Microcredit and Rural Household Outcomes: Evidence from Habru Woreda’s Smallholder Farmers, Ethiopia

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Due to a lack of working capital to diversify their sources of income and acquire new sources, farmers are highly dependent on financial institutions to access microloans. Method This study utilized logistic regression and propensity score matching methods to analyze the primary data collected from a sample of 385 household heads. Results The Estimation results of the study shows that Gender, age, family size, and education, access to irrigation, extension services, dependency ratio, and distance to credit sources were among the significant determinants of access to microcredit service. Propensity score matching results showed that microloans increased household spending but did not improve household asset accumulations. Conclusion While microloans have increased household spending, it has not significantly improved rural households asset accumulation, largely due to high interest rates and repayment pressures. To enhance long-term welfare impacts, microcredit should be complemented with financial literacy, savings and asset-building programs, and redesigned with lower interest rates and more flexible repayment structures in the study area. " } { "@context": "http://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": "1", "item": { "@id": "https://f1000research.com/", "name": "Home" } }, { "@type": "ListItem", "position": "2", "item": { "@id": "https://f1000research.com/browse/articles", "name": "Browse" } }, { "@type": "ListItem", "position": "3", "item": { "@id": "https://f1000research.com/articles/13-1143/v2", "name": "Microcredit and Rural Household Outcomes: Evidence from Habru Woreda’s..." } } ] } Home Browse Microcredit and Rural Household Outcomes: Evidence from Habru Woreda’s... ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article Jejaw Awoke A, Belaye Tefera A and Maru Ayinewa Y. Microcredit and Rural Household Outcomes: Evidence from Habru Woreda’s Smallholder Farmers, Ethiopia [version 2; peer review: 1 approved with reservations] . F1000Research 2025, 13 :1143 ( https://doi.org/10.12688/f1000research.156802.2 ) 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 Revised Microcredit and Rural Household Outcomes: Evidence from Habru Woreda’s Smallholder Farmers, Ethiopia [version 2; peer review: 1 approved with reservations] Previously titled: Micro-loans, macro-impacts: Examining the reverberating gains for Habru Woreda’s small-scale agrarian households Agerwork Jejaw Awoke https://orcid.org/0009-0000-0449-1749 1 , Abate Belaye Tefera 1 , Yigermal Maru Ayinewa https://orcid.org/0009-0002-0035-176X 1 Agerwork Jejaw Awoke https://orcid.org/0009-0000-0449-1749 1 , Abate Belaye Tefera 1 , Yigermal Maru Ayinewa https://orcid.org/0009-0002-0035-176X 1 PUBLISHED 09 Oct 2025 Author details Author details 1 Department of Economics, Woldia University, Weldiya, Amhara, Ethiopia Agerwork Jejaw Awoke Roles: Conceptualization, Methodology, Validation, Writing – Review & Editing Abate Belaye Tefera Roles: Formal Analysis, Investigation, Software, Writing – Original Draft Preparation Yigermal Maru Ayinewa Roles: Data Curation, Validation, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS This article is included in the Agriculture, Food and Nutrition gateway. Abstract Background Most people in third-world countries are impoverished and rely on small-holder farming as a source of income. Due to a lack of working capital to diversify their sources of income and acquire new sources, farmers are highly dependent on financial institutions to access microloans. Method This study utilized logistic regression and propensity score matching methods to analyze the primary data collected from a sample of 385 household heads. Results The Estimation results of the study shows that Gender, age, family size, and education, access to irrigation, extension services, dependency ratio, and distance to credit sources were among the significant determinants of access to microcredit service. Propensity score matching results showed that microloans increased household spending but did not improve household asset accumulations. Conclusion While microloans have increased household spending, it has not significantly improved rural households asset accumulation, largely due to high interest rates and repayment pressures. To enhance long-term welfare impacts, microcredit should be complemented with financial literacy, savings and asset-building programs, and redesigned with lower interest rates and more flexible repayment structures in the study area. READ ALL READ LESS Keywords Microcredit, Household assets, Small holder farmers, Propensity score matching, Habru woreda Corresponding Author(s) Agerwork Jejaw Awoke ( [email protected] ) Close Corresponding author: Agerwork Jejaw Awoke Competing interests: No competing interests were disclosed. Grant information: The author(s) declared that no grants were involved in supporting this work. Copyright: © 2025 Jejaw Awoke A et al . 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: Jejaw Awoke A, Belaye Tefera A and Maru Ayinewa Y. Microcredit and Rural Household Outcomes: Evidence from Habru Woreda’s Smallholder Farmers, Ethiopia [version 2; peer review: 1 approved with reservations] . F1000Research 2025, 13 :1143 ( https://doi.org/10.12688/f1000research.156802.2 ) First published: 07 Oct 2024, 13 :1143 ( https://doi.org/10.12688/f1000research.156802.1 ) Latest published: 09 Oct 2025, 13 :1143 ( https://doi.org/10.12688/f1000research.156802.2 ) Revised Amendments from Version 1 In this revised version of our research article, we have implemented substantial improvements. Specifically, we incorporated additional empirical reviews to strengthen the validity of our findings, and we expanded both the theoretical and empirical literature reviews to enhance the overall coherence of the study. Furthermore, we refined the research title to ensure greater clarity and appeal for readers. In this revised version of our research article, we have implemented substantial improvements. Specifically, we incorporated additional empirical reviews to strengthen the validity of our findings, and we expanded both the theoretical and empirical literature reviews to enhance the overall coherence of the study. Furthermore, we refined the research title to ensure greater clarity and appeal for readers. To read any peer review reports and author responses for this article, follow the "read" links in the Open Peer Review table. READ REVIEWER RESPONSES Introduction The majority of rural households in developing nations depend on smallholder farming as the primary source of livelihood ( Magezi & Nakano, 2020 ). Nonetheless, limited access to financial capital continues to be a hindrance in the adoption of new farm inputs and constrains income diversification opportunities ( Dong et al., 2010 ). Smallholder farmers, with insufficient working capital, depend on financial institutions for access to microcredit to invest in their agriculture and engage in off-farm businesses ( Bhusare & Chanda, 2017 ). Poor households’ lack of access to formal credit markets contributed to the emergence of microfinance as a substitute source of finance. For this purpose, microfinance has become more mainstreamed into poverty alleviation strategies in many developing nations where it is considered a critical tool in improving household welfare and risk reduction by governments and development stakeholders alike ( Feleke, 2011 ). In Ethiopia, the official microfinance industry was founded in 1994–1995, following the issuance of the Microfinance Institution Licensing and Supervision Proclamation No. 40/1996. This act licensed MFIs to mobilize funds legally, issue and accept drafts, and conduct all manner of financial services that are appropriate for the rural poor. The government has proceeded to actively improve the regulatory framework in a bid to deepen financial inclusion in order to enhance economic opportunities and the well-being of low-income households ( Balcha & Tamare, 2017 ; Chirkos, 2014 ; Ramanaiah & Gowri, 2011 ). Improving access to financial services is widely regarded as a way of empowering poor individuals by managing risk, promoting entrepreneurship, diversifying income, and smoothing household expenditure. Microcredit is an important tool to fund productive endeavors and improve socioeconomic status for smallholder farmers, who are usually characterized by small assets, variable incomes, and separation from formal credit markets ( Bauchet et al., 2011 ; Okidim & Obe-Nwaka, 2021 ; Shafique & Khan, 2020 ). Despite such developments, Ethiopia’s agricultural sector continues to face enduring hindrances in feeding its rapidly increasing population, and nearly 29.2% of the rural population continues to live under the poverty line ( Damtew, 2017 ). Structural bottlenecks such as low productivity, poor market linkage and technology access, and low farm incomes remain to constrain rural transformation ( Boere et al., 2016 ; Geffersa, 2023 ; Neglo et al., 2021 ). Smallholder farmers, who account for over 90% of the agricultural production, are still most vulnerable since limited access to productive inputs, liquidity, and credit constrains the application of improved agricultural technologies. Consequently, microcredit has emerged as a vital financial instrument to improve household incomes, protect assets, stabilize consumption, and facilitate easier agricultural investment ( Berhanu et al., 2021 ; Dhillon & Moncur, 2023 ; Obisesan, 2013 ). Empirical evidence regarding microcredit impacts, however, is inconclusive. Siyoum et al. (2012) ; for instance, have indicated that microcredit smoothes household consumption over the short term, while Lawin et al. (2018) ; have found positive and negative impacts, indicating that although microcredit has the potential to enhance investment and technology adoption, it can also reduce technical efficiency and profitability in some cases. Similarly, Geleta et al. (2018) ; concluded microfinance beneficiaries in Oromia Region’s Cheliya District had higher household incomes compared to non-beneficiaries, and Boltana et al. (2023) emphasized its role in improving household food security by means of improved calorie intake. More recently, Mandy (2023) noted that, even though households with entrepreneurial history benefited from incomes and investments, overall poverty-reducing effects of microfinance were limited. These findings suggest that microcredit success depends on context, household type, institutional design, and study areas economic condition. However, despite ACSI’s significant outreach, systematic evidence on the extent to which microcredit influences rural household welfare outcomes in Habru remains scarce and the main objective of this study was to investigate the impact of microcredit utilization on the spending and asset accumulation of smallholder farmers. Literature review Theoretical literature review Microcredit has been widely recognized as an essential instrument for enhancing rural household income and reducing poverty by providing financial access to those excluded from formal markets ( Ukpe et al., 2016 ). Credit in general serves as a bridge between income and expenditure, enabling households to invest in productive activities ( Beckman, 1962 ; Reddall & Miller, 1977 ). Yet, rural credit markets in developing countries are often constrained by collateral requirements, high transaction costs, and information asymmetry, which limit poor households’ ability to borrow from formal institutions ( Binswanger & Rosenzweig, 1986 ; Joan et al., 2022 ). In Ethiopia, where smallholder farmers account for over 90% of agricultural production, financial constraints remain a critical barrier to adopting modern technologies and improving productivity ( Singh et al., 1986 ). In response, the Ethiopian government formalized microfinance through Proclamation No. 40/1996, enabling institutions such as the Amhara Credit and Saving Institution (ACSI) to provide deposit, savings, and loan services across rural areas ( Balcha & Tamare, 2017 ; Chirkos, 2014 ; Ramanaiah & Gowri, 2011 ). While microcredit is theoretically expected to promote income diversification, asset building, and risk management, evidence also suggests that benefits are unevenly distributed, with the “upper poor” often gaining more than the “very poor” ( Gashayie & Singh, 2016 ). Consequently, although microfinance institutions are vital for rural transformation, their effectiveness depends on household characteristics, institutional design, and accessibility ( Boltana et al., 2023 ; Mersland & Strøm, 2010 ). Empirical literature review Empirical studies on the issue of microcredit utilization shows a mixed outcome on rural household’s income and welfare. In Ethiopia, Siyoum et al. (2012) and Lawin (2018) reported that while borrowing improved consumption smoothing and technology adoption, effects on efficiency, profit, and income varied. Geleta et al. (2018) found that beneficiaries of microfinance in Cheliya District had significantly higher incomes than non-users, and Boltana et al. (2023) showed credit programs improved household food security and calorie intake. Similarly, Berhanu et al. (2021) and Dhillon & Moncur (2023) highlighted that microcredit enables smallholders to protect assets, maintain consumption, and improve productivity, thereby enhancing income. Yet, Mandy (2023) observed that while entrepreneurial households benefited from higher income and investment, overall poverty reduction effects were modest. Beyond Ethiopia, Obisesan (2013) found that microcredit positively influenced farm output in Nigeria, while Joan et al. (2022) stressed the ongoing importance of informal lenders for rural households. More recent evidence in the Amhara region indicates that ACSI significantly improved household income ( Beyene & Fentaw, 2023 ; Kassegn & Endris, 2021 ), though coverage gaps persist. Methods Description of the study area Restricted to Ethiopia’s North Wollo Zone, the research was conducted in the Habru District. There are 491 kilometers between it and Addis Ababa. Its total landmass coverage of 1239.79 square kilometers makes it one of the zone’s largest districts. Habru’s economy is based on an unstable rain-fed livelihood system, making it the district in the Amhara Region most vulnerable to drought and food crisis. The district is home to 202,009 people in total, with 99,442 women and 102,567 men, according to the Habru woreda Administration Office ( office, 2023 ). Two urban and thirty-five rural kebeles make up the district. Afar Region borders the district on the east, Guba Lafto borders it on the west, the Alewuha River borders it on the north, and the South Wollo Zone (Ambasel district) borders it on the south as shown by the following Figure 1 . Figure 1. Map of the study area. Note: 'This figure/table has been reproduced from Kassegn, A., & Endris, E. (2022) . Factors affecting loan repayment rate among smallholder farmers got loans from the Amhara Credit and Saving Institution: In the case of Habru District, Amhara Regional State, Ethiopia. International Area Studies Review, 25(1), 73-96. https://doi.org/10.1177/22338659211040993 . The grant is obtained under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0). Type, source and method of data collection This research used only primary data collected between 20 January and 24 February 2023. Structured questionnaires were used to gather the main data on assets, expenses, farm size, and socioeconomic factors that were thought to limit the use of microcredit. Sampling method and sample size determination In the study area, there were 49,880 small holder farm households. To select the representative sample of the target population (small-scale farm household heads), the Cochran (1963) formula of sample size determination for infinite population was used and which is seen as follows. n 0 = Z 2 pq ( e ) 2 n 0 = ( 1.96 ) 2 ( 0.5 ) 2 ( 0.05 ) 2 = 385 Where, n 0 = sample size, e = the level of precision at 95% of confidence interval. To obtain the above representative sample, the study was applied three stage probability sampling method. In the first stage, systematic random sampling was used to select seven (6) out of 35 rural kebeles in the woreda. In this stage, the 5 th kebele, was selected through lottery method and the rest was selected systematically via skimming factor. In the second stage, based on the evidence of clients from ACSI in the study area, rural farm households in the selected kebeles was stratified based on the microcredit program as users and non-users and finally, the sample was selected from each stratum in the selected kebelles through proportional allocation. Model specification Logistic regression model Due to the dichotomous nature of the dependent variable-having or not having access to microcredit services-a univariate binary model called the logit model was chosen. The dependent variable was stated as follows: y i = { 1 if y i ∗ > 0 0 Otherwise Where y* is a latent variable expressing the quantity of microcredit loans contracted by small holder farmer i from micro finance institutions, ACSI. This quantity was a function of the small farmers’ household and his farm characteristics (X i ), as expressed in the form of eq 1 y i ∗ = β 0 + ∑ j = 1 n β i X ij + μ i The cumulative logistic probability function was specified as: eq 2 p i = f ( Z i ) = F ( β 0 + ∑ i = 1 n β i X i ) = 1 1 + e − Z i Where Pi is the probability that a farmer was using Microcredit given his household and farm characteristics X i , and β 0 and β i are the parameters to be estimated. After a simple mathematical manipulation of the above equation, equation 2, the odds ratio can be written as ( Wooldridge, 2004 ) ( 1 − p i ) = 1 1 + e Z i Or ( P i 1 − P i ) = 1 + e Z i 1 + e − Z i = e Z i . By taking the natural logarithms of the odds ratio with the consideration of the expected errors made during the research process, the Logit model becomes Z i = ln ( P i 1 − P i ) = β 0 + ∑ j = 1 n β i X i + μ i Therefore, the coefficient of the logit model β i , presents the change in the log of the odds associated with a change in the explanatory variables, X i . Propensity score matching The program’s features as well as the type and caliber of the data that are currently accessible determine which impact evaluation models should be used. There were no baseline socioeconomic data available in the study area, and it was challenging to locate a non-participant to produce a true counterfactual. Propensity score matching was found to be useful for generating a counterfactual group (comparison) in situations where random assignment is not feasible in order to solve this problem. It was used to calculate how microcredit affected the study area’s small-scale farmers’ revenue and expenses. One of the more useful techniques for estimating impacts with cross-sectional data is the PSM method. Additionally, it provides a useful method for analyzing how credit affects a variety of income indicators, and the outcomes rely less on the operational forms of econometric models. PSM’s key strategy is to maintain as many of the variables as possible constant, ensuring that credit accounts for the difference in income and spending between households that use credit (treated) and those that do not (counterfactual or control group) ( Heinrich et al., 2010 ). The propensity score is a conditional probability estimator that can be applied to any discrete model, including logit and probit, since they produce comparable outcomes ( Caliendo & Kopeinig, 2008 ). By assuming the logistic distribution of the sample mean and variances, the logit model was utilized in this study to estimate the propensity scores. The model of propensity scores is represented as follows: ( p ) = pr ( D = 1 / X i ) = E ( D / X i ) Where D = (1, 0), the indicators of improvement in income, it was the binary variable whether a small holder farmers were using credit or not using credit, (1= yes, 0 = otherwise) where X i is a vector of pre-treatment covariate propensity score, to ensure that matching estimation is done on treatment and control clients that are as similar as possible for ( i ) effective comparison. As a result given a population of units denoted by ( i ) , if the propensity score p ( X i ) is known as average treatment effect (ATE), it can be estimated as ATE = E { Y 1 i − Y 0 i / D i = 1 } = { E { Y 1 i − Y 0 i / D i = 1 , p ( X i ) } } = { E { Y 1 i / D i = 1 , p ( X i ) } − E { Y 0 i / D i = 0 , p ( X i ) / D i = 1 } } Where ATE is the average treatment effect , Y 1 i and Y 0 i is the potential outcome for the two counterfactual situations of the treatment and control small farm households respectively. P ( X i ) , is the propensity score, D is the small farm household’s variable, where D = 1 if the household participated in microcredit program and 0 otherwise. Description and expected sign of working variables The descriptions and expected sign of the working variables were described in the following Table 1 . Table 1. Description and expected sign of working variables. Variable Types of variables Description of variables Expected sign Micro-credit use Dichotomous 1 = if they use credit, 0 = otherwise Annual expenditure Continuous Annual expenditure of the household in ETB - Household asset Continuous Total asset of the household in ETB + Gender Dichotomous 1 = male, 0 = female + Age Continuous Age of the head in year + Marital status Dichotomous 0 = single, 1 = married, 2 = divorced, 3 = widowed + Education Dichotomous 0 = illiterate, 1 = read and write, 2 = primary education, 3 = secondary education, 4 = tertiary education and above + Family size Continuous Number of family member + Dependency ratio Continuous The dependency level of farm household + Farm size Continuous Total land size owned in hectare + Irrigation Dichotomous 1 = have access to irrigation; 0 = otherwise + Extension service Continuous Number of contacts with extension workers in a year + Membership of social organization Dichotomous 1 = member, 0 = otherwise + Remittance Dichotomous 1 = obtaining remittance income, 0 = otherwise + Economic shocks Dichotomous 1 = if the household experience to shocks, 0 = otherwise + Perception for group lending Dichotomous 1 = better perception, 0 = bad perception + Distance from the credit source Continuous Number of km from the credit source _ Ethical considerations This study, entitled “Micro-Loans, Macro-Impacts: Examining the Reverberating Gains for Habru Woreda’s Small-Scale Agrarian Households,” involved human participants and adhered to strict ethical guidelines. A formal written consent was obtained from all participants after providing them with detailed information about the study, ensuring their right to withdraw at any time. Participant confidentiality and anonymity were rigorously maintained, with personal identifiers anonymized and data securely stored. The study received approval from the Institutional Review Board (IRB) committee of college of Business and Economics at Woldia University on January 12, 2023 (Ref: CBE/RCSTT/187/2023). Field research was conducted respectfully, considering the cultural and social context of Habru Woreda, and aimed to benefit the community by enhancing understanding of food security and asset-building strategies. Discussion of results Description of the respondents The study was carried out using primary data gathered from 385 smallholder farmers in the north wollo zone’s Habru woreda, of which 126 had taken out microcredit loans from the Amhara Credit and Saving Institution and 259 had not. According to the descriptive statistics, microcredit loan users had greater mean family sizes and access to irrigation schemes, but non-users had higher mean household head ages. The dependency ratio was higher in non-user households than in user households, but a higher proportion of non-users were married. On the other hand, female-headed farm households were more common in the non-user group, whereas the mean number of male farm household heads was higher for users. Furthermore, social organization memberships accounted for 57% of the household heads. More over the full description of the respondent’s characteristic is given by the extended data at Zenodo: https://doi.org/10.5281/zenodo.13829443 . Determinants of microcredit loan utilization The estimates of the marginal effects from the logit model, as shown by Table 2 below indicates that, in comparison to their male counterparts, female household heads had greater limitations when it came to using microcredit loans. When all other variables are held constant, households headed by men are 24.5% more likely than households headed by women to use microcredit. This result is consistent with the research that Chamboko and Guvuriro (2022) conducted. The likelihood of using microcredit is negatively impacted by the age of the head of the household, and this effect is statistically significant at the 95% confidence level. More specifically, the likelihood of taking out microcredit loans falls by 0.9 percentage points for every year that the head of the household gets older. This outcome agrees with the conclusions of the research that Domanban (2024) conducted. Table 2. Estimates of the marginal effects. Variable dy/dx Std.Err. Z P > z Gender * .245 .057 4.28 0.000 Age -.009 .004 -2.17 0.03 Marital status 0.041 0.048 0.85 0.394 Education 0.059 0.026 2.25 0.024 Family size 0.084 0.015 5.51 0.000 Dependency ratio -0.315 0.128 -2.47 0.013 Farm size 0.054 0.111 0.49 0.624 Irrigation * 0.135 0.0574 2.36 0.018 Extension service 0.0245 0.008 3.10 0.002 Membership of social organization * 0.2 0.055 3.64 0.000 Remittance * 0.158 0.057 2.77 0.006 Economic shocks * 0.139 0.056 2.49 0.013 Perception for group lending * 0.128 0.058 2.21 0.027 Distance from the credit source -0.006 0.002 -2.63 0.008 * dy/dx is for discrete change of dummy variable from 0 to 1. Family size was another significant factor that positively correlated with the use of microcredit. This implies that the likelihood of obtaining formal financial credit increases by 8 percent for every additional family member added, all other things being equal. This can be explained by the fact that, in comparison to smaller families, larger families are able to engage in more self-directed farming activities and provide more family labor for production, which results in higher income. This result is consistent with the earlier theory and the findings of Kiros and Meshesha (2022) . Education also had a beneficial impact because farmers’ access to formal credit institutions can be influenced by their level of literacy. It is believed that farmers with higher levels of literacy would know more about government facilities and the market ( Alemayehu, 2020 ; Ameh & Lee, 2022 ). The last positive effect was that having access to irrigation schemes increased the likelihood that households would use microcredit by 13.5 percentage points. At the one percent significance level, the frequency of extension services is another variable that has a significant impact on rural households’ use of microcredit participation. The hypothesis posited that households receiving social work services more frequently would be more knowledgeable about emerging technologies, strategies for boosting output and efficiency, and the advantages and disadvantages of formal credit. This hypothesis is supported by the coefficient of this variable in the current investigation. The results of earlier research, including Alemayehu (2020) and Fonke (2021) , which claim that providing better extension services encourages smallholder farmers to apply for microcredit, also corroborate this conclusion. Participation in formal microcredit programs by rural households is negatively impacted by dependency ratio. Ceteris paribus, for every additional dependent member in the household, the probability of participation decreases by 31.5 percent. This study is also in line with research done by Ding and Abdulai (2020) & Ferede (2012) . The possibility of utilizing microcredit services was found to be positively and significantly correlated with households’ experience of shocks, remittance income, and improved perception of group lending. Precisely speaking, these variables raised the likelihood of obtaining microcredit by 13.9%, 15.8%, and 12.8%, in that order. Additionally, at the ninety percent significance level, the data showed a positive and significant relationship between farm size and loan usage. A one-hectare increase in farm size is associated with a five percent increase in the use of microcredit loans, according to the positive coefficient. Larger farms are probably more capital-intensive and therefore have a higher demand for loan financing because they need more labor and inputs for production. This result is consistent with the study that Ameh and Lee (2022) recently conducted. The participation of rural households in microfinance institutions (MFIs) was found to be significantly impacted, at the 5 percent significance level, by the distance from the credit source, as previously hypothesized. If all other variables remain unchanged, the marginal effect shows that the likelihood of participating in the microfinance program falls by 0.6 percent for every kilometer added to the distance traveled. The result of the study conducted by Alemayehu (2020) and the outcome of this study is consistent. In comparison to households in closer proximity, the results indicate that those residing further away from credit sources have lower participation rates in microfinance initiatives. To increase rural households’ access to credit services, it is crucial that microfinance institutions (MFIs) have a broad network of branch offices or agents to reach out to them, even in remote areas. The impact of microcredit use on household asset and expenditure Propensity score matching (PSM) was used in the study to calculate how microcredit loans affected annual household expenses and assets. A logit model that found multiple significant explanatory variables and had a pseudo R-squared of 0.4011, indicating a good model fit, was used to estimate the propensity score. The propensity score overlap between the treatment (microloan recipients) and control (non-recipients) groups is represented by the common support region, which was found to be between 0.02596857 and 0.98411727. This indicates that six treated households were left out of the matching exercise because they had propensity scores that fell outside of this range. A kernel density distribution of the propensity scores for the treatment and control groups was plotted in the study to further investigate the shared support. A significant region of overlap between the two groups was found in the kernel density plot, as illustrated in Figure 1 , suggesting the absence of a serious common support issue. Propensity score matching was employed by the researchers to compare the outcomes of loan recipients and non-recipients with similar observed characteristics, allowing them to estimate the impact of microcredit loans on household annual expenditure and asset. More accurate program impact estimates are produced with this method, which also helps to mitigate potential selection bias as shown by the following Figure 2 . Figure 2. Kernel density plot of propensity scores. To make sure that there was no significant difference in the mean of the estimated propensity scores between the treated and control households within each block, the study separated the sample into a maximum of five blocks. This suggests that the matching procedure was effective in forming evenly distributed groups for the impact analysis that followed. Figure 3 below shows the propensity scores’ graphical distribution. The propensity score overlap between the treatment and control groups is displayed in this visualization, which supports the suitability of the matching strategy even more. Figure 3. Graphical distribution of the propensity scores. Estimation of the average treatment effect on the treated The intensity of the matching process is more important in propensity score analysis than the sheer amount of output. By evaluating the balancing property and conducting balance tests, this quality can be evaluated. The estimated propensity scores in this study satisfied the balancing property. All the covariates, or explanatory variables, across all the blocks used in the analysis had the same distributions in households with the same propensity scores. The researchers used a Mantel-Haenszel test statistic to explore the results’ sensitivity in more detail. The absence of hidden bias is demonstrated when the Γ value of this test is 1, indicating that the test statistic’s bounds match the base case. The process of estimating and interpreting the average treatment effect on the treated (ATT) is a crucial output in impact evaluation studies. For the households who actually received the loans, this metric captures the microcredit program’s primary policy-relevant impact. The ATT estimates and their interpretation are shown in the following Table 3 . This illustrates how policy makers’ primary concern in impact evaluation studies is the estimation and interpretation of the average treatment effect on the treated. Table 3. Estimates of average treatment effects on the treated. Outcome variable Matching method n. treated n. control ATT t-value Annual expenditure Kernel matching (0.25) 126 220 1.712 11.561 Annual asset Kernel matching (0.25) 126 220 -8.737 -4.024 Strong empirical evidence of a statistically significant impact of the microcredit program on household asset and expenditure status is provided by the kernel matching results. The program has improved participating households’ expenditure levels, as indicated by the positive value of the average treatment effect on the treated (ATT). On the other hand, a negative ATT value indicates that the study area’s microcredit utilization has resulted in the depletion of household assets. Statistically, the analysis accounted for the variations in demographic and asset distribution traits between participant and non-participant households. The results show that the program has raised the spending of households involved by 1.712% and this outcome aligns with ( Choudhury et al., 2017 ; Phan, 2020 ; Phan et al., 2023 ; Yu et al., 2020 ) while their yearly asset possession has decreased by 8.737%, which is comparable to the earlier research carried out by Ahamad et al. (2021) . This result seems to contradict the main goal of microfinance institutions (MFIs), as the small loans have failed to safeguard the wealth of rural households. However, the findings are statistically significant at the 5% level, as demonstrated by the kernel matching estimators (ATT = 1.712, t = 11.561 for annual spending; ATT = -8.737, t = 4.024 for household yearly asset) with bootstrapped standard errors. Conclusion and recommendations The objective of this study was to investigate the extent to which microloan utilization influences household welfare outcomes, particularly in terms of asset accumulation and expenditure patterns, in Habru Woreda. Larger family size and higher education levels were positively associated with microcredit use, increasing the probability by 8% and 6% respectively. Access to irrigation and more frequent extension services also had positive impacts, while dependency ratio and distance from credit sources were negatively influential, decreasing the probability by 31.5% and 0.6% per kilometer, respectively. Households’ experience of shocks, remittance income, positive perception of group lending, and larger farm size were all positively correlated with microcredit access, suggesting the multifaceted nature of factors influencing credit utilization among rural households. The bootstrapped standard errors support the estimates obtained from the propensity score matching method, which show a mixed Average Treatment Effect on the Treated. Compared to the control group, respondents in the treatment category reported higher spending over the previous two years. The study did discover, however, that the households’ annual asset holdings had not improved. There are various reasons for this polarized result. First of all, households may find it difficult to pay back microloans due to the fixed payback terms and exorbitant interest rates, which force them to put loan repayment ahead of saving and asset creation. Furthermore, the study raises the possibility that, in order to positively affect household assets, microcredit need to be combined with complementary interventions like financial literacy training, asset-building initiatives, or access to other financial services. Microcredit alone may not be adequate to promote asset accumulation. Ethics and consent This study, entitled “Microcredit and Rural Household Outcomes: Evidence from Habru Woreda’s Smallholder Farmers, Ethiopia,” involved human participants and adhered to strict ethical guidelines. A formal written consent was obtained from all participants after providing them with detailed information about the study, ensuring their right to withdraw at any time. Participant confidentiality and anonymity were rigorously maintained, with personal identifiers anonymized and data securely stored. The study received approval from the Institutional Review Board (IRB) committee of college of Business and Economics at Woldia University on January 12, 2023 (Ref: CBE/RCSTT/187/2023). Field research was conducted respectfully, considering the cultural and social context of Habru Woreda, and aimed to benefit the community by enhancing understanding of food security and asset-building strategies. Data availability statement Underlying data Zenodo: Underlying data for ‘Micro-Loans, Macro-Impacts: Examining the Reverberating Gains for Habru Woreda’s Small-Scale Agrarian Households, https://doi.org/10.5281/zenodo.13787722 ( Tefera, A. B., et al., 2024a ) This project contains the following underlying data: • changed data.dta Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0). Extended data Zenodo: Micro-Loans, Macro-Impacts: Examining the Reverberating Gains for Habru Woreda’s Small-Scale Agrarian Households. The appendixes that give an additional clarification for the discussion of the results is available at Zenodo: https://doi.org/10.5281/zenodo.13829443 ( Tefera, A. B., et al., 2024b ) This project contains the following extended data: • Supplementary Materials.docx Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0). Reporting guidelines Zenodo: PRISMA Checklist for ‘Micro-Loans, Macro-Impacts: Examining the Reverberating Gains for Habru Woreda’s Small-Scale Agrarian Households, https://zenodo.org/records/13833908 Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0). Software availability Available from: https://www.stata.com/stata14/ License: ©Copyright 1996–2024 StataCorp LLC References Ahamad S, Bhuiyan AB, Solaiman M, et al. : The Impact of Microfinance Programs on Borrowers’ Asset Accumulation_ An Empirical Study in Bangladesh.pdf. Journal of Asian Finance, Economics and Busines. 2021; 8 (5): 1147–1154. Publisher Full Text Alemayehu T: Determinants of Rural Households’ Participation in Microfinance Program: The Case of Omo Microfinance Institution, Sodo Woreda, Southern Nations Nationalities, and Peoples Regional State, Ethiopia. Journal of Economics and Sustainable Development. 2020; 11 (1): 23–41. 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Reddall RG, Miller RL: Instructor’s Manual to Accompany Miller: Intermediate Microeconomics: Theory, Issues, and Applications. McGraw-Hill; 1977. Shafique O, Khan RMN: 1367-Article Text-4180-1-10-20201010.pdf.2020. Singh I, Squire L, Strauss J: Agricultural household models: Extensions, applications, and policy. 1986. Siyoum AD, Hilhorst D, Pankhurst A: The differential impact of microcredit on rural livelihoods: Case study from Ethiopia. International Journal of Development and Sustainability. 2012; 1 (3): 1–19. Tefera AB, Awoke AJ, Ayinewa YM: Micro-Loans, Macro-Impacts: Examining the Reverberating Gains for Habru Woreda’s Small-Scale Agrarian Households. [Data set]. Zenodo. 2024a. Publisher Full Text Tefera AB, Awoke AJ, Ayinewa YM: Micro-Loans, Macro-Impacts: Examining the Reverberating Gains for Habru Woreda’s Small-Scale Agrarian Households. Zenodo. 2024b. 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Publisher Full Text Reference Source Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 07 Oct 2024 ADD YOUR COMMENT Comment Author details Author details 1 Department of Economics, Woldia University, Weldiya, Amhara, Ethiopia Agerwork Jejaw Awoke Roles: Conceptualization, Methodology, Validation, Writing – Review & Editing Abate Belaye Tefera Roles: Formal Analysis, Investigation, Software, Writing – Original Draft Preparation Yigermal Maru Ayinewa Roles: Data Curation, Validation, 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 (2) version 2 Revised Published: 09 Oct 2025, 13:1143 https://doi.org/10.12688/f1000research.156802.2 version 1 Published: 07 Oct 2024, 13:1143 https://doi.org/10.12688/f1000research.156802.1 Copyright © 2025 Jejaw Awoke A et al . 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 Jejaw Awoke A, Belaye Tefera A and Maru Ayinewa Y. Microcredit and Rural Household Outcomes: Evidence from Habru Woreda’s Smallholder Farmers, Ethiopia [version 2; peer review: 1 approved with reservations] . F1000Research 2025, 13 :1143 ( https://doi.org/10.12688/f1000research.156802.2 ) 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 2 VERSION 2 PUBLISHED 09 Oct 2025 Revised Views 0 Cite How to cite this report: Tundui CS. Reviewer Report For: Microcredit and Rural Household Outcomes: Evidence from Habru Woreda’s Smallholder Farmers, Ethiopia [version 2; peer review: 1 approved with reservations] . F1000Research 2025, 13 :1143 ( https://doi.org/10.5256/f1000research.189171.r426871 ) The direct URL for this report is: https://f1000research.com/articles/13-1143/v2#referee-response-426871 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 24 Dec 2025 Charles Stephen Tundui , Mzumbe University, Morogoro, Tanzania Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.189171.r426871 Dear Editor, I appreciate the opportunity to review the manuscript titled “Microcredit and Rural Household Outcomes: Evidence from Habru Woreda’s Smallholder Farmers. Following a detailed assessment, I regret to report that the manuscript requires major revisions before it can ... Continue reading READ ALL Dear Editor, I appreciate the opportunity to review the manuscript titled “Microcredit and Rural Household Outcomes: Evidence from Habru Woreda’s Smallholder Farmers. Following a detailed assessment, I regret to report that the manuscript requires major revisions before it can be considered for indexing. The paper examines the impact of microcredit on household welfare outcomes using logistic regression and propensity score matching (PSM). However, the study suffers from conceptual, methodological, analytical, and interpretative shortcomings that significantly undermine its rigour and contribution. Overall, the manuscript does not currently meet the methodological and analytical standards required for indexing. Substantial revision would be necessary to enhance clarity, conceptual framing, analytical robustness, and alignment across sections. Comments are detailed below. Sincerely, Reviewer 1. Abstract This is poorly written. The authors do not explain the problem they are investigating or provide a clear purpose for the study. 2. Introduction (i) Title and Alignment with Content The title, "Microcredit and Rural Household Outcomes: Evidence from Habru Woreda’s Smallholder Farmers, implies a broad exploration of rural household outcomes (e.g., income, food security, overall welfare). However, the study examines only two specific outcomes: expenditure and asset accumulation. Moreover, methodologically, the use of the broad term rural household outcomes seems an overstatement. This is because the key analytical tool, Propensity Score Matching (PSM), is applied narrowly to assess the impact on only two specific welfare variables. Similarly, although a significant part of the paper discusses the determinants of microcredit access (i.e., who receives microcredit), this crucial analytical aspect is not mentioned in the title at all. (ii) The problem The problem statement is unclear. It does not sufficiently connect to the specific challenges of the study area (Habru) and instead depends on broader national literature. Similarly, it also fails to clearly link the problem with the particular variables being examined, such as why asset depletion, expenditure increases, or access factors are relevant to household welfare. 3. Study Objective The objective is “to investigate the impact of microcredit utilisation on the spending and asset accumulation… . Although the entire section is dedicated to it, it does not specify the analysis of determinants. “Utilisation” is not explained in this study. The authors should distinguish between microcredit access and utilisation. Utilization could also mean how the credit is being used (utilization patterns). Moreover, the objective is also inconsistent with the abstract. The abstract mentions logistic regression results as determinants; however, this is not part of the stated objectives. 4. Literature Review Theoretical Review While the theoretical review offers a list of general statements about credit markets, it lacks a clear theoretical model of how microcredit influences welfare and a conceptual pathway linking credit to investment, assets, or expenditures. The section appears more descriptive, failing to identify contradictions or theoretical debates that justify the study. Empirical Review This section is very limited and lacks focus. The authors summarise several studies that appear unrelated (from Ethiopia, Nigeria, Vietnam, and Bangladesh) without linking them to the outcome variables of the current research or to the specific gaps in Habru. Precisely, the authors failed to critically assess the limitations of previous studies, including inconsistent methods, inconsistent findings, and the rationale for selecting expenditure and asset accumulation as variables of interest. 5. Methodology This section is wanting. The section is overly descriptive, and the geographical details appear irrelevant and fail to enhance methodological rigour. Sampling and Data Collection The authors suggest selecting six kebeles, but earlier state seven . …. In the first stage, systematic random sampling was used to select seven (6) out of 35 rural kebeles in the woreda . Which one is correct? Moreover, while the authors used a three-stage sampling design, they don’t provide adequate justification for the need for multi-stage sampling. Also, the units of analysis and enquiry are not clearly explained. Sample size: The authors used the Cochran formula, which is generally acceptable. The formula is appropriate for infinite or large populations. Given that the population size is large and finite (49,880), an uncorrected formula is not suitable. The authors should have used the Finite Population Correction (FPC) formula instead. Measurement and operationalization of study variables First, the authors do not justify the choice of the study variables. This choice should be informed by the literature review, not otherwise. Second, several variables (e.g., marital status, education) are poorly operationalised. Marital status and education are coded as categorical but are inconsistently used. This violates the assumptions. Similarly, the authors do not provide descriptions of how assets were valued (market prices or replacement cost?) or the justification for using annual expenditure as a welfare proxy. Data analysis The study used logistic regression and PSM for data analysis. However, the authors do not justify their use. Moreover, while the objective focuses on welfare impact, the authors focused more on the determinants of access, suggesting that the model is misaligned with the study objective. Multicollinearity tests are also missing. On the other hand, although the study also used PSM, the PSM analysis failed to adequately test and report covariate balance and omitted standardised quality indicators and statistical tests. There was non-reporting of common support issues (six treated observations were dropped), further undermining the validity. Furthermore, the authors have not conducted a robust sensitivity analysis (for example, Rosenbaum bounds) to assess for hidden bias/potential impact of unobserved confounders. 6. Findings The analysis of respondents' characteristics is shallow and lacks theoretical grounding, failing to link observed characteristics to expected outcomes. Descriptive findings are also missing. Also, the interpretation of the microcredit determinant coefficients lacks depth. Moreover, the authors incorrectly infer causation from cross-sectional findings. Cross-sectional data only establishes correlation, not a causal relationship. Likewise, the explanation of the Average Treatment Effect on the Treated (ATT) results is inadequate. While observed outcomes, such as increases in expenditure and decreases in assets, could result from various underlying economic dynamics (e.g., risk-taking or prudent investment decisions), the authors offer only a limited and simplistic interpretation of these results Lack of Robustness Checks: The study lacks adequate robustness checks. Although kernel matching was employed, the authors ignored other standard matching algorithms, such as nearest neighbour or radius matching. Implementing these additional methods would have helped improve the precision and credibility of their results by verifying consistency across different specifications. 7. Discussion The discussion is very limited and largely restates findings without critical interpretation. The authors do not link their discussion to broader literature. For example, they don’t compare the contradiction between expenditure and asset impacts as observed in previous research. The effect of microcredit access on household welfare is partially or minimally discussed. The discussion and interpretation of findings also lack theoretical grounding. 8. Policy Implications The recommendations are weak. Several suggestions, such as those concerning lower interest rates and financial literacy programs, appear generic and are not empirically derived from the study's findings. Furthermore, the discussion offers no actionable suggestions for the ACSI (e.g., how to redesign repayment schedules or the specific type of financial literacy needed). Importantly, the policy implications ignore major findings, such as the decline in assets and fail to differentiate between short-term and long-term outcomes, treating all results uniformly without considering their causality or time horizons. 9. References Some in-text citations that do not appear in the reference list, for example, Boere et al. (2016) Some references appear in the reference list but are not cited anywhere, for example, Reddall & Miller (1977). Some in-text citations do not match reference entries, eg. Neglo et al. (2021) Some references in the list are incomplete, incorrect, or poorly formatted Some references are duplicate. 10. Overall Assessment The article attempts to evaluate the impact of microcredit on rural household outcomes using logistic regression and PSM, however, it contains major conceptual, methodological, analytical, and interpretive weaknesses. 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? No If applicable, is the statistical analysis and its interpretation appropriate? No Are all the source data underlying the results available to ensure full reproducibility? No Are the conclusions drawn adequately supported by the results? Partly Competing Interests: No competing interests were disclosed. Reviewer Expertise: Microfinance, Gender and Entrepreneurship, Project Planning and Management, Strategic Planning 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 Tundui CS. Reviewer Report For: Microcredit and Rural Household Outcomes: Evidence from Habru Woreda’s Smallholder Farmers, Ethiopia [version 2; peer review: 1 approved with reservations] . F1000Research 2025, 13 :1143 ( https://doi.org/10.5256/f1000research.189171.r426871 ) The direct URL for this report is: https://f1000research.com/articles/13-1143/v2#referee-response-426871 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 2 VERSION 2 PUBLISHED 07 Oct 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 Version 2 (revision) 09 Oct 25 read Version 1 07 Oct 24 Charles Stephen Tundui , Mzumbe University, Morogoro, Tanzania 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 © 2025 Tundui C. 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. 24 Dec 2025 | for Version 2 Charles Stephen Tundui , Mzumbe University, Morogoro, Tanzania 0 Views copyright © 2025 Tundui C. 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 Dear Editor, I appreciate the opportunity to review the manuscript titled “Microcredit and Rural Household Outcomes: Evidence from Habru Woreda’s Smallholder Farmers. Following a detailed assessment, I regret to report that the manuscript requires major revisions before it can be considered for indexing. The paper examines the impact of microcredit on household welfare outcomes using logistic regression and propensity score matching (PSM). However, the study suffers from conceptual, methodological, analytical, and interpretative shortcomings that significantly undermine its rigour and contribution. Overall, the manuscript does not currently meet the methodological and analytical standards required for indexing. Substantial revision would be necessary to enhance clarity, conceptual framing, analytical robustness, and alignment across sections. Comments are detailed below. Sincerely, Reviewer 1. Abstract This is poorly written. The authors do not explain the problem they are investigating or provide a clear purpose for the study. 2. Introduction (i) Title and Alignment with Content The title, "Microcredit and Rural Household Outcomes: Evidence from Habru Woreda’s Smallholder Farmers, implies a broad exploration of rural household outcomes (e.g., income, food security, overall welfare). However, the study examines only two specific outcomes: expenditure and asset accumulation. Moreover, methodologically, the use of the broad term rural household outcomes seems an overstatement. This is because the key analytical tool, Propensity Score Matching (PSM), is applied narrowly to assess the impact on only two specific welfare variables. Similarly, although a significant part of the paper discusses the determinants of microcredit access (i.e., who receives microcredit), this crucial analytical aspect is not mentioned in the title at all. (ii) The problem The problem statement is unclear. It does not sufficiently connect to the specific challenges of the study area (Habru) and instead depends on broader national literature. Similarly, it also fails to clearly link the problem with the particular variables being examined, such as why asset depletion, expenditure increases, or access factors are relevant to household welfare. 3. Study Objective The objective is “to investigate the impact of microcredit utilisation on the spending and asset accumulation… . Although the entire section is dedicated to it, it does not specify the analysis of determinants. “Utilisation” is not explained in this study. The authors should distinguish between microcredit access and utilisation. Utilization could also mean how the credit is being used (utilization patterns). Moreover, the objective is also inconsistent with the abstract. The abstract mentions logistic regression results as determinants; however, this is not part of the stated objectives. 4. Literature Review Theoretical Review While the theoretical review offers a list of general statements about credit markets, it lacks a clear theoretical model of how microcredit influences welfare and a conceptual pathway linking credit to investment, assets, or expenditures. The section appears more descriptive, failing to identify contradictions or theoretical debates that justify the study. Empirical Review This section is very limited and lacks focus. The authors summarise several studies that appear unrelated (from Ethiopia, Nigeria, Vietnam, and Bangladesh) without linking them to the outcome variables of the current research or to the specific gaps in Habru. Precisely, the authors failed to critically assess the limitations of previous studies, including inconsistent methods, inconsistent findings, and the rationale for selecting expenditure and asset accumulation as variables of interest. 5. Methodology This section is wanting. The section is overly descriptive, and the geographical details appear irrelevant and fail to enhance methodological rigour. Sampling and Data Collection The authors suggest selecting six kebeles, but earlier state seven . …. In the first stage, systematic random sampling was used to select seven (6) out of 35 rural kebeles in the woreda . Which one is correct? Moreover, while the authors used a three-stage sampling design, they don’t provide adequate justification for the need for multi-stage sampling. Also, the units of analysis and enquiry are not clearly explained. Sample size: The authors used the Cochran formula, which is generally acceptable. The formula is appropriate for infinite or large populations. Given that the population size is large and finite (49,880), an uncorrected formula is not suitable. The authors should have used the Finite Population Correction (FPC) formula instead. Measurement and operationalization of study variables First, the authors do not justify the choice of the study variables. This choice should be informed by the literature review, not otherwise. Second, several variables (e.g., marital status, education) are poorly operationalised. Marital status and education are coded as categorical but are inconsistently used. This violates the assumptions. Similarly, the authors do not provide descriptions of how assets were valued (market prices or replacement cost?) or the justification for using annual expenditure as a welfare proxy. Data analysis The study used logistic regression and PSM for data analysis. However, the authors do not justify their use. Moreover, while the objective focuses on welfare impact, the authors focused more on the determinants of access, suggesting that the model is misaligned with the study objective. Multicollinearity tests are also missing. On the other hand, although the study also used PSM, the PSM analysis failed to adequately test and report covariate balance and omitted standardised quality indicators and statistical tests. There was non-reporting of common support issues (six treated observations were dropped), further undermining the validity. Furthermore, the authors have not conducted a robust sensitivity analysis (for example, Rosenbaum bounds) to assess for hidden bias/potential impact of unobserved confounders. 6. Findings The analysis of respondents' characteristics is shallow and lacks theoretical grounding, failing to link observed characteristics to expected outcomes. Descriptive findings are also missing. Also, the interpretation of the microcredit determinant coefficients lacks depth. Moreover, the authors incorrectly infer causation from cross-sectional findings. Cross-sectional data only establishes correlation, not a causal relationship. Likewise, the explanation of the Average Treatment Effect on the Treated (ATT) results is inadequate. While observed outcomes, such as increases in expenditure and decreases in assets, could result from various underlying economic dynamics (e.g., risk-taking or prudent investment decisions), the authors offer only a limited and simplistic interpretation of these results Lack of Robustness Checks: The study lacks adequate robustness checks. Although kernel matching was employed, the authors ignored other standard matching algorithms, such as nearest neighbour or radius matching. Implementing these additional methods would have helped improve the precision and credibility of their results by verifying consistency across different specifications. 7. Discussion The discussion is very limited and largely restates findings without critical interpretation. The authors do not link their discussion to broader literature. For example, they don’t compare the contradiction between expenditure and asset impacts as observed in previous research. The effect of microcredit access on household welfare is partially or minimally discussed. The discussion and interpretation of findings also lack theoretical grounding. 8. Policy Implications The recommendations are weak. Several suggestions, such as those concerning lower interest rates and financial literacy programs, appear generic and are not empirically derived from the study's findings. Furthermore, the discussion offers no actionable suggestions for the ACSI (e.g., how to redesign repayment schedules or the specific type of financial literacy needed). Importantly, the policy implications ignore major findings, such as the decline in assets and fail to differentiate between short-term and long-term outcomes, treating all results uniformly without considering their causality or time horizons. 9. References Some in-text citations that do not appear in the reference list, for example, Boere et al. (2016) Some references appear in the reference list but are not cited anywhere, for example, Reddall & Miller (1977). Some in-text citations do not match reference entries, eg. Neglo et al. (2021) Some references in the list are incomplete, incorrect, or poorly formatted Some references are duplicate. 10. Overall Assessment The article attempts to evaluate the impact of microcredit on rural household outcomes using logistic regression and PSM, however, it contains major conceptual, methodological, analytical, and interpretive weaknesses. 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? No If applicable, is the statistical analysis and its interpretation appropriate? No Are all the source data underlying the results available to ensure full reproducibility? No Are the conclusions drawn adequately supported by the results? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise Microfinance, Gender and Entrepreneurship, Project Planning and Management, Strategic Planning 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) Tundui CS. Peer Review Report For: Microcredit and Rural Household Outcomes: Evidence from Habru Woreda’s Smallholder Farmers, Ethiopia [version 2; peer review: 1 approved with reservations] . F1000Research 2025, 13 :1143 ( https://doi.org/10.5256/f1000research.189171.r426871) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. 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