Beyond the Farm: Small and Micro Agricultural Enterprises as Engines for Youth Employment and Income in Maya City, Ethiopia

Tamirat Tsegaye; Rekiku Yohannes; Habtamu Abaynew; Assefa Dinku

doi:doi:10.11648/j.ajtab.20261201.13

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Beyond the Farm: Small and Micro Agricultural Enterprises as Engines for Youth Employment and Income in Maya City, Ethiopia

Tamirat Tsegaye

, Rekiku Yohannes, Habtamu Abaynew^*

, Assefa Dinku

Published in American Journal of Theoretical and Applied Business (Volume 12, Issue 1)

Received: 3 January 2026 Accepted: 14 January 2026 Published: 30 January 2026

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Abstract

Despite the Ethiopian government's efforts to promote youth participation in Small and Micro Agricultural Enterprises (SMAEs) as a strategy for economic development, youth involvement in these sectors remains low. The purpose of the study was to identify the variables affecting youth participation in small and micro agricultural enterprises and how this influences employment creation and income in Maya city, Oromia region, Ethiopia. The study employed a multi-stage sampling technique to select 180 youths from Maya City. Data were analyzed using descriptive statistics and econometric models. The probit model results showed that land size, comfort in group work, extension services, and awareness of agribusiness positively influenced youth participation in SMAEs, while risk aversion and educational level had a negative effect. Additionally, the endogenous regression model revealed that youth employment creation and income were significantly and positively impacted by participation in SMAEs (0.5 full-time jobs and 7009.2 Birr, respectively). If non-participants had engaged in SMAEs, employment would have increased by 0.35 full-time equivalents and income by 5499.7 Birr. This highlights the vital role of SMAEs in boosting income and employment opportunities for youth. Therefore, the study recommends introducing agricultural insurance and financial safety nets to mitigate risks, raising awareness through campaigns and educational programs, improving access to credit with tailored financial products, and fostering comfort in group work through team building and mentorship. These strategies can significantly enhance youth participation in small and micro agricultural enterprises, thereby improving income levels and employment opportunities.

Published in	American Journal of Theoretical and Applied Business (Volume 12, Issue 1)
DOI	10.11648/j.ajtab.20261201.13
Page(s)	28-44
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2026. Published by Science Publishing Group

Keywords

Agricultural Enterprises, Endogenous Switching Regression, Employment, Income

1. Introduction

Youth unemployment remains a significant development challenge in many low- and middle-income countries, including Ethiopia

[1]

. Globally, the youth unemployment rate was estimated at 5.8% in 2023, showing little improvement since 2019

[2]

. Although the rise after the COVID-19 pandemic has been modest, rapid population growth in Sub-Saharan Africa has greatly expanded the labor force, increasing the risk of joblessness among youth

[3, 4]

. Ethiopia, as the second most populous country in the region, is especially affected. With a population nearing 123 million

[5]

, the country continues to struggle with widespread youth unemployment, particularly in rural and peri-urban areas where most young people live

[6]

. Agriculture remains the main sector in Ethiopia’s economy, contributing about 32.4% to GDP, 85% of exports, and employing roughly 75% of the population

[7, 8]

. Despite its vast potential, the sector faces challenges like inadequate institutional support, limited access to finance, land, and technology, and inefficient marketing systems. These issues disproportionately impact young people, who often encounter additional barriers such as landlessness, limited financial literacy, and poor infrastructure

[9-11]

In this context, small and micro agricultural enterprises (SMAEs) have emerged as viable pathways to promote rural development, youth empowerment, and sustainable economic transformation. SMAEs encompass a range of activities, crop and livestock production, agro-processing, aquaculture, and agri-marketing, that can serve as engines of job creation and income generation

[12-14]

. Globally, micro and small enterprises (MSEs) contribute over 40% to GDP and more than 60% of total employment

[15, 16]

. In Africa, they account for over 80% of jobs and more than 50% of GDP

[17-19]

, underscoring their developmental importance. In Ethiopia, MSEs have gained prominence as key contributors to growth and industrialization. Over the past decade, the sector has become more dynamic, creating millions of jobs and supporting Ethiopia’s shift toward an industrial economy

[20, 21]

. The government has prioritized youth entrepreneurship within national development frameworks through the Growth and Transformation Plan II (GTP II). The GTP II aims to enhance youth participation and provide access to training, credit, equipment, workspaces, and legal recognition through group certification

[22-24]

. Promoting youth participation in SMAEs contributes to the three pillars of sustainable development. Economically, it enhances youth income and employment, fostering entrepreneurship-driven growth. Socially, it reduces rural-urban migration and strengthens community cohesion. Environmentally, youth-led enterprises have the potential to adopt resource-efficient and climate-smart practices, enhancing the resilience of rural livelihoods.

Despite these enabling conditions, youth participation in SMAEs remains limited. Many young people perceive agriculture as unattractive or low-return, leading to underutilization of public support and missed economic opportunities

[25-27]

. This is particularly true in Maya City, where engagement in SMAEs remains suboptimal. Several studies have examined youth participation in agriculture in Ethiopia. Most of them focus on exploring determinants only; others do not disaggregate by enterprise size nor evaluate the direct impact of youth engagement in SMAEs on employment and income

[28, 29]

. Furthermore, few have rigorously quantified the causal impact of youth participation in SMAEs on employment creation and income, and those that do often rely on methods such as propensity score matching that account only for observable factors while overlooking unobservable heterogeneity, thereby limiting the robustness and policy relevance of their findings

[30-34]

This study addresses these gaps by focusing on SMAEs, applying the Endogenous Switching Regression (ESR) model to account for observable and unobservable selection bias, and providing localized evidence from Maya City, Oromia, Ethiopia. It identifies socio-economic, institutional, and behavioral factors shaping participation and estimates the impact of SMAE engagement on youth income and employment, providing actionable insights for sustainable rural development.

2. Related Literature

This section reviews relevant literature on youth participation in small and micro enterprises, with a particular focus on agricultural and agribusiness activities. It first presents key operational definitions used in the study, including youth, income, employment, and micro and small enterprises, as adopted in the Ethiopian context. The section then synthesizes empirical evidence on the determinants of youth participation in agricultural enterprises and examines the effects of such participation on income and employment outcomes.

2.1. Operational Definitions

Youth: The definition of “youth” varies across organizations and contexts. The International Labour Organization (ILO) defines youth as those aged 15–24

[21]

, while the African Youth Charter extends the definition to 15–35 years. According to the Government of Ethiopia, youth are officially defined as individuals aged 15–29

[12]

Income: Income broadly refers to the financial gain obtained through wages, business profits, or other economic activities

[18]

. For this study, income is specifically defined as the net profit earned by youth from micro and small enterprise (MSE) activities, after deducting all business-related expenses.

Employment: refers to participation in productive activities for pay, whether through wages, profits, or contributions to family businesses

[1]

. In this study, employment includes both self-employment youth managing or operating their MSEs and wage-employed youth working in MSEs owned by others, consistent with definitions used in agricultural and non-agricultural enterprise contexts.

Micro and Small Enterprises (MSEs): There is no universal definition of MSEs, as criteria differ by country. In Ethiopia, MSEs are officially categorized based on employment size and capital

[10]

Micro enterprises employ up to 5 persons (including owners and family) with assets not exceeding ETB 100,000 in the industrial sector or ETB 50,000 in services.

Small enterprises employ 6–30 persons, with assets ranging from ETB 100,001 to ETB 1,500,000 in industry, and ETB 50,001 to ETB 500,000 in services.

MSEs, particularly in agribusiness (crop production, livestock rearing, agro-processing), play a vital role in local economic development, youth employment, and poverty reduction. However, limited capital, skills, and managerial capacity often constrain them.

2.2. Empirical Review

Youth: The definition of “youth” varies across organizations and contexts. The International Labour Organization (ILO) defines youth as those aged 15–24

[21]

, while the African Youth Charter extends the definition to 15–35 years. According to the Government of Ethiopia, youth are officially defined as individuals aged 15–29

[12]

. Several studies have investigated the determinants of youth participation in small and micro agricultural enterprises (SMAEs) across Ethiopia and other countries. Gitore et al.

[25]

examined rural and peri-urban youth participation in southern Ethiopia using a probit model, identifying sex, education level, distance to credit institutions, institutional arrangements, and agricultural enterprise awareness as key determinants. Similarly, Seid and Destaw

[35]

applied a logistic regression model in Gurage and Silte Zones, Ethiopia, and found land size, total income, lack of awareness, lack of initial savings, and limited commitment by officials as significant barriers to youth participation. Wordofa et al.

[27]

conducted a study in the SNNP region of Ethiopia using a probit model and highlighted variables such as education level, extension contact, credit bureaucracy, lack of initial capital, risk aversion, fear of group participation, and lack of working space as critical determinants. Yami et al.

[28]

analyzed youth engagement in Kuyyu district, North Shewa Zone, Oromia, using logistic regression, reporting extension services, access to land, migration plans, credit, and career prospects as influential factors.

Other studies, including Giwu et al.

[29]

, emphasized the lack of land, credit, and market information as major barriers, while Adeyanju et al.

[36]

reported access to land, credit, and technology, as well as the availability of off-farm employment, as critical factors. Sosina and Holden

[9]

found that limited access to land and restrictive market conditions significantly deterred youth participation in Southern Ethiopia, with increased migration in search of alternative livelihoods. Begho and Daubry

[37]

in Eastern Cape Province, South Africa, identified sex, age, education, farm size, farm organization membership, access to credit, extension services, and distance to markets as significant factors affecting youth participation. Studies from other regions support these findings. Nyathi et al.

[38]

in Zimbabwe found that educational attainment, training, non-agricultural activities, youth organization membership, access to credit, and productive resources significantly influenced youth participation. Ouko et al.

[39]

in Kenya highlighted attitudes, family support, government support, and promotional activities, while Daudu et al.

[40]

in Nigeria identified high returns, interest, experience, market availability, and government support as positive influences.

The impact of youth participation in agricultural enterprises on income and employment has been explored in several studies, though the literature remains relatively limited. Abraham et al.

[41]

in Hadiya Zone, Ethiopia, using propensity score matching (PSM), found that households participating in agricultural enterprises earned higher incomes and generated more employment opportunities than non-participants. Bello et al.

[42]

in Nigeria evaluated the Youth-in-Agribusiness program using both PSM and the endogenous switching model (ESM). The study revealed that participation was associated with a substantial increase in gainful employment, with consistent results across both models, highlighting the effectiveness of the program in promoting youth employment in agribusiness.

3. Study Methodology

This chapter presents a summary of the study area, outlines the research design, details the methods used for data collection, describes the sampling strategy and sample size determination, and explains the data analysis techniques, along with definitions of key variables and the associated hypotheses.

3.1. Description of the Study Area

The study was conducted in Maya City, Eastern Hararghe, Oromia, Ethiopia. The city comprises four towns: Aweday, Haramaya, Adele, and Bate town, each with 12 kebeles. Geographically, it lies between 9°20′1″ and 9°35′1″ N latitude and 41°51′1″ and 42°04′1″ E longitude. The area is located 508 km east of the capital, Addis Ababa, and shares borders with Kurfa Chele to the south, Kersa to the west, Dire Dawa to the north, Kombolcha to the east, and the Harari Region to the southeast. The elevation of the area ranges from 1,900 to 2,450 meters above sea level, placing it within the Dega and Woinadega agro-ecological zones. The mean annual rainfall is 74.1 mm, and the average annual temperature is 16.9°C. The dry season, receiving less than 30 mm of rainfall monthly, extends from October to February. The main rainy season (autumn rains) occurs between September and November, while a shorter rainy period (spring rains) takes place from March to May

[43]

The livelihood of the community in the study area primarily depends on smallholder mixed farming, combining both crop and livestock production, with limited engagement in off-farm activities, particularly among urban residents. Khat and vegetables serve as the main cash crops cultivated in the area. Information from the Maya City Enterprise Development Office indicates that the city hosts a significant number of micro and small agricultural enterprises, many of which are youth-owned and contribute to the local economy.

Source: Authors’ own design using ArcGIS

Download: Download full-size image

Figure 1. Location Map of the Study Area.

3.2. Types of Data, Sources, and Collection Methods

The study used a mixed-methods approach that combined both primary and secondary data sources to gather qualitative and quantitative information. Primary data were collected through structured questionnaires prepared for youth involved in micro and small enterprises, along with informal interviews to gain additional insights. Additionally, four focus group discussions (FGDs) were held, each with seven male and female youth participants. Secondary data were collected from a variety of published and unpublished materials, including reports, maps, statistical records, and other relevant documents.

3.3. Procedures for Sampling and Sample Size Determination

A multistage sampling technique was used to obtain a representative sample from the study area. In the first stage, Maya City was purposively selected because of its relatively high concentration of youth involved in agricultural activities. From this city, Aweday town was randomly chosen as the study site, and the town shares similar socio-economic, demographic, and enterprise distribution characteristics with other towns in the city. In the second stage, a complete list of youth operating micro and small enterprises was collected from the Aweday Town Enterprise Development Office. This sampling frame was then divided into two groups: those engaged in youth agricultural enterprises and those involved in non-agricultural enterprises.

A total of 180 respondents were randomly selected, with 44% from agricultural enterprises (participants) and 56% from non-agricultural enterprises (non-participants). Although this distribution might introduce bias, the Endogenous Switching Regression (ESR) model used in the analysis addresses such bias by adjusting for both observed and unobserved differences between groups. The total sample size was determined using the formula recommended by Yamane

[44]

, which is suitable given the relative similarity of enterprises in the study area. Based on this formula, the sample was calculated at a 95% confidence level with a margin of error of ±5%. The formula is as follows:

n = \frac{N}{1 + N ({e)}^{2}}

(1)

Where n is the required sample size, N is the population size, and e is the level of precision.

n = \frac{326}{1 + 326 ({0.05)}^{2}} = 180

3.4. Data Analysis Techniques

Descriptive statistics were employed to summarize the institutional, socio-economic, and demographic characteristics of both participants and non-participants in small and micro agricultural enterprises (SMAEs). The chi-square (χ²) and t test were applied for both categorical and continuous variables to examine the differences between the two groups. Qualitative data obtained from focus group discussions (FGDs) and key informant interviews (KIIs) were analyzed using narrative analysis to extract key themes and insights. In addition, econometric models were applied to rigorously evaluate participation determinants and impacts. The probit model was used to identify factors influencing youth participation in SMAEs, while the endogenous switching regression (ESR) model was employed to estimate the impact of participation on employment creation and income levels.

3.5. Endogenous Switching Regression Model Specification

As highlighted in previous studies

[30-34]

, the Endogenous Switching Regression (ESR) model is well-suited to address potential selection bias arising from unobserved heterogeneity and the non-random nature of adopting agricultural innovations, such as participation in Small and Micro Agricultural Enterprises (SMAEs). In contrast, Propensity Score Matching (PSM), the widely used method limited to controlling for observable covariates and fails to account for unobservable factors that jointly influence both the decision to participate and the resulting outcomes

[42, 45]

Given that unobserved characteristics may affect both the decision to engage in SMAEs and the outcome variables (income and employment), the ESR model is preferred over PSM to address this problem in this study. The ESR approach employs a two-stage modeling framework. The probit model is employed to identify the determinants of youth participation in SMAEs as the first stage of the ESR model. This selection equation serves to correct for endogeneity and selection bias. The second stage then estimates separate outcome equations for participants and non-participants, thus allowing the model to capture both observable and unobservable influences. Following the methodologies of Degefu et al. [32], Jaleta et al. [33], Haileslasie and Gidey [34], and Abraham et al.

[41]

, the ESR model aligns with the dual objectives of this study. To identify the factors that affect youth participation in SMAEs, and to assess the impact of participation on employment generation and income levels. In line with previous literature

[30-35, 38]

, the decision to participate is modeled using a random utility framework. According to this framework, a rational youth (i) participates in SMAEs if the expected utility from participating in SMAEs (

U_{1}

) is higher than the expected utility from non-participating (

U_{0}

) such that:

U_{1} - U_{0} > 0

(2)

Where B_𝑖^∗ is the latent variable capturing the unobserved factors influencing a youth’s decision to participate in small and micro agricultural enterprises (SMAEs). This latent construct is assumed to be a function of observed characteristics

(Z_{i})

and a random error term (

U_{i})

. Formally, the selection equation is specified as:

{B_{i}}^{*} = Z_{i} α + U_{i}

(3)

where α is a vector of parameters to be estimated. The observed binary decision variable B_𝑖^∗ takes the value 1 if a youth participates in micro and small Agricultural enterprises (SAMEs) and zero otherwise.

B_{i} = \{\begin{matrix} 1, if {B_{i}}^{*} is > 0 \\ 0, Ot h erwise \end{matrix}

(4)

Since the decision to participate in SMAEs may be endogenous in the outcome equations (income and employment generation), ignoring this potential endogeneity could result in biased and inconsistent estimates. To address this concern, the ESR model incorporates an Instrumental Variable (IV) approach that separates the selection equation from the outcome equations, as suggested by Sileshi et al. [30], Tsegaye et al. [31], Degefu et al. [32], Jaleta et al. [33]. A valid instrument should affect the participation decision but have no direct influence on the outcome variables. In this study, awareness of agricultural enterprises was used as the instrument, as it is expected to influence the likelihood of youth participation in SMAEs but not directly affect income or employment outcomes

[34, 41]

. In the second stage of the ESR model, the relationship between the outcome variables and explanatory factors is estimated conditionally on participation status. Specifically, an Ordinary Least Squares (OLS) regression is used for modeling household income. In contrast, a Tobit model is applied to estimate employment creation, due to the censored nature of the employment variable. The outcome equations are estimated separately for participants (Regime 1) and non-participants (Regime 2), as follows:

Regime 1:

Y_1i=β₁X_1i+ε_1iifB_i=1(Participant)(5)

Regime 2:

Y_2i=β₂X_2i+ε_2iifB_i=0(Non-participant)(6)

In this model, the outcome variables (income and employment) are represented by Y_1i for SMAE participants and Y₂ for nonparticipants. The covariate vectors X_1i and X_2i describe the farmer's characteristics. The parameters β₁ and β₂ are the coefficients to be estimated. The associated error terms are 𝜺₁ and 𝜺₂. According to the ESR framework, the error terms in the selection and outcome equations (Equations 4, 5, and 6) are assumed to have a trivariate normal distribution with a mean of zero and a covariance structure represented as:

cov (ε, ϵ_{1,} ϵ_{0}) = [\begin{matrix} σ_{u}^{2} & σ_{1 u} & σ_{2 u} \\ σ_{ε 1 u} & σ_{ε 1}^{2} & . \\ σ_{ε 2 u} & . & σ_{ε 2}^{2} \end{matrix}]

(7)

Where,

σ_{u}^{2}

denoted the variance of the error term in the selection equation, while

σ_{ε 1}^{2} and σ_{ε 2}^{2}

represent the variances of the error terms in the outcome equation for participants and non-participants, respectively. The term

σ_{ε 1 u} and σ_{ε 2 u}

represent the covariances between the selection and outcome equations. Since the outcomes for participants and non-participants are not observed simultaneously. This means the covariance of the corresponding error terms (ϵ_1i and ϵ_2i) is not defined. This structure suggests that the error terms in the outcome equation are correlated with the error term in the selection equation, resulting in a non-zero value of ε_1i, and 𝜀_2𝑖. The correlation between the selection and outcome equations suggests the presence of selection bias, requiring correction through the inclusion of the inverse Mills ratio (IMR). The expected values of the truncated error terms (𝜀₁|B = 1) and (𝜀₂|B = 0) are, therefore, given below:

ϵ (ε 1 | B = 1) = ϵ (ε 1 | u > - Zα) = σ_{ε 1 u} \frac{φ (Zα σ)}{Φ (Zα σ)} \equiv σ_{ε 1 u} λ_{1}

(8)

ϵ (ε 1| B = 0) = ϵ (ε 2| u \leq - Zα) = σ_{ε 2 u} \frac{φ (Zα σ)}{1 - Φ (Zα σ)} \equiv σ_{ε 2 u} λ_{2}

(9)

Where 𝜑 and 𝛷 denote the probability density and cumulative distribution function of the standard normal distribution, respectively. The ratio of φ and Φ evaluated at Zα yields the inverse Mills ratio (IMR). The selectivity terms λ₁ and λ₂ are obtained from the selection equation and incorporated in equations 8 and 9 to correct the biases arising from the selection equation. Within this framework, the impacts of participation on income and employment creation were examined by contrasting actual outcomes with their counterfactual equivalents. The average treatment effect on the treated (ATT) was estimated by comparing the observed outcomes of participants with the expected counterfactual outcomes had they not participated. Similarly, the average treatment effect on the untreated (ATU) was computed by comparing the actual outcomes of non-participants with the hypothetical scenario of their participation

[30-32]

. The expected outcome values for both participants and non-participants under observed and counterfactual conditions are expressed as follows:

Participants participating in Small and Micro Agricultural enterprises:

E (Y_{1 i} | Bi = 1, X_{1 i}) = β_{1} X_{1 i} + σ_{ε 1 u} λ_{1}

(10)

Non-participants not participating in Small and Micro Agricultural enterprises:

E (Y_{2 i} | Bi = 0, X_{2 i}) = β_{2} X_{2 i} + σ_{ε 2 u} λ_{2}

(11)

Participants had decided not to participate in Small and Micro Agricultural enterprises:

E (Y_{2 i} | Bi = 1, X_{2 i}) = β_{2} X_{2 i} + σ_{ε 2 u} λ_{1}

(12)

Non-participants had they decided to participate in micro and small Agricultural enterprises:

E (Y_{1 i} | Bi = 0, X_{1 i}) = β_{2} X_{1 i} + σ_{ε 1 u} λ_{2}

(13)

The Average Treatment Effect on the Treated (ATT) captures the difference in employment creation and income between the observed outcomes of SMAE participants and the counterfactual outcomes they would have experienced in the absence of participation. In other words, it quantifies the extent to which participation in SMAEs enhanced employment opportunities and income for those who engaged in the program. Based on the scenarios described above, the ATT is obtained by taking the difference between equation (10) and equation (12), expressed as follows:

ATT = E (Y_{1 i} | Bi = 1, X_{1 i}) - E (Y_{2 i} | Bi = 1, X_{1 i}

(14)

The Average Treatment Effect on the Untreated (ATU) reflects the expected difference in employment creation and income for non-participants between their actual outcomes without participation and the hypothetical outcomes they would have achieved had they participated in SMAEs. In other words, it indicates the potential gains that non-participants could have realized through participation. The ATU is computed by taking the difference between equation (13) and equation (11), as shown below:

ATU = E (Y_{1 i} | Bi = 0, X_{1 i}) - E (Y_{2 i} | Bi = 0, X_{2 i}

(15)

Treatment Heterogeneity (TH) captures the variability in the impact of SMAEs on youth employment creation and income. While some youths may experience substantial improvements in these outcomes from participation, others may benefit less, relative to their counterparts who did not participate, but are considered under the hypothetical scenario of participation. Such variation can largely be attributed to unobserved factors influencing youth outcomes. This concept is represented by the difference between equations (14) and (15).

Base Heterogeneity (BH1 and BH2): These terms capture the underlying differences between SMAE participants and non-participants that influence outcome variables independently of program participation. BH1 (Confounding Base Heterogeneity): Refers to factors that simultaneously affect both the decision to participate in SMAEs and the associated outcome variables. BH2 (Non-Confounding Base Heterogeneity): Refers to factors that influence the outcome variables but have no effect on the decision to participate. Thus, even when youths participate in SMAEs, they may exhibit higher productivity than their non-participating peers due to the influence of such unobserved characteristics. The basic heterogeneity between participants and non-participants can be represented in the following equation, with further details provided in Table 1.

BH 1 = E (y_{1}| B = 1) - E (y_{1}| B = 0)

(16)

BH 2 = E (y_{2}| B = 1) - (y_{2}| B = 0)

(17)

Table 1. Average Treatment and Heterogeneity Effects estimation for actual and counterfactual outcomes.

Household participation categories	Participation decision estimations		Treatment effect
Household participation categories	To Participate	Not to Participate	Treatment effect
Youth participation in SMAE	$(a) E (y_{1}\| B = 1)$	$(c) E (y_{2}\| B = 1)$	ATT
Youths who did not participate in SMAE	$(b) E (y_{1}\| B = 0)$	$(d) E (y_{2}\| B = 0)$	ATU
Heterogeneity effect	BH1	BH2	TH

3.6. Definitions of Variables and Working Hypothesis

3.6.1. Dependent Variable

The decision to engage in small and micro agricultural enterprises (SMAEs) serves as the dependent variable in the analysis. This variable is binary, assuming a value of 1 if the youth is involved in SMAEs and 0 if not.

3.6.2. Outcome Variable

Income (INC): This represents the net earnings obtained by youth engaged in small and micro agricultural enterprises. It is calculated as the total revenue generated from agricultural activities over the past year, after deducting all production-related costs. The resulting net income, measured in Ethiopian Birr, is treated as a continuous variable and allocated proportionally among members of the enterprise. For non-participants, those running micro and small enterprises outside the agricultural sector, income refers to the total annual business revenue minus all associated expenses, also distributed proportionally among group members.

Employment Creation (EMC): This refers to the total number of jobs created within both agricultural and non-agricultural small and micro enterprises over a given year. It encompasses both self-employment and wage employment. Each fully employed individual is counted as one job, while part-time employment is converted into full-time equivalents (FTE) by dividing the actual hours worked by the standard full-time working hours. The total number of FTEs thus provides a consistent and comprehensive measure of employment generated by these enterprises.

3.6.3. Independent Variable

The independent variables, also known as explanatory variables, are those factors presumed to affect the decision of youth to engage in small and micro agricultural enterprises. These variables were selected and incorporated into the model based on their potential influence. They encompass a range of socio-demographic characteristics of the household as well as institutional factors, all of which are presented in Table 2.

Dummy and continuous variables

Dummy variables are binary variables that take the value of 1 if a condition is met and 0 otherwise. They are used in this study to capture categorical characteristics of youth that may influence participation in small and micro agricultural enterprises (SMAEs). For example, sex of the household head (1 = male, 0 = female), access to credit (1 = yes, 0 = no), participation in training (1 = yes, 0 = no), and membership in cooperatives (1 = yes, 0 = no) are modeled as dummy variables. These helps quantify qualitative attributes that may have a significant effect on youth decisions and enterprise outcomes.

Continuous variables are those that can take a wide range of numeric values, not limited to categories. In this study, variables such as age of the youth (measured in years), household size (measured as the number of members), land size (measured in hectares), income (measured in Ethiopian Birr), and years of schooling are treated as continuous variables. These provide measurable quantities that vary across individuals and allow for analysis of how incremental changes in these values influence participation in and benefits from SMAEs.

Table 2. Description of the independent variables and their anticipated effect on participation choices in SAME.

Variables	Description of variables	Variable type	Measurement	Effect on Participation
Dependent variables
PSMAE	Participation in SMAE	Dummy	1 if participated in SMAE, 0 otherwise
Outcome variables
NI	Net income	Continuous	ETB
EC	Employment creation	Continuous	Between 0 and 1
Explanatory variables
SEX	Sex of the youth	Dummy	1 if male and 0, otherwise	-
AGE	Age of the respondent	Continuous	Years	+
MARR	Marital status of the respondent	Continuous	1, if married: 0, if not	+
EDUC	Educational status of the youth	Continuous	Years	+
EXT	Extension contacts	Continuous	Frequency	-
LAND	Land size	Continuous	Hectare	-
INST	Institutional support	Dummy	1 if weak organizational institute and 0 otherwise	+
CRED	Access to credit	Dummy	1 if credit is available, otherwise 0	+
MGPL	Migration plan	Dummy	1 if planned to move from the area in the near future, otherwise 0	-
MINF	Market Information	Dummy	1 if they have access to information about the market, otherwise 0	+
CR	Youth career	Dummy	1 If youths’ career choice is in agriculture, and otherwise 0	+
RAV	Risk Aversion	Dummy	has a value of 1 if they are afraid to take a risk, otherwise 0	+
CGW	Comfort in group work	Dummy	1 if the youth is comfortable owning a business with others, and 0 if not	+

4. Results and Discussion

This chapter presents and discusses the results of the study on youth participation in small and micro agricultural enterprises (SMAEs), key determinants of participation, and their impacts on income and youth employment.

4.1. Descriptive Results of Dummy Variables

Table 3 displays the summary statistics for the binary explanatory variables related to youth involvement in SMAEs. The results indicate statistically significant differences between participants and non-participants in terms of risk aversion, willingness to work in groups, awareness of agricultural enterprises, and access to credit. In contrast, variables such as gender, marital status, institutional support, intention to migrate, access to market information, and career aspirations did not differ significantly between the two groups. Awareness of agricultural enterprises was notably greater among participants (75%) than non-participants (46%), with the chi-square test confirming a statistically significant difference at the 1% level. This suggests that increased awareness equips individuals with critical information and motivation to participate in SMAEs, emphasizing the value of targeted information dissemination. Similarly, access to credit was significantly higher for participants (56.25%) compared to non-participants (39%), also significant at the 1% level. This access plays a crucial role in enabling youth to cover startup and operational costs, invest in inputs and tools, and actively engage in agricultural enterprises.

Table 3. Descriptive analysis of dummy explanatory variables.

Variables	Participant of SMAE (N=80)		Non-participant of SMAE (N=100)		Total (N=180)		χ² - value
Variables	Freq.	%	Freq.	%	Freq.	%	χ² - value
Sex	28	35	34	34	62	34.44	0.02
Sex	52	65	66	66	118	65.56	0.02
Marriage	38	47.5	45	45	83	46.11	0.1
Marriage	42	52.5	55	55	97	53.89	0.1
Agri-enterprise awareness	20	25	54	54	77	42.78	15.44^***
Agri-enterprise awareness	60	75	46	46	103	57.22	15.44^***
Institutional support	40	50	45	45	85	47.22	0.45
Institutional support	40	40	55	55	95	52.78	0.45
Access to credit	35	43.75	61	61	92	46.67	3.61^**
Access to credit	45	56.25	39	39	88	51.11	3.61^**
Migration plan	58	72.5	63	63	121	48.89	1.82
Migration plan	22	27.5	37	37	59	32.78	1.82
Market information	39	58.75	47	47	86	47.78	0.06
Market information	41	41.25	53	53	94	52.22	0.06
Career	45	56.25	66	66	111	61.67	1.79
Career	35	43.75	34	34	69	38.33	1.79
Risk aversion	58	72.5	42	42	100	55.56	16.75 ^***
Risk aversion	22	27.5	58	58	80	44.44	16.75 ^***
Comfort in group work	14	12.5	38	38	52	28.89	9.1^***
Comfort in group work

Note: ***, **, and * are significant at 1, 5, and 10 percent probability levels, respectively

A significant variation in risk aversion was observed, with 72.5% of participants reporting no fear of engaging in SMAEs compared to 42% of non-participants, and the chi-square test confirmed this difference at the 1% significance level. This highlights the critical role of risk tolerance in entrepreneurial engagement, as it influences individuals’ readiness to commit time and resources despite potential uncertainties. Similarly, comfort in group work was more prevalent among participants (87.5%) than non-participants (62%), also significant at the 1% level. This suggests that group collaboration, which offers shared support, pooled resources, and collective problem-solving, likely encourages greater youth involvement in SMAEs.

4.2. Descriptive Results of Continuous Variables

Table 4 displays the descriptive statistics for continuous explanatory variables examined in the study. The findings reveal statistically significant differences between SMAE participants and non-participants in terms of education level, landholding size, and access to extension services. Conversely, the average age of individuals in both groups did not show a significant difference. Participants in SMAEs had an average education level of 7.35 years, which was significantly lower than the 9.67 years for non-participants. This difference was statistically significant at the 1% of significance level. Regarding land size, participants held an average of 0.4 hectares of land, notably larger than the 0.23 hectares owned by non-participants. This difference being significant at t 5% level. Across the entire sample, the mean education level was 8.64 years, while the average landholding size was 0.3 hectares. These findings align with previous studies that emphasize the importance of land access as a key enabling factor for youth participation in agricultural enterprises

[35, 37]

. Similarly, while higher education is often expected to increase engagement in entrepreneurial activities, some research indicates that in rural contexts, youth with more formal education may prefer non-agricultural opportunities, which could explain the lower education level among participants in SMAEs

[36, 38]

. Thus, landholding appears to be a critical resource for engaging in small-scale agriculture, even when formal education levels are relatively lower.

Table 4. Descriptive analysis of continuous explanatory variables.

Variables	Participant of SMAE (N=80)		Non-participant of SMAE (N=100)		Total (N=180)		T-value
Variables	Mean	SD	Mean	SD	Mean	SD	T-value
Age	24.88	3.01	25.45	2.25	25.19	2.62	1.47
Education (year of schooling)	7.35	2.01	9.67	2.56	8.64	2.6	6.6^***
Land size (in hectares)	0.4	0.48	0.23	0.39	0.3	0.44	-2.5^**
Extension service	1.25	1.58	0.45	1.18	0.81	1.42	-3.9^***

Note: ***, **, and * imply significant at 1%, 5%, and 10% probability levels.

Participants in SMAEs received an average of 1.25 extension service visits per month, which was higher than the 0.45 visits accessed by non-participants. The difference between the two groups was statistically significant at the 1% of significance level. This finding underscores the critical role of extension services in facilitating youth engagement in agricultural enterprises and provides essential technical support, information, and resources that improve productivity and encourage sustained participation. Similar studies

[35, 37]

have also demonstrated that greater access to extension services is positively correlated with increased involvement and success in smallholder agricultural ventures.

4.3. Distribution of Small-Scale and Micro Agricultural Enterprises

Figure 2 depicts the distribution of youth engagement in micro and small-scale agricultural enterprises across six categories, each represented by a distinct color and percentage of total participation. Shoat fattening leads with 25%, making it accessible for young entrepreneurs due to its high meat demand and relatively low space and feed requirements. Poultry farming follows at 23%, valued for its minimal land and capital needs and quick income generation due to fast production cycles and steady demand for eggs and meat. Dairy farming accounts for 19%, providing essential milk products and a stable market demand. Fattening oxen represents 15%, offering higher returns due to the animals' size and utility, but demanding more land and inputs than shoats. Small-scale crop farming constitutes 11%, involving the cultivation of crops like sorghum, maize, Khat, and vegetables; its lower youth participation may reflect land limitations and labor intensity. Lastly, beekeeping is the smallest segment at 7%, notable for its low land and resource needs, making it viable for those with limited agricultural space.

Download: Download full-size image

Figure 2. Distribution of youth participation in small-scale and micro agricultural enterprises.

4.4. Econometric Results

4.4.1. Factors Affecting Youth Participation in Micro and Small-Scale Enterprises

The binary probit model is widely used in conjunction with the Endogenous Switching Regression (ESR) model to estimate treatment effects on outcome variables, as used in previous studies

[30, 34, 41]

. Prior to model estimation, diagnostic tests were conducted to check for multicollinearity and heteroscedasticity. The Variance Inflation Factor (VIF) values were all below 2, indicating no serious multicollinearity concerns. Additionally, the Breusch-Pagan/Cook-Weisberg test for heteroscedasticity yielded an insignificant result (Prob > chi² = 0.3576), confirming the absence of heteroscedasticity. In this study, the binary probit model was applied as the first stage of the ESR to identify factors influencing youth participation in SMAEs. The model’s overall goodness-of-fit was confirmed by a Wald test statistic of 88.984 with a p-value of 0.000, which is significant at the 1% level, supporting the rejection of the null hypothesis that all coefficients of the explanatory variables are jointly zero. Table 5 summarizes the results, revealing that seven out of the fourteen hypothesized explanatory variables significantly affect the decision to participate in SMAEs. The significant variables are discussed in detail below.

Education Level (Years of Schooling): The negative coefficient of -0.26 suggests that each additional year of schooling reduces the likelihood of youth participation in SMAEs by approximately 10%, holding other factors constant. This relationship is statistically significant at the 5% level. This may reflect a common perception among more educated youth that agricultural work is less prestigious or does not match their qualifications, leading them to prefer careers in sectors they view as offering higher social status or better opportunities. This finding is consistent with

[25]

, who reported that higher education levels discourage youth involvement in agricultural enterprises due to such perceptions. Likewise,

[23-25, 29]

found that education is a key factor influencing youth to pursue employment outside the agricultural sector.

Agricultural Enterprises Awareness: The positive coefficient of 0.54 indicates that youth awareness of agricultural enterprises raises their likelihood of participating in SMAEs by approximately 20.7%, holding other factors constant. This effect is statistically significant at the 5% level. Awareness likely equips individuals with crucial information and motivation about the opportunities and benefits available within the agricultural sector, increasing their willingness to engage. Consequently, well-designed awareness campaigns and effective information dissemination can play a vital role in boosting youth participation in SMAEs. This result aligns with findings by

[46]

, who emphasized the critical role of awareness in youth engagement, as well as

[47]

, who similarly identified awareness as a key factor encouraging youth involvement in agricultural enterprises.

Land Size (in hectares): The coefficient of 0.61 indicates that each additional hectare of land increases the probability of youth participation in SMAEs by about 23.7%, holding other factors constant. This relationship is statistically significant at the 5% level. Land access is a vital resource for agricultural engagement, providing the necessary space to establish and operate enterprises. Enhancing youths’ access to larger landholdings can significantly improve their involvement in SMAEs. This finding is consistent with

[48]

, who identified limited land access as a major obstacle for youth engagement in agriculture, and

[29]

, who similarly reported that land access difficulties negatively affect youth participation in SMAEs in Ethiopia’s Gurage and Silte Zones.

Access to Credit: The coefficient of 0.51 indicates that access to credit increases the likelihood of youth participation in SMAEs by about 19.7%, holding other factors constant. This effect is statistically significant at the 1% level. Credit access provides essential financial resources that enable youth to initiate and sustain agricultural enterprises by investing in inputs, equipment, and other necessary resources. Enhancing financial support mechanisms and expanding accessible credit opportunities can therefore play a crucial role in encouraging youth engagement in agriculture. This finding is consistent with

[25]

and

[29]

, who identified credit access as a key determinant of youth participation in agricultural enterprises in Ethiopia, and

[38]

, who similarly found that credit access significantly influenced youth involvement in agribusiness initiatives in Nigeria.

Risk Aversion: The coefficient of -0.54 suggests that youth who are risk-averse are about 20.7% less likely to participate in SMAEs. This negative relationship is statistically significant at the 5% level. The uncertainty and potential losses associated with agricultural enterprises may discourage risk-averse youths from involvement. This result aligns with

[27]

, who found that fear of risk significantly hinders youth participation in agricultural activities.

Comfort in Group Work: The coefficient of 0.60 indicates that youths who feel comfortable working in groups are approximately 22.2% more likely to participate in SMAEs as hypothesized, holding other factors constant. This relationship is statistically significant at the 1% level. The positive effect of group comfort can be explained by benefits such as social support, shared resources, collaborative problem-solving, and risk-sharing, which collectively facilitate engagement in agricultural enterprises. Group participation also enhances access to information and can boost productivity while reducing individual uncertainties. This finding is consistent with

[27]

, who identified discomfort with group participation as a barrier to youth involvement in agricultural activities. Therefore, fostering a supportive group environment could substantially increase youth engagement in SMAEs.

Extension Services (Number of Contacts): The coefficient of 0.29 suggests that each additional extension service contact increases the likelihood of youth participation in SMAEs by about 11.3% as hypothesized, holding other factors constant. This effect is statistically significant at the 1% level. Extension services likely enhance participation by providing essential technical support, training, and information critical to the success of agricultural enterprises. This result aligns with findings from

[27]

and

[40]

, who emphasize the vital role of extension services in promoting youth engagement in small and micro agricultural enterprises.

The information obtained from the Focus Group Discussion (FGD) indicates that youth participants perceive access to credit and access to land as major factors influencing their decision to engage in small and micro agricultural enterprises (SMAEs). They emphasized that without adequate credit, it is challenging to invest in necessary resources and technologies. Additionally, the availability of land was seen as crucial, as it provides the physical space needed for agricultural activities. The participants noted that improving these two areas could significantly enhance youth participation in SMAEs.

Table 5. Factors Affecting SMAE Participation: Probit Regression.

Variables	Coef.	Std. Err.	t-value	P-Value	Marginal effect
Age	-0.07	0.047	-1.45	0.148	-0.026
Sex	0.031	0.26	0.12	0.905	0.012
Marriage	0.18	0.25	0.73	0.467	0.070
Education	-0.26	0.06	-4.53	0.001	-0.100
Agri-enterprises awareness	0.54	0.26	2.13	0.033	0.207
Land size	0.61	0.28	2.17	0.030	0.237
Institutional support	0.23	0.25	0.92	0.356	0.091
Access to credit	0.51	0.25	2.06	0.039	0.197
Market information	.045	0.26	0.17	0.864	0.017
Career	0.18	0.26	0.71	0.475	0.072
Migration plan	-0.41	0.26	-1.55	0.12	-0.155
Risk aversion	-0.54	0.25	-2.15	0.031	-0.207
Comfort in group work	0.60	0.27	2.20	0.028	0.222
Extension service	0.29	0.09	3.34	0.001	0.113
Constant	2.34	1.27	1.84	0.066
Mean dependent var	0.444		SD dependent var		0.498
Pseudo r-squared	0.360		Number of obs.		180
Chi-square	88.984		Prob > chi2		0.000
Akaike crit. (AIC)	188.322		Bayesian crit. (BIC)		236.216

Note: ***, **, and * implies significant at 1%, 5%, and 10% probability levels.

4.4.2. Impact of Youth Participation in SMAEs on Income and Employment

Youth participation in Small and Micro Agricultural Enterprises (SMAEs) was modeled using an instrumental variable that is hypothesized to influence participation but not directly affect the outcome variables. In this study, agricultural enterprise awareness was selected as the instrumental variable. The validity of this instrument was assessed using a falsification test. The test confirmed that the instrument significantly influences youth participation in SMAEs (treatment equation: Chi² = 15.85, p = 0.0001), satisfying the relevance condition. However, the instrumental variable was found to have no significant direct effect on the outcome variables. Specifically, the impact of agricultural enterprise awareness on income and employment creation was statistically insignificant across both participant and non-participant groups. For income: F = 1.33 (p = 0.252) among participants and F = 1.45 (p = 0.2316) among non-participants. For employment: F = 0.68 (p = 0.4125) among participants and F = 1.52 (p = 0.2204) among non-participants.

Income Impact of Participation in SMAEs

The Endogenous Switching Regression (ESR) model results reported in the Table 6 below indicate that participation in Small and Micro Agricultural Enterprises (SMAEs) has a statistically significant positive impact on youth income. On average, participants in SMAEs earned Birr 112,616.5 annually. In the counterfactual scenario, had they not participated their income would have been Birr 105,607.3. This yields an Average Treatment Effect on the Treated (ATT) of Birr 7,009.2, which is statistically significant at the 1% level, highlighting the tangible financial benefit of SMAE engagement. Similarly, for non-participants, the Average Treatment Effect on the Untreated (ATU) indicates that, had they participated in SMAEs, their income would have increased by Birr 5,499.7, also significant at the 1% level. These findings underscore that participation in SMAEs can substantially enhance income levels for both groups, with actual participants realizing greater income gains. This supports the broader case for promoting youth involvement in SMAEs as a strategy for improving economic well-being.

Employment Impact of Participation in SMAEs

The findings from the Endogenous Switching Regression (ESR) model also reveal a significant positive effect of SMAE participation on employment generation. Youth participants in SMAEs created an average of 6.62 jobs. In the absence of participation, this figure would have been 6.12, resulting in an Average Treatment Effect on the Treated (ATT) of 0.5 jobs. This increase is statistically significant at the 1% level, underscoring the employment-generating potential of SMAEs for youth already engaged in the sector. For non-participants, the Average Treatment Effect on the Untreated (ATU) suggests that, had they participated in SMAEs, they would have created an additional 0.35 jobs. This result is also statistically significant, indicating that SMAEs can play a pivotal role in promoting youth employment. The findings reaffirm the value of supporting youth entrepreneurship in agriculture as a pathway to job creation and economic development.

Table 6. Results from the Endogenous Switching Regression Model.

Outcome variables	Youth group	Decision		Treatment effect
Outcome variables	Youth group	Participant	Non-participant	Treatment effect
Income generated (in birr)	Participant	(a) 112616.5	(c) 105607.3	ATT=7009.2^***
	Non-participant	(d) 107429.3	(b) 101929.6	ATU= 5499.7 ^**
	Heterogeneous effect	BH1= 5,187.2	BH2 = 3677.7	TH= 1509.5
Employment created	Participant	(a) 6.62	(c) 6.12	ATT = 0.5^***
	Non-participant	(d) 5.96	(b) 5.61	ATU = 0.35^***
	Heterogeneous effect	BH1 = 0.66	BH2 = 0.51	TH = 0.15

Note: ***, **, and * implies significant at 1%, 5%, and 10% probability levels.

Base Heterogeneity Analysis

The base heterogeneity (BH) analysis provides insight into the inherent differences between participants and non-participants in terms of income and job creation potential, irrespective of their engagement in SMAEs. The first base heterogeneity effect (BH1) indicates that non-participants would have earned Birr 5,187.2 less than participants even if they had participated in SMAEs. Additionally, they would have created 0.66 fewer jobs, suggesting that participants possess relatively better entrepreneurial capacity or resource endowments that enable superior outcomes. Conversely, the second base heterogeneity effect (BH2) shows that participants would have earned Birr 3,677.7 more and created 0.51 more jobs even if they had not participated in SMAEs. This implies that participants tend to have higher income and employment-generating capabilities than non-participants, regardless of program involvement. Together, these results highlight that youths who participate in SMAEs are positively selected; they are inherently more capable or better positioned to benefit from entrepreneurial activities. These findings underscore the need for targeted support to build the capacities of non-participants and bridge the capability gap.

Transitional Heterogeneity Effect

The transitional heterogeneity effect evaluates the different influence of Small and Micro Agricultural Enterprises (SMAEs) on participants compared to non-participants. The analysis shows that the benefits of participation are more significant for current participants than the projected gains for non-participants. Specifically, participants earned Birr 7,009.2 more in income because of their involvement with SMAEs, while non-participants would have earned only Birr 5,499.7 if they had participated. Regarding employment, participants created 0.5 additional jobs, whereas non-participants are expected to create 0.35 more jobs if they engage in SMAEs. These findings indicate that participants not only enjoy greater financial and employment benefits but also have a stronger ability to capitalize on SMAE opportunities. This result aligns with expectations since SMAEs aim to improve youth livelihoods by boosting income and employment outcomes. These findings also match previous studies. For example, Tarekegn et al.

[48]

, in their assessments in Hadiya Zone, Ethiopia, found that youth involvement in agricultural and dairy enterprises significantly increased both income levels and job creation. Similarly, Daudu et al.

[40]

reported that the youth-in-agribusiness program in Nigeria had a notable positive impact on employment opportunities among participating youth. Overall, the transition heterogeneity results and supporting evidence from earlier studies highlight the effectiveness of SMAEs in enhancing youth economic outcomes.

5. Conclusion and Policy Implications

This study examined the factors influencing youth participation in Small and Micro Agricultural Enterprises (SMAEs) and evaluated their effects on income and employment in Maya City, Oromia, Ethiopia. The econometric analysis, using the Endogenous Switching Regression (ESR) model, provided strong evidence that involvement in SMAEs significantly increases both income and job creation among youth. Important factors that positively affect participation include access to land, credit services, awareness of agricultural enterprises, and extension support. On the other hand, higher educational levels and risk aversion were negatively linked to participation, highlighting potential gaps between formal education and entrepreneurial activity in agriculture. Notably, socio-cultural views of agriculture as a low-status job and the preference of more educated youth for off-farm employment seem to lessen their willingness to engage in SMAEs, despite the sector’s proven economic advantages. The impact estimate further emphasizes the value of SMAEs. The Average Treatment Effect on the Treated (ATT) indicates that participants in SMAEs earned Birr 7,009.2 more and generated 0.5 additional jobs compared to if they had not participated. Similarly, the Average Treatment Effect on the Untreated (ATU) indicates that non-participants could have earned an extra Birr 5,499.7 and created 0.35 more jobs had they engaged in SMAEs. The Base Heterogeneity (BH1) analysis also reveals that non-participants would have earned Birr 5,187.2 less and created 0.66 fewer jobs than participants, even if both groups had participated. In contrast, the BH2 analysis shows that participants would have still outperformed non-participants by earning Birr 3,677.7 more and generating 0.51 additional jobs even without participation.

Based on the study’s results, short-term policy priorities should focus on improving access to credit, enhancing agricultural extension services, raising awareness of SMAE opportunities, fostering group-based enterprise models, and mitigating risk aversion. Financial institutions and microfinance providers, in collaboration with youth enterprise offices, should improve access to credit by offering tailored loan products, simplifying application procedures, and delivering financial literacy training. Agricultural extension services, under the Ministry of Agriculture and regional bureaus, should enhance both the frequency and quality of technical support provided to young farmers. Local government agencies, NGOs, and youth associations should raise awareness of SMAE opportunities through targeted campaigns, workshops, and digital platforms. Cooperative promotion offices and youth enterprise support agencies should foster group-based enterprise models by facilitating team-building, mentorship programs, and cooperative formation. Finally, insurance providers and agricultural policy-makers should develop agricultural insurance products, establish financial safety nets, and provide risk management training to address risk aversion among youth.

Our findings show that Higher education was negatively associated with SMAE participation, likely due to socio-cultural perceptions of agriculture as a low-status occupation and the preference of educated youth for off-farm employment. Policy interventions should therefore consider both ATT (benefits for participants) and ATU (potential benefits for non-participants) perspectives. Strategies should aim to consolidate gains for current participants while enabling access for non-participants, including targeted agribusiness training, awareness campaigns, and facilitating access to resources.

Abbreviations

ATT	Average Treatment Effect on the Treated
ATU	Average Treatment Effect on the Untreated
BH	Base Heterogeneity
ESR	Endogenous Switching Regression
FGDs	Focus Group Discussions
SMAEs	Small and Micro Agricultural Enterprises

Author Contributions

Tamirat Tsegaye: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing

Rekiku Yohannes: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Software, Visualization, Writing – original draft

Habtamu Abaynew: Formal Analysis, Investigation, Software, Visualization, Writing – original draft, Writing – review & editing

Assefa Dinku: Writing – review & editing

Funding

This research has not obtained any financial support.

Data Availability Statement

The data that support our research findings are available from the corresponding author on request.

Conflicts of Interest

The authors declare no conflicts of interest.

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Tsegaye, T., Yohannes, R., Abaynew, H., Dinku, A. (2026). Beyond the Farm: Small and Micro Agricultural Enterprises as Engines for Youth Employment and Income in Maya City, Ethiopia. American Journal of Theoretical and Applied Business, 12(1), 28-44. https://doi.org/10.11648/j.ajtab.20261201.13

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ACS Style

Tsegaye, T.; Yohannes, R.; Abaynew, H.; Dinku, A. Beyond the Farm: Small and Micro Agricultural Enterprises as Engines for Youth Employment and Income in Maya City, Ethiopia. Am. J. Theor. Appl. Bus. 2026, 12(1), 28-44. doi: 10.11648/j.ajtab.20261201.13

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AMA Style

Tsegaye T, Yohannes R, Abaynew H, Dinku A. Beyond the Farm: Small and Micro Agricultural Enterprises as Engines for Youth Employment and Income in Maya City, Ethiopia. Am J Theor Appl Bus. 2026;12(1):28-44. doi: 10.11648/j.ajtab.20261201.13

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@article{10.11648/j.ajtab.20261201.13,
  author = {Tamirat Tsegaye and Rekiku Yohannes and Habtamu Abaynew and Assefa Dinku},
  title = {Beyond the Farm: Small and Micro Agricultural Enterprises as Engines for Youth Employment and Income in Maya City, Ethiopia},
  journal = {American Journal of Theoretical and Applied Business},
  volume = {12},
  number = {1},
  pages = {28-44},
  doi = {10.11648/j.ajtab.20261201.13},
  url = {https://doi.org/10.11648/j.ajtab.20261201.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtab.20261201.13},
  abstract = {Despite the Ethiopian government's efforts to promote youth participation in Small and Micro Agricultural Enterprises (SMAEs) as a strategy for economic development, youth involvement in these sectors remains low. The purpose of the study was to identify the variables affecting youth participation in small and micro agricultural enterprises and how this influences employment creation and income in Maya city, Oromia region, Ethiopia. The study employed a multi-stage sampling technique to select 180 youths from Maya City. Data were analyzed using descriptive statistics and econometric models. The probit model results showed that land size, comfort in group work, extension services, and awareness of agribusiness positively influenced youth participation in SMAEs, while risk aversion and educational level had a negative effect. Additionally, the endogenous regression model revealed that youth employment creation and income were significantly and positively impacted by participation in SMAEs (0.5 full-time jobs and 7009.2 Birr, respectively). If non-participants had engaged in SMAEs, employment would have increased by 0.35 full-time equivalents and income by 5499.7 Birr. This highlights the vital role of SMAEs in boosting income and employment opportunities for youth. Therefore, the study recommends introducing agricultural insurance and financial safety nets to mitigate risks, raising awareness through campaigns and educational programs, improving access to credit with tailored financial products, and fostering comfort in group work through team building and mentorship. These strategies can significantly enhance youth participation in small and micro agricultural enterprises, thereby improving income levels and employment opportunities.},
 year = {2026}
}

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TY  - JOUR
T1  - Beyond the Farm: Small and Micro Agricultural Enterprises as Engines for Youth Employment and Income in Maya City, Ethiopia
AU  - Tamirat Tsegaye
AU  - Rekiku Yohannes
AU  - Habtamu Abaynew
AU  - Assefa Dinku
Y1  - 2026/01/30
PY  - 2026
N1  - https://doi.org/10.11648/j.ajtab.20261201.13
DO  - 10.11648/j.ajtab.20261201.13
T2  - American Journal of Theoretical and Applied Business
JF  - American Journal of Theoretical and Applied Business
JO  - American Journal of Theoretical and Applied Business
SP  - 28
EP  - 44
PB  - Science Publishing Group
SN  - 2469-7842
UR  - https://doi.org/10.11648/j.ajtab.20261201.13
AB  - Despite the Ethiopian government's efforts to promote youth participation in Small and Micro Agricultural Enterprises (SMAEs) as a strategy for economic development, youth involvement in these sectors remains low. The purpose of the study was to identify the variables affecting youth participation in small and micro agricultural enterprises and how this influences employment creation and income in Maya city, Oromia region, Ethiopia. The study employed a multi-stage sampling technique to select 180 youths from Maya City. Data were analyzed using descriptive statistics and econometric models. The probit model results showed that land size, comfort in group work, extension services, and awareness of agribusiness positively influenced youth participation in SMAEs, while risk aversion and educational level had a negative effect. Additionally, the endogenous regression model revealed that youth employment creation and income were significantly and positively impacted by participation in SMAEs (0.5 full-time jobs and 7009.2 Birr, respectively). If non-participants had engaged in SMAEs, employment would have increased by 0.35 full-time equivalents and income by 5499.7 Birr. This highlights the vital role of SMAEs in boosting income and employment opportunities for youth. Therefore, the study recommends introducing agricultural insurance and financial safety nets to mitigate risks, raising awareness through campaigns and educational programs, improving access to credit with tailored financial products, and fostering comfort in group work through team building and mentorship. These strategies can significantly enhance youth participation in small and micro agricultural enterprises, thereby improving income levels and employment opportunities.
VL  - 12
IS  - 1
ER  -

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Author Information

Tamirat Tsegaye

Department of Economics, Arsi University, Asella, Ethiopia

http://orcid.org/0009-0001-8675-8132
Rekiku Yohannes

Department of Agricultural Economics, Haramaya University, Haramaya, Ethiopia
Habtamu Abaynew

Department of Agricultural Economics, Dilla University, Dilla, Ethiopia

Contact Email

http://orcid.org/0000-0003-4436-1388
Assefa Dinku

Department of Economics, Arsi University, Asella, Ethiopia

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Tsegaye, T., Yohannes, R., Abaynew, H., Dinku, A. (2026). Beyond the Farm: Small and Micro Agricultural Enterprises as Engines for Youth Employment and Income in Maya City, Ethiopia. American Journal of Theoretical and Applied Business, 12(1), 28-44. https://doi.org/10.11648/j.ajtab.20261201.13

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ACS Style

Tsegaye, T.; Yohannes, R.; Abaynew, H.; Dinku, A. Beyond the Farm: Small and Micro Agricultural Enterprises as Engines for Youth Employment and Income in Maya City, Ethiopia. Am. J. Theor. Appl. Bus. 2026, 12(1), 28-44. doi: 10.11648/j.ajtab.20261201.13

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AMA Style

Tsegaye T, Yohannes R, Abaynew H, Dinku A. Beyond the Farm: Small and Micro Agricultural Enterprises as Engines for Youth Employment and Income in Maya City, Ethiopia. Am J Theor Appl Bus. 2026;12(1):28-44. doi: 10.11648/j.ajtab.20261201.13

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@article{10.11648/j.ajtab.20261201.13,
  author = {Tamirat Tsegaye and Rekiku Yohannes and Habtamu Abaynew and Assefa Dinku},
  title = {Beyond the Farm: Small and Micro Agricultural Enterprises as Engines for Youth Employment and Income in Maya City, Ethiopia},
  journal = {American Journal of Theoretical and Applied Business},
  volume = {12},
  number = {1},
  pages = {28-44},
  doi = {10.11648/j.ajtab.20261201.13},
  url = {https://doi.org/10.11648/j.ajtab.20261201.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtab.20261201.13},
  abstract = {Despite the Ethiopian government's efforts to promote youth participation in Small and Micro Agricultural Enterprises (SMAEs) as a strategy for economic development, youth involvement in these sectors remains low. The purpose of the study was to identify the variables affecting youth participation in small and micro agricultural enterprises and how this influences employment creation and income in Maya city, Oromia region, Ethiopia. The study employed a multi-stage sampling technique to select 180 youths from Maya City. Data were analyzed using descriptive statistics and econometric models. The probit model results showed that land size, comfort in group work, extension services, and awareness of agribusiness positively influenced youth participation in SMAEs, while risk aversion and educational level had a negative effect. Additionally, the endogenous regression model revealed that youth employment creation and income were significantly and positively impacted by participation in SMAEs (0.5 full-time jobs and 7009.2 Birr, respectively). If non-participants had engaged in SMAEs, employment would have increased by 0.35 full-time equivalents and income by 5499.7 Birr. This highlights the vital role of SMAEs in boosting income and employment opportunities for youth. Therefore, the study recommends introducing agricultural insurance and financial safety nets to mitigate risks, raising awareness through campaigns and educational programs, improving access to credit with tailored financial products, and fostering comfort in group work through team building and mentorship. These strategies can significantly enhance youth participation in small and micro agricultural enterprises, thereby improving income levels and employment opportunities.},
 year = {2026}
}

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TY  - JOUR
T1  - Beyond the Farm: Small and Micro Agricultural Enterprises as Engines for Youth Employment and Income in Maya City, Ethiopia
AU  - Tamirat Tsegaye
AU  - Rekiku Yohannes
AU  - Habtamu Abaynew
AU  - Assefa Dinku
Y1  - 2026/01/30
PY  - 2026
N1  - https://doi.org/10.11648/j.ajtab.20261201.13
DO  - 10.11648/j.ajtab.20261201.13
T2  - American Journal of Theoretical and Applied Business
JF  - American Journal of Theoretical and Applied Business
JO  - American Journal of Theoretical and Applied Business
SP  - 28
EP  - 44
PB  - Science Publishing Group
SN  - 2469-7842
UR  - https://doi.org/10.11648/j.ajtab.20261201.13
AB  - Despite the Ethiopian government's efforts to promote youth participation in Small and Micro Agricultural Enterprises (SMAEs) as a strategy for economic development, youth involvement in these sectors remains low. The purpose of the study was to identify the variables affecting youth participation in small and micro agricultural enterprises and how this influences employment creation and income in Maya city, Oromia region, Ethiopia. The study employed a multi-stage sampling technique to select 180 youths from Maya City. Data were analyzed using descriptive statistics and econometric models. The probit model results showed that land size, comfort in group work, extension services, and awareness of agribusiness positively influenced youth participation in SMAEs, while risk aversion and educational level had a negative effect. Additionally, the endogenous regression model revealed that youth employment creation and income were significantly and positively impacted by participation in SMAEs (0.5 full-time jobs and 7009.2 Birr, respectively). If non-participants had engaged in SMAEs, employment would have increased by 0.35 full-time equivalents and income by 5499.7 Birr. This highlights the vital role of SMAEs in boosting income and employment opportunities for youth. Therefore, the study recommends introducing agricultural insurance and financial safety nets to mitigate risks, raising awareness through campaigns and educational programs, improving access to credit with tailored financial products, and fostering comfort in group work through team building and mentorship. These strategies can significantly enhance youth participation in small and micro agricultural enterprises, thereby improving income levels and employment opportunities.
VL  - 12
IS  - 1
ER  -

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