From pilots to policy: scaling mobile phone data for statistics in West Africa

In an era where decisionmakers in the public and private sectors need faster, more granular and high-frequency information to navigate complex development challenges, traditional data sources are struggling to keep pace. Censuses happen once a decade. Household surveys follow a sampling approach and take time. At the same time, in every country across West Africa, millions of people carry a device that generates a continuous stream of signals: their mobile phone. 

Last month, the World Bank Group (WBG) and ECOWAS (Economic Community of West African States) with financial support from the Harmonizing and Improving Statistics in West and Central Africa (HISWACA) Project organized a landmark workshop in Abidjan, Côte d’Ivoire, bringing together technical experts from across the ECOWAS region to take stock of what insights mobile phone data (MPD) can deliver, and what it will take to make it work at scale.

A first generation of case studies

The workshop made one thing clear: mobile phone data is no longer a niche, experimental tool. Across West Africa, a first generation of case studies demonstrates that call detail records and other mobile network data can generate statistics that are timelier, more spatially granular, and far cheaper to produce than traditional methods.

In The Gambia, the National Statistical Office became the first in the ECOWAS region to formally institutionalize access to MPD through a collaboration with the telecom regulator. The partnership has produced migration statistics that would otherwise have required a dedicated survey costing multiples of what the data agreement cost to negotiate.

In Ghana, MPD has been deployed for mobility, migration, and displacement analysis. During the COVID-19 pandemic, movement indicators derived from mobile network data provided near-real-time insights into how populations were responding to restrictions, information that no traditional survey could have delivered on that timeline.

In Côte d’Ivoire, a 2024 study combined household survey data with MPD indicators to build high-resolution welfare maps, ranking communes by living standards. This kind of geographical targeting, precise enough to inform sub-national social protection programs, is simply not achievable with conventional data at an affordable cost.

These examples share a common thread: mobile phone data generates population-level insights that are updated in days rather than months, disaggregated to the district or corridor level rather than the national average, and produced at a fraction of traditional survey costs. Evidence on the value of investing in data systems is compelling: a valuation of the census in New Zealand, for instance, found a 5:1 ROI, and OECD estimates place the value of open government data at between 1% and 2.5% of GDP.

Scaling up 

The excitement around MPD is justified, but the workshop also brought into sharp focus the required investments that stand between a promising pilot and a sustainable national data asset. 

First, an institutional and regulatory framework must be in place. MPD does not flow automatically from telecom operators to statistical offices. It requires trust, legal authority, and governance. 

Who hosts the data and maintains the necessary infrastructure? Who determines the purposes of data processing? Who processes the data? Who ensures compliance with privacy protection requirements? Without answers to these questions, even technically sophisticated teams will find themselves blocked at the negotiating table. The workshop underscored that collaboration, across national statistical offices, telecom regulators, mobile network operators, and data protection authorities, is the precondition for everything else. Memoranda of Understanding and legally binding data sharing agreements translate goodwill into reliable data governance systems.

Second, infrastructure and technical capacity require further investments. Processing raw call detail records into policy-relevant indicators requires an integrated IT infrastructure and data science capability. These are not resources that most national statistical offices currently have in abundance. Investing in staff training, secure data environments, and reproducible analytical pipelines is as important as negotiating data access in the first place. Without this, even a well-negotiated data sharing agreement will remain unused.

Third, the knowledge frontier still needs to be pushed. The case studies from The Gambia, Ghana, and Côte d’Ivoire are important precisely because they prove the concept in an African context. But more applications are needed, across sectors such as transport planning, disaster response, dynamic population monitoring, and social protection, and across more countries. Each new study builds the evidentiary base that makes it easier for the next country to make the case internally for investing in MPD. More case studies also help identify the limits of what MPD can reliably measure, including biases that underrepresent women, the elderly, and the poor in mobile ownership data, and how to correct for them.

Setting the stage with the ECOWAS Regional Framework 

This is precisely why the regional dimension of what was launched in Abidjan matters so much. The workshop marked the formal launch of the ECOWAS Technical Working Group on Mobile Phone Data, a regional mechanism designed to support member states as they navigate the institutional, regulatory, and technical journey described above.

At the heart of the effort is a regional framework: a set of practical guidelines that help countries move from interest to implementation. This is built around a series of Guiding Principles that form the foundation of all Mobile Phone Initiatives. This informs the convening of a Stakeholder Ecosystem and governance to foster collaboration and resolve bottlenecks. Finally, these feed into the Data Arrangements, technical agreements outlined in a Memorandum of Understanding and then detailed in a Data Sharing Agreement.

READ MORE: https://blogs.worldbank.org/en/opendata/from-pilots-to-policy–scaling-mobile-phone-data-for-statistics-