Published on Data Blog

How we can better measure migration through the integration of different sources of data

This page in:

Image of a girl using and charging her smartphone in a refugee camp
Girl using and charging her smartphone in a refugee camp. Photo: ©Pressmaster / Shutterstock

Last year saw a rebound in migration flows following a pandemic-induced decline in 2020-21. Today, an estimated 184 million migrants live outside the country of their citizenship. Of this total, refugees number 37 million, including the 7.8 million Ukrainians who have been seeking shelter in the EU since the start of the Russia-Ukraine war.

Cross-border mobility is an integral part of the development process – and forms the basis of the World Bank’s World Development Report 2023 titled “Migrants, Refugees, and Societies.” The report explores migration in a holistic way, proposing an integrated framework to maximize the gains for both origin and destination countries and on migrants themselves. The framework, based on both labor economics and international law, enables policymakers to clearly identify different types of movements and design appropriate policies for each.

Given the overlapping crises in many parts of the world, it is more important than ever that we are able to accurately measure migration in its various forms. This is going to take multiple types of data sources, validated and integrated in innovative ways, as explored in a “Let’s Talk Data” event titled “Opportunities and challenges using alternative data sources to study migratory phenomena.”

Traditional sources of migration data – like censuses, surveys, population registers, administrative data –  are collected by trained professionals and tend to have certain advantages, such as transparency, validated methodology, and high demographic representativeness. However, there are now many innovative data sources – including  mobile phones, social media data, traffic information, satellite imagery, mobile payment apps, and internet activity – that can provide data with greater geographic and temporal granularity, extensive coverage, and nearly real-time availability.

Given all this, many benefits can be achieved by combining innovative and traditional sources of data to arrive at better measurement and analysis of different aspects of migration. For example, the Facebook Social Connected Index was recently used to create models for nowcasting estimates, as well as estimating remittances where official statistics were missing (Kalantaryan et al.). And asylum seekers have also recently been forecast by mixing official statistics and machine learning with data at scale (Carammia et al.).

To expand the scope of such work, migration data will need to undergo a “data innovation transition.” To facilitate such a transition, three conditions are necessary: 1) legislation that establishes guidelines and regulations for incorporating innovative data sources in official statistics, 2) technological approaches that facilitate safe and trusted data sharing, and 3) investments in capacity for low-income countries to be able to take advantage of these options.

Bridging foundational and frontier approaches to data is vital for development. As emphasized in the “World Development Report 2021: Data for Better Lives,” not only do we need to bring together public and private data to take full advantage of the potential of linking these valuable data sources, but we also need to forge a new social contract on data, based on value, equity, and trust.

However, the unfortunate reality is that most of the innovative data integration work right now is happening in high-income countries because of the many obstacles that prevent poor countries from being able to benefit from these latest innovations and techniques. And what we really need to avoid is to increase the inequity that already exists between high- and low-income countries when it comes to data and statistics – we want to be able to leverage these latest approaches to reduce inequity and the data divide.

To help low-income countries scale up and expand their access to these new approaches, financing is key. That is why, the new World Bank-hosted Global Data Facility, an innovative data financing mechanism, prioritizes funding to help build capacity and data infrastructure in low-income countries.

Another key role for the Bank is our convening power – we’re well-positioned to create interdisciplinary spaces to bring together data producers and users, researchers and operational staff, private companies and national statistics offices. Our Development Data Partnership does exactly that: alongside our colleagues at the IMF, the IADB, the OECD, and UNDP, we have created a partnership that makes private data from companies such as LinkedIn, Meta, Google, Twitter, and MapBox available for sustainable development projects – more than 200 projects to date and counting.

We are also working on a new program on Mobile Big Data, to see how we can support expanding access to mobile big data so that policymakers in low-income countries are able to use it to gain real-time, dynamic insights into issues like migration, as well as emergencies like climate change and health shocks.

To ensure we understand cross-border mobility in all its complexity, we need to allow for a migration data innovation transition that is truly anchored in value, equity, and trust. This means we need to scale up new approaches on combining traditional and innovative data sources to reach the places where they will be of most benefit. And as we do, we need to focus on making sure that everyone benefits equally and appropriate safeguards are in place so that data privacy is respected.


Haishan Fu

Chief Statistician of the World Bank and Director of the Development Data Group

Stefano Iacus

Senior Research Scientist and Director, Data Science and Product Research of the Institute for Quantitative Social Science (IQSS)

Join the Conversation

The content of this field is kept private and will not be shown publicly
Remaining characters: 1000