Research project Over-coverage in Population Registers: measurements, determinants and consequences
This project examines and discusses ways of improving current estimation methods using Swedish total population register data.
The need for evidence-based policy built on rigorous research has probably never been greater than throughout the COVID-19 pandemic, which has exposed the need for accurate population data. Recent findings, however, have raised concerns regarding the quality of population registers due to the increase of over-coverage.
Project description
Over-coverage is introduced through individuals whose death or migration out of the country is not registered. Not only does it give biased estimates of the size of the total population and of subgroups, it also affects measures such as average income, fertility, health and mortality - measures that underpin most policy decisions.
Over-coverage is found to be more pronounced among migrants. Despite general acknowledgment by government agencies and
researchers that over-coverage might induce serious bias into population estimates, there is no common understanding on how to deal with over-coverage in population estimates and social science research.
This project examines and discusses ways of improving current estimation methods using Swedish total population register data. We examine whether over-coverage can explain demographic paradoxes, test how different socioeconomic outcomes are affected by over-coverage, assess if there are common determinants of over-coverage and their impact on policies at national level and across international contexts. This project will enable a more qualified use of registers.
Project members
Project managers
Eleonora Mussino
Researcher, Docent
Members
Lucy Yasmin Brown
Gästforskare
Sven Drefahl
Senior Lecturer, Docent
Andrea Fuentes Monti
Researcher
Mimmi Aurora Lounela
PhD Student
Eleni Matechou
Senior Lecturer in Statistics
Vitor Miranda
Demographer
Bruno Ramos dos Santos
Guest researcher
Matthew Wallace
Affiliate Researcher, Docent
