A sample of rural smallholder households will be randomly drawn from the population of maize farmers in two aflatoxin-prone counties in upper eastern Kenya, Meru and Tharaka-Nithi. Working closely with local extension, we will identify maize-producing sub-counties, wards, location, and sub-locations in the two counties. Then, we will randomly select 60 sub-locations from within these. Lastly, we will randomly select 4 villages from complete village lists of each sub-location, that will generate 240 villages in total. Sub-locations will serve as randomization blocks (i.e., one village within each sub-location will be randomly assigned to each of the three treatments and one village will be assigned to the control group in each block). Aflatoxin levels in maize depend on climate conditions and the contamination levels can vary significantly between different geographical areas. Sub-location is the lowest administrative unit in Kenya, after village. Therefore, using sub-locations as blocking factor reduces the impact of geographical characteristics as a source of variability in our dependent variable.
The assignment to input treatment groups will be randomized at the village level. The 4 villages from each sub-location will be randomly assigned to one of four groups (1 village to each group): a control (C) group and three treatment groups, including (T1) pre-harvest input only; (T2) post-harvest input only; (T3) pre- and post-harvest inputs. In this design, sub-locations (lowest administrative unit after village) serve as blocking factors.
After villages have been randomly assigned to control or treatment groups, we will work with village elders to obtain up to date household lists for each village. We will then randomly select 8 dual-adult households from each village to participate in the study. All eight households in each treatment village will receive the relevant input(s), a training on its (their) use, and aflatoxin training. In control villages, eight households will be surveyed at the same interval as in the treatment villages, but they will not receive inputs nor training.
In addition to the main research question on the (cost) effectiveness of pre- and post-harvest inputs, we will also study the effect of gendered input and training allocation on aflatoxin levels in maize, knowledge retention, and diffusion of information within a household. Therefore, orthogonal to random assignment to treatment at the village level, we will randomly determine whether a female or a male adult member from each sampled household (all of which will be dual-adult households) will receive the input(s) and training.
Before the second follow-up survey, we decided that we wanted to understand more about the demand for Aflasafe. Therefore, we will add a Becker-Degroot-Marschak (BDM) auction at the end of the second follow-up survey. The BDM auction gives farmers the chance to purchase a 2-kilogram bag of Aflasafe, which is how the product is currently packaged for sale in Kenya. Participants are also given evidence from the first round of the follow-up survey, that based on data from fellow farmers in the study, those households who received Aflasafe at harvest were on average 10 percentage points less likely to have an unsafe level of aflatoxin in their maize at time of harvest than were those who did not receive Aflasafe.
The potential bids for Aflasafe in the BDM auction range from Kenya Shillings (Ksh) 250 to Ksh 550 with a uniform distribution around the market price of Ksh 400. The bids increase in increments of Ksh 50. The participants will play two practice rounds of the BDM auction where they bid for cookies and pens before they bid for Aflasafe. The participants are given Ksh 600 for their maize samples at the beginning of the survey. This will potentially alleviate any credit constraints around purchasing the Aflasafe that participants may experience. However, the payment is not tied to the auction and participants are under no obligation to buy the Aflasafe.
The objective of this BDM auction is to answer the following questions.
1. What percentage of farmers in the sample are willing to pay at or above the market price of Ksh 400 for 2 kilograms of Aflasafe?
2. Is there a difference in willingness to pay for Aflasafe between participants who were randomly chosen to receive Aflasafe for free in the previous season and those who were not selected to receive it for free in the previous season?