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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 RAPHAEL
3, ALIOU DIAGNE
4
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: [email protected]
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 doesn’t 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-R², 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
household’s 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
household’s 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, doesn’t 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
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