Contract farming preferences by smallholder rice producers ... farming... · Contract farming preferences…
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Contract farming preferences by smallholder rice producers in Africa: a stated choice model using mixed logit AMINOU AROUNA1,*, PATRICE ADEGBOLA2, BABATUNDE RAPHAEL3, ALIOU DIAGNE4 1 Africa Rice Center (AfricaRice), Cotonou, Benin 2 Agricultural Research Institute of Benin (INRAB), Cotonou, Benin 3 University of Ilorin, Nigeria 4 University of Gaston Berger, Saint-Louis, Senegal * Corresponding author: firstname.lastname@example.org Abstract In developing countries, smallholder farmers face many constraints including lack of information, access to credit and to markets. To overcome these constraints, resource-poor farmers can engage in contract farming. However, contracts farming need to meet farmers demand in order to be sustainable. This study aimed to analyze the preferences of rice farmers for agricultural contracts in Benin. Stated choice data were collected from 579 rice farmers. In order to account for heterogeneity, data were analyzed using mixed logit model. Results showed that producers preferred contracts under the following terms: short term contract (one season), payment at delivery, group selling and having processor as partner. However, the contract preference is different for men and women. The study suggests that this difference and the attribute of preferred contract need to be taking into account for the design of best-fit contract farming by rice value chain actors and policy makers in Sub-Saharan Africa. Keywords: Rice, stated preferences, contract farming, mixed logit, Benin. JEL codes: C5; C90 2 1. Introduction In Sub-Saharan African (SSA) countries, smallholder farmers face many constraints including lack of information, access to credit and to markets. Smallholder farmers are subject to market access problems, and as consequence they receive relatively low prices for their produce. Both market information and markets themselves are not accessible to the rural poor and farmers capture little of the value that they create and transaction costs for rural products are very high. In addition, market risk in terms of fluctuating prices is a great problem concerning smallholders in SSA countries. To overcome these constraints, resource-poor farmers can engage in contract farming. Under contract farming, farmers usually agree to deliver specific commodities in predetermined quantities and to meet predetermined quality standards, while contractors agree to provide production support (e.g., supply of input and provision of technologies) and accept products at predetermined prices. It is suggested that contrat farming can help to remove market imperfection in products, capital (credit), land, labor and information and can also help in reducing transaction costs (Key and Runsten, 1999). Linking farmers, processors and marketers through contracts farming in SSA has become an important challenge to positively impact the economic well-being of rural communities. At the farm level, contracts farming can play important role by dealing with many smallholder farmers constraints related to problem of access to production resources (inputs, services, and information) and access to outputs markets. Thus, contract arrangements can be seen as an institutional solution to the problems of market failure (Key and Runsten, 1999). Although contrat farmings are comon with cash crops in developping countries, they are limited in food crop production. In the rice value chains, farmers, traders, processors and supermarket buyers can use various types of contracts farming to respond to market requirements. The contracts could be of three types: (i) procurement contracts under which only sale and purchase conditions are specified; (ii) partial contracts where only some of the inputs are supplied by the contracting stakeholders to the farmers and produce is bought at pre-agreed prices; and (iii) total contracts under which the contracting stakeholders supplies and manages all the inputs on the farm and the farmer becomes just a supplier of land and labour. Different models of contract among value chain stakeholders may work differently depending on the context (crop, institutions, stage in the rice value chain, stakeholders, etc.). To be sustainable, contract farming 3 needs to benefit each party. Resource-poor farmers will not engage in any potential contract farming if it doesnt meet with their preferences. Both farmers and contractors will consider the risk return trade-offs of each model of contract. Farmers choice may also depend on the risk attitude and financial positions. The following research questions can be raised: what are the motivations for contracting? What are the factors acting as incentives and impediments to the participation of rice producers to contract farming? Is there any gender difference for contract farming preferences by rice farmers? This paper addressed these questions by analyzing the preferences of rice farmers for contract farming in Benin. Few authors have explored the field of contract farming in general and particularly those relating to food crops (Lajili et al., 1997; Fafchamps and Gabre-Madhin, 2006). More importantly, none of these studies have addressed contract farming for rice value chains in Benin. In addition, we use a choice model based on stated preference data collected from male and female from the same household to analyze intra-household preference for contract farming. Stated preference is a research technic in which information about decision makers preference is elicited by using speciffically designed hypothetical situation. Hence, data generated in stated preference survey are derived from experiments which is the main difference from an analysis of revealed preferences. There are various reasons why a stated preference study may be preferred to an analysis of revealed preference. In this survey, the main reason is the limitation of real contrats on rice production in the study area. Therefore, stated choice method offers a great opportunity to estimate demand for new potential contracts. State choice method was originally developped in marketing research and has been applied in contract (Lajili et al., 1997). The remainder part of the paper is divided into in three sections. Section 2 presents the methodology used while results are shown in section 3. The conclusion and recommendations are in section 4. 2. Methodology 2.1.Estimation of stated preference for contract farming It is well known that farmers preference exhibit substantial heterogeneity. In choice modeling, adequate modeling of heterogeneity is important for many reasons. Most obviously, estimates of own- and cross-price elasticities of demand may be severely biased if one does not properly 4 account for preference heterogeneity. In order to elicited rice farmers preference for contract farming, the choice modeling approach with heterogeneity is used. The conceptual foundation of the choice model is the theory of random utility. The random utility theory states that consumer preferences are latent and unobservable (Manski, 1977; McFadden; 1974; Blandon et al., 2009). The latent value of utility of an individual n associated with a contract farming j, , can be expressed as a function of two components: an observable systematic component , and a random component which comprises unobservable part. The utility function is: (1) The systematic utility is presumed to be a function of various predictors that can be formulated as a generalized regression function (Ben-Akiva and Lerman, 1985) given by: (2) If is independently and identically distributed, the probability that the contract j is chosen from the set of J potential contracts (and dropping reference to individual n for simplicity) is the standard multinomial logit model (MNL) and can be expressed as follows: (3) The multinomial logit model (MNL) is for many years a fundamental basis for the analysis of discrete choice. Due to several shortcomings of the basic of the MNL and especially its inherent assumption of independence of irrelevant alternatives (IIA), researchers have developed a variety of alternative models. In addition, the MNL does not allow for unobserved preference heterogeneity. To avoid IIA assumption and take into account unobservable preference heterogeneity in , one commonly used method is the random coefficient specification of the mixed logit model (MXL) which extend the basic MNL model as follows (McFadden and Train, 2000): [ ] (4) Here is the vector of mean attribute in the population whereas is the vector of individual specific attributes deviation from the mean. is the vector of covariates. The parameters of the mixed logit can be estimated with simulated maximum likelihood (McFadden and Train, 2000). 5 Empirically, this model has been used in a number of studies of choice experiment (e.g. Bartels et al., 2006; Brownstone and Train, 1999; Hall et al., 2006; McFadden and Train, 2000). Based on this model, the generalized multinomial logit (GMNL) has been introduced recently by Fiebig et al. (2009). The GMNL allows to account for both preference and scale heterogeneities. However, Greene and Hensher (2010) showed that in absence of scale heterogeneity, GMNL is equivalent to MXL. In addition, failure to account for scale heterogeneity may be of such great empirical consequence in respect of behavioral outputs such as direct elasticity and willingness to pay. Using Akaike Information Criterion (AIC)1, the MXL model fits better the empirical data analyzed in this paper. The estimated parameters of the MXL model are relatively easy to analyze in terms of their marginal effects, which measure the change in the probability of an event given a unit change in one explanatory variable, keeping constant all other variables (Liao, 1994; Louviere et al., 2000). The marginal effects are the partial derivatives of the probability of the event. However, in the case of categorical variables, these marginal effects are represented by the difference in the predicted probability of each category (Greene, 2000; Liao, 1994). A common measure of goodness of fit in choice models is the pseudo-R, which is estimated as: Where LLF is the log-likelihood of the model and LL0 represents the log-likelihood of the function with only the interception (Lattin et al., 2003). 2.2.Method of data collection and empirical model Two methods exist for data collection on consumer preferences: method based on revealed preference and method based on stated preferences (Lajili et al., 1997). The revealed preference method is used in real situations or conditions experienced by consumers. The questions are therefore asked to the respondents so as to appeal to their memories and respondents reveal what they did. In contrast, surveys based on stated preferences are based on hypothetical situation. In this case, each respondent must declare a choice he would do if he was confronted to it in reality 1 AIC = 2k-2Ln(L) where k is the number of parameters in the model, and L is the maximized value of the likelihood function for the estimated model. The lower the value of AIC, the better the model is. 6 (Damien, 2011). The use of stated preference method has increased significantly in the agricultural and food economy, environment and resources, health economics, trade and marketing since the last decade (Louviere et al., 2010). Stated preference method has the advantage of testing the consumers preference before the release of a product to the market. In that respect, stated preference is used in this study to analyze which types of contract farming are more likely to be adopted by farmers. The data was collected in the south and central parts of Benin. These areas have been subject of numerous studies on rice production given the volume of rice production in the country. The study focused on a sample of rice farming households which were randomly in the rice sector development hub in the south and central parts of Benin. Two household members (husband and wife) were interviewed in 2014, in order to analyze intra-household and gender differentiation for contract preference. In total, 579 rice farmers were surveyed in 38 villages. Data were collected through a structure questionnaire comprising two parts. The first part was used to collect socio-economic and demographic characteristics of producers. The second part focuses on experimental choice on preference of contract farming for rice production. In the experimental design, attributes and attributes levels that might be important for farmers and which may influence in the real world the contract between rice producers and other rice value chain actors are selected. In total, ten attributes are selected: type of partner, length of contract, credit, type of organization, quality agreement, control over production activities, price of rice, agreement on quantity, method of payment and moment when the agreement is signed. Following the choice model described above, the empirical model estimated is: With is the utility associated with the choice made by the producers, are parameters to be estimated. The definition of the explanatory variables is in Table 1. Two attributes (PRICE and QUALITY) have each three levels, while other eight attributes have each two levels (Table 1). A combination of attributes and their levels involves a total of 2304 alternatives. Given 7 that it is impossible to evaluate such number of contract in reality, a fractional orthogonal design was used to select potential contract for evaluation by rice farmers (Louviere et al., 2000). Indeed, 16 hypothetical contracts were selected. The 16 alternatives were divided into four groups each comprising five choice alternatives. The first four were taken from 16 orthogonal alternatives selected and the fifth identically assigned to all groups is the alternative with the lowest of the four alternatives orthogonal group levels: alternative specific constant (ASC) or the "status quo" situation with no contract farming for rice production. Table 1: Description of variables No Attributes Description Values Expected sign 1 TYP_PARTNER Type of partner 0=Trader 1=Processor +/- 2 CONTRACT_LENGTH Length of contract 0= one season 1= Long term (Two or three seasons) +/- 3 CREDIT Granting of credit 0=No 1=Yes +/- 4 TYP_ORGANIZATION Type of organization 0=Individual 1=Group +/- 5 CONTROL Control over the production activities 0=No 1=Yes - 6 AMOUNT Agreement on quantity 0=No 1=Yes - 7 MOMENT_ENGAGMT Moment of reaching agreement 0=Before sowing/planting 1=After sowing/planting -/+ 8 MOMENT_PAYEMNT Payment mechanism 0=Immediatly after delivering 1=Two weeks after delivering - 9 QUALITY Agreement on quality 0=No 1=Yes 2=Yes with premium +/- 10 PRICE Rice price 0=10% less than market price 1=Market price 2=10% more than market price +/- 8 3. Results and discussion 3.1.Experience with contract farming Results showed that contracts farming for rice production are not well developed in the study area. Indeed, amongst all rice producers interviewed, only 7.47% and 8.90% of women have made contract farming for rice respectively in 2011 and 2012 (Table 2). These values are also low for men and represent 9.73% and 10.74% in 2011 and 2012, respectively. The adoption rate of contract farming is also low for the year 2013. The trend showed that the percentage of rice producer engaged in contract farming is decreasing in 2013. This can be explained by the fact that existing contract farming models are not compatible with smallholders preferences. Therefore, new schemes adapted to socio-economic conditions of smallholder farmers need to be developed. Table 2: Distribution of agricultural producers concluded contract in the past three years 2011 2012 2013 Number Percentage Number Percentage Number Percentage Male (N=298) 29 9.73 32 10.74 24 8.05 Female (N=281) 21 7.47 25 8.90 16 5.69 Total 50 8.64 57 9.84 40 6.91 3.2.Estimation of model for the rice farmers preference for contract farming Three models (multinomial logit (MNL), generalized multinomial logit (GMNL) and mixed logit (MXL)) were tested during the analysis. Using Akaike Information Criterion (AIC), the MXL model fits better the empirical data analyzed in this paper. In addition, the coefficients of standard deviations of MXL for men and women are large and significant. Therefore the MXL model is more robust than the MNL model, and thus produces better quality estimations. This result confirms the presence of the preferences heterogeneity for contract farming among rice farmers in Benin. Results of the mixed logit estimation for men and women are presented in Table 3. Estimation of the mixed logit for men confirms that, with the exception of the quality attribute, the coefficients of the average of all attributes are statistically significant. Similar results were obtained for women model in which the coefficients of the average of all attributes are statistically significant except for "type of organization" and "the agreement on quantity of rice". In both models, the mean of variables which the coefficients are statistically significant are: the type of contractor desired by the farmer, the duration of the contract, the agreement on 9 rice price, the credit provision, the agreement on monitoring of production activities by the contractor, the agreement on the quantity to deliver, the moment when the contract will be signed and the moment when the farmers will be paid. Table 3: Estimation of the mixed logit model for men and women VARIABLES Estimation for men Estimation for women Coef. Std. Err. Marginal effect Coef. Std. Err. Marginal effect PRICE 0.349*** 0.029 -- 0.371*** 0.033 -- TYP_PARTNER -1.497*** 0.270 -0.079 -1.462*** 0.306 -0.075 CONTRACT_LENGTH -1.584*** 0.326 -0.072 -1.69*** 0.413 -0.075 CREDIT -2.060*** 0.308 -0.093 -2.475*** 0.364 -0.107 TYP_ORGANIZATION 0.438* 0.259 0.021 0.328 0.287 0.015 NQUALIT2 1.883*** 0.324 0.077 2.440*** 0.361 0.096 NQUALIT3 29.669 6882.18 0.759 27.486 3793.86 0.754 CONTROL -0.615*** 0.191 -0.000 -0.923*** 0.207 -0.000 AMOUNT 0.545** 0.260 0.001 0.402 0.300 0.002 MOMENT_ENGAGMT 0.922*** 0.265 0.000 1.112*** 0.316 0.000 MOMENT_PAYEMNT 3.174*** 0.409 0.02 2.497*** 0.370 0.023 Standard Error of the random variables TYP_PARTNER 0.602*** 0.222 0.022 0.266 CONTRACT_LENGTH -0.189 0.3722 0.150 0.310 CREDIT 0.093 0.322 0.237 0.371 TYP_ORGANIZATION -1.130*** 0.263 1.197*** 0.296 NQUALIT2 2.250*** 0.353 1.719*** 0.353 NQUALIT3 0.243 7378.43 0.035 3634.49 CONTROL 0.266 0.290 -0.186 0.359 AMOUNT 0.083 0.4098 -0.166 0.635 MOMENT_ENGAGMT 0.119 0.540 -0.371 0.337 MOMENT_PAYEMNT 2.079*** 0.373 1.397*** 0.351 Number of observations= 5860 Log likelihood = -668.576 Prob >chi2 =0.0000 LR chi2 (df=10) = 67.66 Number of observations= 5860 Log likelihood= -572.568 Prob >chi2= 0.0040 LR chi2 (df=10) = 25.82 *** significant at 1% ; ** significant at 5% ; * significant at 10%. The coefficients of standard deviations showed that there is heterogeneity in preference for male for four attributes: the type of contractor, the type of organization, the quality agreement with premium and the moment when the farmers will be paid. For female, the coefficients of standard deviations are significant for three attributes: the type of organization, the quality agreement with 10 premium and the moment when the farmers will be paid. These results confirmed heterogeneity in the contract preference by rice farmer and showed that the preference is different for male and female rice producers. In addition, the sign of the coefficients of each attribute indicated how the attribute influences rice farmer decision to participate in contract farming. The price is often the first parameter discussed in a contract between two parties. The coefficient of the attribute on the agreement of the rice price is positive and significant at the 1% level for both men and female. This indicated that contracts with higher prices increase the probability for farmers to enter into a contract with a partner for the production and sale of rice. Indeed, high price will increase farmers income. In addition, high price will allow the rice farmer to reduce the uncertainty associated with changes in the market price at the deliver time. The coefficient of the variable Type of partner is negative and significant. This means that rice farmers would like to make contract with processors instead of traders. Indeed, farmers are considered traders as intermediate and when to deal directly with processors in order to gain all profit from their rice. It could also be that farmers have traders as partners for long time and want to change. In addition, rice farmers preferred contract under which they will receive their money with no delay, i.e. the contract will pay upon receiving the products. The coefficient on the variable "duration of the contract" is significant at 1% and negatively related to the participation to contract farming. The marginal effects are estimated at -0.072 and -0.076 for men and women, respectively. This implies that a contractual agreement whose duration is two or three seasons reduces the probability of rice farmers participation by 7.2% and 7.6% for men and women, respectively. This implies that rice farmers prefer short duration contract, namely contract for each growing season. This preference could be explained by the fact rice farmers are risk averse. By engaging in short duration contract, farmers will have the possibility to withdraw from the contract at any time if the contractor does not fulfill the agreement. However, one can argue that long term contract may offer market guaranty to farmer and allows them to plan for long term investment in rice production. The agreement on the quantity of rice to deliver appears also to be an important aspect for male farmers, as indicated by the coefficient of the variable "agreement on the amount of product to deliver" which is positive and significant at 5% level. This shows that agreement on rice quantity 11 is important for men to participate in a farming contract. On contrary, the agreement on quantity seems not to be a major factor for women to be involved in contract farming. This difference between male and female can be explained by the fact that men in the study area are used to sell together the harvested rice which can help them to have money for investment on production or households equipment. On the other hand, for the security of food for the household, women prefer usually to sell the production in several parts. This strategy allows women to sell rice when they need money and to have safe quantity of rice to assure the consumption of the households members. Similarly, the coefficient of the variable Type of organization showed that men want to make the collective contrat (through group of rice producers), while the form of contract, either individual or collectively, doesnt seem to be an issue for women. The coefficient of the variable credit is negative and significant for the model of men and women. The marginal effects are estimated at -0.09 and -0.11 for men and women, respectively. This result means that credit has negative effect of the contract farming adoption by rice farmers. This is contrary to our expectation. However, this result can be explained by the fact that rice farmers are not use to taking credit from a contractor. Alternatively, this result can also be explained by the fact that rice production in the survey area is mainly rain-fed which is subject to high climatic risk. Therefore, rice farmers may be risk averse and avoid taking credit for an activity which is highly related to climatic variability. However, due to the importance of credit especially to guaranty the quality of paddy rice, it is important to find out conditions under which rice farmers are willing to take credit for contract farming. One condition might be the introduction of agricultural insurance. Indeed, agricultural insurance may help farmer to reduce the climatic risk and encourage them to take credit for rice production. 4. Conclusion The study analyzed contract farming preferences and heterogeneity for rice producers in Benin. Results showed that there is heterogeneity in producer preferences for contracts farming. Producers generally preferred contracts under the following terms: short term contract (one season), payment at delivery, group selling and having a processor as a partner. In both men and women models, the variables which the coefficients are statistically significant are: the type of contractor desired by the farmer, the duration of the contract, the agreement on rice price, a 12 credit provision, the agreement on monitoring of production activities by the contractor, the agreement on the quantity to deliver, the moment when the contract will be signed and the moment when the farmers will be paid. However, there is difference in the preference for male and female. For instance, the agreement on rice quantity is important for men to participate in a farming contract which seems not to be a major factor for women. The study suggests that these attributes of contract and this difference needs to be taking into account for the design of best-fit contract farming for rice policy in Sub-Saharan Africa. 13 References Bartels, R., Denzil, G., and van Soest, A. (2006). Consumers and experts: An econometric analysis of the demand for water heaters. 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