Experimental Design
The aim of this research is to examine if access to credit increases the extent of smallholder farmers’ adoption of modern technologies in agriculture and ultimately farm productivity and income. To do so, we will conduct a randomized controlled trial design in rural part of Ethiopia as follows.
We will choose to districts (Woredas) in Oromia Regional State, the largest and most populous state in Ethiopia. In Ethiopia for administrative purposes, regions are subdivided into districts (Woreds). The districts (Woreds) are sub divided into Kebeles and Kebeles into villages (groups). Rural Villages comprise up to 32 households and on average there are 24 villages in a kebele. Farming is the primary sources of income for rural households in Ethiopia in general.
We first identify farmers who meet the new criteria for getting credit from financial institutions in randomly selected villages in the two districts and register them. Second, inform farmers in the selected villages about the availability of financial credit and the minimum criteria to get the credit. Then after, we ask those who meet the minimum criteria and willing to take the financial credit to go to the nearest agricultural extension service center and get registered by providing all the necessary documents (e.g., land ownership certificate, etc.). Households in selected villages will be contacted through their respective village representatives (all villages have formal representatives who are accountable to kebele managers). Next, we obtain list of farmers who are willing to take financial credit and assess their eligibility for the credit against the minimum criteria set by financial institutions providing it.
After obtaining lists of willing and eligible farmers from each selected villages, we randomly assign the villages into treatment and control groups. Households who are in treatment village will be provided financial credit while households in the control group will not receive anything. That is, we cluster households at village levels.
Prior to assigning villages to treatment or control groups, we will collect baseline data from each village regarding population, land size, weather condition, main crop, soil type, distance from city (town), technology adoption history, credit history, etc. The baseline information is vital to understand the status quo and make sure that the two groups are balanced so that the two groups are different only in terms of access to financial credit. In addition, we will gather baseline information at household level in both treatment and control groups using indicators such as household size, technology adoption history, land size, livestock size, gender of head of a household, credit history, education, etc.
Having randomly assigned the villages to treatment or control groups, and collected baseline data, we provide credit to selected households in the treatment villages through financial institutions (Commercial banks), our implementing partners. The amount of credit will be decided depending on several factors such as the size of collateral presented by the credit applicant (in this case the smallholder farmer), amount requested by the applicant, and credit ceiling set by the borrowing institution.
After one year of financial credit provision to the treatment villages, we undertake end-line survey to collect data on our outcome variables including extent of adoption of technologies in agriculture and farm productivity, and income from both treatment and control villages.