Rice is an important crop in West Africa and globally. However, local rice in West Africa is viewed as a second choice among consumers as they prefer imported foreign rice because of perceived quality. From the supply side, there are problems with post-harvest losses and lack of processing. To overcome this problem related to quality and to reduce the post-harvest losses on the supply side, a novel parboiling technology, called GEM (grain-quality enhancer, energy-efficient durable material) technology, was developed and has since been disseminated to some farmers in Benin, Ivory Coast, Togo, and Nigeria. However, like most technologies in Africa, its adoption remains low.
There are multiple reasons that have been advanced for low adoption of technologies, among them is the lack of information, inappropriate technology, credit constraints, risk averseness of the farmers, lack of accompanying inputs, and institutional factors such poor roads that make these technologies less profitable. Incentives to encourage adoption have been recommended in literature and shown to have a long-lasting impact on the use of technologies. However, what type of incentives and where to place to have the maximum behavioural impact (uptake) remains heavily unexplored. In this study, our goal is three-fold. Firstly, we want to compare the effectiveness of a price-matching incentive and a cost-saving (transport coupon) offered to randomly selected group of rice-parboilers on adoption. We expect that credit constrained households will respond more to the cost-saving incentive compared to the price-matching incentive. Secondly, we want to test if encouraged use is correlated with adoption by offering varying amounts of incentives. The hypothesis is that those who experience the technology more under an incentive will use it more after the incentive period as they would have learned and overcome the imperfect information barrier. The last objective is to understand the impact of using the technology on profit, and other household livelihood outcomes.
To achieve these objectives, we select a sample size of 690, a third in the price matching incentive and a third in the cost coupon while the last third is in the control (i.e. not encouraged to adopt). Because there are few actors along the higher end of the agricultural value chain, we do a census of all parboilers in the selected sites (Gagnoa and Boauke in Ivory Coast and Lafia in Nigeria). The sample size is calculated and selected using profit as the main variable even though we wish to understand how the incentives affect adoption, the sample size required for the latter is generally lower than the sample size required to understand the impact of adoption on rice income.
We envisage the use of an instrumental variable approach in the estimation of the impact of the adoption of GEM on rice profit/income with two instruments used in the first stage regression-- categorical variable for the type of incentive (including none), and a continuous variable for the value of the incentive. We will further employ more detailed models to understand the relationships between the types of incentives and adoption like including measures of access to credit, perception of costs, and market.