Accepted author manuscript, 526 KB, PDF document
Available under license: CC BY: Creative Commons Attribution 4.0 International License
Final published version
Licence: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
Research output: Contribution to Journal/Magazine › Journal article › peer-review
<mark>Journal publication date</mark> | 1/06/2024 |
---|---|
<mark>Journal</mark> | Regional Studies |
Issue number | 6 |
Volume | 58 |
Number of pages | 18 |
Pages (from-to) | 1264-1281 |
Publication Status | Published |
Early online date | 11/08/23 |
<mark>Original language</mark> | English |
A rural–urban poverty gap exists in most countries around the world, and this paper employs a novel approach to explain this difference, using logistic regression to examine the effects of rural–urban residence type, individual socio-economic and demographic characteristics, and changes in government policies on the likelihood of being poor in England. Unusually, rural areas in England have lower poverty rates than urban areas, so the direction of the typical rural–urban poverty gap is reversed, but the method employed here would be applicable in either direction. We disaggregate micro-data from the Understanding Society Survey (USS) into three residence types (predominantly rural; significantly rural and predominantly urban), and combine these USS data with information on changes in councils’ spending power, in service spending and in per capita income lost from cuts to welfare benefits since 2010. The results demonstrate that rural residence provides a buffer against poverty in England, a so-called ‘rural advantage effect’, but this is reduced or becomes non-significant after controlling for individual socio-economic and demographic characteristics and changes in government policies. Furthermore, working-age poverty has increased more rapidly in rural areas than urban between 2010 and 2018. Our analysis also reveals how national policies have differential spatial impacts on local populations according to their diverse characteristics.