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.

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.

Members

Vitor Miranda

Demographer

the Department for Population and Welfare at the Swedish national statistical service

Eleni Matechou

Senior Lecturer in Statistics

University of Kent

Department of Sociology

New model provides better population estimates

Researchers in demography have developed a new model that provides more accurate estimates of population sizes than the models currently used by government organisations. This alternative method has the potential to affect policy decisions, according to Elenora Mussino, one of the researchers behind the study. Current methods for population size estimation rely heavily on information in population registers. If individuals who have left the country or passed away remain listed in some registers, it can lead to an overestimation of the population size. This error can have negative societal effects. – For example, it can look like inactivity of migrants is higher than it is, or that the fertility is lower than it is. This can affect the public narrative, which in turn can lead to other consequences like policy decisions, says Eleonora Mussino.

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