Improving Women’s Access to Digital Mobile Technology in Sub-Saharan Africa

Last registered on December 17, 2025

Pre-Trial

Trial Information

General Information

Title
Improving Women’s Access to Digital Mobile Technology in Sub-Saharan Africa
RCT ID
AEARCTR-0016908
Initial registration date
September 29, 2025

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
October 01, 2025, 8:03 AM EDT

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

Last updated
December 17, 2025, 5:15 PM EST

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

Locations

Region

Primary Investigator

Affiliation
University of Southern California

Other Primary Investigator(s)

PI Affiliation
Yale University
PI Affiliation
University of Michigan
PI Affiliation
University of Nairobi
PI Affiliation
Yale University

Additional Trial Information

Status
In development
Start date
2025-09-30
End date
2027-08-31
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Mobile phones can enable economic and social empowerment by lowering communication costs and reducing information frictions. Yet, gender gaps in smartphone ownership in sub-Saharan Africa are substantial. The high up-front cost of smartphone devices remains a primary barrier, especially for women. Credit is one way to overcome this barrier, but it is often difficult for lenders to extend credit to low-income borrowers, who lack collateral and therefore pose a risk to default on their loans. Recent research indicates that a “pay-as-you-go” (PAYG) structure -- which relies on technology that locks the asset being lent in the event of non-payment -- could be an effective means of lending to the large numbers in low- and middle-income countries with no credit history. However, the rigid nature of these contracts may pose problems, especially when loans finance income-generating assets like smartphones, and could be especially challenging for groups earning irregular income that is subject to shocks. Working with a large asset-financing platform providing PAYG smartphones to individuals in Kenya, we conduct two randomized controlled trials to assess (a) whether offering payment flexibility enhances digital inclusion and raises economic wellbeing and (b) how marketing under these flexible contracts affects selection. In the first experiment, we randomly surprise existing customers into one of 3 experimental contracts offering different types of flexibility and repayment incentive; this lets us estimate the impact of the new contracts holding selection constant. In the second experiment, we assess selection by comparing borrower demographics and repayment outcomes among those surprised with the experimental contracts versus those marketed the contracts. Findings will inform both product design for financial service providers and consumer protection policies amid the rapid growth of digital credit markets in Sub-Saharan Africa.
External Link(s)

Registration Citation

Citation
Jorgensen, Erik et al. 2025. "Improving Women’s Access to Digital Mobile Technology in Sub-Saharan Africa ." AEA RCT Registry. December 17. https://doi.org/10.1257/rct.16908-2.1
Experimental Details

Interventions

Intervention(s)
We will conduct two randomized controlled trials in Kenya to understand how payment flexibility affects borrower repayment, financial stress, smartphone use and livelihoods, and how marketing under these contracts affects adverse selection.

Under the partner’s standard lending model, customers receive a smartphone after making a down payment. They must then make 365 daily payments to clear their loan. The partner remotely locks the phone on days that a payment is due but the customer does not pay. Working with our partner organization, we will study the impact of three contracting variations designed to ease borrower liquidity constraints:
- Under “fixed flex”, customers receive a non-renewable bank of flex days, which can each be redeemed for a day of phone usage on a day the customer cannot repay their loan.
- Under “earned flex”, customers accrue one flex day for every 10 days they repay and, once earned, may redeem their days whenever they choose throughout their contract.
- Under “earned rewards”, customers accrue a cash reward equal to one daily payment for every 10 days they repay. Once earned, customers may redeem their rewards whenever they like.
Intervention Start Date
2025-09-30
Intervention End Date
2026-07-31

Primary Outcomes

Primary Outcomes (end points)
We will further detail our primary and secondary outcomes for the RCT in a pre-analysis plan, which will be posted on the registry before endline data collection is complete. Our primary outcomes include:
- Loan repayment
- Phone lockout by the lender
- Smartphone use
- Income generation and labor supply (experiment 1 only)

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary outcomes include:
- Use of other financial services (saving and debt - experiment 1 only)
- Psychosocial well being (experiment 1 only)

When studying adverse selection (experiment 2) we will also consider selection in terms of a small number of customer demographics, including gender and education.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Our study includes two experiments. In the first experiment, we will individually randomize 3,764 new smartphone customers into one of four arms (941 in each), where they will either remain with the status quo contract or be “surprised” with one of the three contracts discussed above (fixed flex, earned flex, earned rewards; S1-S3). These arms allow us to isolate the behavioral effect of each contract, holding constant selection.

The second experiment involves two layers of randomization. First, we worked with our partner to identify 140 geographically relevant sales markets serviced by a set of sales agents with a common supervisor. We will randomly assign the sale contract available to agents in these markets for a predetermined special offer period. The markets will be randomly assigned to selling conditions such that the number of clusters marketing under each treatment is as follows:
- M0: Status quo (60 markets)
- M2: Earned flex (40 markets)
- M3: Earned rewards (40 markets)

The marketing period length will be set to target enrolling 40 randomly-selected customers per market into the study, for a total enrollment of approximately 5,600 borrowers.

Second, customers who received their devices under the status quo contract (M0) will be individually randomized to be “surprised” with one of two contracts discussed above (earned flex or earned rewards; S2-S3). 1200 customers will be assigned to each of the contracts (S2 and S3). By comparing those who selected into each contract type (M2 and M3) to those surprised (S2 and S3), we isolate the selection effect of being offered the contract.
Experimental Design Details
Not available
Randomization Method
Randomization will be conducted via STATA in an office computer, using administrative data from our partner.
Randomization Unit
Marketing conditions are randomized at the market level. Surprise offers are randomized at the individual level from amongst those marketed the status quo contract.
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
Experiment 1: no clustering.
Experiment 2: 140 clusters.
Sample size: planned number of observations
The total number of observations across both experiments is 9,364 (3,764 in experiment one and 5,600 in experiment two).
Sample size (or number of clusters) by treatment arms
Experiment one:
S0: customers will remain with status-quo contracts, 941 individuals.
S1: customers will be surprised with fixed flex, 941 individuals.
S2: customers will be surprised with earned flex, 941 individuals.
S3: customers will be surprised with earned rewards, 941 individuals.

Experiment two:
M0: Agents will market the status-quo contract. 60 clusters, 2400 individuals
- M0-S2: customers will be surprised with earned flex, 1200 individuals.
- M0-S3: customers will be surprised with earned rewards, 1200 individuals.
M2: Agents will market contracts with earned flex. 40 clusters, 1600 individuals
M3: Agents will market contracts with earned rewards. 40 clusters, 1600 individuals
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
Yale Human Research Protection Program
IRB Approval Date
2023-06-12
IRB Approval Number
2000035311
IRB Name
United States International University - Africa
IRB Approval Date
2023-05-10
IRB Approval Number
USIU-A/IRB/SS0169-2023
Analysis Plan

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