黑料福利社

Natural disasters, conflict, pandemics, and other crises are more frequently and affecting more people. Unconditional cash transfers 鈥� delivered quickly before or after a shock 鈥� help families withstand these shocks. But to deliver cash quickly, governments and aid organizations need to solve a basic problem: How do you find the people most in need fast?

Quickly identifying people in need after a crisis is very challenging

Climate and economic shocks are already reversing progress on poverty and health. In Malawi, for every three people who escaped poverty between 2010 and 2019, four fell back due to . Cash can help. During COVID-19, worldwide received a cash transfer 鈥� and shows those who got money were less likely to lose income or face food insecurity.

But most low and middle-income countries lack accurate, up-to-date information on household income. Many governments rely on national social registries created years ago, which can families who have just lost work or been displaced.

黑料福利社 has been testing if mobile phone usage can quickly and accurately predict need

In 2021, 黑料福利社 tested a new innovation: setting up technology so that people could self-enroll for emergency cash assistance via their phones (through a text message survey or our call center), get verified, and receive mobile money in minutes. This approach 鈥� called 鈥楳obileAid鈥� 鈥� is based on extensive research showing that anonymized phone usage patterns analyzed with machine learning can tell us where someone is located and if he/she is likely poor or in need:

Since then, we鈥檝e the machine learning methodology and launched five more studies in Bangladesh, Malawi, Kenya, and DRC 鈥� three completed and two still underway 鈥� to understand where and when MobileAid works best. Here are five lessons so far:

1锔忊儯 Phone metadata 鈥楳obileAid鈥� is more accurate than most other targeting methods 鈥� except in-person surveys

馃搳 Across studies in , , and , phone metadata outperformed most common ways of identifying people in need 鈥� such as community-based targeting (where local leaders nominate recipients) or simple proxies like location or occupation. The main exception was in-person proxy means tests (PMT), which are 20鈥�30 question surveys on demographics, spending, and assets used to predict how much a family lives on per day. 

Researchers tested each method against detailed in-person surveys, which revealed who was truly most in need and how many people each method correctly included 鈥� or wrongly left out. By this measure, phone metadata was less accurate than PMT but more accurate than other rapid methods typically used in emergencies.

馃搷 What鈥檚 next: We recommend using phone metadata when more detailed information (like recent PMT data) isn鈥檛 available 鈥� especially in fast-moving or large-scale crises. In Malawi, for example, we鈥檙e working with the government to update gaps in its urban social registry using phone metadata when it is prohibitively costly or time-consuming to conduct PMTs, so the government can more quickly screen and deliver assistance to over a hundred thousand households during crises.

2锔忊儯 Combining MobileAid and in-person surveys is important for reaching everyone, and can still cut costs in half

MobileAid is often more expensive to set up than the tools required for in-person surveys, but once built, it is much cheaper per each additional recipient enrolled.

馃嚙馃嚛 In , research showed that MobileAid was the most cost-effective option (resulting in the most amount of money reaching the neediest) for larger-scale programs that need to screen many households on a tight budget (e.g. government programs spending less than $10鈥�50 to screen each household). But as the chart above shows, in-person surveying is more accurate. 

馃嚥馃嚰 So in , we ran a hybrid program: we identified and enrolled 35% of recipients via phone metadata and the other 65% in person. This combined approach still cut the cost of identifying and enrolling each additional person in half, compared to a fully in-person operation.

馃搷 What鈥檚 next: We鈥檙e combining phone metadata with in-person surveys to reduce costs while ensuring no one is left out. In Kenya, we鈥檙e enrolling households 鈥� digitally and in-person 鈥� in flood- and drought-prone areas, so we can send cash when early warning systems predict extreme weather.

3锔忊儯 Once built, MobileAid can identify people in seconds, but systems currently take 6鈥�18 months to set up

馃彈锔� Setting up MobileAid systems can require regulatory approval, data-sharing agreements with telecoms or governments, and integration with mobile communications, KYC and fintech companies to remotely enroll, verify, and pay people. It鈥檚 a complex process 鈥� systems can take 6鈥�18 months to launch, compared to 4鈥�6 months for our traditional . Over the last four years, we鈥檝e partnered with 11 telecoms and other data providers (such as social registries) to test these systems across six countries.

鈿� While upfront costs and effort are high, once live, these systems can identify recipients days after they choose to enroll 鈥� or even seconds if automated via API. That鈥檚 the core advantage: they take time to build, but once built, they enable rapid, flexible response.

馃搷 What鈥檚 next: We鈥檙e shifting toward longer-term partnerships, supporting telecom and government partners to run the data analytics themselves rather than relying on our teams. We鈥檙e also expanding to new countries so these systems can help digital cash reach more people in need 鈥� quickly, and at scale.

4锔忊儯 Phone metadata is highly sensitive, but there are safe ways to handle it with the right systems and protocols

馃敀 While each country has its own data privacy laws, we鈥檝e learned a few practices are essential: conducting data protection impact assessments, setting up clear data-sharing protocols in formal agreements, minimizing the data exchanged between parties, pseudonymizing phone numbers (replacing them with secure keys), and ensuring people give informed consent to share their data.

Setting up privacy-protecting systems takes additional work but is feasible. In Bangladesh, we partnered with government agencies to store and process the phone metadata on in-country government servers. In DRC, the raw data is also processed directly on in-country telco servers.

馃搷 What鈥檚 next: We鈥檙e moving toward models where data processing happens inside telecom or government systems, rather than externally. In Kenya, for example, we鈥檙e working with Safaricom to process phone metadata in-house, while ensuring visibility into code and model performance. 

5锔忊儯 Explaining how MobileAid works is hard, but stories and visuals make it easier

馃柤锔� Even after testing different ways of explaining phone-based targeting, we found it鈥檚 difficult to create an explanation that most people can easily grasp and that works at scale. But when we used a four-paragraph script with real-life scenarios and visuals, people were much more likely to explain the approach back in their own words.

馃搷 What鈥檚 next: We鈥檙e testing different ways to explain MobileAid at scale 鈥� via phone calls, videos, and text messages 鈥� in a randomized controlled trial. The goal is to ensure everyone can have better understanding and give truly informed consent when participating in these programs.

Now we鈥檙e testing more questions and applications

In addition those improvements above, we鈥檙e focused on three other big questions:

馃弮鈥嶁檪锔� How well can phone metadata identify people who are displaced?
We鈥檙e working with Flowminder in DRC to study how well mobile data can detect people displaced by major crises such as conflicts. Findings expected 2026鈥�2027.

馃搲 Can phone metadata detect changes in poverty after a crisis?
So far, we鈥檝e measured accuracy at a single point in time: did the method correctly measure someone鈥檚 daily spending before a shock. But crises change people鈥檚 financial stability quickly. We鈥檙e working with the Government of Malawi and researchers at U.C. Berkeley and U.C. San Diego to study whether phone metadata can flag people whose poverty deepens after a shock. Findings expected in 2026.

馃棧锔� How do recipients prefer to be identified?
Some people prefer to be identified remotely; others want face-to-face interaction. We鈥檙e running more qualitative research to understand preferences and eventually build them into program design.

MobileAid is ready to scale and improve humanitarian responses worldwide

With a total of four research studies complete and two ongoing, we鈥檙e now focused on helping governments and telecoms adopt MobileAid at a national scale. 

While there are certainly limitations, mobile phone ownership is growing rapidly. In Kenya, of households now own at least one phone); in Bangladesh, . Rates are lower in rural and lower-income areas, but rising. That makes mobile phones one of the most scalable tools available to reach people in poverty, fast. The next step is expanding the systems and partnerships to use them 鈥� when the next flood, fire, or pandemic hits.