Estimating the local employment impacts of immigration: A dynamic spatial panel model. (October 2020)

• Record Type: Journal Article
• Authors: Fingleton, Bernard; Olner, Daniel; Pryce, Gwilym
• Is Part Of: Urban studies. Volume 57:Number 13(2020:Oct.)
• Page Start: 2646
• Page End: 2662
• Publication Date: 2020-10
• Subjects: demographics -- diversity/cohesion/segregation -- employment/labour -- lump of labour fallacy -- migration -- race/ethnicity -- spatial panel models; 人口统计 -- 多样性/凝聚力/隔离 -- 就业/劳动力 -- 固定劳动总量谬论 (lump of labour fallacy) -- 移民 -- 种族/族裔 -- 空间面板模型; Cities and towns -- Periodicals; City planning -- Periodicals; 307.1216
• DOI: 10.1177/0042098019887916
• Languages: English
• ISSNs: 0042-0980

Abstract

This paper highlights a number of important gaps in the UK evidence base on the employment impacts of immigration, namely: (1) the lack of research on the local impacts of immigration – existing studies only estimate the impact for the country as a whole; (2) the absence of long-term estimates – research has focused on relatively short time spans – there are no estimates of the impact over several decades, for example; (3) the tendency to ignore spatial dependence of employment which can bias the results and distort inference – there are no robust spatial econometric estimates we are aware of. We aim to address these shortcomings by creating a unique data set of linked Census geographies spanning five Censuses since 1971. These yield a large enough sample to estimate the local impacts of immigration using a novel spatial panel model which controls for endogenous selection effects arising from migrants being attracted to high-employment areas. We illustrate our approach with an application to London and find that no migrant group has a statistically significant long-term negative effect on employment. EU migrants, however, are found to have a significant positive impact, which may have important implications for the Brexit debate. Our approach opens up a new avenue of inquiry into subnational variations in the impacts of immigration on employment.