Leveraging digital finance in Ethiopia

Last registered on May 23, 2023


Trial Information

General Information

Leveraging digital finance in Ethiopia
Initial registration date
February 15, 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
February 21, 2023, 6:53 AM EST

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

Last updated
May 23, 2023, 9:42 AM EDT

Last updated is the most recent time when changes to the trial's registration were published.



Primary Investigator

Addis Ababa University

Other Primary Investigator(s)

PI Affiliation
Social Innovation & Impact Institute (Si3)
PI Affiliation
Ethiopian Economic Association
PI Affiliation
University of Copenhagen
PI Affiliation
Food and Agriculture Organization of the United Nations (FAO)
PI Affiliation
Addis Ababa University
PI Affiliation
Massachusetts Institute of Technology

Additional Trial Information

On going
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Studies found that mobile money transfer increases consumption and remittance by reducing time, transport, and transaction costs. Despite having enormous potential for unbanked and marginalized persons, mobile phone-based money and digital financing system remain low in developing countries. Using beneficiaries of the Ethiopian Safety Net program, largest in Africa, this study aims to test the efficiency of two interventions, i.e., Training and Financial Incentive, to promote uptake and use of mobile money and its intensive use and impacts for the poor and women. Identifying the relative efficacy of training and financial incentives for mobile money adoption is crucial for policy makers promoting financial inclusion of the poor in resource constrained settings of the developing world. Unlike most previous studies, this project does not take the uptake of mobile financial services for granted. In fact, adoption of MM remains the most challenging problem for financial inclusion among the poor. Results will also inform feasibility and identify modifications needed when designing follow-up large-scale RCT.
External Link(s)

Registration Citation

Fetene, Gebeyehu Manie et al. 2023. "Leveraging digital finance in Ethiopia." AEA RCT Registry. May 23. https://doi.org/10.1257/rct.10928-1.1
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Experimental Details


This study aims to investigate if provision of targeted training and financial incentives for marginalized groups who have limited financial literacy induce them to adopt mobile money. We randomly draw clusters and assign them in to two treatment groups (one receiving targeted technical training and brochure that shows the steps about adopting and using mobile money fro various transactions, and the other group received financial training and addition to the technical training conditional on they adopting mobile money) and control groups (who do not receive any intervention).
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
Adoption of mobile money if the key outcome variable. As secondary outcome variables, we investiage also if the interventions (targeted training and incentives) increase mobile banking and induce the marginalized people to be willing to accept their salary in mobile money account.
Primary Outcomes (explanation)
Mobile money adoption is the primary outcome variable.

Secondary Outcomes

Secondary Outcomes (end points)
We also consider intensity of use of MM, mobile banking adoption and willingness to receive their salary in mobile money.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Informed by a combination of our research and work experiences in the country, feedback from our implementing partners, and an initial quantitative study of the relevant population, we propose two interventions: (i) Information and Training (IT): we hypothesize that knowledge on technology and return on investment act as binding constraints. We assume it is difficult for urban poor to acquire information about the technology and harness potential benefits of Mobile money (MM). Thus, providing information about the potential returns from MM and knowledge about how to use the service should increase MM adoption and use. To relax these constraints, we follow successful studies using training and information treatments in other domains such as technology adoption (e.g., Aker, 2011; Campenhout, 2021) and tax compliance (e.g., Doerrenberg and Schmitz, 2017; Shimeles et al., 2017) and introduce practical training combined with information as our first treatment. The treatment includes information about the benefits of mobile payment and practical training on how to use MM. It involves in-person visual and practical demonstration of how to download the mobile app (for smart phone users), install and use the mobile pay service (both for smart phone and USSD users). We expect the training will remove knowledge and skill binding constraints and increase adoption and use of MM. The study will use ‘telebirr’ and and MM provided by banks. (ii) Information and Financial incentive (IF): we hypothesize limited financial resource, uncertainty about return on investment, and lack of trust constitute additional binding constraints in adoption of MM. To remove this binding constraint, we use financial incentive to try the service. We will deposit the incentive on the MM account of beneficiaries for those who will have account at the time of the payment. Telebirr allows also to transfer money to any person who has mobile number regardless of subscription to Telebirr service. Those who do not subscribe to the Telebirr service can then withdraw the money at the Telebirr agent or at any Ethio-telecom sales office showing identity card and the text notification message that Telebirr sends when money is transferred to them via their mobile number. For the others who will refuse to get the payment in mobile transfer form, we have a couple of options: we will, deposit the money in their bank accounts, pay in mobile airtime, and if these all options do not work for any reason, we will pay them in cash money.
We will pay beneficiaries different amounts that could help to estimate elasticity. We will also vary the financial incentive (for instance, 1.5, 3, & 4.5 USD) to gauge the difference in response because of variation in financial payouts. We expect the financial incentive will improve trust, reduce uncertainty, and hence increase adoption and use of MM. Similar to the first treatment, the second treatment also includes information about potential returns from MM. To shed light on the sustainability of mobile money transactions after the incentive expires, we will collect the endline data months after the incentive period expires.
Experimental Design Details
Randomization Method
The UPSNP beneficairies are organized (by the safety-net program) in clusters based on the village they live in. The beneficaries in Addis Ababa are organized in 827 clusters. We received the sampling frame in 827 folders, denoting each cluster. However, the clusters are heterogeneous in terms of beneficiary size. Hence, we assign weights to clusters based on member size after combining the clusters in STATA. Then, we randomly selected 400 clusters to collect fixed number of beneficairies from each cluster, picking four respondents from each cluster, and interviewing additional respondents from new clusters (reserve) whenever the member size of a cluster is less than four.
Randomization Unit
UPSNP beneficaries are oranized in clusters based on the village they live in.They have a representative who facilitates communication between government distric officials who are responsible for the program and the beeficairies. The members in a cluster usually work together in public work, and they receive training together. Hence, there is high potential spillover effect. Conseqently, we randomize clusters, and then we randonly select fixed number of beneficairies from each cluster.
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
300 clusters
Sample size: planned number of observations
1200 individuals
Sample size (or number of clusters) by treatment arms
400 individuals for each treatment arm
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Institutional Review Boards (IRBs)

IRB Name
College of Business and Economics Institutional Review Board, Addis Ababa University
IRB Approval Date
IRB Approval Number


Post Trial Information

Study Withdrawal

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Is the intervention completed?
Data Collection Complete
Data Publication

Data Publication

Is public data available?

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials