Evaluating the impact of alternative interventions to improve adoption of digital innovations by smallholder farmers in Egypt.

Last registered on December 21, 2023

Pre-Trial

Trial Information

General Information

Title
Evaluating the impact of alternative interventions to improve adoption of digital innovations by smallholder farmers in Egypt.
RCT ID
AEARCTR-0012723
Initial registration date
December 19, 2023

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
December 21, 2023, 8:01 AM EST

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Locations

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Primary Investigator

Affiliation
International Food Policy Research Institute & Wageningen University

Other Primary Investigator(s)

PI Affiliation
Wageningen University
PI Affiliation
International Food Policy Research Institute (IFPRI)

Additional Trial Information

Status
On going
Start date
2023-07-01
End date
2024-11-30
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Digital innovations could facilitate access to markets and smallholder commercialization through the following ways: (1) reduce communication and information costs; (2) improve farmers’ knowledge and know- how about market options and prices; (3) improve access to input and output markets; (4) enable and build social networks and connections; (5) facilitate the delivery of other services associated with agricultural markets such as credits and finance; (6) improve management of input and output supply chains; (7) increase communication linkages with other stakeholders involved in agricultural marketing. Despite these advantages that digital innovations could offer, their adoption remains low and heterogenous, especially in Africa, where agricultural markets remain underdeveloped (e.g., Abate et al., 2023; Aker and Cariolle, 2023). Despite the proliferation of digital tools targeting smallholder farmers in recent years in Africa, the vast majority of these remained at pilot stages, with limited evidence of successful scaling and limited impacts to transform agricultural markets.

There exists several demand and supply-side factors that may explain the low adoption of digital innovations and associated heterogeneities across smallholder farmers in Africa. From a supply perspective, this low adoption could be due to several factors, including but not limited to insufficient public and private investment in complementary infrastructure, unsustainable business models, and asynchronous pace of change (Abate et al., 2023). The demand side factors may include: lack of digital literacy, lack of context-specific needs assessment, and digital divide and more importantly accessibility and usability as well as user trust and confidence. However, we lack empirically grounded evidence on alternative and cost-effective interventions to improve adoption and scale-up of digital innovations in various settings. In particular, empirical evidence on most effective strategies to improve access and usability of digital agricultural innovations to smallholders with limited level of literacy remains scarce.

Taking Egypt as a case study, the Egyptian market has an array of innovative digital agricultural tools that offer different services to farmers such as sourcing inputs, providing post-harvest and logistical support, to accessing market information and selling crops online. However, the uptake of these technologies has been quite low, as Egyptian farmers lack the awareness of the benefits of digital tools and can easily get excluded from this agricultural digital revolution due to a lack of accessible training (OBG, 2022; AGBI, 2023). This project aims to test alternative interventions to promote the adoption of digital agricultural innovations in Egypt.
External Link(s)

Registration Citation

Citation
Abay, Kibrom, Fatma Abdelaziz and Erwin Hendricus Bulte. 2023. "Evaluating the impact of alternative interventions to improve adoption of digital innovations by smallholder farmers in Egypt.." AEA RCT Registry. December 21. https://doi.org/10.1257/rct.12723-1.0
Experimental Details

Interventions

Intervention(s)
The alternative interventions we launch and evaluate in this project are part of the German Corporation for International Cooperation (GIZ)’s Agricultural Innovation Project (AIP). The AIP seeks to promote the use of digital mobile apps to smallholder farmers to increase their incomes. Accordingly, the AIP project of GIZ designed various digital promotion strategies to promote the adoption of digital innovations. These interventions are meant to encourage and enable smallholder access and use of agricultural digital mobile applications. The AIP project and hence these interventions focused on two governorates in Egypt: Minya and Benisuef .
The International Food Policy Research Institute (IFPRI) partnered with GIZ to redesign some of these digital promotion strategies and eventually evaluate them using rigorous impact evaluation methods.
1- Digital literacy training through Trainers of Trainees (TOT) sessions
2- Pre-recorded community play videos
3- Real Live Community Play
Intervention Start Date
2023-09-18
Intervention End Date
2024-01-06

Primary Outcomes

Primary Outcomes (end points)
1. Participation in digital literacy training
2. Awareness of digital agricultural tools
3. Uptake of digital tools
4. Digital literacy knowledge test scores
5. Adoption and utilization of digital agricultural tools for marketing purposes
6. Market participation and commercialization behavior and associated outcomes
Primary Outcomes (explanation)
1. Participation in digital literacy training. This will be constructed based on comparing the different compliance rates to the 3 treatment arms, to evaluate which treatment was likely most attractive for farmers to participate in and attend.
2. Awareness of digital agricultural tools. Respondents will be asked to report on their awareness of the 3 agricultural digital apps, in the baseline (before they receive any digital literacy training), and in the midline (after they receive digital literacy training) and later in the endline.
3. Farmer uptake of digital tools. This will be constructed based on downloads data available in the backend database of the 3 different digital apps that farmers will receive instruction on, this data will be shared by the mobile app developers. Self-reported downloads in the baseline, midline and endline data will also be compared.
4. Digital App literacy knowledge test scores, this will be constructed based on a 1 month recall of the training material covered during the training sessions. A test score will be constructed based on the farmers responses to the set of questions on the different features of the 3 digital apps farmers received instruction on in the midline survey. The research team will generate binary indicators of pass or fail test scores.
5. Adoption of digital agricultural tools. This will be constructed using the midline and later, the endline survey data; respondents will be asked to report whether they have attempted to sell their crops/ actually sold their crops / attempted to make an order and purchase an agricultural input / actually ordered and received an agricultural input via any of the 3 digital apps. The reported number of attempts will be used to construct an adoption variable.

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The intervention will follow a randomized control trial design. The randomization takes place at the farmer/ individual level. The invitation of eligible farmers to participate in different kinds of digital support schemes in each farmer organization will be led by the active 30 farmer organizations in Minya and Benisuef, that GIZ have partnered with under the GIZ AIP project.
Following the farmer data received from each FO, a baseline phone survey took place between July 1st-Sepetmeber 7th 2023, a total of 4000 surveys were covered in Minya and Beni Suef. After ruling out respondents that do not assume farming as their main occupation, we were left with a total of 3350 respondents; 2400 of which have access to smart phones and the remaining 950 do not.
For the 2400 farmers that own smart phones, they will be eligible to receive a package of interventions to encourage the use and uptake of existing digital tools. The 2400 farmers from the different farmer organizations will be randomly assigned into three treatment arms, based on the type of treatment/ type of digital promotional strategy they receive. These farmers will be interviewed at midline and endline. These interventions/ dissemination methods include:
 Control Group: no intervention.
 Group A: Digital App Literacy training sessions
 Group B: Pre-recorded community play videos + Digital App Literacy training sessions (Training sessions will be provided, but here trainees will see video of play before the training)
 Group C: Real Live Community Plays + Digital App Literacy training sessions (Training sessions will be provided, but here trainees will see the actual play before the training)
Experimental Design Details
Not available
Randomization Method
The randomization was done at the farmer level using the baseline data and after excluding those farmers without smartphone and those whose main occupation is not farming.
Randomization Unit
Farmer
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
Treatment was not clustered, but randomization was stratified.
Number of clusters by treatment arm:
Control: 573 Farmers
T1 (Group A): 601 Farmers
T2 (Group B): 578 Farmers
T3 (Group C): 649 Farmers
Sample size: planned number of observations
2401 farmers (based on baseline survey data)
Sample size (or number of clusters) by treatment arms
Number of clusters by treatment arm:
Control: 573 Farmers
T1 (Group A): 601 Farmers
T2 (Group B): 578 Farmers
T3 (Group C): 649 Farmers
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Our power calculations aim to achieve the standard and widely adopted 80 percent power at a significance level of 5 percent.
Supporting Documents and Materials

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IRB

Institutional Review Boards (IRBs)

IRB Name
IFPRI IRB
IRB Approval Date
2023-06-27
IRB Approval Number
DS-23-0624
Analysis Plan

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