Empowering rural women: key factors driving participation in livelihood credit programs in Gubalafto woreda, North Wollo, Ethiopia

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Understanding these determinants is crucial for improving program effectiveness and supporting women’s economic empowerment in the region. Methods A cross-sectional survey was conducted with a sample of 364 rural women from four kebeles, selected through stratified random sampling based on agro-ecological characteristics. Data were collected via interviews and analyzed using descriptive statistics, chi-square tests, t-tests, and logit regression models to determine the significance of various factors affecting participation. Results Descriptive statistics revealed key associations between participation and factors such as household size, income level, educational status, perception of group collateral, skill training, and distance from lending offices. Chi-square tests showed significant relationships, while t-tests identified differences between participants and non-participants. Logit regression analysis indicated that household size, income level, educational status, group collateral, skill training, and proximity to lending offices significantly influenced credit participation at the 1% level (p<0.01). Experience with income-generating activities and perception of interest rates were significant at the 5% level (p<0.05). Market access was significant at the 10% level (p<0.10). Positive factors included household size and income level, while age, distance from lending offices, and perception of interest rates had negative effects. Conclusion The study highlights that specific factors significantly impact rural women’s participation in credit programs. To enhance program effectiveness, stakeholders should address these determinants and develop robust institutional frameworks with effective monitoring and evaluation systems. 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F1000Research 2024, 13 :1000 ( https://doi.org/10.12688/f1000research.155113.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Article Empowering rural women: key factors driving participation in livelihood credit programs in Gubalafto woreda, North Wollo, Ethiopia [version 1; peer review: 2 approved with reservations] Moges Asmare Sisay https://orcid.org/0009-0001-7140-4497 1 , Atnafu Amare Mulaw 2 Moges Asmare Sisay https://orcid.org/0009-0001-7140-4497 1 , Atnafu Amare Mulaw 2 PUBLISHED 03 Sep 2024 Author details Author details 1 Economics, Woldia University, Weldiya, Amhara, 400, Ethiopia 2 Economics, North Wollo Agriculture office, Woldia, Amhara, 400, Ethiopia Moges Asmare Sisay Roles: Conceptualization, Formal Analysis, Investigation, Methodology, Resources, Software, Supervision, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Atnafu Amare Mulaw Roles: Methodology, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS Abstract Background This study investigates the factors influencing rural women’s participation in credit programs in Gubalafto Wereda. Understanding these determinants is crucial for improving program effectiveness and supporting women’s economic empowerment in the region. Methods A cross-sectional survey was conducted with a sample of 364 rural women from four kebeles, selected through stratified random sampling based on agro-ecological characteristics. Data were collected via interviews and analyzed using descriptive statistics, chi-square tests, t-tests, and logit regression models to determine the significance of various factors affecting participation. Results Descriptive statistics revealed key associations between participation and factors such as household size, income level, educational status, perception of group collateral, skill training, and distance from lending offices. Chi-square tests showed significant relationships, while t-tests identified differences between participants and non-participants. Logit regression analysis indicated that household size, income level, educational status, group collateral, skill training, and proximity to lending offices significantly influenced credit participation at the 1% level (p<0.01). Experience with income-generating activities and perception of interest rates were significant at the 5% level (p<0.05). Market access was significant at the 10% level (p<0.10). Positive factors included household size and income level, while age, distance from lending offices, and perception of interest rates had negative effects. Conclusion The study highlights that specific factors significantly impact rural women’s participation in credit programs. To enhance program effectiveness, stakeholders should address these determinants and develop robust institutional frameworks with effective monitoring and evaluation systems. READ ALL READ LESS Keywords Keywords: Rural women, credit programs, Gubaflto Wereda, participation determinants, logit regression, economic empowerment, agro-ecological sampling Corresponding Author(s) Moges Asmare Sisay ( [email protected] ) Close Corresponding author: Moges Asmare Sisay Competing interests: No competing interests were disclosed. Grant information: The author(s) declared that no grants were involved in supporting this work. Copyright: © 2024 Sisay MA and Mulaw AA. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Sisay MA and Mulaw AA. Empowering rural women: key factors driving participation in livelihood credit programs in Gubalafto woreda, North Wollo, Ethiopia [version 1; peer review: 2 approved with reservations] . F1000Research 2024, 13 :1000 ( https://doi.org/10.12688/f1000research.155113.1 ) First published: 03 Sep 2024, 13 :1000 ( https://doi.org/10.12688/f1000research.155113.1 ) Latest published: 03 Sep 2024, 13 :1000 ( https://doi.org/10.12688/f1000research.155113.1 ) 1. Introduction Rural women in Ethiopia play a crucial role in the agricultural sector, which remains the backbone of the country's economy, especially in regions like Gubalafto Woreda, North Wollo, Amhara Region, where 84% of the population resides in rural areas. Women are indispensable to agricultural productivity and the improvement of rural living standards. Despite their significant contributions, they often face systemic barriers, particularly in accessing credit, which is essential for enhancing their economic autonomy and socio-economic status. This study explores the determinants of rural women’s participation in livelihood credit programs within Gubalafto Woreda, aiming to illuminate the factors influencing their engagement and the broader socio-economic implications. Historically, Ethiopia’s approach to women’s development has evolved from welfare-oriented initiatives to more comprehensive, development-focused strategies. These strategies increasingly recognize women as pivotal economic actors whose empowerment is crucial for sustainable development. Access to credit is now widely acknowledged as a key driver of this empowerment, enabling women to invest in income-generating activities, improve their asset base, and contribute more effectively to household and community well-being ( Akhter, S., 2003 ). However, access to credit remains uneven, with rural women particularly disadvantaged due to factors such as limited financial literacy, inadequate collateral, and socio-cultural barriers. Credit programs designed specifically for rural women are critical for poverty alleviation and household empowerment. These programs not only aim to increase women’s income but also extend their production knowledge, enhance their decision-making power, and facilitate market access, all of which are vital for improving their economic and social standing ( Abate and Sheferaw, 2023 ). One prominent example is the Productive Safety Net Program, launched in 2005, which seeks to bolster household assets and income through skill development, credit services, and livelihood diversification, thereby supporting food security and resilience among rural populations ( Masa et al., 2021 ). Despite these initiatives, there remains a substantial gap in understanding the specific factors that influence rural women’s participation in livelihood credit programs. Existing research has largely focused on the supply side of credit, such as the availability of financial services and institutional frameworks, while neglecting the demand side, particularly the individual determinants of women’s engagement ( Hameed & Irfan, 2019 ). Challenges such as gender bias in extension services, limited access to information, and socio-economic constraints often hinder women's ability to fully participate in these programs ( Morton et al., 2014 ). Moreover, cultural norms and household dynamics can further restrict women’s access to and control over credit, affecting their capacity to benefit from such initiatives. This study addresses these gaps by analyzing the determinants influencing rural women’s participation in livelihood credit programs in Gubalafto Woreda. It will explore the distribution of livelihood credit within the woreda, identify key factors that affect women’s participation, and evaluate the impact of credit on their income, asset accumulation, and overall socio-economic status. By answering these critical questions, the research aims to provide actionable insights for policymakers, rural development agencies, and other stakeholders involved in designing and implementing credit programs. The findings are expected to contribute significantly to the understanding of the complex factors affecting women's participation in livelihood credit programs, offering guidance for future policy development and program design. 2. Literature review 2.1 Theoretical literature review 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. • Productive Assets: These are items utilized for production, such as animals or crops, that either reproduce themselves or are used to produce something of value. 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. • Informal Credit Markets: Informal credit markets are characterized by private individuals, such as moneylenders, traders, landlords, and relatives, providing loans. While formal and informal credits serve different needs, they are not perfect substitutes, as formal credit does not entirely replace the need for informal borrowing. Informal markets often offer greater flexibility in terms of loan terms and access but may come with higher interest rates and fewer protections ( Kayamo and Tebeka, 2021 ). 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 Empirical studies 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. 3. Methods 3.1 Description of the study area 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. Figure 1. Geographical structure of the study area. 3.2 Research design 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 Data collection and sampling 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. • Women aged 18 and above. Exclusion Criteria : • Women residing in urban areas. • Women under the age of 18. 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 • Weynadega kebeles : n 2 • Dega kebeles : n 3 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, • N is the total population of women program clients, • e is the margin of error. 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. Table 1. Population and sample respondents of the study area. Sn Kebeles Total progra m female clients at each Credit Partici- pants Sample of credit Particip ants % of particip ants sample Non particip ants Sample of non- particip ants % of non- particip ants sample Total sample (n) 1 Doro-Gibr 163 88 37 42.05 75 32 42.67 69 2 Ameya Mecha 178 100 38 38 78 33 42.31 71 3 Gedober 169 90 35 38.89 79 34 43 69 4 Bekulo Manekiya 180 120 100 83.33 60 55 91.67 155 Total 690 398 210 52.76 292 154 52.74 364 3.5 Methods of data analysis 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: (1) 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 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 (2) P ( Y = 1 ) = 1 1 + e − ( ß 0 + ß 1 X 1 + ß 2 X 2 + … + ß KXK ) 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 (3) 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 (4) 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 Table 2 below presents a summary of the variables, their definitions, measurements, and associated hypotheses for this study. Table 2. Summary of variables definition, measurement and hypothesis. Dependent Variable: whether women participated in livelihood program credit service or not (WPLC) Explanatory Variables Type Definition Measurement Hypothesis AGE Continuous Age of women Number of years (-) HHS Continuous Family size Family Number (+) MRTS Categorical Marital status of Women 1 if married, 0 others (+) IL Continuous Annual Income level of women Income in ETB (+) LHS Continuous Land owned by Women Number of hectare (+) MKTACC Dummy Market Access to Women 1 if yes, 0 Otherwise (+) EDUL categorical Education level Of Women 1 literate 0,others (+) EXPIGA Continuous Experience in IGA of Women Number of years (+) EXCTC Continuous Participation of women in extension Number of contacts (+) PRGCO Dummy Women perception on group collateral 1 if obstacle, 0 not obstacle (-) PRIR Dummy Women perception on lending interest rate 1 if unfair, 0 fair (-) SKT Continuous Skill training of women Number of trainings (+) DR Continuous Distance of women residence from lending office No of kilometer (-) 3.6 Ethical consideration 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. 4. Results and discussions This chapter is divided into two sections. The first section provides a description of credit disbursement and the characteristics of the sample women. The second section focuses on the analysis of the econometric model results, specifically logistic regression. 4.1 Descriptive analyses 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. Table 3. Credit disbursement in the woreda (2013-2024). S.no Year Credit participants Credit amount in ETB Average credit amount in ETB Male Female Total Male Female Total credit amount No % No % No Credit amoun Credit amoun 1 2013 383 74.47 134 25.53 517 4213000 1474000 5687000 11000 2 2014 604 74.66 205 25.34 809 7157400 2429250 9586650 11850 3 2015 552 60.1 367 39.9 919 6706800 4459050 11165850 12150 4 2016 606 60.3 399 39.7 1005 7484100 4927650 12411750 12350 5 2017 776 71.2 315 28.8 1091 9920500 4016250 13936750 12774.29 6 2018 622 61.5 389 38.5 1011 6516820 4088779 10605599 10490.207 7 2019 751 64.5 413 35.5 1164 9989051 5493313 15482364 13301 8 2020 647 53.5 563 46.5 1210 12480750 10894050 23374800 19318.017 9 2021 746 53.3 655 46.7 1401 12085200 10611000 22696200 16200 10 2022 597 71.2 242 28.8 839 3575550 4392000 7967550 9496.4839 11 2023 384 57.8 280 42.2 664 7315250 5390000 12705250 19134.413 12 2024 275 68.9 124 31.1 399 10137698 3702676 13840374 34687.654 Total 6943 62.9 4086 37.1 11029 97582119 61878018 159460137 14458.259 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 2. Credit amount distributed in each year (2013-2024). 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. Figure 3. Women participant in each year. 4.2 Econometric analysis: results of the logit model 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 4. Logit regression results of women household credit participation. Logit Regression Wplcstatus Coef. St.Err. t-value P-value [95% Conf Interval] Sig Age -.339 .106 -3.20 .001 -.546 -.132 *** Hhs 4.548 1.446 3.14 .002 1.714 7.382 *** Mrts 2.879 1.241 2.32 .02 .446 5.312 ** Il 0 0 3.49 0 0 0 *** Lhs 6.77 3.229 2.10 .036 .44 13.099 ** Mktacc 7.496 3.394 2.21 .027 .844 14.149 ** Edul -1.694 .742 -2.28 .022 -3.148 -.24 ** Expiga 1.009 .438 2.30 .021 .151 1.867 ** Prgco 6.293 2.219 2.84 .005 1.943 10.643 *** Extc .875 .414 2.11 .035 .064 1.687 ** Prir -1.027 1.411 -0.73 .467 -3.791 1.738 Skt 2.43 .933 2.61 .009 .602 4.258 *** Dr -.564 .27 -2.09 .037 -1.094 -.034 ** Constant -26.585 9.24 -2.88 .004 -44.695 -8.475 *** Mean dependent var 0.577 SD dependent var 0.495 Pseudo r-squared 0.944 Number of obs 364 Chi-square 468.141 Prob > chi2 0.000 Akaike crit. (AIC) 55.821 Bayesian crit. (BIC) 110.381 *** p<.01. ** p<.05. * p<.1. 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. Table 5. The logistic regression/oods ratio. Wplcstatus Coef. St.Err. t-value p-value [95% Conf Interval] Sig Age .713 .075 -3.20 .001 .579 .877 *** Hhs 94.402 136.504 3.14 .002 5.548 1606.197 *** Mrts 17.795 22.088 2.32 .02 1.562 202.694 ** Il 1.001 0.001 3.49 0.08 1.001 1.003 *** Lhs 870.972 2812.771 2.10 .036 1.553 488570.47 ** Mktacc 1801.648 6114.911 2.21 .027 2.326 1395417.1 ** Edul .184 .136 -2.28 .022 .043 .787 ** Expiga 2.742 1.2 2.30 .021 1.163 6.466 ** Prgco 540.792 1200.194 2.84 .005 6.981 41891.523 *** Extc 2.4 .994 2.11 .035 1.066 5.404 ** Prir .358 .505 -0.73 .467 .023 5.685 Skt 11.36 10.594 2.61 .009 1.826 70.664 *** Dr .569 .154 -2.09 .037 .335 .966 ** Constant 2.81 2.63 -2.88 .004 3.88 0.001 *** Mean dependent var 0.577 SD dependent var 0.495 Pseudo r-squared 0.944 Number of obs 364 Chi-square 468.141 Prob > chi2 0.000 Akaike crit. (AIC) 55.821 Bayesian crit. (BIC) 110.381 *** p<.01. ** p<.05. * p<.1. 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. 5. Conclusion and recommendations 5.1 Conclusion 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. 5.2 Recommendations 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. Compliance with ethical standards Ethics and consent 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. Author contributions statement Moges Asmare conceptualized and designed the research, formulated the study objectives, and played a pivotal role in data collection and analysis. As the corresponding author, Moges Asmare took the lead in drafting and revising the manuscript, ensuring its coherence and adherence to academic standards. Atnafu Amare actively participated in the literature review, contributed to the theoretical framework, and played a key role in data interpretation. He also contributed to writing specific sections of the manuscript and provided critical feedback during the revision process. Data availability statement The anonymized datasets of this research are available in the Zenodo Empowering rural women: key factors driving participation in livelihood credit programs in Gubalafto woreda, North Wollo, Ethiopia, DOI: https://zenodo.org/records/13284274 ( Sisay, M. A et al., 2024a ). The project contains the following underlying data: • Rural Women Data.xlsx Data is available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0). Extended data Zenodo: The questionnaire that supports the findings of this research is available in the Zenodo repository Title of project: Empowering rural women: key factors driving participation in livelihood credit programs in Gubalafto woreda, North Wollo, Ethiopia, DOI: https://zenodo.org/records/13284881 ( Sisay, M. A et al., 2024b ). The project contains the following extended data: • Empowring Women Household Survey Questionnaire.docx Data is available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0). Reporting guidelines Zenodo: STORBE checklist for reporting guidelines for this retasearch is available at zenodo: https://zenodo.org/records/13305037 (Sisay, M. A et al., 2024). Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0). 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Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 03 Sep 2024 ADD YOUR COMMENT Comment Author details Author details 1 Economics, Woldia University, Weldiya, Amhara, 400, Ethiopia 2 Economics, North Wollo Agriculture office, Woldia, Amhara, 400, Ethiopia Moges Asmare Sisay Roles: Conceptualization, Formal Analysis, Investigation, Methodology, Resources, Software, Supervision, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Atnafu Amare Mulaw Roles: Methodology, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information The author(s) declared that no grants were involved in supporting this work. Article Versions (1) version 1 Published: 03 Sep 2024, 13:1000 https://doi.org/10.12688/f1000research.155113.1 Copyright © 2024 Sisay MA and Mulaw AA. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Sisay MA and Mulaw AA. Empowering rural women: key factors driving participation in livelihood credit programs in Gubalafto woreda, North Wollo, Ethiopia [version 1; peer review: 2 approved with reservations] . F1000Research 2024, 13 :1000 ( https://doi.org/10.12688/f1000research.155113.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 1 VERSION 1 PUBLISHED 03 Sep 2024 Views 0 Cite How to cite this report: Khosla S. Reviewer Report For: Empowering rural women: key factors driving participation in livelihood credit programs in Gubalafto woreda, North Wollo, Ethiopia [version 1; peer review: 2 approved with reservations] . F1000Research 2024, 13 :1000 ( https://doi.org/10.5256/f1000research.170240.r322435 ) The direct URL for this report is: https://f1000research.com/articles/13-1000/v1#referee-response-322435 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 04 Nov 2024 Sunil Khosla , VIT-AP School of Social Sciences and Humanities, VIT-AP University, Amaravati, Andhra Pradesh, India Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.170240.r322435 Empowering rural women: key factors driving participation in livelihood credit programs in Gubalafto woreda, North Wollo, Ethiopia Thank you for the opportunity to review this intriguing manuscript titled “Empowering rural women: key factors driving participation in ... Continue reading READ ALL Empowering rural women: key factors driving participation in livelihood credit programs in Gubalafto woreda, North Wollo, Ethiopia Thank you for the opportunity to review this intriguing manuscript titled “Empowering rural women: key factors driving participation in livelihood credit programs in Gubalafto woreda, North Wollo, Ethiopia.” Using 364 rural women data and logistic regression model, the paper examined the determinants of rural women participation in livelihood credit programs in rural Ethiopia. While the study offers several valuable insights, a few areas require further attention for clarity and completeness: Please interpret the results instead of writing the results in abstract. Additionally, the abstract should present specific implications rather than a general conclusion. The contributions of this study should be clearly articulated. Consider adding a dedicated section or paragraph in the introduction to emphasize how this study differs from existing literature. Descriptive statistic table is missing . Include a descriptive statistics table to provide insight into the data structure, enhancing reader understanding of the sample characteristics. Tables 4 and 5 appear confusing; consider merging these into a single table for better clarity. Simplify the table by focusing on coefficients, standard errors, and significant indicators. Table 4, IL (Annual Income level of women) coefficient is zero. Discuss the rationale for including this variable if it offers no explanatory power. Sources for the figures are missing. Significance of the study area is missing. Explanation for the outcome variable (Wplcstatus) is missing. How is this variable estimated? How much % of surveyed women are falls under the which binary category? In introduction, the authors mentioned the challenging factors such as gender bias in extension services, limited access to information, and socio-economic constraints but did not consider them in their analysis. How then this study filled the gap? What is the source used for selecting these independent variables used in the logistic regression? Why not used other variables such as social capital (member in groups like SHG etc.), decision making, health status etc. Rewrite Section 2 to concisely focus on social protection programs, specifically the Productive Safety Net Program (PSNP), and its relationship with women’s empowerment and household well-being. Establish connections to women’s participation in such programs and highlight relevant research gaps. Tables should be properly formatted, where c oefficients and standard errors, along with significance indicators, are sufficient for presenting results effectively. As the authors emphasized more on the livelihood credit program and estimating just determinants might not be good idea. Therefore, exploring the impact of livelihood credit programs on women’s empowerment and wellbeing using rigorous methods like impact evaluation (instrumental variable, endogenous switching regression, propensity score matching) would provide additional insights and strengthen the study’s relevance in rural Ethiopia. Citations are mostly missing in the manuscript. For instance, first paragraph of introduction, section 3.3.3, 3.3.4 etc. Multicollinearity test (VIF) table is missing. There exists confusion between literature review and research objective. Everyone knows the benefit of social protection such as social safety net (The Productive Safety Net Program (PSNP)). Why would women not participate? Is there any statistics that suggest low participation or any issue with the government program? Please make it clear. The literature section deals with the definition, and other information but does not connect it related to the women participation in the program nor it explains about the how the factors can make difference. It does not conclude with the research gaps. Therefore, the literature review should extend beyond definitions to relate factors directly to women’s participation in the program. Conclude with a statement of research gaps based on this discussion. The objective is confusing because the sample selection criteria (3.3.3) are those women who received the benefit. So, how the dependent variable is a binary one when all women have participated or received the benefit from the program? Further, the variable creation (dependent variable) explanation is not provided neither the % of women that have received any benefit. The conclusion would be strengthened by providing specific policy implications based on the study’s findings. In the introduction, data is missing on how much % of rural women are currently participating in these programs? If the participation rate is less, then the study make sense. Instead of presenting odds ratios (OR), it is recommended to include the marginal effects for the coefficients, which may provide more intuitive interpretations of the results. Describe how the study’s recommendations could be practically implemented. Providing details on feasible actions enhances the applied relevance of your suggestions. Table 4 & 5 results interpretations need attention. Please revise it or use the marginal effect coefficient. For example, education level is negatively significant, how is that possible? The interpretation might be biased because women in developing areas generally less educated. What is the mean education of women in the study area? To claim your point education square could be used. Limitations of the study and suggestions for the future research is missing. Overall, the manuscript addresses a vital topic but requires revisions to improve clarity, methodological rigor, and presentation. I look forward to seeing how these concerns are addressed in future drafts. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Partly Competing Interests: No competing interests were disclosed. Reviewer Expertise: Development economics (poverty, vulnerability, food security, impact evaluation etc.) I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Khosla S. Reviewer Report For: Empowering rural women: key factors driving participation in livelihood credit programs in Gubalafto woreda, North Wollo, Ethiopia [version 1; peer review: 2 approved with reservations] . F1000Research 2024, 13 :1000 ( https://doi.org/10.5256/f1000research.170240.r322435 ) The direct URL for this report is: https://f1000research.com/articles/13-1000/v1#referee-response-322435 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Ghouse SM. Reviewer Report For: Empowering rural women: key factors driving participation in livelihood credit programs in Gubalafto woreda, North Wollo, Ethiopia [version 1; peer review: 2 approved with reservations] . F1000Research 2024, 13 :1000 ( https://doi.org/10.5256/f1000research.170240.r322436 ) The direct URL for this report is: https://f1000research.com/articles/13-1000/v1#referee-response-322436 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 30 Sep 2024 Suhail M. Ghouse , Dept. of Marketing and Entrepreneurship, College of Commerce and Business Administration, Dhofar University, Salalah, Dhofar Governorate, Oman Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.170240.r322436 Summary of the Article: The study examines the factors influencing rural women’s participation in livelihood credit programs in Gubalafto Woreda, Ethiopia. Using a cross-sectional survey of 364 rural women and a mixed-methods approach, the research identifies the socio-economic and ... Continue reading READ ALL Summary of the Article: The study examines the factors influencing rural women’s participation in livelihood credit programs in Gubalafto Woreda, Ethiopia. Using a cross-sectional survey of 364 rural women and a mixed-methods approach, the research identifies the socio-economic and demographic determinants of participation in these credit programs. Key factors such as household size, income level, education, skill training, and market access are analyzed through descriptive statistics, chi-square tests, t-tests, and logistic regression. The findings underscore the importance of household size, skill training, and access to markets as positive predictors of participation, while age and distance from lending offices are negative predictors. The authors conclude with recommendations to improve access to credit for rural women, particularly through enhanced training programs and better market access. Clarity and Citation of Current Literature: The article is clearly presented, with a logical structure that moves from the introduction through to the conclusion. The use of tables and figures enhances understanding of the data. However, while the article includes relevant references, the literature review could benefit from the inclusion of more recent studies, particularly those from the last 3-5 years. Moreover, there is some repetition in the literature review, particularly in the discussion of theoretical frameworks. Recommendations: 1. Ref-1,2,3,4,5,6,7,8 2. Avoid repeating theoretical concepts in multiple sections of the literature review. Study Design and Technical Soundness: The cross-sectional survey design is appropriate for the study’s goals, and the stratified random sampling technique ensures representativeness across different agro-ecological zones. The statistical methods used—descriptive statistics, chi-square tests, t-tests, and logistic regression—are suitable for the research question and data structure. However, certain methodological details require more elaboration. Variables such as "market access" and "perception of interest rates" are not clearly defined or operationalized. Furthermore, there is no discussion of how missing data were managed, which could influence the study’s robustness. Additionally, while the sample size is justified, the rationale for selecting only four kebeles could be more clearly articulated to enhance the generalizability of the findings. Recommendations: 1. Clearly define and operationalize key variables such as "market access" and "perception of group collateral." 2. Provide a description of how missing data were handled. 3. Offer a stronger justification for the selection of the four kebeles and explain how representative they are of the larger population. Reproducibility of Methods and Analysis: The study provides a solid overview of the methods, but additional detail is required for full reproducibility. Although the sampling procedure and statistical techniques are explained, the questionnaire and the coding of variables are not made available, which limits the ability of others to replicate the study. Moreover, while the authors mention using STATA for data analysis, the specific commands or procedures are not shared. Recommendations: 1. Include the full questionnaire in an appendix or supplementary material. 2. Provide a detailed codebook that explains how all variables were measured or coded. 3. Share the STATA commands or code used for statistical analysis to ensure complete reproducibility. Statistical Analysis and Interpretation: The statistical methods used in the paper are appropriate, and the results are presented clearly. Logistic regression is a good choice for examining the binary dependent variable (credit participation), and the interpretation of results is generally accurate. However, more detail is needed regarding the operationalization of certain variables and the interpretation of the odds ratios. Additionally, the paper mentions testing for multicollinearity but does not provide the results (e.g., VIF scores) or explain how this issue was addressed. The reported pseudo-R-squared of 0.944 for the logistic regression model is unusually high, which could indicate overfitting. The implications of this high value should be discussed, and adjustments should be made if necessary. Recommendations: 1. Provide more detail on the operationalization of key variables in the logistic regression model. 2. Report the results of multicollinearity tests (e.g., VIF scores) to ensure the stability of the regression coefficients. 3. Address the high pseudo R-squared value, explaining whether this might indicate overfitting, and suggest steps to mitigate it if needed. Support of Conclusions by Results: The conclusions are generally supported by the results of the study. The authors correctly identify the factors that influence rural women’s participation in credit programs and provide recommendations that align with these findings. However, the conclusions could be more nuanced in terms of acknowledging the limitations of the study’s cross-sectional design, which only allows for correlation, not causality. Additionally, while the conclusions have broader policy implications, there is limited discussion of the regional specificity of the findings and how they might apply to other areas. Recommendations: 1. Clarify that the study identifies correlations rather than causal relationships between the variables and credit participation. 2. Discuss the limitations of the study, including the cross-sectional design and reliance on self-reported data. 3. Offer suggestions for future research that could address these limitations, such as longitudinal studies or studies that explore specific mechanisms. Final Assessment: This article makes a valuable contribution to understanding the factors influencing rural women’s participation in livelihood credit programs in Ethiopia. However, to enhance the clarity, reproducibility, and impact of the work, the authors should address the specific concerns outlined above. By refining the methodological transparency, strengthening the statistical interpretation, and providing a more nuanced discussion of the findings, the study can offer more robust academic and policy insights. Based on the detailed review, major revision is recommended. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Partly References 1. Durrah O, Ghouse S, Alkhalaf T: Motivations and behaviours of rural women entrepreneurs in Oman. International Journal of Gender and Entrepreneurship . 2024; 16 (3): 402-421 Publisher Full Text 2. Elkafrawi N, Refai D: Egyptian rural women entrepreneurs: Challenges, ambitions and opportunities. The International Journal of Entrepreneurship and Innovation . 2022; 23 (3): 203-214 Publisher Full Text 3. Ghouse S, Durrah O, Shekhar R, Arslan A: Personal Characteristics and Strategic Entrepreneurial Behaviour of Rural Female Entrepreneurs: Insights From Oman. Journal of Small Business Strategy . 2023; 33 (2). Publisher Full Text 4. Ghouse S, Durrah O, McElwee G: Rural women entrepreneurs in Oman: problems and opportunities. International Journal of Entrepreneurial Behavior & Research . 2021; 27 (7): 1674-1695 Publisher Full Text 5. Ghouse S, McElwee G, Meaton J, Durrah O: Barriers to rural women entrepreneurs in Oman. International Journal of Entrepreneurial Behavior & Research . 2017; 23 (6): 998-1016 Publisher Full Text 6. Kitole F, Genda E: Empowering her drive: Unveiling the resilience and triumphs of women entrepreneurs in rural landscapes. Women's Studies International Forum . 2024; 104 . Publisher Full Text 7. Gashi Nulleshi S, Kalonaityte V: Gender roles or gendered goals? Women's return to rural family business. International Journal of Gender and Entrepreneurship . 2023; 15 (1): 44-63 Publisher Full Text 8. Oridi F, Uddin M, Faisal-E-Alam M, Husain T: Prevailing factors of rural women entrepreneurship in Bangladesh: evidence from handicraft business. Journal of Global Entrepreneurship Research . 2022; 12 (1): 305-318 Publisher Full Text Competing Interests: No competing interests were disclosed. Reviewer Expertise: Entrepreneurship & Marketing I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Ghouse SM. Reviewer Report For: Empowering rural women: key factors driving participation in livelihood credit programs in Gubalafto woreda, North Wollo, Ethiopia [version 1; peer review: 2 approved with reservations] . F1000Research 2024, 13 :1000 ( https://doi.org/10.5256/f1000research.170240.r322436 ) The direct URL for this report is: https://f1000research.com/articles/13-1000/v1#referee-response-322436 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 03 Sep 2024 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 Version 1 03 Sep 24 read read Suhail M. Ghouse , Dhofar University, Salalah, Oman Sunil Khosla , VIT-AP University, Amaravati, India Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2024 Khosla S. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The author(s) is/are employees of the US Government and therefore domestic copyright protection in USA does not apply to this work. The work may be protected under the copyright laws of other jurisdictions when used in those jurisdictions. 04 Nov 2024 | for Version 1 Sunil Khosla , VIT-AP School of Social Sciences and Humanities, VIT-AP University, Amaravati, Andhra Pradesh, India 0 Views copyright © 2024 Khosla S. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The author(s) is/are employees of the US Government and therefore domestic copyright protection in USA does not apply to this work. The work may be protected under the copyright laws of other jurisdictions when used in those jurisdictions. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Empowering rural women: key factors driving participation in livelihood credit programs in Gubalafto woreda, North Wollo, Ethiopia Thank you for the opportunity to review this intriguing manuscript titled “Empowering rural women: key factors driving participation in livelihood credit programs in Gubalafto woreda, North Wollo, Ethiopia.” Using 364 rural women data and logistic regression model, the paper examined the determinants of rural women participation in livelihood credit programs in rural Ethiopia. While the study offers several valuable insights, a few areas require further attention for clarity and completeness: Please interpret the results instead of writing the results in abstract. Additionally, the abstract should present specific implications rather than a general conclusion. The contributions of this study should be clearly articulated. Consider adding a dedicated section or paragraph in the introduction to emphasize how this study differs from existing literature. Descriptive statistic table is missing . Include a descriptive statistics table to provide insight into the data structure, enhancing reader understanding of the sample characteristics. Tables 4 and 5 appear confusing; consider merging these into a single table for better clarity. Simplify the table by focusing on coefficients, standard errors, and significant indicators. Table 4, IL (Annual Income level of women) coefficient is zero. Discuss the rationale for including this variable if it offers no explanatory power. Sources for the figures are missing. Significance of the study area is missing. Explanation for the outcome variable (Wplcstatus) is missing. How is this variable estimated? How much % of surveyed women are falls under the which binary category? In introduction, the authors mentioned the challenging factors such as gender bias in extension services, limited access to information, and socio-economic constraints but did not consider them in their analysis. How then this study filled the gap? What is the source used for selecting these independent variables used in the logistic regression? Why not used other variables such as social capital (member in groups like SHG etc.), decision making, health status etc. Rewrite Section 2 to concisely focus on social protection programs, specifically the Productive Safety Net Program (PSNP), and its relationship with women’s empowerment and household well-being. Establish connections to women’s participation in such programs and highlight relevant research gaps. Tables should be properly formatted, where c oefficients and standard errors, along with significance indicators, are sufficient for presenting results effectively. As the authors emphasized more on the livelihood credit program and estimating just determinants might not be good idea. Therefore, exploring the impact of livelihood credit programs on women’s empowerment and wellbeing using rigorous methods like impact evaluation (instrumental variable, endogenous switching regression, propensity score matching) would provide additional insights and strengthen the study’s relevance in rural Ethiopia. Citations are mostly missing in the manuscript. For instance, first paragraph of introduction, section 3.3.3, 3.3.4 etc. Multicollinearity test (VIF) table is missing. There exists confusion between literature review and research objective. Everyone knows the benefit of social protection such as social safety net (The Productive Safety Net Program (PSNP)). Why would women not participate? Is there any statistics that suggest low participation or any issue with the government program? Please make it clear. The literature section deals with the definition, and other information but does not connect it related to the women participation in the program nor it explains about the how the factors can make difference. It does not conclude with the research gaps. Therefore, the literature review should extend beyond definitions to relate factors directly to women’s participation in the program. Conclude with a statement of research gaps based on this discussion. The objective is confusing because the sample selection criteria (3.3.3) are those women who received the benefit. So, how the dependent variable is a binary one when all women have participated or received the benefit from the program? Further, the variable creation (dependent variable) explanation is not provided neither the % of women that have received any benefit. The conclusion would be strengthened by providing specific policy implications based on the study’s findings. In the introduction, data is missing on how much % of rural women are currently participating in these programs? If the participation rate is less, then the study make sense. Instead of presenting odds ratios (OR), it is recommended to include the marginal effects for the coefficients, which may provide more intuitive interpretations of the results. Describe how the study’s recommendations could be practically implemented. Providing details on feasible actions enhances the applied relevance of your suggestions. Table 4 & 5 results interpretations need attention. Please revise it or use the marginal effect coefficient. For example, education level is negatively significant, how is that possible? The interpretation might be biased because women in developing areas generally less educated. What is the mean education of women in the study area? To claim your point education square could be used. Limitations of the study and suggestions for the future research is missing. Overall, the manuscript addresses a vital topic but requires revisions to improve clarity, methodological rigor, and presentation. I look forward to seeing how these concerns are addressed in future drafts. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise Development economics (poverty, vulnerability, food security, impact evaluation etc.) I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (0) Khosla S. Peer Review Report For: Empowering rural women: key factors driving participation in livelihood credit programs in Gubalafto woreda, North Wollo, Ethiopia [version 1; peer review: 2 approved with reservations] . F1000Research 2024, 13 :1000 ( https://doi.org/10.5256/f1000research.170240.r322435) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-1000/v1#referee-response-322435 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2024 Ghouse S. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 30 Sep 2024 | for Version 1 Suhail M. Ghouse , Dept. of Marketing and Entrepreneurship, College of Commerce and Business Administration, Dhofar University, Salalah, Dhofar Governorate, Oman 0 Views copyright © 2024 Ghouse S. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Summary of the Article: The study examines the factors influencing rural women’s participation in livelihood credit programs in Gubalafto Woreda, Ethiopia. Using a cross-sectional survey of 364 rural women and a mixed-methods approach, the research identifies the socio-economic and demographic determinants of participation in these credit programs. Key factors such as household size, income level, education, skill training, and market access are analyzed through descriptive statistics, chi-square tests, t-tests, and logistic regression. The findings underscore the importance of household size, skill training, and access to markets as positive predictors of participation, while age and distance from lending offices are negative predictors. The authors conclude with recommendations to improve access to credit for rural women, particularly through enhanced training programs and better market access. Clarity and Citation of Current Literature: The article is clearly presented, with a logical structure that moves from the introduction through to the conclusion. The use of tables and figures enhances understanding of the data. However, while the article includes relevant references, the literature review could benefit from the inclusion of more recent studies, particularly those from the last 3-5 years. Moreover, there is some repetition in the literature review, particularly in the discussion of theoretical frameworks. Recommendations: 1. Ref-1,2,3,4,5,6,7,8 2. Avoid repeating theoretical concepts in multiple sections of the literature review. Study Design and Technical Soundness: The cross-sectional survey design is appropriate for the study’s goals, and the stratified random sampling technique ensures representativeness across different agro-ecological zones. The statistical methods used—descriptive statistics, chi-square tests, t-tests, and logistic regression—are suitable for the research question and data structure. However, certain methodological details require more elaboration. Variables such as "market access" and "perception of interest rates" are not clearly defined or operationalized. Furthermore, there is no discussion of how missing data were managed, which could influence the study’s robustness. Additionally, while the sample size is justified, the rationale for selecting only four kebeles could be more clearly articulated to enhance the generalizability of the findings. Recommendations: 1. Clearly define and operationalize key variables such as "market access" and "perception of group collateral." 2. Provide a description of how missing data were handled. 3. Offer a stronger justification for the selection of the four kebeles and explain how representative they are of the larger population. Reproducibility of Methods and Analysis: The study provides a solid overview of the methods, but additional detail is required for full reproducibility. Although the sampling procedure and statistical techniques are explained, the questionnaire and the coding of variables are not made available, which limits the ability of others to replicate the study. Moreover, while the authors mention using STATA for data analysis, the specific commands or procedures are not shared. Recommendations: 1. Include the full questionnaire in an appendix or supplementary material. 2. Provide a detailed codebook that explains how all variables were measured or coded. 3. Share the STATA commands or code used for statistical analysis to ensure complete reproducibility. Statistical Analysis and Interpretation: The statistical methods used in the paper are appropriate, and the results are presented clearly. Logistic regression is a good choice for examining the binary dependent variable (credit participation), and the interpretation of results is generally accurate. However, more detail is needed regarding the operationalization of certain variables and the interpretation of the odds ratios. Additionally, the paper mentions testing for multicollinearity but does not provide the results (e.g., VIF scores) or explain how this issue was addressed. The reported pseudo-R-squared of 0.944 for the logistic regression model is unusually high, which could indicate overfitting. The implications of this high value should be discussed, and adjustments should be made if necessary. Recommendations: 1. Provide more detail on the operationalization of key variables in the logistic regression model. 2. Report the results of multicollinearity tests (e.g., VIF scores) to ensure the stability of the regression coefficients. 3. Address the high pseudo R-squared value, explaining whether this might indicate overfitting, and suggest steps to mitigate it if needed. Support of Conclusions by Results: The conclusions are generally supported by the results of the study. The authors correctly identify the factors that influence rural women’s participation in credit programs and provide recommendations that align with these findings. However, the conclusions could be more nuanced in terms of acknowledging the limitations of the study’s cross-sectional design, which only allows for correlation, not causality. Additionally, while the conclusions have broader policy implications, there is limited discussion of the regional specificity of the findings and how they might apply to other areas. Recommendations: 1. Clarify that the study identifies correlations rather than causal relationships between the variables and credit participation. 2. Discuss the limitations of the study, including the cross-sectional design and reliance on self-reported data. 3. Offer suggestions for future research that could address these limitations, such as longitudinal studies or studies that explore specific mechanisms. Final Assessment: This article makes a valuable contribution to understanding the factors influencing rural women’s participation in livelihood credit programs in Ethiopia. However, to enhance the clarity, reproducibility, and impact of the work, the authors should address the specific concerns outlined above. By refining the methodological transparency, strengthening the statistical interpretation, and providing a more nuanced discussion of the findings, the study can offer more robust academic and policy insights. Based on the detailed review, major revision is recommended. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Partly References 1. Durrah O, Ghouse S, Alkhalaf T: Motivations and behaviours of rural women entrepreneurs in Oman. International Journal of Gender and Entrepreneurship . 2024; 16 (3): 402-421 Publisher Full Text 2. Elkafrawi N, Refai D: Egyptian rural women entrepreneurs: Challenges, ambitions and opportunities. The International Journal of Entrepreneurship and Innovation . 2022; 23 (3): 203-214 Publisher Full Text 3. Ghouse S, Durrah O, Shekhar R, Arslan A: Personal Characteristics and Strategic Entrepreneurial Behaviour of Rural Female Entrepreneurs: Insights From Oman. Journal of Small Business Strategy . 2023; 33 (2). Publisher Full Text 4. Ghouse S, Durrah O, McElwee G: Rural women entrepreneurs in Oman: problems and opportunities. International Journal of Entrepreneurial Behavior & Research . 2021; 27 (7): 1674-1695 Publisher Full Text 5. Ghouse S, McElwee G, Meaton J, Durrah O: Barriers to rural women entrepreneurs in Oman. International Journal of Entrepreneurial Behavior & Research . 2017; 23 (6): 998-1016 Publisher Full Text 6. Kitole F, Genda E: Empowering her drive: Unveiling the resilience and triumphs of women entrepreneurs in rural landscapes. Women's Studies International Forum . 2024; 104 . Publisher Full Text 7. Gashi Nulleshi S, Kalonaityte V: Gender roles or gendered goals? Women's return to rural family business. International Journal of Gender and Entrepreneurship . 2023; 15 (1): 44-63 Publisher Full Text 8. Oridi F, Uddin M, Faisal-E-Alam M, Husain T: Prevailing factors of rural women entrepreneurship in Bangladesh: evidence from handicraft business. Journal of Global Entrepreneurship Research . 2022; 12 (1): 305-318 Publisher Full Text Competing Interests No competing interests were disclosed. Reviewer Expertise Entrepreneurship & Marketing I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (0) Ghouse SM. Peer Review Report For: Empowering rural women: key factors driving participation in livelihood credit programs in Gubalafto woreda, North Wollo, Ethiopia [version 1; peer review: 2 approved with reservations] . F1000Research 2024, 13 :1000 ( https://doi.org/10.5256/f1000research.170240.r322436) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. 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Register $(document).ready(function () { signIn.createSignInAsRow($("#sign-in-form-gfb-popup")); $(".target-field").each(function () { var uris = $(this).val().split("/"); if (uris.pop() === "login") { $(this).val(uris.toString().replace(",","/")); } }); });