An Empirical Analysis Agricultural Cooperatives in Thailand
Jumtip Seneerattanaprayuland Christopher Gan
Faculty of Agribusiness and Commerce, Department of Financial and Business Systems, PO Box 85084, Lincoln University, Canterbury, New Zealand
Abstract. Several studies have exhibited that microcredit is not successful in pro- moting rural household welfare. In order to improve the effectiveness of credit, some researchers suggest that non-credit support services could be provided sim- ultaneously with credit. In spite of Thai agricultural cooperatives (ACs) provid- ing credit and non-credit support programs to help the rural households, the num- bers of poor in rural areas have not been reduced significantly as forecasted. One possible cause impeding the effectiveness of ACs to improve rural households’
welfare is low participation rate in ACs. This study examines the determinants of households’ decisions to become AC members and participate in AC credit and marketing services using the Heckman selection model. The data is collected us- ing a household survey questionnaire from AC members and non-members. A two-stage stratified sampling technique were employed to select rural household sample in order to obtain best representatives of the study population. The find- ings confirm that ACs play a significant role in assisting middle-class households to access credit and markets. Households’ farm risks and their expected benefits from AC service participation have significant effects on their decisions to par- ticipate in AC services. Moreover, attributes of AC services significantly deter- mine level of participation in AC services.
Keywords: Credit Service, Marketing Services, Agricultural Cooperatives, Heckman Selection Model, Thailand
1 Introduction
Thailand faces significant challenges regarding poverty. Although the national poverty rate has reduced substantially over the last three decades, from 67% in 1986 to 7.1% in 2015, more than 6.7 million Thais remain vulnerable to falling back into poverty (The World Bank, 2018). Most of them live in rural areas (Lewis, Tambunlertchai, Tambunlertchai, Adair, & Hickson, 2013). Rural households remain impoverished while confronting constraints such as limited access to resources, low education out- comes, insufficient financial capital and inability to access markets (Mojo, Fischer, &
Degafa, 2017).
Credit and non-credit support services (marketing services and agricultural extension services) play a crucial role in encouraging Thai rural households to move beyond pov- erty. Credit access allows households to unlock liquidity constraints leading to more investment in economic activities and smooth their consumption (Reed, 2011). Non- credit support services assist farm households to improve their productivity and access input and output markets by reducing transaction costs, raising bargaining power, and obtaining market information (Ma, 2016). As a result, rural people improve their productivity, asset formation, and income. Increasing income enhances the opportunity for the poor to attain improved nutrition and access to essential services.
In spite of Thai ACs providing credit and non-credit support programs to help the rural households, rural poverty statistics have not been reduced as significantly as fore- cast. One possible cause impeding the effectiveness of ACs to improve rural house- holds’ welfare is low participation rate in ACs. Only 18.59 percent of the total Thai rural population are AC members (Cooperative Promotion Department, 2014). Another possible reason is that AC members do not use integrated services. Many AC members prefer to borrow and to buy inputs at low prices instead of using existing non-credit services such as marketing, processing, and agricultural extension services (Chiengkul, 2007). Only 2.40 and 8.35 percent of AC members join the marketing and processing programs (Cooperative Auditing Department, 2014). Strong members’ participation in ACs’ services is a crucial factor affecting the ACs’ performances and members’ bene- fits (Ruete, 2014; Williams & Crowther, 2016). Based on the Cooperative concept, ACs are operated by members and their capital and cash flow depend on members’ transac- tions. If there is low member participation or trade through ACs, the ACs do not have enough capital to survive and enable the rural poor to be helped. Therefore low member participation in Thai ACs must be critically analyzed in order to make their function work more effectively in alleviating poverty.
A good understanding of households’ decisions to become AC members and partic- ipate in credit and non-credit support services is essential in order to design appropriate policies meeting the welfare of rural households. Therefore, this study examines the determinants influencing Thai rural households’ decisions to become AC members and participate in AC credit and non-credit support programs, particularly input and output marketing service. The study provides the significant information to practitioners and policymakers about the types of AC products and services that work best for the target customers. Both policymakers and the ACs administrations can apply the findings from this study to improve rural household welfare and to improve ACs operation efficiently.
2 Related Literature
Several empirical studies on ACs have attempted to explain who benefits from ACs.
Generally, the participation in ACs is found to be closely related to household head and household characteristics and geographic factors. Household head characteristics, such as age, education level, and being member of other groups, are the important factors affecting participation in ACs since they are related to individual risk preferences, un- derstanding the benefits obtained through AC services, and social network (Alene et
al., 2008; Chagwiza, Muradian, & Ruben, 2016; Muthyalu, 2013; Zhang et al., 2017).
The impact of household head characteristics on AC membership vary.
Household characteristics influence AC participation since they reflect household wealth and production. Household characteristics are captured by household size, farm size, and assets. Effects of household characteristics on AC participation vary. Land ownership and farm size are the key factors influencing farmers’ decision regarding AC membership. They have positive effects on AC participation (Mensah, Karantininis, Adegbidi, & Okello, 2012; Zhang et al., 2017). Fischer and Qaim (2012) and Bernard and Spielman's (2009) provide more comprehensive results that those effects are high- est in the middle-class farmers. In contrast, Chagwiza et al. (2016) and Wollni and Zeller (2007) argue that farm size has a negative impact on AC marketing participation.
The most likely explanation for this is that larger farmers have a higher bargaining power to negotiate with private traders (Wollni & Zeller, 2007).
Geographic factors affect choices of AC participation. Such factors relate to physical environments, economic activities and infrastructures, which affect household joining AC credit and marketing services (Khoi et al, 2012). Empirically, geographic factors are used to control for differences in location. Generally, geographical factors are cap- tured by regions, distance to the nearest market or road access. Distance to the nearest market or access to a road has a positive effect on participation in AC (Abebaw & Haile, 2013; Fischer & Qaim, 2012). Households living far from the markets or road are more likely to participate in ACs.
Furthermore, household perception toward risk, benefit expectation from ACs, and AC performance are discussed as significant determinants of AC participation (Zheng et al., 2011; Ito et al., 2012; Hoken & Su, 2015; Hoken, 2016; Fischer & Qaim, 2011, 2012; Mensah et al., 2012). Risks toward farm production, financial capital, and market have positive effects of AC participation (Zheng et al., 2011; Ito, Bao, & Su, 2012;
Hoken & Su, 2015; Hoken, 2016). It means farmers with high risks are more likely to be AC members since ACs assist them to overcome the credit constraints, access mar- kets, and improve their production.
Household perceptions of AC performance and benefits received from ACs influ- ence their decision to participate in AC and AC services (Fischer & Qaim, 2011, 2012;
Mensah et al., 2012). Mensah et al. (2012) found that households with high levels of AC satisfaction are far more likely to participate in ACs. Regarding AC benefits, Men- sah et al. (2012) and Fischer and Qaim (2011) have shown that only AC prices posi- tively affect AC marketing participation. AC members compare prices offered by ACs to prices from alternatives. If ACs provide a higher price than others, they will sell their products via ACs (Mensah et al., 2012).
There are limited studies focusing on the level of participation in AC services. The empirical studies focus on output amount sold through ACs. Determinants of the level of AC marketing participation can be estimated using the Tobit model or the Heckman model. Model selection depends on the selection bias in the model. The selection bias may take place in AC participation because of non-random samples. The participating households have decided to participate in it themselves (Abebaw & Haile, 2013; Ma &
Abdulai, 2016; Verhofstadt & Maertens, 2015). For example, Mensah et al. (2012) in- vestigated the determinants influencing the number of cashew nuts sold through AC
marketing. A two-limit Tobit model was applied since the proportion of sales via AC is a censored variable which falls into the range between 0 and 100 percent. Some stud- ies investigated factors affecting household participation in AC output marketing by taking account selection bias and using the Heckman model to control for the selection bias (Alene et al., 2008; Fischer & Qaim, 2011; Winter-Nelson & Temu, 2005).
3 Research Method
3.1 Empirical Model
This study explores the determinants of participation in AC and AC services taking into account a selection bias. The participation in AC services in this study is defined as the decision to participate in AC services and the level of participation. The study applies the Heckman selection model to identify factors influencing households’ decisions to participate in AC and AC services.
The Heckman selection model consists of two stages: participation or selection pro- cess and outcome process. In the first stage, a probit model is applied to estimate house- holds’ probability of participation in ACs and AC services. Household decision to par- ticipate in ACs and AC services is based on the utility maximization (Abebaw & Haile, 2013; Fischer & Qaim, 2012; Mojo, Fischer, & Degefa, 2015; Wollni & Zeller, 2007).
The actual utility level of each household is unobservable, however, the utility function can be expressed by equation (1). The explanatory variables (Zij) are the observable factors influencing the utility. The error term (εij) captures the unobservable factors af- fecting the utility but are not included in the explanatory variables.
( 0,1)
ij ij ij
U
z
j (1)Where j is a choice of decision (participation in AC service =1 and = 0 otherwise). To capture the AC service participation, households are assumed to be risk neutral and consider the potential net returns (Ma, 2016). Household decides to participate in AC services, if the expected net returns obtained from participating in AC services (Ui1) is higher than not participating(Ui0). The empirical model of households’ decisions to participate in AC and AC services is illustrated by equation (2).
*
1 0
i i i i i
D U U z
1,
*0
0,
i i
D if D
if otherwise
(2)Where Di is a binary outcome. Di=1 if Ui1> Ui0, and Di=0 if Ui1≤ Ui0. δ is a vector of parameters. The error term is assumed to be normally distributed with zero mean. Based on theories and literature (Bernard, Taffesse, & Gabre-Madhin, 2008; Fischer & Qaim, 2011, 2012; Francesconi & Heerink, 2010; Ma, 2016; Mojo et al., 2017), the observable factors included in the study model are household head and household characteristics
(e.g., age, education, family size, and land size), household’s perception on risk and AC performance (e.g. attitude toward farm risk and AC benefits), and geographic factors (e.g., region and distance to the nearest market).
In the second stage, the level of participation in AC credit and marketing services are analyzed. The level of participation are measured by loan size, input expenditure, and amount of farm product sold through ACs. The participation level in AC services is estimated using the information of households’ participation in the first stage. The participation level in AC services is expressed by the following equation:
i i i
y x
, GivenD
iequals to one (3) Where xi is a vector of explanatory variables determining participation level in AC ser- vices (yi), and (μ) is an error term. The error terms (ε, μ) are bivariate normally distrib- uted with zero means. If both error terms are correlated, the expected value of μ condi- tional on the sample selection are non-zero which is denoted by E(μǀε) = γε. Since the level of AC service participation (yi) is observed only when the household participate in AC service (Di=1). The expect level of AC service participation can be expressed as follows:
i i, i
i, i
E y z x E z x
(4) γ equals zero, indicating the error term of participation and outcome equations are not related. It means the model does not have a sample selection bias. Then β is consistently estimated by OLS using the selected sample. However, if γ does not equal zero, then the level of AC service participation can be rewritten as follows:
i i1 i
i
i
i
E y D x E z x z
(5) Let E(εǀε>-δzi) is λ(δzi). Where λ(δzi) = ϕ(δzi)/Ф(δzi); ϕ and Ф are the standard normal density function and standard normal cumulative distribution function, respectively.λ(δzi) is defined as the inverse Mills ratio. The inverse Mills ratio captures the omitted variable in the selected sample (Wooldridge, 2002). The consistent estimate of β and γ can be obtained by regressing yi on xi and the inverse Mills ratio. Although δ is un- known, the estimate of δ can be derived from a probit estimation of the participation equation. Therefore, the Heckman two-step method can solve the selection bias and provide unbiased and consistent estimators using the inverse Mills ratio.
3.2 Data
The data for this study was collected from rural households in Nakhonratchasima prov- ince, Northeast of Thailand using a structured questionnaire. Nakhonratchasima was selected as the study site since it has the greatest number of AC members in Thailand (with 304,110 members and 96 ACs as of January 2016) (Cooperative Promotion De- partment, 2015). Nakhonratchasima can be categorized into six area groups based on the provincial development plan and policy. Two-stage stratified sampling technique
was employed to select the rural household sample in order to obtain the best represent- atives of the study population. The first stage involves selecting one district from each group that has the greatest number of AC members in the group. Pak Chong, Pak thong chai, Phimai, Non sung, Dan khunthot, and Bua yai are selected as the study areas.
Household selection from each district is the second stage. The sample from each dis- trict is calculated by disproportionate stratified random sampling method.
4 Results and Discussion
4.1 Descriptive Statistics of the Respondents
The major respondents are female and their age ranges from 46 to 55 (30%) and 56 to 65 years old (33%). Most of them (98.47%) attained some levels of education, mostly at the primary level. Most of the respondents engage in farming such as paddy, cassava, corn, and sugarcane. Respondents’ farm size is around 3.72 ha of land and around 64.5% of them own land. Households perceive that they face production, market, and financial risks in the middle level with mean values ranging from 2.28 to 2.97. Mean household annual income equals 245,064 baht.
In total 851 households were interviewed, of which 560 (66%) are AC members, and 291 (34%) are non-AC members. Among AC members, there are 458 (82%) respond- ents borrowing loan from ACs. Among AC borrowers, 323 (71%) AC borrowers also borrow loans from other credit sources. Some farmers borrow from ACs by using group lending (Co-guarantor) (50.34%) and some use land or house as collateral (53.29%).
The major AC loans are borrowed for farm production (99%) and they are short term loans (91%) with interest rate around 8.16%. There are 249 (45%) respondents who joined AC input marketing services, and 100 (18%) trading to AC output marketing service. Most of AC input participants purchase fertilizers (90.36%), pesticides or agro chemical (38.15%), as well as seeds and saplings (36.95%) from ACs. Purchasing in- puts from AC on credit is the most popular payment method (57.43%). Among AC output marketing participants, there are 45% of AC output marketing participants sell- ing their farm outputs to ACs and other channels. Most of AC output marketing partic- ipants (87%) receive the sale revenue from ACs on time.
4.2 Empirical Results
This study applied the probit model (equation 2) to estimate the probability of partici- pation in ACs. Regarding AC services, the Heckman selection model is used to examine the determinants influencing participation in AC services. This study considers three AC services: credit, input and output marketing services. Thus, there are three Heckman selection models in this study. The Heckman selection model for each AC service con- sists of participation equation (equation 2) and participation level equation (equation 3). The dependent variable (yi) in the participation level equation for AC credit is loan amounts. While the dependent variable for AC input marketing service is input ex- penses through ACs and for output marketing service is farm product quantity sold to
ACs. These systems of equations can be estimated using the Heckman two-stage ap- proach. To avoid collinearity among the regressors, this study uses household’s benefit expectation from AC services as a selection instrument variable in the Heckman selec- tion model since it affects a household’s decision to participate in AC services, but does not affect their level of participation.
Multicollinearity and heteroskedasticity test are performed to check for the con- sistency and robustness of the coefficients. The VIF tests of both participation equation (equation 2) and the participation level equation (equation 3) for AC membership and AC services confirm that collinearity does not exist (see Table 1). However, the results of Breusch-Pagan/Cook-Weisberg test show all equation has heteroskedasticity prob- lem at the 1% level except for the equation of participation level in the AC input mar- keting service (equation 3). Therefore, to solve heteroskedasticity problem, the equa- tions are estimated using robust variance.
Determinants of Participation in ACs.
Results of the probit model in which AC membership variable is regressed on individual characteristics, household factors, households’ perception on farm risks and AC bene- fits, and geographical factors are presented in Table 2. The goodness of fit tests indi- cates that the covariates in model provide good estimates. The overall percentage of correctly specified values is 85.23%. The Wald χ2 is statistic significantly at 99% con- fidence level, indicating the explanatory variables are jointly statistically significant.
Table 1. Test for multicollinearity and heteroscedasticity
Participation equation in AC and AC services (equation 2)
Membership Credit Input
marketing
Output marketing
Mean VIF for multicollinearity 2.3 1.03 3.46 3.28
Reject/not reject H0 Not reject Not reject Not reject Not reject Breusch-Pagan/Cook-Weisberg 90.68
(0.000)
157.89 (0.000)
12.42 (0.8248)
70.48 (0.000) Reject/not reject H0 Reject at 1% Reject at 1% Not reject Reject at 1%
Participation level equation in AC services (equation 3)
Credit Input
marketing
Output marketing
Mean VIF for multicollinearity 1.36 5.67 3.19
Reject/not reject H0 Not reject Not reject Not reject
Breusch-Pagan/Cook-Weisberg 44.04
(0.0075)
44.67 (0.0019)
41.22 (0.0014) Reject/not reject H0 Reject at 1% Reject at 1% Reject at 1%
Computed by the authors using STATA software.
Note: number in parentheses are the probability > chi2 for BP/CW test
Table 2. Determinants of participation in AC membership and marginal Effects
Coeff R.S.E MEs Coeff R.S.E MEs
Constant -2.451 0.711
Individual Characteristics Risk and AC Performance
Sex -0.278* 0.145 -0.057 Pro_Risk -0.572*** 0.111 -0.127 Paddy -0.231 0.247 -0.051 Mkt_ Risk -0.068 0.106 -0.015 Orchard -0.511* 0.273 -0.113 Fin_Risk 0.345*** 0.088 0.077 Cash Crop 0.501** 0.226 0.111 AC_Sat 0.143 0.118 0.032 AC rela-
tives 1.2341*** 0.201 0.275 AC_Eff -0.072 0.092 -0.016
Household Characteristics Cre_Be 0.224*** 0.056 0.050
HHinc_2 0.3371 0.325 0.101 Ext_Be -0.043 0.043 -0.010 HHinc _3 0.464 0.313 0.132 Inp_Acc -0.041 0.118 -0.009 HHinc _4 0.609** 0.293 0.162 Out_Acc -0.036 0.110 -0.008 HHinc _5 0.404 0.380 0.117 Inf_Acc 0.173** 0.072 0.039 Inc_Earner -0.168** 0.072 -0.037 Geographical Factors
Farm_Siz
e 0.0137** 0.007 0.003 Dankhuntod -0.554** 0.235 -0.137 Farm_Square -0.0001** 0.000 0.000 Nonsong 0.074 0.236 0.013 Cre_Use 0.913*** 0.159 0.203 Pakchong 0.029 0.315 0.005 Ext_Acc 0.683*** 0.157 0.152
Pak-
thongchai -0.752*** 0.254 -0.203
Phimai 0.611* 0.358 0.074
Res
(Cre_Use) 0.34 0.051 Res
(Ext_Acc) 0.594 0.925
Wald Chi2 196.78***
Observation 616
Predicted Probability 85.23%
Sensitivity 93.75%
Specificity 59.21%
Chi2 of Hosmer-
Lemeshow gof 570.03
Note: *, **, and *** Indicate significance level at 10%, 5%, and 1%, respectively.
HHinc_2, HHinc_3, HHinc_4, HHinc_5 Indicate household income which equal 20,001-50,000 baht, 50,001-100,000 baht, 100,001-500,000 baht, and >500,000 baht respectively.
To obtain consistent estimates, the endogeneity problem of AC participation is ad- dressed. Access to credit and agricultural extension services might be potentially en- dogenous variables in the AC participation equation (equation 2) since both variables
may be jointly determined with AC participation (Abebaw & Haile, 2013 Ma & Ab- dulai, 2016). The endogeneity problem is tested using Rivers and Vuong’s (1988) ap- proach. The result shows that the estimates of the residual for credit access and agricul- tural extension service access are not significantly different from zero, indicating that they are exogenous variables in AC participation equation (see Table 2).
Probability of participation in ACs is dominated by AC relatives, farm size, credit use (Cre_Use), access to agricultural extension services (Ext_Acc), production risk (Pro_Risk), financial risk (Fin_Risk), credit benefits (Cre_Be), and district. Households with relatives or neighbor as AC members have about 41 % higher probability of join- ing ACs. This implies that social network is the important factor to motivate households to access AC membership.
Our results on household income and farm size support the idea of the middle effect discussed by Bernard and Spielmen (2008) and Mojo et al. (2015). A probability of AC participation increases for each additional farm size, up to certain level. It means the probability of AC participation is maximized for an intermediate level of those. It may be explained that benefits earning from ACs tend to increase with household transaction via ACs (Bernard & Spielmen, 2008; Mojo et al., 2015). Farmers holding more farms and more production obtain more benefits from buying and selling products to ACs.
But the benefits from ACs will reduce when farmers own large farms since large-scale farmers have greater bargaining power, thus they gain high benefits by individual sell- ing. This is consistent with effect of income. Households living above the national min- imum wage (HHinc_4) are more likely apply for AC membership compared to the poor households.
In addition, credit use has a strong impact on AC participation. Farmers using credit have a greater probability to joining ACs than non-credit users by 35 %, indicating that farmers participate in AC when they face liquidity constraints and need to borrow loans from ACs. Farmers accessing agricultural extension services tend to participate in ACs.
A possible explanation is they can perform better by following ACs’ standard.
In terms of household risks and perception on AC benefits, our results show that the more farm production risk households face, the less probability of AC participation. In contrast, household financial risk and perception on credit benefit and market infor- mation have positive effects on being AC members, suggesting that farmers join AC since they would like to obtain loans and believe that ACs could support them to access credit and market information.
Determinants of AC Credit Participation.
To obtain consistent estimates, the endogeneity of other loan in AC credit participation is considered. Rivers and Vuong’s (1988) approach is applied to solve endogeneity problem. Table 3 shows that the estimates of the residual for other loans are not signif- icantly different from zero, indicating that the endogeneity problem does not exist in the participation equation for AC credit service (equation 2).
The results of the Heckman selection model of determinants of AC members’ deci- sion to participate in AC credit service are presented in Table 3. The participation in AC credit is strongly influenced by being a member of other groups (Oth_Mem), farm
Table 3. Determinants of AC credit participation using the Heckman two-step method
Participation log AC Loan Size
Coeff R.S.E MEs Coeff R.S.E Change
in Y (%)
Constant 1.067 0.61 Constant 8.224 0.671
Oth_Mem -0.82** 0.419 -0.076 Age_36-45 0.450** 0.208 56.89 Farm_Yr -0.013** 0.007 -0.001 Age_46-55 0.321 0.202 37.79 Age_56-65 0.361* 0.204 43.52 Age_Over 6 0.081 0.219 8.47 Farm_Si 0.017** 0.008 0.002 Paddy 0.126 0.110 13.48 Farm_Sq -0.0001** 0.000 -8.04e-06 Orchard 0.051 0.188 5.19 Farm_Ass -0.628*** 0.227 -0.059 Cash_Crop 0.206* 0.118 22.87 Oth_Loan -7.17e-07* 0.000 -6.69e-08 lgHHmem -0.327*** 0.107
Ext_Acc 0.478** 0.224 0.045 Dep_Rat 0.147** 0.059 15.83 lgFarm_Mem 0.210** 0.104
lgFarm_Size 0.123** 0.054 lgHH_Inc 0.105** 0.044
AC_Eff 0.188** 0.090 0.017 Pro_Risk -0.032 0.048 -3.16 Mkt_Risk 0.005 0.052 0.51 Fin_Risk -0.092** 0.042 -8.83 lgAC_Share 0.147*** 0.032
ACdur_Mid 0.690*** 0.182 99.42 ACdur_Lon 0.727*** 0.182 106.80 Coll_Prop 0.417*** 0.094 51.75 Dis_Town 0.045*** 0.013 0.004 Dan -0.257* 0.136 -22.63
Non 0.019 0.133 1.91
Pak 0.206 0.156 22.90
Pakth -0.046 0.169 -4.46
Phi 0.294** 0.142 34.21
Lambda -0.414 0.277
Res (Other Loans) 2.33e-06 3.33e-06
Wald Chi2 190.04***
Observation 450
Wald test of Exogeneity (Chi2) 0.4
Note: *, **, and *** Indicate significance level at 10%, 5%, and 1%, respectively.
AC_Eff is used as selection instrument variable for this model.
asset ownership (Farm_Ass), and access to agricultural extension services (Ext_Acc).
Probability of participation in AC credit decreases by 7.6 percent, if AC members are members of other associations. The other associations that AC members are involved in are BAAC and the village fund. Main objective of both organizations is credit pro- vision. The result indicates that farmers will choose to borrow from other organizations before borrowing from ACs. This implies that ACs tend to be an alternative credit source.
However, the results infer that ACs are the credit source for poor farmers. This is confirmed by a statistically significant negative effect of other loans (Oth_Loan) and farm assets on AC credit participation. The results show that farmers who cannot access other credit sources are more likely to participate in AC credit. Households who have less farm capital have a higher probability of access to AC credit than those having farm capitals by 5.8 percent. Households holding small endowments cannot access to formal credit but can borrow credit from ACs since AC credit borrowing can use co-guarantees in place of collaterals.
The results of the participation level equation for AC credit (equation 3) confirm that loan size is significantly determined by household characteristics and AC credit attrib- utes. Household member (lgHHmem), loan duration (ACdur), and collateral type (Coll_Prop) highly influence loan amounts granted by ACs. AC loan amounts signifi- cantly decrease with the number of members in the household while increases with the dependency ratio. Household members indicate the number of working labor. Larger families have enough income therefore they are less likely to borrow AC credit, con- trary to Benjamin, Timo, Stefan, and Jukka (2015) and Nguyen (2007). Furthermore, households with a higher dependency ratio tend to access AC credit. According to the results of household members and dependence ratio, it infers that AC members access- ing to AC credit are households with few income earners but many dependent members.
Loan amount granted by ACs is strongly dominated by AC credit attributes: AC share, loan duration, and using property as collateral. Besides co-guarantee collateral, AC share and property are used as a collateral to borrow loans from ACs. The results show that the number of AC share and property collateral positively influence AC loan amounts. Moreover, loan amounts depend on loan duration since larger loan amounts are allowed to have a longer period for repayment.
Determinants of AC Marketing Services.
To obtain consistent estimates, the estimation considers the endogeneity problem in participation in AC marketing services. Household income may be potential endoge- nous variable of input use (Winter-Nelson & Temu, 2005). Similarly, farm size might be potential endogenous variable in AC output marketing participation (Fischer
&Qaim, 2011). The endogeneity problem of farm income and farm size is solved using Rivers and Vuong’s (1988) approach. Our result shows that household income and farm size are not significantly difference from zero (see Table 4 and 5), thus, the study can ignore the endogeneity problem of household income and farm size.
Table 4. Determinants of Participation in AC Input Marketing Service Using Heckman Two- Step Method
Participation Log Input Expense
Coeff R.S.E MEs Coeff R.S.E Change
in Y (%)
Constant -1.198 0.502 6.753 0.997
Individual Characteristics
Age_36-45 -0.531 0.374 -0.187 Edu_2 0.776 0.565 117
Age_46-55 -0.859** 0.365 -0.317 Edu_3 0.578 0.597 78
Age_56-65 -0.546 0.364 -0.193 Edu_4 0.667 0.580 95
Age_over 66 -0.747* 0.388 -0.272 Edu_5 1.097* 0.655 199 Household Characteristics
Paddy -0.334* 0.196 -0.132 Paddy -0.141 0.170 -13
Cash_Crop -0.335* 0.194 -0.133 Cash_Crop 0.336* 0.179 40
Orchard -0.412 0.308 -0.163 Orchard 0.495* 0.293 64
Farm_Size 0.018*** 0.005 0.007 lgFarm_Size 0.597*** 0.086 Farm_Square -0.0001** 0.000 -2E-05 lgHHmem 0.330** 0.144
Irr_Acc -0.261 0.194 -0.103 HHinc_2 -0.727** 0.328 -52 Tech_Acc 0.429** 0.194 0.170 HHinc_3 -0.619** 0.297 -46 Farm_Income -1.1E-07 0.000 -5E-08 HHinc_4 -0.705*** 0.267 -51
HHinc_5 -0.483 0.313 -38
Perception toward Risk and AC Performance
Inp_Acc 0.223*** 0.063 0.088 Pro_Risk -0.135* 0.071 -13 AC Marketing attributes
ACdistance -0.015** 0.006 -0.006 lgAC_Share 0.044 0.039
ACInp_cre 0.250** 0.121 28 Geographical Factors
Dankhuntod 1.478*** 0.245 0.525 Dankhuntod 0.128 0.370 Nonsong 1.201*** 0.222 0.421 Nonsong 0.256 0.325 Pakchong 0.773*** 0.283 0.252 Pakchong 0.366 0.327 Pakthongchai 1.358*** 0.268 0.481 Pakthongchai 0.364 0.382 Phimai 0.719*** 0.250 0.231 Phimai -0.033 0.317 Res (Farm
Income) 2.61e-06 1.75e-06
Lambda -0.271 0.268
Wald Chi2 170.92***
Observation 517
Wald test of Exogeneity
Chi2 1.52
P-Value 0.217
Note: *, **, and *** Indicate significance level at 10%, 5%, and 1%, respectively.
Inp_Acc is used as selection instrument variable for this model.
Table 5. Determinants of Participation in AC Output Marketing Service Using Heckman Two-Step Method
Participation Log Output Amount
Coeff R.S.E MEs Coeff R.S.E Change
in Y (%)
Constant -3.111 0.769 7.549 1.146
Individual Characteristics Household Characteristics
Age_36-45 0.098 0.415 0.038 Paddy 0.038 0.339 4
Age_46-55 -0.326 0.407 -0.116 Cash_Crop -0.197 0.367 -18 Age_56-65 0.024 0.400 0.009 Orchard -0.858** 0.355 -58 Age_over 66 -0.317 0.441 -0.113 lgFarm_Size 0.645*** 0.134
Household Characteristics Commerce 1.603** 0.725 397
Farm_Mem 0.155* 0.086 0.057 HHinc_2 -1.270** 0.624 -72
Cre_use 0.450* 0.250 0.166 HHinc_3 -0.258 0.425 -23
Farm_Size 0.018*** 0.006 0.007 HHinc_4 -0.239 0.372 -21
HHinc_5 0.0427 0.464 4
Farm_Squarq -7.8e-05** 0.000 -3e-05 Perception toward Risk and AC Performance HHinc_2 -0.843* 0.470 -0.260 Cons_Change -0.133* 0.070 -12 HHinc_3 -0.02 0.364 -0.009 AC Marketing Attributes
HHinc_4 -0.14 0.325 -0.054 lgAC_Dist 0.154 0.127 HHinc_5 -0.04 0.390 -0.014 lgAC_P -1.139*** 0.235
Perception toward Risk and AC Performance ACothsale -0.392** 0.182 -32 Fin_Risk -0.184** 0.080 -0.068 Geographical Factors
Cons_Change -0.13** 0.059 -0.049 Dankhuntod 0.670 0.502 96
Out_Acc 0.397*** 0.087 0.146 Nonsong 0.760 0.463 114
AC Marketing Attributes Pakchong 0.849 0.591 134
ACdistance -0.019** 0.007 -0.007 Pakthonchai 0.698 0.611 101
Res(Farm_Si) 0.002 0.006 Phimai 0.944* 0.516 157
Mills Lambda -0.095 0.308
Wald Chi2 208.77*** Observation 465
Wald Test of Exogeneity (Chi2) 0.000
P-Value 0.946
Note: *, **, and *** Indicate significance level at 10%, 5%, and 1%, respectively.
Out_Acc is used as selection instrument variable for this model.
The findings show that the AC members’ decisions to purchase farm inputs and sell farm products through ACs are highly significant influenced by perception of the ben- efits, farm size, and distance to ACs. The probability of participation in marketing sig- nificantly increases with household attitude as to whether ACs help them access inputs (Inp_Acc) and output markets (Out_Acc). Farmers, who are in a stronger position, be- lieve that ACs assist them to reach materials of good quality and reasonable price and support them to access modern technology and inputs, are more likely to purchase farm materials from ACs. Similarly, farmers selling their products to ACs have higher level of belief that ACs provides good farm product prices compared to non-participants.
As expected, the effects of farm size on participation in AC input and output mar- keting service are positively significant, consistent with Fischer and Qaim (2011,2012), Ma (2016), Mensah et al. (2012), Muthyalu (2013), and Zhang et al.’s (2017) studies.
Farm size relates to household wealth and farm production scale. An increase in farm size by one hectare raises AC members’ probabilities of joining in both AC input and output marketing services by 0.2% and 0.7%, respectively. However, this will trend down when farm size reaches a certain level. This findings confirm the middle-class effect that is middle class farmers have the greatest probability of access to AC market- ing services (Fischer and Qaim, 2012).
The effect of AC distance on AC output marketing participation is consistent with Alelne et al. (2008), Chagwiza et al. (2016), Muthyalu (2013), and Winter-Nelson and Temu (2005). Since distance to ACs refers to transaction cost, it reduces the returns of farm production (Winter-Nelson & Temu, 2005). Therefore, the probability of partici- pation in AC output marketing service decreases with distance from farm to ACs.
The level of participation in AC input marketing service in this study is measured by input expenditure. Input expense includes cost for seed, saplings, fertilizer, and pesti- cide. The level of participation in AC output marketing is measured by quantity of farm products sold to ACs. The input expenditure through ACs is highly related to farm size, family size, household income, and buying on credit (ACInp_cre). While, farm product quantity sold to ACs is highly affected by crop type, farm size, household income, com- mercialization, and output price offered by ACs. The findings show that the amount of farm products sold to ACs seems to be less for orchard farmers since ACs in our study do not buy vegetable and fruits from members.
Farm size has positive effects on input expense and sale amount through ACs. If farm size increases one percent, farmers will spend more money to buy inputs from AC around 59% and farmers will sell more outputs to ACs around 65%. Regarding house- hold income, households in every income group except the rich farmers (HHinc_5) have negative effects on input expenditure. The results indicate that these farmers pur- chase inputs from ACs less than the poor farmers staying under poverty line (based group). Similarly, households staying slightly above Thai national poverty line income (HHinc_2) sell outputs to ACs less than the poor income farmers by 52%.
In addition, level of marketing participation depends on AC service attributes. The percentage of input expense through ACs significantly increases with input purchasing on credit. Input expense of farmers who buy inputs from ACs on credit is greater than those not using credit by 25%. The relationship between quantity of products sold to
ACs and their price received from ACs is negative. When price of farm product in mar- ket increases, ACs have to offer higher prices to induce members to sell their products to ACs. However, ACs confront fund scarcity and their funds are limited, thus ACs can buy less quantity of farm products from their members.
5 Conclusion
This study explores the determinants of household participation in AC and AC services:
credit and marketing services in Thailand. The estimated results indicate that ACs play a significant role in assisting middle-class households to access credit and markets.
Households’ farm risks and their attitude toward benefits obtained from AC services have significant effects on their decisions to participate in AC services. Moreover, at- tributes of AC services significantly determine level of participation in AC services.
Our findings confirm that households’ decisions to be AC members are positively influenced by the following factors: crop type, having relatives in ACs, credit use, ac- cess to agricultural extension services, financial risk, and believe in AC support for credit access and market information. In contrast, the choice regarding AC participation is negatively affected by gender, orchard farm, and production risk. Among the signif- icantly statistical factors, AC relatives, credit use, access to agricultural extension ser- vices, production risk, financial risk, and expectation of credit benefit from ACs strongly influence AC participation. These factors suggest that social network has an important role to induce farmers to become AC members. The AC members confront liquidity constraints and have high expectations that AC can support them to obtain credit. Moreover, the middle-class farmers who face low production risk and access agricultural extension services are more likely accepted to be AC members. According to the results, we may conclude that households want to participate in ACs since they need ACs to support them in credit.
The determinants influencing AC credit participation include farm size, access to agricultural extension services, household perception on AC effectiveness, distance to the nearest market, being member of other associations, farming experience, farm asset ownership, and access to other credit sources. According to the findings, we can con- clude that ACs play a significant role in rural credit market since they are the compli- mentary credit lenders for households who are not able to borrow from formal credit providers. The findings indicate that households who are not members of other groups and cannot access other credit sources are more likely to borrow loans from ACs. ACs support the low income farmers to access to credit by applying group lending. Those farmers cannot access formal credit due to low asset ownership. Loan amounts are sig- nificantly dominated by AC credit attributes: AC share, loan duration, and property as collateral. Households who have higher AC share values and provide some properties as collateral are more likely to obtain greater loan amount.
Similarly, AC members’ decisions to participate in AC marketing are significantly influenced by a household’s perception of the benefits gained from AC marketing ser- vices and farm size. Households participating in AC marketing services believe that ACs assist them to reach the good quality inputs with reasonable prices, and provide
them with reasonable prices for farm products. The middle class farmers have the great- est probability of access to AC marketing services since the probability of AC market- ing participation increases with farm size but decreases with farm size squared. In con- trast, AC marketing participation is negatively influenced by distance to ACs. Regard- ing participation level in AC marketing services, the input expenditure and product quantity sold through ACs is determined by household characteristics, such as farm size, family size, household income, and AC marketing attributes such as, buying inputs on credit and AC price of output.
The results of this study provide some policy implications with respect to improving household participation in ACs and their services. First, high production risk is an im- portant obstacle for farmers to be an AC member. In addition, households participating in AC credit tend to have high financial risk. To reduce household risks on production and financial capital, ACs might extent training services targeting both agricultural ac- tivities and off-farm activities to develop households’ skills, such as training on-farm product processing to add value on their farm products and training other skills to in- crease opportunity to generate income. This is essential for building up farmers’ capa- bility to mitigate farm risks and increase their opportunity to participate in ACs. Second, ACs might reduce farmers’ transaction costs and reduce the fluctuation of output prices to motivate members to join in AC marketing services. Increasing in distance to ACs tends to reduce the household probability of participation in AC marketing services.
AC network creation may reduce transportation costs and increase AC bargaining power. Both vertical and horizontal integration networks should be promoted. A verti- cal integration network with other stakeholders in the supply chain may reduce trans- portation costs for AC members. For example, making contract with the input whole- salers leads to lower input prices and transportation cost. Since wholesalers may have several distributors in the cities, AC members can take inputs from the closest distrib- utors. AC horizontal integration networks among ACs which produce the same prod- ucts reduce production costs and increases bargaining power.
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