Using administrative data to improve the estimation of immigration to local areas in England

TL;DR

This paper explores how administrative data sources can enhance the estimation of international migration at local levels in England, addressing measurement challenges and proposing an integrated model for improved accuracy.

Contribution

It introduces a novel model combining administrative data with national estimates to better estimate local immigration, overcoming data inconsistencies.

Findings

Administrative data improves local migration estimates

The model provides alternative immigration figures for policy use

Integration of data sources enhances measurement accuracy

Abstract

International migration is now a significant driver of population change across Europe but the methods available to estimate its true impact upon sub-national areas remain inconsistent, constrained by inadequate systems of measurement and data capture. In the absence of a population register for England, official statistics on immigration and emigration are derived from a combination of survey and census sources. This paper demonstrates how administrative data systems such as those which capture registrations of recent migrants with a local doctor, National Insurance Number registrations by workers from abroad and the registration of foreign students for higher education, can provide data to better understand patterns and trends in international migration. The paper proposes a model for the estimation of immigration at a local level, integrating existing national estimates from the Office for National Statistics with data from these administrative sources. The model attempts to circumvent conceptual differences between datasets through the use of proportional distributions rather than absolute migrant counts in the estimation process. The model methodology and the results it produces provide alternative estimates of immigration for consideration by the Office for National Statistics as it develops its own programme of improvement to sub-national migration statistics.