Anatomy of a Misfit: International Migration Statistics

Abstract

Migration is one of the key aspects of the Sustainable Development Goals (SDGs). To understand global migration patterns, develop scenarios, design effective policies, focus on the population’s needs, and identify how these needs change over time, we need accurate, reliable and timely data. The gaps in international migration data have persisted since international organizations collect data. To improve the data gaps, there is a need to conceptualize the types of gaps and pinpoint the gaps within the international data systems. To that end, the ultimate objective of this paper is twofold, (i) to review and categorize the gaps in the literature and (ii) assess the statistical data sources, i.e., United Nations Department of Social and Economic Affairs (UN DESA), Organization for Economic Co-operation and Development (OECD), International Organization for Migration (IOM), Eurostat, and the United Nations High Commissioner for Refugees (UNHCR). Our results demonstrate that the gaps could be categorized under (1) definitions and measures, (2) drivers or reasons behind migration, (3) geographic coverage, (4) gaps in demographic characteristics and (5) the time lag in the availability of data. The reviewed sources suffer from the gaps, which are not mutually exclusive (they are interlinked): the quality and availability of both migration flows and stocks data vary across regions and countries, and migration statistics highly rely on immigrants’ arrival.

A critique of gender‐blind migration theories and data sources

“…The need for sex‐disaggregated data and gender statistics in international migration data has long been understood. Many scholars and policymakers acknowledge the pressing lack of adequate data in this field (Ahmad‐Yar & Bircan, 2021; Bircan et al, 2020). We argue that in addition to improving accuracy and coverage of sex‐disaggregated statistics on international migration, both regular and irregular, it is crucial to develop quantitative as well as qualitative indicators to monitor the gender dimension in migration.…”

“…The availability of the gender‐responsive indicators on migration is restricted to sex‐disaggregated data of migration flows and stocks. Despite the urgent need for gender statistics on migration, gender being limited to biological sex is one of the major shortcomings of the aggregated data on international migration (Ahmad‐Yar & Bircan, 2021; Bircan et al, 2020).…”

“…There is a lack of detailed data on migration drivers (Ahmad-Yar & Bircan, 2021). The international migration statistics reported by a range of agencies, such as the United Nations, the Organisation for Economic Co-operation and Development (OECD), and the International Organisation for Migration (IOM), among others, do not fully capture the reasons for migration in terms of consistent categories (economic, education, family, asylum, other) or how such drivers intersect with demographic dimensions (Bircan et al, 2020).…”

EMAKG: An Enhanced Version Of The Microsoft Academic Knowledge Graph

“…There exists a notable gap in the understanding of researchers' mobility, knowledge exchange, and scientific collaboration networks, besides a few exceptions [5][6][7]. One of the challenges with modelling researchers' mobility and collaborations is the lacking of data and international statistics providing definitions and specific indicators, e.g., socio-economic, educational, and professional indicators [8,9]. The most recent research has focused on alternative data sources to fill the gaps posed by traditional data, e.g., register statistics.…”

“…Scopus: https://www.scopus.com/ 6. DBLP: https://dblp.uni-trier.de/7 Google Scholar: https://scholar.google.com/ 8. CiteSeerX: http://citeseerx.ist.psu.edu/index 9.…”