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Does agricultural cooperative membership influence off‐farm work decisions of farm couples?

Does agricultural cooperative membership influence off‐farm work decisions of farm couples? INTRODUCTIONOff‐farm work, an essential source of rural livelihood, is critical to alleviating rural poverty and improving household well‐being in many developing countries (Aikaeli et al. 2023; Das & Mahanta 2023; Duong et al. 2021). In China, rural migrant workers leaving for more rewarding off‐farm employment opportunities increased from 242 million in 2010 to 285 million in 2020 (NBSC 2020). In some rural areas in Vietnam and India, off‐farm income represents more than half of the total household income (Imai et al. 2015; Rajkhowa & Qaim 2022). In practice, the rural labor migration from the farm to the off‐farm sector often proceeds incrementally. Typically, only a few household members migrate, whereas others remain in their farming community to support farm production (Giles & Mu 2018). This is because, despite being toilsome and not highly profitable, farm production is a familiar source of livelihood to rural people, offering food security. Thus, farm production is treated preferentially in rural households; their aversion to risk prevents them from abandoning farm production even when off‐farm work may yield greater monetary compensation (Dubbert 2019; Ge et al. 2020; Huang et al. 2020). Removing barriers to securing and pursuing off‐farm work through policies and development programs may help farmers improve the quality of their life.This study is devoted to examining whether cooperative membership positively or negatively affects off‐farm work participation. We contribute to the literature in four ways. First, we explore the effects of cooperative membership on both husbands’ and wives’ participation in off‐farm work. Previous studies have emphasized that women's participation in off‐farm work enhances households’ dietary diversity (Sangwan & Kumar 2021), empowers women (Maligalig et al. 2019), and improves their subjective well‐being (Van den Broeck & Maertens 2017). Should more women have off‐farm work, the quality of life in rural regions may improve markedly, and agricultural cooperatives can play an important role. Second, we employ a recursive bivariate probit (RBP) model to account for the self‐selection bias inherent in cooperative membership—after all, farmers choose to participate in agricultural cooperatives; membership is not randomly assigned (Grashuis & Skevas 2022; Ma et al. 2022b; Neupane et al. 2022). The RBP model accounts for the endogeneity and self‐selection bias arising from both observed and unobserved factors (Gopalan et al. 2022; Owusu et al. 2021).Third, we explore how cooperative membership affects the time spent on off‐farm work. To this end, we use two variables reflecting the number of months husbands and wives spend on off‐farm work in a reference year. Because the time devoted to off‐farm work is measured as a count variable (0–12 months), we employ an endogenous treatment Poisson regression (ETPR) model to address the endogeneity of cooperative memberships (Ojo et al. 2021). Fourth, we analyze the impact of cooperative membership on joint off‐farm work decisions, wages earned from off‐farm, and on‐farm labor allocation, thus providing a comprehensive view of the relationship between cooperative membership and farm couples’ off‐farm work decisions.This study uses household survey data collected from 595 banana farmers from rural China in 2019. Four primary reasons have motivated us to study Chinese banana farmers. First, banana crops, with a growth cycle of 9–13 months, are harvested about once a year (Slaven 2020); cereal crops, such as rice, have shorter growth cycles and can be harvested twice or thrice annually. Thus, damage to banana crops can set farmers back financially for lengthy periods, making it all the more important for farmers to manage every stage of the growth cycle carefully. Agricultural cooperatives can provide farmers with technical support and training, helping them to manage their farming operations and optimize their yields. Second, unlike grains, which can be stored for many years after harvesting, bananas last for only three or four weeks. Even bananas stored in high‐density or low‐density polyethylene bags last for about 36 days (Hailu et al. 2014). Consequently, farmers must sell bananas quickly to avoid losses. Agricultural cooperatives can help farmers monetize their harvests within the desired timelines by bringing buyers and sellers together and providing access to local and global supply chains. Third, banana production is more vulnerable to the threats of diseases than cereal crops (Drenth & Kema 2021). Extreme weather events and pathogens have compromised banana harvests in China. For example, typhoon Mangkhut ravaged banana crops in Guangdong in 2018, and Fusarium Sacchari (a fungal plant pathogen affecting banana leaves) was also discovered in the province in 2016 (Cui et al. 2021). The resources available to farmers via agricultural cooperatives can help them learn new adaptation and mitigation strategies to cope with such adversities. This may become increasingly important should the southern provinces of China become more susceptible to extreme weather events induced by climate change. Fourth, agricultural cooperatives can help accelerate the mechanization of banana farming, which, unlike grain farming, continues to rely significantly on manual labor (Ma et al. 2022b).The rest of this article proceeds as follows: Section 2 comprises a review of related literature. Section 3 introduces the research hypothesis and describes the econometric methods. Section 4 presents the data and descriptive statistics. The empirical results are presented and discussed in Section 5. Section 6 concludes the paper and provides policy recommendations.LITERATURE REVIEWAgricultural cooperatives and agricultural and rural developmentAgricultural cooperatives have proven effective in promoting agricultural and rural development. Studies have shown that cooperatives help smallholder farmers access input and output markets and technology extension services and bring farmers together, thereby promoting collective action (Demont 2022; Lin et al. 2020; Liu et al. 2017; Neupane et al. 2022). Membership in cooperatives also affects smallholder farmers’ decisions to adopt improved technologies and invest in their farming operations, helping farmers achieve higher crop yields and production efficiency (Kumar et al. 2018; Lin et al. 2022; Ma et al. 2023; Verhofstadt & Maertens 2014; Zhang et al. 2020).Cooperative membership has also been shown to increase household income and consumption expenditure and improve subjective well‐being in rural areas (e.g., Ahmed & Mesfin 2017; Demont 2022; Dohmwirth & Liu 2020; Gava et al. 2021; Kumar et al. 2018; Verhofstadt & Maertens 2015). For example, Ahmed and Mesfin (2017) showed that joining agricultural cooperatives positively affected the consumption expenditure of smallholder farmers in Ethiopia. Dohmwirth and Liu (2020) concluded that cooperative membership empowered women working in India's dairy sector. Analyzing data collected from the Shandong province of China, Wu et al. (2022) found that joining agricultural cooperatives improved rural households’ subjective well‐being. They pointed out that the positive effects may have been driven by higher incomes and social capital resulting from joining agricultural cooperatives.Factors affecting off‐farm work participationFarmers’ decisions to participate in off‐farm work are influenced by on‐farm labor requirements. Given this, previous studies have analyzed various strategies that encourage off‐farm work participation, such as promoting contract farming to reduce labor requirements (Ruml & Qaim 2021), cultivating crops like oil palm that are less labor‐intensive than traditional crops (Chrisendo et al. 2021), and the adoption of labor‐saving agricultural technologies (Diiro et al. 2021; Zheng et al. 2022). For example, mechanized agriculture saves farm labor, thus enabling rural households to allocate more time to off‐farm activities (Zheng et al. 2022). Besides, land‐related factors, such as land access (Kosec et al. 2018), land property rights, and land tenure (Giles & Mu 2018), and the development of rural land rental markets (Daymard 2022) are also important determinants of off‐farm work participation. For example, Giles and Mu (2018) found that secure property rights facilitated rural labor moving out of the agricultural sector.Cooperative membership and off‐farm work participationNotwithstanding the voluminous literature on the effects of cooperative membership on various aspects of rural life and the factors affecting rural households’ participation in off‐farm work, there is a paucity of research on the relationship between cooperative membership and decisions to participate in off‐farm work (Demont 2022; Liu et al. 2017; Zhou et al. 2020). And the findings are mixed. For example, Liu et al. (2017) found that land transfer through land cooperatives did not affect household heads’ off‐farm employment in China. In contrast, using data collected from rice farmers in China, Zhou et al. (2020) showed that cooperative membership significantly decreased the probability of husbands’ off‐farm work while leaving that of wives unchanged. However, they did not account for the endogeneity of cooperative membership. This study contributes to this limited body of research by applying a recursive bivariate probit model and an endogenous‐treatment Poisson regression model to estimate data collected from banana‐producing households in China.RESEARCH HYPOTHESIS AND ECONOMETRIC APPROACHESResearch hypothesisBased on the classical farm household model (Min et al. 2017; Tsiboe et al. 2016), this study assumes that farm couples maximize household income earned from on‐ and off‐farm activities by joining agricultural cooperatives and participating in off‐farm work. These two activities are interrelated as farm couples need to trade off their labor force and time allocated to each activity. Allocating more labor force in a farming household to off‐farm work would reduce the labor force that can be allocated to farm work and time allocated to cooperative activities. However, participating in off‐farm work allows farmers to earn additional income that would be unavailable to them if they chose to allocate all the available household labor to farm work. The extra income improves their quality of life and provides them with the wherewithal to invest in their farming operations. To maximize household welfare, household members jointly decide who should work on or off the farm. It is common for men in rural areas to seek off‐farm employment, which often entails migrating to cities; women stay behind to care for the children and the elderly and undertake farming activities (Giles & Mu 2018; Zhou et al. 2020). Thus, women often face considerable barriers to working off‐farm. Access to employment opportunities, social support systems, mentors, training, and career counseling can help women overcome these barriers—membership in agricultural cooperatives can be beneficial in this regard. With this in mind, in the present study, we investigate how membership in agricultural cooperatives influences farm couples’ decisions to work off the farm.Having a cooperative membership may influence farm couples’ off‐farm work decisions in different ways. We discuss three reasons here. First, agricultural cooperatives improve their members’ farm management skills and production efficiency, helping save time and effort expended on farming activities, allowing them to allocate more time to off‐farm work (Demont 2022; Liu et al. 2017; Neupane et al. 2022). Second, agricultural cooperatives provide production and marketing services to their members, helping them increase farm output and sales revenue. The cooperatives also run training and development programs designed to upskill the farmers, making them more adept at farming activities and more attractive to potential employers in non‐agricultural industries (Bachke 2019; Hao et al. 2018). Third, joining cooperatives may help farmers build their social and professional networks, opening up opportunities for off‐farm work and, thus, making cooperative members more likely to be employed in non‐farm roles (Jia & Xu 2021). These reasons have motivated us to test the following two hypotheses:H1: Cooperative membership increases the probability of farm couples participating in off‐farm work.H2: Cooperative membership increases farm couples’ time allocated to off‐farm work.Estimation issuesWe aim to obtain unbiased estimates of the effects of cooperative membership on the off‐farm work of farm couples. Since our analysis is based on household survey data, which precludes randomly selecting households to join and not join cooperatives, self‐selection of households into agricultural cooperative membership is distinctly possible. Furthermore, the estimates of the influence of cooperative membership may suffer from self‐selection bias related to both observable and unobservable factors. To mitigate this bias, previous studies have utilized various approaches, such as the propensity score matching (PSM) technique (Chagwiza et al. 2016; Ji et al. 2019), the inverse probability weighted regression adjustment (IPWRA) estimator (Neupane et al. 2022), the endogenous treatment regression model (Li et al. 2023b; Vatsa et al. 2022), and the RBP model (Gopalan et al. 2022; Li et al. 2023a).In this study, we employ the RBP model to estimate the impact of cooperative membership on the off‐farm work decisions of farm couples. Two notable features of the RBP model are that it can address selection bias from observable and unobservable factors—thus, it is advantageous to use this model rather than the PSM and IPWRA methods that only account for observable bias (Li et al. 2021; Owusu et al. 2021). In addition, it also estimates the direct marginal effects of cooperative membership—a binary explanatory variable—on off‐farm work participation of husbands and wives—a binary dependent variable.To clarify, the RBP model is not applicable when analyzing the relationship between cooperative membership and off‐farm work time because the latter is measured as a count variable, months spent on off‐farm work, rather than a binary variable. In this case, the ETPR model is apt as it can estimate the impact of a binary treatment variable on the counted outcome variables (Ojo et al. 2021). In the following two subsections, we first discuss the RBP model and then introduce the ETPR model.RBP modelThe RBP model, which accounts for endogeneity and selection bias, is used to jointly estimate households’ decisions regarding cooperative membership and the impact of becoming members on farm couples’ off‐farm work participation. The decisions to join cooperatives can be modeled within an optimization framework, assuming that banana‐producing households are risk‐neutral and maximize the net benefits from cooperative membership. Let Ci∗$C_i^*$ denote the differences in the net benefits derived by households with cooperative membership and those without. Households would prefer to join cooperatives should Ci∗$C_i^*$ exceed zero. Although Ci∗$C_i^*$ is subjective and unobservable, it can be expressed using a latent variable function as follows:1Ci∗=αXi+εi,whereCi=1ifCi∗>00otherwise$$\begin{equation}C_i^* = \alpha {X}_i + {\varepsilon }_i,{\rm{\ where\ }}{C}_i = \left\{ { \def\eqcellsep{&}\begin{array}{@{}*{1}{c}@{}} {1\ if\ C_i^* &gt; 0\ }\\ {0\ otherwise} \end{array} } \right.\end{equation}$$where Ci∗$C_i^*$ represents a latent variable of cooperative membership, which is determined by the observed membership status variable Ci${C}_i$. The binary variable Ci${C}_i$ takes the value of one if households have cooperative membership and zero otherwise; Xi${X}_i$ refers to a vector of explanatory variables that are expected to affect the likelihood of cooperative membership. εi${\varepsilon }_i$ represents the error term.Assuming that the decisions of farm couples apropos off‐farm work are binary, the influence of cooperative membership and other variables on farm couples’ off‐farm work participation decisions can be modeled as follows:2Oi∗=βCi+γZi+μi,whereOi=1ifOi∗>00otherwise$$\begin{equation}O_i^* = \beta {C}_i + \gamma {Z}_i + {\mu }_i,{\rm{\ where\ }}{O}_i = \left\{ { \def\eqcellsep{&}\begin{array}{@{}*{1}{c}@{}} {1\ if\ O_i^* &gt; 0\ }\\ {0\ otherwise} \end{array} } \right.\end{equation}$$where Oi∗$O_i^*$ is a latent variable denoting farm couples’ off‐farm work status; Oi${O}_i$ is a dichotomous variable equal to one for off‐farm workers and zero for non‐farm workers; Zi${Z}_i$ is a vector of variables affecting off‐farm work decisions; β and γ are vectors of parameters to be estimated; μi${\mu }_i$ is the error term.The RBP model jointly estimates Equation (1), the treatment equation, and Equation (2), the outcome equation, using the full information maximum likelihood (FIML) estimator. The error terms of the two equations are assumed to follow a bivariate distribution, which can be expressed as:3εiμi∼N00,1ρεμρεμ1$$\begin{equation}\left( { \def\eqcellsep{&}\begin{array}{@{}*{1}{c}@{}} {{\varepsilon }_i}\\ {{\mu }_i} \end{array} } \right)\sim {\rm{N}}\left( {\left[ { \def\eqcellsep{&}\begin{array}{@{}*{1}{c}@{}} 0\\ 0 \end{array} } \right],\ \left[ { \def\eqcellsep{&}\begin{array}{@{}*{2}{c}@{}} 1&{{\rho }_{\varepsilon \mu }}\\ {{\rho }_{\varepsilon \mu }}&1 \end{array} } \right]} \right)\end{equation}$$where ρεμ${\rho }_{\varepsilon \mu }$ is the correlation coefficient between the error terms of Equations (1) and (2). A significant ρεμ${\rho }_{\varepsilon \mu }$ suggests that the error terms between the two equations are correlated, indicating that cooperative membership is endogenous. After estimating the coefficients using the RBP model, we calculate the corresponding marginal effects to facilitate the interpretation of the results. Moreover, the marginal effects can also be estimated at specific values of the covariates.A crucial prerequisite for estimating the RBP model is the exclusion restriction on the explanatory variables; that is, at least one instrumental variable is included in Xi${X}_i$ but not in Zi${Z}_i$ (Gopalan et al. 2022; Li et al. 2021; Owusu et al. 2021). A variable representing the distance from the respondent's residence to the nearest agricultural cooperative is used as the instrumental variable. It affects households’ decisions to join agricultural cooperatives—the closer people live to an agricultural cooperative, the higher their likelihood of joining it. However, the proximity of a household to agricultural cooperatives is not directly associated with its members’ participation in off‐farm work. Following previous studies (Gopalan et al. 2022; Li et al. 2020; Pizer 2016), we perform a falsification test to verify the admissibility of the instrumental variable. The results in Table A1 in the Appendix confirm that distance to the nearest agricultural cooperatives is significantly associated with cooperative membership but not with off‐farm work for households without a cooperative membership.ETPR modelThe ETPR model comprises two stages (Ojo et al. 2021). The first stage models farmers’ decisions to join cooperatives and is the same as Equation (1). The second stage models the influence of cooperative membership and other explanatory variables on the time spent on off‐farm work. The assumed linear function can be expressed as follows:4Ti=δiOi+θiZi+vi$$\begin{equation}{T}_i = {\delta }_i{O}_i + {\theta }_i{Z}_i + {v}_i\end{equation}$$where Ti${T}_i$ represents a count variable that reflects off‐farm work time of farm couples, that is, how many months a husband or wife spends on off‐farm work in a reference year; Oi${O}_i$ and Zi${Z}_i$ are as defined above; δi${\delta }_i$ and θi${\theta }_i$ are unknown parameters to be estimated, vi${v}_i$ is the error term.The maximum likelihood estimator jointly estimates Equations (1) and (4). The error terms in the two equations are assumed to be bivariate normal with zero mean and covariance matrix:5εivi∼σ2σεvρεvσεvρεv1$$\begin{equation}\left( { \def\eqcellsep{&}\begin{array}{@{}*{1}{c}@{}} {{\varepsilon }_i}\\ {{v}_i} \end{array} } \right)\sim \left[ { \def\eqcellsep{&}\begin{array}{@{}*{2}{c}@{}} {{\sigma }^2}&{{\sigma }_{\varepsilon v}{\rho }_{\varepsilon v}}\\ {{\sigma }_{\varepsilon v}{\rho }_{\varepsilon v}}&1 \end{array} } \right]\end{equation}$$where σ2 represents the variance of the error term vi${v}_i$; σεv${\sigma }_{\varepsilon v}$ is the covariance of the two error terms; and ρεv${\rho }_{\varepsilon v}$ refers to the correlation coefficient between the two error terms. A statistically significant ρεv${\rho }_{\varepsilon v}$ indicates the presence of selection bias and confirms the appropriateness of using the ETPR model rather than a simple Poisson regression model.DATA AND DESCRIPTIVE STATISTICSDataThis study utilizes data collected from a household survey on banana farmers in China. The survey was carried out from July to October 2019, covering three major banana‐producing provinces: Hainan, Yunnan, and Guangdong. The banana production of the three provinces was 7.89 million tons in 2020, accounting for 68.55% of the total banana output in China (NBSC 2020). The sample households were selected using a multistage sampling approach. After choosing the three provinces in the first stage, we randomly selected three to five counties in each province in the second stage and then two towns in each county in the third stage. In the fourth stage, one to two villages were chosen in each selected town. Finally, about 10–20 households were randomly chosen in each selected village. Since we were interested in farm couples’ off‐farm work decisions, the observations comprising unmarried, divorced, or widowed respondents were left out of the analysis. This resulted in a sample of 595 households. Among these, 129 households joined a cooperative, whereas 466 did not.During the survey, we gathered information on the off‐farm work status of the respondents by asking, “Did you participate in any off‐farm work last year?”. If the answer was “Yes”, we asked the respondent, “How many months did you spend on off‐farm work last year?”. The respondents were also queried about their marital status. If the respondent reported being married, we asked, “Did your spouse participate in any off‐farm work last year?” If the respondent answered “Yes”, we prompted the respondent with the following question: “How many months did your spouse spend on off‐farm work last year?”. The answers to these questions generated two dichotomous variables representing the off‐farm work status of husbands and wives and two count variables capturing their off‐farm work time.To determine farming households’ cooperative membership status, we asked farmers in our survey, “Did your households participate in any agricultural cooperatives?”. Mindful of the practical realities of agricultural cooperatives in China in that many do not provide adequate support and services to their members (Deng et al. 2010; Huang & Liang 2018), we asked farmers who answered “Yes” (i.e., cooperative members) to the previous question, “What kinds of production services do you receive from the cooperatives?” and “What kinds of marketing services did you receive from the cooperatives?”. The answers to these questions shed light on the extent to which farmers benefitted from joining cooperatives. For example, in some cases, the surveyed members reported that they received information and services related to seedlings, fertigation, agricultural innovations, and farm management, suggesting that joining cooperatives was indeed beneficial—the cooperatives were achieving their core objectives of helping farmers succeed in their endeavors.Drawing upon the literature on cooperative membership (Lin et al. 2022; Ma et al. 2022a, 2022b; Manda et al. 2020; Neupane et al. 2022) and rural residents’ off‐farm work decisions (Balachandran et al. 2022; Benjamin & Kimhi 2006; El‐Osta et al. 2008; Maligalig et al. 2019; Zhou et al. 2020), we prepared a number of questions in our questionnaire to collect information on the individual, household‐level, and locational characteristics. In the econometric model, we included age, education, and health status of husbands and wives to capture individual‐level characteristics; household size, whether the household had students in primary school, dependency ratio, farm size, and car ownership captured household‐level characteristics; and distance to input markets, road conditions, and provincial dummies denoted locational characteristics.Descriptive statisticsTable 1 summarizes the descriptive statistics for the selected variables. Among the interviewed households, 11% of husbands and 12% of wives reported working off‐farm. On average, husbands and wives spent 0.48 and 0.67 months on off‐farm work, respectively. Around 22% of households were cooperative members. Figure 1 illustrates the proportions of cooperative members and non‐members participating in off‐farm work. Among households with cooperative membership, 20.93% of husbands and 24.81% of wives worked off‐farm. In comparison, among non‐member households, only 8.37% of husbands and the same proportion of wives had worked off‐farm. Figure 1 shows a potential positive association between cooperative membership and participation in off‐farm work for both husbands and wives; furthermore, the association appears stronger for wives than husbands.1TABLEDefinition and descriptive statistics of variablesVariableDefinitionMean (S.D.)Dependent variablesOff‐farm work (husband)1 = Husband participated in off‐farm work, 0 = otherwise0.11 (0.31)Off‐farm work (wife)1 = Wife participated in off‐farm work, 0 = otherwise0.12 (0.32)Off‐farm work time (husband)Time allocated to off‐farm work by husband (months)0.48 (1.75)Off‐farm work time (wife)Time allocated to off‐farm work by wife (months)0.67 (2.28)Cooperative membership1 = Cooperative member, 0 = otherwise0.22 (0.41)Independent variablesAge (husband)Age of husband (years)48.88 (9.56)Education (husband)Educational level of husband (years)8.31 (2.85)Health (husband)Self‐rated health status of husband: 1 = very unhealthy, 2 = unhealthy, 3 = well, 4 = healthy, 5 = very healthy3.98 (0.96)Age (wife)Age of wife (years)46.77 (9.94)Education (wife)Educational level of wife (years)6.58 (3.30)Health (wife)Self‐rated health status of wife: 1 = very unhealthy, 2 = unhealthy, 3 = well, 4 = healthy, 5 = very healthy3.88 (0.98)Household sizeNumber of household members (persons)5.98 (2.35)Student member1 = having primary student member(s), 0 = otherwise0.35 (0.48)Dependency ratioRatio of child (≤15 years old) and elder (>65 years old) to other members (15–65 years)0.48 (0.59)Farm sizeCultivated land size for banana production (mu) a28.87 (73.36)Car ownership1 = car owner, 0 = otherwise0.31 (0.46)Distance to input marketsDistance to the nearest input markets (km)5.78 (8.59)Road condition1 = the road to the nearest transportation is good, 0 = otherwise0.81 (0.39)Hainan1 = Hainan province, 0 = otherwise0.37 (0.48)Yunnan1 = Yunnan province, 0 = otherwise0.29 (0.46)Guangdong1 = Guangdong province, 0 = otherwise0.34 (0.47)IVDistance to the nearest agricultural cooperatives (km)4.46 (6.26)Observations595Note:a1 mu = 1/15 hectare; S.D. refers to standard deviation.1FIGUREProportions of husbands and wives with off‐farm work by membership status [Colour figure can be viewed at wileyonlinelibrary.com]As for the other characteristics, Table 1 shows that husbands and wives were, on average, 48.88 and 46.77 years old, respectively. Also, husbands were slightly more educated than wives: on average, the former had 8.31 years of education, whereas the latter had 6.58 years. The self‐rated health status was measured on a five‐point scale. The mean health score was 3.98 for husbands and 3.88 for wives. On average, six members lived together in a household. Around 35% of the households had children attending primary school, and 31% owned cars. The average area cultivated was 28.87 mu (1 mu = 1/15 hectare).Table 2 presents the mean comparisons between the selected variables for cooperative members and non‐members, revealing systematic differences. For example, the proportions of husbands and wives who engaged in off‐farm work were greater among cooperative members than non‐members. The comparisons also show that cooperative members tended to be younger, better educated, and healthier than those without memberships. In addition, households with cooperative membership had more household members and a higher dependency ratio than those without memberships. Also, cooperative members cultivated larger farms and were more likely to own a car than their non‐member counterparts.2TABLEMean differences in variables between cooperative members and non‐membersVariableMembersNon‐membersMean differencesOff‐farm work (husband)0.21 (0.41)0.08 (0.28)0.13***Off‐farm work (wife)0.25 (0.43)0.08 (0.28)0.16***Off‐farm work time (husband)0.76 (2.11)0.41 (1.63)0.35**Off‐farm work time (wife)1.15 (2.80)0.54 (2.10)0.61***Age (husband)47.40 (9.07)49.29 (9.67)−1.89**Education (husband)8.84 (3.07)8.17 (2.78)0.67**Health (husband)4.20 (0.84)3.92 (0.98)0.29***Age (wife)45.11 (10.14)47.23 (9.84)−2.12**Education (wife)7.36 (3.21)6.36 (3.30)1.00***Health (wife)4.06 (0.87)3.83 (1.01)0.23**Household size6.48 (2.39)5.85 (2.32)0.64***Student member0.38 (0.49)0.34 (0.47)0.04Dependency ratio0.56 (0.60)0.46 (0.59)0.10*Farm size41.71 (71.98)25.32 (73.42)16.39**Car ownership0.48 (0.50)0.26 (0.44)0.22***Distance to input markets6.84 (7.62)5.49 (8.82)1.35Road condition0.76 (0.43)0.83 (0.38)−0.07*Hainan0.13 (0.34)0.44 (0.50)−0.30***Yunnan0.42 (0.50)0.26 (0.44)0.16***Guangdong0.45 (0.50)0.31 (0.46)0.14***IV3.59 (4.66)4.70 (6.62)−1.11*Observations129466Note:***p < 0.01,**p < 0.05, and*p < 0.10.RESULTS AND DISCUSSIONSResults of the RBP modelTable 3 presents the empirical results of the RBP model. The estimates of ρεμ${\rho }_{\varepsilon \mu }$ at the bottom of Table 3 are negative and significant, implying negative selection bias. In such cases, one‐stage models (e.g., a probit model) would underestimate the marginal effects of cooperative membership on the off‐farm work decisions of farm couples. The significance of ρεμ${\rho }_{\varepsilon \mu }$ also vindicates the use of the IV‐based RBP approach comprising the joint estimation of two probit equations (Gopalan et al. 2022; Li et al. 2021; Zhu et al. 2021).3TABLEDeterminants of cooperative membership and its impacts on farm couples’ off‐farm work participation: Marginal effects of the RBP model estimatesHusbandsWivesVariablesCooperative membership (marginal effects)Off‐farm work participation (marginal effects)Cooperative membership (marginal effects)Off‐farm work participation (marginal effects)Cooperative membership0.378 (0.036)***0.313 (0.074)***Age (husband)−0.003 (0.002)−0.001 (0.001)Education (husband)0.003 (0.006)0.001 (0.004)Health (husband)0.053 (0.017)***−0.008 (0.012)Age (wife)−0.004 (0.002)**−0.002 (0.001)Education (wife)0.006 (0.005)0.010 (0.005)**Health (wife)0.023 (0.018)0.017 (0.014)Household size0.016 (0.007)**−0.004 (0.006)0.017 (0.007)**0.005 (0.006)Student member−0.029 (0.037)0.034 (0.027)−0.036 (0.038)0.012 (0.028)Dependency ratio0.022 (0.030)0.010 (0.022)0.010 (0.032)0.021 (0.023)Farm size0.000 (0.000)−0.000 (0.000)0.000 (0.000)−0.000 (0.000)Car ownership0.051 (0.036)−0.131 (0.032)***0.055 (0.037)−0.057 (0.030)*Distance to input markets−0.001 (0.002)−0.002 (0.002)−0.001 (0.002)−0.004 (0.002)Road condition−0.032 (0.041)0.003 (0.029)−0.021 (0.043)−0.009 (0.032)Hainan−0.182 (0.043)***0.017 (0.032)−0.183 (0.045)***0.049 (0.033)Yunnan0.050 (0.047)0.049 (0.037)0.049 (0.047)0.048 (0.039)IV−0.005 (0.003)*−0.006 (0.003)**ρεμ${\rho }_{\varepsilon \mu }$−1.382 (0.431)***−0.821 (0.470)*Observations595595595595Note: *** p < 0.01, ** p < 0.05, and * p < 0.10; Standard errors are presented in parentheses; The reference province is Guangdong. Marginal effects in Table 3 are predicted after estimating the results of Table A2.Determinants of cooperative membershipColumns 2 and 4 of Table 3 present the marginal effects of the control variables and the IV on the probability of joining cooperatives. We discuss the marginal effects as they provide an intuitive interpretation of the associations between cooperative membership and participation in off‐farm work on the one hand and the explanatory variables on the other. The underlying coefficients are presented in Table A2 in the Appendix.The husbands’ perceived health status is positively associated with cooperative membership. For a one‐point increase—on a five‐point Likert scale—in husbands’ self‐reported health status, the probability of having a cooperative membership rises by 5.3%. The results are largely in line with the findings of Lin et al. (2022), who reported a positive relationship between health and cooperative membership among rice farmers in China. Cooperative membership places additional demands on one's time and may require people to contribute in kind. Being in good physical health helps keep up with such demands. In contrast, wives’ health status does not affect the likelihood of joining cooperatives. Interestingly, only wives’ age affects the likelihood of being a cooperative member—specifically, the predicted probability of them being cooperative members declines by 0.4% for a one‐year increase in wives’ age. This result is consistent with Ma et al. (2022b), who found a negative association between age and cooperative membership for banana farmers in rural China. Nevertheless, it contradicts Mojo et al. (2017)—who report a positive association between age and cooperative membership for coffee farmers in Ethiopia.Household size, however, raises the probability of joining cooperatives. Moreover, the effect size is similar for the two sexes. An additional household member is associated with around 1.6–1.7% increases in the probability of joining cooperatives. This is consistent with the findings of Chagwiza et al. (2016). But to be clear, notwithstanding the statistical significance of household size, its effect is relatively small. Individual locations also influence their probability of joining cooperatives. Households in Hainan province are around 18% less likely to be cooperative members than those in Guangdong. Finally, the longer the distance to the nearest cooperative, the lower the predicted probability of being cooperative members—the marginal effect of the IV, that is, distance to the nearest cooperative, is around −0.005 and statistically significant. These results confirm findings from previous studies (see, for example, Manda et al., 2020), showing a negative association between distance to cooperatives and cooperative membership. This stands to reason as larger distances entail longer commutes and thus a more considerable time commitment and higher travel costs, which may discourage acquiring cooperative memberships.Impact of cooperative membership on farm couples’ off‐farm work participation decisionsColumns 3 and 5 of Table 3 show the marginal effects of the key explanatory variable, cooperative membership, and other control variables on the probability of husbands and wives working off‐farm. The positive marginal effects of cooperative membership in column 3 suggest that cooperative membership increases the probability of husbands working off‐farm by 37.8%. Although the effect is relatively small for wives, it is appreciable; nevertheless, cooperative membership is associated with a 31.3% increase in the likelihood of wives working off‐farm. The findings support hypothesis 1 and confirm the importance of agricultural cooperatives in creating vibrant rural economies and increasing local employment incentives for rural dwellers (Demont 2022; World Bank 2019).Heterogeneous analysesTo study the heterogeneous effects of cooperative membership on farm couples’ off‐farm work, we predict the marginal effects of cooperative membership for different household sizes. Because household size is mainly distributed between 4 and 7 (this range accounts for more than 78% of the total observations), we only report the predicted marginal effects for household sizes 4, 5, 6, and 7. The disaggregated results are illustrated in Figure 2.2FIGUREPredicted marginal effects of cooperative membership on farm couples’ off‐farm work at different household sizes [Colour figure can be viewed at wileyonlinelibrary.com]Interestingly, Figure 2 shows that the marginal effects of cooperative membership on the likelihood of husbands having off‐farm work decrease monotonically from 0.387 to 0.370 as household size increases from four to seven. In contrast, cooperative membership increases the probability of wives working off‐farm by 30.2% when the household size is four and by 32.1% when the household size increases to seven—the marginal effects of cooperative membership on off‐farm work for wives rise with the increase in household size. These differences in marginal effects for husbands and wives can be partially explained by the different roles they have within households.Husbands tend to be household heads and hold sway in household decisions—Chinese society is, after all, patriarchal. Tending to a large household entails more commitments, leaving less time for off‐farm work. In comparison, wives are usually responsible for day‐to‐day household tasks, such as cooking and caring for the elderly and children. Indeed, having more members in the household may increase the amount of work needed to be done; however, with more members in the household, wives may receive assistance from other members to complete household work. The net effect of having more members may be more time for wives to pursue off‐farm work.Results of the ETPR modelTable 4 presents the results of the second stage of the ETPR model expressed by Equation (4). The results obtained from the first stage showing the association between the control variables and cooperative membership are reported in Table A3 in the Appendix for reference. We present the coefficients and the corresponding incidence rate ratios (IRRs) in Table 4. Since the coefficients do not provide a directly interpretable and meaningful interpretation, we will discuss the results using the IRRs.4TABLEDeterminants of cooperative membership and its impacts on farm couples’ off‐farm work time: Second‐stage estimation of the ETPR modelHusbandsWivesVariablesOff‐farm work time (coefficients)Off‐farm work time (IRRs)Off‐farm work time (coefficients)Off‐farm work time (IRRs)Cooperative membership0.951 (0.197)***2.5870.412 (0.153)***1.509Age (husband)−0.043 (0.016)***0.958Education (husband)−0.036 (0.028)0.964Health (husband)0.017 (0.102)1.017Age (wife)−0.031 (0.009)***0.969Education (wife)0.201 (0.026)***1.223Health (wife)0.169 (0.099)*1.185Household size−0.104 (0.089)0.9010.089 (0.026)***1.093Student member0.701 (0.204)***2.015−0.133 (0.160)0.875Dependency ratio−0.032 (0.193)0.968−0.240 (0.139)*0.787Farm size−0.009 (0.005)*0.991−0.004 (0.001)***0.996Car ownership−2.545 (0.330)***0.079−0.792 (0.155)***0.453Distance to input markets0.007 (0.007)1.007−0.068 (0.011)***0.934Road condition0.164 (0.302)1.178−0.443 (0.183)**0.642Hainan−0.883 (0.210)***0.414−1.679 (0.221)***0.187Yunnan0.605 (0.306)**1.831−1.385 (0.189)***0.250Constant−1.799 (0.675)***0.165−3.967 (0.754)***0.019Observations595595595595Note: *** p < 0.01, ** p < 0.05, and * p < 0.10. Standard errors are presented in parentheses. The reference province is Guangdong.The results show that cooperative membership exerts a positive and statistically significant impact on the time spent on off‐farm work for husbands and wives—the coefficients of cooperative membership in columns 2 and 4 are positive and significant. The IRRs imply that relative to those without cooperative membership, husbands with memberships spend 2.587 times the number of months working off‐farm; the effect on wives’ duration of off‐farm work, while not as large as that on husbands’, is still appreciable at 1.509 times. These results support hypothesis 2. The results are expected as the social ethos in rural China confers advantages on males allowing them access to a larger variety of employment opportunities. Males also tend to be more adaptive to physical jobs that are commonplace in rural China, and having more opportunities translates into more time spent working off‐farm. Although cooperative membership increases the duration of off‐farm work for females, a lack of information impedes their transition to off‐farm work (Rajkhowa & Qaim 2022; Zhou et al. 2020).Additional analysesImpact of cooperative membership on farm couples’ joint off‐farm work decisionsMarried couples’ decisions to work off‐farm tend to be interdependent. Depending upon intra‐household labor division, the husbands and the wives could make four types of exclusive off‐farm work participation decisions: neither husbands nor wives participate in off‐farm work; only husbands work off‐farm; only wives work off‐farm; and both husbands and wives work off‐farm. Table A4 in the Appendix presents the descriptive statistics of the variables. It shows that the majority of households in the sample (83%) have neither husbands nor wives working off‐farm. The households with only husbands and only wives working off‐farm account for 6% and 5%, respectively. Only 6% of households have both husbands and wives working off the farm.To deepen our understanding, we empirically examine the impact of cooperative membership on farm couples’ joint off‐farm work decisions. The cooperative membership variable is potentially endogenous, and those four types of off‐farm work are mutually exclusive. Given this, we combine the two‐stage residual inclusion (2SRI) method (Terza 2018; Zhu et al. 2020) with the multinomial logit (MNL) model to address the endogeneity issue of cooperative membership variable and explore how cooperative membership affects farm couples’ joint off‐farm work decisions. For the sake of simplicity, we only report the results estimated from the second‐stage estimations of the 2SRI‐MNL model and the coefficients of the cooperative membership variable. The residual term that is predicted from the first‐stage estimation is also included.1In the first‐stage, Equation (1) is estimated using a probit model and then the residual term is predicted. Table 5 presents the empirical results, showing that the marginal effects of cooperative membership are statistically significant in columns 2 and 5. The findings suggest that cooperative membership is associated with a 73.1% decline in the predicted probability of neither the husband nor the wife working off‐farm but a 62% increase in that of both working off‐farm. These considerable effect sizes illustrate how important cooperative membership is to promoting off‐farm employment of farm couples in rural China.5TABLEImpacts of cooperative membership on farm couples’ joint off‐farm work decisions: Second‐stage results of the 2SRI‐MNL modelVariablesOff‐farm work‐neither husbands nor wives (marginal effects)Off‐farm work‐husbands only (marginal effects)Off‐farm work‐wives only (marginal effects)Off‐farm work‐both (marginal effects)Cooperative membership−0.731 (0.352)**0.219 (0.236)−0.108 (0.203)0.620 (0.248)**Control variablesYesYesYesYesResidual0.582 (0.356)−0.165 (0.238)0.153 (0.208)−0.570 (0.246)**Sample size595595595595Note: Standard errors are presented in parentheses. *** p < 0.01, ** p < 0.05, and * p< 0.10. The reference province is Guangdong. We present the marginal effects rather than coefficients here because the estimated coefficients of the MNL model cannot be interpreted straightforwardly.Impacts on off‐farm work wages and on‐farm labor allocationAgricultural cooperatives may also directly provide employment opportunities to their members. These opportunities, which are often in the form of seasonal or long‐term odd jobs, are generally provided to poor members who need extra support. Thus, cooperative membership may lead to higher off‐farm incomes for farm households, who may use the additional income to finance their farming operations; they may purchase seedlings, fertilizers, pesticides, machinery, and irrigation apparatus.To explore these possibilities, we estimate the impact of cooperative membership on the off‐farm work wages of husbands and wives and on‐farm labor allocation decisions. On‐farm labor allocation decisions are measured by labor input and capital input. Specifically, labor input captures the amount of family and hired labor, measured in 100 labor‐days/mu. Capital input captures the total expenditure on seedlings, fertilizers, pesticides, machinery, and irrigation, measured in 1000 yuan/mu. The lower part of Table A4 in the Appendix presents the definitions and descriptive statistics of the relevant variables.Table 6 presents the empirical results. For brevity, we only report the results estimated in the second stage of the ETR model. The results show that the coefficients of cooperative membership, 2.731 and 1.632 for husbands and wives, respectively, are statistically significant, suggesting that being a cooperative member is associated with higher non‐farm income. However, the gains in wages are higher for husbands than for wives. Furthermore, the significant and positive coefficients of cooperative membership in columns 4 and 5 suggest that cooperative membership significantly increases labor and capital inputs used by farm households—not only do farmers invest more in seedlings, fertilizes, machinery, and irrigation, but they also allocate more labor to banana farming. This raises another question: where does the increase in labor come from? In other words, do farmers hire labor or allocate more family labor to their farming operations? To answer this, we estimate the impact of cooperative membership on the labor input ratio, defined as the ratio of hired labor to family labor used for producing bananas. The results (last column of Table 6) show that the coefficient of cooperative membership is positive but insignificant, suggesting that cooperative membership motivates farmers to increase both hired and family labor.6TABLEImpact of cooperative membership on farm couples’ off‐farm work wages and on‐farm labor allocations: Second‐stage estimations of the ETR modelFarm couples’ off‐farm work wagesOn‐farm labor allocationsVariablesOff‐farm work wages of husbands (coefficients)Off‐farm work wages of wives (coefficients)Labor input (coefficients)Capital input (coefficients)Labor input ratio (coefficients)Cooperative membership2.731 (0.172)***1.632 (0.159)***1.542 (0.110)***2.530 (0.170)***0.312 (0.236)Control variablesYesYesYesYesYesConstant0.436 (0.700)−0.041 (0.431)−0.248 (0.487)3.509 (0.823)***−0.159 (0.600)Observations595595595595580Note: *** p < 0.01, ** p < 0.05, and * p < 0.10. Standard errors are presented in parentheses. The reference province is Guangdong.CONCLUSIONS AND POLICY IMPLICATIONSMore and more rural households in China are turning to off‐farm employment to supplement farm income. However, rural families are reluctant to abandon farming operations due to their familiarity with farm work and the accompanying food security and employment stability. Consequently, they are faced with important intra‐household labor‐reallocation decisions: Who should work on the farm, and who is best suited to undertake off‐farm employment? Is it better for both the husband and wife to split their time between the farm and off‐farm work? How can they improve the prospects of finding suitable off‐farm work? Of course, there is a multitude of factors that dictate their course of action. This study focuses on the effects of cooperative membership on farm couples’ decisions regarding off‐farm work. Because these decisions involve both the husband and the wife, we ask whether cooperative membership increases the likelihood and time of each working off‐farm. Noting that the decision to join cooperatives is endogenous and that farmers opt to become members, we employ the IV‐based RBP model and the ETPR model to analyze rural household survey data collected in 2019. These models account for the said endogeneity and self‐selection bias.The results show that cooperative membership increases the probability of husbands having off‐farm work by 37.8% and wives by 31.3%. On average, husbands with cooperative membership work 2.587 times more months on off‐farm work than those who do not have cooperative memberships, while wives work 1.509 times more. The results provide strong evidence for the efficacy of agricultural cooperatives in helping rural farming households secure off‐farm employment. The results of additional analyses reveal that cooperative membership is associated with a 73.1% reduction in the predicted probability of neither the husband nor the wife working off‐farm but a 62% increase in that of both working off‐farm. Furthermore, it significantly increases off‐farm work wages for both husbands and wives. Last, cooperative members use more inputs—labor and capital—to grow bananas.The results should be of strong interest to those tasked with rural development, agricultural cooperatives, and, most importantly, rural households. The positive effects of rural cooperatives on alleviating labor shortages and overcoming the challenges of working with poor technology and diseconomies of scale are well documented. We show that cooperative membership can help rural households secure off‐farm work to enhance their income and living standards. Thus, rural households would be well advised to join agricultural cooperatives. Although China has eradicated extreme poverty, many rural households contend with low living standards. Increasing fiscal outlays such as subsidies and providing tax incentives to cooperatives may pay rich dividends in improving the quality of life in rural China. Given the regional differences in the effectiveness of cooperative membership in helping households secure off‐farm work, a one‐size‐fits‐all policy framework is unsuitable. Policies ought to be designed with these differences in mind.This study focuses on banana farmers in only three provinces, which presents a localized perspective. Because banana farming is quite labor‐intensive, the results may not translate to farmers growing grains, legumes, and other kinds of fruit. Future studies may focus on the effects of cooperative membership on rural households growing different crops in other regions of the country. 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Tourism Economics, 135481662110004.APPENDIXA1TABLEFalsification test of instrument variableOutcome variablesStatisticsOff‐farm work (husband)χ2 = 0.67; p‐value = 0.412Off‐farm work (wife)χ2 = 2.08; p‐value = 0.149Cooperative membershipχ2 = 5.11**; p‐value = 0.028Note: ** p < 0.05.A2TABLEDeterminants of cooperative membership and its impacts on farm couples’ off‐farm work participation: Coefficients of the RBP modelHusbandsWivesVariablesCooperative membership (coefficients)Off‐farm work participation (coefficients)Cooperative membership (coefficients)Off‐farm work participation (coefficients)Cooperative membership2.202 (0.251)***1.809 (0.465)***Age (husband)−0.011 (0.007)−0.005 (0.008)Education (husband)0.013 (0.022)0.006 (0.026)Health (husband)0.205 (0.069)***−0.046 (0.072)Age (wife)−0.016 (0.008)**−0.012 (0.009)Education (wife)0.023 (0.021)0.055 (0.028)**Health (wife)0.089 (0.069)0.100 (0.078)Household size0.062 (0.027)**−0.022 (0.034)0.067 (0.028)**0.029 (0.033)Student member−0.114 (0.143)0.199 (0.156)−0.140 (0.148)0.072 (0.162)Dependency ratio0.084 (0.116)0.056 (0.127)0.038 (0.123)0.119 (0.131)Farm size0.000 (0.001)−0.002 (0.002)0.000 (0.001)−0.002 (0.002)Car ownership0.199 (0.142)−0.766 (0.181)***0.213 (0.143)−0.328 (0.174)*Distance to input markets−0.003 (0.009)−0.010 (0.010)−0.003 (0.009)−0.022 (0.014)*Road condition−0.125 (0.160)0.018 (0.170)−0.080 (0.164)−0.051 (0.188)Hainan−0.704 (0.171)***0.098 (0.184)−0.704 (0.178)***0.284 (0.192)Yunnan0.192 (0.182)0.287 (0.213)0.189 (0.181)0.280 (0.225)Constant−1.333 (0.600)**−1.004 (0.662)−0.698 (0.592)−1.929 (0.674)***IV−0.021 (0.011)*−0.023 (0.011)**Observations595595595595Note: *** p < 0.01, ** p < 0.05, and * p < 0.10. Standard errors are presented in parentheses. The reference province is Guangdong.A3TABLEDeterminants of cooperative membership: First‐stage estimation of the ETPR modelHusbandsWivesVariablesCooperative membership (coefficients)Cooperative membership (coefficients)Age (husband)−0.013 (0.007)*Education (husband)0.020 (0.024)Health (husband)0.167 (0.068)**Age (wife)−0.012 (0.008)Education (wife)0.026 (0.022)Health (wife)0.059 (0.067)Household size0.056 (0.030)*0.059 (0.031)*Student member−0.128 (0.150)−0.127 (0.148)Dependency ratio0.068 (0.115)0.026 (0.114)Farm size0.000 (0.001)0.000 (0.001)Car ownership0.236 (0.141)*0.251 (0.137)*Distance to input markets0.001 (0.008)−0.001 (0.008)Road condition−0.068 (0.160)−0.079 (0.159)Hainan−0.714 (0.171)***−0.755 (0.173)***Yunnan0.177 (0.175)0.105 (0.172)Constant−1.112 (0.582)*−0.741 (0.587)IV−0.028 (0.014)**−0.024 (0.012)*Sample size595595Note: *** p < 0.01, ** p < 0.05, and * p < 0.10. Standard errors are presented in parentheses. The reference province is Guangdong.A4TABLEDefinition and descriptive statistics of variables for additional analysisVariableDefinitionMean (S.D.)Off‐farm work: neither husband nor wife1 = Husband and wife did not participate in off‐farm work, 0 = otherwise0.83 (0.38)Off‐farm work: husband only1 = Husband participated in off‐farm work while wife did not, 0 = otherwise0.06 (0.23)Off‐farm work: wife only1 = Wife participated in off‐farm work while husband did not, 0 = otherwise0.05 (0.23)Off‐farm work: both1 = Husband and wife participated in off‐farm work, 0 = otherwise0.06 (0.24)Off‐farm work wage (husband)Wage earned from off‐farm work by husband among workers (1000 yuan/month) a3.99 (2.57)Off‐farm work wage (wife)Wage earned from off‐farm work by wife among workers (1000 yuan/month)2.81 (1.07)Hired labor inputAmount of hired labor (100 labor‐days/mu)0.75 (1.02)Family labor inputAmount of family labor (100 labor‐days/mu)0.02 (0.05)Labor inputAmount of family labor and hired labor (100 labor‐days/mu) b0.77 (1.01)Capital inputTotal expenditure on seedlings, fertilizers, pesticides, machinery, and irrigation (1000 yuan/mu)2.33 (1.62)Labor input ratioRatio of hired labor input to family labor input0.31 (1.58)Note:aYuan is Chinese currency (1 US dollar = 6.90 yuan in 2019).b1 mu = 1/15 hectare. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Annals of Public and Cooperative Economics Wiley

Does agricultural cooperative membership influence off‐farm work decisions of farm couples?

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1370-4788
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1467-8292
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10.1111/apce.12417
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Abstract

INTRODUCTIONOff‐farm work, an essential source of rural livelihood, is critical to alleviating rural poverty and improving household well‐being in many developing countries (Aikaeli et al. 2023; Das & Mahanta 2023; Duong et al. 2021). In China, rural migrant workers leaving for more rewarding off‐farm employment opportunities increased from 242 million in 2010 to 285 million in 2020 (NBSC 2020). In some rural areas in Vietnam and India, off‐farm income represents more than half of the total household income (Imai et al. 2015; Rajkhowa & Qaim 2022). In practice, the rural labor migration from the farm to the off‐farm sector often proceeds incrementally. Typically, only a few household members migrate, whereas others remain in their farming community to support farm production (Giles & Mu 2018). This is because, despite being toilsome and not highly profitable, farm production is a familiar source of livelihood to rural people, offering food security. Thus, farm production is treated preferentially in rural households; their aversion to risk prevents them from abandoning farm production even when off‐farm work may yield greater monetary compensation (Dubbert 2019; Ge et al. 2020; Huang et al. 2020). Removing barriers to securing and pursuing off‐farm work through policies and development programs may help farmers improve the quality of their life.This study is devoted to examining whether cooperative membership positively or negatively affects off‐farm work participation. We contribute to the literature in four ways. First, we explore the effects of cooperative membership on both husbands’ and wives’ participation in off‐farm work. Previous studies have emphasized that women's participation in off‐farm work enhances households’ dietary diversity (Sangwan & Kumar 2021), empowers women (Maligalig et al. 2019), and improves their subjective well‐being (Van den Broeck & Maertens 2017). Should more women have off‐farm work, the quality of life in rural regions may improve markedly, and agricultural cooperatives can play an important role. Second, we employ a recursive bivariate probit (RBP) model to account for the self‐selection bias inherent in cooperative membership—after all, farmers choose to participate in agricultural cooperatives; membership is not randomly assigned (Grashuis & Skevas 2022; Ma et al. 2022b; Neupane et al. 2022). The RBP model accounts for the endogeneity and self‐selection bias arising from both observed and unobserved factors (Gopalan et al. 2022; Owusu et al. 2021).Third, we explore how cooperative membership affects the time spent on off‐farm work. To this end, we use two variables reflecting the number of months husbands and wives spend on off‐farm work in a reference year. Because the time devoted to off‐farm work is measured as a count variable (0–12 months), we employ an endogenous treatment Poisson regression (ETPR) model to address the endogeneity of cooperative memberships (Ojo et al. 2021). Fourth, we analyze the impact of cooperative membership on joint off‐farm work decisions, wages earned from off‐farm, and on‐farm labor allocation, thus providing a comprehensive view of the relationship between cooperative membership and farm couples’ off‐farm work decisions.This study uses household survey data collected from 595 banana farmers from rural China in 2019. Four primary reasons have motivated us to study Chinese banana farmers. First, banana crops, with a growth cycle of 9–13 months, are harvested about once a year (Slaven 2020); cereal crops, such as rice, have shorter growth cycles and can be harvested twice or thrice annually. Thus, damage to banana crops can set farmers back financially for lengthy periods, making it all the more important for farmers to manage every stage of the growth cycle carefully. Agricultural cooperatives can provide farmers with technical support and training, helping them to manage their farming operations and optimize their yields. Second, unlike grains, which can be stored for many years after harvesting, bananas last for only three or four weeks. Even bananas stored in high‐density or low‐density polyethylene bags last for about 36 days (Hailu et al. 2014). Consequently, farmers must sell bananas quickly to avoid losses. Agricultural cooperatives can help farmers monetize their harvests within the desired timelines by bringing buyers and sellers together and providing access to local and global supply chains. Third, banana production is more vulnerable to the threats of diseases than cereal crops (Drenth & Kema 2021). Extreme weather events and pathogens have compromised banana harvests in China. For example, typhoon Mangkhut ravaged banana crops in Guangdong in 2018, and Fusarium Sacchari (a fungal plant pathogen affecting banana leaves) was also discovered in the province in 2016 (Cui et al. 2021). The resources available to farmers via agricultural cooperatives can help them learn new adaptation and mitigation strategies to cope with such adversities. This may become increasingly important should the southern provinces of China become more susceptible to extreme weather events induced by climate change. Fourth, agricultural cooperatives can help accelerate the mechanization of banana farming, which, unlike grain farming, continues to rely significantly on manual labor (Ma et al. 2022b).The rest of this article proceeds as follows: Section 2 comprises a review of related literature. Section 3 introduces the research hypothesis and describes the econometric methods. Section 4 presents the data and descriptive statistics. The empirical results are presented and discussed in Section 5. Section 6 concludes the paper and provides policy recommendations.LITERATURE REVIEWAgricultural cooperatives and agricultural and rural developmentAgricultural cooperatives have proven effective in promoting agricultural and rural development. Studies have shown that cooperatives help smallholder farmers access input and output markets and technology extension services and bring farmers together, thereby promoting collective action (Demont 2022; Lin et al. 2020; Liu et al. 2017; Neupane et al. 2022). Membership in cooperatives also affects smallholder farmers’ decisions to adopt improved technologies and invest in their farming operations, helping farmers achieve higher crop yields and production efficiency (Kumar et al. 2018; Lin et al. 2022; Ma et al. 2023; Verhofstadt & Maertens 2014; Zhang et al. 2020).Cooperative membership has also been shown to increase household income and consumption expenditure and improve subjective well‐being in rural areas (e.g., Ahmed & Mesfin 2017; Demont 2022; Dohmwirth & Liu 2020; Gava et al. 2021; Kumar et al. 2018; Verhofstadt & Maertens 2015). For example, Ahmed and Mesfin (2017) showed that joining agricultural cooperatives positively affected the consumption expenditure of smallholder farmers in Ethiopia. Dohmwirth and Liu (2020) concluded that cooperative membership empowered women working in India's dairy sector. Analyzing data collected from the Shandong province of China, Wu et al. (2022) found that joining agricultural cooperatives improved rural households’ subjective well‐being. They pointed out that the positive effects may have been driven by higher incomes and social capital resulting from joining agricultural cooperatives.Factors affecting off‐farm work participationFarmers’ decisions to participate in off‐farm work are influenced by on‐farm labor requirements. Given this, previous studies have analyzed various strategies that encourage off‐farm work participation, such as promoting contract farming to reduce labor requirements (Ruml & Qaim 2021), cultivating crops like oil palm that are less labor‐intensive than traditional crops (Chrisendo et al. 2021), and the adoption of labor‐saving agricultural technologies (Diiro et al. 2021; Zheng et al. 2022). For example, mechanized agriculture saves farm labor, thus enabling rural households to allocate more time to off‐farm activities (Zheng et al. 2022). Besides, land‐related factors, such as land access (Kosec et al. 2018), land property rights, and land tenure (Giles & Mu 2018), and the development of rural land rental markets (Daymard 2022) are also important determinants of off‐farm work participation. For example, Giles and Mu (2018) found that secure property rights facilitated rural labor moving out of the agricultural sector.Cooperative membership and off‐farm work participationNotwithstanding the voluminous literature on the effects of cooperative membership on various aspects of rural life and the factors affecting rural households’ participation in off‐farm work, there is a paucity of research on the relationship between cooperative membership and decisions to participate in off‐farm work (Demont 2022; Liu et al. 2017; Zhou et al. 2020). And the findings are mixed. For example, Liu et al. (2017) found that land transfer through land cooperatives did not affect household heads’ off‐farm employment in China. In contrast, using data collected from rice farmers in China, Zhou et al. (2020) showed that cooperative membership significantly decreased the probability of husbands’ off‐farm work while leaving that of wives unchanged. However, they did not account for the endogeneity of cooperative membership. This study contributes to this limited body of research by applying a recursive bivariate probit model and an endogenous‐treatment Poisson regression model to estimate data collected from banana‐producing households in China.RESEARCH HYPOTHESIS AND ECONOMETRIC APPROACHESResearch hypothesisBased on the classical farm household model (Min et al. 2017; Tsiboe et al. 2016), this study assumes that farm couples maximize household income earned from on‐ and off‐farm activities by joining agricultural cooperatives and participating in off‐farm work. These two activities are interrelated as farm couples need to trade off their labor force and time allocated to each activity. Allocating more labor force in a farming household to off‐farm work would reduce the labor force that can be allocated to farm work and time allocated to cooperative activities. However, participating in off‐farm work allows farmers to earn additional income that would be unavailable to them if they chose to allocate all the available household labor to farm work. The extra income improves their quality of life and provides them with the wherewithal to invest in their farming operations. To maximize household welfare, household members jointly decide who should work on or off the farm. It is common for men in rural areas to seek off‐farm employment, which often entails migrating to cities; women stay behind to care for the children and the elderly and undertake farming activities (Giles & Mu 2018; Zhou et al. 2020). Thus, women often face considerable barriers to working off‐farm. Access to employment opportunities, social support systems, mentors, training, and career counseling can help women overcome these barriers—membership in agricultural cooperatives can be beneficial in this regard. With this in mind, in the present study, we investigate how membership in agricultural cooperatives influences farm couples’ decisions to work off the farm.Having a cooperative membership may influence farm couples’ off‐farm work decisions in different ways. We discuss three reasons here. First, agricultural cooperatives improve their members’ farm management skills and production efficiency, helping save time and effort expended on farming activities, allowing them to allocate more time to off‐farm work (Demont 2022; Liu et al. 2017; Neupane et al. 2022). Second, agricultural cooperatives provide production and marketing services to their members, helping them increase farm output and sales revenue. The cooperatives also run training and development programs designed to upskill the farmers, making them more adept at farming activities and more attractive to potential employers in non‐agricultural industries (Bachke 2019; Hao et al. 2018). Third, joining cooperatives may help farmers build their social and professional networks, opening up opportunities for off‐farm work and, thus, making cooperative members more likely to be employed in non‐farm roles (Jia & Xu 2021). These reasons have motivated us to test the following two hypotheses:H1: Cooperative membership increases the probability of farm couples participating in off‐farm work.H2: Cooperative membership increases farm couples’ time allocated to off‐farm work.Estimation issuesWe aim to obtain unbiased estimates of the effects of cooperative membership on the off‐farm work of farm couples. Since our analysis is based on household survey data, which precludes randomly selecting households to join and not join cooperatives, self‐selection of households into agricultural cooperative membership is distinctly possible. Furthermore, the estimates of the influence of cooperative membership may suffer from self‐selection bias related to both observable and unobservable factors. To mitigate this bias, previous studies have utilized various approaches, such as the propensity score matching (PSM) technique (Chagwiza et al. 2016; Ji et al. 2019), the inverse probability weighted regression adjustment (IPWRA) estimator (Neupane et al. 2022), the endogenous treatment regression model (Li et al. 2023b; Vatsa et al. 2022), and the RBP model (Gopalan et al. 2022; Li et al. 2023a).In this study, we employ the RBP model to estimate the impact of cooperative membership on the off‐farm work decisions of farm couples. Two notable features of the RBP model are that it can address selection bias from observable and unobservable factors—thus, it is advantageous to use this model rather than the PSM and IPWRA methods that only account for observable bias (Li et al. 2021; Owusu et al. 2021). In addition, it also estimates the direct marginal effects of cooperative membership—a binary explanatory variable—on off‐farm work participation of husbands and wives—a binary dependent variable.To clarify, the RBP model is not applicable when analyzing the relationship between cooperative membership and off‐farm work time because the latter is measured as a count variable, months spent on off‐farm work, rather than a binary variable. In this case, the ETPR model is apt as it can estimate the impact of a binary treatment variable on the counted outcome variables (Ojo et al. 2021). In the following two subsections, we first discuss the RBP model and then introduce the ETPR model.RBP modelThe RBP model, which accounts for endogeneity and selection bias, is used to jointly estimate households’ decisions regarding cooperative membership and the impact of becoming members on farm couples’ off‐farm work participation. The decisions to join cooperatives can be modeled within an optimization framework, assuming that banana‐producing households are risk‐neutral and maximize the net benefits from cooperative membership. Let Ci∗$C_i^*$ denote the differences in the net benefits derived by households with cooperative membership and those without. Households would prefer to join cooperatives should Ci∗$C_i^*$ exceed zero. Although Ci∗$C_i^*$ is subjective and unobservable, it can be expressed using a latent variable function as follows:1Ci∗=αXi+εi,whereCi=1ifCi∗>00otherwise$$\begin{equation}C_i^* = \alpha {X}_i + {\varepsilon }_i,{\rm{\ where\ }}{C}_i = \left\{ { \def\eqcellsep{&}\begin{array}{@{}*{1}{c}@{}} {1\ if\ C_i^* &gt; 0\ }\\ {0\ otherwise} \end{array} } \right.\end{equation}$$where Ci∗$C_i^*$ represents a latent variable of cooperative membership, which is determined by the observed membership status variable Ci${C}_i$. The binary variable Ci${C}_i$ takes the value of one if households have cooperative membership and zero otherwise; Xi${X}_i$ refers to a vector of explanatory variables that are expected to affect the likelihood of cooperative membership. εi${\varepsilon }_i$ represents the error term.Assuming that the decisions of farm couples apropos off‐farm work are binary, the influence of cooperative membership and other variables on farm couples’ off‐farm work participation decisions can be modeled as follows:2Oi∗=βCi+γZi+μi,whereOi=1ifOi∗>00otherwise$$\begin{equation}O_i^* = \beta {C}_i + \gamma {Z}_i + {\mu }_i,{\rm{\ where\ }}{O}_i = \left\{ { \def\eqcellsep{&}\begin{array}{@{}*{1}{c}@{}} {1\ if\ O_i^* &gt; 0\ }\\ {0\ otherwise} \end{array} } \right.\end{equation}$$where Oi∗$O_i^*$ is a latent variable denoting farm couples’ off‐farm work status; Oi${O}_i$ is a dichotomous variable equal to one for off‐farm workers and zero for non‐farm workers; Zi${Z}_i$ is a vector of variables affecting off‐farm work decisions; β and γ are vectors of parameters to be estimated; μi${\mu }_i$ is the error term.The RBP model jointly estimates Equation (1), the treatment equation, and Equation (2), the outcome equation, using the full information maximum likelihood (FIML) estimator. The error terms of the two equations are assumed to follow a bivariate distribution, which can be expressed as:3εiμi∼N00,1ρεμρεμ1$$\begin{equation}\left( { \def\eqcellsep{&}\begin{array}{@{}*{1}{c}@{}} {{\varepsilon }_i}\\ {{\mu }_i} \end{array} } \right)\sim {\rm{N}}\left( {\left[ { \def\eqcellsep{&}\begin{array}{@{}*{1}{c}@{}} 0\\ 0 \end{array} } \right],\ \left[ { \def\eqcellsep{&}\begin{array}{@{}*{2}{c}@{}} 1&{{\rho }_{\varepsilon \mu }}\\ {{\rho }_{\varepsilon \mu }}&1 \end{array} } \right]} \right)\end{equation}$$where ρεμ${\rho }_{\varepsilon \mu }$ is the correlation coefficient between the error terms of Equations (1) and (2). A significant ρεμ${\rho }_{\varepsilon \mu }$ suggests that the error terms between the two equations are correlated, indicating that cooperative membership is endogenous. After estimating the coefficients using the RBP model, we calculate the corresponding marginal effects to facilitate the interpretation of the results. Moreover, the marginal effects can also be estimated at specific values of the covariates.A crucial prerequisite for estimating the RBP model is the exclusion restriction on the explanatory variables; that is, at least one instrumental variable is included in Xi${X}_i$ but not in Zi${Z}_i$ (Gopalan et al. 2022; Li et al. 2021; Owusu et al. 2021). A variable representing the distance from the respondent's residence to the nearest agricultural cooperative is used as the instrumental variable. It affects households’ decisions to join agricultural cooperatives—the closer people live to an agricultural cooperative, the higher their likelihood of joining it. However, the proximity of a household to agricultural cooperatives is not directly associated with its members’ participation in off‐farm work. Following previous studies (Gopalan et al. 2022; Li et al. 2020; Pizer 2016), we perform a falsification test to verify the admissibility of the instrumental variable. The results in Table A1 in the Appendix confirm that distance to the nearest agricultural cooperatives is significantly associated with cooperative membership but not with off‐farm work for households without a cooperative membership.ETPR modelThe ETPR model comprises two stages (Ojo et al. 2021). The first stage models farmers’ decisions to join cooperatives and is the same as Equation (1). The second stage models the influence of cooperative membership and other explanatory variables on the time spent on off‐farm work. The assumed linear function can be expressed as follows:4Ti=δiOi+θiZi+vi$$\begin{equation}{T}_i = {\delta }_i{O}_i + {\theta }_i{Z}_i + {v}_i\end{equation}$$where Ti${T}_i$ represents a count variable that reflects off‐farm work time of farm couples, that is, how many months a husband or wife spends on off‐farm work in a reference year; Oi${O}_i$ and Zi${Z}_i$ are as defined above; δi${\delta }_i$ and θi${\theta }_i$ are unknown parameters to be estimated, vi${v}_i$ is the error term.The maximum likelihood estimator jointly estimates Equations (1) and (4). The error terms in the two equations are assumed to be bivariate normal with zero mean and covariance matrix:5εivi∼σ2σεvρεvσεvρεv1$$\begin{equation}\left( { \def\eqcellsep{&}\begin{array}{@{}*{1}{c}@{}} {{\varepsilon }_i}\\ {{v}_i} \end{array} } \right)\sim \left[ { \def\eqcellsep{&}\begin{array}{@{}*{2}{c}@{}} {{\sigma }^2}&{{\sigma }_{\varepsilon v}{\rho }_{\varepsilon v}}\\ {{\sigma }_{\varepsilon v}{\rho }_{\varepsilon v}}&1 \end{array} } \right]\end{equation}$$where σ2 represents the variance of the error term vi${v}_i$; σεv${\sigma }_{\varepsilon v}$ is the covariance of the two error terms; and ρεv${\rho }_{\varepsilon v}$ refers to the correlation coefficient between the two error terms. A statistically significant ρεv${\rho }_{\varepsilon v}$ indicates the presence of selection bias and confirms the appropriateness of using the ETPR model rather than a simple Poisson regression model.DATA AND DESCRIPTIVE STATISTICSDataThis study utilizes data collected from a household survey on banana farmers in China. The survey was carried out from July to October 2019, covering three major banana‐producing provinces: Hainan, Yunnan, and Guangdong. The banana production of the three provinces was 7.89 million tons in 2020, accounting for 68.55% of the total banana output in China (NBSC 2020). The sample households were selected using a multistage sampling approach. After choosing the three provinces in the first stage, we randomly selected three to five counties in each province in the second stage and then two towns in each county in the third stage. In the fourth stage, one to two villages were chosen in each selected town. Finally, about 10–20 households were randomly chosen in each selected village. Since we were interested in farm couples’ off‐farm work decisions, the observations comprising unmarried, divorced, or widowed respondents were left out of the analysis. This resulted in a sample of 595 households. Among these, 129 households joined a cooperative, whereas 466 did not.During the survey, we gathered information on the off‐farm work status of the respondents by asking, “Did you participate in any off‐farm work last year?”. If the answer was “Yes”, we asked the respondent, “How many months did you spend on off‐farm work last year?”. The respondents were also queried about their marital status. If the respondent reported being married, we asked, “Did your spouse participate in any off‐farm work last year?” If the respondent answered “Yes”, we prompted the respondent with the following question: “How many months did your spouse spend on off‐farm work last year?”. The answers to these questions generated two dichotomous variables representing the off‐farm work status of husbands and wives and two count variables capturing their off‐farm work time.To determine farming households’ cooperative membership status, we asked farmers in our survey, “Did your households participate in any agricultural cooperatives?”. Mindful of the practical realities of agricultural cooperatives in China in that many do not provide adequate support and services to their members (Deng et al. 2010; Huang & Liang 2018), we asked farmers who answered “Yes” (i.e., cooperative members) to the previous question, “What kinds of production services do you receive from the cooperatives?” and “What kinds of marketing services did you receive from the cooperatives?”. The answers to these questions shed light on the extent to which farmers benefitted from joining cooperatives. For example, in some cases, the surveyed members reported that they received information and services related to seedlings, fertigation, agricultural innovations, and farm management, suggesting that joining cooperatives was indeed beneficial—the cooperatives were achieving their core objectives of helping farmers succeed in their endeavors.Drawing upon the literature on cooperative membership (Lin et al. 2022; Ma et al. 2022a, 2022b; Manda et al. 2020; Neupane et al. 2022) and rural residents’ off‐farm work decisions (Balachandran et al. 2022; Benjamin & Kimhi 2006; El‐Osta et al. 2008; Maligalig et al. 2019; Zhou et al. 2020), we prepared a number of questions in our questionnaire to collect information on the individual, household‐level, and locational characteristics. In the econometric model, we included age, education, and health status of husbands and wives to capture individual‐level characteristics; household size, whether the household had students in primary school, dependency ratio, farm size, and car ownership captured household‐level characteristics; and distance to input markets, road conditions, and provincial dummies denoted locational characteristics.Descriptive statisticsTable 1 summarizes the descriptive statistics for the selected variables. Among the interviewed households, 11% of husbands and 12% of wives reported working off‐farm. On average, husbands and wives spent 0.48 and 0.67 months on off‐farm work, respectively. Around 22% of households were cooperative members. Figure 1 illustrates the proportions of cooperative members and non‐members participating in off‐farm work. Among households with cooperative membership, 20.93% of husbands and 24.81% of wives worked off‐farm. In comparison, among non‐member households, only 8.37% of husbands and the same proportion of wives had worked off‐farm. Figure 1 shows a potential positive association between cooperative membership and participation in off‐farm work for both husbands and wives; furthermore, the association appears stronger for wives than husbands.1TABLEDefinition and descriptive statistics of variablesVariableDefinitionMean (S.D.)Dependent variablesOff‐farm work (husband)1 = Husband participated in off‐farm work, 0 = otherwise0.11 (0.31)Off‐farm work (wife)1 = Wife participated in off‐farm work, 0 = otherwise0.12 (0.32)Off‐farm work time (husband)Time allocated to off‐farm work by husband (months)0.48 (1.75)Off‐farm work time (wife)Time allocated to off‐farm work by wife (months)0.67 (2.28)Cooperative membership1 = Cooperative member, 0 = otherwise0.22 (0.41)Independent variablesAge (husband)Age of husband (years)48.88 (9.56)Education (husband)Educational level of husband (years)8.31 (2.85)Health (husband)Self‐rated health status of husband: 1 = very unhealthy, 2 = unhealthy, 3 = well, 4 = healthy, 5 = very healthy3.98 (0.96)Age (wife)Age of wife (years)46.77 (9.94)Education (wife)Educational level of wife (years)6.58 (3.30)Health (wife)Self‐rated health status of wife: 1 = very unhealthy, 2 = unhealthy, 3 = well, 4 = healthy, 5 = very healthy3.88 (0.98)Household sizeNumber of household members (persons)5.98 (2.35)Student member1 = having primary student member(s), 0 = otherwise0.35 (0.48)Dependency ratioRatio of child (≤15 years old) and elder (>65 years old) to other members (15–65 years)0.48 (0.59)Farm sizeCultivated land size for banana production (mu) a28.87 (73.36)Car ownership1 = car owner, 0 = otherwise0.31 (0.46)Distance to input marketsDistance to the nearest input markets (km)5.78 (8.59)Road condition1 = the road to the nearest transportation is good, 0 = otherwise0.81 (0.39)Hainan1 = Hainan province, 0 = otherwise0.37 (0.48)Yunnan1 = Yunnan province, 0 = otherwise0.29 (0.46)Guangdong1 = Guangdong province, 0 = otherwise0.34 (0.47)IVDistance to the nearest agricultural cooperatives (km)4.46 (6.26)Observations595Note:a1 mu = 1/15 hectare; S.D. refers to standard deviation.1FIGUREProportions of husbands and wives with off‐farm work by membership status [Colour figure can be viewed at wileyonlinelibrary.com]As for the other characteristics, Table 1 shows that husbands and wives were, on average, 48.88 and 46.77 years old, respectively. Also, husbands were slightly more educated than wives: on average, the former had 8.31 years of education, whereas the latter had 6.58 years. The self‐rated health status was measured on a five‐point scale. The mean health score was 3.98 for husbands and 3.88 for wives. On average, six members lived together in a household. Around 35% of the households had children attending primary school, and 31% owned cars. The average area cultivated was 28.87 mu (1 mu = 1/15 hectare).Table 2 presents the mean comparisons between the selected variables for cooperative members and non‐members, revealing systematic differences. For example, the proportions of husbands and wives who engaged in off‐farm work were greater among cooperative members than non‐members. The comparisons also show that cooperative members tended to be younger, better educated, and healthier than those without memberships. In addition, households with cooperative membership had more household members and a higher dependency ratio than those without memberships. Also, cooperative members cultivated larger farms and were more likely to own a car than their non‐member counterparts.2TABLEMean differences in variables between cooperative members and non‐membersVariableMembersNon‐membersMean differencesOff‐farm work (husband)0.21 (0.41)0.08 (0.28)0.13***Off‐farm work (wife)0.25 (0.43)0.08 (0.28)0.16***Off‐farm work time (husband)0.76 (2.11)0.41 (1.63)0.35**Off‐farm work time (wife)1.15 (2.80)0.54 (2.10)0.61***Age (husband)47.40 (9.07)49.29 (9.67)−1.89**Education (husband)8.84 (3.07)8.17 (2.78)0.67**Health (husband)4.20 (0.84)3.92 (0.98)0.29***Age (wife)45.11 (10.14)47.23 (9.84)−2.12**Education (wife)7.36 (3.21)6.36 (3.30)1.00***Health (wife)4.06 (0.87)3.83 (1.01)0.23**Household size6.48 (2.39)5.85 (2.32)0.64***Student member0.38 (0.49)0.34 (0.47)0.04Dependency ratio0.56 (0.60)0.46 (0.59)0.10*Farm size41.71 (71.98)25.32 (73.42)16.39**Car ownership0.48 (0.50)0.26 (0.44)0.22***Distance to input markets6.84 (7.62)5.49 (8.82)1.35Road condition0.76 (0.43)0.83 (0.38)−0.07*Hainan0.13 (0.34)0.44 (0.50)−0.30***Yunnan0.42 (0.50)0.26 (0.44)0.16***Guangdong0.45 (0.50)0.31 (0.46)0.14***IV3.59 (4.66)4.70 (6.62)−1.11*Observations129466Note:***p < 0.01,**p < 0.05, and*p < 0.10.RESULTS AND DISCUSSIONSResults of the RBP modelTable 3 presents the empirical results of the RBP model. The estimates of ρεμ${\rho }_{\varepsilon \mu }$ at the bottom of Table 3 are negative and significant, implying negative selection bias. In such cases, one‐stage models (e.g., a probit model) would underestimate the marginal effects of cooperative membership on the off‐farm work decisions of farm couples. The significance of ρεμ${\rho }_{\varepsilon \mu }$ also vindicates the use of the IV‐based RBP approach comprising the joint estimation of two probit equations (Gopalan et al. 2022; Li et al. 2021; Zhu et al. 2021).3TABLEDeterminants of cooperative membership and its impacts on farm couples’ off‐farm work participation: Marginal effects of the RBP model estimatesHusbandsWivesVariablesCooperative membership (marginal effects)Off‐farm work participation (marginal effects)Cooperative membership (marginal effects)Off‐farm work participation (marginal effects)Cooperative membership0.378 (0.036)***0.313 (0.074)***Age (husband)−0.003 (0.002)−0.001 (0.001)Education (husband)0.003 (0.006)0.001 (0.004)Health (husband)0.053 (0.017)***−0.008 (0.012)Age (wife)−0.004 (0.002)**−0.002 (0.001)Education (wife)0.006 (0.005)0.010 (0.005)**Health (wife)0.023 (0.018)0.017 (0.014)Household size0.016 (0.007)**−0.004 (0.006)0.017 (0.007)**0.005 (0.006)Student member−0.029 (0.037)0.034 (0.027)−0.036 (0.038)0.012 (0.028)Dependency ratio0.022 (0.030)0.010 (0.022)0.010 (0.032)0.021 (0.023)Farm size0.000 (0.000)−0.000 (0.000)0.000 (0.000)−0.000 (0.000)Car ownership0.051 (0.036)−0.131 (0.032)***0.055 (0.037)−0.057 (0.030)*Distance to input markets−0.001 (0.002)−0.002 (0.002)−0.001 (0.002)−0.004 (0.002)Road condition−0.032 (0.041)0.003 (0.029)−0.021 (0.043)−0.009 (0.032)Hainan−0.182 (0.043)***0.017 (0.032)−0.183 (0.045)***0.049 (0.033)Yunnan0.050 (0.047)0.049 (0.037)0.049 (0.047)0.048 (0.039)IV−0.005 (0.003)*−0.006 (0.003)**ρεμ${\rho }_{\varepsilon \mu }$−1.382 (0.431)***−0.821 (0.470)*Observations595595595595Note: *** p < 0.01, ** p < 0.05, and * p < 0.10; Standard errors are presented in parentheses; The reference province is Guangdong. Marginal effects in Table 3 are predicted after estimating the results of Table A2.Determinants of cooperative membershipColumns 2 and 4 of Table 3 present the marginal effects of the control variables and the IV on the probability of joining cooperatives. We discuss the marginal effects as they provide an intuitive interpretation of the associations between cooperative membership and participation in off‐farm work on the one hand and the explanatory variables on the other. The underlying coefficients are presented in Table A2 in the Appendix.The husbands’ perceived health status is positively associated with cooperative membership. For a one‐point increase—on a five‐point Likert scale—in husbands’ self‐reported health status, the probability of having a cooperative membership rises by 5.3%. The results are largely in line with the findings of Lin et al. (2022), who reported a positive relationship between health and cooperative membership among rice farmers in China. Cooperative membership places additional demands on one's time and may require people to contribute in kind. Being in good physical health helps keep up with such demands. In contrast, wives’ health status does not affect the likelihood of joining cooperatives. Interestingly, only wives’ age affects the likelihood of being a cooperative member—specifically, the predicted probability of them being cooperative members declines by 0.4% for a one‐year increase in wives’ age. This result is consistent with Ma et al. (2022b), who found a negative association between age and cooperative membership for banana farmers in rural China. Nevertheless, it contradicts Mojo et al. (2017)—who report a positive association between age and cooperative membership for coffee farmers in Ethiopia.Household size, however, raises the probability of joining cooperatives. Moreover, the effect size is similar for the two sexes. An additional household member is associated with around 1.6–1.7% increases in the probability of joining cooperatives. This is consistent with the findings of Chagwiza et al. (2016). But to be clear, notwithstanding the statistical significance of household size, its effect is relatively small. Individual locations also influence their probability of joining cooperatives. Households in Hainan province are around 18% less likely to be cooperative members than those in Guangdong. Finally, the longer the distance to the nearest cooperative, the lower the predicted probability of being cooperative members—the marginal effect of the IV, that is, distance to the nearest cooperative, is around −0.005 and statistically significant. These results confirm findings from previous studies (see, for example, Manda et al., 2020), showing a negative association between distance to cooperatives and cooperative membership. This stands to reason as larger distances entail longer commutes and thus a more considerable time commitment and higher travel costs, which may discourage acquiring cooperative memberships.Impact of cooperative membership on farm couples’ off‐farm work participation decisionsColumns 3 and 5 of Table 3 show the marginal effects of the key explanatory variable, cooperative membership, and other control variables on the probability of husbands and wives working off‐farm. The positive marginal effects of cooperative membership in column 3 suggest that cooperative membership increases the probability of husbands working off‐farm by 37.8%. Although the effect is relatively small for wives, it is appreciable; nevertheless, cooperative membership is associated with a 31.3% increase in the likelihood of wives working off‐farm. The findings support hypothesis 1 and confirm the importance of agricultural cooperatives in creating vibrant rural economies and increasing local employment incentives for rural dwellers (Demont 2022; World Bank 2019).Heterogeneous analysesTo study the heterogeneous effects of cooperative membership on farm couples’ off‐farm work, we predict the marginal effects of cooperative membership for different household sizes. Because household size is mainly distributed between 4 and 7 (this range accounts for more than 78% of the total observations), we only report the predicted marginal effects for household sizes 4, 5, 6, and 7. The disaggregated results are illustrated in Figure 2.2FIGUREPredicted marginal effects of cooperative membership on farm couples’ off‐farm work at different household sizes [Colour figure can be viewed at wileyonlinelibrary.com]Interestingly, Figure 2 shows that the marginal effects of cooperative membership on the likelihood of husbands having off‐farm work decrease monotonically from 0.387 to 0.370 as household size increases from four to seven. In contrast, cooperative membership increases the probability of wives working off‐farm by 30.2% when the household size is four and by 32.1% when the household size increases to seven—the marginal effects of cooperative membership on off‐farm work for wives rise with the increase in household size. These differences in marginal effects for husbands and wives can be partially explained by the different roles they have within households.Husbands tend to be household heads and hold sway in household decisions—Chinese society is, after all, patriarchal. Tending to a large household entails more commitments, leaving less time for off‐farm work. In comparison, wives are usually responsible for day‐to‐day household tasks, such as cooking and caring for the elderly and children. Indeed, having more members in the household may increase the amount of work needed to be done; however, with more members in the household, wives may receive assistance from other members to complete household work. The net effect of having more members may be more time for wives to pursue off‐farm work.Results of the ETPR modelTable 4 presents the results of the second stage of the ETPR model expressed by Equation (4). The results obtained from the first stage showing the association between the control variables and cooperative membership are reported in Table A3 in the Appendix for reference. We present the coefficients and the corresponding incidence rate ratios (IRRs) in Table 4. Since the coefficients do not provide a directly interpretable and meaningful interpretation, we will discuss the results using the IRRs.4TABLEDeterminants of cooperative membership and its impacts on farm couples’ off‐farm work time: Second‐stage estimation of the ETPR modelHusbandsWivesVariablesOff‐farm work time (coefficients)Off‐farm work time (IRRs)Off‐farm work time (coefficients)Off‐farm work time (IRRs)Cooperative membership0.951 (0.197)***2.5870.412 (0.153)***1.509Age (husband)−0.043 (0.016)***0.958Education (husband)−0.036 (0.028)0.964Health (husband)0.017 (0.102)1.017Age (wife)−0.031 (0.009)***0.969Education (wife)0.201 (0.026)***1.223Health (wife)0.169 (0.099)*1.185Household size−0.104 (0.089)0.9010.089 (0.026)***1.093Student member0.701 (0.204)***2.015−0.133 (0.160)0.875Dependency ratio−0.032 (0.193)0.968−0.240 (0.139)*0.787Farm size−0.009 (0.005)*0.991−0.004 (0.001)***0.996Car ownership−2.545 (0.330)***0.079−0.792 (0.155)***0.453Distance to input markets0.007 (0.007)1.007−0.068 (0.011)***0.934Road condition0.164 (0.302)1.178−0.443 (0.183)**0.642Hainan−0.883 (0.210)***0.414−1.679 (0.221)***0.187Yunnan0.605 (0.306)**1.831−1.385 (0.189)***0.250Constant−1.799 (0.675)***0.165−3.967 (0.754)***0.019Observations595595595595Note: *** p < 0.01, ** p < 0.05, and * p < 0.10. Standard errors are presented in parentheses. The reference province is Guangdong.The results show that cooperative membership exerts a positive and statistically significant impact on the time spent on off‐farm work for husbands and wives—the coefficients of cooperative membership in columns 2 and 4 are positive and significant. The IRRs imply that relative to those without cooperative membership, husbands with memberships spend 2.587 times the number of months working off‐farm; the effect on wives’ duration of off‐farm work, while not as large as that on husbands’, is still appreciable at 1.509 times. These results support hypothesis 2. The results are expected as the social ethos in rural China confers advantages on males allowing them access to a larger variety of employment opportunities. Males also tend to be more adaptive to physical jobs that are commonplace in rural China, and having more opportunities translates into more time spent working off‐farm. Although cooperative membership increases the duration of off‐farm work for females, a lack of information impedes their transition to off‐farm work (Rajkhowa & Qaim 2022; Zhou et al. 2020).Additional analysesImpact of cooperative membership on farm couples’ joint off‐farm work decisionsMarried couples’ decisions to work off‐farm tend to be interdependent. Depending upon intra‐household labor division, the husbands and the wives could make four types of exclusive off‐farm work participation decisions: neither husbands nor wives participate in off‐farm work; only husbands work off‐farm; only wives work off‐farm; and both husbands and wives work off‐farm. Table A4 in the Appendix presents the descriptive statistics of the variables. It shows that the majority of households in the sample (83%) have neither husbands nor wives working off‐farm. The households with only husbands and only wives working off‐farm account for 6% and 5%, respectively. Only 6% of households have both husbands and wives working off the farm.To deepen our understanding, we empirically examine the impact of cooperative membership on farm couples’ joint off‐farm work decisions. The cooperative membership variable is potentially endogenous, and those four types of off‐farm work are mutually exclusive. Given this, we combine the two‐stage residual inclusion (2SRI) method (Terza 2018; Zhu et al. 2020) with the multinomial logit (MNL) model to address the endogeneity issue of cooperative membership variable and explore how cooperative membership affects farm couples’ joint off‐farm work decisions. For the sake of simplicity, we only report the results estimated from the second‐stage estimations of the 2SRI‐MNL model and the coefficients of the cooperative membership variable. The residual term that is predicted from the first‐stage estimation is also included.1In the first‐stage, Equation (1) is estimated using a probit model and then the residual term is predicted. Table 5 presents the empirical results, showing that the marginal effects of cooperative membership are statistically significant in columns 2 and 5. The findings suggest that cooperative membership is associated with a 73.1% decline in the predicted probability of neither the husband nor the wife working off‐farm but a 62% increase in that of both working off‐farm. These considerable effect sizes illustrate how important cooperative membership is to promoting off‐farm employment of farm couples in rural China.5TABLEImpacts of cooperative membership on farm couples’ joint off‐farm work decisions: Second‐stage results of the 2SRI‐MNL modelVariablesOff‐farm work‐neither husbands nor wives (marginal effects)Off‐farm work‐husbands only (marginal effects)Off‐farm work‐wives only (marginal effects)Off‐farm work‐both (marginal effects)Cooperative membership−0.731 (0.352)**0.219 (0.236)−0.108 (0.203)0.620 (0.248)**Control variablesYesYesYesYesResidual0.582 (0.356)−0.165 (0.238)0.153 (0.208)−0.570 (0.246)**Sample size595595595595Note: Standard errors are presented in parentheses. *** p < 0.01, ** p < 0.05, and * p< 0.10. The reference province is Guangdong. We present the marginal effects rather than coefficients here because the estimated coefficients of the MNL model cannot be interpreted straightforwardly.Impacts on off‐farm work wages and on‐farm labor allocationAgricultural cooperatives may also directly provide employment opportunities to their members. These opportunities, which are often in the form of seasonal or long‐term odd jobs, are generally provided to poor members who need extra support. Thus, cooperative membership may lead to higher off‐farm incomes for farm households, who may use the additional income to finance their farming operations; they may purchase seedlings, fertilizers, pesticides, machinery, and irrigation apparatus.To explore these possibilities, we estimate the impact of cooperative membership on the off‐farm work wages of husbands and wives and on‐farm labor allocation decisions. On‐farm labor allocation decisions are measured by labor input and capital input. Specifically, labor input captures the amount of family and hired labor, measured in 100 labor‐days/mu. Capital input captures the total expenditure on seedlings, fertilizers, pesticides, machinery, and irrigation, measured in 1000 yuan/mu. The lower part of Table A4 in the Appendix presents the definitions and descriptive statistics of the relevant variables.Table 6 presents the empirical results. For brevity, we only report the results estimated in the second stage of the ETR model. The results show that the coefficients of cooperative membership, 2.731 and 1.632 for husbands and wives, respectively, are statistically significant, suggesting that being a cooperative member is associated with higher non‐farm income. However, the gains in wages are higher for husbands than for wives. Furthermore, the significant and positive coefficients of cooperative membership in columns 4 and 5 suggest that cooperative membership significantly increases labor and capital inputs used by farm households—not only do farmers invest more in seedlings, fertilizes, machinery, and irrigation, but they also allocate more labor to banana farming. This raises another question: where does the increase in labor come from? In other words, do farmers hire labor or allocate more family labor to their farming operations? To answer this, we estimate the impact of cooperative membership on the labor input ratio, defined as the ratio of hired labor to family labor used for producing bananas. The results (last column of Table 6) show that the coefficient of cooperative membership is positive but insignificant, suggesting that cooperative membership motivates farmers to increase both hired and family labor.6TABLEImpact of cooperative membership on farm couples’ off‐farm work wages and on‐farm labor allocations: Second‐stage estimations of the ETR modelFarm couples’ off‐farm work wagesOn‐farm labor allocationsVariablesOff‐farm work wages of husbands (coefficients)Off‐farm work wages of wives (coefficients)Labor input (coefficients)Capital input (coefficients)Labor input ratio (coefficients)Cooperative membership2.731 (0.172)***1.632 (0.159)***1.542 (0.110)***2.530 (0.170)***0.312 (0.236)Control variablesYesYesYesYesYesConstant0.436 (0.700)−0.041 (0.431)−0.248 (0.487)3.509 (0.823)***−0.159 (0.600)Observations595595595595580Note: *** p < 0.01, ** p < 0.05, and * p < 0.10. Standard errors are presented in parentheses. The reference province is Guangdong.CONCLUSIONS AND POLICY IMPLICATIONSMore and more rural households in China are turning to off‐farm employment to supplement farm income. However, rural families are reluctant to abandon farming operations due to their familiarity with farm work and the accompanying food security and employment stability. Consequently, they are faced with important intra‐household labor‐reallocation decisions: Who should work on the farm, and who is best suited to undertake off‐farm employment? Is it better for both the husband and wife to split their time between the farm and off‐farm work? How can they improve the prospects of finding suitable off‐farm work? Of course, there is a multitude of factors that dictate their course of action. This study focuses on the effects of cooperative membership on farm couples’ decisions regarding off‐farm work. Because these decisions involve both the husband and the wife, we ask whether cooperative membership increases the likelihood and time of each working off‐farm. Noting that the decision to join cooperatives is endogenous and that farmers opt to become members, we employ the IV‐based RBP model and the ETPR model to analyze rural household survey data collected in 2019. These models account for the said endogeneity and self‐selection bias.The results show that cooperative membership increases the probability of husbands having off‐farm work by 37.8% and wives by 31.3%. On average, husbands with cooperative membership work 2.587 times more months on off‐farm work than those who do not have cooperative memberships, while wives work 1.509 times more. The results provide strong evidence for the efficacy of agricultural cooperatives in helping rural farming households secure off‐farm employment. The results of additional analyses reveal that cooperative membership is associated with a 73.1% reduction in the predicted probability of neither the husband nor the wife working off‐farm but a 62% increase in that of both working off‐farm. Furthermore, it significantly increases off‐farm work wages for both husbands and wives. Last, cooperative members use more inputs—labor and capital—to grow bananas.The results should be of strong interest to those tasked with rural development, agricultural cooperatives, and, most importantly, rural households. The positive effects of rural cooperatives on alleviating labor shortages and overcoming the challenges of working with poor technology and diseconomies of scale are well documented. We show that cooperative membership can help rural households secure off‐farm work to enhance their income and living standards. Thus, rural households would be well advised to join agricultural cooperatives. Although China has eradicated extreme poverty, many rural households contend with low living standards. Increasing fiscal outlays such as subsidies and providing tax incentives to cooperatives may pay rich dividends in improving the quality of life in rural China. Given the regional differences in the effectiveness of cooperative membership in helping households secure off‐farm work, a one‐size‐fits‐all policy framework is unsuitable. Policies ought to be designed with these differences in mind.This study focuses on banana farmers in only three provinces, which presents a localized perspective. Because banana farming is quite labor‐intensive, the results may not translate to farmers growing grains, legumes, and other kinds of fruit. Future studies may focus on the effects of cooperative membership on rural households growing different crops in other regions of the country. 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Tourism Economics, 135481662110004.APPENDIXA1TABLEFalsification test of instrument variableOutcome variablesStatisticsOff‐farm work (husband)χ2 = 0.67; p‐value = 0.412Off‐farm work (wife)χ2 = 2.08; p‐value = 0.149Cooperative membershipχ2 = 5.11**; p‐value = 0.028Note: ** p < 0.05.A2TABLEDeterminants of cooperative membership and its impacts on farm couples’ off‐farm work participation: Coefficients of the RBP modelHusbandsWivesVariablesCooperative membership (coefficients)Off‐farm work participation (coefficients)Cooperative membership (coefficients)Off‐farm work participation (coefficients)Cooperative membership2.202 (0.251)***1.809 (0.465)***Age (husband)−0.011 (0.007)−0.005 (0.008)Education (husband)0.013 (0.022)0.006 (0.026)Health (husband)0.205 (0.069)***−0.046 (0.072)Age (wife)−0.016 (0.008)**−0.012 (0.009)Education (wife)0.023 (0.021)0.055 (0.028)**Health (wife)0.089 (0.069)0.100 (0.078)Household size0.062 (0.027)**−0.022 (0.034)0.067 (0.028)**0.029 (0.033)Student member−0.114 (0.143)0.199 (0.156)−0.140 (0.148)0.072 (0.162)Dependency ratio0.084 (0.116)0.056 (0.127)0.038 (0.123)0.119 (0.131)Farm size0.000 (0.001)−0.002 (0.002)0.000 (0.001)−0.002 (0.002)Car ownership0.199 (0.142)−0.766 (0.181)***0.213 (0.143)−0.328 (0.174)*Distance to input markets−0.003 (0.009)−0.010 (0.010)−0.003 (0.009)−0.022 (0.014)*Road condition−0.125 (0.160)0.018 (0.170)−0.080 (0.164)−0.051 (0.188)Hainan−0.704 (0.171)***0.098 (0.184)−0.704 (0.178)***0.284 (0.192)Yunnan0.192 (0.182)0.287 (0.213)0.189 (0.181)0.280 (0.225)Constant−1.333 (0.600)**−1.004 (0.662)−0.698 (0.592)−1.929 (0.674)***IV−0.021 (0.011)*−0.023 (0.011)**Observations595595595595Note: *** p < 0.01, ** p < 0.05, and * p < 0.10. Standard errors are presented in parentheses. The reference province is Guangdong.A3TABLEDeterminants of cooperative membership: First‐stage estimation of the ETPR modelHusbandsWivesVariablesCooperative membership (coefficients)Cooperative membership (coefficients)Age (husband)−0.013 (0.007)*Education (husband)0.020 (0.024)Health (husband)0.167 (0.068)**Age (wife)−0.012 (0.008)Education (wife)0.026 (0.022)Health (wife)0.059 (0.067)Household size0.056 (0.030)*0.059 (0.031)*Student member−0.128 (0.150)−0.127 (0.148)Dependency ratio0.068 (0.115)0.026 (0.114)Farm size0.000 (0.001)0.000 (0.001)Car ownership0.236 (0.141)*0.251 (0.137)*Distance to input markets0.001 (0.008)−0.001 (0.008)Road condition−0.068 (0.160)−0.079 (0.159)Hainan−0.714 (0.171)***−0.755 (0.173)***Yunnan0.177 (0.175)0.105 (0.172)Constant−1.112 (0.582)*−0.741 (0.587)IV−0.028 (0.014)**−0.024 (0.012)*Sample size595595Note: *** p < 0.01, ** p < 0.05, and * p < 0.10. Standard errors are presented in parentheses. The reference province is Guangdong.A4TABLEDefinition and descriptive statistics of variables for additional analysisVariableDefinitionMean (S.D.)Off‐farm work: neither husband nor wife1 = Husband and wife did not participate in off‐farm work, 0 = otherwise0.83 (0.38)Off‐farm work: husband only1 = Husband participated in off‐farm work while wife did not, 0 = otherwise0.06 (0.23)Off‐farm work: wife only1 = Wife participated in off‐farm work while husband did not, 0 = otherwise0.05 (0.23)Off‐farm work: both1 = Husband and wife participated in off‐farm work, 0 = otherwise0.06 (0.24)Off‐farm work wage (husband)Wage earned from off‐farm work by husband among workers (1000 yuan/month) a3.99 (2.57)Off‐farm work wage (wife)Wage earned from off‐farm work by wife among workers (1000 yuan/month)2.81 (1.07)Hired labor inputAmount of hired labor (100 labor‐days/mu)0.75 (1.02)Family labor inputAmount of family labor (100 labor‐days/mu)0.02 (0.05)Labor inputAmount of family labor and hired labor (100 labor‐days/mu) b0.77 (1.01)Capital inputTotal expenditure on seedlings, fertilizers, pesticides, machinery, and irrigation (1000 yuan/mu)2.33 (1.62)Labor input ratioRatio of hired labor input to family labor input0.31 (1.58)Note:aYuan is Chinese currency (1 US dollar = 6.90 yuan in 2019).b1 mu = 1/15 hectare.

Journal

Annals of Public and Cooperative EconomicsWiley

Published: Sep 1, 2023

Keywords: China; cooperative membership; farm couples; off‐farm work; recursive bivariate probit

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