The opinions expressed in this article are solely those of the author, and do not necessarily reflect the opinions or views of the Mo Ibrahim Foundation.
Incomplete and poorly presented migration data often lead to misconceptions about the scale of migrations and its effects.
Various data sources provide information about migrations. Traditional sources such as statistical and administrative data are currently insufficient to build a comprehensive analysis of migration. Statistical sources include censuses, household surveys and labour force surveys. While providing detailed information on international migrant stocks that can be compared across countries, statistical data are collected infrequently and at a high cost. Moreover, censuses’ incomplete and poorly presented migration data often lead to misconceptions about the scale of migrations and its effects. Household and labour surveys generally do not include enough questions on migration to allow for a comprehensive analysis.
Administrative sources include visa processes, residence and work permits, administrative registers and border data collection systems. Though timely, data collected through these sources are seldom comprehensive. Additionally, each country has its own administrative processes, migration definitions and entry requirements, making the data not comparable across countries.
The need for better data and the potential of big data
The formal inclusion of the topic of migration in the UN 2030 Agenda for Sustainable Development (2015), as well as the adoption of the UN Global Compact for Safe, Orderly and Regular Migration (GCM) and the UN Global Compact for Refugees (both December 2018), have heightened the need for reliable, timely and internationally comparable definitions and data. The joint effect of growing demand for more and better migration data on the one hand, and widening technological progress on the other, have prompted the international statistics community to strengthen the traditional sources of migration data, and to look for alternatives to enhance collection and analysis.
Innovative data sources such as big data are generated automatically through mobile phones, social media, internet platforms and applications, as well as via digital sensors and meters. These are usually managed at transnational level by tech giants such as Google, Facebook, NASA and GSM. Big data could present an opportunity to complement traditional data sources in Africa and to leapfrog migration data availability and analysis. With 444 million estimated mobile phone subscribers (44% of the population) in sub-Saharan Africa and a total of 453.3 million internet users and 177 million Facebook subscribers in Africa in 2017, big data could prove a timelier, more frequent and less costly data source which also entails a broader coverage.