{"paper_id":"09180867-85f9-46eb-bfbd-9264ff383a69","body_text":"Impact of Agricultural Cooperative Membership on Households’ Welfare in South Ethiopia Region, Ethiopia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Impact of Agricultural Cooperative Membership on Households’ Welfare in South Ethiopia Region, Ethiopia Amanuel Shewa, Eric Ndemo, Tibebu Bezabh, Chanyalew Seyoum, Fassil Eshetu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6844791/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Increasing agricultural production and productivity is indispensable for improvement of household welfare. In developing countries however it is challenged by various factors including market failures. Agricultural cooperatives are among the institutional arrangements that are widely recognized for improving the market access of smallholders in developing countries and improve welfare. This study therefore aimed to assess the impact of being a member of agricultural cooperatives on smallholder household welfare in southern Ethiopia. The multistage sampling techniques were followed with a survey research design. Data were collected from 422 households. Welfare was approached with farm income and food security (household dietary diversity and food consumption scores). The average treatment on treated and untreated of farm income were 0.74 and 0.19, respectively, whereas that of dietary diversity were 1.86 and 1.5, respectively, and the food consumption scores of members and non-members were 20.88 and 5.77, respectively. The treatment effect shows that farm income increased by 7.65% for members, whereas non-member income increased by 1.87%. The average treatment effect of non-members is 5.77; that is, the food consumption score of non-members of agricultural cooperatives would be higher by 16.49% if they had joined. The study revealed that membership positively influences farm income, dietary diversity and food consumption scores. This study emphasizes the importance of promoting cooperatives to improve the welfare of smallholder households, but future research should consider other forms of cooperatives and other dimensions of welfare. impact agricultural cooperatives household welfare farm income food security endogenous switching regression Figures Figure 1 Figure 2 1. Introduction Agriculture is a basis for economic growth and private sector investment and drives agriculture-related industries, and it is important for food security through employment and sources of income ( 1 ) Globally, 475 million smallholdings with less than 2 hectares of land provide a livelihood for almost two billion, and in Asia and Sub Saharan Africa, these small holdings provide 80% of the food consumed. A report by the FAO highlights that food production should at least double in 2050 in developing countries, particularly Africa and South Asia, for increasing food demand ( 2 ). Modernizing agricultural production systems and commercializing smallholder agriculture are considered indispensable for the economic growth and development of most developing countries ( 3 ). Increased production and productivity also have implications for the livelihoods of low-income urban populations, the capacity to mobilize the rural economy and the ability to serve as engines for the national economy through its multiplier effects ( 3 – 5 ) Nevertheless, following market liberalization and a free market economy system, developing countries face challenges in addressing the development needs of agricultural- and rural-based populations due to pervasive market failures. These market failures of the free-market economy require institutional arrangements that can fill the gaps of markets for agricultural inputs and outputs to enhance the livelihood of smallholders. Agricultural cooperatives are among the institutional arrangements that are widely recognized for improving the market access of smallholders in developing countries ( 6 – 8 ). In Ethiopia, agricultural cooperatives are important service providers of agricultural inputs and means to access output markets for smallholders, but their contribution to smallholders’ household welfare is inconclusive. Furthermore, agricultural cooperatives in Ethiopia serve the whole community regardless of membership, and it is not well known that joining cooperatives truly matters for smallholder household welfare ( 4 ). The empirical results concerning the impact of cooperatives on household benefits from collective actions are mixed. As cooperatives provide services and benefit the community in general, regardless of membership, some studies reported no significant impact of agricultural cooperatives ( 10 ), whereas other studies reported a significant positive impact of membership ( 11 – 13 ). A study by ( 14 ) in Eastern Ethiopia revealed a significant positive impact of membership in agricultural cooperatives on household welfare measured in consumption expenditures. It was also reported that membership has a significant effect on the household income of maize-producing farmers ( 15 ). In terms of welfare indicators considered by scholars, impact studies in Ethiopia have focused mainly on the price received by farmers ( 16 ), the profitability of farmers ( 17 ), and total household income ( 10 , 11 , 13 ). This study contributes to the growing literature on the impact of agricultural cooperatives in that it considers the specific impact of these cooperatives in increasing farm income, as few studies have considered this indicator. Furthermore, it considers food security dimensions through dietary diversity and food consumption scores, which are considered relevant indicators for household-level studies because they value the diversity of foods consumed as well as the nutritional dimension (quality) of food consumed by households ( 18 – 21 ). Moreover, studies on the impact of household welfare in Ethiopia and the study area in particular are scarce. Therefore, this study contributes to the growing literature on the impact of agricultural cooperatives with a focus on household welfare in southern Ethiopia. 2. Related Literature Review 2.1. Theoretical Framework This research is based on the new institutional economics (NIE) perspective, specifically the application of collective action theory and transaction cost economics (TCE) to the context of ACs. Institutional economics theory of collective action, sociological and anthropological theories, and the theory of transaction cost economics (TCE). Institutional economists focus on how collectives find solutions for social dilemmas where collective action generates more benefit for the collective than does acting individually. It focuses on factors that influence actors’ capacity to cooperate, and this cooperation creates institutions called “rules in use”. This theory is founded on methodological individualism, where the center of explanation of social phenomena is grounded in the interests and behavior of individuals ( 22 ). Likewise, sociological and anthropological theories of collective action consider societal-level interactions, the heterogeneity of social members and factors that are exogenous to the community ( 23 ). Collective action theories and collectivism are also explained by three dominant schools of thought. These schools of thought are traditional collective action theory or Olson’s theory, resource mobilization or social movement, and social psychological theory ( 6 , 23 – 25 ). The traditional theory of collective action is known by two prominent theories, rational choice and zero contribution theories, where Olson (1965), in his work of “ The logic of collective action: public goods and the theory of groups,” coined that, in collective groups, when the group size increases, a rational individual wants to maximize his/her benefit without contributing to collective action, which results in the problem of free riders unless there are successful institutional norms of the group or otherwise unless externally enforced coercion mechanisms are used to control the opportunistic behavior of individuals ( 25 ). Rational decision theory dates back a long time, and it is about individuals’ decision-making behavior, where individuals make decisions by analyzing all possible options to make optimal decisions. First introduced by Adam Smith with the work “an inquiry in to the nature and causes of the wealth of nations” in 1776, Adam Smith coined that the human nature of the tendency toward self-interest is a basis for prosperity ( 26 ). The zero contribution theory of Olson (1965) concerns the social dilemma game of public goods; those who believe that others cooperate continue to contribute, whereas rational egoist individuals should not be affected by this belief that others’ contributions continue and benefit from the public good without any contribution called “zero contribution”. ( 27 ) also introduced the concept of the theory of social dilemmas called “ The tragedy of the commons ”, where short-term individual interest is against long-term group interest in open access resources ( 27 , 28 ). However, scholars such as Ostrom argued with the theory of contribution, where strong institutions force individuals to contribute to common goals ( 29 – 33 ), whereas the theory of embeddedness criticizes both theories for not considering other social, cultural and political contexts when individuals make economic decisions ( 34 , 35 ). Regarding motives of joining agricultural cooperatives, theory of resource mobilization argues that grievances are reasons for joining collective groups to access and control resources collectively rather than acting individually, which is later called resource mobilization ( 36 ). Resource mobilization theory is a process of acquiring the resources needed to achieve predetermined goals of the collective through different mechanisms. This finding suggests that the realization of collective goals depends mainly on the capital that society owns. Resource mobilization theory therefore argues that access to available resources and opportunities for resource mobilization are important determinants of collective action. Agricultural cooperatives are meant to achieve market power by joining together, investing in cooperatives and participating in the affairs of cooperatives with the intention of mutual benefit. Membership in agricultural cooperatives is also affected by resource endowments and access to resources. Households join cooperatives to solve problems related to resources (knowledge, capital, physical resources and others), which are important for the production and marketing of farm products. Transaction cost theory was first introduced by ( 37 ), who described, in his seminal paper, that transaction costs are created during business relations and that their level affects the type of economic activity that is organized and run, which he thought is the root cause of market failure ( 37 ). He argued that inefficient resource utilization causes market failure, which could be resolved by introducing new methods of doing business through institutional arrangements. Later, Williamson (1975) formally introduced transaction costs into neo-institutional economics by assuming that transactions are risky and unpredictable and that those involved have problems of opportunity and bounded rationality. For this and other reasons, he suggested reforming governance structures and bringing institutional arrangements such as cooperatives could solve market failures. Economic activity in formal organizations and the spot market varies with considerable economic activity in formal organizations, with high transaction costs because of imperfect information ( 38 ). Institutional arrangements such as agricultural cooperatives can solve or minimize transaction costs by introducing new ways of doing business ( 39 ). Agricultural cooperatives achieve a reduction in transaction costs through their influence on market information, improving the bargaining power of members through economies of scale and group marketing and the provision of services. A reduction in transaction costs due to the joining of agricultural cooperatives encourages smallholder farmers to join. Theory of social capital illuminates that social capital facilitates collective action mainly through “trust and norms of reciprocity embedded in social networks” ( 40 , 41 ). Social capital has important economic implications through lowering the transaction costs of exchange, reducing the cost of enforcing rules in provision and appropriation, providing informal insurance mechanisms by facilitating adaptation in risky environments and improving local authorities’ performance by drawing them into networks ( 42 ). Social capital through social interaction works through observation, hierarchies and reciprocity (networks and clubs). Hierarchies and clubs help in providing capacity for a group decision. It is useful because social interaction generates positive externalities through knowledge about the behavior of agents in the group and knowledge about non-behavior factors such as price and technologies, and it avoids the free-rider problem through norms ( 42 ). In collective action, social capital assets are categorized as cognitive, altruism or structural ( 43 ). Social capital facilitates collective action, and it is recommended that both concepts be considered during empirical studies ( 40 ). Social capital can contribute to the reduction of transaction costs through improving trust and confidence in other actions, which can contribute to the reduction of opportunism and free rider problems of collective action. A high level of trust results in compliance with rules and the regulation of cooperatives, which in turn results in agency problems. 2.2. Empirical literature 2.2.1. Factors affecting membership decisions Factors like age, farming experience, household size, and wealth all had an impact on cooperative involvement in Nigeria's Abia State ( 44 ). In order to determine the factors influencing participation in coffee cooperatives in Rwanda's Huye district, ( 45 ) conducted a study in which he discovered that the following variables were statistically significant influences on membership in the coffee cooperative: the age of the household head, the size of the household, the distance to the cooperative washing station, the availability of credit, prior experience with coffee cultivation, and the volume of coffee produced. ( 46 ) in study of “drivers of agricultural cooperative formation and farmers' membership decision in Ethiopia” found that location, the scale of operation, specialization, and human and relational capital were strongly related to household decision to join agricultural cooperatives while a households’ membership and patronage decisions are also affected by the size, specialization and integration of the cooperatives. ( 14 ) identified variables related to social responsibility, distance from the market centre, and distance from the Office of cooperatives and land size owned as significant variables affecting membership in cooperatives. ( 47 ) in their study in Arsi Zone's Lemu-Arya and Bekoji dairy marketing cooperatives reported that a household's decision to join a dairy cooperative is significantly influenced by educational level, livestock holdings, number of dairy cows owned, labor, off-farm participation, and credit access, perception towards cooperative organizations, extension service, family size, and distance from the cooperative milk collection centre to the farmer's home. In their study of agricultural cooperatives in the Bench Maji Zone of Ethiopia, ( 48 ) looked at opportunities and challenges as well as levels of awareness, information access, marketing and cooperative promotion efforts' promotion and supporting roles, respondents' educational attainment, embezzlements, training, farmers' attitudes toward cooperatives, trust between members and the management committee, and leadership commitment. ( 11 ) also reported age, education level, and family size are positively correlated with the chance of joining a cooperative, whereas land size, agricultural experience, and proximity to a milk collection centre are adversely correlated. ( 49 ) reported access to information, special skill, membership in rural associations, frequency of attendance at public meetings or workshops, household head education, According to the study conducted on agricultural cooperatives age and education of household head, household size, level and land property are variables significantly affect membership in agricultural cooperatives ( 50 ). By employing tobit model regression, ( 13 ) ( 51 ) analysed community level factors affecting participation in agricultural cooperatives in Amhara and Oromia regions of Ethiopia and found that access to road, access to information and land size owned significantly affect level of participation. This research considered Kebele level participation of household as dependent variable where mean of households in the Kebele on selected variables was considered as independent variable. Analysing the participation level at Kebele level is problematic as household level factors are very important in affecting households to decide to participate. Participation decision is mainly affected by socio-economic, demographic and psychological factors of household and household head rather than physical and institutional factors though their effect is renowned. 2.2.2. Impacts of membership in agricultural cooperatives on household welfare Agricultural cooperatives have a major impact on members' income, production, and fertilizer unit costs, according to ( 13 ). Active cooperative members would have escaped a 1.37 quintal per hectare decline in yield and an 1804 ETB drop in income. Similarly, the cost of fertilizer would have gone up by 22 birr if a member had not joined. Their results, however, show that there was no appreciable difference in the marketable surplus or fertilizer adoption between members and non-members. This defies the authors' theory, which held that cooperatives had less of an effect on market orientation and agricultural production performance. This generalization is limited because the authors did not examine the nature of multipurpose cooperatives. Typically, these cooperatives provide services to both members and non-members. ( 11 ) reported that membership in dairy cooperatives in Selale area of Oromia region had significant impact on number of impact indicators. Dairy cooperative membership had positive impact on total annual income of between 14,799 Birr and 15,483 Birr higher total annual dairy income than the non-members, higher proportion of crossbreeds than non-members, cooperative members purchase between 9872.80 kg and 10910.50 kg more than the non-members. Milk production, milk productivity and level of commercialization were also higher for members of dairy cooperatives. According to ( 10 ) agricultural cooperatives positively contributing towards smallholder commercialization and the need to improve internal affairs of cooperatives, strong relationship with GOs and NGOs and encouraging smallholders to join cooperatives. The authors also reported that agricultural cooperatives play a great role in encouraging farmers to produce high value crops, provide agricultural inputs and improved technologies. ( 50 ) in their study in Oromia region on dairy cooperatives employed quasi experimental research design and used both quantitative and qualitative methods for data collection in cross sectional research. Study by ( 50 ) addressed decision to join agricultural cooperatives and impact of agricultural cooperatives membership on household income and assets which may have indirect effect on smallholder commercialization. They pointed cooperative membership improve household income and importance of encouraging smallholders to join cooperatives. The authors also stressed on government intervention in property right related issues. ( 17 ) reported that cooperative membership positively affect profitability of potato farmers. The result however, does not show clear implication on contribution of cooperatives on profitability of potato farmers in the area. This may be associated with services delivered by cooperatives to all including non-members calling for addressing boundary issue and property right as mentioned by ( 52 ). ( 11 ) studied impact of cooperatives by selecting ten indicators including proportion of dairy income to household income, total dairy income, proportion of crossbreed cows to total number of cows in the herd, amount of feed bought, milk production, productivity, price per litre of milk, price per kilogram of butter and the share of milk production that is processed at the household level. The authors also reported that age, education level, household size, access to cooperatives as important variables. 2.3. Conceptual Framework of the Study Household membership in agricultural cooperatives is the decision of households to join agricultural cooperatives and is expected to be affected by endogenous and exogenous factors, including household demographic characteristics and institutional, socioeconomic, natural, physical and psychological factors. The welfare of smallholder households was expected to be positively impacted by membership in agricultural cooperatives. As depicted in Fig. 1 , membership in agricultural cooperatives is expected to be affected by household and household characteristics; socioeconomic, psychological (member value), physical, natural (agro-ecological) and institutional factors. Membership in agricultural cooperatives is expected to affect the welfare of households. 3. Research methodology The Gamo Zone is one of the zones of the southern Ethiopian region, with an astronomical location of approximately 50.57–60 .71\"N latitude and 36 0 . 37–370 .98\" E longitude. It borders the Wolaita Zone in North China, Konso, South Omo and Derashe Zones in South China, the Amaro Zone in East China and the Gofa and Dawro Zones in West China (Gamo Zone Plan Department, 2023 unpublished). The Zone has a total area of 8,222.4 square kilometers or 822,242.8 hectares. In the Gamo Zone, there are 14 rural Woredas and 4 city administrations. Rural Woreda s include Arba Minch Zuria, Gacho Baba, Geresse, Bonke, Kucha, Kucha Alfa, Kemba Zuria, Garda Marta, Boreda, Chencha, Dita, Kogota, Daramlo and Mirab Abaya Woredas. The city administrations in the Zone are Arba Minch, Chencha, Kamba and Selam ber (Gamo Zone Plan Department unpublished report, 2023). According to the 2007 National Population and Housing Census results, the total population size of the Gamo Zone was 1,123,388. On the basis of population projection, the total population of the zone in 2022, considering the annual growth rate of 2.9, was 1,775,403 (883,207 males and 892,197 females). Among the total population in the zone, more than 85% of the population depends on agriculture as their main livelihood (Plan Department of the Gamo Zone, 2023 unpublished). The zone is classified into three ecological zones, i.e., dega (highland) 30.1%, woina-dega (midland) 41.44% and kola (lowland) 28.46%. The annual mean temperature ranges from 10.1°C–27.5°C, whereas the total annual rainfall distribution ranges from 801 mm–2000 mm. There are two distinct rainy seasons: “belg” and “keremt”. Most of the parts of the Gamo Zone experience summer/ kiremt /rainfall caused by the equatorial westerly/Guinea monsoon and southern easterly winds. The rainy season months are June, July, and August, which constitute the summer season. The mean annual rainfall ranges from the lowest value of approximately 801 mm in Kamba Woreda to over 2000 mm in Chencha Woreda . Almost 85% of the rural population in the Zone is employed in agriculture, which is the region's main economic sector. However, it suffers from recurrent droughts, deforestation, high population density, and degradation of the soil from conventional farming methods and overgrazing (Gamo Zone Plan Department, 2023 unpublished report). The Zone has extremely small and dispersed farmland holdings. The main cause of this was the area's high population density, particularly in the highland areas. According to the report, 34.8–36.8% of households own less than 0.5 hectares of land. The majority of households (29.6–32.3%) own between 0.5 and 1 hectare of farmland. In low land areas, only a very small percentage of households (1.93–3.2%) own farmland that is two hectares or more. A sizeable fraction of households (10.2–12.5%) do not employ any farm laborers. Most of them work in needlework, ceramics, small-scale business, and other related fields (Gamo Zone Plan Department, unpublished report, 2023). The Zone has 499,154.9 ha of cultivated land, 133,759 ha of forestland, 88,222 ha of pasture land, and 107,281.8 ha of land. The Zone has 42445.27 ha of arable land, 9496.891 hectares of land are not arable, and the area covered by water bodies is 78,020.5 hectares. A total of 17,753 ha of land is cultivated by agricultural clusters as part of national commercialization clusters, while only 6,206.3 ha of land is mechanized agriculture (Gamo Zone Plan Department, 2023 unpublished report). According to an unpublished report from the Gamo Zone Department of Agriculture, maize, sorghum , barley, wheat and sorghum are major cereal crops cultivated, whereas sweet potato, potato, enset and cassava are major root and tuber crops cultivated in the Zone. The fruit crops banana, mango and apple are dominant in the area, whereas coffee, groundnut, cotton and sesame are known cash crops. Data sources, types and collection tools A mixed research approach was employed. In this mixed research approach, quantitative research was based on descriptive surveys, whereas qualitative research was conducted through focus group discussion (FGD) and key informant interviews (KIIs). The research was based on cross-sectional data from households (members and nonmembers). In this study, a convergent design was employed in which quantitative and qualitative data were collected and analyzed separately for later combination or comparison. This approach was chosen because household-level responses to quantitative questions might not yield the required data, and qualitative data would play a complementary role, as many exogenous and endogenous factors are included in the study. Both quantitative and qualitative data were collected from primary and secondary sources. Quantitative data were collected from selected agricultural cooperatives for a cooperative-level study on the performance of agricultural cooperatives. Quantitative data on factors affecting membership and smallholder farmers’ crop commercialization were collected from members and nonmembers of agricultural cooperatives. Primary data were collected on household and household characteristics, household resource endowments, institutional factors, physical factors, socioeconomic factors, and member value factors (psychological factors) from sample households (members and nonmembers of agricultural cooperatives). Qualitative data related to crop out marketing, challenges, and service delivery were collected from FGD and KII participants. A household-level survey was conducted to collect data on household-level characteristics related to membership and the impact of membership on household market participation. Primary quantitative data on household and household head characteristics, resource endowment, and access to institutional services, physical infrastructure and social factors were collected by conducting household surveys via structured questionnaires through an interview schedule. Sampling Procedures and Sample Size Determination The multistage sampling technique was followed. First, three Woredas with a large number of multipurpose cooperatives were purposively selected from fourteen Woredas. Second, a total of 10 kebeles were randomly selected for the study, comprising three from Arba Minch Zuria (out of 16), four from Boreda (out of 29), and three from Dita (out of 24). Third, after the list (sampling frame) of members and nonmembers of cooperatives in the Kebeles ( stratified into two groups ) was obtained , sample respondents were selected from two strata following a systematic random sampling technique. The sample size for the household survey of the study is determined according to Cochran (1963), which is recommended when the population is large and known ( 53 ). This formula is considered for minimizing the availability of error and bias. The formula for sample determination at the 95% confidence level is described as follows: $$\\:N=\\frac{{Z}^{2}pq}{{e}^{2}}$$ 1 where N is the sample size, Z 2 is the area under the acceptance region in a normal distribution (1 – α), e is the level of precision, p is the proportion of an attribute present in the population, and q is 1- p. On the basis of the formula above, the sample size for the household survey is 385. By adding 10% to 385, the sample size was 424, and by excluding 2 incomplete responses, this study used a sample size of 422 for analysis. Following the probability proportional to size (PPS) method, 160, 147 and 115 sample households from Arba Minch Zuria, Boreda and Dita Woredas, respectively, were included in the study. Among the 422 sample households, 222 were nonmembers, whereas 200 sample households were members. Methods of Data Analysis Qualitative data were analyzed through a content analysis technique that employs a directed data analysis method. In the content analysis technique, qualitative data are analyzed by creating themes for the data gathered. In a theme created, subthemes are also created for issues following the major theme. Data that do not fall under any of the themes are assigned to a new theme. Content analysis helps us understand different themes associated with the issue under consideration. It is also important to understand contexts in different settings. The quantitative data were analyzed via descriptive and inferential statistics. Descriptive statistics, including the mean, percentage, standard deviation, and minimum, maximum and frequency distributions, were used. Inferential statistical tools such as chi-square tests and t tests were used to analyze the associations between explanatory and response variables and to compare the mean differences in household farm income, HDDS and FCS between groups (members and nonmembers of agricultural cooperatives). An endogenous switching regression model was used to analyze the impacts of membership in agricultural cooperatives on the household welfare of smallholder farmers. Model Specification Cooperative members and nonmembers are not directly comparable due to initial differences before joining cooperatives and a bias in selection ( 4 , 14 ). In impact studies, it is therefore important to control bias due to observable and unobservable community- and household-level characteristics ( 11 , 50 , 54 , 55 ). Farming households join agricultural cooperatives to obtain the services delivered by the collective group. A rational farmer would join collective groups when the expected utility gained from joining the collectives is greater than that of nonmembers. In such studies, it is strictly recommended to avoid bias arising from selection on observables and endogeneity from unobservables. The PSM approach generates a control group and then addresses the bias due to selection-on-observables, overt bias ( 56 ). PSM is used in observational studies to adjust for differences in pretreatment variables and to draw inferences about the effects of binary treatments or membership in agricultural cooperatives. However, it fails to control for unobservable selection bias. According to ( 55 ), the utility gain from joining agricultural cooperatives (M* = M 1 – M 0 ) as a function of the observable vector of covariates (Z) can be expressed in a latent model as: M i * = αZ i + η i , M i = 1 if M i * >0 ( 2 ) where M i is a binary variable that is 1 if the household is a member of agricultural cooperatives and 0 otherwise; α is a vector of parameters; Z i is a vector of household and household characteristics and socioeconomic, institutional, social and psychological factors; and η i is a random error term assumed to be normally distributed. Membership in agricultural cooperatives was expected to positively impact household welfare through its multidimensional influences. Household welfare is measured in terms of farm income, HDDS and FCS and is a function of the vector of exogenous variables X i and endogenous membership in agricultural cooperatives (M i ): Y i = βX i + δM i + e i (3 ) where Y i represents the outcome variables (farm income, HDDS and FCS); M i is defined as previously described; β and δ are parameters to be estimated; and e i is the error term. Households may self-select themselves into agricultural cooperatives, which results in bias. PSM can resolve selection bias by controlling for observable covariates; however, it cannot control for bias from unobservable covariates. Therefore, endogenous switching regression, which controls the endogeneity of membership or both observable and unobservable sources of bias, is used ( 14 , 50 , 55 ). Endogenous switching regression (ESR) is employed to evaluate the impact of membership in agricultural cooperatives on welfare in two steps. The first step involves the decision to join agricultural cooperatives, and the second step involves the outcome equation for members and nonmembers separately: Regime 1: Y1i = β1Xi + e1i if Mi = 1 (Members) ( 4 ) Regime 2: Y2i = β2Xi + e2i if Mi = 0 (Nontembers) ( 5 ) where Y 1 and Y 2 represent the outcome (welfare) for members and nonmembers, respectively; x i represents the vectors of covariates of farmer i; β 1 and β 2 are parameters to be estimated; and e 1i and e 2i are error terms associated with the outcome variables. The covariance for the error terms of equations (5) and ( 6 ) above are not determined, as they are not observed simultaneously. The expected values of the error terms e 1i and e 2i are nonzero, as the error term of Eq. 3 is correlated with the error terms in equations 5 and 6 ( ( 57 ) ( 55 ) as: $$\\:E\\:\\left[e1i\\:\\right|\\:M\\:=\\:1]\\:=\\:\\sigma\\:1\\eta\\:\\:(\\phi\\:\\:\\left(Zi\\alpha\\:\\right))/\\:(\\varPhi\\:\\:\\left(Zi\\alpha\\:\\right))\\:=\\:\\sigma\\:1\\eta\\:\\lambda\\:1i$$ 6 $$\\:E\\:\\left[e2i\\:\\right|\\:M\\:=\\:0]\\:=\\:\\sigma\\:2\\eta\\:\\:(\\phi\\:\\:\\left(Zi\\alpha\\:\\right))/\\:(1\\:-\\:\\varPhi\\:\\:\\left(Zi\\alpha\\:\\right))\\:=\\:\\sigma\\:2\\eta\\:\\lambda\\:2i$$ 7 where \\(\\:\\phi\\:\\) is the standard normal probability density function; φ is the standard normal cumulative density function; and λ1i and λ2i are the inverse mill ratios (IMRs) from Eq. 3 and are included in equations ( 7 ) and (8) to correct for selection biases from unobservable factors. On the basis of ( 55 ), the observed and unobserved counterfactual outcomes for agricultural cooperative members can be as follows: E [Y 1i | M = 1] = β 1 X i + σ 1η λ 1i ( 8 ) E [Y 2i | M = 0] = β 2 X i + σ 2η λ 2i ( 9 ) E [Y 2i | M = 1] = β 2 X i + σ 2η λ 1i ( 10 ) E [Y 1i | M = 0] = β 1 X i + σ 1η λ 2i ( 11 ) Equation (8) and Eq. (9) yield welfare for members and nonmembers of agricultural cooperatives, whereas equations (10) and (11) provide counterfactuals for observed welfare for members and nonmembers of agricultural cooperatives. Eq. (10) expresses what would have happened to members if they had not joined agricultural cooperatives, and Eq. (11) is counterfactual for the observed outcome of nonmembers to express the scenario if the households decided to join agricultural cooperatives. From these equations, it is possible to compute the average treatment effect on treated (ATT, the difference between Eq. 8 and Eq. 10) and the average treatment effect on untreated (ATU, the difference between equations 9 and 11), which results in unbiased treatment effects (Adjin et al ., 2020; Mensabo, 2020). AT T = E [Y 1i | M = 1] − E [Y 2i | M = 1] = (β 1 − β 2 ) X i + λ 1i (σ 1ν − σ 2ν ) ( 12 ) AT U = E [Y 1i | M = 0] − E [Y 2i | M = 0] = (β 1 − β 2 ) X i + λ 2i (σ 1ν − σ 2ν ) ( 13 ) In this study, there are two related objectives. Hence, first, factors affecting membership in agricultural cooperatives were analyzed, and then their impact on household welfare was evaluated. However, this article focuses on only the impact aspect. The outcome variable used to measure the impact of membership in agricultural cooperatives is household welfare, which is measured in terms of farm income, household dietary diversity and the food consumption score. The analysis focused on the factors of heterogeneity in the impact of agricultural cooperatives on welfare. In this study, household-level data on farm income (income from the sale of major crops cultivated) were measured by income from the sale of crops, which was used as the output variable. The food security dimension of welfare was approached through HDDSs and FCSs on the basis of ( 59 ) procedures. HDDS was measured by 24-hour food consumption of the household where food groups consumed by 50%+ household members were considered out of twelve food groups. The FCS was measured by considering the food consumption of twelve food groups by 50%+ household member data from the previous seven days. The FCS was computed by considering the weight given to each food group on the basis of ( 59 ). 4. Results and Discussion 4.1. Descriptive Statistics of the Background Characteristics of Respondent Households The data were collected from 422 households, 222 (52.6%) of which are nonmembers of agricultural cooperatives, whereas 200 (47.4%) are members of agricultural cooperatives. The data were collected on household demographic, physical, socioeconomic, and institutional characteristics. In the discussion below, background characteristics are separately discussed for continuous and discrete variables. 4.1.1. Descriptive Statistics of the Respondents (Continuous Variables) The mean age of the respondents in the study was 46.93 years, with a maximum of 85 years and a minimum of 25 years. Education is also another important factor affecting household membership in collective action. The mean years of education completed in the study area was 4.5 years. The survey results also revealed that approximately 37% of the respondents were unable to read and write, whereas 4% of the respondents did not attend formal education and were able to read and write. Thirty-three percent of the respondents reported that they had completed primary education, 20% had completed secondary education, and 6% had completed tertiary education. The survey results revealed that the literacy level in the area is above the national average of 54% ( 60 ), but below sub-Saharan Africa, the average literacy level is 68% (World Bank data bank, 2021) 1 . Household size is another important factor expected to affect household membership in agricultural cooperatives. The mean household size in the adult equivalent is 5.19, with a maximum of 14.65 AE and a minimum of 0.70 AE. Land, as a household asset endowment, is usually considered a proxy for wealth and is expected to affect households’ membership in collective action, as wealth is an important factor affecting households’ ability to join collective action. In this study, the mean land owned by households was 1.35 hectares, which is above the national average of 1.1 hectares ( 60 ). In the study area, 95.7% of the respondents reported that they own land ranging from 0.01 hectares to 15 hectares, which is slightly below the regional average of 97% ( 60 ). Table 1 Descriptive statistics of household background characteristics (continuous variables) Variables Mean Standard Deviation Min Max. Age 46.93 12.19 25 85 Education 4.50 4.50 0 16 HH size 5.19 2.12 0.70 14.65 Land size 1.35 1.77 0.00 15.00 Livestock holding 3.10 3.853 0 25.37 Distance to coop. 2.83 5.14 .00 50.00 Distance to market 8.67 9.16 .00 38.00 Social networks 1.47 1.038 0 5 Business networks .98 1.30 0 8 The results in Table 3 show that 42% of the sampled households had no livestock, whereas 58% reported that they own livestock. The mean number of livestock held in the area is 3.1 TLU. Physical factors such as distance from cooperative offices and market centers are important factors that affect household likelihood of joining agricultural cooperatives. When cooperative offices are far from households, they are less likely to access cooperative extension services such as awareness creation, training, market information and other marketing functions, which makes them less likely to join agricultural cooperatives. The mean distances to cooperative offices and market centers in kilometers are 2.83 and 8.67, respectively. Networks are among the factors expected to affect households’ likelihood of joining collective action. In this study, the number of social and business networks that household heads have was expected to affect membership decisions in agricultural cooperatives. The mean numbers of social and business network household heads are 1.47 and 0.98, respectively. 4.1.2. Descriptive statistics of the respondents (discrete variables) Households living in different agro-ecosystems are expected to join agricultural cooperatives differently, as agro-ecology is an important factor affecting agricultural production, the input needs of farmers, the type of market and other agricultural services that farmers seek to access from cooperatives. Therefore, agro-ecology is considered an important variable affecting households’ decision to join agricultural cooperatives. Among the 422 households included in the study, 109 (25.8%) were from Dega (highland), 106 (25.1%) were from Woinadega (midland), and 207 (49.1%) were from Kolla (lowland agro-ecologies). Psychological factors (perceptions of and trust in the leaders of cooperatives) are expected to affect household membership in agricultural cooperatives. Given that most collective actions in Ethiopia are externally induced, understanding the level of perception and trust and their influence on collective actions should be considered critically ( 61 – 63 ). However, such psychological factors are difficult to measure via dummy questions, and items that are proxies for the perceptions and trust of members have been employed. Measuring Perception: Perception was measured via the following factor analysis technique, where 11 variables indicating households’ perceptions of the benefits that cooperatives deliver were used. Households evaluated their perceptions of the benefits of cooperatives on a five-point scale (1 = strongly disagree, 2 = disagree, 3 = neutral, 4 = agree and 5 = strongly agree). Out of 11 variables, one variable (perception of buying inputs at a good price) was removed because of its insignificant correlation with other variables. After checking for internal consistency through correlation analysis, the retained variables were checked for reliability by using Cronbach’s alpha, and the alpha value was 0.861, indicating reliability. Table 2 Reliability analysis of perception items Items Alpha Selling outputs with good price 0.860 Acquiring extension services 0.857 Acquiring market information 0.838 Time of services delivery 0.847 Accessing preferred markets 0.844 Enable to make business and personal 0.843 Enhance social support 0.847 Quality of services delivered 0.841 Cooperatives have promising growth 0.846 Cooperatives can improve welfare of their members 0.854 Number of Items = 10 Cronbach's alpha value = 0.858 Cronbach's alpha Based on Standardized Items = 0.861 The mean composite score of the 10 items of perception was 34.6. Respondents with composite score values below the mean score were considered to have poor perceptions of the benefits of cooperatives and vice versa. Measuring trust: Trust is an important psychological factor for collective action, as it improves commitment to collective action and reduces transaction costs by lowering the risk of free-rider problems (Ostrom, 2010). Measuring trust is difficult, and relevant items for measuring trust were identified by consulting cooperative experts and leaders and reviewing the theoretical and empirical literature. Five variables for each type of trust in cooperative leaders and trust in the actions of other members are employed. The respondents evaluated their level of trust in the identified items on a five-point Likert-type scale. All five items of trust were retained, as there was a significant correlation among the items, and there was also acceptable reliability, as Cronbach’s alpha test revealed that trust in leaders and trust in the actions of other members were 0.83 and 0.92, respectively. The trust composite score was computed for every respondent, and the respondents were categorized into two trust groups (low and high) on the basis of the composite score computation. Respondents within the score range of minimum to mean have low trust, and those within the range of mean to maximum have high trust in their cooperative leaders and the actions of other members. Accordingly, respondents with composite scores ranging from 5–16.95 and above 16.95 were categorized as having low and high levels of trust in the leaders of cooperatives, respectively. With respect to trust in the actions of other members, those within the range of 5-17.80 were categorized as having low trust, whereas those within the composite score range above 17.80 were categorized as having high trust in other members’ actions. Empirical studies reveal that households headed by females and males join agricultural cooperatives differently. For this purpose, both female- and male-headed households were included in this study. Among the 422 respondent household heads, 110 were female, whereas 312 (73.9%) were male. Table 3 Descriptive Statistics of the Discrete Variables Variables Categories Numbers Percent Agro-ecology Dega (Highland) 109 25.8 Woina-dega (Midland) 106 25.1 Kolla (Lowland) 207 49.1 Gender Female 110 26.1 Male 312 73.9 Off-/Nonfarm participation No 291 68.96 Yes 131 31.04 Access to irrigated land No 304 79 Yes 118 21 Access to cooperative extension/agents No 165 39.10 Yes 257 60.90 Perception on benefits of cooperatives Negative 207 49.05 Positive 215 50.95 Trust on leaders of cooperatives No 130 30.81 Yes 292 69.19 Household participation in off-/nonfarm income activities improves households’ financial position and is expected to encourage households to join agricultural cooperatives, as they can easily meet financial requirements to join collective actions. In this study, 291 (68.96%) households did not participate in off-/nonfarm income activities, whereas only 131 (31.04%) households reported that they were involved in these income activities, which is still above the national average of 27% ( 60 ). Household access to irrigation is an important factor in agricultural production; thus, inputs are also needed. Households with access to agricultural land with access to irrigation were expected to increase their chance of membership in agricultural cooperatives. In the Ethiopian agricultural development strategy, the government designed an extension system to reach rural areas through the deployment of agricultural development agents, cooperative promotion and the organization of experts and animal health workers. In this study, households’ access to cooperative extensions was also expected to affect their probability of joining agricultural cooperatives. The survey results revealed that 165 (39.1%) households had no access to cooperative extensions, whereas 257 (60.9%) households had access to cooperative extensions and contact with cooperative extension workers. Psychological factor perception and trust are believed to be important factors affecting household membership decisions in cooperatives in general and agricultural cooperatives in particular. In the study area, the majority of the respondent households (215 (50.95%) had positive perceptions of the benefits of cooperatives in improving livelihoods, whereas 207 (49.05%) households reported that they did not believe that cooperatives could improve livelihoods. With respect to the trust of respondents in the leaders of cooperatives, 165 (39.1%) had no trust in the leaders of cooperatives, whereas 257 (61.9%) had trust in the leaders of cooperatives. 4.2. Simple Inferential statistics 4.2.1. Mean difference (t test) statistics The mean age of members of cooperatives (48.29 years) is greater than the mean age of nonmembers (45.71 years), highlighting that households with aged heads are more likely to join agricultural cooperatives. This might be because aged household heads have access to agricultural resources (land, livestock, and others) that help them engage in farming activities and use agricultural marketing services from cooperatives. The mean comparison indicates a significant mean difference between the two groups at less than a 5% significance level. This result is similar to those of other previous studies by Gashaw (2018), Bizualem and Saron (2018), Mbagwu (2018), and Miroro et al. (2023). Table 4 Mean difference statistics of continuous variables Variables Mean Std. error of mean difference t test Nonmembers Members Mean difference Age of hh in years 45.71 48.29 2.58 0.59 -2.179** Education completed 4.27 4.76 0.49 0.22 -1.095 HH size in AE 4.80 5.61 0.81 0.10 -4.052*** Land size in hectare 1.12 1.59 0.47 0.09 -2.774*** Livestock holding in TLU 2.81 3.43 0.62 0.19 -1.64 Distance to coop. in km 2.78 2.87 0.09 0.25 − .169 Distance to market in km 7.88 9.54 1.66 0.45 -1.864 The mean education level of cooperative members (4.76) is higher than that of nonmembers (4.27), which is in agreement with previous studies by Gashaw (2018), Hiskeal, Senapathy and Bojago (2022), Miroro et al. (2023) and against the works of Bizualem and Saron (2018). However, there is an insignificant mean difference in education level between members and nonmembers of agricultural cooperatives in the area. Household size is another important factor that is expected to affect household membership in agricultural cooperatives. The mean household size of members (5.61) is greater than that of nonmembers (4.8). The t test statistics of the mean comparison also reveal significant mean differences between members and nonmembers of agricultural cooperatives. The mean land size owned by members (1.59 hectares) is greater than the mean land size owned by nonmembers (1.12 hectares). The t test statistics also reveal that there is a significant difference in the mean land size owned between members and nonmembers of agricultural cooperatives, and this result is analogous to that of studies by Gashaw (2018). Similarly, the results in Table 5 show that the mean number of livestock held by members of agricultural cooperatives is 3.43 TLU, whereas it is 2.81 for nonmembers. The t test statistics, however, do not reveal a significant difference in the means between the two groups. On the other hand, the results in Table 5 show the presence of an insignificant mean difference in distance from cooperative offices and market centers for members and nonmembers of agricultural cooperatives. 4.2.2. Chi-square test statistics The results revealed that the majority of the nonmembers, 179 (80.63%) and 137 (68.5%), were from lowland agro-ecosystems. In contrast, 43 (19.4%) nonmembers were from the midland area, whereas 63 (31.5%) members were from the midland agro-ecology area. The chi-square test (0.01) also reveals a significant association between agro-ecology and household membership in agricultural cooperatives at less than the 5% significance level. The results in Table 6 show that out of 222 nonmember respondents, 150 (67.6%) reported that they had not engaged in off-/nonfarm income activities. Similarly, 141 (70.5%) member respondents reported that they did not engage in off-/nonfarm income-generating activities. The chi-square test (0.53), however, does not reveal a significant association between household participation in these income activities and the decision to join agricultural cooperatives in the study area. In the study area, among the sampled households, 163 (73.42%) nonmember and 136 (71%) member households reported that they had no access to irrigation, whereas 59 nonmember and 64 (21%) member households reported that they had access to irrigation. The chi-square test, however, does not reveal an association between membership in agricultural cooperatives and access to irrigation. Table 5 Chi-square test statistics of discrete variables Variables Categories Membership X 2 value Nonmembers Members Agro-ecology Dega (Highland) 66 43 0.010** Woina-dega (Midland) 43 63 Kolla (Lowland) 113 94 Woina-dega No 179 137 0.004*** Yes 43 63 Kolla No 109 106 0.423 Yes 113 94 Gender Female 47 63 0.02** Male 175 137 Off-/Nonfarm participation No 150 141 0.53 Yes 72 59 Access to irrigated land No 163 141 0.24 Yes 59 59 Access to cooperative extension/agents No 137 28 0.00*** Yes 85 172 Perception on benefits of cooperatives Negative 141 66 0.00*** Positive 81 134 Trust on leaders of cooperatives No 108 22 0.00*** Yes 114 178 Regarding household access to cooperative extension services, 137 (61.7%) nonmembers reported that they had no access to this service, whereas only 28 (14%) member respondents reported that they had no access to these services, highlighting the significance of cooperative extension services. The chi-square test also reveals a significant association between household access to cooperative extensions and membership in agricultural cooperatives in the area at less than the 1% significance level. Among the 207 (49.05%) respondents with negative perceptions of cooperatives, 141 (68.12%) did not join agricultural cooperatives, whereas only 66 (31.88%) of the respondents with negative perceptions were members of agricultural cooperatives. The results in Table 6 show that 165 respondents reported that they had no trust in the leaders of cooperatives; 109 (66.06%) of these did not join agricultural cooperatives, whereas only 56 (33.94%) with no trust joined agricultural cooperatives. Similarly, 144 (56.03%) member respondents had trust in the cooperative leaders before they joined agricultural cooperatives, indicating the importance of building trust among members and leaders of the cooperative organization to attract nonmembers and keep members in the cooperatives. The chi-square test also revealed a significant association between both perceptions and membership decisions and between trust and membership decisions at less than the 1% significance level. 4.3. Factors affecting membership and Impact of agricultural cooperative membership on household welfare 4.3.1. Determinants of membership in agricultural cooperatives: ESR results Household membership in agricultural cooperatives is expected to be affected by various endogenous and exogenous factors. We identified demographic, socioeconomic, physical, institutional and psychological factors. In this study, fourteen ( 14 ) variables related to demographics (sex, age and education level completed by HH and HH size in AE), socioeconomics (farm size, access to irrigation, livestock holding size, and participation in off-/nonfarm activities), physical factors (distance to cooperative offices and the market center), institutional factors (access to extension services) and psychological factors (perception of the benefits of cooperatives and trust in leaders of cooperatives) were included and fitted to the model on the basis of the theoretical and empirical literature. Two variables (perception of the benefits of cooperatives and trust in the leaders of cooperatives) were included as exclusion variables (instrumental). As shown on Table 7 , out of 14 variables, nine ( 9 ) variables statistically significantly affect the membership decisions of households. Among these nine variables, eight variables, including age and education level of the household head, household size in the adult equivalent, access to irrigation, distance to the market center, perceptions of the benefits of agricultural cooperatives and trust in leaders of cooperatives, positively affect the membership decisions of households at less than 1% (four variables), 5% (one variable) and 10% (three variables) significance levels, with all other variables held constant. However, being a male-headed household statistically significantly and negatively affects membership decisions at less than the 1% significance level when all other variables are held constant. Household gender is an important factor for participation in agricultural extension and other social activities and decision-making processes in developing countries. In Ethiopia, mainstream gender in development activities and decision-making processes has been focus for at least the last three decades (Mihrete and Bayu 2021; Alebie 2023). Previous studies in Ethiopia reported mixed results concerning the gender of household heads and the membership decisions of cooperatives in general and agricultural cooperatives in particular. When the household is male, there is a greater probability of joining agricultural cooperatives (Abebayehu 2024), whereas others have reported an insignificant influence of the gender of the household head on membership decisions, including (Woldegebrial, van Huylenbroeck, and Buysse, 2013; Bizualem and Saron, 2018; Dereje, 2021). In this study, the gender of the household head was also included, with the expectation of affecting membership decisions positively when the household head was male. However, the result is against the prior expectation and previous works by Abebayehu (2024). When the household head is male, the probability of joining agricultural cooperatives decreases. Being a female-headed household positively and significantly affects membership in agricultural cooperatives at less than the 5% significance level when all other predictors are held constant and vice versa. The result is against prior expectations, and the reason for this is that agricultural cooperatives in the area are more focused on consumer goods and that cooperative offices have made efforts to bring more women to cooperatives, even as leaders, in many kebeles. This was also assured during focus group discussions and field visits. The age of the household head is an imperative variable affecting household membership in collective action, as it is associated with interest and the ability to fulfill requirements as a member. At less than the 10% significance level, the age of household head positively and significantly affects household membership in agricultural cooperatives in the area. The reason is that aged household heads have better access to agricultural resources, experiences and participation in agricultural activities, which triggers the need for collective actions to access inputs and access output markets. Previous theories also argue that old farmers determine themselves to cooperatives for loyalty to their friends, whereas young farmers focus on the leverage ratio that they obtain from the collective group. The results support the initial expectations of the study and previous studies by Abate (2018), Bizualem and Saron (2018), Mbagwu (2018). This result contrasts with the work of Balgah (2018) on the factors influencing coffee farmers’ ability to join cooperatives in Cameron. The inconsistency with this study might be due to the specialization of production and the geographical variation, which affects the production system, access to agricultural markets, information, market failures and other government services (infrastructure, market facilities and information) and households’ level of literacy and other factors. Education is a tool for innovation and the adoption of better practices for agricultural and rural development interventions. The ESR result is consistent with initial expectations, as education significantly and positively affects household membership in agricultural cooperatives in the area at less than the 1% significance level. The result is also consistent with previous works of Gashaw ( 2018) Dendup, Tashi and Satit (2021), Hiskeal, Senapathy, and Bojago (2022) and against the works of Balgah (2018), Beyene and Nachimuthu (2018) and Bizualem and Saron (2018). The inconsistency with these works may be due to the geographical location and type of cooperative under consideration. Household size is an important factor in agricultural production and food consumption. In this study, it was measured in terms of the adult equivalent, as our targets are farmers, and labor is an important factor for agricultural input needs to join agricultural cooperatives. In this study, this variable was expected to positively affect membership in agricultural cooperatives, as households with large household sizes are expected to participate in agricultural activities and have high consumption needs. In this study, this variable positively and significantly affects membership decisions at less than the 1% significance level. Table 6 ESR results for agricultural cooperative membership and its impact on smallholder farmers’ farm income Log likelihood = -900.80117 Wald chi2( 11 ) = 101.75 Prob > chi2 = 0.0000 LR test of indep. eqns.: chi2( 1 ) = 4.33 Prob > chi2 = 0.0375 Agricultural cooperative Membership Farm income (log of farm income) Variables Coef. Std. Err. Member Nonmember Coef. Std. Err. Coef. Std. Err. Midland 0.278 0.219 Lowland 0.337 0.178* Sex -0.333 0.157** 0.111 0.154 -0.195 0.252 Age 0.018 0.008** 0.009 0.008 0.019 0.011* Education level 0.048 0.018*** 0.090 0.018*** 0.033 0.028 Off-/Nonfarm activity -0.112 0.150 -0.029 0.152 -0.033 0.214 HH size 0.124 0.037*** -0.021 0.042 -0.068 0.054 Farm land size -0.016 0.044 0.119 0.037*** 0.180 0.090** Farming experience -0.003 0.006 -0.004 0.006 -0.004 0.008 Livestock size -0.011 0.020 0.069 0.020*** 0.100 0.031*** Access to irrigation 0.214 0.159 0.802 0.156*** 0.113 0.231 Distance to coop. -0.016 0.015 -0.007 0.021 0.011 0.021 Distance to market 0.017 0.010* -0.009 0.011 -0.043 0.015*** Perception about cooperatives 0.725 0.141*** Trust on coop leaders 1.096 0.169*** Constant -2.963 0.475 9.178 0.464 9.219 0.621 *** 1% level of significance; **5% level of significance; *10% level of significance This result corresponds with those of studies by Gashaw (2018), Hiskeal, Senapathy, and Bojago (2022), Abateneh, Azanaw and Desyalew (2024) and with those of studies by Mbagwu (2018), who reported that household size negatively influences membership in cooperative societies in Abia State of Nigeria. This author explained the reason for the negative influence, as households with large household sizes have greater loads on domestic activities and lack time for cooperative activities. The difference may also be due to the types of cooperatives included in the study, as this author included other agricultural cooperatives, savings and credit cooperatives and farmer multipurpose cooperatives, whereas our study focused on farmer multipurpose cooperatives. Household heads’ access to extension services motivates households to join agricultural cooperatives by raising awareness and understanding about the benefits of collective actions through training and information about inputs, credit and market services ( 66 ). The model results also indicate a positive and significant influence of access to extension services on membership in agricultural cooperatives. The result is in agreement with initial expectations and other studies by Woldegebrial et al. , (2013) and Dejen and Matthews (2016). Among the factors that affect households’ decision to participate and make use of new technologies, practices and innovations, perception and trust are important psychological factors. These two variables, perceptions of the benefits of agricultural cooperatives and trust (perceived trust in leaders and actions of other members of cooperatives), were included in this study because of their perceived influence on membership in agricultural cooperatives. The results of the model reveal that both households’ perceptions of the benefits of agricultural cooperatives and their trust in cooperative management significantly and positively affect membership decisions at a level of less than 1%. This result is similar to the works of Bizualem and Saron (2018), Habtamu (2021) and Hiskeal, Senapathy, and Bojago (2022). 4.3.2. Impact of agricultural cooperative membership on household welfare 4.3.2.1. Impact on farm income Agricultural cooperatives influence household welfare through their impact on agricultural production. Agricultural cooperatives are crucial in facilitating input and output markets in rural areas of developing countries where market failure is pervasive. Welfare is approached through diverse indicators, and in this study, we used proxies including smallholder household farm income and household food security status measured by household dietary diversity (HDD) and the food consumption score (FCS). Agricultural cooperative membership and household farm income: Household-level data for gross farm income were collected from the sampled households. The data were collected on gross income from grain crops, gross income from farm activities and total gross income of the households. The descriptive statistics result on Table 6 indicates that the mean gross farm income of members of agricultural cooperatives 63,323.10) ETB is greater than those of nonmembers (52,046.34) ETB. Table 7 Agricultural cooperatives and household welfare Welfare Indicator Membership Mean Std. Dev Minimum Maximum Gross total farm income Nonmembers 52046.34 78300.39 0 620000 Members 63323.10 109086.51 0 930000 Total 57390.77 94207.45 0 930000 Endogenous switching regression model result of impact on farm income The factors affecting household membership in agricultural cooperatives were determined and discussed in the previous section of this report. Although the ESR model reported both the determinants of membership and the determinants of farm income level, we have discussed here only the determinants of farm income for both members and nonmembers of agricultural cooperatives separately. Owing to data outliers and a lack of concavity, the farm income data were transformed to logarithms of ten and used as the dependent variable for the model. The model fits the data well, as the chi-square test is significant at less than the 1% level. The Wald chi-square is also a significant indicator of the goodness of fit of the model. The rho_2 is significantly different from zero, which means that nonmembers of agricultural cooperatives would have better income if they had joined agricultural cooperatives. The LR dependence equation test chi-square test is also significant (P < 0.0375), which means that the three equations are dependent on each other. Accordingly, the ESR model results revealed that four variables, including education level completed by the household head, livestock size owned, farm size owned and access to irrigation, significantly and positively affect the farm income level of members of agricultural cooperatives. On the other hand, four variables, including the age of the household head, livestock and farm size, are positively and significantly related, whereas the distance from the market center negatively and significantly affects the farm income of nonmembers of agricultural cooperatives. Average treatment effect on the treated and untreated groups The mean farm income (log) of respondents who belong to agricultural cooperatives and the associated counterfactual results were taken into account when calculating the average treatment effect. According to the model, the average effect of treatment on the treated (ATT) is the net difference between these two averages of farm income. The average farm income of nonmembers and its counterfactual, that is, what the average farm income of nonmembers would have been had they joined agricultural cooperatives, are also produced by the model. These two farm incomes are subtracted to determine the ATU. These average farm incomes (log), together with the estimated ATT and ATU, are shown in Table 8 . Table 8 ESR estimates of farm income ATT and ATU Welfare indicators Membership status Treatment effect Effect Member Nonmember Farm income Member 10.41 9.67 0.74 7.65% Nonmember 10.35 10.16 0.19 1.87% Heterogeneity effect 0.06 -0.49 0.55 For both members and nonmembers of agricultural cooperatives, as can be seen from Table 8 , the ATT and ATU are positive and significant, suggesting that participation in agricultural cooperatives has significantly improved farm income for the sampled smallholder farmer households. Farm income increases by 7.65% as a result of membership in agricultural cooperatives. This value is 0.74 log of the farm income of households. However, their farm income would have increased by 1.87% if nonmembers had chosen to join agricultural cooperatives. The observed heterogeneity between members and nonmembers is shown in the final row of Table 4 . The findings indicate that if nonmembers had decided to join agricultural cooperatives, members’ farm income would have increased by approximately 0.58%. Moreover, if members of agricultural cooperatives had decided not to join cooperatives, their arm income would have been 0.48% lower than that of those who did not. The findings indicate that membership in agricultural cooperatives significantly improved the farm income of the smallholder farmers in the sampled Woredas. The heterogeneity shows that membership in agricultural cooperatives improves farm income by 55%. This result is consistent with prior expectations and with those of previous studies in Asia by ( 78 , 79 ), Eastern Africa by ( 80 ), who reported the potential of membership in voluntary groups to improve the income of coffee-growing farmers in Rwanda, and Ethiopia by ( 14 ), who reported the significant impact of membership in cooperatives on improving farm income in Eastern Ethiopia. ( 50 ) reported a positive significant impact of membership in coffee-growing farmer cooperatives on farm income in rural Ethiopia. ( 81 ) also reported a positive effect of membership in Bonga sheep producer cooperatives on household income in southwestern Ethiopia. In his research on the effects of agricultural cooperative membership on farm income and risk management in southern Ethiopia (Silte and Sidama), ( 82 ) also reported that membership had a positive effect. The study context, however, differs from this study area in terms of farming systems, resources at the household level, and the development of institutional and physical infrastructure. This finding also demonstrates the potential of agricultural cooperatives to improve household welfare in the study area and the substantial impact that cooperative membership has on smallholder households' farm income. 4.3.2.2. Impact of Membership on household food security (Dietary Diversity and Food Consumption Scores) An impact of agricultural cooperative membership was expected to affect household food security status. Food security is an argued concept in literature where many definitions and ways of measurements (indicators) have been developed. Based on recent literature, household dietary diversity and food consumption scores are indicators used in this study. According to the study result, the mean HDDS of the study area is 6.77, whereas the mean FCS score is 35.40. However, there is no significant mean difference between the food security status of members and nonmembers of agricultural cooperatives. In contrast, the mean HDDS of nonmembers (6.85) is slightly greater than that of members (6.68). FCS data also show no significant mean difference, where the mean FCS of members (35.82) is greater than that of nonmembers (35.01). According to the WFP food security classification, the mean FCS of nonmembers is on the borderline, whereas the mean FCS of members of agricultural cooperatives is on an acceptable profile ( 59 ). Table 9 Agricultural cooperatives and household food security (dietary diversity and food consumption scores) Welfare Indicator Membership Mean Std. Dev Minimum Maximum Household Dietary Diversity Nonmembers 6.85 2.31 0 12 Members 6.68 1.97 2 11 Total 6.77 2.15 0 12 Food Consumption Score Nonmembers 35.01 17.76 5 137 Members 35.82 17.84 6 83.5 Total 35.40 17.78 5 137 Membership in agricultural cooperatives influences household food security in multiple ways ( 14 , 15 , 83 ). In this study, data on household food security status were collected via the indicators HDDS and FCS on the basis of ( 59 ) procedures. To analyze the food security status of households via the HDDS indicator, the food consumption of the household in the last 24 hours was collected, and the consumption of twelve food groups by 50%+ household members was considered. To analyze the food security status of the households by the FCS indicator, the food consumption data of twelve food groups by 50%+ household members for the last seven days were collected. The FCS was computed by considering the weight given to each food group on the basis of ( 59 ). The mean HDDS of the study area is 6.77, whereas the mean FCS score is 35.40. However, there is no significant mean difference between the food security status of members and nonmembers of agricultural cooperatives. In contrast, the mean HDDS of nonmembers (6.85) is slightly greater than that of members (6.68). FCS data also show no significant mean difference, where the mean FCS of members (35.82) is greater than that of nonmembers (35.01). According to the WFP food security classification, the mean FCS of nonmembers is on the borderline, whereas the mean FCS of members of agricultural cooperatives is on an acceptable profile ( 59 ). The households’ dietary diversity score was computed for every respondent. According to the survey results, 68.25% of the sampled households had high dietary diversity, whereas 27% had medium dietary diversity. Seventy percent of the members of agricultural cooperatives have high dietary diversity, whereas 66.22% of nonmembers have high dietary diversity. When we compare membership households in agricultural cooperatives, the percentage of nonmember households with the lowest dietary diversity (4.95%) is slightly greater than that of members (4.5%). Table 10 HDDS classification of the sampled households Membership Mean HDDS Food security profile Lowe dietary diversity ( ≤ 3 food groups) Medium dietary diversity (4 and 5 food groups) High dietary diversity ( ≥ 6 food groups N % N % N % Nonmembers (222) 6.85 11 4.95 64 28.83 147 66.22 Members (200) 6.68 9 4.50 50 25.00 141 70.50 Total (422) 6.78 20 4.74 114 27.01 288 68.25 Household food security status (FCS) category The household composite score of the FCS is converted into categories on the basis of the WFP 2008 classification. Households with composite food consumption scores less than 21 are categorized as poor, those with scores ranging from 21.5–35 are borderline, and those with FCSs greater than 35 are considered acceptable. Accordingly, 43.36% of the sampled households in the study area are in the acceptable food security profile, whereas 33.65% and 22.99% are in the borderline and poor food security profiles, respectively. With respect to membership status and the food security profile, 44% and 42.79% of the sampled member and nonmember households, respectively, fall within the acceptable food security profile. Table 11 Food security profile of households Membership Mean FCS Food security profile Poor (0–21) Borderline (21.5–35) Acceptable (> 35) N % N % N % Nonmembers (222) 35.01 51 22.97 76 34.23 95 42.79 Members (200) 35.82 46 23.00 66 33.00 88 44.00 Total (422) 35.40 97 22.99 142 33.65 183 43.36 ESR model results for the HDDS and FCS average treatment effects The impact of agricultural cooperative membership on household food security is determined via the ESR model, where the average treatment (AT) effect on treated (ATT) and untreated (ATU) food security is computed. The results show that for both impacts on HDDS and FCS, the treatment effect is significantly different from 0, suggesting rejection of the null hypothesis and acceptance of the alternative hypothesis, as the p value is significant at less than the 1% and 5% significance levels. As shown in Table 10 , for members of agricultural cooperatives, HDDS increased by 1.86 because their membership or members’ HDDS would be less than 1.86 if they had not been a member. The treatment effect on members’ HDDS is 38.59%, indicating that membership significantly improves the food security status of members and improves the status of nonmembers if they join collective groups. For nonmembers of agricultural cooperatives, HDDS increases by a score of 1.5 if they join agricultural cooperatives, which is an increase of 21.96%. The heterogeneity effect is that if nonmembers had chosen to join agricultural cooperatives, their HDDS would have been approximately 29.43% higher than that of members. Moreover, if members of agricultural cooperatives had not joined, their HDDS would have been 19.81% lower than that of those who did not join agricultural cooperatives. Table 12 ESR estimates of farm income ATT and ATU Outcome Selection Membership status Treatment effect Effect (%) Member Nonmember HDDS Member 6.68 4.82 1.86 38.59 Nonmember 8.33 6.83 1.50 21.96 Heterogeneity effect -1.65 -2.01 0.36 FCS Member 35.82 14.94 20.88 139.76 Nonmember 40.75 34.98 5.77 16.49 Heterogeneity effect -5.68 -18.33 12.65 Regarding the FCS, as indicated in Table 10 , the ATT for members of agricultural cooperatives is 20.88, which is 139.76% higher than their counterfactual value. For nonmembers, ATU has a score of 5.77, which is what the FCS of nonmembers of agricultural cooperatives would be higher by 16.49% if they had joined. The heterogeneity effect indicated in the lower line of Table 10 is that the FCS of nonmembers would be 52.40% greater than that of members if they did not join agricultural cooperatives. The FCS of members would be 13.94% lower than that of nonmembers if they joined agricultural cooperatives. The negative sign of the heterogeneity effect indicates that agricultural cooperative membership would benefit more nonmembers than would members of cooperatives if they joined ( 15 ), suggesting the importance of further work to bring more households as members of agricultural cooperatives. This result is consistent with the initial expectation of the study, as agricultural cooperatives are expected to improve household-level food security through their contribution to agricultural production and productivity improvement and through their multiplier effect on household income, favoring conditions for credit and market information and improving market access for agricultural inputs and outputs. This result is also similar to that of Olumeh & Mithöfer (2023), who reported a significant impact of membership in collective action on household welfare in Malawi. Many previous studies have also reported a positive significant effect of membership on household welfare (food security), including consumption expenditures (Seneerattanaprayul & Gan, 2021) in Thailand and ( 85 ) in the Philippines. The ESR model results indicate that membership in farmer organizations in northern Ghana positively impacts household dietary diversity and reduces food insecurity ( 86 ). This finding is also similar to that of Musa & Hiwot (2017), who reported a positive effect of cooperative membership on household food security in Eastern Ethiopia. Muhammed Abdella & Callo-Concha (2021) also reported a positive impact of access to markets through collective groups on the dietary diversity and food security of coffee-producing smallholders in southwestern Ethiopia. Agricultural cooperative membership in the Halu district of the Oromia region in Ethiopia was also found to positively affect household food balance and the household food insecurity access scale (HFIAS) ( 15 ). 5. Conclusion and policy implications Many empirical findings have reported the impact of membership in developing countries. The impact dimensions considered by different studies also vary, ranging from agricultural production and productivity improvement to the use of agricultural technologies (inputs), market participation, and household welfare (income and food security). The reports are mixed and inconclusive in many instances. In this study, the impact analysis involved at least three indicators of household welfare, including farm income, the household dietary diversity score (HDDS) and the household food consumption score (FCS). By employing the ESR model for its ability to address observable sample selection bias and unobservable factors, the average treatment effect of membership in agricultural cooperatives was determined. The mean income comparison of agricultural cooperative members and nonmembers indicates that the gross mean gross income from the sale of cereal grains, total farm income and total income of household members are higher than those of nonmember households. The ESR model also reveals that there is a heterogeneous impact of household and household characteristics on the level of farm income of respondents. The education level of the household head, livestock and farm size owned and access to irrigation positively and significantly affect the farm income of members, whereas the age of the household head, distance to the market center, livestock and farm size owned significantly affect the farm income level of nonmembers of agricultural cooperatives. The average treatment effect (AT) results show that agricultural cooperatives have a positive significant effect on the farm income of smallholder farmer households. The farm income of members of agricultural cooperatives increased by 7.4%, whereas it would be higher by 1.9% for nonmembers if they had joined collective groups. The HDDS analysis indicated that 70.5% of the members were in the high dietary diversity category, whereas 66.22% of the nonmembers were in the high dietary diversity category. The mean FCS of the members is greater (35.82) than the mean FCS of the nonmembers (35.01). The mean food consumption of members is in the acceptable food profile, whereas that of nonmembers is in the borderline profile. In the study area, only 43.36% of the households fell within the acceptable food security profile, highlighting the vulnerability of these areas to food insecurity. The regression analysis also reveals that the gender and education level of the household head, livestock and farm size of the household significantly positively affect the household dietary diversity score (HDDS), whereas the household size, access to irrigation, and distance to the market center significantly negatively affect the HDDS of members. Household size, distance to the market center and livestock size significantly affect the HDDS of nonmembers. The household FCS, as an indicator of the food security status of members, is also significantly affected by the household head’s education level, market proximity, and access to irrigation, whereas livestock size, farm size, and distance to the market center significantly affect the food security status of nonmembers. The regression results of the average treatment effect highlight that membership in agricultural cooperatives significantly improves and improves the food security status of members and nonmembers. Declarations Acknowledgement We would like to thank Woreda cooperative Office heads, experts, cooperative promotions agents and Kebele leaders who cooperated during data collection by devoting their time for interview and disclosing all necessary secondary data including audit reports. We also appreciate the perseverance and commitment of data enumerator for collecting quality data in desired time period. Finally, we thank all cooperative leaders who cooperated and participated during data collection. Author contribution AS managed the conceptualization of the study design, data collection and cleaning, writing of drafts, and the write up of the final manuscript to its submission. EN, TB, CS and FE provided technical supervision in the study design, data analysis, contextualization of results and discussion, preparation, proof reading, and editing of the final manuscript to its submission. Funding This study was funded by Haramaya University with grant code HUSP_2023_8254 Data Availability Statement The data collected for analysis of this research is available at Mendeley Data and Digital Commons Data at https://data.mendeley.co/datasets m /kd8422nbjj/1 Ethical Statements The study was conducted after ethical approval of IRB of College of Agricultural Sciences of Arba Minch University. It strictly followed the ethical guideline of the University. Ethical approval The research was approved by Institutional Review Board of College of Agricultural Sciences of Arba Minch University before going to field data collection. Consent to participate Informed consent was obtained from all individual participants included in the study. Consent for publication We declare that all of the material in the manuscript is owned by the authors and/or no permissions are required. Competing Interest We here declare that all authors of the article have no conflict of interest. Clinical trial number: not applicable. Author details 1 Department of Rural Development and Agricultural Extension, Arba Minch University 2 School of Rural Development and Agricultural Innovation, Haramaya University 3 Department of Economics, Arba Minch University References World Bank. Agriculture for Development. World Development Report 2008. Washington DC; 2008. 390 p. FAO. 2017 The State of Food and Agrivulture Leveraging Food Systems for Inclusive Rural Transformation. Population and Development Review. 2017. Moti J, Berhanu G, Hoekstra D. Smallholder commercialization: Processes, determinants and impact. Discussion Paper No. 18. Improving Productivity and Market Success (IPMS) of Ethiopian Farmers Project, ILRI (International Livestock Research Institute), Nairobi, Kenya. 55 pp. ILRI. Addis Ababa; 2009. Bernard T, Gabre-Madhin E, Taffesse AS. Smallholders’ Commercialization through Cooperatives: A Diagnostic for Ethiopia. Hist Stud [Internet]. 2007;(October):33. 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1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":38534,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eConceptual framework of the study\\u003c/p\\u003e\\n\\u003cp\\u003eSource: Developed for our own study (2023)\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6844791/v1/b20b4258b06461a275af37c0.png\"},{\"id\":86859147,\"identity\":\"3f13d4e5-6cf6-4229-a71b-8a324b265989\",\"added_by\":\"auto\",\"created_at\":\"2025-07-16 11:52:42\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":579184,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eLocation map of the Gamo Zone\\u003c/p\\u003e\\n\\u003cp\\u003eSource: Gamo Zone Plan Department (2023)\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6844791/v1/c6a79e40c2d8afe15fbb8ed2.png\"},{\"id\":87508531,\"identity\":\"434fabde-1990-400a-95a6-fec07f33bc1a\",\"added_by\":\"auto\",\"created_at\":\"2025-07-24 15:08:43\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":2486301,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6844791/v1/a45362e3-b58b-4510-8639-8f1bb68e17bf.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Impact of Agricultural Cooperative Membership on Households’ Welfare in South Ethiopia Region, Ethiopia\",\"fulltext\":[{\"header\":\"1. Introduction\",\"content\":\"\\u003cp\\u003eAgriculture is a basis for economic growth and private sector investment and drives agriculture-related industries, and it is important for food security through employment and sources of income (\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e) Globally, 475\\u0026nbsp;million smallholdings with less than 2 hectares of land provide a livelihood for almost two billion, and in Asia and Sub Saharan Africa, these small holdings provide 80% of the food consumed. A report by the FAO highlights that food production should at least double in 2050 in developing countries, particularly Africa and South Asia, for increasing food demand (\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eModernizing agricultural production systems and commercializing smallholder agriculture are considered indispensable for the economic growth and development of most developing countries (\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e). Increased production and productivity also have implications for the livelihoods of low-income urban populations, the capacity to mobilize the rural economy and the ability to serve as engines for the national economy through its multiplier effects (\\u003cspan additionalcitationids=\\\"CR4\\\" citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e)\\u003c/p\\u003e\\u003cp\\u003eNevertheless, following market liberalization and a free market economy system, developing countries face challenges in addressing the development needs of agricultural- and rural-based populations due to pervasive market failures. These market failures of the free-market economy require institutional arrangements that can fill the gaps of markets for agricultural inputs and outputs to enhance the livelihood of smallholders. Agricultural cooperatives are among the institutional arrangements that are widely recognized for improving the market access of smallholders in developing countries (\\u003cspan additionalcitationids=\\\"CR7\\\" citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eIn Ethiopia, agricultural cooperatives are important service providers of agricultural inputs and means to access output markets for smallholders, but their contribution to smallholders\\u0026rsquo; household welfare is inconclusive. Furthermore, agricultural cooperatives in Ethiopia serve the whole community regardless of membership, and it is not well known that joining cooperatives truly matters for smallholder household welfare (\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eThe empirical results concerning the impact of cooperatives on household benefits from collective actions are mixed. As cooperatives provide services and benefit the community in general, regardless of membership, some studies reported no significant impact of agricultural cooperatives (\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e), whereas other studies reported a significant positive impact of membership (\\u003cspan additionalcitationids=\\\"CR12\\\" citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e). A study by (\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e) in Eastern Ethiopia revealed a significant positive impact of membership in agricultural cooperatives on household welfare measured in consumption expenditures. It was also reported that membership has a significant effect on the household income of maize-producing farmers (\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e). In terms of welfare indicators considered by scholars, impact studies in Ethiopia have focused mainly on the price received by farmers (\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e), the profitability of farmers (\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e), and total household income (\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e). This study contributes to the growing literature on the impact of agricultural cooperatives in that it considers the specific impact of these cooperatives in increasing farm income, as few studies have considered this indicator. Furthermore, it considers food security dimensions through dietary diversity and food consumption scores, which are considered relevant indicators for household-level studies because they value the diversity of foods consumed as well as the nutritional dimension (quality) of food consumed by households (\\u003cspan additionalcitationids=\\\"CR19 CR20\\\" citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e). Moreover, studies on the impact of household welfare in Ethiopia and the study area in particular are scarce. Therefore, this study contributes to the growing literature on the impact of agricultural cooperatives with a focus on household welfare in southern Ethiopia.\\u003c/p\\u003e\"},{\"header\":\"2. Related Literature Review\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.1. Theoretical Framework\\u003c/h2\\u003e\\u003cp\\u003eThis research is based on the new institutional economics (NIE) perspective, specifically the application of collective action theory and transaction cost economics (TCE) to the context of ACs. Institutional economics theory of collective action, sociological and anthropological theories, and the theory of transaction cost economics (TCE). Institutional economists focus on how collectives find solutions for social dilemmas where collective action generates more benefit for the collective than does acting individually. It focuses on factors that influence actors\\u0026rsquo; capacity to cooperate, and this cooperation creates institutions called \\u0026ldquo;rules in use\\u0026rdquo;. This theory is founded on methodological individualism, where the center of explanation of social phenomena is grounded in the interests and behavior of individuals (\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eLikewise, sociological and anthropological theories of collective action consider societal-level interactions, the heterogeneity of social members and factors that are exogenous to the community (\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e). Collective action theories and collectivism are also explained by three dominant schools of thought. These schools of thought are traditional collective action theory or Olson\\u0026rsquo;s theory, resource mobilization or social movement, and social psychological theory (\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e, \\u003cspan additionalcitationids=\\\"CR24\\\" citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eThe traditional theory of collective action is known by two prominent theories, rational choice and zero contribution theories, where Olson (1965), in his work of \\u0026ldquo;\\u003cem\\u003eThe logic of collective action: public goods and the theory of\\u003c/em\\u003e groups,\\u0026rdquo; coined that, in collective groups, when the group size increases, a rational individual wants to maximize his/her benefit without contributing to collective action, which results in the problem of free riders unless there are successful institutional norms of the group or otherwise unless externally enforced coercion mechanisms are used to control the opportunistic behavior of individuals (\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e). Rational decision theory dates back a long time, and it is about individuals\\u0026rsquo; decision-making behavior, where individuals make decisions by analyzing all possible options to make optimal decisions. First introduced by Adam Smith with the work \\u0026ldquo;an inquiry in to the nature and causes of the wealth of nations\\u0026rdquo; in 1776, Adam Smith coined that the human nature of the tendency toward self-interest is a basis for prosperity (\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eThe zero contribution theory of Olson (1965) concerns the social dilemma game of public goods; those who believe that others cooperate continue to contribute, whereas rational egoist individuals should not be affected by this belief that others\\u0026rsquo; contributions continue and benefit from the public good without any contribution called \\u0026ldquo;zero contribution\\u0026rdquo;. (\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e) also introduced the concept of the theory of social dilemmas called \\u0026ldquo;\\u003cem\\u003eThe tragedy of the commons\\u003c/em\\u003e\\u0026rdquo;, where short-term individual interest is against long-term group interest in open access resources (\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e). However, scholars such as Ostrom argued with the theory of contribution, where strong institutions force individuals to contribute to common goals (\\u003cspan additionalcitationids=\\\"CR30 CR31 CR32\\\" citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e), whereas the theory of embeddedness criticizes both theories for not considering other social, cultural and political contexts when individuals make economic decisions (\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eRegarding motives of joining agricultural cooperatives, theory of resource mobilization argues that grievances are reasons for joining collective groups to access and control resources collectively rather than acting individually, which is later called resource mobilization (\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e). Resource mobilization theory is a process of acquiring the resources needed to achieve predetermined goals of the collective through different mechanisms. This finding suggests that the realization of collective goals depends mainly on the capital that society owns. Resource mobilization theory therefore argues that access to available resources and opportunities for resource mobilization are important determinants of collective action. Agricultural cooperatives are meant to achieve market power by joining together, investing in cooperatives and participating in the affairs of cooperatives with the intention of mutual benefit. Membership in agricultural cooperatives is also affected by resource endowments and access to resources. Households join cooperatives to solve problems related to resources (knowledge, capital, physical resources and others), which are important for the production and marketing of farm products.\\u003c/p\\u003e\\u003cp\\u003eTransaction cost theory was first introduced by (\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e), who described, in his seminal paper, that transaction costs are created during business relations and that their level affects the type of economic activity that is organized and run, which he thought is the root cause of market failure (\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e). He argued that inefficient resource utilization causes market failure, which could be resolved by introducing new methods of doing business through institutional arrangements. Later, Williamson (1975) formally introduced transaction costs into neo-institutional economics by assuming that transactions are risky and unpredictable and that those involved have problems of opportunity and bounded rationality. For this and other reasons, he suggested reforming governance structures and bringing institutional arrangements such as cooperatives could solve market failures. Economic activity in formal organizations and the spot market varies with considerable economic activity in formal organizations, with high transaction costs because of imperfect information (\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e). Institutional arrangements such as agricultural cooperatives can solve or minimize transaction costs by introducing new ways of doing business (\\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e). Agricultural cooperatives achieve a reduction in transaction costs through their influence on market information, improving the bargaining power of members through economies of scale and group marketing and the provision of services. A reduction in transaction costs due to the joining of agricultural cooperatives encourages smallholder farmers to join.\\u003c/p\\u003e\\u003cp\\u003eTheory of social capital illuminates that social capital facilitates collective action mainly through \\u0026ldquo;trust and norms of reciprocity embedded in social networks\\u0026rdquo; (\\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e). Social capital has important economic implications through lowering the transaction costs of exchange, reducing the cost of enforcing rules in provision and appropriation, providing informal insurance mechanisms by facilitating adaptation in risky environments and improving local authorities\\u0026rsquo; performance by drawing them into networks (\\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e). Social capital through social interaction works through observation, hierarchies and reciprocity (networks and clubs). Hierarchies and clubs help in providing capacity for a group decision. It is useful because social interaction generates positive externalities through knowledge about the behavior of agents in the group and knowledge about non-behavior factors such as price and technologies, and it avoids the free-rider problem through norms (\\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e). In collective action, social capital assets are categorized as cognitive, altruism or structural (\\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e43\\u003c/span\\u003e). Social capital facilitates collective action, and it is recommended that both concepts be considered during empirical studies (\\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e). Social capital can contribute to the reduction of transaction costs through improving trust and confidence in other actions, which can contribute to the reduction of opportunism and free rider problems of collective action. A high level of trust results in compliance with rules and the regulation of cooperatives, which in turn results in agency problems.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.2. Empirical literature\\u003c/h2\\u003e\\u003cdiv id=\\\"Sec5\\\" class=\\\"Section3\\\"\\u003e\\u003ch2\\u003e2.2.1. Factors affecting membership decisions\\u003c/h2\\u003e\\u003cp\\u003eFactors like age, farming experience, household size, and wealth all had an impact on cooperative involvement in Nigeria's Abia State (\\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e44\\u003c/span\\u003e). In order to determine the factors influencing participation in coffee cooperatives in Rwanda's Huye district, (\\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e45\\u003c/span\\u003e) conducted a study in which he discovered that the following variables were statistically significant influences on membership in the coffee cooperative: the age of the household head, the size of the household, the distance to the cooperative washing station, the availability of credit, prior experience with coffee cultivation, and the volume of coffee produced.\\u003c/p\\u003e\\u003cp\\u003e(\\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e46\\u003c/span\\u003e) in study of \\u0026ldquo;drivers of agricultural cooperative formation and farmers' membership decision in Ethiopia\\u0026rdquo; found that location, the scale of operation, specialization, and human and relational capital were strongly related to household decision to join agricultural cooperatives while a households\\u0026rsquo; membership and patronage decisions are also affected by the size, specialization and integration of the cooperatives. (\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e) identified variables related to social responsibility, distance from the market centre, and distance from the Office of cooperatives and land size owned as significant variables affecting membership in cooperatives.\\u003c/p\\u003e\\u003cp\\u003e(\\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e47\\u003c/span\\u003e) in their study in Arsi Zone's Lemu-Arya and Bekoji dairy marketing cooperatives reported that a household's decision to join a dairy cooperative is significantly influenced by educational level, livestock holdings, number of dairy cows owned, labor, off-farm participation, and credit access, perception towards cooperative organizations, extension service, family size, and distance from the cooperative milk collection centre to the farmer's home. In their study of agricultural cooperatives in the Bench Maji Zone of Ethiopia, (\\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e48\\u003c/span\\u003e) looked at opportunities and challenges as well as levels of awareness, information access, marketing and cooperative promotion efforts' promotion and supporting roles, respondents' educational attainment, embezzlements, training, farmers' attitudes toward cooperatives, trust between members and the management committee, and leadership commitment. (\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e) also reported age, education level, and family size are positively correlated with the chance of joining a cooperative, whereas land size, agricultural experience, and proximity to a milk collection centre are adversely correlated. (\\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e49\\u003c/span\\u003e) reported access to information, special skill, membership in rural associations, frequency of attendance at public meetings or workshops, household head education,\\u003c/p\\u003e\\u003cp\\u003eAccording to the study conducted on agricultural cooperatives age and education of household head, household size, level and land property are variables significantly affect membership in agricultural cooperatives (\\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e50\\u003c/span\\u003e). By employing tobit model regression, (\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e) (\\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e51\\u003c/span\\u003e) analysed community level factors affecting participation in agricultural cooperatives in Amhara and Oromia regions of Ethiopia and found that access to road, access to information and land size owned significantly affect level of participation. This research considered \\u003cem\\u003eKebele\\u003c/em\\u003e level participation of household as dependent variable where mean of households in the \\u003cem\\u003eKebele\\u003c/em\\u003e on selected variables was considered as independent variable. Analysing the participation level at \\u003cem\\u003eKebele\\u003c/em\\u003e level is problematic as household level factors are very important in affecting households to decide to participate. Participation decision is mainly affected by socio-economic, demographic and psychological factors of household and household head rather than physical and institutional factors though their effect is renowned.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec6\\\" class=\\\"Section3\\\"\\u003e\\u003ch2\\u003e2.2.2. Impacts of membership in agricultural cooperatives on household welfare\\u003c/h2\\u003e\\u003cp\\u003eAgricultural cooperatives have a major impact on members' income, production, and fertilizer unit costs, according to (\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e). Active cooperative members would have escaped a 1.37 quintal per hectare decline in yield and an 1804 ETB drop in income. Similarly, the cost of fertilizer would have gone up by 22 birr if a member had not joined. Their results, however, show that there was no appreciable difference in the marketable surplus or fertilizer adoption between members and non-members. This defies the authors' theory, which held that cooperatives had less of an effect on market orientation and agricultural production performance. This generalization is limited because the authors did not examine the nature of multipurpose cooperatives. Typically, these cooperatives provide services to both members and non-members.\\u003c/p\\u003e\\u003cp\\u003e(\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e) reported that membership in dairy cooperatives in Selale area of Oromia region had significant impact on number of impact indicators. Dairy cooperative membership had positive impact on total annual income of between 14,799 Birr and 15,483 Birr higher total annual dairy income than the non-members, higher proportion of crossbreeds than non-members, cooperative members purchase between 9872.80 kg and 10910.50 kg more than the non-members. Milk production, milk productivity and level of commercialization were also higher for members of dairy cooperatives.\\u003c/p\\u003e\\u003cp\\u003eAccording to (\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e) agricultural cooperatives positively contributing towards smallholder commercialization and the need to improve internal affairs of cooperatives, strong relationship with GOs and NGOs and encouraging smallholders to join cooperatives. The authors also reported that agricultural cooperatives play a great role in encouraging farmers to produce high value crops, provide agricultural inputs and improved technologies.\\u003c/p\\u003e\\u003cp\\u003e(\\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e50\\u003c/span\\u003e) in their study in Oromia region on dairy cooperatives employed quasi experimental research design and used both quantitative and qualitative methods for data collection in cross sectional research. Study by (\\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e50\\u003c/span\\u003e) addressed decision to join agricultural cooperatives and impact of agricultural cooperatives membership on household income and assets which may have indirect effect on smallholder commercialization. They pointed cooperative membership improve household income and importance of encouraging smallholders to join cooperatives. The authors also stressed on government intervention in property right related issues.\\u003c/p\\u003e\\u003cp\\u003e(\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e) reported that cooperative membership positively affect profitability of potato farmers. The result however, does not show clear implication on contribution of cooperatives on profitability of potato farmers in the area. This may be associated with services delivered by cooperatives to all including non-members calling for addressing boundary issue and property right as mentioned by (\\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e52\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003e(\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e) studied impact of cooperatives by selecting ten indicators including proportion of dairy income to household income, total dairy income, proportion of crossbreed cows to total number of cows in the herd, amount of feed bought, milk production, productivity, price per litre of milk, price per kilogram of butter and the share of milk production that is processed at the household level. The authors also reported that age, education level, household size, access to cooperatives as important variables.\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.3. Conceptual Framework of the Study\\u003c/h2\\u003e\\u003cp\\u003eHousehold membership in agricultural cooperatives is the decision of households to join agricultural cooperatives and is expected to be affected by endogenous and exogenous factors, including household demographic characteristics and institutional, socioeconomic, natural, physical and psychological factors. The welfare of smallholder households was expected to be positively impacted by membership in agricultural cooperatives.\\u003c/p\\u003e\\u003cp\\u003eAs depicted in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e, membership in agricultural cooperatives is expected to be affected by household and household characteristics; socioeconomic, psychological (member value), physical, natural (agro-ecological) and institutional factors. Membership in agricultural cooperatives is expected to affect the welfare of households.\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"3. Research methodology\",\"content\":\"\\u003cp\\u003e\\u003cb\\u003eThe\\u003c/b\\u003e Gamo Zone is one of the zones of the southern Ethiopian region, with an astronomical location of \\u003csup\\u003eapproximately 50.57\\u0026ndash;60\\u003c/sup\\u003e.71\\\"N latitude and 36\\u003csup\\u003e0\\u003c/sup\\u003e.\\u003csup\\u003e37\\u0026ndash;370\\u003c/sup\\u003e.98\\\" E longitude. It borders the Wolaita Zone in North China, Konso, South Omo and Derashe Zones in South China, the Amaro Zone in East China and the Gofa and Dawro Zones in West China (Gamo Zone Plan Department, 2023 unpublished). The Zone has a total area of 8,222.4 square kilometers or 822,242.8 hectares. In the Gamo Zone, there are 14 rural \\u003cem\\u003eWoredas\\u003c/em\\u003e and 4 city administrations. Rural \\u003cem\\u003eWoreda\\u003c/em\\u003es include Arba Minch Zuria, Gacho Baba, Geresse, Bonke, Kucha, Kucha Alfa, Kemba Zuria, Garda Marta, Boreda, Chencha, Dita, Kogota, Daramlo and Mirab Abaya \\u003cem\\u003eWoredas.\\u003c/em\\u003e The city administrations in the Zone are Arba Minch, Chencha, Kamba and Selam ber (Gamo Zone Plan Department unpublished report, 2023).\\u003c/p\\u003e\\u003cp\\u003eAccording to the 2007 National Population and Housing Census results, the total population size of the Gamo Zone was 1,123,388. On the basis of population projection, the total population of the zone in 2022, considering the annual growth rate of 2.9, was 1,775,403 (883,207 males and 892,197 females). Among the total population in the zone, more than 85% of the population depends on agriculture as their main livelihood (Plan Department of the Gamo Zone, 2023 unpublished).\\u003c/p\\u003e\\u003cp\\u003eThe zone is classified into three ecological zones, i.e., \\u003cem\\u003edega\\u003c/em\\u003e (highland) 30.1%, \\u003cem\\u003ewoina-dega\\u003c/em\\u003e (midland) 41.44% and kola (lowland) 28.46%. The annual mean temperature ranges from 10.1\\u0026deg;C\\u0026ndash;27.5\\u0026deg;C, whereas the total annual rainfall distribution ranges from 801 mm\\u0026ndash;2000 mm. There are two distinct rainy seasons: \\u003cem\\u003e\\u0026ldquo;belg\\u0026rdquo;\\u003c/em\\u003e and \\u0026ldquo;keremt\\u0026rdquo;. Most of the parts of the Gamo Zone experience summer/\\u003cem\\u003ekiremt\\u003c/em\\u003e/rainfall caused by the equatorial westerly/Guinea monsoon and southern easterly winds. The rainy season months are June, July, and August, which constitute the summer season. The mean annual rainfall ranges from the lowest value of approximately 801 mm in Kamba \\u003cem\\u003eWoreda\\u003c/em\\u003e to over 2000 mm in Chencha \\u003cem\\u003eWoreda\\u003c/em\\u003e.\\u003c/p\\u003e\\u003cp\\u003eAlmost 85% of the rural population in the Zone is employed in agriculture, which is the region's main economic sector. However, it suffers from recurrent droughts, deforestation, high population density, and degradation of the soil from conventional farming methods and overgrazing (Gamo Zone Plan Department, 2023 unpublished report). The Zone has extremely small and dispersed farmland holdings. The main cause of this was the area's high population density, particularly in the highland areas. According to the report, 34.8\\u0026ndash;36.8% of households own less than 0.5 hectares of land. The majority of households (29.6\\u0026ndash;32.3%) own between 0.5 and 1 hectare of farmland. In low land areas, only a very small percentage of households (1.93\\u0026ndash;3.2%) own farmland that is two hectares or more. A sizeable fraction of households (10.2\\u0026ndash;12.5%) do not employ any farm laborers. Most of them work in needlework, ceramics, small-scale business, and other related fields (Gamo Zone Plan Department, unpublished report, 2023).\\u003c/p\\u003e\\u003cp\\u003eThe Zone has 499,154.9 ha of cultivated land, 133,759 ha of forestland, 88,222 ha of pasture land, and 107,281.8 ha of land. The Zone has 42445.27 ha of arable land, 9496.891 hectares of land are not arable, and the area covered by water bodies is 78,020.5 hectares. A total of 17,753 ha of land is cultivated by agricultural clusters as part of national commercialization clusters, while only 6,206.3 ha of land is mechanized agriculture (Gamo Zone Plan Department, 2023 unpublished report). According to an unpublished report from the Gamo Zone Department of Agriculture, maize, \\u003cem\\u003esorghum\\u003c/em\\u003e, barley, wheat and sorghum are major cereal crops cultivated, whereas sweet potato, potato, \\u003cem\\u003eenset\\u003c/em\\u003e and cassava are major root and tuber crops cultivated in the Zone. The fruit crops banana, mango and apple are dominant in the area, whereas coffee, groundnut, cotton and sesame are known cash crops.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eData sources, types and collection tools\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eA mixed research approach was employed. In this mixed research approach, quantitative research was based on descriptive surveys, whereas qualitative research was conducted through focus group discussion (FGD) and key informant interviews (KIIs). The research was based on cross-sectional data from households (members and nonmembers). In this study, a convergent design was employed in which quantitative and qualitative data were collected and analyzed separately for later combination or comparison. This approach was chosen because household-level responses to quantitative questions might not yield the required data, and qualitative data would play a complementary role, as many exogenous and endogenous factors are included in the study.\\u003c/p\\u003e\\u003cp\\u003eBoth quantitative and qualitative data were collected from primary and secondary sources. Quantitative data were collected from selected agricultural cooperatives for a cooperative-level study on the performance of agricultural cooperatives. Quantitative data on factors affecting membership and smallholder farmers\\u0026rsquo; crop commercialization were collected from members and nonmembers of agricultural cooperatives.\\u003c/p\\u003e\\u003cp\\u003ePrimary data were collected on household and household characteristics, household resource endowments, institutional factors, physical factors, socioeconomic factors, and member value factors (psychological factors) from sample households (members and nonmembers of agricultural cooperatives). Qualitative data related to crop out marketing, challenges, and service delivery were collected from FGD and KII participants. A household-level survey was conducted to collect data on household-level characteristics related to membership and the impact of membership on household market participation. Primary quantitative data on household and household head characteristics, resource endowment, and access to institutional services, physical infrastructure and social factors were collected by conducting household surveys via structured questionnaires through an interview schedule.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eSampling Procedures and Sample Size Determination\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eThe multistage sampling technique was followed. First, three \\u003cem\\u003eWoredas\\u003c/em\\u003e with a large number of multipurpose cooperatives were purposively selected from fourteen Woredas. Second, a total of 10 kebeles were randomly selected for the study, comprising three from Arba Minch Zuria (out of 16), four from Boreda (out of 29), and three from Dita (out of 24). Third, after the list (sampling frame) of members and nonmembers of cooperatives in the \\u003cem\\u003eKebeles (\\u003c/em\\u003estratified into two groups\\u003cem\\u003e) was obtained\\u003c/em\\u003e, sample respondents were selected from two strata following a systematic random sampling technique.\\u003c/p\\u003e\\u003cp\\u003eThe sample size for the household survey of the study is determined according to Cochran (1963), which is recommended when the population is large and known (\\u003cspan citationid=\\\"CR53\\\" class=\\\"CitationRef\\\"\\u003e53\\u003c/span\\u003e). This formula is considered for minimizing the availability of error and bias. The formula for sample determination at the 95% confidence level is described as follows:\\u003cdiv id=\\\"Equ1\\\" class=\\\"Equation\\\"\\u003e\\u003cdiv format=\\\"TEX\\\" class=\\\"mathdisplay\\\" id=\\\"FileID_Equ1\\\" name=\\\"EquationSource\\\"\\u003e\\n$$\\\\:N=\\\\frac{{Z}^{2}pq}{{e}^{2}}$$\\u003c/div\\u003e\\u003cdiv class=\\\"EquationNumber\\\"\\u003e1\\u003c/div\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003ewhere N is the sample size, Z\\u003csup\\u003e2\\u003c/sup\\u003e is the area under the acceptance region in a normal distribution (1 \\u0026ndash; α), e is the level of precision, p is the proportion of an attribute present in the population, and q is 1- p.\\u003c/p\\u003e\\u003cp\\u003eOn the basis of the formula above, the sample size for the household survey is 385. By adding 10% to 385, the sample size was 424, and by excluding 2 incomplete responses, this study used a sample size of 422 for analysis. Following the probability proportional to size (PPS) method, 160, 147 and 115 sample households from Arba Minch Zuria, Boreda and Dita Woredas, respectively, were included in the study. Among the 422 sample households, 222 were nonmembers, whereas 200 sample households were members.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eMethods of Data Analysis\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eQualitative data were analyzed through a content analysis technique that employs a directed data analysis method. In the content analysis technique, qualitative data are analyzed by creating themes for the data gathered. In a theme created, subthemes are also created for issues following the major theme. Data that do not fall under any of the themes are assigned to a new theme. Content analysis helps us understand different themes associated with the issue under consideration. It is also important to understand contexts in different settings. The quantitative data were analyzed via descriptive and inferential statistics. Descriptive statistics, including the mean, percentage, standard deviation, and minimum, maximum and frequency distributions, were used.\\u003c/p\\u003e\\u003cp\\u003eInferential statistical tools such as chi-square tests and t tests were used to analyze the associations between explanatory and response variables and to compare the mean differences in household farm income, HDDS and FCS between groups (members and nonmembers of agricultural cooperatives). An endogenous switching regression model was used to analyze the impacts of membership in agricultural cooperatives on the household welfare of smallholder farmers.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eModel Specification\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eCooperative members and nonmembers are not directly comparable due to initial differences before joining cooperatives and a bias in selection (\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e). In impact studies, it is therefore important to control bias due to observable and unobservable community- and household-level characteristics (\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e50\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR54\\\" class=\\\"CitationRef\\\"\\u003e54\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR55\\\" class=\\\"CitationRef\\\"\\u003e55\\u003c/span\\u003e). Farming households join agricultural cooperatives to obtain the services delivered by the collective group. A rational farmer would join collective groups when the expected utility gained from joining the collectives is greater than that of nonmembers. In such studies, it is strictly recommended to avoid bias arising from selection on observables and endogeneity from unobservables.\\u003c/p\\u003e\\u003cp\\u003eThe PSM approach generates a control group and then addresses the bias due to selection-on-observables, overt bias (\\u003cspan citationid=\\\"CR56\\\" class=\\\"CitationRef\\\"\\u003e56\\u003c/span\\u003e). PSM is used in observational studies to adjust for differences in pretreatment variables and to draw inferences about the effects of binary treatments or membership in agricultural cooperatives. However, it fails to control for unobservable selection bias.\\u003c/p\\u003e\\u003cp\\u003eAccording to (\\u003cspan citationid=\\\"CR55\\\" class=\\\"CitationRef\\\"\\u003e55\\u003c/span\\u003e), the utility gain from joining agricultural cooperatives (M* = M\\u003csub\\u003e1\\u003c/sub\\u003e \\u0026ndash; M\\u003csub\\u003e0\\u003c/sub\\u003e) as a function of the observable vector of covariates (Z) can be expressed in a latent model as:\\u003c/p\\u003e\\u003cp\\u003e\\u003cem\\u003eM\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003ei\\u003c/em\\u003e\\u003c/sub\\u003e\\u003csup\\u003e\\u003cem\\u003e* =\\u003c/em\\u003e\\u003c/sup\\u003e \\u003cem\\u003eαZ\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003ei\\u003c/em\\u003e\\u003c/sub\\u003e\\u0026thinsp;\\u003cem\\u003e+\\u0026thinsp;η\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003ei\\u003c/em\\u003e\\u003c/sub\\u003e, \\u003cem\\u003eM\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003ei\\u003c/em\\u003e\\u003c/sub\\u003e \\u003cem\\u003e= 1 if M\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003ei\\u003c/em\\u003e\\u003c/sub\\u003e\\u003csup\\u003e\\u003cem\\u003e*\\u003c/em\\u003e\\u003c/sup\\u003e \\u003cem\\u003e\\u0026gt;0\\u003c/em\\u003e (\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e)\\u003c/p\\u003e\\u003cp\\u003ewhere M\\u003csub\\u003ei\\u003c/sub\\u003e is a binary variable that is 1 if the household is a member of agricultural cooperatives and 0 otherwise; α is a vector of parameters; Z\\u003csub\\u003ei\\u003c/sub\\u003e is a vector of household and household characteristics and socioeconomic, institutional, social and psychological factors; and η\\u003csub\\u003ei\\u003c/sub\\u003e is \\u003csub\\u003ea\\u003c/sub\\u003e random error term assumed to be normally distributed. Membership in agricultural cooperatives was expected to positively impact household welfare through its multidimensional influences. Household welfare is measured in terms of farm income, HDDS and FCS and is a function of the vector of exogenous variables X\\u003csub\\u003ei\\u003c/sub\\u003e and endogenous membership in agricultural cooperatives (M\\u003csub\\u003ei\\u003c/sub\\u003e):\\u003c/p\\u003e\\u003cp\\u003e\\u003cem\\u003eY\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003ei\\u003c/em\\u003e\\u003c/sub\\u003e\\u0026thinsp;\\u003cem\\u003e=\\u0026thinsp;βX\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003ei\\u003c/em\\u003e\\u003c/sub\\u003e\\u0026thinsp;\\u003cem\\u003e+\\u0026thinsp;δM\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003ei\\u003c/em\\u003e\\u003c/sub\\u003e \\u003cem\\u003e+ e\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003ei\\u003c/em\\u003e \\u003cb\\u003e(3\\u003c/b\\u003e\\u003c/sub\\u003e\\u003cb\\u003e)\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003ewhere Y\\u003csub\\u003ei\\u003c/sub\\u003e represents the outcome variables (farm income, HDDS and FCS); M\\u003csub\\u003ei\\u003c/sub\\u003e is defined as previously described; β and δ are parameters to be estimated; and e\\u003csub\\u003ei\\u003c/sub\\u003e is the error term. Households may self-select themselves into agricultural cooperatives, which results in bias.\\u003c/p\\u003e\\u003cp\\u003ePSM can resolve selection bias by controlling for observable covariates; however, it cannot control for bias from unobservable covariates. Therefore, endogenous switching regression, which controls the endogeneity of membership or both observable and unobservable sources of bias, is used (\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e50\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR55\\\" class=\\\"CitationRef\\\"\\u003e55\\u003c/span\\u003e). Endogenous switching regression (ESR) is employed to evaluate the impact of membership in agricultural cooperatives on welfare in two steps. The first step involves the decision to join agricultural cooperatives, and the second step involves the outcome equation for members and nonmembers separately:\\u003c/p\\u003e\\u003cp\\u003e\\u003cem\\u003eRegime 1: Y1i\\u0026thinsp;=\\u0026thinsp;β1Xi\\u0026thinsp;+\\u0026thinsp;e1i if Mi\\u0026thinsp;=\\u0026thinsp;1 (Members)\\u003c/em\\u003e (\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e)\\u003c/p\\u003e\\u003cp\\u003e\\u003cem\\u003eRegime 2: Y2i\\u0026thinsp;=\\u0026thinsp;β2Xi\\u0026thinsp;+\\u0026thinsp;e2i if Mi\\u0026thinsp;=\\u0026thinsp;0 (Nontembers)\\u003c/em\\u003e (\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e)\\u003c/p\\u003e\\u003cp\\u003ewhere Y\\u003csub\\u003e1\\u003c/sub\\u003e and Y\\u003csub\\u003e2\\u003c/sub\\u003e represent the outcome (welfare) for members and nonmembers, respectively; x\\u003csub\\u003ei\\u003c/sub\\u003e represents the vectors of covariates of farmer i; β\\u003csub\\u003e1\\u003c/sub\\u003e and β\\u003csub\\u003e2\\u003c/sub\\u003e are parameters to be estimated; and e\\u003csub\\u003e1i\\u003c/sub\\u003e and e\\u003csub\\u003e2i\\u003c/sub\\u003e are error terms associated with the outcome variables.\\u003c/p\\u003e\\u003cp\\u003eThe covariance for the error terms of equations (5) and (\\u003cspan refid=\\\"Equ2\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e) above are not determined, as they are not observed simultaneously. The expected values of the error terms e\\u003csub\\u003e1i\\u003c/sub\\u003e and e\\u003csub\\u003e2i\\u003c/sub\\u003e are nonzero, as the error term of Eq.\\u0026nbsp;3 is correlated with the error terms in equations 5 and \\u003cspan refid=\\\"Equ2\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e ( (\\u003cspan citationid=\\\"CR57\\\" class=\\\"CitationRef\\\"\\u003e57\\u003c/span\\u003e) (\\u003cspan citationid=\\\"CR55\\\" class=\\\"CitationRef\\\"\\u003e55\\u003c/span\\u003e) as:\\u003cdiv id=\\\"Equ2\\\" class=\\\"Equation\\\"\\u003e\\u003cdiv format=\\\"TEX\\\" class=\\\"mathdisplay\\\" id=\\\"FileID_Equ2\\\" name=\\\"EquationSource\\\"\\u003e\\n$$\\\\:E\\\\:\\\\left[e1i\\\\:\\\\right|\\\\:M\\\\:=\\\\:1]\\\\:=\\\\:\\\\sigma\\\\:1\\\\eta\\\\:\\\\:(\\\\phi\\\\:\\\\:\\\\left(Zi\\\\alpha\\\\:\\\\right))/\\\\:(\\\\varPhi\\\\:\\\\:\\\\left(Zi\\\\alpha\\\\:\\\\right))\\\\:=\\\\:\\\\sigma\\\\:1\\\\eta\\\\:\\\\lambda\\\\:1i$$\\u003c/div\\u003e\\u003cdiv class=\\\"EquationNumber\\\"\\u003e6\\u003c/div\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Equ3\\\" class=\\\"Equation\\\"\\u003e\\u003cdiv format=\\\"TEX\\\" class=\\\"mathdisplay\\\" id=\\\"FileID_Equ3\\\" name=\\\"EquationSource\\\"\\u003e\\n$$\\\\:E\\\\:\\\\left[e2i\\\\:\\\\right|\\\\:M\\\\:=\\\\:0]\\\\:=\\\\:\\\\sigma\\\\:2\\\\eta\\\\:\\\\:(\\\\phi\\\\:\\\\:\\\\left(Zi\\\\alpha\\\\:\\\\right))/\\\\:(1\\\\:-\\\\:\\\\varPhi\\\\:\\\\:\\\\left(Zi\\\\alpha\\\\:\\\\right))\\\\:=\\\\:\\\\sigma\\\\:2\\\\eta\\\\:\\\\lambda\\\\:2i$$\\u003c/div\\u003e\\u003cdiv class=\\\"EquationNumber\\\"\\u003e7\\u003c/div\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003ewhere \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:\\\\phi\\\\:\\\\)\\u003c/span\\u003e\\u003c/span\\u003e is the standard normal probability density function; φ is the standard normal cumulative density function; and λ1i and λ2i are the inverse mill ratios (IMRs) from Eq.\\u0026nbsp;3 and are included in equations (\\u003cspan refid=\\\"Equ3\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003e) and (8) to correct for selection biases from unobservable factors. On the basis of (\\u003cspan citationid=\\\"CR55\\\" class=\\\"CitationRef\\\"\\u003e55\\u003c/span\\u003e), the observed and unobserved counterfactual outcomes for agricultural cooperative members can be as follows:\\u003c/p\\u003e\\u003cp\\u003e\\u003cem\\u003eE [Y\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003e1i\\u003c/em\\u003e\\u003c/sub\\u003e \\u003cem\\u003e| M\\u0026thinsp;=\\u0026thinsp;1] = β\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003e1\\u003c/em\\u003e\\u003c/sub\\u003e\\u003cem\\u003eX\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003ei\\u003c/em\\u003e\\u003c/sub\\u003e\\u0026thinsp;\\u003cem\\u003e+\\u0026thinsp;σ\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003e1η\\u003c/em\\u003e\\u003c/sub\\u003e\\u003cem\\u003eλ\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003e1i\\u003c/em\\u003e\\u003c/sub\\u003e (\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e)\\u003c/p\\u003e\\u003cp\\u003e\\u003cem\\u003eE [Y\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003e2i\\u003c/em\\u003e\\u003c/sub\\u003e \\u003cem\\u003e| M\\u0026thinsp;=\\u0026thinsp;0] = β\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003e2\\u003c/em\\u003e\\u003c/sub\\u003e\\u003cem\\u003eX\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003ei\\u003c/em\\u003e\\u003c/sub\\u003e\\u0026thinsp;\\u003cem\\u003e+\\u0026thinsp;σ\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003e2η\\u003c/em\\u003e\\u003c/sub\\u003e\\u003cem\\u003eλ\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003e2i\\u003c/em\\u003e\\u003c/sub\\u003e (\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e)\\u003c/p\\u003e\\u003cp\\u003e\\u003cem\\u003eE [Y\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003e2i\\u003c/em\\u003e\\u003c/sub\\u003e \\u003cem\\u003e| M\\u0026thinsp;=\\u0026thinsp;1] = β\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003e2\\u003c/em\\u003e\\u003c/sub\\u003e\\u003cem\\u003eX\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003ei\\u003c/em\\u003e\\u003c/sub\\u003e\\u0026thinsp;\\u003cem\\u003e+\\u0026thinsp;σ\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003e2η\\u003c/em\\u003e\\u003c/sub\\u003e\\u003cem\\u003eλ\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003e1i\\u003c/em\\u003e\\u003c/sub\\u003e (\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e)\\u003c/p\\u003e\\u003cp\\u003e\\u003cem\\u003eE [Y\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003e1i\\u003c/em\\u003e\\u003c/sub\\u003e \\u003cem\\u003e| M\\u0026thinsp;=\\u0026thinsp;0] = β\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003e1\\u003c/em\\u003e\\u003c/sub\\u003e\\u003cem\\u003eX\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003ei\\u003c/em\\u003e\\u003c/sub\\u003e\\u0026thinsp;\\u003cem\\u003e+\\u0026thinsp;σ\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003e1η\\u003c/em\\u003e\\u003c/sub\\u003e\\u003cem\\u003eλ\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003e2i\\u003c/em\\u003e\\u003c/sub\\u003e (\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e)\\u003c/p\\u003e\\u003cp\\u003eEquation (8) and Eq.\\u0026nbsp;(9) yield welfare for members and nonmembers of agricultural cooperatives, whereas equations (10) and (11) provide counterfactuals for observed welfare for members and nonmembers of agricultural cooperatives. Eq.\\u0026nbsp;(10) expresses what would have happened to members if they had not joined agricultural cooperatives, and Eq.\\u0026nbsp;(11) is counterfactual for the observed outcome of nonmembers to express the scenario if the households decided to join agricultural cooperatives.\\u003c/p\\u003e\\u003cp\\u003eFrom these equations, it is possible to compute the average treatment effect on treated (ATT, the difference between Eq.\\u0026nbsp;8 and Eq.\\u0026nbsp;10) and the average treatment effect on untreated (ATU, the difference between equations 9 and 11), which results in unbiased treatment effects (Adjin \\u003cem\\u003eet al\\u003c/em\\u003e., 2020; Mensabo, 2020).\\u003c/p\\u003e\\u003cp\\u003eAT T\\u0026thinsp;=\\u0026thinsp;E [Y\\u003csub\\u003e1i\\u003c/sub\\u003e | M\\u0026thinsp;=\\u0026thinsp;1]\\u0026thinsp;\\u0026minus;\\u0026thinsp;E [Y\\u003csub\\u003e2i\\u003c/sub\\u003e | M\\u0026thinsp;=\\u0026thinsp;1] = (β\\u003csub\\u003e1\\u003c/sub\\u003e\\u0026thinsp;\\u0026minus;\\u0026thinsp;β\\u003csub\\u003e2\\u003c/sub\\u003e) X\\u003csub\\u003ei\\u003c/sub\\u003e\\u0026thinsp;+\\u0026thinsp;λ\\u003csub\\u003e1i\\u003c/sub\\u003e (σ\\u003csub\\u003e1ν\\u003c/sub\\u003e\\u0026thinsp;\\u0026minus;\\u0026thinsp;σ\\u003csub\\u003e2ν\\u003c/sub\\u003e) (\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e)\\u003c/p\\u003e\\u003cp\\u003eAT U\\u0026thinsp;=\\u0026thinsp;E [Y\\u003csub\\u003e1i\\u003c/sub\\u003e | M\\u0026thinsp;=\\u0026thinsp;0]\\u0026thinsp;\\u0026minus;\\u0026thinsp;E [Y\\u003csub\\u003e2i\\u003c/sub\\u003e | M\\u0026thinsp;=\\u0026thinsp;0] = (β\\u003csub\\u003e1\\u003c/sub\\u003e\\u0026thinsp;\\u0026minus;\\u0026thinsp;β\\u003csub\\u003e2\\u003c/sub\\u003e) X\\u003csub\\u003ei\\u003c/sub\\u003e\\u0026thinsp;+\\u0026thinsp;λ\\u003csub\\u003e2i\\u003c/sub\\u003e (σ\\u003csub\\u003e1ν\\u003c/sub\\u003e\\u0026thinsp;\\u0026minus;\\u0026thinsp;σ\\u003csub\\u003e2ν\\u003c/sub\\u003e) (\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e)\\u003c/p\\u003e\\u003cp\\u003eIn this study, there are two related objectives. Hence, first, factors affecting membership in agricultural cooperatives were analyzed, and then their impact on household welfare was evaluated. However, this article focuses on only the impact aspect. The outcome variable used to measure the impact of membership in agricultural cooperatives is household welfare, which is measured in terms of farm income, household dietary diversity and the food consumption score. The analysis focused on the factors of heterogeneity in the impact of agricultural cooperatives on welfare.\\u003c/p\\u003e\\u003cp\\u003eIn this study, household-level data on farm income (income from the sale of major crops cultivated) were measured by income from the sale of crops, which was used as the output variable. The food security dimension of welfare was approached through HDDSs and FCSs on the basis of (\\u003cspan citationid=\\\"CR59\\\" class=\\\"CitationRef\\\"\\u003e59\\u003c/span\\u003e) procedures. HDDS was measured by 24-hour food consumption of the household where food groups consumed by 50%+ household members were considered out of twelve food groups. The FCS was measured by considering the food consumption of twelve food groups by 50%+ household member data from the previous seven days. The FCS was computed by considering the weight given to each food group on the basis of (\\u003cspan citationid=\\\"CR59\\\" class=\\\"CitationRef\\\"\\u003e59\\u003c/span\\u003e).\\u003c/p\\u003e\"},{\"header\":\"4. Results and Discussion\",\"content\":\"\\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e4.1. Descriptive Statistics of the Background Characteristics of Respondent Households\\u003c/h2\\u003e\\u003cp\\u003eThe data were collected from 422 households, 222 (52.6%) of which are nonmembers of agricultural cooperatives, whereas 200 (47.4%) are members of agricultural cooperatives. The data were collected on household demographic, physical, socioeconomic, and institutional characteristics. In the discussion below, background characteristics are separately discussed for continuous and discrete variables.\\u003c/p\\u003e\\u003cdiv id=\\\"Sec11\\\" class=\\\"Section3\\\"\\u003e\\u003ch2\\u003e4.1.1. Descriptive Statistics of the Respondents (Continuous Variables)\\u003c/h2\\u003e\\u003cp\\u003eThe mean age of the respondents in the study was 46.93 years, with a maximum of 85 years and a minimum of 25 years. Education is also another important factor affecting household membership in collective action. The mean years of education completed in the study area was 4.5 years. The survey results also revealed that approximately 37% of the respondents were unable to read and write, whereas 4% of the respondents did not attend formal education and were able to read and write. Thirty-three percent of the respondents reported that they had completed primary education, 20% had completed secondary education, and 6% had completed tertiary education. The survey results revealed that the literacy level in the area is above the national average of 54% (\\u003cspan citationid=\\\"CR60\\\" class=\\\"CitationRef\\\"\\u003e60\\u003c/span\\u003e), but below sub-Saharan Africa, the average literacy level is 68% (World Bank data bank, 2021)\\u003csup\\u003e1\\u003c/sup\\u003e.\\u003c/p\\u003e\\u003cp\\u003eHousehold size is another important factor expected to affect household membership in agricultural cooperatives. The mean household size in the adult equivalent is 5.19, with a maximum of 14.65 AE and a minimum of 0.70 AE. Land, as a household asset endowment, is usually considered a proxy for wealth and is expected to affect households\\u0026rsquo; membership in collective action, as wealth is an important factor affecting households\\u0026rsquo; ability to join collective action. In this study, the mean land owned by households was 1.35 hectares, which is above the national average of 1.1 hectares (\\u003cspan citationid=\\\"CR60\\\" class=\\\"CitationRef\\\"\\u003e60\\u003c/span\\u003e). In the study area, 95.7% of the respondents reported that they own land ranging from 0.01 hectares to 15 hectares, which is slightly below the regional average of 97% (\\u003cspan citationid=\\\"CR60\\\" class=\\\"CitationRef\\\"\\u003e60\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eDescriptive statistics of household background characteristics (continuous variables)\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"5\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eVariables\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eMean\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eStandard\\u003c/p\\u003e\\u003cp\\u003eDeviation\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eMin\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eMax.\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAge\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e46.93\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e12.19\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e25\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e85\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eEducation\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e4.50\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e4.50\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e16\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eHH size\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e5.19\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e2.12\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.70\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e14.65\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eLand size\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.35\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.77\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.00\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e15.00\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eLivestock holding\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e3.10\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e3.853\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e25.37\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eDistance to coop.\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e2.83\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e5.14\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e.00\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e50.00\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eDistance to market\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e8.67\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e9.16\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e.00\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e38.00\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSocial networks\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.47\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.038\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e5\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eBusiness networks\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e.98\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.30\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e8\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003eThe results in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e show that 42% of the sampled households had no livestock, whereas 58% reported that they own livestock. The mean number of livestock held in the area is 3.1 TLU. Physical factors such as distance from cooperative offices and market centers are important factors that affect household likelihood of joining agricultural cooperatives. When cooperative offices are far from households, they are less likely to access cooperative extension services such as awareness creation, training, market information and other marketing functions, which makes them less likely to join agricultural cooperatives. The mean distances to cooperative offices and market centers in kilometers are 2.83 and 8.67, respectively.\\u003c/p\\u003e\\u003cp\\u003eNetworks are among the factors expected to affect households\\u0026rsquo; likelihood of joining collective action. In this study, the number of social and business networks that household heads have was expected to affect membership decisions in agricultural cooperatives. The mean numbers of social and business network household heads are 1.47 and 0.98, respectively.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec12\\\" class=\\\"Section3\\\"\\u003e\\u003ch2\\u003e4.1.2. Descriptive statistics of the respondents (discrete variables)\\u003c/h2\\u003e\\u003cp\\u003eHouseholds living in different agro-ecosystems are expected to join agricultural cooperatives differently, as agro-ecology is an important factor affecting agricultural production, the input needs of farmers, the type of market and other agricultural services that farmers seek to access from cooperatives. Therefore, agro-ecology is considered an important variable affecting households\\u0026rsquo; decision to join agricultural cooperatives. Among the 422 households included in the study, 109 (25.8%) were from \\u003cem\\u003eDega\\u003c/em\\u003e (highland), 106 (25.1%) were from \\u003cem\\u003eWoinadega\\u003c/em\\u003e (midland), and 207 (49.1%) were from \\u003cem\\u003eKolla\\u003c/em\\u003e (lowland agro-ecologies).\\u003c/p\\u003e\\u003cp\\u003ePsychological factors (perceptions of and trust in the leaders of cooperatives) are expected to affect household membership in agricultural cooperatives. Given that most collective actions in Ethiopia are externally induced, understanding the level of perception and trust and their influence on collective actions should be considered critically (\\u003cspan additionalcitationids=\\\"CR62\\\" citationid=\\\"CR61\\\" class=\\\"CitationRef\\\"\\u003e61\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR63\\\" class=\\\"CitationRef\\\"\\u003e63\\u003c/span\\u003e). However, such psychological factors are difficult to measure via dummy questions, and items that are proxies for the perceptions and trust of members have been employed.\\u003c/p\\u003e\\u003cp\\u003eMeasuring Perception: Perception was measured via the following factor analysis technique, where 11 variables indicating households\\u0026rsquo; perceptions of the benefits that cooperatives deliver were used. Households evaluated their perceptions of the benefits of cooperatives on a five-point scale (1\\u0026thinsp;=\\u0026thinsp;strongly disagree, 2\\u0026thinsp;=\\u0026thinsp;disagree, 3\\u0026thinsp;=\\u0026thinsp;neutral, 4\\u0026thinsp;=\\u0026thinsp;agree and 5\\u0026thinsp;=\\u0026thinsp;strongly agree). Out of 11 variables, one variable (perception of buying inputs at a good price) was removed because of its insignificant correlation with other variables. After checking for internal consistency through correlation analysis, the retained variables were checked for reliability by using Cronbach\\u0026rsquo;s alpha, and the alpha value was 0.861, indicating reliability.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eReliability analysis of perception items\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"2\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eItems\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eAlpha\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSelling outputs with good price\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.860\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAcquiring extension services\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.857\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAcquiring market information\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.838\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eTime of services delivery\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.847\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAccessing preferred markets\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.844\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eEnable to make business and personal\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.843\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eEnhance social support\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.847\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eQuality of services delivered\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.841\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eCooperatives have promising growth\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.846\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eCooperatives can improve welfare of their members\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.854\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e\\u003cp\\u003eNumber of Items\\u0026thinsp;=\\u0026thinsp;10\\u003c/p\\u003e\\u003cp\\u003eCronbach's alpha value\\u0026thinsp;=\\u0026thinsp;0.858\\u003c/p\\u003e\\u003cp\\u003eCronbach's alpha Based on Standardized Items\\u0026thinsp;=\\u0026thinsp;0.861\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003eThe mean composite score of the 10 items of perception was 34.6. Respondents with composite score values below the mean score were considered to have poor perceptions of the benefits of cooperatives and vice versa.\\u003c/p\\u003e\\u003cp\\u003eMeasuring trust: Trust is an important psychological factor for collective action, as it improves commitment to collective action and reduces transaction costs by lowering the risk of free-rider problems (Ostrom, 2010). Measuring trust is difficult, and relevant items for measuring trust were identified by consulting cooperative experts and leaders and reviewing the theoretical and empirical literature. Five variables for each type of trust in cooperative leaders and trust in the actions of other members are employed. The respondents evaluated their level of trust in the identified items on a five-point Likert-type scale. All five items of trust were retained, as there was a significant correlation among the items, and there was also acceptable reliability, as Cronbach\\u0026rsquo;s alpha test revealed that trust in leaders and trust in the actions of other members were 0.83 and 0.92, respectively.\\u003c/p\\u003e\\u003cp\\u003eThe trust composite score was computed for every respondent, and the respondents were categorized into two trust groups (low and high) on the basis of the composite score computation. Respondents within the score range of minimum to mean have low trust, and those within the range of mean to maximum have high trust in their cooperative leaders and the actions of other members. Accordingly, respondents with composite scores ranging from 5\\u0026ndash;16.95 and above 16.95 were categorized as having low and high levels of trust in the leaders of cooperatives, respectively. With respect to trust in the actions of other members, those within the range of 5-17.80 were categorized as having low trust, whereas those within the composite score range above 17.80 were categorized as having high trust in other members\\u0026rsquo; actions.\\u003c/p\\u003e\\u003cp\\u003eEmpirical studies reveal that households headed by females and males join agricultural cooperatives differently. For this purpose, both female- and male-headed households were included in this study. Among the 422 respondent household heads, 110 were female, whereas 312 (73.9%) were male.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eDescriptive Statistics of the Discrete Variables\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"4\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eVariables\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eCategories\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eNumbers\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003ePercent\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e\\u003cp\\u003eAgro-ecology\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eDega (Highland)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e109\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e25.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eWoina-dega (Midland)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e106\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e25.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eKolla (Lowland)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e207\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e49.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003eGender\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eFemale\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e110\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e26.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eMale\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e312\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e73.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003eOff-/Nonfarm participation\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eNo\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e291\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e68.96\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eYes\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e131\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e31.04\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003eAccess to irrigated land\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eNo\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e304\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e79\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eYes\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e118\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e21\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003eAccess to cooperative extension/agents\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eNo\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e165\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e39.10\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eYes\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e257\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e60.90\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003ePerception on benefits of cooperatives\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eNegative\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e207\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e49.05\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003ePositive\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e215\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e50.95\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003eTrust on leaders of cooperatives\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eNo\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e130\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e30.81\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eYes\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e292\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e69.19\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003eHousehold participation in off-/nonfarm income activities improves households\\u0026rsquo; financial position and is expected to encourage households to join agricultural cooperatives, as they can easily meet financial requirements to join collective actions. In this study, 291 (68.96%) households did not participate in off-/nonfarm income activities, whereas only 131 (31.04%) households reported that they were involved in these income activities, which is still above the national average of 27% (\\u003cspan citationid=\\\"CR60\\\" class=\\\"CitationRef\\\"\\u003e60\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eHousehold access to irrigation is an important factor in agricultural production; thus, inputs are also needed. Households with access to agricultural land with access to irrigation were expected to increase their chance of membership in agricultural cooperatives. In the Ethiopian agricultural development strategy, the government designed an extension system to reach rural areas through the deployment of agricultural development agents, cooperative promotion and the organization of experts and animal health workers. In this study, households\\u0026rsquo; access to cooperative extensions was also expected to affect their probability of joining agricultural cooperatives. The survey results revealed that 165 (39.1%) households had no access to cooperative extensions, whereas 257 (60.9%) households had access to cooperative extensions and contact with cooperative extension workers.\\u003c/p\\u003e\\u003cp\\u003ePsychological factor perception and trust are believed to be important factors affecting household membership decisions in cooperatives in general and agricultural cooperatives in particular. In the study area, the majority of the respondent households (215 (50.95%) had positive perceptions of the benefits of cooperatives in improving livelihoods, whereas 207 (49.05%) households reported that they did not believe that cooperatives could improve livelihoods. With respect to the trust of respondents in the leaders of cooperatives, 165 (39.1%) had no trust in the leaders of cooperatives, whereas 257 (61.9%) had trust in the leaders of cooperatives.\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e4.2. Simple Inferential statistics\\u003c/h2\\u003e\\u003cdiv id=\\\"Sec14\\\" class=\\\"Section3\\\"\\u003e\\u003ch2\\u003e4.2.1. Mean difference (t test) statistics\\u003c/h2\\u003e\\u003cp\\u003eThe mean age of members of cooperatives (48.29 years) is greater than the mean age of nonmembers (45.71 years), highlighting that households with aged heads are more likely to join agricultural cooperatives. This might be because aged household heads have access to agricultural resources (land, livestock, and others) that help them engage in farming activities and use agricultural marketing services from cooperatives. The mean comparison indicates a significant mean difference between the two groups at less than a 5% significance level. This result is similar to those of other previous studies by Gashaw (2018), Bizualem and Saron (2018), Mbagwu (2018), and Miroro et al. (2023).\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 4\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eMean difference statistics of continuous variables\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"6\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003eVariables\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c4\\\" namest=\\\"c2\\\"\\u003e\\u003cp\\u003eMean\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003eStd. error of mean difference\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003et test\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eNonmembers\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eMembers\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eMean difference\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAge of hh in years\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e45.71\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e48.29\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2.58\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.59\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e-2.179**\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eEducation completed\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e4.27\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e4.76\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.49\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.22\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e-1.095\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eHH size in AE\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e4.80\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e5.61\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.81\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.10\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e-4.052***\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eLand size in hectare\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.12\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.59\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.47\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.09\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e-2.774***\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eLivestock holding in TLU\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e2.81\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e3.43\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.62\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.19\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e-1.64\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eDistance to coop. in km\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e2.78\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e2.87\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.09\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.25\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e\\u0026minus;\\u0026thinsp;.169\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eDistance to market in km\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e7.88\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e9.54\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.66\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.45\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e-1.864\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003eThe mean education level of cooperative members (4.76) is higher than that of nonmembers (4.27), which is in agreement with previous studies by Gashaw (2018), Hiskeal, Senapathy and Bojago (2022), Miroro et al. (2023) and against the works of Bizualem and Saron (2018). However, there is an insignificant mean difference in education level between members and nonmembers of agricultural cooperatives in the area. Household size is another important factor that is expected to affect household membership in agricultural cooperatives. The mean household size of members (5.61) is greater than that of nonmembers (4.8). The t test statistics of the mean comparison also reveal significant mean differences between members and nonmembers of agricultural cooperatives.\\u003c/p\\u003e\\u003cp\\u003eThe mean land size owned by members (1.59 hectares) is greater than the mean land size owned by nonmembers (1.12 hectares). The t test statistics also reveal that there is a significant difference in the mean land size owned between members and nonmembers of agricultural cooperatives, and this result is analogous to that of studies by Gashaw (2018). Similarly, the results in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e show that the mean number of livestock held by members of agricultural cooperatives is 3.43 TLU, whereas it is 2.81 for nonmembers. The t test statistics, however, do not reveal a significant difference in the means between the two groups. On the other hand, the results in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e show the presence of an insignificant mean difference in distance from cooperative offices and market centers for members and nonmembers of agricultural cooperatives.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec15\\\" class=\\\"Section3\\\"\\u003e\\u003ch2\\u003e4.2.2. Chi-square test statistics\\u003c/h2\\u003e\\u003cp\\u003eThe results revealed that the majority of the nonmembers, 179 (80.63%) and 137 (68.5%), were from lowland agro-ecosystems. In contrast, 43 (19.4%) nonmembers were from the midland area, whereas 63 (31.5%) members were from the midland agro-ecology area. The chi-square test (0.01) also reveals a significant association between agro-ecology and household membership in agricultural cooperatives at less than the 5% significance level.\\u003c/p\\u003e\\u003cp\\u003eThe results in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e show that out of 222 nonmember respondents, 150 (67.6%) reported that they had not engaged in off-/nonfarm income activities. Similarly, 141 (70.5%) member respondents reported that they did not engage in off-/nonfarm income-generating activities. The chi-square test (0.53), however, does not reveal a significant association between household participation in these income activities and the decision to join agricultural cooperatives in the study area.\\u003c/p\\u003e\\u003cp\\u003eIn the study area, among the sampled households, 163 (73.42%) nonmember and 136 (71%) member households reported that they had no access to irrigation, whereas 59 nonmember and 64 (21%) member households reported that they had access to irrigation. The chi-square test, however, does not reveal an association between membership in agricultural cooperatives and access to irrigation.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab5\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 5\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eChi-square test statistics of discrete variables\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"5\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003eVariables\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003eCategories\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e\\u003cp\\u003eMembership\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003eX\\u003csup\\u003e2\\u003c/sup\\u003e value\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eNonmembers\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eMembers\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e\\u003cp\\u003eAgro-ecology\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eDega (Highland)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e66\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e43\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e\\u003cp\\u003e0.010**\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eWoina-dega (Midland)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e43\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e63\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eKolla (Lowland)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e113\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e94\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003eWoina-dega\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eNo\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e179\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e137\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e0.004***\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eYes\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e43\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e63\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003eKolla\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eNo\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e109\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e106\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e0.423\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eYes\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e113\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e94\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003eGender\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eFemale\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e47\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e63\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e0.02**\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eMale\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e175\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e137\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003eOff-/Nonfarm participation\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eNo\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e150\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e141\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e0.53\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eYes\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e72\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e59\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003eAccess to irrigated land\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eNo\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e163\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e141\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e0.24\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eYes\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e59\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e59\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003eAccess to cooperative extension/agents\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eNo\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e137\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e28\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e0.00***\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eYes\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e85\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e172\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003ePerception on benefits of cooperatives\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eNegative\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e141\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e66\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e0.00***\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003ePositive\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e81\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e134\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003eTrust on leaders of cooperatives\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eNo\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e108\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e22\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e0.00***\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eYes\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e114\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e178\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003eRegarding household access to cooperative extension services, 137 (61.7%) nonmembers reported that they had no access to this service, whereas only 28 (14%) member respondents reported that they had no access to these services, highlighting the significance of cooperative extension services. The chi-square test also reveals a significant association between household access to cooperative extensions and membership in agricultural cooperatives in the area at less than the 1% significance level.\\u003c/p\\u003e\\u003cp\\u003eAmong the 207 (49.05%) respondents with negative perceptions of cooperatives, 141 (68.12%) did not join agricultural cooperatives, whereas only 66 (31.88%) of the respondents with negative perceptions were members of agricultural cooperatives.\\u003c/p\\u003e\\u003cp\\u003eThe results in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e show that 165 respondents reported that they had no trust in the leaders of cooperatives; 109 (66.06%) of these did not join agricultural cooperatives, whereas only 56 (33.94%) with no trust joined agricultural cooperatives. Similarly, 144 (56.03%) member respondents had trust in the cooperative leaders before they joined agricultural cooperatives, indicating the importance of building trust among members and leaders of the cooperative organization to attract nonmembers and keep members in the cooperatives. The chi-square test also revealed a significant association between both perceptions and membership decisions and between trust and membership decisions at less than the 1% significance level.\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec16\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e4.3. Factors affecting membership and Impact of agricultural cooperative membership on household welfare\\u003c/h2\\u003e\\u003cdiv id=\\\"Sec17\\\" class=\\\"Section3\\\"\\u003e\\u003ch2\\u003e4.3.1. Determinants of membership in agricultural cooperatives: ESR results\\u003c/h2\\u003e\\u003cp\\u003eHousehold membership in agricultural cooperatives is expected to be affected by various endogenous and exogenous factors. We identified demographic, socioeconomic, physical, institutional and psychological factors. In this study, fourteen (\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e) variables related to demographics (sex, age and education level completed by HH and HH size in AE), socioeconomics (farm size, access to irrigation, livestock holding size, and participation in off-/nonfarm activities), physical factors (distance to cooperative offices and the market center), institutional factors (access to extension services) and psychological factors (perception of the benefits of cooperatives and trust in leaders of cooperatives) were included and fitted to the model on the basis of the theoretical and empirical literature.\\u003c/p\\u003e\\u003cp\\u003eTwo variables (perception of the benefits of cooperatives and trust in the leaders of cooperatives) were included as exclusion variables (instrumental). As shown on Table\\u0026nbsp;\\u003cspan refid=\\\"Tab7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003e, out of 14 variables, nine (\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e) variables statistically significantly affect the membership decisions of households. Among these nine variables, eight variables, including age and education level of the household head, household size in the adult equivalent, access to irrigation, distance to the market center, perceptions of the benefits of agricultural cooperatives and trust in leaders of cooperatives, positively affect the membership decisions of households at less than 1% (four variables), 5% (one variable) and 10% (three variables) significance levels, with all other variables held constant. However, being a male-headed household statistically significantly and negatively affects membership decisions at less than the 1% significance level when all other variables are held constant.\\u003c/p\\u003e\\u003cp\\u003eHousehold gender is an important factor for participation in agricultural extension and other social activities and decision-making processes in developing countries. In Ethiopia, mainstream gender in development activities and decision-making processes has been focus for at least the last three decades (Mihrete and Bayu 2021; Alebie 2023). Previous studies in Ethiopia reported mixed results concerning the gender of household heads and the membership decisions of cooperatives in general and agricultural cooperatives in particular. When the household is male, there is a greater probability of joining agricultural cooperatives (Abebayehu 2024), whereas others have reported an insignificant influence of the gender of the household head on membership decisions, including (Woldegebrial, van Huylenbroeck, and Buysse, 2013; Bizualem and Saron, 2018; Dereje, 2021).\\u003c/p\\u003e\\u003cp\\u003eIn this study, the gender of the household head was also included, with the expectation of affecting membership decisions positively when the household head was male. However, the result is against the prior expectation and previous works by Abebayehu (2024). When the household head is male, the probability of joining agricultural cooperatives decreases. Being a female-headed household positively and significantly affects membership in agricultural cooperatives at less than the 5% significance level when all other predictors are held constant and vice versa. The result is against prior expectations, and the reason for this is that agricultural cooperatives in the area are more focused on consumer goods and that cooperative offices have made efforts to bring more women to cooperatives, even as leaders, in many kebeles. This was also assured during focus group discussions and field visits.\\u003c/p\\u003e\\u003cp\\u003eThe age of the household head is an imperative variable affecting household membership in collective action, as it is associated with interest and the ability to fulfill requirements as a member. At less than the 10% significance level, the age of household head positively and significantly affects household membership in agricultural cooperatives in the area. The reason is that aged household heads have better access to agricultural resources, experiences and participation in agricultural activities, which triggers the need for collective actions to access inputs and access output markets. Previous theories also argue that old farmers determine themselves to cooperatives for loyalty to their friends, whereas young farmers focus on the leverage ratio that they obtain from the collective group. The results support the initial expectations of the study and previous studies by Abate (2018), Bizualem and Saron (2018), Mbagwu (2018). This result contrasts with the work of Balgah (2018) on the factors influencing coffee farmers\\u0026rsquo; ability to join cooperatives in Cameron. The inconsistency with this study might be due to the specialization of production and the geographical variation, which affects the production system, access to agricultural markets, information, market failures and other government services (infrastructure, market facilities and information) and households\\u0026rsquo; level of literacy and other factors.\\u003c/p\\u003e\\u003cp\\u003eEducation is a tool for innovation and the adoption of better practices for agricultural and rural development interventions. The ESR result is consistent with initial expectations, as education significantly and positively affects household membership in agricultural cooperatives in the area at less than the 1% significance level. The result is also consistent with previous works of Gashaw ( 2018) Dendup, Tashi and Satit (2021), Hiskeal, Senapathy, and Bojago (2022) and against the works of Balgah (2018), Beyene and Nachimuthu (2018) and Bizualem and Saron (2018). The inconsistency with these works may be due to the geographical location and type of cooperative under consideration.\\u003c/p\\u003e\\u003cp\\u003eHousehold size is an important factor in agricultural production and food consumption. In this study, it was measured in terms of the adult equivalent, as our targets are farmers, and labor is an important factor for agricultural input needs to join agricultural cooperatives. In this study, this variable was expected to positively affect membership in agricultural cooperatives, as households with large household sizes are expected to participate in agricultural activities and have high consumption needs. In this study, this variable positively and significantly affects membership decisions at less than the 1% significance level.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab6\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 6\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eESR results for agricultural cooperative membership and its impact on smallholder farmers\\u0026rsquo; farm income\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"7\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"7\\\" nameend=\\\"c7\\\" namest=\\\"c1\\\"\\u003e\\u003cp\\u003eLog likelihood = -900.80117 Wald chi2(\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e) = 101.75 Prob\\u0026thinsp;\\u0026gt;\\u0026thinsp;chi2 = 0.0000\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"7\\\" nameend=\\\"c7\\\" namest=\\\"c1\\\"\\u003e\\u003cp\\u003eLR test of indep. eqns.: chi2(\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e) = 4.33 Prob\\u0026thinsp;\\u0026gt;\\u0026thinsp;chi2\\u0026thinsp;=\\u0026thinsp;0.0375\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e\\u003cp\\u003eAgricultural cooperative Membership\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"4\\\" nameend=\\\"c7\\\" namest=\\\"c4\\\"\\u003e\\u003cp\\u003eFarm income (log of farm income)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003eVariables\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003eCoef.\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003eStd. Err.\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c5\\\" namest=\\\"c4\\\"\\u003e\\u003cp\\u003eMember\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c7\\\" namest=\\\"c6\\\"\\u003e\\u003cp\\u003eNonmember\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eCoef.\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eStd. Err.\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eCoef.\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003eStd. Err.\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eMidland\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.278\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.219\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eLowland\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.337\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.178*\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSex\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e-0.333\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.157**\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.111\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.154\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e-0.195\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.252\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAge\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.018\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.008**\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.009\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.008\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.019\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.011*\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eEducation level\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.048\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.018***\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.090\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.018***\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.033\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.028\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eOff-/Nonfarm activity\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e-0.112\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.150\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e-0.029\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.152\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e-0.033\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.214\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eHH size\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.124\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.037***\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e-0.021\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.042\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e-0.068\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.054\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eFarm land size\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e-0.016\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.044\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.119\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.037***\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.180\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.090**\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eFarming experience\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e-0.003\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.006\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e-0.004\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.006\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e-0.004\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.008\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eLivestock size\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e-0.011\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.020\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.069\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.020***\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.100\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.031***\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAccess to irrigation\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.214\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.159\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.802\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.156***\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.113\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.231\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eDistance to coop.\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e-0.016\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.015\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e-0.007\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.021\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.011\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.021\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eDistance to market\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.017\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.010*\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e-0.009\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.011\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e-0.043\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.015***\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePerception about cooperatives\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.725\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.141***\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eTrust on coop leaders\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.096\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.169***\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eConstant\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e-2.963\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.475\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e9.178\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.464\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e9.219\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.621\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003ctfoot\\u003e\\u003ctr\\u003e\\u003ctd colspan=\\\"7\\\"\\u003e*** 1% level of significance; **5% level of significance; *10% level of significance\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tfoot\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003eThis result corresponds with those of studies by Gashaw (2018), Hiskeal, Senapathy, and Bojago (2022), Abateneh, Azanaw and Desyalew (2024) and with those of studies by Mbagwu (2018), who reported that household size negatively influences membership in cooperative societies in Abia State of Nigeria. This author explained the reason for the negative influence, as households with large household sizes have greater loads on domestic activities and lack time for cooperative activities. The difference may also be due to the types of cooperatives included in the study, as this author included other agricultural cooperatives, savings and credit cooperatives and farmer multipurpose cooperatives, whereas our study focused on farmer multipurpose cooperatives.\\u003c/p\\u003e\\u003cp\\u003eHousehold heads\\u0026rsquo; access to extension services motivates households to join agricultural cooperatives by raising awareness and understanding about the benefits of collective actions through training and information about inputs, credit and market services (\\u003cspan citationid=\\\"CR66\\\" class=\\\"CitationRef\\\"\\u003e66\\u003c/span\\u003e). The model results also indicate a positive and significant influence of access to extension services on membership in agricultural cooperatives. The result is in agreement with initial expectations and other studies by Woldegebrial \\u003cem\\u003eet al.\\u003c/em\\u003e, (2013) and Dejen and Matthews (2016).\\u003c/p\\u003e\\u003cp\\u003eAmong the factors that affect households\\u0026rsquo; decision to participate and make use of new technologies, practices and innovations, perception and trust are important psychological factors. These two variables, perceptions of the benefits of agricultural cooperatives and trust (perceived trust in leaders and actions of other members of cooperatives), were included in this study because of their perceived influence on membership in agricultural cooperatives. The results of the model reveal that both households\\u0026rsquo; perceptions of the benefits of agricultural cooperatives and their trust in cooperative management significantly and positively affect membership decisions at a level of less than 1%. This result is similar to the works of Bizualem and Saron (2018), Habtamu (2021) and Hiskeal, Senapathy, and Bojago (2022).\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec18\\\" class=\\\"Section3\\\"\\u003e\\u003ch2\\u003e4.3.2. Impact of agricultural cooperative membership on household welfare\\u003c/h2\\u003e\\u003cdiv id=\\\"Sec19\\\" class=\\\"Section4\\\"\\u003e\\u003ch2\\u003e4.3.2.1. Impact on farm income\\u003c/h2\\u003e\\u003cp\\u003eAgricultural cooperatives influence household welfare through their impact on agricultural production. Agricultural cooperatives are crucial in facilitating input and output markets in rural areas of developing countries where market failure is pervasive. Welfare is approached through diverse indicators, and in this study, we used proxies including smallholder household farm income and household food security status measured by household dietary diversity (HDD) and the food consumption score (FCS).\\u003c/p\\u003e\\u003cp\\u003eAgricultural cooperative membership and household farm income: Household-level data for gross farm income were collected from the sampled households. The data were collected on gross income from grain crops, gross income from farm activities and total gross income of the households. The descriptive statistics result on Table\\u0026nbsp;\\u003cspan refid=\\\"Tab6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e indicates that the mean gross farm income of members of agricultural cooperatives 63,323.10) ETB is greater than those of nonmembers (52,046.34) ETB.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab7\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 7\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eAgricultural cooperatives and household welfare\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"6\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eWelfare Indicator\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eMembership\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eMean\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eStd. Dev\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eMinimum\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eMaximum\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e\\u003cp\\u003eGross total farm income\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eNonmembers\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e52046.34\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e78300.39\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e620000\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eMembers\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e63323.10\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e109086.51\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e930000\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eTotal\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e57390.77\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e94207.45\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e930000\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eEndogenous switching regression model result of impact on farm income\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eThe factors affecting household membership in agricultural cooperatives were determined and discussed in the previous section of this report. Although the ESR model reported both the determinants of membership and the determinants of farm income level, we have discussed here only the determinants of farm income for both members and nonmembers of agricultural cooperatives separately.\\u003c/p\\u003e\\u003cp\\u003eOwing to data outliers and a lack of concavity, the farm income data were transformed to logarithms of ten and used as the dependent variable for the model. The model fits the data well, as the chi-square test is significant at less than the 1% level. The Wald chi-square is also a significant indicator of the goodness of fit of the model. The rho_2 is significantly different from zero, which means that nonmembers of agricultural cooperatives would have better income if they had joined agricultural cooperatives. The LR dependence equation test chi-square test is also significant (P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.0375), which means that the three equations are dependent on each other.\\u003c/p\\u003e\\u003cp\\u003eAccordingly, the ESR model results revealed that four variables, including education level completed by the household head, livestock size owned, farm size owned and access to irrigation, significantly and positively affect the farm income level of members of agricultural cooperatives. On the other hand, four variables, including the age of the household head, livestock and farm size, are positively and significantly related, whereas the distance from the market center negatively and significantly affects the farm income of nonmembers of agricultural cooperatives.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eAverage treatment effect on the treated and untreated groups\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eThe mean farm income (log) of respondents who belong to agricultural cooperatives and the associated counterfactual results were taken into account when calculating the average treatment effect. According to the model, the average effect of treatment on the treated (ATT) is the net difference between these two averages of farm income. The average farm income of nonmembers and its counterfactual, that is, what the average farm income of nonmembers would have been had they joined agricultural cooperatives, are also produced by the model. These two farm incomes are subtracted to determine the ATU. These average farm incomes (log), together with the estimated ATT and ATU, are shown in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab8\\\" class=\\\"InternalRef\\\"\\u003e8\\u003c/span\\u003e.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab8\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 8\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eESR estimates of farm income ATT and ATU\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"5\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003eWelfare indicators\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e\\u003cp\\u003eMembership status\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003eTreatment effect\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003eEffect\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eMember\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eNonmember\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eFarm income\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eMember\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e10.41\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e9.67\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.74\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e7.65%\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eNonmember\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e10.35\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e10.16\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.19\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1.87%\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eHeterogeneity effect\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.06\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e-0.49\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.55\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003eFor both members and nonmembers of agricultural cooperatives, as can be seen from Table\\u0026nbsp;\\u003cspan refid=\\\"Tab8\\\" class=\\\"InternalRef\\\"\\u003e8\\u003c/span\\u003e, the ATT and ATU are positive and significant, suggesting that participation in agricultural cooperatives has significantly improved farm income for the sampled smallholder farmer households. Farm income increases by 7.65% as a result of membership in agricultural cooperatives. This value is 0.74 log of the farm income of households. However, their farm income would have increased by 1.87% if nonmembers had chosen to join agricultural cooperatives. The observed heterogeneity between members and nonmembers is shown in the final row of Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e. The findings indicate that if nonmembers had decided to join agricultural cooperatives, members\\u0026rsquo; farm income would have increased by approximately 0.58%. Moreover, if members of agricultural cooperatives had decided not to join cooperatives, their arm income would have been 0.48% lower than that of those who did not. The findings indicate that membership in agricultural cooperatives significantly improved the farm income of the smallholder farmers in the sampled Woredas.\\u003c/p\\u003e\\u003cp\\u003eThe heterogeneity shows that membership in agricultural cooperatives improves farm income by 55%. This result is consistent with prior expectations and with those of previous studies in Asia by (\\u003cspan citationid=\\\"CR78\\\" class=\\\"CitationRef\\\"\\u003e78\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR79\\\" class=\\\"CitationRef\\\"\\u003e79\\u003c/span\\u003e), Eastern Africa by (\\u003cspan citationid=\\\"CR80\\\" class=\\\"CitationRef\\\"\\u003e80\\u003c/span\\u003e), who reported the potential of membership in voluntary groups to improve the income of coffee-growing farmers in Rwanda, and Ethiopia by (\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e), who reported the significant impact of membership in cooperatives on improving farm income in Eastern Ethiopia. (\\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e50\\u003c/span\\u003e) reported a positive significant impact of membership in coffee-growing farmer cooperatives on farm income in rural Ethiopia. (\\u003cspan citationid=\\\"CR81\\\" class=\\\"CitationRef\\\"\\u003e81\\u003c/span\\u003e) also reported a positive effect of membership in Bonga sheep producer cooperatives on household income in southwestern Ethiopia.\\u003c/p\\u003e\\u003cp\\u003eIn his research on the effects of agricultural cooperative membership on farm income and risk management in southern Ethiopia (Silte and Sidama), (\\u003cspan citationid=\\\"CR82\\\" class=\\\"CitationRef\\\"\\u003e82\\u003c/span\\u003e) also reported that membership had a positive effect. The study context, however, differs from this study area in terms of farming systems, resources at the household level, and the development of institutional and physical infrastructure. This finding also demonstrates the potential of agricultural cooperatives to improve household welfare in the study area and the substantial impact that cooperative membership has on smallholder households' farm income.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec20\\\" class=\\\"Section4\\\"\\u003e\\u003ch2\\u003e4.3.2.2. Impact of Membership on household food security (Dietary Diversity and Food Consumption Scores)\\u003c/h2\\u003e\\u003cp\\u003eAn impact of agricultural cooperative membership was expected to affect household food security status. Food security is an argued concept in literature where many definitions and ways of measurements (indicators) have been developed. Based on recent literature, household dietary diversity and food consumption scores are indicators used in this study.\\u003c/p\\u003e\\u003cp\\u003eAccording to the study result, the mean HDDS of the study area is 6.77, whereas the mean FCS score is 35.40. However, there is no significant mean difference between the food security status of members and nonmembers of agricultural cooperatives. In contrast, the mean HDDS of nonmembers (6.85) is slightly greater than that of members (6.68). FCS data also show no significant mean difference, where the mean FCS of members (35.82) is greater than that of nonmembers (35.01). According to the WFP food security classification, the mean FCS of nonmembers is on the borderline, whereas the mean FCS of members of agricultural cooperatives is on an acceptable profile (\\u003cspan citationid=\\\"CR59\\\" class=\\\"CitationRef\\\"\\u003e59\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab9\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 9\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eAgricultural cooperatives and household food security (dietary diversity and food consumption scores)\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"6\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eWelfare Indicator\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eMembership\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eMean\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eStd. Dev\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eMinimum\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eMaximum\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e\\u003cp\\u003eHousehold Dietary Diversity\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eNonmembers\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e6.85\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2.31\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e12\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eMembers\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e6.68\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.97\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e11\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eTotal\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e6.77\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2.15\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e12\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e\\u003cp\\u003eFood Consumption Score\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eNonmembers\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e35.01\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e17.76\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e137\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eMembers\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e35.82\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e17.84\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e83.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eTotal\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e35.40\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e17.78\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e137\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003eMembership in agricultural cooperatives influences household food security in multiple ways (\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR83\\\" class=\\\"CitationRef\\\"\\u003e83\\u003c/span\\u003e). In this study, data on household food security status were collected via the indicators HDDS and FCS on the basis of (\\u003cspan citationid=\\\"CR59\\\" class=\\\"CitationRef\\\"\\u003e59\\u003c/span\\u003e) procedures. To analyze the food security status of households via the HDDS indicator, the food consumption of the household in the last 24 hours was collected, and the consumption of twelve food groups by 50%+ household members was considered. To analyze the food security status of the households by the FCS indicator, the food consumption data of twelve food groups by 50%+ household members for the last seven days were collected. The FCS was computed by considering the weight given to each food group on the basis of (\\u003cspan citationid=\\\"CR59\\\" class=\\\"CitationRef\\\"\\u003e59\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eThe mean HDDS of the study area is 6.77, whereas the mean FCS score is 35.40. However, there is no significant mean difference between the food security status of members and nonmembers of agricultural cooperatives. In contrast, the mean HDDS of nonmembers (6.85) is slightly greater than that of members (6.68). FCS data also show no significant mean difference, where the mean FCS of members (35.82) is greater than that of nonmembers (35.01). According to the WFP food security classification, the mean FCS of nonmembers is on the borderline, whereas the mean FCS of members of agricultural cooperatives is on an acceptable profile (\\u003cspan citationid=\\\"CR59\\\" class=\\\"CitationRef\\\"\\u003e59\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eThe households\\u0026rsquo; dietary diversity score was computed for every respondent. According to the survey results, 68.25% of the sampled households had high dietary diversity, whereas 27% had medium dietary diversity. Seventy percent of the members of agricultural cooperatives have high dietary diversity, whereas 66.22% of nonmembers have high dietary diversity. When we compare membership households in agricultural cooperatives, the percentage of nonmember households with the lowest dietary diversity (4.95%) is slightly greater than that of members (4.5%).\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab10\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 10\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eHDDS classification of the sampled households\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"8\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e\\u003cp\\u003eMembership\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e\\u003cp\\u003eMean HDDS\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"6\\\" nameend=\\\"c8\\\" namest=\\\"c3\\\"\\u003e\\u003cp\\u003eFood security profile\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e\\u003cp\\u003eLowe dietary diversity (\\u003cspan type=\\\"Underline\\\" class=\\\"Underline\\\" name=\\\"Emphasis\\\"\\u003e\\u0026le;\\u003c/span\\u003e\\u0026thinsp;3 food groups)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e\\u003cp\\u003eMedium dietary diversity (4 and 5 food groups)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c8\\\" namest=\\\"c7\\\"\\u003e\\u003cp\\u003eHigh dietary diversity (\\u003cspan type=\\\"Underline\\\" class=\\\"Underline\\\" name=\\\"Emphasis\\\"\\u003e\\u0026ge;\\u003c/span\\u003e\\u0026thinsp;6 food groups\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eN\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e%\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eN\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e%\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003eN\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e%\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eNonmembers (222)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e6.85\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e11\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e4.95\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e64\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e28.83\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e147\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e66.22\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eMembers (200)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e6.68\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e9\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e4.50\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e50\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e25.00\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e141\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e70.50\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eTotal (422)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e6.78\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e20\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e4.74\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e114\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e27.01\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e288\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e68.25\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cstrong\\u003eHousehold food security status (FCS) category\\u003c/strong\\u003e\\u003c/p\\u003e\\u003cp\\u003eThe household composite score of the FCS is converted into categories on the basis of the WFP 2008 classification. Households with composite food consumption scores less than 21 are categorized as poor, those with scores ranging from 21.5\\u0026ndash;35 are borderline, and those with FCSs greater than 35 are considered acceptable. Accordingly, 43.36% of the sampled households in the study area are in the acceptable food security profile, whereas 33.65% and 22.99% are in the borderline and poor food security profiles, respectively. With respect to membership status and the food security profile, 44% and 42.79% of the sampled member and nonmember households, respectively, fall within the acceptable food security profile.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab11\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 11\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eFood security profile of households\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"8\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e\\u003cp\\u003eMembership\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e\\u003cp\\u003eMean FCS\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"6\\\" nameend=\\\"c8\\\" namest=\\\"c3\\\"\\u003e\\u003cp\\u003eFood security profile\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e\\u003cp\\u003ePoor (0\\u0026ndash;21)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e\\u003cp\\u003eBorderline (21.5\\u0026ndash;35)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c8\\\" namest=\\\"c7\\\"\\u003e\\u003cp\\u003eAcceptable (\\u0026gt;\\u0026thinsp;35)\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eN\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e%\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eN\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e%\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003eN\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e%\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eNonmembers (222)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e35.01\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e51\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e22.97\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e76\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e34.23\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e95\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e42.79\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eMembers (200)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e35.82\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e46\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e23.00\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e66\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e33.00\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e88\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e44.00\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eTotal (422)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e35.40\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e97\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e22.99\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e142\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e33.65\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e183\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e43.36\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eESR model results for the HDDS and FCS average treatment effects\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eThe impact of agricultural cooperative membership on household food security is determined via the ESR model, where the average treatment (AT) effect on treated (ATT) and untreated (ATU) food security is computed. The results show that for both impacts on HDDS and FCS, the treatment effect is significantly different from 0, suggesting rejection of the null hypothesis and acceptance of the alternative hypothesis, as the p value is significant at less than the 1% and 5% significance levels. As shown in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab10\\\" class=\\\"InternalRef\\\"\\u003e10\\u003c/span\\u003e, for members of agricultural cooperatives, HDDS increased by 1.86 because their membership or members\\u0026rsquo; HDDS would be less than 1.86 if they had not been a member. The treatment effect on members\\u0026rsquo; HDDS is 38.59%, indicating that membership significantly improves the food security status of members and improves the status of nonmembers if they join collective groups. For nonmembers of agricultural cooperatives, HDDS increases by a score of 1.5 if they join agricultural cooperatives, which is an increase of 21.96%.\\u003c/p\\u003e\\u003cp\\u003eThe heterogeneity effect is that if nonmembers had chosen to join agricultural cooperatives, their HDDS would have been approximately 29.43% higher than that of members. Moreover, if members of agricultural cooperatives had not joined, their HDDS would have been 19.81% lower than that of those who did not join agricultural cooperatives.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab12\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 12\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eESR estimates of farm income ATT and ATU\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"6\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003eOutcome\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003eSelection\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e\\u003cp\\u003eMembership status\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003eTreatment effect\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003eEffect (%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eMember\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eNonmember\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e\\u003cp\\u003eHDDS\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eMember\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e6.68\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e4.82\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1.86\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e38.59\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eNonmember\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e8.33\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e6.83\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1.50\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e21.96\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eHeterogeneity effect\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e-1.65\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e-2.01\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.36\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e\\u003cp\\u003eFCS\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eMember\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e35.82\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e14.94\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e20.88\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e139.76\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eNonmember\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e40.75\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e34.98\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e5.77\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e16.49\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eHeterogeneity effect\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e-5.68\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e-18.33\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e12.65\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003eRegarding the FCS, as indicated in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab10\\\" class=\\\"InternalRef\\\"\\u003e10\\u003c/span\\u003e, the ATT for members of agricultural cooperatives is 20.88, which is 139.76% higher than their counterfactual value. For nonmembers, ATU has a score of 5.77, which is what the FCS of nonmembers of agricultural cooperatives would be higher by 16.49% if they had joined. The heterogeneity effect indicated in the lower line of Table\\u0026nbsp;\\u003cspan refid=\\\"Tab10\\\" class=\\\"InternalRef\\\"\\u003e10\\u003c/span\\u003e is that the FCS of nonmembers would be 52.40% greater than that of members if they did not join agricultural cooperatives. The FCS of members would be 13.94% lower than that of nonmembers if they joined agricultural cooperatives. The negative sign of the heterogeneity effect indicates that agricultural cooperative membership would benefit more nonmembers than would members of cooperatives if they joined (\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e), suggesting the importance of further work to bring more households as members of agricultural cooperatives.\\u003c/p\\u003e\\u003cp\\u003eThis result is consistent with the initial expectation of the study, as agricultural cooperatives are expected to improve household-level food security through their contribution to agricultural production and productivity improvement and through their multiplier effect on household income, favoring conditions for credit and market information and improving market access for agricultural inputs and outputs. This result is also similar to that of Olumeh \\u0026amp; Mith\\u0026ouml;fer (2023), who reported a significant impact of membership in collective action on household welfare in Malawi. Many previous studies have also reported a positive significant effect of membership on household welfare (food security), including consumption expenditures (Seneerattanaprayul \\u0026amp; Gan, 2021) in Thailand and (\\u003cspan citationid=\\\"CR85\\\" class=\\\"CitationRef\\\"\\u003e85\\u003c/span\\u003e) in the Philippines.\\u003c/p\\u003e\\u003cp\\u003eThe ESR model results indicate that membership in farmer organizations in northern Ghana positively impacts household dietary diversity and reduces food insecurity (\\u003cspan citationid=\\\"CR86\\\" class=\\\"CitationRef\\\"\\u003e86\\u003c/span\\u003e). This finding is also similar to that of Musa \\u0026amp; Hiwot (2017), who reported a positive effect of cooperative membership on household food security in Eastern Ethiopia. Muhammed Abdella \\u0026amp; Callo-Concha (2021) also reported a positive impact of access to markets through collective groups on the dietary diversity and food security of coffee-producing smallholders in southwestern Ethiopia. Agricultural cooperative membership in the Halu district of the Oromia region in Ethiopia was also found to positively affect household food balance and the household food insecurity access scale (HFIAS) (\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e).\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\"},{\"header\":\"5. Conclusion and policy implications\",\"content\":\"\\u003cp\\u003eMany empirical findings have reported the impact of membership in developing countries. The impact dimensions considered by different studies also vary, ranging from agricultural production and productivity improvement to the use of agricultural technologies (inputs), market participation, and household welfare (income and food security). The reports are mixed and inconclusive in many instances. In this study, the impact analysis involved at least three indicators of household welfare, including farm income, the household dietary diversity score (HDDS) and the household food consumption score (FCS). By employing the ESR model for its ability to address observable sample selection bias and unobservable factors, the average treatment effect of membership in agricultural cooperatives was determined.\\u003c/p\\u003e\\u003cp\\u003eThe mean income comparison of agricultural cooperative members and nonmembers indicates that the gross mean gross income from the sale of cereal grains, total farm income and total income of household members are higher than those of nonmember households. The ESR model also reveals that there is a heterogeneous impact of household and household characteristics on the level of farm income of respondents. The education level of the household head, livestock and farm size owned and access to irrigation positively and significantly affect the farm income of members, whereas the age of the household head, distance to the market center, livestock and farm size owned significantly affect the farm income level of nonmembers of agricultural cooperatives. The average treatment effect (AT) results show that agricultural cooperatives have a positive significant effect on the farm income of smallholder farmer households. The farm income of members of agricultural cooperatives increased by 7.4%, whereas it would be higher by 1.9% for nonmembers if they had joined collective groups.\\u003c/p\\u003e\\u003cp\\u003eThe HDDS analysis indicated that 70.5% of the members were in the high dietary diversity category, whereas 66.22% of the nonmembers were in the high dietary diversity category. The mean FCS of the members is greater (35.82) than the mean FCS of the nonmembers (35.01). The mean food consumption of members is in the acceptable food profile, whereas that of nonmembers is in the borderline profile. In the study area, only 43.36% of the households fell within the acceptable food security profile, highlighting the vulnerability of these areas to food insecurity. The regression analysis also reveals that the gender and education level of the household head, livestock and farm size of the household significantly positively affect the household dietary diversity score (HDDS), whereas the household size, access to irrigation, and distance to the market center significantly negatively affect the HDDS of members. Household size, distance to the market center and livestock size significantly affect the HDDS of nonmembers. The household FCS, as an indicator of the food security status of members, is also significantly affected by the household head\\u0026rsquo;s education level, market proximity, and access to irrigation, whereas livestock size, farm size, and distance to the market center significantly affect the food security status of nonmembers. The regression results of the average treatment effect highlight that membership in agricultural cooperatives significantly improves and improves the food security status of members and nonmembers.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgement\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe would like to thank \\u003cem\\u003eWoreda\\u003c/em\\u003e cooperative Office heads, experts, cooperative promotions agents and \\u003cem\\u003eKebele\\u003c/em\\u003e leaders who cooperated during data collection by devoting their time for interview and disclosing all necessary secondary data including audit reports. We also appreciate the perseverance and commitment of data enumerator for collecting quality data in desired time period. Finally, we thank all cooperative leaders who cooperated and participated during data collection.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthor contribution\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAS managed the conceptualization of the study design, data collection and cleaning, writing of drafts, and the write up of the final manuscript to its submission. EN, TB, CS and FE provided technical supervision in the study design, data analysis, contextualization of results and discussion, preparation, proof reading, and editing of the final manuscript to its submission.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis study was funded by Haramaya University with grant code HUSP_2023_8254\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData Availability Statement\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe data collected for analysis of this research is available at \\u003cstrong\\u003eMendeley Data and Digital Commons Data at\\u0026nbsp;\\u003c/strong\\u003ehttps://data.mendeley.co/datasets m /kd8422nbjj/1\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthical Statements\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe study was conducted after ethical approval of IRB of College of Agricultural Sciences of Arba Minch University. It strictly followed the ethical guideline of the University.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthical approval \\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe research was approved by Institutional Review Board of College of Agricultural Sciences of Arba Minch University before going to field data collection.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent to participate \\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eInformed consent was obtained from all individual participants included in the study.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe declare that all of the material in the manuscript is owned by the authors and/or no permissions are required.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting Interest\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe here declare that all authors of the article have no conflict of interest.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eClinical trial number: \\u003c/strong\\u003enot applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u0026nbsp;Author details\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003csup\\u003e1\\u003c/sup\\u003e Department of Rural Development and Agricultural Extension, Arba Minch University\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003csup\\u003e2\\u003c/sup\\u003eSchool of Rural Development and Agricultural Innovation, Haramaya University\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003csup\\u003e3\\u0026nbsp;\\u003c/sup\\u003eDepartment of Economics, Arba Minch University\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eWorld Bank. 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The Role of Agricultural Cooperatives in Risk Management and Impact on Farm Income: Evidence from Southern Ethiopia. Int J Econ Behav Organ. 2016;4(4):28. \\u003c/li\\u003e\\n\\u003cli\\u003eShumeta Z, D\\u0026rsquo;Haese M. Do Coffee Farmers Benefit in Food Security from Participating in Coffee Cooperatives? Evidence from Southwest Ethiopia Coffee Cooperatives. Food Nutr Bull. 2018;39(2):266\\u0026ndash;80. \\u003c/li\\u003e\\n\\u003cli\\u003eOlumeh DE, Mith\\u0026ouml;fer D. Impact of collective action on household welfare: Empirical evidence from baobab collectors in Malawi. Ann Public Coop Econ. 2023;(August 2022):1\\u0026ndash;27. \\u003c/li\\u003e\\n\\u003cli\\u003eJimenez CD, Catelo SP, Elauria M, Sajise AJU. Impact of Cooperative Membership on Household Welfare , Evidence from Calamansi Farmers in Oriental Mindoro , Philippines. J Econ Manag Agric Dev. 2018;4(2):27\\u0026ndash;43. \\u003c/li\\u003e\\n\\u003cli\\u003eAddai KN, Ng\\u0026rsquo;ombe JN, Temoso O. Can farmer organization membership improve household food security and nutrition? Evidence from Northern Ghana. World Food Policy. 2024;(November 2023):180\\u0026ndash;202. \\u003c/li\\u003e\\n\\u003c/ol\\u003e\"},{\"header\":\"Footnotes\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003e (The World Bank data bank, 2021) Ethiopia Adult Literacy Rate Yearly Analysis: World Development Indicators | YCharts\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":true,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"impact, agricultural cooperatives, household welfare, farm income, food security, endogenous switching regression\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-6844791/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-6844791/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003e\\u003cem\\u003eIncreasing agricultural production and productivity is indispensable for improvement of household welfare. In developing countries however it is challenged by various factors including market failures. Agricultural cooperatives are among the institutional arrangements that are widely recognized for improving the market access of smallholders in developing countries and improve welfare. This study therefore aimed to assess the impact of being a member of agricultural cooperatives on smallholder household welfare in southern Ethiopia. The multistage sampling techniques were followed with a survey research design. Data were collected from 422 households. Welfare was approached with farm income and food security (household dietary diversity and food consumption scores). The average treatment on treated and untreated of farm income were 0.74 and 0.19, respectively, whereas that of dietary diversity were 1.86 and 1.5, respectively, and the food consumption scores of members and non-members were 20.88 and 5.77, respectively. The treatment effect shows that farm income increased by 7.65% for members, whereas non-member income increased by 1.87%. The average treatment effect of non-members is 5.77; that is, the food consumption score of non-members of agricultural cooperatives would be higher by 16.49% if they had joined. The study revealed that membership positively influences farm income, dietary diversity and food consumption scores. This study emphasizes the importance of promoting cooperatives to improve the welfare of smallholder households, but future research should consider other forms of cooperatives and other dimensions of welfare.\\u003c/em\\u003e\\u003c/p\\u003e\",\"manuscriptTitle\":\"Impact of Agricultural Cooperative Membership on Households’ Welfare in South Ethiopia Region, Ethiopia\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-07-16 11:44:37\",\"doi\":\"10.21203/rs.3.rs-6844791/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"7d5c1aff-9903-4261-9bfe-6bced0b6bd47\",\"owner\":[],\"postedDate\":\"July 16th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-07-24T15:08:19+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-07-16 11:44:37\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-6844791\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-6844791\",\"identity\":\"rs-6844791\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}