Phân tích mô hình logit đa thức quyết định tham gia hợp tác xã của nông hộ

Nội dung text: Phân tích mô hình logit đa thức quyết định tham gia hợp tác xã của nông hộ

1. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 A MULTINOMIAL LOGIT MODEL ANALYSIS OF FARMER’S PARTICIPATION IN AGRICULTURAL COOPERATIVES MODELS PHÂN TÍCH MÔ HÌNH LOGIT ĐA THỨC QUYẾT ĐỊNH THAM GIA HỢP TÁC XÃ CỦA NÔNG HỘ Luu Tien Dung Faculty of Postgraduate Studies, Lac Hong University dunglt@lhu.edu.vn ABSTRACT The present study aimed to assess determinants of farmers’ participating behavior in agricultural cooperatives models. The study based on the survey data of 421 farmers in the Mekong Delta of Vietnam, using multinomial logistic regression model. Findings of the study indicate that farmers are more likely to participate in agricultural cooperatives actions when they have better favorable resources, include Education level, Farm land size, Access to credit, Social capital, Access to extension, and Constraint to market. Farmers participate in cooperatives because they view it as the institution that can help them to reduce production and marketing risks and ultimately enhance their chances of expanding their business operations and income level. To an extent, the results from this study suggest that Vietnam’s agricultural policies and efforts to promote cooperatives are effective in the coming periods. Keywords: Agricultural cooperatives, cooperative types, farmers’ participation, multinomial logit, Vietnam. TÓM TẮT Nghiên cứu này nhằm phân tích các yếu tố tác động đến hành vi tham gia các hợp tác xã nông nghiệp của nông hộ. Nghiên cứu dựa trên dữ liệu khảo sát của 421 nông dân ở đồng bằng sông Cửu Long, Việt Nam, sử dụng mô hình hồi quy logistic đa thức. Những phát hiện của nghiên cứu chỉ ra rằng nông dân có nhiều khả năng tham gia vào các tổ chức hợp tác xã nông nghiệp khi họ có nguồn lực thuận lợi hơn, bao gồm Trình độ học vấn, Diện tích đất nông nghiệp, Tiếp cận tín dụng, Vốn xã hội, Tiếp cận khuyến nông, và Rào cản tiếp cận thị trường. Nông dân tham gia vào các hợp tác xã vì họ coi đây là tổ chức có thể giúp họ giảm rủi ro trong sản xuất và tiếp thị, và cuối cùng nâng cao cơ hội mở rộng hoạt động kinh doanh và mức thu nhập của họ. Ở một mức độ nào đó, kết quả từ nghiên cứu này cho thấy các chính sách nông nghiệp của Việt Nam và nỗ lực thúc đẩy hợp tác xã có hiệu quả trong giai đoạn tới có thể được đề nghị từ nghiên cứu này. Từ khóa: Hợp tác xã nông nghiệp, mô hình hợp tác xã, tham gia của nông dân, logistic đa thức, Việt Nam. 1. Introduction Agriculture is the backbone of the Vietnamese economy employing around 60% of the workforce and accounting for 17% of GDP (GSO, 2018). This means that an average farmer earns less than half of the average income per capita in Vietnam. The challenge is to integrate farmers and their produce into the agricultural value chain so that they benefit more equally to other chain partners. Evidence from both research and practice indicates that smallholders can overcome market failures and maintain their position in the market, improving economic and technical efficiency by organizing themselves into groups or producer organizations, the farmer organizations can serve as a platform for capacity building, information exchange, and innovation in rural settings (Abate, Francesconi, & Getnet, 2014; Abebaw & Haile, 2013; Ahmed & Mesfin, 2017; Ma & Abdulai, 2016; Verhofstadt & Maertens, 2014; Wollni & Zeller, 2007). Consequently, increased participation in agricultural cooperatives should further enhance efficiency gains among smallholder farmers. The Cooperatives Law of 2012 has generated a lot of interest in the activities of cooperatives. Vietnam is also opening its agricultural markets rapidly as a consequence of various international trade agreements. Due to the favorable climate many high value crops are growing in Vietnam and this results in huge opportunities for farmers to export. However, farmers need to be organized in order to have access to the more profitable and high-end markets or at 453
2. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 least organize negotiating power. But, participation of members is very limited. Currently, there are around 13.856 thousand formally registered agricultural cooperatives with 4.1 million members and 26.978 thousand agricultural collaboration groups with 638.24 thousand members in Vietnam, accounted only 20% of employed population in agriculture, 55% of all cooperatives are considered effective (GSO, 2018). This becomes a barrier to the success of the agricultural cooperatives in Vietnam. Several studies have found that various variables have a positive impact on farmers’ adoption of innovative agricultural practices (Feder, Just, & Zilberman, 1985; Lee, 2005; Rogers, 2003; Teklewold, Kassie, & Shiferaw, 2013; Wollni, Lee, & Thies, 2010; Zbinden & Lee, 2005). Number of studies have investigated the importance that various socioeconomic and psychological member attributes have on how members assess their agricultural cooperatives. During the last two decades, studies contain empirical investigations about member opinions, satisfaction, loyalty, participation, choices, and other behavioral aspects-cooperatives’ degree of success such as member trust, commitment, and intensity participation in the cooperatives (Esayas & Gecho, 2017; Fischer & Qaim, 2014; Gyau, Mbugua, & Oduol, 2016; Karlı, Bilgiç, & Çelik, 2006; Kidane, Lemma, & Tesfay, 2018; Mensah, Karantininis, Adégbidi, & Okello, 2012; Mojo, Fischer, & Degefa, 2017; Österberg & Nilsson, 2009; Zeweld Nugusse, Van Huylenbroeck, & Buysse, 2013). A number of the studies focus on member behavior in specific decision situations, such as the farmers’ choice between cooperative and investor-owned partner firms (De Moura Costa, Chaddad, & Furquim de Azevedo, 2013; Lind & Åkesson, 2005). In the context of a transforming agriculture market in Vietnam, there are very few existing researches in this area were focused predominantly on the aggregate macroeconomic level (Cox & Le, 2014; Luu Tien Dung, Pham Van Trinh, & Van Nu Thuy Linh, 2016; Wolz & Duong, 2010). In contrast, very little research has been conducted at the microeconomic level with emphasis on the behavior of farming households in cooperatives participation. The main objective of this study is to investigate the determinants of farmers decision to participate in agricultural cooperatives types in Vietnam, using a multinomial logit regression model. 2. Literature Review and Hypothesis Development A cooperative is an autonomous association of persons united voluntarily to meet their common economic, social, and cultural needs and aspirations through a jointly-owned and democratically- controlled enterprise (ICA, 2005). Agricultural cooperative is a group of farmers who pool their resources together in certain areas of activity to facilitate optimal production through efficient use of these resources. This pooling of resources includes the joint purchase of farm inputs like seed, farm machinery, aiding members morally and financially during cultivation and seeking marketing channels for farm products to ensure better and fair prices. Farmers formed cooperatives with the objective to generate greater profits, i) by obtaining inputs and services at lower costs than they could obtain elsewhere or that were not available, and ii) by marketing their products at better prices or in markets that were previously not accessible (Coltrain, Barton, & Boland, 2000). Ortmann and King (2007) maintained that in general, agricultural cooperatives can be classified into three broad categories according to their main activity namely: i) Marketing cooperatives, which may bargain for better prices, handle, process or manufacture and sell farm products, ii) Farm supply cooperatives, which may purchase in volume, manufacture, process or formulate, and distribute farm supplies and inputs such as seed, fertilizer, feed, chemicals, petroleum products, farm equipment, hardware, and building supplies, and iii) Service cooperatives, which provide services such as trucking, storage, ginning, grinding, drying, artificial insemination, irrigation, credit, utilities, and insurance. In Vietnam, agricultural cooperative has developed since 1954 with many stages of rises and falls (Tran, 2014). The transition process of collective farming in Vietnam was not straightforward, but a trial and error process (Sultan & Wolz, 2012). Currently, the agricultural cooperatives in Vietnam operate with three main models, including i) model of agricultural service cooperatives accounts for 70% of total agricultural cooperatives, they mainly perform basic service stages for the production of farmers. In this 454
3. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 model, agricultural production is categorized as private work with households as members of the cooperatives, they conduct it by themselves. The cooperatives only provide services suitable for their requests such as inputs for agricultural production, services in stages of agricultural production, and outputs for agricultural production; ii) model of agricultural services and integrated business cooperatives not only provide services to members, but also raise capital to organize production and integrated business to generate higher profits; and iii) model of specialized cooperatives such as livestock cooperatives, flower and ornamental plant cooperatives, safe vegetable cooperatives, they appear to meet production requirements of markets. For these models, the major task of agricultural cooperatives is providing essential services to their members, include preparing lands, seeds, fertilizers and plant protection chemicals; technical guidance, methods of preservation, harvesting; organizing the processing and distribution of products; supporting funds for their members; organizing the production of crafts and other sectors. In addition to these models, in recent periods, to meet the needs of restructuring the agricultural production and rural area, one cooperative model has just appeared in Vietnam, this is a new type of cooperative model (Cox & Le, 2014). Collaborative group models have significantly increased in quantity and have been a popular choice for many farmers wanting to collaborate alongside the formal cooperatives (Tran, 2014). The model is more suited to the capacity and demands of farmers, and it shares the mutual values and ensures the fundamental principles of voluntary, independent, self-reliant, and efficient expectations of farmers are upheld. This form of cooperation targets structural changes in market power; improvement in access to resources, inputs for production, and public services; fulfillment of community functions; improvement in community resistance and risk sharing; the rise in the voice of farmers, increase in social capital for poor/disadvantaged groups; and increase in community-based social security. The major difference between the legal status of collaborative groups and cooperatives normally carries a psychological impact on enterprises rather than the assurance of contract compliance and an increase in dispute resolution, especially with small-scale contracts and alliances. The cooperative linkage is successful when farmers have a high demand for cooperative production, supply quality products, and achieve mutual benefit and risk-sharing (Cox & Le, 2014). Helmberger and Hoos (1962) can be regarded as having developed the first complete mathematical model of behavior of an agricultural cooperative. The authors use the neo-classical theory of the firm to develop short-run and long-run models of a cooperative including behavioral relations and positions of equilibrium for a cooperative and its members under different sets of assumptions using traditional marginal analysis. In their model, the cooperative’s optimization objective is to maximize benefits to members by maximizing the per-unit value or average price by distributing all earnings back to members in proportion to their patronage volume or use. A variety of explanatory variables occur in the previous studies in the prediction of farmer’s behavior to participate in agricultural cooperatives. Recent empirical studies emphasizing the following variables as the main determinants of farmers participation in agricultural cooperatives include farmer’s gender, age, education level, farm land size, off-farm income, credit access, social capital, extension access, perceived trust, land tenure status, and market access (Agbonlahor, Enilolobo, Sodiaya, Akerele, & Oke, 2012; Arayesh, 2011; Fisher & Qaim, 2012; Gijselinckx & Bussels, 2014; Gyau, Mbugua, & Oduol, 2016; Karlı, Bilgiç, & Çelik, 2006; Mojo, Fischer, & Degefa, 2017; Zeweld Nugusse, Van Huylenbroeck, & Buysse, 2013; Zheng, Wang, & Awokuse, 2012). The human capital of farmer such as the education level of household head, age, agricultural knowledge, and experience may affect decisions to participate cooperatives action because of the imperfect markets. The education level of farmer correlates positively with adoption decisions because of the assumed link between education and knowledge. Education is likely to have a positive influence on participation because well-educated farmers are more likely to possess the skills and networks necessary 455
4. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 to initiate and manage an association (Gyau, Mbugua, & Oduol, 2016). Mojo, Fischer, and Degefa (2017) indicate that the probability of farmers' membership decision increases with education level. Gender influences farmers’ participation in collective action because group activities can be time-consuming, thereby lowering the incentive for women to participate (Weinberger & Jütting, 2001). Ownership of assets is strongly gendered, reflecting existing gender norms and limiting women’s ability to invest in more profitable livelihood strategies (Quisumbing et al., 2015). The participation of men and women members in producer organizations are conditioned by economic, social and cultural factors, including their access to natural and other productive resources (Bacon, 2010; Kaaria, Osorio, Wagner, & Gallina, 2016). Studies concluded that young heads of households are more likely to acquire new knowledge and learn new techniques than the orders (Arayesh, 2011; Gyau, Mbugua, & Oduol, 2016; Kidane, Lemma, & Tesfay, 2018; Mojo, Fischer, & Degefa, 2017; Ouma & Abdulai, 2009; Weinberger, 2001). The financial resources of farm operation include off-farm income, farm land size, and financial credit accessibility. Farmers who engage in farming as their primary occupation are more likely to produce more and hence will need to engage more in input and output markets (Kirui, 2013). Financial shortage is one of the main reasons for formation of cooperative societies (Gertler, 2006). This justifies that people have to join cooperatives to solve short of financial resources (Zeweld Nugusse, Van Huylenbroeck, & Buysse, 2013). Farm land size refers to the total land available to a farmer for an agricultural production. Feder, Just, and Zilberman (1985) show that given the uncertainty, and the fixed transaction and information costs associated with technologies, there may be a critical lower limit on farm size which prevents smaller farms from adoption decision. Farm land tenure is a descriptor differentiating self-owned land from a property which is rented from a third party. A farmer is more likely to manage self-owned land in a more favorable manner than rented land (Chirwa, 2005; Isgin, Bilgic, Forster, & Batte, 2008; Teklewold, Kassie, & Shiferaw, 2013), because of the effect of the land tenure status of the household on cooperatives participation to the fact that the benefits of long-term practices accrue over time. Social capital is indicated in terms of three dimensions, i.e., the external, relational, and cognitive dimensions (Liang, Huang, Lu, & Wang, 2015). This represents a combination of variables, such as membership in farmers’ groups or associations, the number of relatives in and outside the village that a household can rely on for critical support, and the number of traders that a farmer knows in and outside the village. Social capital literature treats social networks as a means to access information, secure a job, obtain credit, protection against unforeseen risks, information exchange market, reduce information asymmetries and enforce contracts. Social networks also reduce transaction costs and increase farmers’ bargaining power, helping farmers earn higher returns when marketing their products. Gijselinckx and Bussels (2014) find that social capital and the legacy of communism are significantly correlated with the attractiveness of the co‐ operative sector for farmers. Social capital can be developed by agricultural cooperatives and the amount of social capital within the organization theoretically will enhance economic efficiency and enhance long-term success (Hong & Sporleder, 2007). The extension is a source of information for many farmers, either directly, through contact with extension agents, or indirectly, through farmers who have prior exposure transmitting information to other farmers. With the added responsibility, the average farmer seeks ways to enhance his farming business through regular extension contact and access to farm-related information, credit, exchange of ideas and access to affordable inputs (Gyau, Mbugua, & Oduol, 2016). Access to information through training, information tools and exposure visit is also an important factor to motivate rural people to join cooperatives (Zeweld Nugusse, Van Huylenbroeck, & Buysse, 2013). Cooperatives can serve to reduce transaction costs associated with producing, marketing and distributing products and can mitigate risks faced by small farmers like low farm prices (Zeweld Nugusse, Van Huylenbroeck, & Buysse, 2013). Perceived trust and attitudes towards collective action are also likely to be important. There is substantial experimental evidence showing that individuals are willing to 456
5. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 take actions towards shared goals of a group when they trust that other group members will also take such action (Fehr & Gachter, 2000). Similarly, within groups, the intensity of participation and commitment could vary given the different motivations, perceived benefits and trust in collective action (Meier zu Selhausen, 2016). The key variables used were centered on knowledge gained from group activities, perception of trust, benefits in terms of technology and economic gains from collective action (Gyau, Mbugua, & Oduol, 2016). Access to market can influence the farmers’ decision in cooperatives action in various ways. Access to market is directly associated with the transaction costs that occur when households participate in input and output marketing activities. Transaction costs are barriers to market participation by smallholder farmers and are factors responsible for significant market failures in developing countries (Dimara & Skuras, 2003; Pretty, Toulmin, & Williams, 2011). Farmers participate in agricultural cooperatives to overcome barriers such as poverty, market failure, missing services in the production process, decreased income, reduce transaction costs with traders and contribution to the development of the cooperative communities (Msimango & Oladele, 2013). Hovhannisyan and Gould (2012) identified that cooperative organizations are supportive in overcoming barriers that impede farmers’ access to assets, information, services and input and output markets. Participation in cooperatives may need up-to- dated information on the day to day activities of the cooperatives and operations. Therefore, the nearby farmers have better chance of getting reliable information related to seed production from the cooperative member than farmers far away (Kidane, Lemma, & Tesfay, 2018). 3. Methodology Research model and measurements The logit model is the most popular model used in choice behavioral studies, was based on the theory of Maximum Likelihood suggested by Ben-Akiva and Lerman (1985) (Agbonlahor, Enilolobo, Sodiaya, Akerele, & Oke, 2012; Esayas & Gecho, 2017; Gyau, Mbugua, & Oduol, 2016; Karlı, Bilgiç, & Çelik, 2006; Zheng, Wang, & Awokuse, 2012; Zeweld Nugusse, Van Huylenbroeck, & Buysse, 2013). The logit model is generally classified into two major categories, including logit model of binary and multinomial models. Multinomial logistic regression is used to predict the categorical placement in or the probability of category membership on a dependent variable based on multiple independent variables. The multinomial logistic regression is a simple extension of binary logistic regression that allows for more than two categories of the dependent or outcome variable. Like the binary logistic regression, the multinomial logistic regression uses maximum likelihood estimation to evaluate the probability of categorical membership. Tabachnick, Fidell and Osterlind (2001) argued that multinomial logistic regression technique has number of major advantages: i) it is more robust to violations of assumptions of multivariate normality and equal variance-covariance matrices across groups; and ii) it is similar to linear regression, but more easily interpretable diagnostic statistics. Further, advantages of the analysis that raise its popularity come from the following assumptions: iii) most importantly, multinomial logistic regression does not assume a linear relationship between the dependent and independent variables; iv) independent variables need not to be interval v) MLR does not require that the independents be unbounded; and lastly vi) normally distributed error terms are not assumed. In applying the multinomial logit model of agricultural cooperatives of rice farmers, they chose a member of cooperatives and collaborative groups comparable to none-member decision. The probability of choosing cooperatives participation j is given by: pj = exp(X’βj)/D, j = 1,2, , m-1. (1) And pm = 1/D 457
6. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 Where D = 1 + (j = 1, 2, , m) is the different alternative, pj is the probability of choosing cooperative participation j, X is a vector of characters, and βj is the vector of coefficients pertaining to participates j. Table 1: Definition of variables Variable Definition Expected sign Source of variables Dependent variable Y Dummy, cooperatives participation: 2 = cooperatives member; 1 = collaboration groups member; 0 = non-member. Independent variables Gender Dummy, gender of household -/+ Agbonlahor, Enilolobo, Sodiaya, head: 1= male, 0= female Akerele, & Oke, 2012; Gyau, Mbugua, & Oduol, 2016; Kaaria, Osorio, Wagner, & Gallina, 2016; Weinberger & Jütting, 2001. Age Continuous, age of household - Arayesh, 2011; Barrett, 2008; head (years) Fisher & Qaim, 2012; Gyau, Mbugua, & Oduol, 2016; Kaaria, Osorio, Wagner, & Gallina, 2016; Kidane, Lemma, & Tesfay, 2018; Kirui, 2013; Mojo, Fischer, & Degefa, 2017; Weinberger & Jütting, 2001. Education Continuous, the number of + Agbonlahor, Enilolobo, Sodiaya, level formal education year of Akerele, & Oke, 2012; Arayesh, household head 2011; Barrett, 2008; Gyau, Mbugua, & Oduol, 2016; Kaaria, Osorio, Wagner, & Gallina, 2016; Karlı, Bilgiç, & Çelik, 2006; Mojo, Fischer, & Degefa, 2017; Weinberger & Jütting, 2001; Zheng, Wang, & Awokuse, 2012; Zeweld Nugusse, Van Huylenbroeck, & Buysse, 2013. Farm land Continuous, total farm land + Arayesh, 2011; Barrett, 2008; size (1000m2) Fisher & Qaim, 2012; Kaaria, Osorio, Wagner, & Gallina, 2016; Karlı, Bilgiç, & Çelik, 2006; Mojo, Fischer, & Degefa, 2017; Weinberger & Jütting, 2001; Zeweld Nugusse, Van Huylenbroeck, Buysse, 2013. 458
7. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 Access to Dummy, access to credit of + Weinberger & Jütting, 2001; credit household in cooperatives action: Zeweld Nugusse, Van 1= yes, 0 = otherwise Huylenbroeck, & Buysse, 2013. Off-farm Dummy, Off-farm income of + Arayesh, 2011; Kirui, 2013. income household: 1= yes, 0 = otherwise Social Continuous, the number of traders + Arayesh, 2011; Gijselinckx & capital that farmer contacts Bussels, 2014; Hong & Sporleder, 2007; Karlı, Bilgiç, & Çelik, 2006; Liang, Huang, Lu, & Wang, 2015; Mojo, Fischer, & Degefa, 2017; Weinberger & Jütting, 2001; Zeweld Nugusse, Van Huylenbroeck, Buysse, 2013. Access to Continuous, the number of + Arayesh, 2011; Zeweld Nugusse, extension agricultural knowledge sources Van Huylenbroeck, & Buysse, that farmer accesses by 2013. extension (television-radio, agricultural paper-book, smartphone, extension officer, extension-education courses, others) Perceived Dummy, perceived trust in + Arayesh, 2011; Fehr & Gachter, trust cooperatives action: 1=yes, 0 = 2000; Gyau, Mbugua, & Oduol, otherwise 2016; Österberg & Nilsson, 2009; Zheng, Wang, & Awokuse, 2012. Land Land tenure status: 1=secure, 0 + Agbonlahor, Enilolobo, Sodiaya, tenure = otherwise Akerele, & Oke, 2012; Mojo, Fischer, & Degefa, 2017; Zeweld Nugusse, Van Huylenbroeck, & Buysse, 2013. Constraint Continuous, access to markets - Arayesh, 2011; Pretty, Mojo, to market (Distance to input/product Fischer, & Degefa, 2017; Kidane, market, km) Lemma, & Tesfay, 2018; Pretty, Toulmin, & Williams, 2011; Zeweld Nugusse, Van Huylenbroeck, & Buysse, 2013; Zheng, Wang, & Awokuse, 2012. Sample and data According to Yamane (1967), the minimum sample size in the study should be: 2pp(1 )(1.96)2 0.5(1 0.5) n  384.16 e220.05 Where: Z: The significance of 95%, the value of the distribution table Z = 1.96. P: The estimate of correct prediction of n for p = 0.5. 459
8. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 e: Sampling error allowed with + -0.05 (5%). In addition, a sample size requirement for the multinomial logistic regression indicates a minimum of 10 cases per independent variable (Schwab, 2002). The sample size in this study is 421 farmers in the Mekong Delta, which is selected by the non- probability method based on the quota and convenience sampling techniques. The Mekong Delta is the largest rice production area in Vietnam, is located in the Southwestern of Vietnam. The Delta covers 39.000 km2 with about 600 km of coastline and is divided into 12 provinces (Long An, Tien Giang, Ben Tre, Tra Vinh, Vinh Long, Dong Thap, An Giang, Kien Giang, Hau Giang, Soc Trang, Bac Lieu and Ca Mau) and 1 central city, Can Tho. The sample areas include 7 communes among 7 provinces, including An Giang, Dong Thap, Long An, Kien Giang, Tien Giang, Can Tho, and Soc Trang which represents for three agro- ecological zones based on the depth and extent of flooding in the Delta, including: deep flood area (most of Long Xuyen Quadrangle, Plain of Reeds and An Giang, Dong Thap and Long An); average flood area (most of Hau Giang, Vinh Long, Tien Giang and Can Tho; part of the Trans-bassac depression and freshwater alluvial zone); shallow or no flood area (most of the coastal area and Kien Giang, Ha Tien, Soc Trang, Bac Lieu, Ben Tre and Tra Vinh) (Huynh, 2015). In each selected commune, the author interviewed 60 farmers based on supporting from extension officers and farmers’ cooperative organizations. 4. Results Demographic Data The survey data showed that 152 cases (36.1%) were involved in cooperation and collaboration groups while 269 cases (63.9%) were not. In which cases of participation, 80 cases (19.0%) adopted agricultural cooperatives, 72 cases (17.1%) adopted collaboration groups. About 94.10% of the farm households, both participants and non-participants were male headed. Other characteristics of the respondents in the survey sample is presented in Table 2. Table 2: Respondent’s characteristics (all cases) Variable Min Max Mean S. D Gender 0 1 0.94 0.24 Age 20.00 63.00 40.26 11.43 Education level 1.00 16.00 8.69 4.08 Farm land size 5.00 11.00 4.21 2.301 Access to credit 0.00 1.00 0.57 0.50 Off-farm income 0.00 1.00 0.81 0.39 Social capital 1.00 7.00 3.29 0.93 Access to extension 2.00 5.00 2.67 1.01 Perceived trust 0.00 1.00 0.73 0.44 Land tenure 0.00 1.00 0.79 0.41 Constraint to market 1.00 13 4.32 1.96 ANOVA and Chi square test results in Table 3 indicate that farmers who have better favorable resources, including education level, farm land size, extension access, market access, social capital, financial capital, perceived trust, and land tenure status are more likely to participate cooperatives than farmers whose do not (Table 3). 460
9. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 Table 3: Farmers’ characteristics, and mean statistics among cases Non-members Cooperatives Collaboration groups member member Genderc 0.81 0.97 0.99 Age 41.01 40.40 39.54 Education levela 5.33 10.00 10.19 Farm land sizea 2.07 4.08 4.44 Access to creditc 0.44 0.56 0.66 Off-farm incomec 0.16 0.88 0.88 Social capitala 0.22 0.22 0.84 Access to extensiona 0.54 0.87 2.90 Perceived trustc 0.45 0.74 0.77 Land tenurec 0.64 0.84 0.89 Constraint to marketa 5.12 4.45 4.33 a, c corresponds to the statistical significance level at 1% of ANOVA and Chi-square tests. Hypothesis Testing The results in Table 4 show the logistic coefficient for each predictor variable for each alternative category of the outcome variable; alternative category meaning, not the reference category. The logistic coefficient is the expected amount of change in the logit for each one-unit change in the predictor. The logit is what is being predicted; it is the odds of membership in the category of the outcome variable which has been specified (the first value: 0 was specified, rather than the alternative values 1 or 2). The closer a logistic coefficient is to zero, the less influence the predictor has in predicting the logit. The Table 4 also displays the standard error, t-statistic, and the p-value. The t test for each coefficient is used to determine if the coefficient is significantly different from zero. The Pseudo R2 (McFadden R2) is treated as a measure of effect size, similar to how R² is treated in standard multiple regression. However, these types of metrics do not represent the amount of variance in the outcome variable accounted for by the predictor variables. Higher values indicate better fit, but they should be interpreted with caution. The Likelihood Ratio chi-square test is alternative test of goodness-of-fit. The chi-square results show that likelihood ratio statistics are highly significant (p < .0001) suggesting the model has a strong explanatory power for behavior to participate cooperatives forms. The distribution in Table 4 reveals that the value of pseudo R2 was at 0.3511, suggesting that 35.11% of the variability is explained by this set of variables used in the model. The finding also revealed that the probability of the model chi-square (267.15) was 0.005, less than the level of significance of 0.05 (p < .05). The results show that most of the important explanatory variables in the model are statistically significant at 5% or higher and the signs on most variables are as expected. An assessment of Table 4 revealed that there is a statistically significant relationship between Education level, Farm land size, Access to credit, Social capital, Access to extension, and Constraint to market and the dependent variable cooperatives participation among farmers. 461
10. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 Table 4: Parameter estimates and marginal effects of explanatory variables from the multinomial logit adoption model Variables Cooperatives member Collaboration groups member Estimated Marginal Estimated Marginal coefficients effects coefficients effects 0.016 -0.016 13.80 0.18 Gender (1.169) (586.68) Age -0.007 -0.0004 -0.015 -0.0012 (0.016) (0.014) Education level 0.15 0.012 0.008 0.0004 (0.048) (0.04) Farm land size 0.42 0.031 0.31 0.022 (0.097) (0.086) Access to credit 1.06* 0.075 0.63* 0.042 (0.530) (0.38) Off-farm income 0.86 0.052 0.50 0.031 (0.80) (0.59) Social capital 0.65 0.045 0.70 0.0005 (0.20) (0.18) Access to 0.64 0.045 0.55 0.035 extension (0.18) (0.16) Perceived trust 0.46 0.041 0.27 0.024 (0.49) (0.44) Land tenure 0.27 0.017 0.60 0.041 (0.61) (0.52) Constraint to -0.35 -0.026 -0.14 -0.008 market (0.113) (0.093) Constant -8.59 - -20.55 - (1.93) (586.68) Number of obs = 421; LR chi2(22) = 267.15; Prob > chi2 = 0.0000 Log likelihood = -246.91425; Pseudo R2 = 0.3511 *, , denote significance at the 10%, 5% and 1%, respectively; Standard errors in parentheses. The marginal effects are presented in Table 4 by variable category. As can be observed from the Table, the most important determinants of cooperatives types participation among farmers include Education level, Fam land size, Access to credit, Social capital, Access to extension, and Constraint to market (i.e. a one unit increase in the education level of farmers will in turn increase the probability of participation by 1.2%; farmer who has an credit accessibility associated with agricultural cooperative 7.5% more likely, 4.2% the collaboration groups than farmers who has not an credit accessibility). 462
11. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 Discussion Increased encouragement of farmers to participate in the market through cooperatives puts a premium on understanding farmer’s commitment to cooperatives. The findings align with other research results (Agbonlahor, Enilolobo, Sodiaya, Akerele, & Oke, 2012; Arayesh, 2011; Fisher & Qaim, 2012; Gijselinckx & Bussels, 2014; Gyau, Mbugua, & Oduol, 2016; Kaaria, Osorio, Wagner, & Gallina, 2016; Karlı, Bilgiç, & Çelik, 2006; Mojo, Fischer, & Degefa, 2017; Zeweld Nugusse, Van Huylenbroeck, & Buysse, 2013; Zheng, Wang, & Awokuse, 2012). Farmers participate in cooperatives because they view it as an institution that can help them to reduce production and marketing risks and ultimately enhance their chances of expanding their business operations and income level. The membership probability increases with increases in Education level, Fam land size, Access to credit, Social capital, Access to extension, and Access to market. Among the farmer’s human capital variables, the effect of the number of schooling years indicates that households head with higher education level are more likely to participate agricultural cooperatives compared to households with few schooling years. The increased probabilities of the decision to enter agricultural cooperatives with higher educational attainment is presumably due in large part to foreseeing the diversification and make the use of available opportunities provided by the cooperatives. In Vietnam, approximately 42 million farmers engage in the agricultural sector, the proportion of skilled workers is very small. In 2016, 84.55 percent of the labor force are under-trained, causing low labor productivity and posing constraints to the application of technology in agricultural production. Among farmer’s financial variables, the farm land size of household increases the likelihood of participation in agricultural cooperatives and collaboration groups. On the other hand, the availability of credit accessibility has a positive effect on participation in agricultural cooperatives and collaboration groups. A small-scale production base accounts for more than 9 million households. Vietnam is in the group of 7 ranked countries in terms of agricultural land size per capita (147th of 204 countries ranked in 2015), only 0.11 ha/person or on average only 0.46 ha/household. These small pieces of land are often scattering and non-adjacent, which cause difficulties when applying technology in agriculture that requires large areas. Among social capital of farmer, as the number of traders increases in an individual’s household, farmer is more likely to join agricultural cooperatives and collaboration groups. The scale of land for agricultural production is very small and fragmented. Economic and social organizations in rural areas such as farmers' associations, women's unions, and the Vietnamese Fatherland Front are supporting and playing an increasingly important role in the implementation of economic and social objectives in general and promoting agricultural cooperatives in particular. Among extension access, the effect of the number of agricultural knowledge source indicates that households with higher agricultural knowledge are more likely to participate agricultural cooperatives and collaboration groups compared to households with few sources. The agricultural extension system in Vietnam is well organized with 91.36 percent of communes having representative extension staff and 97.32 percent having representative veterinary staff. Farmers can easily access new knowledge, skills and technologies from official sources and thus reduce risks and increase the level of application of new technologies. However, extension services are facing many difficulties because of lack of funding for R&D, poor human resources, top-down approach, and limited participation of the private sector. Among market access, the distance to input/output market of farmer increases, farmer is less likely to join agricultural cooperatives and collaboration groups. The market system, traffic and irrigation system in rural areas of Vietnam is well organized, with 79.04 percent of communes having access to the markets, 193.035 km of irrigation canals, and concretized roads. The infrastructure is constantly being improved under the new rural program, with a system of agro-enterprises and agro-processing enterprises that facilitate farmers' access to markets, reduce transaction costs, access to information on output and 463
12. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 input markets, thereby promoting the application of new technologies in production. However, infrastructure in rural areas is still poor and under-treated due to slow local infrastructure projects, scattered infrastructure and low efficiency. The study has certain limitations. Firstly, the study has considered only farmer's participation as the dependent variable in the research model. Other alternative variables such as farmer commitment and intensity in cooperatives, and performance of agricultural cooperatives have not been considered in this study. Secondly, the data sets have only collected in the Mekong delta area, therefore the model might not fit for the other areas or whole country. In further studies, it should be having a study for other areas and different types of agricultural cooperatives models. 5. Conclusion Farmers play a significant role in the supply chain of the agricultural sector and their adoption behavior of cooperatives will determine the sustainability of agricultural development on the economic, environment, and social pillar. Based on the survey data of 421 rice farmers in the Mekong Delta, the study analyzed the factors determine the probability of adoption of cooperatives models among rice farmers in Vietnam, using the multinomial logit model. The estimation results indicated a statistically significant relationship between Education level, farm land size, Access to credit, Social capital, Access to extension, and Constraint to market and the dependent variable cooperatives participation among farmers. In order to improve their performance and serve their members better these cooperatives need to overcome a set of challenges including limited market access, excessive regulations, underdeveloped infrastructure, and climate change, in addition to internal challenges related to governance and management, policies should focus on: i) Improving the quality of human capital in order to improve the quality of growth, productivity, and income for producers; ii) Revising policies on the management and use of agricultural land; iii) Investments for improving the quality of social capital as a new dynamic factor for the growth; iv) Improving the quality of extension system based on strengthening agricultural extension socialization according to a demand-based extension service, human resources training and building up regional agriculture extension network, and v) Strengthen the input and product markets on the basis of development in the supply chain of the agricultural production system. REFERENCES [1] Abate, G. T., Francesconi, G. N., & Getnet, K. (2014), ‘Impact of agricultural cooperatives on smallholders’technical efficiency: empirical evidence from Ethiopia’, Annals of Public and Cooperative Economics, 85(2), 257-286. [2] Abebaw, D., & Haile, M. G. (2013), ‘The impact of cooperatives on agricultural technology adoption: Empirical evidence from Ethiopia’, Food policy, 38, 82-91. [3] Agbonlahor, M.U., O.S. Enilolobo, C.I. Sodiaya, D. Akerele & J.T. Oke, (2012), Accelerating rural growth through collective action: groups’ activities and determinants of participation in southwestern Nigeria’, Journal of Rural Social Sciences, 27, 114-13. [4] Ahmed, M. H., & Mesfin, H. M. (2017), ‘The impact of agricultural cooperatives membership on the wellbeing of smallholder farmers: empirical evidence from eastern Ethiopia’, Agricultural and Food Economics, 5(1), 6-19. [5] Arayesh, B. (2011), ‘Identifying the factors affecting the participation of agricultural cooperatives’ members’, American Journal of Agricultural and Biological Sciences, 6(4), 560-566. [6] Esayas, B., & Gecho, Y. (2017), ‘Determinants of Women’s Participation in Agricultural Cooperatives Activities: The Case of Sodo Zuria Woreda, Wolaita Zone, Southern Ethiopia’, Journal of Culture, Society and Development, 27, 27-35. [7] Bacon, C. M. (2010), ‘A spot of coffee in crisis: Nicaraguan smallholder cooperatives, fair trade networks, and gendered empowerment’, Latin American Perspectives, 37(2), 50-71. 464
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