2.1.1 Definition and key concepts

2.1.1.1 Livelihood concepts and livelihood framework

Livelihood programs are integral components of food security strategies, designed to offer comprehensive support to food-insecure households by enhancing their asset base and diversifying income sources. These programs, initiated in 2005 in Ethiopia, aim to enable households to transition from dependence on the Productive Safety Net Program (PSNP) to sustainable livelihoods ( Fre, Woldu, & Livingston, 2022).

Livelihood Defined: A livelihood encompasses the capabilities, assets (both material and social), and activities necessary for a means of living. In the Ethiopian context, food security is defined as the availability, access, and utilization of sufficient food to maintain a healthy life. Availability refers to the sustainable production of adequate food, access involves having the economic means to obtain food, and utilization pertains to the effective use of food for nutrition and health, often challenged by inadequate sanitation and health facilities ( Eskola et al., 2020; World Health Organization, 2024). These interconnected elements of livelihood highlight the multifaceted nature of food security and the importance of addressing all aspects to achieve long-term stability.

PSNP Overview: The Productive Safety Net Program (PSNP) is a cornerstone of Ethiopia's strategy to improve the livelihoods of its beneficiaries. It aims to increase household assets, incomes, and resilience against economic shocks, with the ultimate goal of enabling households to “graduate” from the PSNP, thereby reflecting improved livelihoods and economic stability ( Ranganathan et al., 2022). The PSNP’s approach underscores the importance of integrating asset-building initiatives with income-generating activities to create a sustainable path out of poverty.

2.1.1.2 The concepts of asset, asset building, and income-generating activities

Assets Explained: Assets are items of economic value owned by an individual that can be converted into cash or used productively. For farming households, these assets include livestock, land, and rentable houses, which serve as critical resources for generating income and sustaining livelihoods.

Asset Types in Ethiopia: •

Livestock Assets: These include cattle, sheep, goats, and poultry, which are pivotal for the survival and economic well-being of rural households.

Household Asset Building: Asset building involves increasing the real value of household assets over time, often linked to the duration of a program or intervention aimed at fostering asset accumulation. This process is crucial for improving household resilience and economic stability, enabling families to invest in income-generating activities and secure their livelihoods.

Household Income: Household income encompasses all income generated by household members from various activities, including on-farm activities (e.g., crop production, livestock), non-farm activities (e.g., handicrafts, carpentry), and off-farm activities (e.g., wage labor on other people's farms). Diversifying income sources is essential for reducing vulnerability to economic shocks and achieving sustainable development.

2.1.1.3 Household structure

Definition of household: A household is defined as a group of individuals who live together, share meals, and often share economic responsibilities. Households can range from single-person units to extended families or groups of unrelated individuals ( Smeeding and Weinberg, 2001).

Economic importance of households: Households are fundamental units of economic activity, playing a critical role in production, distribution, and consumption. Decisions made within households, such as those regarding labor market participation and expenditure, are vital for resource allocation and economic stability ( Sanson, 2024). Understanding the dynamics within households is essential for designing effective livelihood programs that address the needs and constraints of different household members.

Household head: The household head is recognized as the primary administrator and manager of the household's activities and resources. This role is crucial in decision-making processes related to resource allocation, labor distribution, and participation in economic activities ( Ranganathan et al., 2022).

2.1.2 The concept and types of rural credit

Rural Credit Overview: Rural credit is essential for providing the startup capital necessary for poor individuals to initiate, sustain, or expand agricultural production, thus increasing productivity and helping them move out of poverty. Access to credit enables rural households to invest in income-generating activities, purchase necessary inputs, and build assets, which are vital for enhancing their economic well-being and reducing poverty.

Types of rural credit markets: •

Formal Credit Markets: Formal credit markets involve institutions that mediate between depositors and lenders, often providing loans at relatively low, sometimes government-subsidized, interest rates. These institutions include banks, cooperatives, and microfinance institutions that offer structured financial products to rural households.

2.1.2.1 Government food security program

Food Security Program (FSP): The Ethiopian government’s Food Security Program (FSP) offers formal credit through local agricultural sectors and institutions like Tsedey Bank (Amhara Credit and Saving Institution - ACSI) to individual farmers using collateral agreements. The program aims to enhance farmers' productivity and asset base, helping them escape poverty by providing access to financial resources necessary for investing in agricultural inputs and technologies ( Alemaw et al., 2024). The FSP's focus on credit provision is designed to complement other food security initiatives, ensuring that farmers have the means to improve their livelihoods.

2.1.2.2 Group-based credit and gender participation

Group-Based Lending: Group-based lending has proven to be highly effective in low-income societies due to its advantages in information sharing, peer support, and risk mitigation. Group members can monitor and assist each other, making it a beneficial system for both lenders and borrowers. This method is particularly advantageous for women, who often face barriers in accessing formal credit independently due to lack of collateral, limited financial literacy, and socio-cultural constraints. Group lending models, therefore, provide a viable pathway for women to access credit, build assets, and improve their economic standing.

Gender Participation: Women's participation in group-based credit schemes has been shown to significantly improve their economic empowerment and household food security. By participating in these schemes, women gain the support and confidence needed to engage in economic activities, thereby contributing to the overall development of their communities ( Armendáriz & Morduch, 2010). The success of these models highlights the importance of designing credit programs that are inclusive and sensitive to the unique challenges faced by women in rural areas.

By integrating these concepts and mechanisms, livelihood and food security programs aim to create sustainable economic stability for rural households. These programs enable households to build assets, increase income, and achieve food security, ultimately contributing to the reduction of poverty and the promotion of equitable development.

2.2.1 Global empirical studies on credit programs

Credit programs have become increasingly important in developing economies, with numerous studies documenting their impact. For instance, in Bangladesh, the Grameen Bank, a pioneer in microfinance, had reached a membership of 2.4 million by the end of 2000, with 95% of these members being women, and an outstanding loan portfolio of $225 million ( Cull, 2007). Similarly, Thailand’s Bank for Agriculture and Agricultural Cooperatives has effectively expanded its reach through agricultural lending initiatives, while in Cambodia, the growth of microfinance institutions underscores the widespread influence of credit programs ( Caballero-Montes, 2022).

Globally, various studies have analyzed the factors that influence participation in credit programs. In Pakistan, Khan et al. (2020) found a strong relationship between education levels and access to credit for livestock, indicating that illiterate farmers often struggle with loan applications, thereby limiting their access to credit. In Namibia, Kaukumangera (2021) observed a positive correlation between experience in income-generating activities and access to agricultural credit. Similarly, research in Ghana by the African Development Review (2020) supports the notion that larger landholdings significantly enhance access to credit ( Nguyen, 2017), although Dzadze et al. (2012) did not find this association in their study.

Farm size is frequently highlighted as a determinant of credit demand. Research in Bangladesh by Rayhan, Rahman, and Lyu (2024) and in Ghana by Twumasi et al. (2020) demonstrated that larger farms tend to have a higher demand for credit. Conversely, Chaiya et al. (2023) in Pakistan found that family size plays a significant role in influencing credit access, as larger families may benefit from diversified agricultural activities, impacting their credit requirements. However, the effect of family size varies by context, with mixed results reported in Bangladesh ( Rayhan et al., 2024).

Age is another factor influencing credit demand. Mpuga (2010) in Uganda found a positive relationship between age and loan demand, with older farmers more likely to seek credit due to greater access to assets such as land. However, studies in Qatar ( Charfeddine, Umlai & El-Masri, 2024) and China ( Dong, Lu & Featherstone, 2012) found either negative or insignificant associations between age and credit demand. In India, Swain (2007) also reported no significant correlation between age and credit demand.

Education consistently emerges as a critical factor in credit participation. In Ethiopia, studies by Waje (2020) and Mamuye (2021) found that formal education significantly enhances access to credit. Similar findings were reported in Pakistan by Kumar et al. (2021), who noted that higher education levels are associated with increased participation in credit programs, although results vary across different regions.

The distance to lending institutions also plays a crucial role in credit demand. Studies have shown that greater distances from financial institutions negatively impact credit demand ( Anang et al., 2015). Additionally, interest rates and collateral requirements have been found to deter potential borrowers, as demonstrated in research conducted in Romania ( Chaves et al., 2001). Furthermore, extension services have been shown to positively influence credit participation by improving access to information and enhancing decision-making capabilities ( Kwabena and Opoku, 2023; Anang et al., 2015).

2.2.2 Empirical studies on credit programs in ethiopia

In Ethiopia, research on credit programs has largely focused on the supply side, particularly from the perspective of lending institutions. However, several studies have examined the factors that influence access to credit. Waje (2020) found that formal education significantly improves access to credit, while Abreu et al. (2024) noted that participation in extension package programs increases the likelihood of obtaining credit.

The impact of landholding size on credit participation in Ethiopia has produced mixed results. Tura et al. (2017) found that larger landholdings negatively affect credit participation, whereas Kassegn and Endris (2021) reported a positive relationship between land size and credit access. Engagement in income-generating farming activities has also been found to positively influence access to credit ( Kassegn & Endris, 2021).

Interest rates have been identified as a key deterrent to credit demand in Ethiopia. Negatu (2021) reported that higher interest rates significantly reduce credit demand. Family size, however, has shown inconsistent effects across different studies. For example, Kassegn and Endris (2021) found a negative influence of family size on credit demand in Ethiopia, which contrasts with findings from other regions.

The diverse findings from these studies underscore the importance of context-specific research in understanding the determinants of credit demand and access. The inconsistencies in results reflect the varying influence of national policies, regulatory frameworks, and socio-economic conditions on credit participation. The rich dataset used in this study allows for the inclusion of additional variables to better analyze their relationships with credit participation, providing a more nuanced understanding of the factors at play.

Gubalafto Woreda is located in the Amhara Region of Ethiopia, part of the North Wollo Zone, approximately 520 kilometers from the capital city, Addis Ababa. It is one of the eleven woredas in North Wollo Zone, situated between 39°12'9" and 39°45'58” East longitude, and 11°34'54" and 11°58'59” North latitude. The woreda is bordered to the south-by-South Wollo Zone, to the west by Delanta and Wadla, to the northwest by Meket, to the north by Gidan, to the northeast by the Alawuha River (Logiya River), which separates it from Kobo, and to the southeast by Habru. Weldia is an enclave within this woreda, and notable towns include Hara.

The geographical landscape of Gubalafto is predominantly mountainous, with elevations ranging from 1,300 to 3,900 meters above sea level. The woreda comprises three agro-ecological zones: Lowland (Kolla), ranging from 1,379 to 1,500 meters above sea level; Mid-altitude (Weynadega), from 1,500 to 2,300 meters; and Highland (Dega), above 2,500 meters. The majority of the rural population resides in the highlands and plateaus, with Weynadega and Dega areas being densely populated, while Kolla is relatively sparsely populated. Major crops cultivated include barley, teff, and sorghum, with small land holdings being a significant constraint for households (Gubalafto Woreda Agricultural Office, 2024).

According to the 2007 national census by the Central Statistical Agency ( CSA, 2007) of Ethiopia, Gubalafto Woreda had a population of 139,825, marking a 0.48% increase from the 1994 census. The population comprises 70,750 men and 69,075 women, with 4,886 (3.49%) residing in urban areas. Covering an area of 900.49 square kilometers, the woreda has a population density of 155.28 persons per square kilometer, which is higher than the zone average of 123.25 persons per square kilometer. There are 33,676 households, averaging 4.15 persons per household, and 32,824 housing units. Gubalafto Wereda is categorized among the 48 most drought-prone and food-insecure woredas in the Amhara Region . The predominant religion is Orthodox Christianity (86.58%), followed by Islam (13.32%). As of 2024, the estimated population is 158,324, comprising 80,948 males and 77,376 females.

The woreda has been supported by the Productive Safety Net Program (PSNP) since 2015, with the Livelihood Program being a key component introduced at that time. Currently, there are 10,500 beneficiaries of the Livelihood Credit Program in the woreda, with 6,416 men and 4,086 women participating. Figure 1 below illustrates the geographical structure of the study area. This figure provides a visual representation of the region under study, which is essential for understanding the spatial context of the research.

This study investigates the determinants of rural women's participation in livelihood credit programs, utilizing both descriptive and explanatory research designs. A mixed methods design was employed, integrating both qualitative and quantitative data collection and analysis concurrently. Combining quantitative and qualitative research approaches can mitigate the limitations inherent in using a single approach, as these methods separately may not sufficiently address complex social and cultural phenomena.

The quantitative approach was used to analyze statistical data and test hypotheses by specifying a narrow hypothesis and collecting data to support or refute it. Concurrently, the qualitative approach involved conducting in-depth interviews with key informants to gather rich, contextual data. The study relied on both primary and secondary data sources.

3.3.1 Data type and source

This study, which commenced in February 2023, explores the factors influencing rural women's participation in livelihood credit programs in Gubalafto Woreda through both qualitative and quantitative methods. Participant recruitment occurred from March 1, 2023, to March 25, 2023. Primary data collection took place between May 4, 2023, and July 28, 2023, using structured questionnaires distributed to female household heads, encompassing both participants and non-participants of the credit program. These questionnaires gathered comprehensive information on demographic, economic, social, and contextual factors. Additionally, secondary data were obtained from program documentation, academic literature, and prior research to enrich and provide context to the primary findings.

3.3.2 Method of data collection

Primary data collection involved administering structured questionnaires with both closed and open-ended questions. These questions addressed factors such as age, marital status, household size, market access, landholding size, income level, education, experience in income-generating activities, skill training, extension contact, perceptions of interest rates, group collateral, and religious affiliation. Interviews were conducted with female household heads to gain insights into these variables.

Secondary data were obtained from program documents and relevant literature to provide additional context and support the primary findings.

3.3.2.1 Data quality management

Experienced field staff conducted the data collection, guided by a one-day training session on data collection methods and questionnaire administration. A pre-test was performed with non-sample respondents to refine the questionnaire. Data were double-entered, checked for consistency, and cleaned to ensure accuracy.

3.3.3 Sampling techniques

Gubalafto Woreda was selected for its relevance to the study of women's participation in livelihood credit programs. The woreda includes 34 rural kebeles, with 11,672 female household clients in the program, distributed across three ecological zones: dega, weyna dega, and kola.

A multi-stage sampling approach was used. Four kebeles were randomly selected based on agro-ecological characteristics: one from kola, two from weynadega, and one from dega. On the four selected Kebeles, there 4086 female headed clients in the program. A total of 364 beneficiaries were sampled: 163 from Doro-Gibr, 178 from Ameya Mecha, 169 from Gedober, and 180 from Bekulo Manekiya.

Inclusion Criteria: •

Women who are beneficiaries of the livelihood credit program and reside in rural areas.

Exclusion Criteria: •

Women residing in urban areas.

3.3.4 Sample size calculation

Sample size was determined using the formula for single population proportion as per Yamane (1973), ensuring a 95% confidence level and a 5% margin of error. The sample was proportionally distributed across the three ecological zones: •

Kola kebeles: n ₁

Where n=n ₁+n ₂+n _3, n represents the total sample size. This approach ensured a representative sample across the kebeles, facilitating a comprehensive analysis of credit program participation factors. n = N 1 + N e 2

Where: •

n is the sample size,

Accordingly, with N = 4086, e = 5% (at least 95 % confidence level), sample size for this study is determined using the above equation as: n = 4086 1 + 4086 ∗ 0.05 2 = 364

Agro-ecologically, the classification of the total population becomes: n 1 = n ∗ total women client in kola agro − ecological zone total women client in the sampled four kebeles = 364 ∗ 773 4086 = 69 n 2 = n ∗ total women client in woynadega agro − ecological zone total women client in the sampled four kebeles = 364 ∗ 1571 4086 = 140 n 3 = n ∗ total women client in dega agro − ecological zone total women client in the sampled four kebeles = 364 ∗ 1742 4086 = 155

With this we come up with n= n1+n2+n3= 69+140+155=364 sample size. Finally, 69 clients from Dorogibr kebele, 140 clients from Ameya Mecha kebele and Gedober kebele,180 clients from Beklo Manekiya kebele totally 364 women are selected randomly and assessed for credit participation as shown in Table 1 below.

This study employed both descriptive and econometric methods to analyze the determinants of rural women's participation in livelihood credit programs.

3.5.1 Descriptive analysis

Descriptive statistics were used to summarize and describe the main features of the dataset. Key statistics such as minimum, maximum, mean, and standard deviation were calculated for each variable. Data were also presented through tables and graphs to illustrate trends and distributions.

3.5.2 Econometric analysis

A logistic regression model was utilized to identify the determinants of participation in livelihood credit programs. The analysis was conducted using STATA 17, with a sample size of 364 observations. The logistic regression model was chosen for its effectiveness in handling dichotomous dependent variables and explaining the relationship between these variables and the explanatory factors.

3.5.3 Model specification

The logit model was selected due to its mathematical simplicity and practical application in examining binary outcomes. It is appropriate for ensuring that estimated probabilities lie between 0 and 1. The logit model, with its logistic distribution, was preferred over the probit model for this analysis ( Wooldridge, 2002; Gujarati and Porter, 2009).

The cumulative logistic probability model is econometrically specified as follows: Logit [ P ( x ) ] = ln P ( x ) 1 − P ( x ) = X ß + ß 0 + ß 1 X 1 + ß 2 X 2 + … + ß k X k + μ (1)

Where ln is natural logarithm

(xi) is the probability women participated in livelihood credit program

1 − (Xi) is the probability women not participated in livelihood credit program.

Xi = represents the i th explanatory variables

βi = is coefficient of i th explanatory variable P ( Y = 1 ) = 1 1 + e − ( ß 0 + ß 1 X 1 + ß 2 X 2 + … + ß KXK ) (2)

Where P (Yi = 1) is the probability of women participated in livelihood credit program.

Xi = represents the i ^th explanatory variables

e = denotes the base of natural logarithms, which is approximately equal to 2.718

βi = is coefficient of i ^th explanatory variables Y = β 0 + β 1 X 1 + β 2 X 2 + β 3 X 3 + β 4 X 4 + β 5 X 5 + β 6 X 6 + β 7 X 7 + β 8 X 8 + β 9 X 9 + β 10 X 10 + β 11 X 11 + β 12 X 12 + β 13 X 13 + μ (3) WPLC = β 0 + β 1 AGE + β 2 HHS + β 3 MRTS + β 4 IL + β 5 LHS + β 6 MKTACC + β 7 EDUL + β 8 EXPIGA + β 9 EXTC + β 10 PRGCO + β 11 PRIR + β 12 SKT + β 13 DR + μ (4)

Table 2 below presents a summary of the variables, their definitions, measurements, and associated hypotheses for this study.

Ethical approval for this study, titled "Empowering rural women: key factors driving participation in livelihood credit programs in Gubalafto Woreda, North Wollo, Ethiopia," was obtained from the Research Ethics Approval Committee of Woldia University, Ethiopia, and authorized by the Institutional Review Board (IRB) of the Faculty of Business and Economics at Woldia University under Reference No: FBE/RCSTT/300/2023 on February 12, 2023. All participants provided written informed consent and authorized the collection of necessary data for analyzing the factors influencing rural women’s participation in livelihood credit programs. The analysis was conducted using anonymized data, ensuring that no information could identify individual participants.

In the descriptive statistics section, the status of credit disbursement in the woreda, the key characteristics of the sample women, and related variables are analyzed using appropriate statistical tools.

4.1.1 Livelihood credit disbursement in the woreda

The livelihood program was initiated in the woreda in 2013, offering capacity-building services along with credit disbursement for RPSNP (PSNP) participants.

Table 3 below presents a 12-year overview (2013-2024) of credit disbursement in Gubalafto Woreda, focusing on participation by gender, total credit amounts, and average credit per participant. A total of 11,029 participants accessed credit, with males constituting 62.9% and females 37.1%. While male participation consistently exceeded female participation, the gender gap varied, with closer parity observed in 2020 and 2021. The total credit disbursed reached ETB 159.5 million, with males receiving a larger share (ETB 97.6 million) compared to females (ETB 61.9 million). The average credit amount per participant increased from ETB 11,000 in 2013 to ETB 34,687.65 in 2024, indicating larger loans in recent years. The data reflect a growing reliance on credit, though gender disparities persist, with females receiving less credit on average. Overall, the trends suggest increasing access to credit, but further improvements are needed to achieve equitable participation and credit distribution across genders.

Figure 2 below illustrates the trend of credit distribution in ETB from 2013 to 2024. Initially, there was a steady increase in credit distribution from 2013, culminating in a peak in 2020. This marks the highest point of credit distribution during the observed period. However, in 2021, there was a noticeable decline, followed by a significant drop in 2022. In the subsequent years, 2023 and 2024, the chart shows a gradual recovery with an increase in credit distribution. This pattern highlights fluctuations in financial activities over the years.

Figure 3 below also shows the mean number of women participants in credit activities from 2014 to 2024. Initially, there was a gradual increase from 2014, continuing up to 2017. From 2018 to 2020, the participation remained fairly stable, with a slight rise. The peak occurred in 2021, marking the highest level of female participation. However, there was a sharp decline in 2022, followed by a minor recovery in 2023 and a slight decrease again in 2024. This trend highlights the fluctuations in women's involvement in credit activities over the years.

This section presents the results of the logistic regression analysis used to identify factors affecting rural women's participation in livelihood credit programs. Before interpreting the model, we assessed multicollinearity and heteroscedasticity to ensure the reliability of the regression coefficients. Multicollinearity was evaluated using the Variance Inflation Factor (VIF) and correlation coefficients; results indicated no serious multicollinearity, with VIF values well below 10 and correlation coefficients under 0.85. Heteroscedasticity was tested with the chi-square test, revealing a prob > chi2 of 0.0492, which was below the 5% significance level. This indicated the presence of heteroscedasticity. To address this, White’s heteroscedasticity-robust standard errors were used in STATA, effectively resolving the issue and ensuring the validity of the regression results.

The results from the binary logit model as shown in Table 4 below, derived from maximum likelihood estimates, reveal that rural women’s participation in credit programs is influenced by a complex interplay of demographic, socio-economic, and program-related variables. The logit model analysis highlights the importance of examining the combined effects of these variables on participation. The logistic regression, expressed through odds ratios, compares the likelihood of participation between those who are participants and those who are non-participants, providing insights into the factors driving participation in the study area.

Table 5 below provides a comprehensive overview of the significant variables influencing rural women's participation in livelihood credit programs. The logistic regression results reveal several critical factors affecting participation.

Household size is significantly associated with increased participation, evidenced by an odds ratio of 94.4. This finding aligns with research by [23], who noted that larger family sizes can diversify economic activities, thus enhancing credit access. Conversely, studies such as [21] found mixed results regarding the impact of family size on credit demand, suggesting that the effect may vary by context.

Market access also plays a significant role, with better access strongly linked to higher participation (odds ratio of 1802). This supports findings from [30], who emphasized that improved market connectivity boosts credit engagement. However, this contrasts with [20], who did not observe a significant relationship, indicating regional differences in the impact of market access.

Age shows a negative correlation with participation, indicating that older individuals are less likely to engage in credit programs. This finding is consistent with [24], who observed that older farmers might have more assets and thus less need for credit. This differs from studies by [25] and [26], which reported varying or insignificant associations between age and credit demand, highlighting the potential influence of different regional or contextual factors.

Education levels are associated with decreased participation, aligning with [27] and [28], who found that higher education might limit involvement due to opportunity costs or misalignment with program goals. This contrasts with [29], who reported that higher education often facilitates better credit access, indicating that the effect of education on credit participation can vary depending on regional specifics.

Marital status positively impacts participation, with married women showing higher odds of engagement. This finding reflects similar conclusions by [14], who noted the benefits of group-based lending models for married women. This result diverges from some studies that did not find strong associations, suggesting that marital status effects can differ based on local practices and program structures.

Landholding size is positively associated with increased participation, consistent with [19], who noted that larger landholdings improve credit access. This supports the notion that land assets are crucial for securing credit. However, [34] found a negative relationship between land size and credit participation in Ethiopia, contrasting with [35], who observed a positive correlation, indicating that the impact of landholding size may vary depending on local conditions and policies.

Distance to credit facilities negatively impacts participation, aligning with [30], who emphasized the importance of proximity to financial institutions. This result resonates with [36], who identified interest rates as a significant deterrent to credit demand, although the study did not specifically address distance. The role of interest rates and accessibility reflects a broader understanding of the barriers to credit engagement.

Overall, the results highlight a complex interplay of demographic, socio-economic, and program-specific factors influencing rural women’s participation in livelihood credit programs. The findings reflect both agreements with and deviations from existing research, emphasizing the need for context-specific approaches to address the diverse factors affecting credit participation in different regions. The inconsistencies in the literature underscore the importance of tailored research to better understand local determinants of credit demand and access.

The livelihood program targeting women in the woreda has been operational from 2013 to 2024, distributing a total of 159,460,137 ETB to 11,029 beneficiaries. Of this, males received 97,582,119 ETB, while females received 61,878,008 ETB. In 2024 alone, 27 male clients were allocated 10,137,698 ETB, and 129 female clients received 3,702,676 ETB. This study set out to evaluate the distribution of livelihood credit and identify the key factors influencing women’s participation. Data were gathered from 364 women through structured questionnaires and analyzed using STATA 17 logistic regression. Four kebeles were randomly selected based on agro-ecological characteristics, and the data were analyzed using descriptive statistics such as percentages, frequency distributions, means, and standard deviations. The results of chi-square and t-tests revealed that most of the hypothesized variables significantly impact women's participation in credit programs.

The findings indicate that the livelihood program has had a positive impact on participants’ income and asset accumulation, particularly in livestock. Out of the 13 independent variables examined, 12 were found to be significant. Variables such as household size, income level, perception of group collateral, skill training, marital status, landholding size, market access, education level, experience in income-generating activities (IGA), extension contact, and distance from the lending office were identified as influential factors. Specifically, age, education level, and distance from the lending office were negatively associated with credit participation, while household size, marital status, income level, landholding size, market access, experience in IGA, perception of group collateral, extension contact, and skill training had positive effects.

This study underscores the complexity of factors influencing rural women's participation in credit programs and highlights the need for tailored approaches in policy and practice. The variability in determinants across different contexts suggests that a one-size-fits-all approach may not be effective. These findings provide valuable insights for future policy formulation and research, particularly in designing interventions that enhance rural women's access to credit and support their economic empowerment.

To enhance rural women's participation in livelihood credit programs, several key recommendations are proposed based on the analysis:

Focus on Younger Women: Emphasize increasing credit participation among younger women by providing targeted training and awareness programs, aligning with the program’s objectives.

Leverage Family Size: Recognize the positive association between family size and credit access. Families with more members can diversify income sources through agriculture and have more trading contacts. Microfinance institutions should extend their reach to support women-headed households with larger families.

Diversify Income Sources: Improve rural household incomes by encouraging participation in both on-farm and off-farm activities. Expanding agricultural and non-agricultural income sources will help increase overall financial stability.

Enhance Human Capital Development: Invest in education and vocational training to build human capital. Strengthening both formal education and skill training will address perception issues and increase participation in livelihood programs.

Promote Income Generating Activities (IGAs): Encourage rural women to engage in various income-generating activities with confidence. Support from government officers and development actors at the village level can foster smallholder business activities and integrate them with agricultural production.

Increase Financial Institution Presence: Expand the physical presence of financial institutions in rural areas through additional branches, mobile banking units, and agent banking. Utilize digital platforms for loan applications, repayments, and financial literacy training to overcome geographical barriers.

Provide Financial and Technical Training: Ensure timely delivery of financial and technical skills training to program clients. This will enhance participation, improve business effectiveness, and encourage better saving habits.

Improve Infrastructure: Enhance access to basic infrastructure such as roads and veterinary services. Reducing the distance between residences and lending offices will support the nutritional and income gains of agro-pastoral households.

Simplify Application Procedures: Streamline credit application processes by minimizing paperwork and reducing bureaucratic hurdles. Develop women-supportive policies and allocate appropriate resources to facilitate easier access to credit for rural women.

Ethical approval for this study, titled "Empowering rural women: key factors driving participation in livelihood credit programs in Gubalafto Woreda, North Wollo, Ethiopia," was obtained from the Research Ethics Approval Committee of Woldia University, Ethiopia, and authorized by the Institutional Review Board (IRB) of the Faculty of Business and Economics at Woldia University under Reference No: FBE/RCSTT/300/2023 on February 12, 2023. All participants provided written informed consent and authorized the collection of necessary data for analyzing the factors influencing rural women’s participation in livelihood credit programs. The analysis was conducted using anonymized data, ensuring that no information could identify individual participants.