebeles and bounded with Sinja farmers’ association in south, Hawusho farmers’ association in south west, Hora Boka farmers’ association in West, Sanbitu peasant association in north and Shallo farmers’ association in East direction. The town has located on 2492m above sea level with mean annual temperature of 15oc which experienced cool temperature [10].

Research Design

Due to lack of panel and time series data, the methodological scope of the study was used the cross sectional research design.

Sources and Methods of Data Collection

Both primary and secondary sources of data were used for this study. Primary sources of data was collected using structured questionnaire, whereas the secondary data sources relied on relevant literatures, available official documents and demographic figures accessed from local, zone and district government offices were also used.

The questionnaires have prepared for self-administered and interviewer administered by using close ended questions to get information from the respondents.

The sample size of farmers is determined by following: 

(????) is equals to 0.5, (the maximum level of variability taken when the previous population variability is unknown), the confidence level of 95% which corresponds to Z -value of 1.96 and an error or precision (e) of 5%.

Methods of data analysis

To achieve the objective the empirical data was analyzed using descriptive statistics and econometric models. The descriptive analysis were performed using average and mean difference test to compare the socio economic and socio demographic characteristics of youths.

Specification of probit Regression model

Following Gujarati (Gujarati (2004) the probit regression model becomes

Where is not observed. It is commonly called a latent variable. What we observe is a

The Probit and Logit models differ in the specification of the distribution of the error term

For instance, if the observed dummy variable is whether or not a person is employed or not, defined as ‘propensity or ability to find employment. 'Where, F is the cumulative density function of, Xi are independent variables and ????i are the coefficients of the independent variables and the variable e is a random error introduced to accommodate the effect of other predictors which were not included in the model but have relationship with rural-urban migration (Figure 1).

Figure 1: Adapted from different literatures

Figure 2: Source: computed from World Bank Data (2022). From the above figure, it can be seen that unemployment has been rising in the last few years

Figure below shows trends of unemployment rate for Ethiopia. Data was collected from World Bank database. In the last few years the unemployment of Ethiopia has been rising. From the above table it as observed that there is statistically significance difference between unemployed youths and employed youths in terms of family income and educational attainment of youths. This indicates that skill and education is very important to get jobs. In addition, the family background is also helpful to encourage youths during job search. The average monthly income of family was 5.673 thousand birr for employed and 3.242 thousand birr for unemployed youths. Educational attainment of the youth: this variable was statistically significant at less than 5% significance level. The negative coefficient of the variable is negative and it indicates that an increase in year of schooling by one grade decreases the probability of being unemployed by 2.4%. The probable justification is that the more youth achieve more years of schooling, his experience and skill will be improved. Employers usually prefer workers who fit their vacant positions in terms of years of schooling. This result confirms the finding of [11]. However, found that educated young is unemployed due to unavailability of resources to support their full-time job search in Ethiopia.

Table 1: Tabulation of dummy variables

Table 2: Descriptive statistics of continuous variables.

Table 3: Econometric model result

Being single: This variable has a negative coefficient and shows that being single reduces the probability of being unemployed by 35%. The probable justification is that single youths can easily move from place and apply for more and more jobs. The more they apply the more they can get jobs. This result corroborates the findings of also showed that youth that were married had less odds of being unemployed. This result contradicts the findings of and consistent with the findings of [12].

 Parents’ education: Both mother’s and father’s education were important variables affecting the probability of being unemployed.Work experience: This variable has a negative coefficient. It is statistically significant. Most of the time vacancy posted by employing organization usually attached with job experience. The coefficient of (=0. 246) shows that an increase in experience by one year reduces the probability of being unemployed by 24.6%.

Being migrant: this variable was included to indicate whether youths with migration history are unemployed. The positive sign of the marginal effect result (=0.258) shows that being migrant from another areas increases the chance of falling in the unemployed category by 25.8%).

Social capital: Social network is important in order to get relevant information about different job opportunities. This study found that social network affects individual’s unemployment status negatively and significantly at less than 5%.  This result corroborates the findings of [13]. Also found that members of minority groups, may face discrimination during their job search and unemployed [14-16].

Family income: parents' income is statistically significant at 1% significance level and affects the probability of being unemployed by 4.2%. Parents,\ economic status is very much important to cover expenses related to search of job and those youths from rich families are usually either self-employed or can easily get available jobs. This finding is congruent with the findings of who found that youths from relatively poorer families are most likely unemployed as compared to youths from richer households.

Youth unemployment has been one of the most challenging problems in countries with large young and rapidly growing populations. By taking into account the challenges of unemployment, the major objective of this study was identify factors affecting urban youth unemployment in Ethiopia in the case of Bale-Robe town, bale zone. Both primary data and secondary data were collected. Primary data was collected using structured questionnaire. Probability sampling methods was applied to select 384 sample respondents from the town. Descriptive and econometric models were used for data analysis. Both logistic and probit regression models were fitted and compared. Base on the goodness of fit test result indicated by AIC and BIC, probit regression was used in this research. The result of this study revealed that social capital variable, educational attainment of the youth, being single, mother’s education, work experience and family income in etb (000) were among the significant variables affecting the status of unemployment at less than 5%. However, being migrant has positive effect on the probability of being unemployed. Based on the findings of the study, government and all concerned bodies must expand higher education centres which helps youths to acquire technical skill to create their own jobs, provision of basic education for youth parents, and creating conducive environment to get more job opportunities are the areas that must be taken into consideration.

1. MoPD, Ministry of planning and development, Ethiopia voluntary national rev. 2022, Addis Ababa, Ethiopia. 2022.
2. Berhe MW. Empirical analysis of urban youth unemployment in Ethiopia. African Development Rev. 2021.
3. Ababa A. Central Statistics Agency (CSA). Labour force and migration survey key findings. Ethiopia. 2021.
4. Acemoglu D, Laibson D, John A. List (2022) MACROECONOMICS, Third Edition, Global Edition. 2022.
5. Weerasiri ARP, Samaraweera GRSRC. Factors influencing Youth Unemployment in Sri Lanka. Asian J Management Studies. 2021; 1: 49-72.
6. Nganwa P, Assefa D, Mbaka P. The nature and determinants of urban youth unemployment in Ethiopia.  Pub Policy  Adm Res. 2015; 5.
7. Batu MM. Determinants of youth unemployment in urban areas of Ethiopia. Int J Sci Res Publications. 2016; 6.
8. Larry D, Abdusemed A. Factors affecting youth employment in Ethiopia: A combination of attitudes, behaviors, interventions, and context. 2019.
9. Tegegne TK, Socioeconomic determinants of youth unemployment in Ethiopia, the Case of Wolaita Sodo Town, Southern Ethiopia. Developing Country Studies. 2019; 9.
10. BoFED, bureau of finance and economic development. The national regional government of oromiya bureau of finance and economic development physical and soc eco. 2016.
11. Duguma AL, Tolcha FT. Determinants of urban youth unemployment: The Case of Guder Town, Western Shoa Zone, Ethiopia. Int J Res-Granthaalayah. 2019; 7: 318-327.
12. Disassa A. Determinants of youth unemployment; Evidence from Ethiopia. Global J Human-Soc Sci. Arts Humanities - Psychology. 2016; 16.
13. Aynalem S, Mulugeta D. Determinants of urban youth unemployment; Evidence from East Gojjam Zone, Ethiopia. Int J Eco Develop. 2018; 11: 245-265.
14. Connie W. The individual experience of unemployment. Annual review of psychology. 2010; 63: 369-96.
15. Jacques DJ. Factors affecting youth employment: An analysis of the Emfuleni and Metsimaholo local municipal areas. Conference: 2nd International Conference Contemporary Issues In Theory and Practice of Management CITPM 2018At: Czestochowa University of Technology, Czestochowa, Poland. 2018.
16. Gregory MN. Brief principles of macroeconomics, Ninth Edition, Harvard University. 2021