HEALTH BEGINS AT HOME

UNDERSTANDING HEALTH INEQUALITIES IN OXFORDSHIRE

REPORT COMPILED BY YEONJOO LA PUBLISHED 28/05/2016

HEALTH BEGINS AT HOME UNDERSTANDING HEALTH INEQUALITIES IN OXFORDSHIRE

THIS REPORT WAS COORDINATED & COMPILED BY YEONJOO LA IT WAS PUBLISHED ON 28/05/2016.

ACKNOWLEDGEMENTS

This paper was written by a team of Oxford University students: Benjamin Mappin-Kasirer, Caspar Kaiser, Claudette Hewitt, Dipal Patel, Emily Rosen, Gabriel Bernard-Harding, Jessica Mundy, Nancy Colombe, Thomas Colthorpe, Tobias Gill, and Yeonjoo La, with research help from Tommy Lees. Particular thanks go to Stephen Jones for his inspiring talk at our report launch event. Operational support was provided by the committee members: Cristian Leata, Rachael Midlen, Natascha Eichinger, Bastian Betthaeuser, and Rosa von Gleichen. The report was edited by Yeonjoo La. We thank the Oxford Hub for their continued support of OxPolicy.

ABOUT OXPOLICY

OxPolicy is a student-run think tank based in Oxford. We are linked with the Oxford Hub, and seek to bring together different elements of the university and local community to produce inter-disciplinary work and generate a fresh perspective on policy work. We aim to engage people in the policy process, educate people on pressing social issues and empower voices that would otherwise have no influence on important issues. Our reports and events provide concise overviews of complex issues and outline practical, implementable solutions. We are structurally and operationally independent of the University and our sponsors. As such the views expressed in this summary and in the paper are our own, and do not necessarily reflect those of the Oxford Hub as a whole, or of our sponsors. Get in touch at contact@oxpolicy.co.uk.

THIS REPORT IS ALSO AVAILABLE ONLINE OR BY REQUEST. COVER PHOTO CREDIT: TOMMY LEES

HEALTH BEGINS AT HOME

CONTENTS

Executive Summary .......................................................................................... 4

Introduction ......................................................................................................... 5

Section One | Background .............................................................................. 6

Section Two | Empirical Analysis ............................................................. 10

Section Three | Current Policy within Oxfordshire ........................... 25

Section four | Policy Suggestions .............................................................. 31

Conclusion .......................................................................................................... 33

Appendix | Robustness Checks and Validity of Results ................... 34

Bibliography ...................................................................................................... 41

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EXECUTIVE SUMMARY

The impact of socio-economic backgrounds on health outcomes in contemporary society is both extensive and avoidable. Theoretically, all inhabitants of England have access to the same NHS health services and the quality of that care should remain geographically constant. However, people facing different social circumstances experience avoidable differences in health. It is therefore safe to assume that health inequalities are inextricably linked to social disparities. As a focused case study, we investigated the most relevant factors for determining health inequalities in Oxfordshire. We collected cross-sectional data at the level of the Middle-Layer Super Output Area (MSOA) from Public Health England and the Office for National Statistics. Using a multiple regression model, we tested the significance of income, education, unemployment, income deprivation, overcrowding, fuel poverty, and urban-rural divide on health outcomes. Life expectancy and subjective health were used as primary dependent variables, both of which capture health outcomes on a broad basis. Our results show that income deprivation and the overcrowding of housing are the two factors that have a significant negative impact on health outcomes in Oxfordshire. Based on these results and an analysis on the current policies in the county, we proposed the following five policy suggestions:

(1) We recommend the County Council should advocate all employers within the county to

become accredited Living Wage employers.

(2) We recommend the County Council targets spending and strengthens the provision of early

childhood development initiatives.

(3) We recommend the implementation of an Oxfordshire-wide program modelled after the city

of Liverpool’s ‘Healthy Homes’ initiative.

(4) We recommend providing substantial improvement in information and education for both

landlords and tenants.

(5) We recommend the City Council should attempt to improve the standard of HMO housing.

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INTRODUCTION

It is documented that in England people facing different social circumstances experience

avoidable differences in health. Across a range of health markers, people from poorer areas

exhibit worse health than people from wealthier areas.1 The term ‘health inequities’ is applied

to differences in health that are controllable or remediable, such as those arising from

inequalities in the conditions in which people are born, grow, live, work and age. These

conditions can affect a range of health markers, including life expectancy, disability-free life

expectancy and subjective health. The social gradient that exists in health dictates that the

higher one’s social position, the better one’s health is likely to be. In 2008 the World Health

Organization (WHO) Commission on Social Determinants of Health went so far as to claim that

‘social injustice is killing on a grand scale’.2 Tackling health inequalities is widely considered to

be necessitated by a concern for fairness and social justice, thus becoming an important goal for

policymakers nationwide.

We set out to identify the most relevant factors that determine health inequalities in

Oxfordshire. We examine health inequalities at a county level because local government is best

placed to influence wider factors that determine health inequalities. Specifically, Oxfordshire is

interesting as it is a relatively affluent county compared to other parts of England, yet it still

exhibits considerable variation in health outcomes amongst people of different socio-economic

status.

We use cross-sectional data collected at the level of the Middle-Layer Super Output Area

(MSOA)3 to identify causes of health inequalities in Oxfordshire. Data were sourced from Public

Health England (PHE) and the Office for National Statistics (ONS) and encompass many

variables that have previously been associated with health. The relationship between

independent variables and health outcomes can then be empirically evaluated. Policy changes

that can reduce systemic health inequalities within Oxfordshire are then recommended on the

basis of this analysis.

1 Marmot M et al. Fair society, healthy lives: the marmot review: strategic review of inequalities in England post-2010. London: The Marmot Review, 2010. 2 Commission on Social Determinants of Health. CSDH final report: closing the gap in a generation: health equity through action on the social determinants of health. Geneva: World Health Organization, 2008. 3 According to the Office for National Statistics, MSOAs are geographic units designed to improve the reporting of small area statistics. In consultation with local authorities, 7,194 MSOAs in England (6,781) and Wales (413) were built. MSOAs have a minimum population of 5,000 (an average of 7,200) and a minimum resident household of 2,000 (an average of 3,000).

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SECTION ONE | BACKGROUND

Health inequalities in England are evident across a number of measures of health outcomes

including life expectancy, self-reported health, disease specific mortality, and death from

injuries.4 Despite universal access to health care provided by the NHS, people living in the

poorest neighbourhoods of England will die an average of seven years earlier than those living

in the richest neighbourhoods.5 The disparity in health, however, becomes even more

prominent than this suggests if we consider disability free life expectancy, for which the

correlative difference is 17 years.6

While Oxfordshire is a relatively affluent county compared to other parts of England, there is

still considerable variation in deprivation and health outcomes. In Oxford city, for example, men

in the wealthiest areas live an average of 8.8 years longer than those in the most deprived

areas.7 Given that, theoretically, everyone living in Oxfordshire has access to the same level of

health care provision, the social determinants of health are likely to explain, at least some of, the

health differences. Experiences in the early years of life, education provision, employment,

income, and community all have the potential to influence health.8

Experiences begin to influence health outcomes in-utero. The nutrients received during foetal

stages can permanently alter development and low-birth weight is often associated with poorer

health outcomes in later life.9 Disadvantaged mothers are more likely to give birth to a baby of

lower birth weight due to the lack of proper nutrition, higher levels of stress, alcohol and

tobacco use during pregnancy.10 The first year of life is critical for neuro-development and a

child’s social, emotional, and physical development influence his or her readiness for school,

educational achievement, and job prospects later in life.11 This, in turn, affects socioeconomic

status, perpetuating health inequalities across generations.

Educational attainment also has an impact on long-term health outcomes. Higher educational

attainment is associated with higher self-reported health, lower mortality rates and lower

incidence of depression and obesity.12 Explanations for this association fall into three categories:

work and economic conditions, social-psychological resources and health lifestyle. Better

education indirectly improves health by increasing the likelihood of full-time work and high

income, which are both associated with improved health outcomes. In addition, those with

4 Op. cit., Marmot M et al. Fair society, healthy lives. 5 Ibid. 6 Ibid. 7 Oxford City Council. Oxford’s health. https://www.oxford.gov.uk/info/20127/health/457/oxfords_health (15 February 2016, date last accessed). 8 Op. cit., Marmot, Fair society, healthy lives. 9 Gluckman PD and Hanson MA. Adult disease: echoes of the past. European Journal of Endocrinology 2006:155(suppl_1):S47-S50. 10 Jefferis BJMH, Power C, and Hertzman C. Birth weight, childhood socioeconomic environment, and cognitive development in the 1958: British birth cohort study. BMJ 2002:325:305. 11 Perry BD. Childhood experience and the expression of genetic potential: what childhood neglect tells us about nature and nurture. Brain and Mind 2002:3:79100. 12 Centre for the Wider Benefits of Learning. The wider benefits of learning: a synthesis of findings from the Centre for Research on the Wider Benefits of Learning 1999-2006, Research Brief RCB05-06. Nottingham: DfES, 2006.

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higher educational attainment report a greater sense of control over their lives and their health,

which is associated with good health. Finally higher educational attainment is associated with

better health behaviour: the well-educated are less likely to smoke and are more likely to

exercise and drink moderately, all of which are associated with good health.13 Although

education is universally available and free in England up to the age of 18, there are significant

variations in educational outcomes that are associated with the socioeconomic status of the

learner14 and thus inequalities in education are a factor that can contribute to health

inequalities within England.

Health status has also been shown to vary with work status. An extensive report on the effect of

work on well-being showed that unemployment is associated with higher mortality, poorer

general health, poorer mental health and higher hospital admission rates. These effects were

mitigated by re-employment. Although this evidence could be explained by a health selection

effect - those who stay in work manage to do so because they are healthier - there is also

considerable evidence of cause and effect. It has been shown that work is beneficial for health

and well-being as it provides economic resources, meets important psychosocial requirements

and is central to individual identity.15

Income and health are closely intertwined and the relationship operates in many ways.

Literature on the subject generally postulates two hypotheses to explain the observed

associations. First, the ‘absolute income thesis’16,17 simply states that, on the individual level,

low absolute income causes worse health (e.g. via worse nutrition or reduced access to

healthcare). However, this relationship is seen to be concave, implying that each additional unit

of income is associated with a decreasing positive effect on health. In contrast, the ‘relative

income thesis’18,19 asserts that inequality itself exerts a negative effect on health that is

independent of absolute incomes accrued to individuals. This thesis is typically grounded in a

psychosocial mechanism: greater inequality leads individuals to experience an increased level

of stress and negative effects as a consequence of one’s position in the social hierarchy being

perceived less favourably.12 It is important to remember that this relationship is not one-way:

low income can cause poor health, while poor health can result in a reduced ability to earn

higher incomes.

Health inequalities also become apparent when investigating communities, especially in terms

of urban-rural divides, social gradients and housing. People on low incomes in the UK are more

13 Ross C E and Wu C. The links between education and health. American Sociological Review 1995:60(5):719-45. 14 Cassen R and Kingdon G. Tackling low educational achievement. York: Joseph Rowntree Foundation, 2007. https://www.jrf.org.uk/sites/default/files/jrf/migrated/files/2063-education-schools-achievement.pdf (21 November 2016, date last accessed). 15 Waddell G and Burton A. Is work good for your health and well-being? London: The Stationery Office, 2006. https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/214326/hwwb-is-work-good-for-you.pdf (21 November 2016, data last accessed). 16 Subramanian SV and Kawachi I. Income inequality and health: what have we learned so far? Epidemiologic Reviews 2004:26(1):78-91. 17 Stanistreet D, Scott-Samuel A, and Bellis MA. Income in inequality and mortality in England. Journal of Public Health 1999:21(2):205-207. 18 Op. cit., Subramanian SV and Kawachi I. Income inequality and health: what have we learned so far? 19 Lynch JW, Smith GD, Kaplan GA, and House JS. Income inequality and mortality: importance to health of individual income, psychosocial environment, or material conditions. BMJ 2000:320(7243):1200.

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likely to live in urbanised areas, which are associated with a variety of health problems. For

example, poorer communities are likely to live in homes that are exposed to extreme weather

conditions, and are also less likely to have access to insurance against the risks associated with

storm and flood damage. The urban-rural divide is interesting for looking at health inequalities

due to the importance of green spaces. Research has found that living in close proximity to areas

of green space (such as parks and woodland) improves both physical and mental health, even

when social class is controlled for.20 Natural, green areas have been associated with a decrease

in a multitude of health complaints, such as blood pressure and cholesterol, lowered stress

levels, better perceived general health, the ability to face problems and improved mental health.

Moreover, urban areas have higher concentrations of pollution, meaning poorer communities

are at risk of contracting cardio-respiratory illnesses and other diseases associated with air

pollution. It is estimated that each year in the UK, short-term air pollution is associated with

12,000 to 24,000 premature deaths.21 Therefore, it is clear that income is an important factor in

determining where people live (urban or rural), which has an indirect effect on their health

status, and whether or not they have access to these green spaces which promote health and

well-being. The Marmot Review stresses creating a physical environment in which people can

live healthier lives with a greater sense of well-being as a significant factor in reducing health

inequalities.22

The urban-rural divide is not the only important factor determining health inequalities among

communities in the UK. Poorer populations often comprise estates of mainly socially rented

housing, which is often located in deprived areas.23 Nearly half of all social housing is now

located in the most deprived fifth of neighbourhoods. While the quality of housing is important

for health, a large proportion of the health problems associated with social housing can be

explained by the subset of the population who occupy these residences. Because the supply of

social housing has been reduced over the past 25 years, there has been a ‘residualisation’ effect

on the characteristics of the people who require it. Therefore as a group, they have higher rates

of unemployment, ill health and disability than the average for the UK population.24 To some

extent, this skews the literature and can convey the idea that poor quality social housing is

causing health problems. Although this might be true, it is important to acknowledge that

people occupying social housing may be more predisposed to suffering from poor health

anyway. Poverty rates are double that of the population as a whole for people living in social

housing, with fewer than half in any form of paid work.

The link between quality of housing and the health of occupants is well established in the

literature regarding the social determinants of health.25 Poor housing conditions, including

homelessness, temporary accommodation, overcrowding, insecurity, and housing in poor

physical condition, constitute a risk to health.26 Such environments are associated with a variety

20 Ibid. 21 Ibid. 22 Op. cit., Marmot M et al. Fair society, healthy lives. 23 Op. cit., Lynch JW et al. Income inequality and mortality: importance to health of individual income, psychosocial environment, or material conditions. 24 Ibid. 25 Howden-Chapman P, Isaacs N, Crane J, and Chapman R. Housing and health: The relationship between research and policy. International Journal of Environmental Health Research 1996:6(3):173-185. 26 Op. cit., Marmot M et al. Fair society, healthy lives.

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of health conditions, including respiratory infections, asthma, lead poisoning, injuries, and poor

mental health.27 A British survey that used a double-blind procedure measured dampness and

health in 597 houses in the UK, and found that adults living in damp and mouldy dwellings were

more likely to report a wide range of poor-health symptoms (nausea, vomiting, constipation,

blocked noses, breathlessness, backache, aching joints, fainting and nervousness) than those

living in dry housing conditions. Interestingly, these differences remained after controlling for

socioeconomic status and smoking. Fuel poverty is another causal factor of health inequalities in

the UK. A household is said to be in fuel poverty if it needs to spend more than 10% of its

income on fuel to sustain a satisfactory level of warmth. In England, this is defined as 21°C in the

living room and 18°C in other rooms.28 Fuel poverty needs to be addressed because cold

housing poses a severe risk to health. Cold is believed to be the main explanation for the extra

winter deaths occurring each year between December and March. It is estimated by the World

Health Organisation that 30% of excess winter deaths are due to people living in cold homes.

Since 2010, 155,720 excess winter deaths have occurred in the UK, with 46,700 estimated as

being due to cold homes.29 Being able to afford to keep a warm home is clearly an important

factor for maintaining good health, and these deaths could be prevented if people were kept

warm during the winter months. Single parent households and households with pensioners

appear to be the most vulnerable in terms of fuel poverty.30

27 Krieger J and Higgins DL. Housing and health: time again for public health action. Public Health 2002:92(5):758-768. 28 Energy UK. Fuel Poverty. http://www.energy-uk.org.uk/policy/fuel-poverty.html (3 March 2016, date last accessed). 29 Association for the Conservation of Energy. Chilled to death: the human cost of cold homes. London: Westgate House, 2015. http://www.ukace.org/wp-content/uploads/2015/03/ACE-and-EBR-fact-file-2015-03-Chilled-to-death.pdf (3 March 2016, date last accessed). 30 Op. cit., Marmot M et al. Fair society, healthy lives.

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SECTION TWO | EMPIRICAL ANALYSIS

The aim of the empirical analysis is to evaluate the causes of health inequalities within

Oxfordshire using a cross-sectional multivariate linear regression model. This model is outlined

in section 1, before data sources and definitions of dependent and independent variables will

then be described in detail. Our results are then presented and a discussion of their implications

follow.

2.1 Empirical Model

Health outcomes will be modelled at the MSOA level as a function of a number of determinants

of health: income level, educational attainment, housing quality, employment status and fuel

poverty. Data for nutrition during early childhood were unavailable therefore were not included.

Variables indicating the impact of age and of living in an urban environment compared to living

in a rural environment are included as control variables.

Therefore, the following relationship will be estimated:

(1)

where the dependent variable, is health outcomes in MSOA i. The independent variables are

income, , income squared, , age, , education, , the rate of unemployment,

, the percentage of the population defined as income deprived, , the percentage of houses defined as overcrowded, , and the percentage of individuals in fuel poverty, and a dummy variable capturing whether an MSOA is urban or rural, . The error term, , is a robust error term for heteroskedasticity.

2.2 Data

MSOAs are the unit of analysis for our study and measurements of the variables considered refer to averages within these local areas. The data used in our model came from two sources: Public Health England (PHE)31 and the Office for National Statistics (ONS)32, which are both large databases of population statistics on, amongst other things, variables relating to health and economic well-being. To be included within the study, the data had to be available at the MSOA level within Oxfordshire. Most variables were present for all 86 MSOAs within the county, but there were several which had missing values for different reasons (see column 1 in Table 1). Table 1 shows descriptive statistics for the seven dependent and nine independent variables used within the study.

31 Public Health England. Local Health. http://www.localhealth.org.uk/#sly=ward_2013_DR;sid=5564;v=map4;l=en;z=404698,239790,91209,59144 (18 February 2016, date last accessed). 32Office for National Statistics. Time series explorer. http://www.ons.gov.uk/ons/datasets-and-tables/index.html (18 February 2016, date last accessed).

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Table 1. Descriptive Statistics

(1) (2) (3) (4) (5)

VARIABLES Number/total Mean

Standard deviation Min Max

Dependent Variables

Life Expectancy Males (Years) 86 80.64 2.305 74.70 85.60

Life Expectancy Females (Years) 86 84.45 2.446 77.20 90.80 Percentage of Individuals with bad or very bad subjective Health (%) 85 3.586 0.871 1.600 5.700 Deaths from Coronary Heart Disease (Deviation from National Index set at 100) 85 78.20 20.95 34.60 151.3 Deaths from Circulatory Disease (Deviation from National Index set at 100) 85 84.50 17.89 49.70 160.7 Deaths from Respiratory Diseases (Deviation from National Index set at 100) 85 89.90 27.24 42.40 168.6 Deaths from all forms of Cancer (Deviation from National Index set at 100) 86 96.07 15.02 53.60 142.6

Independent Variables

Equivalised Income after Housing (£, weekly) 86 576.5 79.63 369.0 737.4

Equivalised Income after Housing, squared 86 338,596 88,129 136,191 543,759

Percentage of Individuals over the Age of 65 (% of total) 85 17.03 5.306 4.300 26.50 GCSE Achievement (& of total achieving A*-C in GCSE examination) 83 59.04 11.96 32.90 83.30

Unemployment Rate (%) 85 1.604 0.928 0.500 5.200

Percentage of Individuals Categorised as Income Deprived (%) 85 7.907 4.754 2.800 26.60

Percentage Households that are Overcrowded (%) 85 6.696 4.869 1.800 23.70

Urban Dummy 86 0.961 0.194 0 1

Percentage of Individuals in Fuel Poverty (%) 86 8.165 2.820 2.880 21.72

Oxfordshire has some notable characteristics compared to other British counties which are relevant for our empirical analysis. On average, male inhabitants of Oxfordshire have a lower life expectancy compared to the national average for males of 83.3.33 The standard deviation of income net of housing costs is 0.15% of mean income net of housing which indicates an unequal distribution of income across the 85 MSOA’s within the county. This is confirmed by the high average percentage of individuals categorised as income deprived across MSOA’s which also has high variability. The rate of unemployment is extremely low, however, a high percentage of houses are classified as overcrowded.

2.3 Dependent Variables

Life expectancy and subjective health were used as primary dependent variables, which both capture health outcomes on a broad basis. In addition, using these variables captures how both objective health outcomes (life expectancy) as well as individuals’ own evaluation of their health (subjective health) are affected by the independent variables of interest. These variables are

33 Office for National Statistics. National Life Tables, United Kingdom: 2011-2013. http://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/lifeexpectancies/bulletins/nationallifetablesunitedkingdom/2014-09-25 (22 May 2016, date last accessed).

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further accompanied by disease specific mortality rates to allow for a more detailed analysis. Below a brief description of the exact operationalisation of our variables is provided.

Life expectancy of males or females is period life expectancy, defined as the average number of years a person would live if they experienced the particular MSOA’s age-specific mortality rates for that period throughout their life. It is based on rolling three year aggregate periods (the data are produced on an annual basis), and this study used the data from 2009-13. The percentage of individuals who register as having ‘bad’ or ‘very bad’ health is based on answers to a question on self-rated health in the 2011 Census, which offered five answer categories (very good health, good health, fair health, bad health, very bad health). The percentage is calculated by dividing the number of respondents answering that they have bad or very bad health, divided by the total number of respondents (and then rounded to one decimal place) in order to avoid underestimating the true value.

For the four disease measures (Coronary Heart Disease, Circulatory Disease, Respiratory Disease, and Cancer), PHE’s data for deaths at all ages were chosen. These figures are standardised mortality ratios (SMR), where 100 is the expected value for each MSOA given the national average. More specifically, SMR is equal to the observed deaths divided by the expected deaths (calculated using age-specific death rates for the MSOA’s population), then multiplied by 100. The SMR is calculated from deaths over the period 2008-12. The diseases were selected to cover a broad spectrum of conditions with potentially diverse roots in the social determinants of health.

2.4 Independent Variables

Equivalised net income after housing costs was used because it best captures disposable income available to households. This value squared was also included, an approach often used by literature dealing with the relationship between income and health to account for its concavity.34 This is one of a variety of income measures available at the MSOA level (produced by ONS) and is the sum of net income of every member of the household, which includes income from benefits (such as Working Families Tax Credit), but is net of income tax payments; national insurance contributions; domestic rates/council tax; contributions to occupational pension schemes; all maintenance and child support payments, which are deducted from the income of the person making the payments; and parental contribution to students living away from home. Further, it is subject to the Organisation of Economic Co-operation and Development’s equivalisation scale, which takes into account the household composition, assigning as value of 1 to the primary earner (the ‘first adult’), 0.7 to the second adult and each subsequent person aged over 14, and 0.5 to each child aged under 14. Housing costs are deducted from the income, which include rent (gross of housing benefit); water rates, community water charges and council water charges; mortgage interest payments (net of any tax relief); structural insurance premiums (for owner occupiers); and ground rent and service charges. These data were from 2011/12.

Further, the percentage of the population over 65 was used as a means of understanding the age profile of each MSOA. This figure is based on estimates of single years of age aggregated to total populations and populations aged 65 and over within each MSOA in 2011, and the percentage is calculated by dividing the latter by the former and multiplying by 100. GCSE achievement is

34 Deaton A. Health, inequality, and economic development. Journal of Economic Literature 2003:XLI:113–158.

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defined as the percentage of pupils achieving 5 or more A*-C including English and mathematics, or the equivalent percentage of pupils at the end of Key Stage 4 at schools maintained by Local Authority. These figures are for the school year 2011-12. The unemployment rate is expressed as the percentage of working-age population (16-64) claiming out of work benefit in the period April 2012 to March 2013. This measure was chosen over long term unemployment statistics, which look at individuals claiming Job Seeker’s Allowance (JSA) for over 12 months. Income deprivation is defined as the percentage of the population living in low-income families who are dependent on means-tested benefits. The value of Index of Multiple Deprivation (IMD)35 income domain score was applied to mid-2009 population estimates to obtain an estimate of people living in low-income families. The overcrowding indicator shows the percentage of houses with 1 or more rooms too few given the composition of the household and relationships between occupants. It is used as a way of showing low quality housing. To generate the percentage value, the occupancy ratio (OR) was first calculated using 2011 Census data, and a house was considered as overcrowded if the OR was less than -1.36 The fuel poverty measure assesses the proportion of households who are characterised as ‘fuel poor’37, which is based on a modelled estimate of the number of households within each LSOA that cannot afford to sufficiently heat all the rooms in their house based on household composition, and then aggregated up to MSOA level. The data are from 2013, but calculates the extent of fuel poverty across two years (i.e. 2012-3). Finally, the dummy variable for whether an MSOA is classified as urban or rural was based on this population density data: an area was considered urban if it has a population density of over 1,000, and would receive a value of 1.

2.5 Hypotheses

Given the independent variables to be investigated, a number of hypotheses were posited. Applied to our dataset, they are:

H1: A higher average equivalised income after housing will result in better health outcomes.

H2: A higher percentage of individuals with 5 GCSE’s at grade C-A* will result in better health outcomes.

H3: A higher unemployment rate will result in worse health outcomes.

H4: A higher percentage of individual categorised as income deprived will result in worse health outcomes.

H5: A higher percentage of households that are overcrowded will result in worse health outcomes.

H6: A higher percentage of individuals in fuel poverty will result in worse health outcomes.

35 This measure describes deprivation in terms of proportions of deprived people (according to income), hence allowing a direct comparison between areas of different deprivation levels. The income domain score takes into account various measures on income, and is one of seven different domains which form the full IMD measure. It is important to realise that there will be some deprived people living in not deprived areas, and vice-versa. 36 The occupancy ratio is calculated by subtracting the notional number of bedrooms in a dwelling (recommended by the bedroom standard, which accounts for numerous household occupant characteristics) from the actual number of bedrooms. An OR of <-1 means there is more than 1 too few bedrooms for the household. 37 While this may be thought of as correlated with income deprivation, statistical analyses revealed that it was not, and therefore it is unproblematic to include both measures as independent variables. This is further discussed in the ‘Robustness Checks’ section.

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2.6 Results

For each of our seven dependent variables the favoured specification is found in columns (8) of the following tables 2-8.

Life Expectancy Males: Three independent variables are statistically significant (defined as p<0.05): the percentage of households which are income deprived, the percentage of houses which are overcrowded, and the urban dummy (for all, p<0.01). A one percent increase in the percentage of households’ classified as income deprived is associated with a reduction in the average life expectancy of males by 0.33 years, which is approximately four months. A corresponding increase in the percentage of houses overcrowded is associated with a reduction in the average life expectancy of males by 0.12 years, approximately one and a half months. Therefore, the magnitude of the correlation of income deprivation and health is greater than the correlation of overcrowded housing. The urban dummy shows that on average MSOA’s in an urban area have a life expectancy of males that is 1.17 years, approximately 14 months, higher than in rural areas.

Income is initially statistically significant in regressions of columns (1)-(3) of Table 2 but fails to be so once unemployment is controlled for. In turn, once education is controlled for, unemployment is no longer a statistically significant variable. The age and fuel poverty variables are also insignificant in our preferred specification.

Life Expectancy Females: Only one independent variable is significant for the life expectancy of females: the urban dummy (p<0.05). It indicates that on average MSOA’s in an urban area have a life expectancy of females that is 1.6 years, approximately 20 months, higher than in rural areas. In contrast to the life expectancy of males measure of health, the overcrowded housing variable and the percentage of households which are income deprived are not statistically significant.

Percentage of Individuals with bad or very bad subjective health: Three independent variables are statistically significant. The percentage of households which are income deprived and the percentage of individuals over 65 are significant are highly significant (p<0.01) in our preferred specification (column (8) in Table 4). The square of the income variable is just significant (p<0.1). A one percentage point increase in the proportion of the population over the age of 65 is associated with a 0.063 percentage point increase in the percentage of individuals with bad subjective health. A corresponding increase in the percentage of individuals classified as income deprived is associated with a 0.14 percentage point decrease in the percentage of individuals with bad subjective health. Once again, the magnitude of the correlation of income deprivation on health is larger than that of housing quality.

Income is initially statistically significant (p<0.01) in columns (1)-(3) of Table 4 but fails to be so once unemployment is controlled for. In turn, once income deprivation is controlled for unemployment is no longer a statistically significant variable. In contrast to the life expectancy of males measure of health the overcrowded housing variable and urban dummy are statistically insignificant.

Deaths from All Forms of Cancer: The percentage of households which are income deprived, the percentage of houses which are overcrowded, the rate of unemployment and the percentage of households in fuel poverty are all highly significant (p<0.01) in our preferred specification (column (8) in Table 5). In this regression, it is likely that the reverse causality running from deaths from cancer to unemployment will bias the results, therefore the results from this

15

regression should be treated with caution. This issue is discussed further in the robustness checks.

Deaths from Coronary Heart Disease: Four variables are statistically significant in the final specification (column (8) in Table 6). The quadratic income variable is highly significant (p<0.01). The income variable and the percentage of households that are income deprived are significant at p<0.05, while the urban dummy is significant at p<0.1. A higher average income is associated with an increase in deaths from coronary heart disease, although the impact of a unit increase in weekly income on the incidence of deaths from coronary heart disease will fall as income rises. An increase in the proportion of people who are income deprived is associated with an increase in the incidence of deaths from coronary heart disease. There is a higher incidence of deaths from coronary heart disease in rural areas than in urban areas.

Deaths from Circulatory Disease: Four variables are statistically significant. The urban dummy and the percentage of individuals over the age of 65 are significant at p<0.01, the percentage of households that are income deprived is significant at p<0.05, and the quadratic income variable is significant at p<0.1.

Deaths from Respiratory Diseases: There is one significant variable, the urban dummy (p<0.01, in column (8) in Table 7). Urban areas have more deaths from respiratory diseases relative to the national average than rural areas. This is in contrast to the result for the life expectancy measures.

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Table 2. Life Expectancy Males (1) (2) (3) (4) (5) (6) (7) (8)

Equivalised Income after Housing 0.0585*** 0.0531** 0.0691*** 0.0334 -0.0149 -0.00515 -0.00226 -0.00938

(0.0212) (0.0215) (0.0197) (0.0310) (0.0321) (0.0307) (0.0302) (0.0280)

Equivalised Income after Housing, squared -3.39e-05* -2.95e-05 -4.45e-05** -1.74e-05 1.93e-05 9.85e-06 8.86e-06 1.56e-05

(1.97e-05) (2.00e-05) (1.78e-05) (2.48e-05) (2.61e-05) (2.54e-05) (2.49e-05) (2.33e-05)

Percentage of Individuals over the Age of 65

0.0332 0.0519 0.0366 0.0425 -0.00965 0.0126 0.0478

(0.0339) (0.0364) (0.0335) (0.0315) (0.0379) (0.0371) (0.0415)

GCSE Acheivement

-0.00761 -0.0159 -0.0153 -0.00380 -0.00261 -0.00247

(0.0192) (0.0192) (0.0208) (0.0197) (0.0197) (0.0179)

Unemployment Rate

-0.758 0.525 0.704 0.683 0.505

(0.511) (0.643) (0.615) (0.625) (0.574)

Percentage of Individuals Categorised as Income Deprived

-0.397*** -0.367*** -0.342** -0.327**

(0.131) (0.131) (0.131) (0.125)

Percentage of Households that are Overcrowded

-0.126*** -0.0600 -0.118**

(0.0422) (0.0554) (0.0590)

Percentage of Individuals in Fuel Poverty

-0.124** -0.0692

(0.0542) (0.0571)

Urban Dummy

1.171**

(0.509)

Constant 58.36*** 59.49*** 55.40*** 68.78*** 85.19*** 83.26*** 81.90*** 82.55***

(5.561) (5.620) (4.985) (10.42) (10.09) (9.363) (9.145) (8.442)

Observations 86 85 82 82 82 82 82 82

Adjusted R-squared 0.541 0.544 0.537 0.554 0.606 0.629 0.638 0.665

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

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Table 3. Life Expectancy Females

(1) (2) (3) (4) (5) (6) (7) (8)

Equivalised Income after Housing 0.0408 0.0447 0.0621** 0.0604* 0.0386 0.0396 0.0402 0.0305

(0.0327) (0.0330) (0.0311) (0.0355) (0.0421) (0.0416) (0.0424) (0.0411)

Equivalised Income after Housing, squared -2.19e-05 -2.51e-05 -4.05e-05 -3.91e-05 -2.27e-05 -2.36e-05 -2.38e-05 -1.46e-05

(2.95e-05) (2.94e-05) (2.78e-05) (3.06e-05) (3.59e-05) (3.54e-05) (3.60e-05) (3.50e-05)

Percentage of Individuals over the Age of 65

-0.0218 0.00744 0.00668 0.00934 0.00423 0.00915 0.0572

(0.0606) (0.0649) (0.0678) (0.0672) (0.0916) (0.0909) (0.0880)

GCSE Acheivement

-0.0159 -0.0164 -0.0161 -0.0150 -0.0147 -0.0145

(0.0256) (0.0258) (0.0262) (0.0266) (0.0267) (0.0245)

Unemployment Rate

-0.0375 0.540 0.557 0.553 0.309

(0.471) (0.630) (0.656) (0.660) (0.614)

Percentage of Individuals Categorised as Income Deprived

-0.178 -0.175 -0.170 -0.149

(0.154) (0.151) (0.154) (0.146)

Percentage of Households that are Overcrowded

-0.0123 0.00221 -0.0769

(0.0836) (0.0963) (0.0964)

Percentage of Individuals in Fuel Poverty

-0.0276 0.0479

(0.0932) (0.0886)

Urban Dummy

1.599**

(0.623)

Constant 68.35*** 67.52*** 63.10*** 63.76*** 71.14*** 70.95*** 70.65*** 71.55***

(8.951) (8.985) (8.506) (10.92) (13.29) (13.11) (13.38) (12.74)

Observations 86 85 82 82 82 82 82 82

Adjusted R-squared 0.283 0.269 0.266 0.257 0.257 0.247 0.238 0.281

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

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Table 4. Percentage of Individuals with bad or very bad subjective health (1) (2) (3) (4) (5) (6) (7) (8)

Equivalised Income after Housing -0.0141** -0.0238*** -0.0236*** -0.00772 0.00816 0.00822 0.00747 0.00760

(0.00608) (0.00532) (0.00539) (0.00726) (0.00679) (0.00700) (0.00681) (0.00704)

Equivalised Income after Housing, squared 5.89e-06 1.28e-05*** 1.24e-05** 4.01e-07 -1.16e-05** -1.17e-05** -1.14e-05** -1.16e-05*

(5.49e-06) (4.72e-06) (4.77e-06) (6.03e-06) (5.55e-06) (5.77e-06) (5.59e-06) (5.84e-06)

Percentage of Individuals over the Age of 65

0.0785*** 0.0646*** 0.0714*** 0.0694*** 0.0691*** 0.0634*** 0.0628***

(0.0147) (0.0110) (0.00944) (0.00769) (0.00945) (0.00923) (0.0101)

GCSE Acheivement

0.000355 0.00405 0.00386 0.00392 0.00362 0.00361

(0.00569) (0.00529) (0.00473) (0.00494) (0.00504) (0.00510)

Unemployment Rate

0.337*** -0.0850 -0.0840 -0.0787 -0.0755

(0.0949) (0.0965) (0.0959) (0.0946) (0.0988)

Percentage of Individuals Categorised as Income Deprived

0.130*** 0.131*** 0.124*** 0.124***

(0.0237) (0.0242) (0.0257) (0.0261)

Percentage of Households that are Overcrowded

-0.000709 -0.0177 -0.0167

(0.0111) (0.0154) (0.0158)

Percentage of Individuals in Fuel Poverty

0.0321* 0.0312

(0.0193) (0.0193)

Urban Dummy

-0.0204

(0.129)

Constant 9.690*** 11.62*** 11.84*** 5.898*** 0.504 0.493 0.846 0.834

(1.674) (1.415) (1.391) (2.209) (2.144) (2.181) (2.111) (2.131)

Observations 85 85 82 82 82 82 82 82

Adjusted R-squared 0.469 0.657 0.720 0.751 0.795 0.792 0.797 0.794

Robust standard errors in parentheses *** p<0.01, ** p<0.05, *p<0.1

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Table 5. Deaths from all forms of Cancer (1) (2) (3) (4) (5) (6) (7) (8)

Equivalised Income after Housing -0.418*** -0.463*** -0.476*** -0.476** -0.187 -0.165 -0.199 -0.216

(0.144) (0.139) (0.149) (0.213) (0.231) (0.225) (0.166) (0.172)

Equivalised Income after Housing, squared 0.000263** 0.000292** 0.000310** 0.000310* 9.04e-05 6.94e-05 8.08e-05 9.75e-05

(0.000131) (0.000125) (0.000138) (0.000180) (0.000193) (0.000188) (0.000145) (0.000150)

Percentage of Individuals over the Age of 65

0.442 0.344 0.344 0.309 0.192 -0.0631 0.0239

(0.303) (0.328) (0.323) (0.307) (0.424) (0.392) (0.417)

GCSE Acheivement

-0.0571 -0.0572 -0.0606 -0.0348 -0.0485 -0.0481

(0.102) (0.103) (0.105) (0.107) (0.110) (0.109)

Unemployment Rate

-0.00442 -7.686*** -7.286** -7.049** -7.489***

(2.893) (2.753) (2.803) (2.715) (2.765)

Percentage of Individuals Categorised as Income Deprived

2.374*** 2.441*** 2.156*** 2.194***

(0.814) (0.804) (0.690) (0.710)

Percentage of Households that are Overcrowded

-0.281 -1.035** -1.179***

(0.436) (0.432) (0.402)

Percentage of Individuals in Fuel Poverty

1.429*** 1.566***

(0.406) (0.385)

Urban Dummy

2.896

(3.253)

Constant 248.2*** 256.8*** 263.5*** 263.6*** 165.4** 161.1** 176.7*** 178.4***

(39.16) (37.52) (40.09) (67.71) (74.20) (72.04) (52.41) (53.54)

Observations 86 85 82 82 82 82 82 82

Adjusted R-squared 0.473 0.491 0.488 0.481 0.521 0.518 0.555 0.553

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

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Table 6. Deaths from Circulatory Diseases (1) (2) (3) (4) (5) (6) (7) (8)

Equivalised Income after Housing -0.451* -0.414 -0.449* -0.0924 0.301 0.349 0.344 0.438

(0.259) (0.258) (0.265) (0.303) (0.282) (0.271) (0.273) (0.275)

Equivalised Income after Housing, squared 0.000301 0.000274 0.000321 4.97e-05 -0.000248 -0.000295 -0.000293 -0.000383*

(0.000224) (0.000224) (0.000233) (0.000248) (0.000233) (0.000225) (0.000225) (0.000225)

Percentage of Individuals over the Age of 65

-0.300 -0.365 -0.212 -0.260 -0.519 -0.560 -1.023***

(0.319) (0.346) (0.331) (0.323) (0.413) (0.414) (0.386)

GCSE Acheivement

-0.118 -0.0350 -0.0397 0.0178 0.0156 0.0138

(0.183) (0.200) (0.205) (0.209) (0.209) (0.183)

Unemployment Rate

7.578 -2.866 -1.977 -1.939 0.406

(5.567) (6.736) (6.582) (6.657) (6.026)

Percentage of Individuals Categorised as Income Deprived

3.228** 3.376** 3.331** 3.131**

(1.419) (1.450) (1.497) (1.417)

Percentage of Households that are Overcrowded

-0.626 -0.745 0.0176

(0.519) (0.613) (0.660)

Percentage of Individuals in Fuel Poverty

0.226 -0.501

(0.761) (0.744)

Urban Dummy

-15.42***

(4.423)

Constant 242.5*** 235.1*** 248.2*** 114.5 -19.06 -28.68 -26.19 -34.85

(73.94) (73.73) (75.45) (101.7) (96.30) (91.77) (92.90) (93.61)

Observations 85 85 82 82 82 82 82 82

Adjusted R-squared 0.286 0.284 0.270 0.299 0.351 0.353 0.345 0.426

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

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Table 7. Deaths from Coronary Heart Disease

(1) (2) (3) (4) (5) (6) (7) (8)

Equivalised Income after Housing -0.243 -0.203 -0.278 0.213 0.574** 0.620** 0.611** 0.672**

(0.238) (0.241) (0.228) (0.262) (0.284) (0.273) (0.271) (0.279)

Equivalised Income after Housing, squared 6.22e-05 3.35e-05 0.000127 -0.000246 -0.000520** -0.000564** -0.000561** -0.000620***

(0.000210) (0.000212) (0.000202) (0.000220) (0.000232) (0.000223) (0.000220) (0.000225)

Percentage of Individuals over the Age of 65

-0.327 -0.407 -0.196 -0.240 -0.486 -0.553 -0.857

(0.419) (0.463) (0.450) (0.433) (0.576) (0.552) (0.542)

GCSE Acheivement

-0.192 -0.0775 -0.0818 -0.0274 -0.0310 -0.0322

(0.185) (0.203) (0.195) (0.195) (0.196) (0.177)

Unemployment Rate

10.44** 0.857 1.698 1.760 3.300

(4.381) (4.559) (4.253) (4.342) (4.646)

Percentage of Individuals Categorised as Income Deprived

2.961** 3.101** 3.026** 2.895**

(1.159) (1.179) (1.200) (1.292)

Percentage of Households that are Overcrowded

-0.592 -0.790 -0.289

(0.499) (0.609) (0.670)

Percentage of Individuals in Fuel Poverty

0.375 -0.103

(0.772) (0.805)

Urban Dummy

-10.12*

(5.078)

Constant 197.0*** 189.0*** 214.0*** 29.88 -92.60 -101.7 -97.59 -103.3

(66.47) (66.79) (63.92) (86.22) (97.82) (93.93) (93.39) (95.79)

Observations 85 85 82 82 82 82 82 82

Adjusted R-squared 0.426 0.424 0.401 0.448 0.480 0.481 0.475 0.497

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

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Table 8. Deaths from Respiratory Diseases (1) (2) (3) (4) (5) (6) (7) (8)

Equivalised Income after Housing -0.887*** -0.970*** -1.113*** -1.186*** -0.816* -0.772 -0.790 -0.659

(0.322) (0.313) (0.306) (0.424) (0.485) (0.495) (0.510) (0.480)

Equivalised Income after Housing, squared 0.000627** 0.000686** 0.000811*** 0.000867** 0.000586 0.000544 0.000550 0.000425

(0.000292) (0.000282) (0.000277) (0.000357) (0.000407) (0.000418) (0.000430) (0.000408)

Percentage of Individuals over the Age of 65

0.672 0.347 0.316 0.270 0.0365 -0.100 -0.751

(0.435) (0.429) (0.436) (0.416) (0.563) (0.626) (0.711)

GCSE Acheivement

0.131 0.114 0.109 0.161 0.154 0.151

(0.219) (0.227) (0.232) (0.254) (0.255) (0.234)

Unemployment Rate

-1.544 -11.36 -10.56 -10.43 -7.140

(5.947) (8.238) (8.282) (8.389) (7.503)

Percentage of Individuals Categorised as Income Deprived

3.034* 3.167* 3.014* 2.735

(1.731) (1.746) (1.808) (1.693)

Percentage of Households that are Overcrowded

-0.564 -0.969 0.102

(0.638) (0.903) (0.822)

Percentage of Individuals in Fuel Poverty

0.766 -0.255

(1.140) (1.121)

Urban Dummy

-21.63***

(7.823)

Constant 388.8*** 405.3*** 443.4*** 470.7*** 345.2** 336.5** 344.9** 332.8**

(87.50) (84.77) (81.96) (134.4) (150.2) (152.6) (157.7) (146.3)

Observations 85 85 82 82 82 82 82 82

Adjusted R-squared 0.351 0.357 0.367 0.359 0.374 0.370 0.365 0.434

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

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2.7 Discussion of Results

As subjective health captures not only physical but also mental health, the contrast in the results obtained from the life expectancy of males and females health measure with the subjective health measure can be seen as the differences in the determinants of purely physical health and overall well-being. Our results suggest income deprivation negatively impacts health. The results are present for both more objective measures, i.e., life expectancy for males, and deaths from cancer, circulatory and coronary heart disease, and more subjective measures, i.e., subjective self-reported health, of health. It is also a significant determinant of three of the four specific diseases used in the analysis. The overcrowding of housing appears to impact only on physical health with no impact on subjective health or the incidence of specific diseases. Age

has an impact on subjective health but not on other health indicators. In Oxfordshire living in an urban area appears to have a positive impact on physical health but is not a determinant of subjective health. Furthermore, living in an urban area leads to a higher incidence of cancer and respiratory diseases whilst reducing the incidence of coronary heart disease and circulatory diseases. Therefore, the data give conflicting evidence and no clear conclusion can be drawn concerning the impact of living in an urban area on health.

Overall, income deprivation and the overcrowding of housing are the factors which are significantly and negatively associated with health outcomes in Oxfordshire. Within Oxfordshire, eighteen Lower Super Output Areas (LSOAs) rank among the 20% most deprived in England. These more deprived areas typically experience worse outcomes in terms of health, which seem to be attributed to poverty.38 A number of reasons may help understand our findings. Disadvantaged groups are more likely to demonstrate a number of unhealthy behaviours, including smoking, drinking, inadequate consumption of fruit and vegetables, and low exercise levels.39 If these trends continue, these poorer populations are likely to suffer avoidable illness more frequently in the future, thus allowing health inequalities to grow.40 Moreover, earning a low income prevents people from participating in certain spheres of social life, and as a result, they feel that their status is less worthy than the better-off in society, which can lead to an array of mental health problems. Studies show that an increase in income leads to an increase in psychological well-being and a decrease in anxiety and depression. Moreover, it has been observed that increased indebtedness raises the likelihood of poor mental health outcomes.41

With regard to overcrowding, Oxford city experiences severe housing problems - both in terms of availability and quality. This is mainly due to extremely high house prices in the city: several studies have identified Oxford’s housing to be the least affordable in Britain.42 When taking income into account, housing prices in the city during 2014 outpaced that of London.43 Consequently, house-sharing in Oxford is common, even for adults in their 30s.44 An extensive

38 Oxford Health NHS Foundation Trust. Strategic Plan Document for 2014-2019. https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/392928/OXFORD_HEALTH_Publishable_Summary_Strategic_Plan_1415.pdf (16 October 2016, date last accessed). 39 Ibid. 40 Ibid. 41 Op. cit., Marmot M et al. Fair society, healthy lives. 42 University of Oxford. Average house prices in Oxford 'most unaffordable in Britain'. 2015. http://www.ox.ac.uk/news/2015-02-26-average-house-prices-oxford-most-unaffordable-britain (2 March 2016, date last accessed). 43 Ibid. 44 Osborne H. Oxford ‘struck by housing shortfalls, top prices and rising rents’. 2014. http://www.theguardian.com/uk-news/2014/oct/16/oxford-housing-shortfall-prices-rising-rent-investors (2 March 2016, date last accessed).

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housing survey undertaken in the early 90s concluded that if a person’s living conditions are crowded, noisy, cold and dilapidated, then their ability to deal with both mental and physical problems is lowered, especially if social support is lacking already. In this study, housing-related health problems included rhinorrhoea, colds, rheumatism, headaches and asthmatic symptoms.45 In addition, there is a link between health (both mental and physical) and overcrowding. Many studies support the notion that crowded housing closely related to psychological problems in adults. One such study suggests that crowded or cramped living conditions prevent social interaction between adults and children within the household, which prevents positive relationships from forming. Consequently, their mental health may be damaged or impaired.46 Additionally, associations between physical ailments and health problems have been established. For instance, research demonstrates a relationship between overcrowding and respiratory conditions and meningitis in children. Interestingly, there is a significant correlation between self-reported health and overcrowding; a large-scale study revealed that experiencing overcrowded living conditions in childhood increases the likelihood of poor self-reported health in adulthood.47

45 Op. cit., Howden-Chapman P, Isaacs N, Crane J, and Chapman R. Housing and health: The relationship between research and policy. 46 Evans G, Lercher P, and Kofler W. Crowding and children's mental health: the role of house type. Journal of Environmental Psychology 2002:22:221-231. 47 Office of the Deputy Prime Minister. The Impact of Overcrowding on Health and Education: A review of the Evidence and Literature. London: Office of the Deputy Prime Minister, 2004. http://dera.ioe.ac.uk/5073/1/138631.pdf (21 November 2016, date last accessed).

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SECTION THREE | CURRENT POLICY WITHIN OXFORDSHIRE

This section of our report will begin by identifying relevant policy stakeholders, and reviewing the structure of Public Health England. An overview of current employment policy in Oxfordshire is then provided given the crucial role it plays in supporting individuals out of income deprivation and to their overall health and well-being. We will then move on to an overview of current housing policy, with a particular emphasis on Houses in Multiple Occupation (HMO) policies, so we might later draw out how they relate to health. A good understanding of the policy and infrastructure in place will allow us to formulate targeted policy recommendations.

3.1 Policy stakeholders

Public Health England (PHE) is the executive branch of the government’s Department of Health. PHE was established by the Health and Social Care Act 2012, which put forward an extensive reorganisation of the National Health Service (NHS). PHE has overseen population health policy and interventions in the United Kingdom since its implementation on 1 April 2013.48 In order to make well-targeted policy recommendations, it is thus crucial to understand PHE’s departments and jurisdiction.

PHE’s leadership team carries out efforts in eight directorates: health protection, health improvement and population health, knowledge and information, operations, strategy, programmes, finance and corporate services, and human resources. The health improvement and population health directorate is most relevant to this report, as it is mandated with reducing health inequities.49 For the purposes of research and service delivery, PHE divides the UK into four regions (North of England, Midlands and East of England, London, and South of England), as well as 15 centres.50

Oxfordshire is included in the Thames Valley PHE centre, which is itself part of the South of England region.51 The Thames Valley centre (TVC) describes itself as a mixture of industrial, rural, and urban environments with ‘mostly wealthy local authorities, with some areas of higher deprivation’.52 The TVC spans the three counties of Oxfordshire, Buckinghamshire, and Berkshire, with a total population of 2,012,000 according to the latest report (2014).53 Oxfordshire itself is home to 666,100 people, of which one third lives in towns of less than 10,000.54 Five districts make up the country of Oxfordshire: the city of Oxford, Cherwell, South

48 Department of Health. Public Health England’s Operating Model. London: Department of Health, 2011. https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/216716/dh_131892.pdf (13 February 2016, date last accessed). 49 Ibid. 50 Ibid. 51 Public Health England. Who we are and what we do: Annual Plan 2015/16. London: Public Health England, 2015. https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/452328/Annual_plan_2015-_Aug7-web.pdf (13 February 2016, date last accessed). 52 Op. cit., Department of Health. Public Health England’s Operating Model. 53 Public Health England. Thames Valley Centre Prospectus. London, Public Health England, 2014. https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/271123/Thames_Valley_PHE_Centre_Prospectus_V7_PH_checked.pdf (13 February 2016, date last accessed). 54 Oxfordshire County Council. About Oxfordshire. https://www.oxfordshire.gov.uk/cms/public-site/about-oxfordshire (13 February 2016, date last accessed).

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Oxfordshire, Vale of White Horse, and West Oxfordshire.55 The County council is responsible for some general public health initiatives, which target healthy eating, fitness, smoking cessation, and mental well-being.56 The Oxfordshire County Council has also established a collaborative Health and Wellbeing Board, explained below. District councils are responsible for the majority of local public health initiatives, and will thus be the focus of this report.

Other key stakeholders include the Health and Wellbeing Board established by Oxfordshire County Council. The Health and Wellbeing board is a partnership between local government, the NHS and the people of Oxfordshire.57 It has the responsibility of promoting health and well-being in Oxfordshire and is made up of senior local government officials, relevant representatives from local clinical commissioning groups and other stakeholders such as Healthwatch Oxfordshire.58 Another key stakeholder is Oxfordshire Clinical Commissioning Group (OCCG) which was established on 1 April 2013 to buy health care services on behalf of the Oxfordshire population.59 OCCG is made up of local GPs, local people, hospital clinicians and other partners. OCCG has identified reducing health inequalities in the county as an area of focus in their five year vision and along with Oxfordshire County Council and NHS England have agreed to take a collective approach to tackling inequalities.60 As the current policy approach shows, the involvement and cooperation of all relevant stakeholders will be vital in tackling the issue of health inequalities. This report demonstrates such an approach.

3.2 Employment Policy

Employment is crucial to health and well-being: being in work is good for physical and mental well-being and being unemployed is unequivocally bad.61 The nature of work - whether it is safe, long term and well paid - is also important. Given this and the crucial role employment plays in bringing individuals and families out of income deprivation it is helpful to examine existing initiatives in Oxfordshire targeted at improving employment in the County.

Both 'macro' and 'micro' level interventions to help boost employment in the County can be seen in Oxfordshire. At a 'macro' level, initiatives such as the Oxford and Oxfordshire City Deal which is supported by a range of stakeholders including central government, Oxford and Oxfordshire local authorities and OxLEP (Oxfordshire Local Enterprise Partnership) have been implemented. Amongst a wider remit of driving economic growth in the area, the City Deal also promises to deliver the following employment benefits: 525 additional apprenticeships for young people (aged 16-23) over three years from 2014/15, 350 more employers offering accredited work experience schemes and 250 more employers engaging with school and

55 Ibid. 56 Oxfordshire County Council. Healthy lifestyles. https://www.oxfordshire.gov.uk/cms/public-site/healthy-lifestyles (24 March 2016, date last accessed). 57 Oxfordshire County Council. About the Health and Wellbeing Board. https://www.oxfordshire.gov.uk/cms/content/about-health-and-wellbeing-board (14 February 2016, date last accessed). 58 Ibid. 59 Oxfordshire Clinical Commissioning Group. About us. http://www.oxfordshireccg.nhs.uk/about-us/ (13 February 2016, date last accessed). 60 Oxfordshire Clinical Commissioning Group. Oxfordshire CCG strategy and plan 2014/15-2018/19. https://mycouncil.oxfordshire.gov.uk/documents/s24544/HWB_MAR1314R09.doc.pdf (30 March 2016 date last accessed). 61 Op. cit., Marmot M et al. Fair society, healthy lives.

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colleges. 62 This is an example of cross organisation working to deliver employment opportunities for the region.

Oxfordshire County Council also has initiatives such as the Early Intervention Service in place which supports employment amongst other services. The intervention includes regular contact by phone and email, job clubs and appointments at Early Intervention Hubs, information and guidance, and lists of work, apprenticeships and other opportunities to help young people into employment, education or training. Other more 'micro' level interventions to help specific individuals into employment include job clubs run by some District Councils within Oxfordshire. For example, job clubs run by Cherwell District Council offer help to individuals at all stages of the job search by providing a place for local employers to advertise jobs and running workshops on topics such as CV-writing and interview techniques.63

3.3 Housing policy

Housing policy in England is largely localised and the responsibility of district councils. As such, County Council involvement in housing policy is limited, and for services related to home improvement grants, housing associations, homelessness, housing benefits and affordable housing schemes the County Council signpost district councils as the appropriate decision making authority.

3.3.1 General Housing Policy by District

Housing policy is almost entirely delegated to the District Council level. Within Oxfordshire, the city of Oxford has the most severe problem with housing. The Strategic Housing Market Assessment estimates that between 24,000 and 32,000 new homes need building by 2031 in order to sustain the city. The City Council divides its housing priorities fourfold:

Priority 1 – Increase supply & improve access to affordable housing

Priority 2 – Meet the housing needs of vulnerable groups

Priority 3 – Support the growth of a balanced housing market

Priority 4 – Support sustainable communities

In accordance with these priorities, the Oxford City Council recently signed the ‘City Deal’ with the government to attract funding for additional housing, which aims to accelerate delivery of up to 7500 new homes, mostly between Oxford, Bicester and Didcot.64

62 Oxfordshire County Council et al. Oxford and Oxfordshire City Deal. https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/276205/Oxford-Oxfordshire-City-Deal.pdf (7 March 2016, date last accessed). 63 Cherwell District Council. Job clubs. http://www.cherwell.gov.uk/index.cfm?articleid=4614 (28 February 2016, date last accessed). 64 Oxford City Council. Housing Strategy 2015-18. https://www.oxford.gov.uk/downloads/file/910/housing_strategy_2015-18 (13 February 2016, date last accessed).

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With specific regard to priority 1, the City Council is carrying out direct acquisition of properties for temporary accommodation, delivering Barton Parks affordable housing, and pushing several housing developments with a focus on large family homes. For priority 2, the Council provides specialist accommodation for the homeless, enforces national building standards in guidance for developers for disabled or elderly clients, and is planning to deliver additional care homes for the elderly. With regard to priority 3, the Council is investigating various new schemes and partnerships with private corporations, but very little tangible work is being done at present. To address priority 4, the Council is identifying individuals whose access to education is affected by poor housing conditions, delivering the 2015-18 Housing Investment Program as part of the Housing Asset Management Strategy, improving awareness and access to health services for residents, particularly those in HMOs, improving immunisation rates, providing study spaces and internet access to prevent educational overcrowding, and providing inclusive social activities for identified isolated elderly individuals.65

An overview of housing policy in the other Oxfordshire Districts is also presented here. Cherwell District Council’s housing policy can be summarised as follows:

In partnership with a development partner establish whether there is a business case for a build to (market) rent scheme in Cherwell and identify potential sites to bring this forward. Explore potential links into a ‘rent – save – own’ model of tenure

Undertake a complete re-branding exercise for the Private Accommodation Letting Scheme Continue to promote (and enforce where necessary) the HMO licensing scheme and

undertake the proactive inspection and re- inspection of HMOs to ensure appropriate management

Undertake feasibility and cost benefit analysis of establishing a Private Sector Leasing and/or RP management service for the private sector and ensure that such a scheme provides homes which are affordable for people in receipt of benefits

Continue to use Cherwell Landlords Forum and existing links in the private sector as an opportunity to increase investment in the buy- to-let market and increase suppl.

Maintain excellent in-house (i.e. within Cherwell District Council) expertise in welfare benefits, to ensure the Council is ready for changes as they come in and to ensure accurate advice is given to landlords and tenants.66

South Oxfordshire also experiences a problem with insufficient housing provision due to growing populations. It aims to provide a range of housing developments across the district, a significant proportion of which are intended as affordable housing. On all new developments, they will ensure a 40% rate of affordable housing, and will also include homes to cater for those with special accommodation needs (e.g. disability).67

The Vale of White Horse’s District Council put forward a Local Plan 2031 Part 1: Strategic Sites and Policies to the Secretary of State in 2014, which provides a framework for sustainable housing policy in the district up to the year 2031.68 Much of the report focuses on ‘building

65 Ibid. 66 Cherwell District Council. Cherwell Housing Strategy 2012-2017. http://www.cherwell.gov.uk/media/pdf/d/4/2012_Housing_Strategy.pdf (13 February 2016, date last accessed). 67 South Oxfordshire District Council. Oxfordshire Core Strategy. Wallingford: South Oxfordshire District Council, 2012. http://www.southoxon.gov.uk/sites/default/files/2013-05-01%20Core%20Strategy%20for%20Website%20final_0.pdf (13 February 2016, date last accessed). 68 Vale of White Horse District Council. Local Plan 2013 Part 1: Strategic Sites and Policies, http://www.whitehorsedc.gov.uk/sites/default/files/2014-10-

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healthy and sustainable communities’. One element towards this objective is meeting housing needs, and the report specifies the ‘scale and location of new housing’ projects to ensure ‘development is built in the most appropriate locations.’69 New developments will be subject to a district-wide ‘Core Policy 24’, which aims to ensure 35% affordable housing ‘on all sites capable of a net gain of three or more dwellings’. If the level of affordable housing sought in a site in unviable, ‘alternative tenure mixed and levels of affordable housing provision may be considered’.70 The plan is well formulated and informed by evidence.

3.3.2 HMO Policy

With regard to overcrowding – an important predictor of poor health outcomes in our analysis of Oxfordshire – a key issue problem is Houses In Multiple Occupation (HMOs). A House in Multiple Occupation is a residence:

With 3 or more occupants

Where the occupants form 2 or more ‘households’

Which is the occupants’ main/only residence

Its purposes are solely residential

Rent is paid in some form.

Basic amenities (toilets, showers etc) are shared between households.71

In essence, they are properties playing host to more than one family/household, and are thus inherently associated with overcrowding. Oxford has the 14th highest number of HMOs in England and Wales (almost all boroughs with more are in London, and all are in far more urban areas). In Oxford, 1 in 5 people lives in an HMO. This is a problem, as the 2005 Housing Condition Survey found HMOs to be the most unsafe residences, with 70% deemed unsafe. They are responsible for 2000 service requests/complaints per year, a number which is increasing. The properties are generally kept in very poor condition by landlords, and are associated with social problems such as rubbish and anti-social behaviour. Oxford City Council has received more complaints regarding HMOs than any other district council nationally.72

Oxford City Council’s policy with regard to HMOs is based around licensing, planning laws, and prosecution. All dwellings meeting the above definition must apply for an HMO license, which ensures that:

The landlord is fit to manage the property (or hires someone who is).

27%2001%20VOWHDC%20Local%20Plan%202031%20Contents-Chapters%201.pdf (13 February 2016, date last accessed). 69 Ibid. 70 Vale of White Horse District Council. Local Plan 2013 Part 1: Strategic Sites and Policies: 6. District-Wide Policies. http://www.whitehorsedc.gov.uk/sites/default/files/2014-10-27%2005%20VOWHDC%20Local%20Plan%202031%20Chapters%206-7-Appendix.pdf (13 February 2016, date last accessed). 71 Oxford City Council. Houses or flats that need a HMO license. https://www.oxford.gov.uk/info/20237/properties_that_need_an_hmo_licence/926/houses_or_flats_that_need_an_hmo_licence (13 February 2016, date last accessed). 72 Oxford City Council. Houses in Multiple Occupation: Background, https://www.oxford.gov.uk/info/20113/houses_in_multiple_occupation/374/houses_in_multiple_occupation_background (13 February 2016, date last accessed).

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The property is fit for the number of occupants.

The property is in an acceptable condition.

Vulnerable tenants are protected.

High risk HMOs are targeted for improvement.

A licence may be for 1, 3 or 5 years, with more stringent application requirements for longer licences. Those applying for one year licenses need not necessarily be up to date with tax payments or health and safety certificates, for example. Every license comes with a fee. Most cost between £300 and £400, but the most expensive (for those caught operating without a license beforehand) is £999. Since 2011, the National Government has delegated authority to Oxford City Council to enforce additional planning permission requirements to convert a residence into an HMO.73

The Oxford City Council has prosecuted more individuals than any other District Councils in the country over HMO licensing laws. To maintain a license, an HMO must be subject to inspection by a Council Officer, who will inspect residences against the Housing Health and Safety Rating Scheme, as well as ensuring that all have:

Adequate heating for the space

Ventilated kitchens and bathrooms (extractor fans in kitchens)

Kitchens and bathrooms are of adequate size and layout

Hot and cold water on tap in kitchens and bathrooms

Enough toilets, bathrooms, and sinks for number of residents

Adequate refuse disposal facilities

Adequate fire precautions

Working smoke/fire alarms.74

The officer may enforce the necessary changes. If an HMO is found wanting on any of the above grounds, its license will be rescinded, and the landlord may be subject to fines.

The other Oxfordshire District Councils experience far fewer problems with HMOs, so address them less directly. South Oxfordshire and Vale of the White Horse District Councils do not address HMO policy on their websites. Cherwell District Council does address HMO policy, but seems far less punitive of HMO landlords. The Cherwell fees system is less expensive and simpler (£600 for first time application, £360 for a license renewal), and all licenses are provided for 5 years. There are also instructions on how to appeal a license decision, and no mentions of prosecution.75 All in all, HMOs seem far less of a problem outside the jurisdiction of Oxford City Council.

73 Oxford City Council. HMO Additional Licensing Designations 2015: Proposed Fees and Charges. http://mycouncil.oxford.gov.uk/documents/s25876/Appendix%202%20-%20HMO%20licensing%20Fees%20and%20Charges%20January%202016.pdf (13 February 2016, date last accessed). 74 Oxford City Council. Minimum Standards for Houses in Multiple Occupation. https://www.oxford.gov.uk/info/20113/houses_in_multiple_occupation/373/minimum_standards_for_homes_in_multiple_occupation (13 February 2016, date last accessed). 75 Cherwell District Council. Houses in Multiple Occupancy Licenses. http://www.cherwell.gov.uk/index.cfm?articleid=1892 (13 February 2016, date last accessed).

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SECTION FOUR | POLICY SUGGESTIONS

4.1 Recommendations for Income Deprivation

(1) We recommend the County Council should advocate all employers within the county to become accredited Living Wage employers.

For individuals living in the county working with low wages, receiving a living wage will support the chances of an in­-work household remaining above the poverty threshold and allow them the chance to live a healthy lifestyle. Encouraging the living wage will permit people to buy healthier foods, reduce income inequalities that have been related to health, and will result in better psychological well­-being.76,77We propose that when an employer becomes accredited, the County Council should provide a sticker to display in their window, which may provide extra incentive for retail stores, restaurants, and food suppliers.

(2) We recommend the County Council targets spending and strengthens the provision of early childhood development initiatives.

Early childhood experiences have a significant bearing on the lifelong physical, social and intellectual well-being of individuals.78 As such, many of the inequalities that present later in life have their roots in early childhood (aged 5 and under) development. By dedicating more financial resources to these crucial years of development and strengthening the existing provision of children's centres the Council can help to negate the adverse effects of income deprivation on children and provide them with the best start in life.

4.2 Recommendations for Housing and Overcrowding

(1) We recommend the implementation of an Oxfordshire-­wide program modeled after the city of Liverpool’s ‘Healthy Homes’ initiative.

The Healthy Homes initiative involves the training of ‘healthy housing advocates’, who visit homes in areas of notable overcrowding and housing disparity to gather information about the residents and their health needs.79 They also provide evidence­-based advice and help to occupants, free of charge, aiming to prevent hazards and improve their housing-­related well-being. The Liverpool Healthy Homes model proposes advice on proofing homes from excess cold, dampness, and mould; home safety measures; keeping homes warm in cold seasons; and maximising income by housing­ related measures. Oxfordshire’s ‘healthy housing advocates’ could also sensitise tenants to the county’s standards of living, and to the resources available to them for help with housing problems. This measure would serve as a complement to the National Energy Foundation’s Better Housing, Better Health initiative, which is in effect in

76 Rowlingson K. Does income inequality cause health and social problems? Joseph Rowntree Foundation, 2011. https://www.jrf.org.uk/report/does-income-inequality-cause-health-and-social-problems (21 October, 2016, date last accessed). 77 Trust for London. Costs and Benefits of a Living Wage. https://www.trustforlondon.org.uk/wp-content/uploads/2012/10/Living-Wage-Costs-and-Benefits.pdf (26 February, 2016, date last accessed). 78 Op. cit., Marmot M et al. Fair society, healthy live. 79 Liverpool City Council. Healthy Homes Programme. http://liverpool.gov.uk/council/strategies-plans-and-policies/housing/healthy-homes-programme/healthy-homes-what-we-do/ (22 February 2016, date last accessed).

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Oxfordshire, but for which only residents with cardiovascular disease or respiratory illness referred by health professionals are eligible.80 Adapting an existing public health intervention such as Healthy Homes to the needs of our county would provide an immediate measure towards the betterment of housing­ related health in Oxfordshire.

(2) We recommend providing substantial improvement in information and education for both landlords and tenants.

The first premise of this statement is to improve the education of landlords, so they can improve the quality of housing available for renting within the county. Education will afford landlords greater familiarity with the rules which govern their leases, and ensure the limits and responsibilities of what they can and cannot do for tenants are known. Secondly, this recommendation entails the provision of information for tenants relating to what their rights are, of which many may currently be unaware. We believe that tenants should also be provided with negotiators who have a better understanding of rules and regulations, and who can converse with landlords to reduce power and informational asymmetries and the vulnerability to exploitation of tenants at the hands of negligent landlords. Finally, we propose a landlord rating services for tenants to provide feedback into a centralised database, with the hope of providing an incentive for landlords to improve the quality of their property and services, empowering tenants to avoid negligent landlords and endowing councils with more information about offending landlords and properties. It should be noted that some of these suggestions can equally apply to social renting, such as in relation to reducing power discrepancies and increasing knowledge of tenants.

(3) We recommend the City Council should attempt to improve the standard of HMO housing.

By tightening regulations for HMO license holders, particularly health and safety regulations for one year license holders, the Council could improve the quality of HMO housing, and subsequently health outcomes. Officers inspecting HMOs should also take into account a more specific measure of overcrowding. Financial incentives for landlords to improve the quality of their housing could also be very effective (perhaps in the form of subsidies or low interest loans), to make improvements to health by landlords easier.

80 National Energy Foundation. Better Housing, Better Health. http://www.nef.org.uk/service/programme-management/householder-support/better-housing-better-health (22 February 2016, date last accessed).

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CONCLUSION

While Oxfordshire is a relatively wealthy county, income deprivation and overcrowding of housing have a significant impact on health inequalities by reducing the life expectancies of some groups. Disadvantaged groups participate more frequently in unhealthy behaviours such as smoking, drinking, junk food consumption, and inactivity perpetuating the health inequalities caused by these behaviours.81 Additionally, mental health can be affected by income inequalities when one group feels they are in a lower status than another group.82 Overcrowding of housing can impact mental health, disturb sleep, spread illness, and increase risk of accidents.83

Given the impact of income deprivation and overcrowding of housing on health in Oxford, we suggest that these two areas be target specifically. To combat income deprivation, the County Council should advocate for employers to become accredited Living Wage employers and early childhood development initiatives should be strengthened to break the inheritance of poverty. Housing and overcrowding should be improved with the implementation of a program modelled after the city of Liverpool’s Healthy Homes initiative, the improvement of information for landlords and tenants, and increasing the standards of HMO housing. By implementing these changes, it is probable that housing standards will improve and income inequalities will be reduced thereby decreasing inequalities in health.

A limiting factor in this report has been the lack of data. In particular, measuring poor housing conditions purely via the incidence of overcrowding limits our understanding of the most important ways in which poor housing conditions impact health. Given that overcrowding is an imperfect proxy for poor housing conditions overall, it is likely that our estimates of the negative impact of poor housing are rather conservative. Measures such as the incidence of mould could enrich our understanding of the particular housing challenges Oxfordshire faces with respect to health and thus add further precision to the recommendation towards ameliorating health inequalities given here. We call for such research to be conducted in the context of Oxfordshire.

Moreover, the ways in which children’s health during childhood and later in life is affected by socio-economic disparities could not be directly assessed due to data limitations. Especially because of the particular vulnerabilities associated with this social group, we urge relevant data to be made available and further research to be conducted on this issue on both the national level and the level of Oxfordshire in particular.

81 Op. cit., Marmot M et al. Fair society, healthy lives. 82 Ibid. 83 Shelter. Full House? How overcrowded housing affects families. London: Shelter, 2005. http://england.shelter.org.uk/__data/assets/pdf_file/0004/39532/Full_house_overcrowding_effects.pdf (30 March 2016, date last accessed).

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APPENDIX | ROBUSTNESS CHECKS AND VALIDITY OF RESULTS

The focus of this section will be to highlight issues with the empirical analysis, explain how they were addressed, and to explore the overall validity of the results.

In order to evaluate the robustness of our empirical analysis it is helpful to restate the empirical model which has been estimated and the data used to proxy for the independent variables.

(1)

1 Limitations due to data availability

As is often the case with empirical analysis it was not possible to obtain data for certain factors which the literature highlights as important determinants of health outcomes. With particular relevance to the United Kingdom is differences in healthcare provision between different MSOA’s, as variation in the quality or quantity of healthcare provision could have a significant effect on health outcomes. Information such as doctors per person, healthcare spending per person and average distance to a hospital would have been a valuable addition to our model. Although in theory healthcare provision is uniform across Oxfordshire, it is likely that MSOA’s will have different levels of healthcare provision which could contribute to health inequality. Given the importance of health in utero and during early childhood84,85,86 it would be particularly valuable to have data on the provision and use of antenatal and postnatal care at the MSOA level. The effect of the factors in our empirical analysis on health outcomes could be confounded by differences in the quality of early life healthcare across MSOA’s.

All the independent variables used in the analysis adequately proxy for the influence of the specified factor on health outcomes apart from the education variable. The education variable is defined as the percentage of pupils achieving 5 or more A*-C including English and mathematics, or the equivalent percentage of pupils at the end of Key Stage 4 at schools maintained by Local Authority. It is possible that if the education variable contained information on the percentage of individuals in each MSOA who have completed a degree, the data would reveal a significant impact of education on health outcomes and inequality as it would capture one of the main mechanisms by which education impacts on health – an individual’s employment status. Therefore, our empirical analysis would also be improved by an additional variable capturing the tertiary educational attainment of each MSOA.

A more revealing analysis would involve the use of individual panel data. An empirical investigation using the independent variables specified in equation (1) in addition to factors such as individual weight at birth and anthropometric measures during early childhood would reveal to what extent health inequality is caused by individual specific factors such as health promoting behaviour. In terms of the policy response to health inequality it is extremely useful to know which external factors affect health inequality and to what extent health inequality is determined by individual specific preferences which are more difficult to influence using policy interventions.

84 Op. cit., Gluckman PD, Hanson MA. Adult disease: echoes of the past.. 85 Op.cit., Jefferis BJMH, Power C, and Hertzman C. Birth weight, childhood socioeconomic environment, and cognitive development in the 1958: British birth cohort study. 86 Op. cit., Perry BD. Childhood experience and the expression of genetic potential: what childhood neglect tells us about nature and nurture.

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2 Multicollinearity

The problem of multicollinearity refers to the extent to which the independent variables are correlated with each other. A degree of correlation is expected, however, a very high correlation can lead to changes in the sign of estimated coefficients and can reduce their precision. As a result there is a higher likelihood of incorrectly inferring that a variable has no impact on the dependent variable.

Tables 9 and 10 report the covariances between all nine independent variables, some of which are highly correlated. Some variables are highly correlated due to their construction, for example the income variable is highly negatively correlated with both the unemployment variable and the percentage of individuals categorised as income deprived. The most potentially problematic correlation is between the unemployment rate and the percentage of individuals categorised as income deprived.

Tables 11 and 12 remove each independent variable in turn from the preferred specification in order to investigate the impact of each one on the sign and significance of the estimated coefficients for the two most important dependent variables: the life expectancy of males and the percentage of individuals with bad or very bad subjective health. The preferred specification is in column (9) for convenient comparison. Typical symptoms of multicollinearity include coefficients and standard errors which are sensitive to changes in the independent variables included in the analysis. Coefficients which change sign as different independent variables are removed from the analysis are in bold. In both tables the sign on the unemployment variable changes when the income deprivation variable is removed, which reflects their close relationship. The sign on the income variable also tends to jump around in both tables which is also a consequence of its correlation with some of the other variables. However, removing variables does not change the significance of the factors found to be relevant for explaining the dependent variable in the preferred specification.

There are clearly issues of multicollinearity in the analysis. It is important to acknowledge the presence of multicollinearity and to emphasise that the estimates of the impact of factors on health outcomes are unlikely to be precise. The aim of the analysis was to identify the main determinants of health inequalities and not to predict the impact of varying determinants of health on health outcomes. However, the main conclusions drawn concerning the significance of factors in determining health inequality is robust to all specification changes. Furthermore, as multicollinearity leads to fewer independent variables having a significant effect on the dependent variable our analysis has not falsely identified important determinants of health outcomes. In conclusion, our main findings concerning the health inequality in Oxfordshire are not undermined by the correlation between the independent variables in our empirical analysis.

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Table 9. Correlation between independent variables Equivalised Income

after Housing Equivalised Income

after Housing, squared % Individuals over the

Age of 65 GCSE Acheivement

Equivalised Income after Housing 1

Equivalised Income after Housing, squared 1 1

% Individuals over the Age of 65 0.4 0.39 1

GCSE Acheivement 0.61 0.6 0.3 1

% Individuals Income Deprived -0.82 -0.78 -0.37 -0.61

Unemployment Rate -0.78 -0.75 -0.4 -0.6

% Households that are Overcrowded -0.54 -0.52 -0.69 -0.27

% Individuals in Fuel Poverty -0.15 -0.13 -0.21 -0.01

Urban Dummy -0.48 -0.47 -0.65 -0.32

Table 10. Correlation between independent variables

% Individuals Income

Deprived

Unemployment Rate

% Households Overcrowded

% Individuals in Fuel Poverty

Urban Dummy

Equivalised Income after Housing

Equivalised Income after Housing, squared

% Individuals over the Age of 65

GCSE Acheivement

% Individuals Income Deprived 1

Unemployment Rate 0.94 1

% Households that are Overcrowded 0.51 0.51 1

% Individuals in Fuel Poverty 0.26 0.24 0.59 1

Urban Dummy 0.44 0.48 0.61 0.1 1

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Table 11. Life Expectancy Males - Robustness Variable Drop

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Equivalised Income after Housing 0.00913***

-0.00671 -0.0287 -0.0109 0.0307 -0.0116 -0.0116 -0.00938

(0.00337)

(0.0284) (0.0262) (0.0281) (0.0268) (0.0296) (0.0281) (0.0280)

Percentage of Individuals over the Age of 65 0.0453 0.0467

0.0483 0.0474 0.0429 0.0808** 0.0401 0.0478

(0.0412) (0.0414)

(0.0407) (0.0403) (0.0416) (0.0383) (0.0425) (0.0415)

GCSE Acheivement -0.00152 -0.00220 -8.20e-05

-0.00569 -0.00121 -0.00820 -0.00306 -0.00247

(0.0180) (0.0179) (0.0173)

(0.0167) (0.0162) (0.0193) (0.0177) (0.0179)

Unemployment Rate 0.521 0.513 0.500 0.434

-0.514 0.435 0.497 0.505

(0.577) (0.573) (0.582) (0.552)

(0.430) (0.602) (0.563) (0.574)

Percentage of Individuals Categorised as Income Deprived -0.298*** -0.311*** -0.322** -0.352*** -0.247***

-0.333*** -0.338*** -0.327**

(0.110) (0.108) (0.127) (0.120) (0.0683)

(0.124) (0.124) (0.125)

Percentage of Households that are Overcrowded -0.121** -0.119** -0.151*** -0.100* -0.113* -0.124*

-0.157*** -0.118**

(0.0566) (0.0573) (0.0550) (0.0539) (0.0615) (0.0644)

(0.0452) (0.0590)

Percentage of Individuals in Fuel Poverty -0.0715 -0.0710 -0.0537 -0.0692 -0.0679 -0.0913 -0.150***

-0.0692

(0.0582) (0.0576) (0.0582) (0.0538) (0.0578) (0.0743) (0.0454)

(0.0571)

Urban Dummy 1.142** 1.158** 1.014** 1.251** 1.233** 1.238** 0.910* 1.293*** 1.171**

(0.509) (0.508) (0.471) (0.480) (0.503) (0.531) (0.470) (0.479) (0.509)

Equivalised Income after Housing, squared

7.84e-06*** 1.30e-05 3.24e-05 1.70e-05 -1.47e-05 1.88e-05 1.68e-05 1.56e-05

(2.83e-06) (2.37e-05) (2.27e-05) (2.35e-05) (2.21e-05) (2.44e-05) (2.35e-05) (2.33e-05)

Constant 76.95*** 79.67*** 82.73*** 88.01*** 83.25*** 68.93*** 82.72*** 83.32*** 82.55***

(2.487) (1.758) (8.504) (7.864) (8.494) (8.510) (9.088) (8.515) (8.442)

Observations 82 82 82 84 82 82 82 82 82

Adjusted R-squared 0.668 0.669 0.664 0.679 0.665 0.631 0.655 0.666 0.665

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

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Table 12. Percentage of individuals with bad or very bad subjective health - Robustness Variable Drop

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Equivalised Income after Housing -0.00609***

0.0111 0.0105 0.00782 -0.00759 0.00729 0.00860 0.00760

(0.000963)

(0.00838) (0.00751) (0.00679) (0.00697) (0.00691) (0.00709) (0.00704)

Percentage of Individuals over the Age of 65 0.0646*** 0.0637***

0.0659*** 0.0628*** 0.0646*** 0.0674*** 0.0662*** 0.0628***

(0.0100) (0.00998)

(0.0102) (0.0101) (0.0115) (0.00933) (0.0105) (0.0101)

GCSE Acheivement 0.00291 0.00340 0.00675

0.00410 0.00314 0.00280 0.00388 0.00361

(0.00511) (0.00509) (0.00650)

(0.00522) (0.00544) (0.00492) (0.00509) (0.00510)

Unemployment Rate -0.0878 -0.0817 -0.0819 -0.0575

0.310*** -0.0854 -0.0719 -0.0755

(0.0992) (0.0970) (0.121) (0.113)

(0.100) (0.0998) (0.100) (0.0988)

Percentage of Individuals Categorised as Income Deprived 0.102*** 0.111*** 0.130*** 0.140*** 0.112***

0.123*** 0.129*** 0.124***

(0.0260) (0.0249) (0.0323) (0.0271) (0.0196)

(0.0258) (0.0245) (0.0261)

Percentage of Households that are Overcrowded -0.0145 -0.0160 -0.0597*** -0.0296 -0.0175 -0.0144

0.00115 -0.0167

(0.0165) (0.0159) (0.0185) (0.0194) (0.0156) (0.0154)

(0.0112) (0.0158)

Percentage of Individuals in Fuel Poverty 0.0329 0.0326 0.0515** 0.0351* 0.0310 0.0395 0.0198

0.0312

(0.0211) (0.0201) (0.0256) (0.0177) (0.0191) (0.0245) (0.0170)

(0.0193)

Urban Dummy 0.000811 -0.0102 -0.227 0.0556 -0.0297 -0.0458 -0.0573 -0.0755 -0.0204

(0.127) (0.126) (0.145) (0.131) (0.125) (0.132) (0.125) (0.128) (0.129)

Equivalised Income after Housing, squared

-5.25e-06*** -1.50e-05** -1.29e-05** -1.18e-05** -6.77e-08 -1.11e-05* -1.21e-05** -1.16e-05*

(7.92e-07) (7.05e-06) (6.14e-06) (5.63e-06) (5.92e-06) (5.70e-06) (5.93e-06) (5.84e-06)

Constant 4.979*** 3.171*** 1.067 -0.364 0.731 5.992*** 0.858 0.490 0.834

(0.706) (0.487) (2.574) (2.416) (2.054) (1.996) (2.127) (2.160) (2.131)

Observations 82 82 82 84 82 82 82 82 82

Adjusted R-squared 0.789 0.794 0.721 0.793 0.796 0.754 0.795 0.790 0.794

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

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3 Validity of Inference

In order to perform statistical inference in small samples it is necessary to assume that the unobserved error term is normally distributed. This can be a strong assumption. However, for all regressions apart from the regression which models the determinants of death from respiratory disease the residuals are indeed normal. Statistical inference is therefore valid in all regressions, but Table 8 should be interpreted with some caution.

4 Homoskedasticity

In order for statistical inference to be valid, it is required that the variance of the unobservable error term, conditional on all the independent variables, be constant. In the context of our analysis these factors will be unobservable factors which influence health outcomes at the MSOA level. For three of the seven regressions the residuals are found to be heteroskedastic; their variance does not remain constant as the independent variables vary. The robust standard errors are smaller than the unadjusted standard errors which will lead to a higher incidence of a Type 1 error. Heteroskedasticity robust standard errors are reported throughout the analysis in order to deal with this issue.

5 Alternative Specifications

In order to further investigate the validity of the results of the empirical analysis it is useful to specify the model differently and see if the same determinants are shown to be the relevant factors in explaining income inequality. The two life expectancy dependent variables were transformed into dummy variables which is equal to one if the average life expectancy in the MSOA is above the median age and equal to zero if it was below the median age. The model was then estimated again using both a Linear Probability Model and a Logit Model which now investigates the impact of our chosen determinants of health on the probability of having a life expectancy above the median value for each gender. The results are reported in Table 13 where column (3) and (6) contain the preferred specification for comparison. In all three regressions the signs of all variables are the same for both dependent variables. However, both the income and the income squared variables are significant in both regressions with the binary dependent variable in the life expectancy of males regressions and income deprivation is insignificant in the Logit model while fuel poverty is significant at the five percent level. In the regression on the life expectancy of females no variables are significant in either the LPM or the Logit model. Given that the LPM model uses the same method as the preferred model to estimate the coefficients it is somewhat surprising that the coefficients are different for both dependent variables, however, turning the dependent variable from a continuous variable into a discrete variable leads to a loss of information concerning the dependent variable which is likely to be the cause of these differences.

40

41

BIBLIOGRAPHY

Association for the Conservation of Energy. Chilled to death: the human cost of cold homes. London: Westgate House, 2015. http://www.ukace.org/wp-content/uploads/2015/03/ACE-and-EBR-fact-file-2015-03-Chilled-to-death.pdf (3 March 2016, date last accessed).

Cassen R and Kingdon G. Tackling low educational achievement. York: Joseph Rowntree Foundation, 2007. https://www.jrf.org.uk/sites/default/files/jrf/migrated/files/2063-education-schools-achievement.pdf (21 November 2016, date last accessed).

Centre for the Wider Benefits of Learning. The wider benefits of learning: a synthesis of findings from the Centre for Research on the Wider Benefits of Learning 1999-2006, Research Brief RCB05-06. Nottingham: DfES, 2006.

Cherwell District Council. Cherwell Housing Strategy 2012-2017. http://www.cherwell.gov.uk/media/pdf/d/4/2012_Housing_Strategy.pdf (13 February 2016, date last accessed).

Cherwell District Council. Houses in Multiple Occupancy Licenses. http://www.cherwell.gov.uk/index.cfm?articleid=1892 (13 February 2016, date last accessed).

Cherwell District Council. Job clubs. http://www.cherwell.gov.uk/index.cfm?articleid=4614 (28 February 2016, date last accessed).

Commission on Social Determinants of Health. CSDH final report: closing the gap in a generation: health equity through action on the social determinants of health. Geneva: World Health Organization, 2008.

Deaton A. Health, inequality, and economic development. Journal of Economic Literature 2003:XLI:113–158.

Department of Health. Public Health England’s Operating Model. London: Department of Health, 2011. https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/216716/dh_131892.pdf (13 February 2016, date last accessed).

Energy UK. Fuel Poverty. http://www.energy-uk.org.uk/policy/fuel-poverty.html (3 March 2016, date last accessed).

Evans G, Lercher P, and Kofler W. Crowding and children's mental health: the role of house type. Journal of Environmental Psychology 2002:22:221-231.

Gluckman PD and Hanson MA. Adult disease: echoes of the past. European Journal of Endocrinology 2006:155(suppl_1):S47-S50.

Howden-Chapman P, Isaacs N, Crane J, and Chapman R. Housing and health: The relationship between research and policy. International Journal of Environmental Health Research 1996:6(3):173-185.

Jefferis BJMH, Power C, and Hertzman C. Birth weight, childhood socioeconomic environment, and cognitive development in the 1958: British birth cohort study. BMJ 2002:325:305.

Krieger J and Higgins DL. Housing and health: time again for public health action. Public Health 2002:92(5):758-768.

42

Liverpool City Council. Healthy Homes Programme. http://liverpool.gov.uk/council/strategies-plans-and-policies/housing/healthy-homes-programme/healthy-homes-what-we-do/ (22 February 2016, date last accessed).

Lynch JW, Smith GD, Kaplan GA, and House JS. Income inequality and mortality: importance to health of individual income, psychosocial environment, or material conditions. BMJ 2000:320(7243):1200.

Marmot M et al. Fair society, healthy lives: the marmot review: strategic review of inequalities in England post-2010. London: The Marmot Review, 2010.

National Energy Foundation. Better Housing, Better Health. http://www.nef.org.uk/service/programme-management/householder-support/better-housing-better-health (22 February 2016, date last accessed).

Office for National Statistics. National Life Tables, United Kingdom: 2011-2013. http://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/lifeexpectancies/bulletins/nationallifetablesunitedkingdom/2014-09-25 (22 May 2016, date last accessed).

Office for National Statistics. Time series explorer. http://www.ons.gov.uk/ons/datasets-and-tables/index.html (18 February 2016, date last accessed).

Office of the Deputy Prime Minister. The Impact of Overcrowding on Health and Education: A review of the Evidence and Literature. London: Office of the Deputy Prime Minister, 2004. http://dera.ioe.ac.uk/5073/1/138631.pdf (21 November 2016, date last accessed).

Osborne H. Oxford ‘struck by housing shortfalls, top prices and rising rents’. 2014. http://www.theguardian.com/uk-news/2014/oct/16/oxford-housing-shortfall-prices-rising-rent-investors (2 March 2016, date last accessed).

Oxfordshire Clinical Commissioning Group. About us. http://www.oxfordshireccg.nhs.uk/about-us/ (13 February 2016, date last accessed).

Oxfordshire Clinical Commissioning Group. Oxfordshire CCG strategy and plan 2014/15-2018/19. https://mycouncil.oxfordshire.gov.uk/documents/s24544/HWB_MAR1314R09.doc.pdf (30 March 2016 date last accessed).

Oxfordshire County Council. About Oxfordshire. https://www.oxfordshire.gov.uk/cms/public-site/about-oxfordshire (13 February 2016, date last accessed).

Oxfordshire County Council. About the Health and Wellbeing Board. https://www.oxfordshire.gov.uk/cms/content/about-health-and-wellbeing-board (14 February 2016, date last accessed).

Oxfordshire County Council. Healthy lifestyles. https://www.oxfordshire.gov.uk/cms/public-site/healthy-lifestyles (24 March 2016, date last accessed).

Oxfordshire County Council et al. Oxford and Oxfordshire City Deal. https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/276205/Oxford-Oxfordshire-City-Deal.pdf (7 March 2016, date last accessed).

Oxford City Council. HMO Additional Licensing Designations 2015: Proposed Fees and Charges. http://mycouncil.oxford.gov.uk/documents/s25876/Appendix%202%20-%20HMO%20licensing%20Fees%20and%20Charges%20January%202016.pdf (13 February 2016, date last accessed).

43

Oxford City Council. Houses in Multiple Occupation: Background, https://www.oxford.gov.uk/info/20113/houses_in_multiple_occupation/374/houses_in_multiple_occupation_background (13 February 2016, date last accessed).

Oxford City Council. Houses or flats that need a HMO license. https://www.oxford.gov.uk/info/20237/properties_that_need_an_hmo_licence/926/houses_or_flats_that_need_an_hmo_licence (13 February 2016, date last accessed).

Oxford City Council. Housing Strategy 2015-18. https://www.oxford.gov.uk/downloads/file/910/housing_strategy_2015-18 (13 February 2016, date last accessed).

Oxford City Council. Minimum Standards for Houses in Multiple Occupation. https://www.oxford.gov.uk/info/20113/houses_in_multiple_occupation/373/minimum_standards_for_homes_in_multiple_occupation (13 February 2016, date last accessed).

Oxford City Council. Oxford’s health. https://www.oxford.gov.uk/info/20127/health/457/oxfords_health (15 February 2016, date last accessed).

Oxford Health NHS Foundation Trust. Strategic Plan Document for 2014-2019. https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/392928/OXFORD_HEALTH_Publishable_Summary_Strategic_Plan_1415.pdf (16 October 2016, date last accessed).

Perry BD. Childhood experience and the expression of genetic potential: what childhood neglect tells us about nature and nurture. Brain and Mind 2002:3:79100.

Public Health England. Local Health. http://www.localhealth.org.uk/#sly=ward_2013_DR;sid=5564;v=map4;l=en;z=404698,239790,91209,59144 (18 February 2016, date last accessed).

Public Health England. Thames Valley Centre Prospectus. London, Public Health England, 2014. https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/271123/Thames_Valley_PHE_Centre_Prospectus_V7_PH_checked.pdf (13 February 2016, date last accessed).

Public Health England. Who we are and what we do: Annual Plan 2015/16. London: Public Health England, 2015. https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/452328/Annual_plan_2015-_Aug7-web.pdf (13 February 2016, date last accessed).

Ross C E and Wu C. The links between education and health. American Sociological Review 1995:60(5):719-45.

Rowlingson K. Does income inequality cause health and social problems? Joseph Rowntree Foundation, 2011. https://www.jrf.org.uk/report/does-income-inequality-cause-health-and-social-problems (21 October, 2016, date last accessed).

Shelter. Full House? How overcrowded housing affects families. London: Shelter, 2005. http://england.shelter.org.uk/__data/assets/pdf_file/0004/39532/Full_house_overcrowding_effects.pdf (30 March 2016, date last accessed).

South Oxfordshire District Council. Oxfordshire Core Strategy. Wallingford: South Oxfordshire District Council, 2012. http://www.southoxon.gov.uk/sites/default/files/2013-05-01%20Core%20Strategy%20for%20Website%20final_0.pdf (13 February 2016, date last accessed).

44

Stanistreet D, Scott-Samuel A, and Bellis MA. Income in inequality and mortality in England. Journal of Public Health 1999:21(2):205-207.

Subramanian SV and Kawachi I. Income inequality and health: what have we learned so far? Epidemiologic Reviews 2004:26(1):78-91.

Trust for London. Costs and Benefits of a Living Wage. https://www.trustforlondon.org.uk/wp-content/uploads/2012/10/Living-Wage-Costs-and-Benefits.pdf (26 February, 2016, date last accessed).

University of Oxford. Average house prices in Oxford 'most unaffordable in Britain'. 2015. http://www.ox.ac.uk/news/2015-02-26-average-house-prices-oxford-most-unaffordable-britain (2 March 2016, date last accessed).

Vale of White Horse District Council. Local Plan 2013 Part 1: Strategic Sites and Policies, http://www.whitehorsedc.gov.uk/sites/default/files/2014-10-27%2001%20VOWHDC%20Local%20Plan%202031%20Contents-Chapters%201.pdf (13 February 2016, date last accessed).

Vale of White Horse District Council. Local Plan 2013 Part 1: Strategic Sites and Policies: 6. District-Wide Policies. http://www.whitehorsedc.gov.uk/sites/default/files/2014-10-27%2005%20VOWHDC%20Local%20Plan%202031%20Chapters%206-7-Appendix.pdf (13 February 2016, date last accessed).

Waddell G and Burton A. Is work good for your health and well-being? London: The Stationery Office, 2006. https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/214326/hwwb-is-work-good-for-you.pdf (21 November 2016, data last accessed).