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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.

A rural-urban poverty gap exists in most countries in the world and this paper employs a novel approach to explaining 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. Our 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 2010-18. Our analysis also reveals how national policies have differential spatial impacts on local populations according to their diverse characteristics.

In 1997 approximately two million people aged 60 years or over were living poverty in the UK. In 1999 the UK Government raised real pension incomes of low-income pensioners by around a third through the introduction of the Minimum Income Guarantee (MIG). This study explores the implications of this change for pensioners' mental wellbeing with a focus on differences by area level deprivation in England. We explore mental wellbeing outcomes of 205 men (750 person-year observations) and 367 women (1,336 person-year observations) of state pension age from scores on the General Health Questionnaire from the British Household Panel Survey using a panel difference-in-difference estimation procedure. We compare the mental wellbeing of pensioners receiving MIG to that of low-income pensioners not claiming MIG, from 1998 to 2002. To investigate differences by area deprivation we use quintiles of the of the distributions of the 2000 and 2019 local-authority-level English Index of Multiple Deprivation. Models controlled for age, marital status and year. Between 1998 and 2002, 136 (38%) of low-income women and 57 (28%) of low-income men in the sample were claiming MIG at any one time. Income increased by 31% for men and 22% for women. There was no change in mental wellbeing for women but we found an improvement for men overall and for men living in the most deprived areas, in the latter case with a decrease of the GHQ-12 score of 2.43 points (95% CI: -5.49, 0.02). This estimate was similar across all measures of deprivation, and across both years of IMD. This study provides tentative evidence that the increase in pension income in England for low-income pensioners contributed to a reduction of inequalities in mental wellbeing for men. This needs to be considered in terms of future state pension policies. [Abstract copyright: Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.]

Promoting good health across the life course is high on countries agenda. There is a growing evidence base that health is correlated across generations. We examine the persistence of physical and mental health status across generations and explore how different early life factors and adult outcomes impact on this association. In particular, we focus on childhood disadvantage and childhood health, educational attainment, and social mobility measured by household income compared to one's parents. We use data from 19 waves of the Household, Income and Labor Dynamics in Australia (HILDA) Survey. The analysis is restricted to young adults (aged 25–35 years old in 2019) and their parents. We find an intergenerational correlation in health which ranges from 0.19 for physical health to 0.20 for the QALY and 0.21 for mental health. After we include covariates related to childhood disadvantage, childhood health, educational attainment, and social mobility, the intergenerational correlations are reduced to 0.13 for physical health, 0.18 for mental health, and 0.14 for QALYs. We find that early life disadvantage is the only factor influencing the intergenerational correlation for all health measures. Policy focusing on reducing the negative impact of early life disadvantage is likely to have a larger impact on improving health across the life course and reducing intergenerational health inequalities.

Reducing energy poverty will help improve the nation's health and help achieve sustainability. Using sixteen years of the Household, Income and Labour Dynamics in Australia (HILDA) Survey, we study the dynamics, persistence and determinants of energy poverty. Results indicate that energy poverty in Australia is generally a temporary condition, yet a non-negligible share of the Australian population (ranging from 0.41% to 8.1% depending on the energy poverty indicator used) is exposed to persistent energy disadvantage. Thus, examining the dynamics of energy poverty is essential to make sure that policy targets are effective and reach those in need. Single individuals (whether elderly or not), single-parent households and those with a disabled household member are at high risk of persistently experiencing energy poverty in Australia. This is also true for non-working individuals and immigrants from non-English speaking countries. In contrast, highly educated individuals, those living in metropolitan areas and homeowners face lower likelihoods of persistently experiencing energy poverty. Government investment in energy efficiency for houses and apartments is crucial to generate savings in electricity bills, healthier homes and evident reductions in carbon emissions.

Promoting good health across the life course is high on countries agenda. There is a growing evidence base that health is correlated across generations. We examine the persistence of physical and mental health status across generations and explore how different early life factors and adult outcomes impact on this association. In particular, we focus on childhood disadvantage and childhood health, educational attainment, and social mobility measured by household income compared to one's parents. We use data from 19 waves of the Household, Income and Labor Dynamics in Australia (HILDA) Survey. The analysis is restricted to young adults (aged 25–35 years old in 2019) and their parents. We find an intergenerational correlation in health which ranges from 0.19 for physical health to 0.20 for the QALY and 0.21 for mental health. After we include covariates related to childhood disadvantage, childhood health, educational attainment, and social mobility, the intergenerational correlations are reduced to 0.13 for physical health, 0.18 for mental health, and 0.14 for QALYs. We find that early life disadvantage is the only factor influencing the intergenerational correlation for all health measures. Policy focusing on reducing the negative impact of early life disadvantage is likely to have a larger impact on improving health across the life course and reducing intergenerational health inequalities.