“Analyzing the determinants of rural household welfare in Ethiopia: Does agricultural technology adoption matters”: Using seemingly unrelated multi-variate probit model”

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The agricultural sector plays a pivotal role in Ethiopia’s economy, contributing significantly to income, consumption, and employment. This research aims to analyze how adopting agricultural technologies such as improved seeds, fertilizers, and irrigation affects poverty and food insecurity among rural households. The research examined poverty and food insecurity levels in Ethiopia and investigates the impact of adopting agricultural technologies on rural household welfare. Methods We used both Descriptive and econometric methods, in which descriptive analysis has been used to assess poverty and food insecurity levels, and a seemingly unrelated bivariate and multivariate probit model applied to analyze the effect of agricultural technology adoption. Result From descriptive summary, it is noted that out of a total sample of the study, 47.61% (2322 out of 4877) are adopting agricultural technologies in many forms more than 70% of female-headed households are food secured and non-poor and 68% of male-headed households are food secured and nun poor. The findings from regression model indicate that access to irrigation, improved seeds, and fertilizers significantly reduces the likelihood of poverty and food insecurity among rural households. Conclusion Based on these results, policy recommendations include promoting agricultural technologies, enhancing access to credit, investing in agricultural extension services, diversifying livelihoods, improving education, and implementing targeted poverty alleviation programs. Further research is encouraged to explore the reasons behind insignificant variables and to identify additional factors affecting rural household welfare in Ethiopia. " } { "@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/14-102", "name": "“Analyzing the determinants of rural household welfare in Ethiopia:..." } } ] } Home Browse “Analyzing the determinants of rural household welfare in Ethiopia:... ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article Abate TW, Sheferaw HE, Sitotaw KW and Mulaw SG. “Analyzing the determinants of rural household welfare in Ethiopia: Does agricultural technology adoption matters”: Using seemingly unrelated multi-variate probit model” [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :102 ( https://doi.org/10.12688/f1000research.159875.1 ) 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 “Analyzing the determinants of rural household welfare in Ethiopia: Does agricultural technology adoption matters”: Using seemingly unrelated multi-variate probit model” [version 1; peer review: 2 approved with reservations] Tadesse Wudu Abate https://orcid.org/0000-0001-5048-6457 1 , Henok Ezezew Sheferaw 1 , Kalid Wendimnew Sitotaw 2 , Sisay Genanu Mulaw 1 Tadesse Wudu Abate https://orcid.org/0000-0001-5048-6457 1 , Henok Ezezew Sheferaw 1 , Kalid Wendimnew Sitotaw 2 , Sisay Genanu Mulaw 1 PUBLISHED 17 Jan 2025 Author details Author details 1 Economics, Woldia University, Weldiya, Amhara, Ethiopia 2 Economics, Dilla University, Dilla, Southern Nations, Nationalities, and People's Region, Ethiopia Tadesse Wudu Abate Roles: Conceptualization, Formal Analysis, Methodology Henok Ezezew Sheferaw Roles: Formal Analysis, Investigation, Methodology, Software Kalid Wendimnew Sitotaw Roles: Conceptualization, Data Curation, Software, Writing – Original Draft Preparation Sisay Genanu Mulaw Roles: Formal Analysis, Writing – Original Draft Preparation, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS This article is included in the Agriculture, Food and Nutrition gateway. Abstract Background This study analyzed the nexus between rural household welfare and agricultural technology adoption in Ethiopia using secondary data from the Ethiopian Socioeconomic Survey (ESS). The agricultural sector plays a pivotal role in Ethiopia’s economy, contributing significantly to income, consumption, and employment. This research aims to analyze how adopting agricultural technologies such as improved seeds, fertilizers, and irrigation affects poverty and food insecurity among rural households. The research examined poverty and food insecurity levels in Ethiopia and investigates the impact of adopting agricultural technologies on rural household welfare. Methods We used both Descriptive and econometric methods, in which descriptive analysis has been used to assess poverty and food insecurity levels, and a seemingly unrelated bivariate and multivariate probit model applied to analyze the effect of agricultural technology adoption. Result From descriptive summary, it is noted that out of a total sample of the study, 47.61% (2322 out of 4877) are adopting agricultural technologies in many forms more than 70% of female-headed households are food secured and non-poor and 68% of male-headed households are food secured and nun poor. The findings from regression model indicate that access to irrigation, improved seeds, and fertilizers significantly reduces the likelihood of poverty and food insecurity among rural households. Conclusion Based on these results, policy recommendations include promoting agricultural technologies, enhancing access to credit, investing in agricultural extension services, diversifying livelihoods, improving education, and implementing targeted poverty alleviation programs. Further research is encouraged to explore the reasons behind insignificant variables and to identify additional factors affecting rural household welfare in Ethiopia. READ ALL READ LESS Keywords Agricultural technology adoption, Gender Inequality, Household welfare, Multi-variate probit model, seemingly unrelated regression Corresponding Author(s) Tadesse Wudu Abate ( [email protected] ) Close Corresponding author: Tadesse Wudu Abate Competing interests: No competing interests were disclosed. Grant information: The author(s) declared that no grants were involved in supporting this work. Copyright: © 2025 Abate TW 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: Abate TW, Sheferaw HE, Sitotaw KW and Mulaw SG. “Analyzing the determinants of rural household welfare in Ethiopia: Does agricultural technology adoption matters”: Using seemingly unrelated multi-variate probit model” [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :102 ( https://doi.org/10.12688/f1000research.159875.1 ) First published: 17 Jan 2025, 14 :102 ( https://doi.org/10.12688/f1000research.159875.1 ) Latest published: 17 Jan 2025, 14 :102 ( https://doi.org/10.12688/f1000research.159875.1 ) 1. Introduction Agricultural sector had a paramount contribution and dominant role to the growth and development of many countries in the world ( Nonvide, 2024 ) since it constitutes the highest share of income, consumption and employment generation ( Pan et al., 2020 ). More specifically, in agricultural sector-dominated economies of least developed countries like those in sub Saharan Africa, their success and failure in bringing economic development are highly determined by their performance in agricultural sector ( Adams & Jumpah, 2021 ). This is because of their high dependence on this sector, with more than 80% of their GDP share and employment creation coming form the agricultural sector ( Wordofa & Sassi, 2020 ; Gelata, Han, & Limo, 2024 ; Habtewold, 2021 ; Shikur, 2023 ). Introducing improved innovations and technology in agriculture is an important way to increase the productivity of Africa’s smallholder farmers, promoting economic growth and the well-being of millions of poor households ( Adams & Jumpah, 2021 ). Unlike in many other parts of the world, many governments in Africa do not collect, research, and report critical data in the appropriate format and within the required time frames ( Ali, Menza, Hagos, & Haileslassie, 2023 ). Without basic, descriptive information about who adopts improved agricultural technologies and who does not, it is difficult to develop effective policies to improve agricultural productivity ( Asrat, Anteneh, Adem, & Berhanie, 2022 ). Adopting modern agricultural technology has a significant impact on improving the welfare of a given society by supporting the fundamental need for food and feeding an ever-growing population ( Korir et al., 2023 ). Agricultural productivity can be improved when agricultural technologies are adopted by farmers, thereby reducing the poverty status of household farmers by increasing their income and consumption ( Mutungi et al., 2023 ). Using this idea as starting point, many researchers have investigated the impact of agricultural technology adoption on the welfare of societies in different countries, including Ethiopia ( Tesfay, 2023 ; Gelata et al., 2024 ). They have conducted research to assess the nexus between household welfare and agricultural technology adoption and noted that rural household’s welfare in terms of access to food, income, and expenditure is significantly increased with adopting modern agricultural technologies ( Zegeye, Fikire, & Assefa, 2022 ). Ethiopia, as a key constituent of the burgeoning economies within Sub-Saharan Africa, exhibits a profound economic reliance on agriculture, with over 70% of its export revenues, 80% of its GDP, and 60% of employment originating from this sector ( Legesse, Srivastava, Kuhn, & Gaiser, 2019 ). A substantial portion of the population is still tethered to crop cultivation and pastoral activities, despite the pervasive use of antiquated production methods ( Wassihun, Nega, Kebede, Fenta, & Ayalew, 2022 ). To elevate the living standards and welfare of Ethiopian citizens, it is crucial to supplant these obsolete agricultural practices with state-of-the-art technological innovations, such as enhanced seed varieties, fertilizers, modern irrigation systems, and agrochemicals to ( Gebre, Amekawa, & Rahut, 2023 ). According to Abate (2024a) , the alleviation of poverty and the achievement of sustained economic progress are contingent upon the transformation of the agricultural sector through the embracement of modern agricultural technologies. This sector’s pivotal role in rural livelihoods, in a nation where poverty is endemic, positions it at the epicenter of development strategies and policy interventions ( Biru, Zeller, & Loos, 2020 ). In this vein, various investigations have probed the effects of rural technology adoption on food insecurity, agricultural efficiency, and poverty reduction in Ethiopia. For instance ( Ayenew, Lakew, & Kristos, 2020 ), applied propensity score matching (PSM) techniques to assess the impact of agricultural technology adoption on household welfare in the Amhara region. Their analysis revealed that the adoption of agricultural technologies substantially augmented household consumption, thereby enhancing welfare by mitigating poverty. In a similar vein ( Bekana & Takele, 2020 ), employed PSM methods to examine the repercussions of adopting agricultural technologies on income inequality, finding that while innovations such as chemical fertilizers and improved seed varieties bolstered household income, they simultaneously exacerbated disparities in income distribution. Belay and Mengiste (2023) explored the effect of adopting a combination of complementary technologies on smallholder consumption, poverty, and vulnerability, determining that technology adoption markedly amplified consumption expenditure, with the most pronounced benefits occurring when multiple complementary technologies were integrated. Atinafu, Lejebo, and Alemu (2022) investigated the influence of introducing improved wheat varieties on the welfare of 4,444 households in Ethiopia. Their study found that factors such as credit availability, agricultural extension services, soil fertility, land size, off-farm employment, household head age, proximity to input markets, and farming experience were pivotal in shaping adoption decisions and intensity. Furthermore, BELETE (2023) reported that food insecurity in Sub-Saharan Africa and Eastern Africa stood at 26% and 28%, respectively, based on 2016 data. The trajectory toward achieving the 2030 goal of eradicating hunger is beset by numerous challenges. In alignment with ( Gebre et al., 2023 ), Ethiopia is ranked among the African nations—such as South Sudan, Nigeria, Somalia, and the Central African Republic—experiencing the most acute levels of hunger in the reporting period. 1.1 Motivation of the study As elucidated in numerous scholarly investigations, Ethiopian citizens continue to endure diminished welfare, as evidenced by indicators such as livelihood conditions, food security, and poverty levels ( Sebsibie, Asmare, & Endalkachew, 2014 ). A prime example of this is the elevated incidence of poverty and household food insecurity in rural Ethiopia, which constitutes a formidable obstacle in the pursuit of sustained economic growth and development ( Zegeye et al., 2022 ). The interim report further underscores that rural poverty remains disproportionately higher than its urban counterpart across all temporal intervals ( Abate, 2024a ). Notably, the nation-wide reduction in poverty is predominantly attributed to the alleviation of urban poverty, although both rural and urban areas have witnessed a general decline in poverty (measured in both incidence and depth). Nevertheless, the income distribution among the rural poor has shown negligible improvement ( Ayenew et al., 2020 ). In contrast, the prevailing drivers of hunger and impoverished living conditions in Ethiopia are widely considered to be a confluence of factors, including the widespread devastation wrought by the COVID-19 pandemic, civil unrest, internal strife, and constrained agricultural productivity. These elements are encompassed within the scope of this study ( Bekana & Takele, 2020 ). To ameliorate the prevailing low welfare and living standards, it is imperative to analyze both the extent and the determinants of welfare, alongside its intricate relationship with the adoption of agricultural technology. Thus, the overarching aim of the present study is to explore the nexus between rural household welfare and the uptake of agricultural technology within the Ethiopian context. Specifically, the study seeks to probe the interdependencies between various welfare metrics and examine the influence of agricultural technology adoption on rural household welfare. This research contributes to the extant body of literature in three distinct ways. First, it utilizes the seemingly unrelated multivariate probit model to investigate the impact of agricultural technology adoption on household welfare ( Atinafu et al., 2022 ). This methodological innovation provides new insights by simultaneously considering three welfare dimensions uni-dimensional poverty, multidimensional poverty, and food insecurity rather than relying on a singular measure, as is commonly the case in much of the existing research ( Mulugeta Habtewold & Heshmati, 2023 ). Secondly, this study considers the effect of recurrent internal conflicts and wars on the welfare of households, which was not previously considered. Thirdly, this study analyzes the interdependence of rural household welfare measurements instead of using only one proxy variable to indicate the welfare of a society. This means this study is the first to check whether households facing multidimensional poverty are also facing food insecurity. The rest of the paper follows this structure, and the next section presents a concise review of the theoretical literature on the factors that affect rural household welfare, with special emphasis on agricultural technology adoption. It also includes empirical literature on the determinants of households’ welfare, both in Ethiopia and globally. Section III outlines the methodology, followed by a discussion of the estimation results in Section IV. The final section provides conclusions and recommendations. 2. Literature review Theoretically, the adoption of agricultural technology is expected to enhance household welfare by boosting agricultural productivity and reducing poverty ( Hawas & Degaga, 2023 ). However, the actual impact of adopting new technology largely depends on whether farmers decide to embrace it and, if they do, how quickly they adopt it. The rate of adoption is typically measured by how long it takes for a certain proportion of the population to incorporate the innovation. Additionally, innovations that are perceived as having a substantial relative advantage, high compatibility, low complexity, ease of repair, and clear visibility tend to experience faster adoption ( Korir et al., 2023 ; Legesse et al., 2019 ). Adoption decisions are often framed as the result of an optimization process, where individuals seek to maximize expected returns ( Massresha, Lema, Neway, & Degu, 2021 ). These returns are influenced by factors such as land allocation, the productivity of the technology, input costs, and output prices ( Merga, Sileshi, & Zeleke, 2023 ). The positive effects of agricultural technology on economic growth, in terms of increased productivity and poverty reduction, will only be fully realized if the technology is widely adopted and consistently used ( Mekonnen, 2017 ; Merga et al., 2023 ). The diffusion of innovation transpires through a succession of individual determinations concerning the utilization of novel technologies ( Mogess & Ayen, 2023 ). These choices are frequently made by juxtaposing the ambiguous advantages of a new invention against the uncertain costs of its deployment ( Mujeyi, Mudhara, & Mutenje, 2021 ). When farmers contemplate the adoption of a specific technology, they are compelled to assess its potential ramifications against its economic, social, and technical viability ( Mulugeta Habtewold & Heshmati, 2023 ). Although numerous studies have established that access to credit exerts a positive influence on household well-being ( Mutungi et al., 2023 ), certain research contends that credit may have an adverse effect on household happiness ( Nonvide, 2024 ). Conversely, research by Nsabimana and Adom (2024) and Ogundari and Bolarinwa (2019) has revealed that loans tend to diminish household consumption. Notably, Pan et al. (2020) found a 15 percent reduction in both permanent and non-permanent household consumption within the treatment group in comparison to the control group. Furthermore, Shikur (2020 , 2023) explored the impact of informal credit on poverty and inequality, concluding that credit fosters poverty alleviation. The extant literature substantiates the assertion that technology adoption engenders significant enhancements in productivity, poverty reduction, and overall living standards globally ( Shita, Kumar, & Singh, 2023 ). Similarly, empirical studies within Ethiopia have demonstrated that the adoption of advanced agricultural technologies enhances productivity and societal welfare ( Shokati Amghani, Mojtahedi, & Savari, 2023 ), while simultaneously bolstering food security for smallholder farmers ( Sinha & Nag, 2023 ). According to Tefera, Haji, and Ketema (2023) , the exclusive utilization of enhanced seed varieties and synthetic fertilizers has the potential to augment crop productivity in Ethiopia by 7.38% and 6.32% annually, respectively. Notwithstanding the escalating adoption rates and their salutary effects on agricultural output and productivity, a significant proportion of rural households in Ethiopia continue to subsist under precarious living conditions. Presently, wheat cultivation occupies roughly 16% of the nation’s total arable grain production area, with approximately 36% of grain-producing households being directly dependent on wheat farming. Despite these figures, the national average wheat yield remains suboptimal at 1.83 tons per hectare ( Tesfay, 2023 ), a marginal increase from 2.7 tons per hectare in 2018. Moreover, wheat productivity is projected to rise to 2.77 tons per hectare during the 2019/2020 harvest season, expanding the total cultivated area to 1.66 million hectares. However, Ethiopia remains incapable of fulfilling its domestic wheat requirements, with annual production estimated at approximately 4.6 million tons, while consumption substantially exceeds this output, reaching approximately 6.3 million tons per annum ( Tesfaye & Tirivayi, 2018 ; Tilahun, Bantider, & Yayeh, 2023 ). In addition to the persistently low yield levels, the demand for wheat continues to escalate in both rural and urban sectors of Ethiopia’s economy ( Tilaye, Delele, & Ogeto, 2023 ), intensifying the disparity between supply and demand, thereby exacerbating the prevailing poverty endemic to the nation. According to Tolola (2023) , many countries have contact builders such as Model Builders in Ethiopia and Master or Progressive Builders ( Verkaart, Munyua, Mausch, & Michler, 2017 ) in Malawi to assist government advisors in technology transfer and information provision. There is a long tradition of doing so ( Wordofa et al., 2021 ). According to Wordofa and Sassi (2020) and Wossen et al. (2017) , model farmers represent a distinct cohort characterized by relatively superior resource endowment, a proclivity for early adoption of agricultural innovations, and the capacity to implement a substantial portion typically at least 70% of the technology packages promoted by agricultural extension systems. These farmers are operationalized as those who not only incorporate advanced farming systems but also meet specific definitional thresholds. Their influence within the agricultural milieu is acknowledged by development practitioners and grassroots workers, who regard them as key drivers of agricultural modernization. The discourse surrounding the impact of credit on household welfare is multifaceted. While savings are conceptualized as a mechanism for ensuring future economic stability, credit is positioned as an instrument of financial liberation, providing a conduit for households to transcend immediate fiscal constraints and, thereby, preserve or augment their current well-being ( Ali et al., 2023 ). Creditworthiness is contingent upon mutual trust from both the creditor and debtor’s vantage points ( Bekana & Takele, 2020 ). The creditor’s utility is influenced by the propensity to defer current consumption in favor of future savings, while the debtor’s welfare is shaped by the trade-off between immediate financial relief and the eventual burden of future obligations, including interest payments. Should the arrangement falter, the debtor bears the onus of repayment. Although a plethora of studies assert that credit exerts a salutary effect on household well-being, there exists a corpus of literature that posits a deleterious impact of credit on household felicity. On the negative side, research by Atinafu et al. (2022) identified a deleterious effect of loans on household consumption. Specifically, it revealed a 15% reduction in the consumption of both durable and non-durable goods among 4,444 households in the treatment group relative to the control cohort. Similarly, Atumo and Samago (2023) and Ayenew et al. (2020) explored the ramifications of informal credit on poverty alleviation and inequality, concluding that credit exerts a favorable influence on poverty reduction. Their findings showed that 4,444 impoverished regions predominantly accessed loans from informal rather than formal institutions, with a marked decline in borrower poverty rates by 8 percentage points. BELETE (2023) emphasized the intrinsic link between poverty, consumption, and wealth, suggesting that poverty reduction is inextricably tied to heightened consumption levels and overall welfare enhancement. Additionally, the analysis by Bruno, Florencia, Esther, and William on micro-credit’s effect on household consumption expenditures found a marginally negative influence of credit on consumption patterns ( Belay & Mengiste, 2023 ). The use of aggregate consumption as a household-level welfare indicator to measure poverty has often been criticized ( Gebre et al., 2023 ). This is because people think they don’t really show what folks actually use. This article looks at when and how we can tweak spending data from household surveys to get a clearer picture of what essential services people actually use. Habtewold (2021) asserts that markets impacted by subsidy structures, allocation rationing, and elevated cross-border tariffs are analyzed, with incremental recalibrations suggested to mitigate these economic distortions. The study by Feyisa (2020) conducted a study using the Tobit model to explore the drivers behind the implementation and usage intensity of barley malting technology in the Limna Bilbiro, Shashemene, and Kofere districts within the Oromia National Regional State ( Gelata et al., 2024 ; Assaye, Habte, & Sakurai, 2023 ). Another study was conducted by Kebede (2022) focuses on investigation that delves into the elements swaying the uptake and level of commitment to the barley malting tech package in Marga Woreda, by leveraging the Tobit model. The findings reveal that key aspects influencing the inclination to adopt and used include schooling, family headcount, plot size, loan access, co-op membership, access to hands-on learning, and exposure to demos ( Gebre, Isoda, Amekawa, & Nomura, 2019 ). Moreover, the total number of critters and how far they are from the nearest marketplace play a meaningful role in shaping farmers’ decisions to hop on board and how fully they commit to adopting ( Zegeye et al., 2022 ). After considering the previously conducted studies indicated above, the contextual frame work of the study is developed as follows. In the diagram below ( Figure 1 ), the nexus between rural household’s welfare and agricultural technology adoption is briefly analyzed. How the agricultural technology adoptions in the form of improved seeds, fertilizers and irrigation can affect farmer’s welfare as measured by food insecurity, unidimensional poverty, and multi-dimensional poverty. In addition to those agricultural technology adoptions, the effect of socio-economic and contextual factors such as education level, household age and dependency ratio on welfare is analyzed. Figure 1. Conceptual frame work of the study. 3. Method 3.1 Data and sampling procedure The data used in this study is secondary data obtained from ESS (Ethiopian Socio-economic survey). The Ethiopian Socioeconomic Survey (ESS) data was collected by the Central Statistical Agency (CSA) of Ethiopia in collaboration with the Living Standards Measurement Study Integrated Surveys on Agriculture (LSMS-ISA) of the World Bank. The data collection process included the following steps. The ESS sample was designed to be representative of Ethiopia’s small towns and rural areas. The sampling frame included households from the Amhara, Oromia, Southern Nations, Nationalities, and Peoples’ Region (SNNP), Tigray, Afar, Benshangul Gumuz, Gambella, Harari, and Somali Regional States, as well as the city government of Dire Dawa. The survey involved a two-stage stratified sampling technique. In the first stage, enumeration areas (EAs) were selected from each region based on probability proportional to size. In the second stage, households within these EAs were randomly selected. A total of 4,870 households were included in the sample for the study. This sample size was determined to ensure that the survey results would be statistically significant and representative of the broader population. Data was collected through household interviews using structured questionnaires. The survey covered various socio-economic indicators, including household demographics, education, health, employment, agriculture, and income. To ensure the quality and accuracy of the data, field supervisors conducted regular checks and validation of the collected data. The data collection process also involved training enumerators and supervisors on the survey instruments and data collection procedures. 3.2 Methods of analysis Both qualitative and quantitative methods of analysis with descriptive and econometric applications are used. Descriptive analysis is used to investigate the extents of poverty and food insecurity in the study area while econometric analysis is applied to identify the determinants of rural household’s welfare by considering agricultural technology adoption as the major factor to affect welfare of the society. Uni-dimensional Poverty ( Wossen, Alene, Abdoulaye, Feleke, & Manyong, 2019 ) can be measured using per adult consumption expenditure per households and individual household is uni-dimensional poor if they are living under the absolute poverty line of the area. Household with per adult consumption expenditure represented by “Y” and poverty line by “Z”, and then poverty is defined by Y ∗ = Z − Y , hence household is poor if Y ∗ > 0. Multidimensional poverty measure non income poverty by considering deprivations of households for basic needs. In this study, the UNDP Multidimensional poverty index is developed using ten indicators categorized under three dimensions namely health (nutrition and child mortality), education (school enrollment, years of schooling and living standard with indicators of access to water, electricity, sanitation, cooking fuel, floor and, assets ( Kovacevic & Calderon, 2014 ). Principal component analysis (PCA) was used to estimate the household food security index. Similarly, PCA was employed in empirical research to create indices of household food security ( Hawas & Degaga, 2023 ). Researchers created a food security index for this analysis using food consumption score, dietary diversity, consumption expenditure, calorie intake, and hunger months. The PCA linearly divides the indicator variables into smaller components while retaining most information from the original indicators ( Massresha et al., 2021 ). Stated mathematically, from an initial set of correlated indicator food security variables ( X 1 , X 2 , X 3 …, X n ), PCA creates uncorrelated indices or components whereby each component is a linear weighted combination of the initial variables as follows: (1) PC . M = am 1 X 1 + am 2 X 2 + am 3 X 3 + … . + am n X n Where am n represents the weight for the mth principal component and the n th food security measurement indicators. Components are ordered based on their ability to explain the most significant variance, given the constraint of squared weight sum ( a 2 m 1 + a 2 m 2 + a 2 m 3 + ⋯ + a 2 m n ) is equal to one. Each successive component explains an additional but smaller amount of variable variation. The higher the degree of correlation between the original variables, the fewer components are needed to gather general information. After determining the first component, the household food security index is calculated as follows: (2) FSI . j = ∑ Fi ( X ij − X i ) S i where FSI. j the food security index follows a normal distribution with a mean of 0 and a standard deviation of 1. F i is the weight for the i th variable in the PCA model, X ji is the j th ’ ‘household’s value for the i th variable, and X i and S i are the mean and standard deviations for all households. 3.3 Model specification Seemingly unrelated multivariate probit model is applied for this research. The seemingly unrelated regression model is preferable ( Baltagi, 2012 ) and ( Pan et al., 2020 ) when there are more than one regression models and the dependent variables are affected by similar co-variate or interdependent variables ( Kim & Cho, 2019 ). Rural household’s welfare for this study is measured using two dummy variables of multi-dimensional poverty and food insecurity status when those variables are determined by similar co-variate. The regression model is developed as follows: (3) Yi = ∝ 1 cultivates land + ∝ 2 livestock + ∝ 3 education + ∝ 4 insecticides + ∝ 5 use of imptooved seeds + ∝ 6 use offertilizers + ∝ 7 pastecides + ∝ 8 use of irrigation + ∝ 9 access to agricultural extension service + ∝ 10 access to credit + ∝ 11 age + error term Yi = Rural household welfare and has a multiple category of 1 for unidirectional poverty, 2 for Multidimensional poverty and 3 for Food insecurity (4) y 1 y 2 y 3 = [ α 1 i α 2 i α 3 i − − − α ni α 1 i α 2 i α 3 i − − − α ni α 1 i α 2 i α 3 i − − − α ni ] x 1 i x 2 i x 3 i + ɛ 1 ɛ 2 ɛ 3 Where y1 = 1 for uni-dimensional poor, y2 = 1 for multidimensional poor and y3 = 1 for food in-secured and the variance of the co-variate error is given by (5) Cov ( ɛ 1 , ɛ 2 , ɛ 3 ) = [ Ơ 1 2 Ơ 12 Ơ 13 Ơ 21 Ơ 2 2 Ơ 23 Ơ 31 Ơ 32 Ơ 3 2 ] My merging uni-dimensional and multi-dimensional poverty in to one variable (to be poverty) from the above multi-variate probit model, the bi-variate probit regression model is also specified as follows; Yi = Rural household welfare and has two categories of 1 for Multidimensional poverty and 2 for Food insecurity (1 is assigned for poor and food in secured) (6) y 1 i y 2 i = α 1 i α 2 i − − − − αni α 1 i α 2 i − − − − αni ∗ X 1 X 2 + ɛ 1 ɛ 2 Where y 1 = 1 for poor and y 2 = 1 for food in-secured and the variance of the co-variate error is given by (7) Cov ( ɛ 1 , ɛ 2 ) = [ Ơ 1 2 Ơ 12 Ơ 21 Ơ 2 2 ] 3.4 Description of variables and expected signs Welfare as measured by three proxy variables of uni-dimensional poverty, multi-dimensional poverty and food insecurity is expected to have specified relationship with explanatory variables as presented in the following Table 1 . Table 1. Description of variables and their expected sides. Name of variable Abbreviation Expected sign Nature of variable cultivated land (in hectare) Cultivated land + Continues Numbers of livestock Livestock + Continues Education level of the household head Education - Continues Ag of household head Age - Continues Use of improved seeds improved seeds + Dummy (1 if using it and 0 if not using) Use of fertilizers Fertilizers + Dummy (1 if using it and 0 if not using) Use of Irrigation Irrigation + Dummy (1 if using it and 0 if not using) Use of pesticides Pesticides + Dummy (1 if using it and 0 if not using) Access to agricultural extension services Extension + Dummy (1 if accessing it and 0 otherwise) Access to credit provision Credit - Dummy (1 if accessing it and 0 otherwise) 4. Result and Discussion 4.1. Descriptive analysis Under this descriptive analysis part, the issue of gender is addressed and what proportion of female headed farmers are food-insecure and poor is analyzed. For this sake, the bar graph presented below shows the proportion of females from the total sample and how many of those households are living under low welfare as measured by poverty and food insecurity. Out of a total sample of the study, 47.61% (2322 out of 4877) are adopting agricultural technologies in many forms. The remaining 52.39% are not using modern agricultural inputs and irrigation to increase the productivity of their agricultural outputs. Among those their welfare status is analysed based on their gender as presented below ( Figure 2 ). Figure 2. Bar chart of gender and food insecurity. Out of a total sample of 4887 rural household farmers in Ethiopia, more than 3787 farmers are male headed and the remaining 1090 households are female headed ( Figure 1 ). Out of male headed households, about 68% are food secured and the remaining 32% are food insecure households. When we see their female headed counter parts, out of a total of 1090 female headed rural households, about 70% are food secured and the remaining 30% are food insecure This shows us there is no significant difference of food security status based on their gender. The below Figure 2 presents the male female composition of the sample and the numbers households living under multi-dimensional poverty. About 67% of male headed households are facing multi-dimensional poverty and about 71% of female headed households are multi-dimensional poor. From this result we can observe that majority of the population is facing multi-dimensional poverty either they are male headed or female headed regardless of their gender differences. Moreover, the provision of credit across the male and female-headed households are analyzed here below using pie chart ( Figure 3 ). Majority of female-headed households accessed credit provision when compared with their male-headed counter parts. Figure 3. Multi-dimensional poverty across gender. As seen from the above pie chart, 36% of female-headed households accessed credit even though female-headed household’s accounts for about 40%of the total sample and the remaining 4% are not accessing credit. From those no credit accessed categories, 96.32% are male-headed households and hence we can say that more priority is given for females in the area of credit provision from governmental and non-governmental donors ( Figure 4 ). Figure 4. Gender and access to credit. After analyzing the data obtained from the focused group discussion, the following findings had been drawn relating to the impact of internal conflict on rural household’s agricultural activities and their welfare. Internal conflicts and wars have profound and far-reaching impacts on rural household welfare, particularly in agricultural contexts such as Ethiopia. These impacts can be broadly categorized into several areas: Displacement and migration : Conflicts often lead to the displacement of populations, forcing rural households to abandon their homes and farmland. This displacement disrupts agricultural activities, leading to loss of crops, livestock, and income sources. Migration also increases competition for resources in safer areas, exacerbating food insecurity and poverty. Destruction of infrastructure : Wars and conflicts typically result in the destruction of critical infrastructure such as roads, irrigation systems, storage facilities, and markets. The damage to infrastructure hampers the ability of farmers to access markets, sell their produce, and obtain necessary agricultural inputs like seeds and fertilizers. Loss of agricultural productivity : The direct impacts of conflict, such as destruction of crops and livestock, combined with the disruption of farming activities, result in significant losses in agricultural productivity. Farmers may miss planting and harvesting seasons, leading to reduced yields and food shortages. Economic instability : Conflicts lead to economic instability, characterized by inflation, loss of income, and decreased investment in the agricultural sector. The uncertainty and risk associated with conflict discourage both local and foreign investment, reducing the financial resources available for agricultural development. Food insecurity : The combination of displacement, infrastructure destruction, and reduced agricultural productivity culminates in heightened food insecurity. Rural households struggle to produce and purchase sufficient food, leading to malnutrition and increased vulnerability to diseases. Social disruption : Conflicts erode social cohesion and trust within communities. Traditional support systems, which are crucial for coping with economic and environmental shocks, are weakened. This social fragmentation makes it more difficult for rural households to recover and rebuild their livelihoods post-conflict. Loss of human capital : Wars and conflicts result in the loss of human capital through death, injury, and the disruption of education. Now currently, internal conflict is experienced in many parts of the study area and it leads to devastating human capital level. The absence of able-bodied family members reduces the household labor force, impacting agricultural productivity. Additionally, children’s education is often interrupted, limiting future opportunities and perpetuating the cycle of poverty. Psychological impact : The trauma and stress associated with living in conflict zones have long-term psychological impacts on individuals and communities in the study area. Mental health issues can reduce the ability of individuals to engage in productive activities, further diminishing household welfare. 4.2 Econometrics analysis Under this section, the effect of agricultural technology adoption had been analyzed using econometric results obtained from bi-variate probit regression model. First results obtained from the bi-variate probit ( Table 2 ) is used to analyze the relationship that exists between Welfare and its determinant with special emphasis of the effect of agricultural technology adoption of rural household’s welfare. With this regard, overall significance test of the model is applied using Chi-square test and individual variables statistical significance is tested using simple students t- test with the application of general rule of thumb (the null hypothesis H0: estimated coefficients are zero can be rejected when t-statistics values are greater than two). Table 2. Bi-variate Probit regression model. Multi-dimensional Poverty Food insecurity P>Z Z st.error coeff. Variables P>Z Z st.error coeff. Variables 0.000* 8.64 0.0015 0.013 Age ≤0.001* 8.85 0.0015 0.013 Age 0.000* -8.66 0.0007 -0.007 Fertilizer ≤0.001* -8.63 0.0007 -0.006 Fertilizer 0.000* 4.3 0.057 0.248 Extension ≤0.001* 4.43 0.578 0.256 Extension 0.000* -27.63 0.037 -0.049 improved seeds ≤0.001* -27.58 0.0379 -0.047 improved seeds 0.199 -1.28 0.061 -0.078 access to credit 0.214 -1.24 0.061 -0.758 access to credit -0.045** -2.01 0.052 -0.104 Irrigation 0.063*** -1.86 0.0522 -0.097 Irrigation 0.000* 9.28 0.007 0.067 Dependency ratio 0.000*. 9.1 0.0072 0.066 Dependency ratio 0.422 0.8 0.0377 0.03 Pesticides 0.525 0.64 0.0377 0.024 Pesticides 0.063*** 1.86 0.645 0.11 Education 0.069*** 1.82 0.0645 0.117 Education 0.033** -2.13 0.064 -0.137 Livestock 0.035** -2.1 0.0643 -0.135 Livestock The maximum likelihood estimation result of the bivariate probit model gives us a chi-squared probability value of zero (0.000), indicating the overall significance of the model by rejecting the null hypothesis that states the coefficients of all variables are zero. In addition to the overall significance test, all independent variables are tested to determine if they are significant using Z and probability values. Variables with Z values greater than two or P-values less than 0.05 ( Baltagi 2012 ) are statistically significant in affecting the level of rural welfare as measured by poverty and food insecurity. Negative coefficients for poverty and food insecurity suggest that adopting the specified agricultural technologies is associated with a lower likelihood of being in each respective state of poverty or food insecurity. It’s important to consider the statistical significance of these coefficients (t-values) and their practical significance in the context of the study. As seen from Table 2 , all independent variables included in the model, except for access to credit, education, and access to pesticides, are statistically significant in affecting both poverty and food insecurity. Accordingly, among indicators of agricultural technology adoption, access to irrigation ( Tilahun, Bantider et al. 2023 ), access to improved seeds ( Mutungi, Manda et al. 2023 ), and use of fertilizers ( Ayenew, Lakew et al. 2020 ) significantly and positively affect the status of being non-poor and food-secure households ( Biru, Zeller et al. 2020 ). More specifically, the results of the bivariate probit model state that having access to irrigation and improved seeds is associated with respective probabilities of 0.014 and 0.049 of being under the non-poor and food-secured category when compared with households without access to irrigation and improved seeds. Adopting improved seeds is associated with a statistically significant reduction in the likelihood of poverty. The negative coefficient indicates that, holding other variables constant, adopting improved seeds is linked to a decrease in the probability of being poor. Adopting irrigation has a strong and statistically significant negative association with poverty. Farmers using irrigation are much less likely to be poor compared to those who do not adopt irrigation. This result is supported by many research findings such as ( Legesse, Srivastava et al. 2019 ; Belay and Mengiste 2023 ; Atinafu, Lejebo et al. 2022 ; Korir, Manning et al. 2023 ). Existing research often supports the idea that adopting agricultural technologies like improved seeds leads to increased crop yields and, subsequently, improved welfare of farmers. This is consistent with findings from ( Biru, Zeller et al. 2020 ; Asrat, Anteneh et al. 2022 ; Habtewold 2018 ) that state improved seeds are associated with a lower likelihood of poverty and food insecurity. This result contradicts the findings of ( Wossen, Alene et al. 2019 ; Wordofa and Sassi 2020 ; Tilaye, Delele et al. 2023 ) who state that their results deviate from the theory due to the target population and method of sampling used in the study. Additionally, using one kilogram of fertilizer is associated with increasing the probability of being non-poor by 0.007. Adopting fertilizers is associated with a statistically significant decrease in the likelihood of poverty, as stated by Mulugeta Habtewold and Heshmati (2023) , Mogess and Ayen (2023) , Legesse, Srivastava et al. (2019) Gebre, Amekawa et al. (2023) , though the effect is not as strong as with improved seeds. The negative coefficient suggests a reduction in the probability of poverty when farmers adopt fertilizers. In this study, the age of households and dependency ratio are associated with a high probability of being poor and food-insecure households. When the age of the household head increases by one year, the probability of being poor and food insecure increases by 0.013 and 0.067, respectively. The remaining variable affecting poverty and food insecurity status is the number of livestock owned by households ( Mekonnen 2017 ; Korir, Manning et al. 2023 ). One-unit increase in livestock is associated with increasing the probability of being non-poor and food-secure by 0.137. Hence, we can say that the number of livestock is a key determinant of household welfare, and an increased number of livestock has the power to reduce the level of poverty and food insecurity ( Shikur 2023 ). Existing research often supports the idea that adopting improved seeds leads to increased crop yields and, subsequently, improved economic outcomes for farmers. This is consistent with the findings of ( Bekana and Takele 2020 ; Belay and Mengiste 2023 ; Mogess and Ayen 2023 ), who also state that the adoption of improved seeds is associated with a lower likelihood of poverty and food insecurity. Variables such as access to credit, use of pesticides, and education level are statistically insignificant ( Gebrehiwot 2015 ; Habtewold 2018 ), and access to agricultural extension service is negatively related to rural household welfare as measured by proxy variables of poverty and food security, in contrast to the results of ( Nonvide 2024 ; Kebede 2022 ; Hawas and Degaga 2023 ). These results are somewhat unexpected and deviate from theories. Researchers analyzed the reason behind these results as follows: Access to credit had an insignificant impact on rural household welfare in the study area because many agricultural credits are provided by credit associations to farmers with high interest rates, creating difficulty in repaying the credit. Instead of improving the welfare of rural households, high-interest payments create tension, and farmers are obliged to sell their assets to repay the credited money with high interest, negatively affecting household welfare. Agricultural extension service support is not improving the welfare of households, and farmers with extension support are relatively under low welfare status, necessitating a re-evaluation of the relevance of agricultural extension. Additionally, the education level is statistically insignificant since almost all farmers in rural Ethiopia are either illiterate or have only completed the first cycle of elementary education, resulting in no significant difference in welfare associated with their levels of education. In addition to the bi-variate model, the seemingly unrelated multi-variate probit model with few selected independent variables of the research are presented in the table below ( Table 3 ) and results are interpreted and analyzed as follows. Table 3. Multi-variate Probit regression model. Variables Coefficient Std. err. z P>|z| Food insecurity Cultivated land 0.0170395 0.0018469 9.23 ≤0.001* Dependency ratio 0.1146298 0.0639331 1.79 0.073 Improved seeds -0.0126885 0.0383665 -0.33 0.741 Extension 0.3535153 0.0384274 9.2 ≤0.001* Access to pesticides 0.048119 0.038 1.27 0.205 livestock -0.1229268 0.0638216 -1.93 0.054*** Uni-dimensional poverty Cultivated land -0.0062664 0.0018478 -3.39 0.001* Dependency ratio 0.099901 0.0681262 1.47 0.143 Improved seeds -0.0269907 0.0403048 -0.67 0.503 Extension 0.1718347 0.0400286 4.29 ≤0.001* Access to pesticides -0.0272377 0.0398283 -0.68 0.494 Livestock -0.0961446 0.0679607 -1.41 0.157 Multi-dimensional poverty Cultivated land -0.0055926 0.0018199 -3.07 0.002* Dependency ratio 0.1082926 0.0686382 1.58 0.115 Improved seeds -0.0130351 0.0401107 -0.32 0.745 Extension 0.1847254 0.0395978 4.67 ≤0.001* Access to pesticides -0.0644856 0.039594 -1.63 0.103 Livestock -0.1169272 0.0684731 -1.71 0.088*** The overall significance of the model, with a p-value less than 5% ( Table 3 ), showed that the model is developed with significant co-variates since the null hypothesis, which states that all variables coefficients are zero, can be rejected. With this in mind, we proceed to the interpretation of statistically significance variables by examining individual hypothesis testing results for all variables. According to the estimation result from the multi-variate probit model ( Table 3 ), the size of cultivated land owned by household farmers significantly affect their welfare. Land size is positively related with food security but inversely related with uni-variate and multi-variate poverty. This shows, when household’s cultivated land size increased by one hectare, the probability of being food secured increased by 0.047, while increased land size is associated with a decrease in the probability of being multidimensional and multidimensional poor by 0.006 and 0.0055 respectively. This means that a large size of cultivated land may help households to be food secured ( Shita et al., 2023 ), but households with increased land size are living under uni dimensional and multi-dimensional poverty. This result aggres with the findings of Mekonnen (2017) and Habtewold (2021) . Additionally, provision of agricultural extension services, numbers livestock are significance variables affecting rural households, welfare as measured by three proxy variables of food insecurity, uni dimensional poverty, and multi dimensional poverty. This result is supported by the bi-variate probit model regression result analyzed previously ( Table 2 ). This result is also similar to the findings of Gelata et al. (2024) , Atinafu et al. (2022) Habtewold (2018) , Gebrehiwot (2015) and Wordofa & Sassi (2020) . From the probit regression model, we observed the interdependence of three measures of welfare, all of which are significantly affected by similar explanatory variables. This means that individual household farmers which are facing food insecurity are also facing both uni-dimensional and multi-dimensional poverty. Farmers living under uni-dimensional poverty are also facing multidimensional poverty and food insecurity. Finally, multi-dimensional poverty is occurred in line with food insecurity and uni-dimensional poverty. This result is supported by the findings of Merga et al. (2023) . 5. Conclusion and recommendations 5.1 Conclusion This study analyzed the determinants of rural household welfare in Ethiopia, with a special emphasis on the effect of agricultural technology adoption on improving the welfare of rural household farmers. The analysis using the bivariate probit regression model revealed several key findings regarding the relationship between agricultural technology adoption and rural household welfare in Ethiopia. From descriptive summary, it is noted that out of a total sample of the study, 47.61% (2322 out of 4877) are adopting agricultural technologies in many forms more than 70% of female headed households are food secured and no poor and 68% of male headed households are food secured and nun poor. The overall significance test of the model indicated its relevance in explaining the determinants of welfare, particularly the impact of agricultural technology adoption. The results showed that variables such as access to irrigation, improved seeds, and fertilizers were statistically significant in reducing the likelihood of poverty and food insecurity. These findings are consistent with previous research, suggesting that adopting these technologies can lead to increased crop yields and improved welfare for farmers. However, variables like access to credit, education, and access to pesticides were found to be statistically insignificant, indicating the need for further investigation into their impact on welfare outcomes. Moreover, the seemingly unrelated multivariate probit model provided additional insights into the relationship between selected independent variables and welfare indicators. The results highlighted the significance of variables such as cultivated land, agricultural extension services, and livestock ownership in affecting welfare outcomes. Larger cultivated land size was associated with increased food security but decreased uni-dimensional and multidimensional poverty, suggesting a complex relationship between land size and welfare. Additionally, the provision of agricultural extension services and livestock ownership were found to have a positive impact on welfare, consistent with the findings from the bivariate probit model. 5.2 Recommendations Based on the analysis of the relationship between agricultural technology adoption and rural household welfare in Ethiopia, the following recommendations can be made to improve welfare outcomes: Promotion of Agricultural Technologies: Policymakers should prioritize the promotion and adoption of agricultural technologies such as irrigation, improved seeds, and fertilizers. This can be done through targeted extension programs, subsidies, and incentives to encourage farmers to adopt these technologies. Providing access to high-quality and affordable agricultural inputs can significantly improve crop yields and enhance food security. Enhanced Access to Credit: Although access to credit was found to be statistically insignificant in this study, efforts should be made to improve access to credit for rural farmers. This can be achieved by establishing rural credit institutions, providing credit guarantees, and offering low-interest loans to farmers. Access to credit can help farmers invest in agricultural inputs and technologies, leading to increased productivity and improved welfare. Improvement of Education: Although the education level was found to be statistically insignificant in this study, improving access to education can have long-term benefits for rural households. Policymakers should focus on improving the quality and accessibility of education in rural areas, including investing in school infrastructure, providing scholarships, and promoting adult education programs. Education can help improve livelihood opportunities and empower rural communities to make informed decisions about their welfare. Targeted Poverty Alleviation Programs: Based on the findings regarding age and dependency ratio, policymakers should consider implementing targeted poverty alleviation programs for vulnerable groups such as the elderly and households with high dependency ratios. These programs can include social safety nets, cash transfer programs, and food assistance to help improve the welfare of these groups. Further Research and Monitoring: Finally, further research is needed to explore the reasons behind the insignificant impact of certain variables and to identify additional factors that may affect rural household welfare. Continuous monitoring and evaluation of welfare programs can help policymakers tailor interventions to address specific challenges and improve overall welfare outcomes in rural areas. Ethics and consent This study, titled “Analyzing the determinants of rural household welfare in Ethiopia: does agricultural technology adoption matters”: using seemingly unrelated multi-variate probit model” was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki. The research received ethical approval from the Research and Community Service Bureau of the College of Business and Economics at Woldia University on December 14, 2023, under approval number RCSTT/184/2023. The following ethical standards were adhered to throughout the research process: 1. Compliance with the Declaration of Helsinki: The study was conducted in line with the principles of the Declaration of Helsinki, which emphasizes the protection of human participants, respect for their rights and dignity, and the promotion of well-being. This research prioritized participants’ health, welfare, and confidentiality. 2. Informed Consent: All participants were fully informed about the objectives, methods, risks, and potential benefits of the study. Participation was voluntary, and verbal informed consent was obtained from each participant prior to their involvement. Participants were also made aware of their right to withdraw from the study at any time without consequences. 3. Confidentiality and Data Protection: Personal data was handled in strict confidence. Participant identities were anonymized, and no personally identifiable information was disclosed in the final analysis or publications. All data were securely stored, and only the research team had access to them. Data protection protocols were followed to prevent unauthorized access. 4. Non-Coercion and Autonomy: No participant was coerced or pressured into participating. Autonomy was respected throughout the study, and participants were encouraged to make informed decisions regarding their participation. The research ensured that vulnerable groups, such as women or economically disadvantaged individuals, were not subjected to any form of exploitation or undue influence. 5. Protection of Vulnerable Populations: The study took particular care to protect vulnerable individuals. No minors were involved in the study. “In this study, participants provided verbal consent after being fully informed about the research’s purpose, procedures, and potential risks. Verbal consent was documented as per ethical guidelines, ensuring participants understood their voluntary involvement and right to withdraw at any time. Institutional approval was obtained to use verbal consent instead on written consent because of the presence of many illiterate farmers who can no read and write.” The research team made every effort to minimize any potential risks or harms, both physical and psychological, to participants. Interview questions were designed to be non-intrusive, and participants were assured that they could skip any questions that made them uncomfortable. Should any participant experience distress, protocols were in place to provide support and discontinue the interview if necessary. 7. Transparency and Feedback: Participants were informed about how the research results would be used and how they could access the findings if desired. The results will be disseminated to local communities, policymakers, and other relevant stakeholders, ensuring that the research findings contribute to improving the livelihood and economic well-being of rural households involved in traditional opal mining Data availability statement Underlying data Zenodo: Determinants of Rural Households Welfare In Ethiopia, Doi: https://zenodo.org/records/14282852 ( Abate, 2024b ). This project contains the following underlying data: • Data ATA.xlsx Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0). Extended data Zenodo: Review guidline for determinants of welfare. Doi: https://doi.org/10.5281/zenodo.14560932 ( Tadesse Wudu, 2024 ). This project contains the following extended data: • inretview guide line.xlsx Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0). Software availability statement This study used Stata version [stata 13] for the statistical analysis. Stata is a proprietary software available from StataCorp ( https://www.stata.com/ ). While Stata is widely used for econometric and statistical analysis, an open-source alternative that can perform similar functions is R ( https://www.r-project.org/ ) with packages such as plm (for panel data analysis), MASS, and stats (for econometric functions). 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Publisher Full Text Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 17 Jan 2025 ADD YOUR COMMENT Comment Author details Author details 1 Economics, Woldia University, Weldiya, Amhara, Ethiopia 2 Economics, Dilla University, Dilla, Southern Nations, Nationalities, and People's Region, Ethiopia Tadesse Wudu Abate Roles: Conceptualization, Formal Analysis, Methodology Henok Ezezew Sheferaw Roles: Formal Analysis, Investigation, Methodology, Software Kalid Wendimnew Sitotaw Roles: Conceptualization, Data Curation, Software, Writing – Original Draft Preparation Sisay Genanu Mulaw Roles: Formal Analysis, 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: 17 Jan 2025, 14:102 https://doi.org/10.12688/f1000research.159875.1 Copyright © 2025 Abate TW 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 Abate TW, Sheferaw HE, Sitotaw KW and Mulaw SG. “Analyzing the determinants of rural household welfare in Ethiopia: Does agricultural technology adoption matters”: Using seemingly unrelated multi-variate probit model” [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :102 ( https://doi.org/10.12688/f1000research.159875.1 ) 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 17 Jan 2025 Views 0 Cite How to cite this report: Nonvide GMA. Reviewer Report For: “Analyzing the determinants of rural household welfare in Ethiopia: Does agricultural technology adoption matters”: Using seemingly unrelated multi-variate probit model” [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :102 ( https://doi.org/10.5256/f1000research.175663.r360900 ) The direct URL for this report is: https://f1000research.com/articles/14-102/v1#referee-response-360900 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 10 Feb 2025 Gbêtondji Melaine Armel Nonvide , Université d’Abomey-Calavi, Godomey, Benin Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.175663.r360900 This study analyzed the nexus between rural household welfare and agricultural technology adoption in Ethiopia using secondary data from the Ethiopian Socioeconomic Survey (ESS). The study is interesting and it is well conducted. However, there are still room for improving ... Continue reading READ ALL This study analyzed the nexus between rural household welfare and agricultural technology adoption in Ethiopia using secondary data from the Ethiopian Socioeconomic Survey (ESS). The study is interesting and it is well conducted. However, there are still room for improving its quality. Below are some comments. The motivation section should be include in the introduction. This should demonstrate the contribution of your study What technology are under investigation? What about if multiple technology were adopted? The authors fail to measure any causal relationship between technology adoption and welfare You should test for robustness by using other econometric methods. Is the work clearly and accurately presented and does it cite the current literature? Yes 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? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: technology adoption, poverty, food security, climate change economics 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 Nonvide GMA. Reviewer Report For: “Analyzing the determinants of rural household welfare in Ethiopia: Does agricultural technology adoption matters”: Using seemingly unrelated multi-variate probit model” [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :102 ( https://doi.org/10.5256/f1000research.175663.r360900 ) The direct URL for this report is: https://f1000research.com/articles/14-102/v1#referee-response-360900 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: Han J. Reviewer Report For: “Analyzing the determinants of rural household welfare in Ethiopia: Does agricultural technology adoption matters”: Using seemingly unrelated multi-variate probit model” [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :102 ( https://doi.org/10.5256/f1000research.175663.r360896 ) The direct URL for this report is: https://f1000research.com/articles/14-102/v1#referee-response-360896 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 08 Feb 2025 Jiqin Han , Nanjing Agricultural University, Jiangsu, China Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.175663.r360896 This study analyzed the determinants of rural household welfare in Ethiopia, with a special emphasis on the effect of agricultural technology adoption on improving the welfare of rural household farmers. The agricultural technologies were indicated by improved seeds, fertilizers, and ... Continue reading READ ALL This study analyzed the determinants of rural household welfare in Ethiopia, with a special emphasis on the effect of agricultural technology adoption on improving the welfare of rural household farmers. The agricultural technologies were indicated by improved seeds, fertilizers, and irrigation, while rural household welfare provision of agricultural extension services, numbers livestock are significance variables affecting rural households, welfare as measured by three proxy variables of food insecurity, uni dimensional poverty, and multi dimensional poverty. The major findings indicated that access to irrigation, improved seeds, and fertilizers significantly reduces the likelihood of poverty and food insecurity among rural households. The following areas need to be improved: 1. Marginal contribution needs to be improved This study has some significance to justify the importance of agricultural inputs such as seeds, fertilizers and irrigation in improving rural households’ food security level. The research findings are the general consensus in academia. For example, a large size of cultivated land may help households to be food secured, the significance of variables such as cultivated land, agricultural extension services, and livestock ownership in affecting welfare outcomes. These findings are not contributing to new knowledge. 2. The title The authors use the seemingly unrelated multivariate probit model to analyze the determinants of household welfare. While this model is suitable for analyzing multiple binary outcomes that may be correlated, the rationale for choosing this specific model could be expanded. It is not immediately clear how the different binary outcomes (e.g., technology adoption, improved welfare, etc.) are interrelated in this context. The authors should clearly explain the relationships between the outcomes analyzed in the model. For example, are households that adopt one technology more likely to adopt others? Or, is technology adoption correlated with other welfare-enhancing factors, such as access to credit or extension services? Detailing these connections would help justify the use of the multivariate probit model over alternative methods. 3. Methodology The authors tried to use the multivariate probit model (MVP). The model is typically used to analyze correlated binary outcomes (dependent variables) that may be jointly determined or interdependent. In this study, the three welfare dimensions—unidimensional poverty, multidimensional poverty, and food insecurity—are modeled as interdependent binary outcomes. The MVP model is appropriate if: There is theoretical and empirical justification for interdependence among the dependent variables. The covariance matrix of the error terms captures unobserved correlations between these outcomes. 4. Limitations （1） The study does not provide sufficient justification for why the MVP model was specifically chosen over other models (e.g., ordered probit or structural equation modeling). Are these welfare dimensions truly interdependent, or could they be analyzed independently? （2） If the welfare dimensions are independent or weakly correlated, using an MVP model may be unnecessary and could lead to inefficient or biased estimates. Methodology Vs Result （1） The coefficients for some variables (e.g., access to credit, education) are insignificant or inconsistent with theoretical expectations. This may indicate issues with model specification, variable selection, or the underlying dataset. （2） The discussion does not adequately connect the results of the MVP model to its theoretical purpose—capturing interdependencies among welfare dimensions. 5. Literature: (1) The reference for more than 2 authors should be used according to the requirements of the Journal. (2) There are mistakes in the way of citing literature, e.g. in alignment with (Gebre et al., 2023) on page 3 6. Language: language editing is needed since there are expression problems in the article. Is the work clearly and accurately presented and does it cite the current literature? Yes 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: agribusiness, international agricultural governance 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 Han J. Reviewer Report For: “Analyzing the determinants of rural household welfare in Ethiopia: Does agricultural technology adoption matters”: Using seemingly unrelated multi-variate probit model” [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :102 ( https://doi.org/10.5256/f1000research.175663.r360896 ) The direct URL for this report is: https://f1000research.com/articles/14-102/v1#referee-response-360896 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 17 Jan 2025 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 17 Jan 25 read read Jiqin Han , Nanjing Agricultural University, Jiangsu, China Gbêtondji Melaine Armel Nonvide , Université d’Abomey-Calavi, Godomey, Benin 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 Nonvide G. 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. 10 Feb 2025 | for Version 1 Gbêtondji Melaine Armel Nonvide , Université d’Abomey-Calavi, Godomey, Benin 0 Views copyright © 2025 Nonvide G. 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 This study analyzed the nexus between rural household welfare and agricultural technology adoption in Ethiopia using secondary data from the Ethiopian Socioeconomic Survey (ESS). The study is interesting and it is well conducted. However, there are still room for improving its quality. Below are some comments. The motivation section should be include in the introduction. This should demonstrate the contribution of your study What technology are under investigation? What about if multiple technology were adopted? The authors fail to measure any causal relationship between technology adoption and welfare You should test for robustness by using other econometric methods. Is the work clearly and accurately presented and does it cite the current literature? Yes 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? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise technology adoption, poverty, food security, climate change economics 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) Nonvide GMA. Peer Review Report For: “Analyzing the determinants of rural household welfare in Ethiopia: Does agricultural technology adoption matters”: Using seemingly unrelated multi-variate probit model” [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :102 ( https://doi.org/10.5256/f1000research.175663.r360900) 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/14-102/v1#referee-response-360900 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Han J. 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. 08 Feb 2025 | for Version 1 Jiqin Han , Nanjing Agricultural University, Jiangsu, China 0 Views copyright © 2025 Han J. 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 This study analyzed the determinants of rural household welfare in Ethiopia, with a special emphasis on the effect of agricultural technology adoption on improving the welfare of rural household farmers. The agricultural technologies were indicated by improved seeds, fertilizers, and irrigation, while rural household welfare provision of agricultural extension services, numbers livestock are significance variables affecting rural households, welfare as measured by three proxy variables of food insecurity, uni dimensional poverty, and multi dimensional poverty. The major findings indicated that access to irrigation, improved seeds, and fertilizers significantly reduces the likelihood of poverty and food insecurity among rural households. The following areas need to be improved: 1. Marginal contribution needs to be improved This study has some significance to justify the importance of agricultural inputs such as seeds, fertilizers and irrigation in improving rural households’ food security level. The research findings are the general consensus in academia. For example, a large size of cultivated land may help households to be food secured, the significance of variables such as cultivated land, agricultural extension services, and livestock ownership in affecting welfare outcomes. These findings are not contributing to new knowledge. 2. The title The authors use the seemingly unrelated multivariate probit model to analyze the determinants of household welfare. While this model is suitable for analyzing multiple binary outcomes that may be correlated, the rationale for choosing this specific model could be expanded. It is not immediately clear how the different binary outcomes (e.g., technology adoption, improved welfare, etc.) are interrelated in this context. The authors should clearly explain the relationships between the outcomes analyzed in the model. For example, are households that adopt one technology more likely to adopt others? Or, is technology adoption correlated with other welfare-enhancing factors, such as access to credit or extension services? Detailing these connections would help justify the use of the multivariate probit model over alternative methods. 3. Methodology The authors tried to use the multivariate probit model (MVP). The model is typically used to analyze correlated binary outcomes (dependent variables) that may be jointly determined or interdependent. In this study, the three welfare dimensions—unidimensional poverty, multidimensional poverty, and food insecurity—are modeled as interdependent binary outcomes. The MVP model is appropriate if: There is theoretical and empirical justification for interdependence among the dependent variables. The covariance matrix of the error terms captures unobserved correlations between these outcomes. 4. Limitations （1） The study does not provide sufficient justification for why the MVP model was specifically chosen over other models (e.g., ordered probit or structural equation modeling). Are these welfare dimensions truly interdependent, or could they be analyzed independently? （2） If the welfare dimensions are independent or weakly correlated, using an MVP model may be unnecessary and could lead to inefficient or biased estimates. Methodology Vs Result （1） The coefficients for some variables (e.g., access to credit, education) are insignificant or inconsistent with theoretical expectations. This may indicate issues with model specification, variable selection, or the underlying dataset. （2） The discussion does not adequately connect the results of the MVP model to its theoretical purpose—capturing interdependencies among welfare dimensions. 5. Literature: (1) The reference for more than 2 authors should be used according to the requirements of the Journal. (2) There are mistakes in the way of citing literature, e.g. in alignment with (Gebre et al., 2023) on page 3 6. Language: language editing is needed since there are expression problems in the article. Is the work clearly and accurately presented and does it cite the current literature? Yes 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 agribusiness, international agricultural governance 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) Han J. Peer Review Report For: “Analyzing the determinants of rural household welfare in Ethiopia: Does agricultural technology adoption matters”: Using seemingly unrelated multi-variate probit model” [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :102 ( https://doi.org/10.5256/f1000research.175663.r360896) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. 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