As the digital technology revolution continues to race ahead, its benefits remain unevenly shared. In many low-income countries, women are significantly less likely to own smartphones and use the internet than men. [1] These inequities constrain their economic opportunities, limit access to services, and reinforce or even worsen gender inequality—itself a significant barrier to economic development. [2]
What are effective strategies to close the mobile gender gap? In a large-scale randomized study in southern Malawi, we tested the benefits of providing smartphones and digital training at no upfront cost to women from low-income households. At baseline, participants did not own a mobile phone, and household ownership was just 30%.
To assess the impact of such an approach, and whether it worked any better than simply providing participants with the equivalent value in cash, we partnered with the Institute of Public Opinion and Research and the Girls Empowerment Network of Malawi to implement the following study. Working with village development committees in the poorest regions of Blantyre, we recruited 1,500 women and randomly assigned participants to one of four groups:
- Control (n = 300): No intervention.
- Cash (n = 400): Participants attended individually and received an unconditional cash grant of USD 70, the equivalent value of the smartphone. Participants also received empowerment training emphasizing women’s control of valuable household assets and were encouraged to use the funds to support personal or household well-being.
- Individual Smartphone (n = 400): Participants attended individually and received a smartphone with certificate of ownership, SIM card, and start-up credit. They also received the same empowerment training, plus instruction in how to use the smartphone. This session covered basic functionality (e.g., powering on/off, charging, calling, SMS); using mobile money; accessing the internet and Google Play; and downloading, signing up for, and sending a WhatsApp message.
- Couples Smartphone (n = 400): Participants received the same package as those in the Individual condition, but their husbands also attended the smartphone distribution and training session and were encouraged to recognize women’s property rights over the smartphones.
Throughout this post, we refer to these treatment groups as: Control, Cash, Individual, Couples, and Smartphone groups (when we pool Individual and Couples into a single group). 
We followed participants in the study for nearly three years. Here’s what we learned.
Smartphones and Training Outperform Cash in Building and Sustaining Women’s Digital Skills
We track participants’ digital capabilities over the course of the study. Women in the Smartphone conditions scored 0.46 standard deviations (SD) higher on a Digital Capabilities Index than those in Control, and 0.40 SD higher than Cash. These effect sizes point to large, meaningful differences in digital skills: smartphone recipients reported more frequently making calls and sending and receiving SMS, accessing the internet and WhatsApp, and using a phone for income generation.
The smartphone intervention also increased mobile money use. Women in the Smartphone conditions scored 0.34 standard deviations higher on a Mobile Money Index than those in Control, and 0.24 SD higher than those in Cash. The Mobile Money Index reflects not only self-reported use (e.g., having a mobile money account and frequency of use), but also actual use of mobile money in an incentivized choice task. [3]
These results suggest that smartphone access, combined with a single hands-on training, can help women from low-income households build and sustain digital skills they are unlikely to acquire on their own. The Cash group, despite experiencing significant economic uplift in the first year of the program due to the cash grants, saw little improvement in their own digital capabilities—but their husbands did, a pattern we discuss below.
Among the Smartphone Conditions, the Individual Treatment Proved Most Effective
Our pre-registered expectation was that the Couples treatment would lead to the largest gains in women’s digital capabilities. Specifically, we expected this intervention to increase male support for female mobile phone ownership and encourage intra-household digital knowledge sharing and cooperation.
However, the largest gains in women’s digital capabilities compared to Control occurred in the Individual treatment, which significantly outpaced the Couples, even as the latter also led to substantial gains.
What accounts for this difference? It does not appear to be a function of access; at midline and endline smartphone ownership and possession were remarkably similar among participants in the Individual and Couples treatments, even as both experienced substantial handset attrition. And there is some evidence that, as intended, the Couples treatment led to a positive shift in women’s mobile phone control.
Instead, this difference may reflect a learning effect. Training women on their own in the Individual condition appears to have fostered a deeper understanding and confidence in how to use the technology. In contexts where men entered the study with stronger mobile competencies and likely were seen as the primary holders of digital expertise, the Individual model may have helped to flatten these gendered hierarchies of knowledge by creating space for women to learn autonomously, experiment freely, and become digitally proficient in their own right.
How Digital Gains Were Distributed Within Households
A large body of research focuses on the digital gender gap, but few studies directly measure changes in intra-household digital capabilities. To capture these dynamics, we independently surveyed participants and their spouses at the endline, deploying common instruments measuring mobile phone access and use, digital capabilities, and mobile money uptake and fluency. These findings offer additional insights into the impact of the smartphone distributions and training compared to cash grants.
As the Individual condition boosted women’s digital capabilities, it also significantly reduced intra-household digital inequality. The Couples treatment left inequality unchanged. Whereas in the Cash group, digital gains were disproportionately captured by men, significantly increasing digital inequality compared to Control.
These findings suggest that providing cash transfers to women—even when targeted directly to them—does not necessarily lead to greater digital equality, and may in some cases reinforce or widen existing disparities. This highlights the importance of carefully designed, gender-intentional digital interventions.
Smartphone Participants’ Digital Gains Were Attenuated by Handset Attrition
Nearly three-quarters of smartphone participants no longer had their handsets 32 months after the program began. The primary source of attrition wasn’t that participants sold them, but that many devices failed due to hardware malfunction (especially battery failure), water damage, or breakage.
Handset attrition substantially reduced digital capabilities among smartphone recipients. This points to an important structural challenge: digital gains from smartphones are only as robust as the devices that enable them. Investing in handset durability and increased use of protective cases could significantly improve the long-term impact of digital inclusion programs.
[1] GSMA, The Mobile Gender Gap Report 2023.
[2] World Bank. World Development Report 2012: Gender Equality and Development. Washington, DC: World Bank.
[3] At endline, we offered participants a choice between a fixed cash payment (~$1.17 USD) or an equivalent or larger mobile money payment. One choice was then randomly selected for payout, ensuring real stakes. This allowed us to credibly measure participants’ preferences for and capabilities to use mobile money.
This research was supported by the Gates Foundation (Grand Challenges Call to Action Grant, 2020), the Women’s Economic Empowerment and Digital Finance (WEE-DiFine) Initiative (Pilot and Greenfield Grants), Open Philanthropy, and University of Texas-Austin.
