^1*Corresponding Author: Habtamu Tolera, habtol@yahoo.com(HT), orcid.org/0000-0001-5967-1237.

How people think about youth varies widely depending on their background and perspective (Seatzu, 2022). These views are shaped by a mix of social, economic, political, and cultural factors, as well as the broader systems and structures they live within(Sin, 2024). For example, the UN Secretariat, UNICEF, WHO, UNFPA, and ILO describe youth as individuals aged between 15 and 24 years(United Nations., 2008), UN Habitat(The UN-Habitat, 2025) broadens to 15 to 32 years age, African Youth Charter (Seatzu, 2022) define it 15 to 35 years, and OECD sets 15 to 29 years old. However, all definitions consider change from childhood to adulthood, which includes going to school, seeking work, and learning how to apply responsibility and make informed decisions(OECD, 2018). The complex problem of youth unemployment includes economic, social, and political issues(Gupte and Barnett, 2014). Moreover, exploring global youth unemployment poses challenges since youth unemployment basically depends on a country's specific socio-political and economic conditions, structural and institutional contexts, and educational systems(Sin, 2024)

Currently, there are roughly 1.2 billion people aged between 15 to 24 years in the global south alone, comprising 16% of the total population worldwide(Population Reference Bureau, 2017). Research from Low and Middle Income Countries(LMICs) consistently shows that the rates of unemployment among youth excel those of adult populations(Munyao, 2014; Pieters, 2013). Sources indicate over 88 million individuals are actively looking for decent work, with youth making up 47% of the world unemployment figure (Mastercard Foundation, 2020; Seatzu, 2022; The UN-Habitat, 2025; United Nations Population Fund., 2023). Among more than 1.3 billion youth people aged between 15 and 24 years worldwide, 85% found in LMICs, primarily in Asia and Africa(Fung & Nga, 2022). It was also anticipated that this figure believed to be increased to 89.5% by the year 2025(United Nations., 2019). In sub-Sharan Africa(sSA), total youth population aged between 15 and 24 years accounts for one-fifth of the total (Gupte and Barnett, 2014; ILO, 2024). Even though young people comprises 36% of the economically active labor force, they are the overwhelming majority group without jobs in the area. Likewise, Ethiopia the second-most populous in the part of sSA, is also experiencing a rapid increase in demographic size raising by about 2.3% per year(Ethiopian Statistics Service., 2022; Federal Democratic Republic of Ethiopia, 2020), accompanied by high rate of youth unemployment(Broussar and Tekleselassie, 2012) largely due to the slow job-creating capacity that have not kept pace with the country's significant demographic changes(Industry(, 2019). Youths account for 28.3% of the Ethiopian population (Ethiopian Statistics Service., 2022; United Nations Population Fund., 2023), with an imperative number aged between 15 and 24 years. However, this change faces unemployment rates that surpass those of older age cohort in urban areas of the country including Nekemte City (Ethiopian Statistics Service., 2022; Netherlands Enterprise Agency, 2020).

The engagement of youth in sustainable socioeconomic development program is essential for building resilient economies that are equitable, secure, and enduring(Caribbean Development Bank, 2016; Tegegne, 2019; United Nations., 2019) while also addressing the severe challenges and risks to sustainable development(OECD, 2018). They include climate change impacts, unemployment, poverty, gender inequality, conflict, and migration(United Nations., 2019). The challenges drive significant global social and economic malaise(The UN-Habitat, 2025; UNICEF, 1992; United Nations., 2008). A heavy number of youths in LMICs including Ethiopia, encounter risks in securing decent and productive job market(World Bank Group., 2025). A ranges of sources indicate how crucial it is to address lack of job among youth in rapidly growing urbanization countries like Ethiopia(World Bank Group., 2025). The challenge is caused by deep rooted drivers in society, namely structural obstacles and demographic changes due to rural out-migrations (Abdurahman and Ahmed, 2021), lack of formal job markets(Kassa, 2012; Lam and Leibbrandt, 2014), sluggish economic growth(Mago, 2014), and deviation between educational outcomes and labor market needs(Kibru, 2012; Msigwa and Kipesha, 2013; Mulatu, 2022) together with weak institutional action(Msigwa and Kipesha, 2013; Puente, 2025).

Ethiopia introduced various strategies to overcome problems of job basically driven by weak structural systems(Federal Democratic Republic of Ethiopia, 2020; Habiyaremye et al., 2022; OECD, 2018). The National Youth Development and Change Strategy (2017) was the major initiative to offer young people with jobs and to help the country rely less on farming by fostering industrial parks as a means for industrialization(Zhang et al., 2018, Ethiopian Ministry of Labor and Skills, 2017) ). The Federal Job Creation Commission (2018) also adopted plans to create three million jobs by 2020, fourteen million by 2025, and twenty million by 2030 establishing huge number of industries across Ethiopia for the growing number of urban youths (Federal Democratic Republic of Ethiopia, 2020). Moreover, the Youth Revolving Fund program was formally established in the year 2017 with a proclamation No. 995/2017 to help youth create viable businesses and to provide financial and technical support to realize their productive potential(Ethiopian Ministry of Labor and Skills, 2017). Besides government program, organizations have also significantly promoted young people to find work in Ethiopia(Jubane, 2021). The Master Card Foundation(2019) launched several youth-focused initiatives through skills training, access to finance, entrepreneurship support, and policy reform with a goal of helping 10 million young people, with a focus on young women find job(Mastercard Foundation, 2020). The Challenge Fund for Youth Employment(The World Bank Group, 2021), also produces two thousands of jobs focusing on starting businesses, job training, and improving skills(World Bank Group., 2025).

Despite several efforts to overcome the problems of unemployment, Ethiopia still boasts the largest youth population share at 21.8% at the world level(Population Reference Bureau, 2017). Sources underscore the country must work hard for two-million people looking for jobs each year (Kelly and Martinez, 2018). Youth unemployment rates are very dynamic in the country and in the year 2021, the rate stood at 6.4%(The World Bank Group, 2021). Even though the rate fell a bit to 5.43% in the year 2024, ongoing structural failures still make the problem hard for more youth to find jobs (United Nations Population Fund., 2023). In Oromia, the rate is highly increasing at 40%(Wakgari, 2022). More recent studies showed continued deep-rooted structural problems unfavorably affect urban youths’ employment in Oromia(Mulu et al., 2022). Likewise, in Nekemte, there are more than sixty thousand young people (aged between 15 and 29 years), and over half are women (Ethiopian Statistics Service., 2022).

Existing empirical studies report about youth unemployment problems in urban Ethiopia (Abdurahman and Ahmed, 2021; Munyao, 2014), however, very few focus on Nekemte City. Assessing the specific local contexts why urban youths are unemployed is, however, very important to inform the prevailing job market policy(Abebe, 2015; Duguma, 2019; Lebakeng and Matebese-Notshulwana, 2024).

Furthermore, earlier studies typically explore trends across the whole Ethiopia, most often without considering factors such as population, social-economic problems, psychology problems, local institutions and structural issues that significantly influencing urban unemployment in cities including Nekemte (Caribbean Development Bank, 2016; De Lannoy et al., 2018; Hove et al., 2013; Suva, 2015). Particularly, structural/institutional and psychological factors are often overlooked dimensions in urban youth unemployment studies(Abdurahman and Ahmed, 2021). psychological stress is more importantly a powerful and often overlooked aspects that I can use it as a variable to explore how mental and emotional strain affects job-seeking behavior, employability, and long-term career outcome(Duresso and Gebreegziabher, 2020; Pereira, et. al., 2021; Ronzon et al., 2025) Hence, this research aims at exploring factors influencing urban unemployment among youth in Nekemte City, Eastern Wollega, Oromia Region, Ethiopia.

The study was conducted from December 2, 2024, to April 30, 2025 in the Nekemte City, which lies approximately 330 km directly west of the Addis Ababa (the national capital), in the east Wollega Zone, Oromia Region. The total area of the city was 3.043 square kilometers and divided into six administrative sub-cities. According to the 2019 population projection release and Oromia Regional State (2019), the city had a total population of 125,157, including 61,634 urban youth aged between 15 and 29(Ethiopian Statistics Service., 2022). See Fig. 1

Redlands, CA: Environmental Systems Research Institute

A community-based, cross-sectional design was employed to study urban youths aged between 15 and 29-years-old who had been living in the city in the last six months preceding to the survey.

A two-stage sampling method was employed in order to select respondents who lived in the study area during the reference period. In stage one four sub-cities (Darge, Chalalaki, Bake Jama, and Burka Jato) were purposively selected due to high concentration of unemployed youth compared to the rest and in the second stage simple random sampling technique (a random number table) was employed to randomly select samples with the consideration given to the size of the respective sub-cities.

The source population for this study was all youth aged between 15 and 29 years old living in the four sub-cities under investigation in Nekemte and from this youth population, a sample of 463 participants was randomly selected to serve as the study subjects. To determine the suitable sample size (n) required for the study a researcher used the formula proposed by(Kothari, 2004). That was,

n=

In the first step initial sample (n) calculation was determined using the standard formula for sample size estimation as follows (Kelsey et al., 1996) with:

Next, since the source population was less than ten thousand finite population correction was applied to adjust for population size(Pallant, 2020). Therefore, a design effect adjustment and a two-stage sampling method was applied due to selection at sub-city and household levels(Ethiopian Statistics Service., 2022). A design effect of two (2) was employed, resulting in a sample size of 454 (227 * 2). Furthermore, a 2% contingency was added to account for potential non-response and data loss. Thus, the final sample size became 463 urban youth(Kelsey., 1996).

Youths were considered eligible if they fulfilled the definition of young individuals(Ethiopian Statistics Service., 2022), as used in this study and had resided constantly in Nekemte City for at least six months prior to the time of data collection (Kelsey., 1996). participants were excluded from the study if they were younger than 15 or older than 29 years, or if they had resided in the study area for less than six months prior to data collection, as this duration was deemed insufficient for meaningful exposure to local conditions.

Data were collected using a structured survey questionnaire. The questionnaire was designed in English and translated to Afan Oromo by staff of the English and Afan Oromo departments of Wollega University. The questionnaire comprise current status of employment, demographic, structural and institutional, socio-economic, psychological and financial characteristics affecting urban youth’s occupational status in Nekemte(OECD, 2018). Data were collected by eight enumerators holding BA degrees in the social science disciplines and all had prior experience directing field work in similar studies in the area. Data collection process was supervised by four supervisors recruited from among local officials and coordinated by the principal investigator. The enumerators and supervisors were trained for 3–4 days in data collection techniques and ethics. A pilot study involving 10% of the study population was conducted an adjacent sub-city to validate the data collection tools and procedure(Pallant, 2020). Important revisions, therefore, were made based on the analysis from pretest. The supervisors and the investigator monitored field activities and checked all the complete questionnaires and missing information daily to ensure validity and reliability. Questionnaires thought incomplete were returned to data collectors for completion. To ensure respondents anonymity, no personal identifiers were noted, and confidentiality was completely preserved(Berg, 2009).

Urban youth employment status was here redefined into three (International Labour Organization (ILO)5) nominal groups: (1) Employed youth who had worked for pay or profit during a reference period, including formal and salaried employment); informal employment like street vending, casual labor, etc; self-employment or unpaid work in a family business(Group I), (2) Unemployed youth who were not working but were actively seeking work, and were available to start work(Group II), (3) Inactive youth who were not in employment, education, or training (Group III). The outcome variable in this study was the employment status of urban youth in Nekemte City the case of the first two youth group. This study did not include inactive youth due to the absence of such cases in the sampled population of Nekemte City. Therefore, the analysis emphasized completely on youth who were either employed or unemployed and the outcome variable was code as (the studied group) and employed (the reference group) in the model. This means the regression estimates the log-odds of being unemployed compared to being employed. This binary classification, therefore, enables the use of logistic regression the right choice to identify factors influencing urban youth unemployment in the study setting.

According to (International Labour Organization, 2019) youth were individuals aged between 15 and 24 years. However, this study adopts a wider range between 15 and 29 years that alignment with national demographic classifications (CSA, 2019).

Demographic factors such as gender of urban youth has a binary categorical variable defined as (1 = female youth, 0 = male youth), age group of urban youth (in year) classified as a binary variable (19 years or younger, 20 years or older), marital status coded as a binary categorical variable (1 = single/divorced/widowed/separated, 0 = married); youth background by geographic origin as a binary category (1 = rural youth, 0 = urban area), security-induced rural youth migration was a binary categorical variable (1 = youth migrated out from a rural area due to security concerns, otherwise, no).

Perceived structural and institutional factors were educational level of both urban youth and youth’s household (College/university/vocational training; secondary education (Grades 9 − 2); primary education (Grades 1–8), cannot read & write). Youth’s sub-city as a nominal variable (Darge, Bakanisa Kesso, Chalalaki, Burka Jato), local job creation deficit of municipal coded as a binary variable (1 = yes if youth perceives a lack of adequate job creation efforts by the municipality, 0 = no if did not perceive deficit), local government capacity to attract investment as a binary variable (1 = yes if it is perceived as weak and discouraging investment attraction; otherwise, no) and population pressure is a binary variable (1 = yes if youth perceives high population growth as a contributing challenge, otherwise, no).

Perceived psychological factors were self-reported psychological impact of unemployment is a binary variable (1 = yes if youth perceives psychological stress that affects unemployment like stress, anxiety and loss of motivation; otherwise, no), job aspiration as binary category (1 = public job, 0 = any available job).

Perceived socio-economic factors such as youth’s exposure to market information as a binary variable coded as (1 = no if youth lacks exposure; otherwise, yes), mismatch between acquired skills and labor market demands (1 = no if acquired skills did not match labor market demands, otherwise, yes), prior work history coded as (1 = no if youth had no previous employment experience, otherwise, yes), medium of job search (0 = media, 1 = job board, 2 = friends), job search habit (1 = no if youth did not actively engage in job-seeking behavior, 0 = yes), sector-based job vacancy disparities (1 = yes if youth thought unequal job opportunities across economic sectors, otherwise, no), experienced socioeconomic shocks (1 = yes if youth experienced serious socioeconomic problems, particularly financial hardships reduced living standard; otherwise, no), deficiencies in entrepreneurial competencies(1 = yes if they had no basic skills like business planning, financial literacy and innovation; otherwise, no).

Perceived financial factors were access to financial credit as nominal variable (1 = yes if youth had access to any financial lending, otherwise, no), income level of the youth’s household (1 = low income if the households fell in the bottom 40%; 0 = high income if they fell in the top 60%). See Fig. 2

A logistic regression model for the likelihood of an event occurring (youth unemployment) was expressed as(Lemeshow and Hosmer, 2000; Pallant, 2020):

Where,

Data processing and analysis

All the questionnaires were checked manually, coded and entered into EpiData version 3.1, and exported to SPSS Version 24.0 (SPSS; IBM Corp; USA) for analysis. The data were cleaned to check for errors and missed values and any error identified was corrected. Descriptive statistics, particularly cross-tabs were used to calculate the frequency distribution and proportions for categorical variables. The Variance Inflation Factor (VIF) > 10 indicates redundancy among explanatory variables. Urban youth’s employment status model satisfied this criterion with VIF < 2.0 and therefore all variables were retained. Associations between binary outcome variable (urban youth employment status) and explanatory variables were determined by the use of the univariate and multivariate binary logistic regression models.

All the significant variables in the bivariate analysis (p < 0.05) were included in the multivariable logistic (MVL) regression model because univariate association between two variables did not necessarily tell us a significant causal relationship between them(Rana and Singhal, 2015; Teshita, 2018). Therefore, a multivariate approach was applied to determine which factors best explain and predict urban youth employment outcome. The adequacy of the developed model was verified through the standard statistical mean of likelihood ratio test of goodness of fit(Lemeshow and Hosmer, 2000). Multicollinearity in the MVL model was detected by examining the standard error for the coefficients(Pallant, 2020) Adjusted odds ratios (AOR) with corresponding 95% CI estimates were used to describe the strength of associations of factors with unemployed urban youth versus employed one. The association of variables was found to be statistically significant at p < 0.05(Rana and Singhal, 2015).