The document analyzes differences in life satisfaction between local authority areas in England based on data from the 2011/12 Annual Population Survey. Some key findings include: - Life satisfaction varied significantly between local authority areas, ranging from 7.1 to 8.1 out of 10 on average. - Areas with higher levels of unemployment, disability, and poor health tended to have more residents reporting very low life satisfaction between 0-4. Rural areas were associated with slightly higher satisfaction. - Areas with larger rural populations, more retired residents or younger couples without children, and higher senses of belonging reported higher proportions of residents with high life satisfaction of 9-10. The analysis suggests tailoring actions to populations with very low versus

1. Understanding differences in life
satisfaction between local
authority areas
Analysis based on the 2011/12 Annual Population Survey
21 October 2013
Baljit Gill and Rob Green
Statistical and Spatial Analysis Team, Analysis and Innovation
Directorate, DCLG
1

2. Contents
1 Summary
3
2 Introduction
5
3 Background
10
4 Setting the scene: differences in life satisfaction between local
authority areas
15
5 Characteristics of places with low or high life satisfaction:
correlations
20
6 Understanding differences in life satisfaction between local
authority areas: regression modelling
29
7 Annex: Underlying models and the method of modelling
37
2

3. 1. Summary
Differences in self-reported wellbeing between local authority areas can be large. In 2011/12, average ratings of life
satisfaction among local authority districts ranged between 7.1 to 8.1 (out of 10). This one-point difference in average life
satisfaction is almost as large as that between employed and unemployed people.
ONS have found the biggest drivers of life satisfaction are people’s employment status, their health, and their relationships
(in terms of their marital status). Insofar as ONS have been able to explain differences between local authority areas in the
life satisfaction levels of their residents, they find the differences between areas reflect the circumstances of the types of
people who live there, with an additional positive effect on satisfaction associated with living in rural areas.
Distribution of Life Satisfaction (England)
Nationally, the distribution of people’s life satisfaction ratings
on a scale of 0 to 10 (where ‘10’ is completely satisfied) is
highly skewed and there is a long tail of low life satisfaction:
7% of the population rate their satisfaction as 0 to 4.
But this distribution can differ markedly between places. In
2011/12, the proportion rating their satisfaction from 0 to 4
ranged widely between districts, from 1% to 16%, and the
proportion who were almost or completely satisfied with their
lives (rating their satisfaction as a 9 or a 10) ranged from 11%
to 43%.
35
30
25
High: 26%
20
Low: 18%
%
This distribution is relevant in local policy making and delivery
to address wellbeing issues: should efforts be focused on
those people who are experiencing the lowest levels of
wellbeing? Are there relatively cost-effective things that can
be done to maintain or improve everyone’s wellbeing?
Medium: 50%
15
Very Low: 7%
10
5
0
0
Not at all
satisfied
1
2
3
4
5
6
Life Satisfaction
7
8
9
10
Completely
satisfied
3

4. Summary continued
The analysis presented here explores differences between local authority districts in how residents rate their life
satisfaction in terms of the types of people who live there and attributes of the places themselves. The findings suggest
that actions to improve life satisfaction should be tailored to populations with very low and high life satisfaction.
35
30
Distribution of Life Satisfaction
25
%
20
15
10
5
0
0
1
2
3
4
5
6
7
8
9
10
This analysis finds that the characteristics of
places where a relatively high proportion of
residents report very low life satisfaction
correspond well with ONS’s key drivers;
these were generally areas with higher levels
of poor health, disability or unemployment,
and relatively few people living in small
market towns.
LA areas where a relatively high proportion of residents report high life satisfaction
are characterised by having large rural or market town populations, a relatively high
proportion of retired people or younger couples without dependent children, and
relatively few women working full time in the workforce. In addition to these factors,
residents in these districts tended to report a high sense of belonging to their
neighbourhood.
Actions to increase employment and improve
health outcomes should therefore lead to
improved wellbeing. Other initiatives to
mitigate the detrimental impacts of ill-health,
disability or unemployment on wellbeing
could be considered. This could include
delivering services in ways which meet
people’s needs for social contact and building
social networks, and building their
confidence. Actions could also focus on
tackling anti-social behaviour and crime. The
findings suggest a key role for Health and
Wellbeing boards.
It may be possible to maintain or increase life satisfaction from moderate to high
levels by focusing on increasing people’s sense of belonging to their
neighbourhoods and considering how some of the benefits of rural or market town
living can be ‘designed into’ urban areas. This might include actions such as
building community spirit and promoting volunteering; reducing fear of crime;
supporting wider community wellbeing through the design of housing and the built
environment; maintaining a thriving high street and building a sense of distinct
identity in areas.
4
Maintaining or increasing good employment opportunities is important in areas of
high life satisfaction too, to meet the needs of all segments of the population, such4

5. 2. Introduction
5

6. Introduction
The first ONS annual estimates of personal wellbeing were published in July 2012 and showed
considerable variation between the 90 county and unitary authorities in the UK.
Our aim:
DCLG wish to understand how far differences between places can be explained by the
characteristics of people who live there, and to what extent there are specific features of
places which may influence wellbeing.
The purpose of this paper is to analyse the factors that are associated
with wellbeing, as measured by life satisfaction, at local authority level.
Along with other research on wellbeing by the ONS and Cabinet
Office, this exploratory analysis can support discussions with local
authorities and Health and Wellbeing boards as they seek to
understand wellbeing in their areas and identify actions to improve it.
This analysis aims to cast some light on differences between places
alongside ONS’s publication of 23 October 2013 which, for the first
time, gives official estimates of wellbeing at LA district level.
We hope it will inspire others to drill more deeply into the data to
understand differences in wellbeing between areas and the role of
place. ONS have already made a substantial contribution through their
‘What matters most to personal wellbeing’ report and further work is
underway to enhance this modelling with an expanded set of place
characteristics.
Approach
• We have focused on the life satisfaction
measure of wellbeing from ONS’s 2011/12
survey.
• We explore mean, high and very low levels
of life satisfaction by exploring characteristics
of areas that are correlated with these, and
using linear regression.
• We analyse life satisfaction at local authority
district level.
District councils are the main administrative bodies in
local government. Where data are sufficiently robust,
ONS estimates describe average levels of wellbeing and
the distribution in each District. This is a more granular
level of analysis than the county/unitary level reported by
ONS in 2012. Districts are more heterogeneous than
counties with respect to wellbeing. For example, ONS
estimates from the 2011/12 survey show West Midlands
metropolitan county to have particularly poor wellbeing,
6
but this area includes such varied districts/cities as
Solihull, Wolverhampton and Coventry.
6

7. ONS measures of personal wellbeing
As part of the Measuring National Wellbeing Programme, the Office for National Statistics has
developed a set of experimental survey-based measures of wellbeing.
The first annual estimates of national wellbeing, published in July 2012, are based on the Annual
Population Survey (APS) 2011-2012. This is a very large UK-wide survey of adults living in private
households. There were 122,000 respondents In England.
Adults rated their life satisfaction on a scale from 0 to 10 in answer to the question: Overall, how
satisfied are you with your life nowadays?
Adults were also asked to rate their wellbeing according to their feeling that the things they do in life are
worthwhile, and their levels of happiness and anxiety yesterday.
As with any survey estimate, wellbeing estimates are subject to random sampling error.
As such the confidence intervals around wellbeing estimates for local authority areas, particularly at
district level, are generally very wide. This means that many of the apparent differences between local
authorities are not statistically significant.
Nevertheless, it is possible to explore the data to identify patterns in how respondents rated their
wellbeing in different areas and the socio-demographic characteristics of the area or particular attributes
of the places themselves.
The APS is a large sample survey of adults aged 16 and over in private households in the UK. The
survey is particularly focused on measuring economic activity but includes questions on subjective
wellbeing. All interviews in England were conducted face-to-face. Wellbeing questions were not
asked of another household member by proxy. The survey is boosted in some local authorities to
provide greater numbers for analysis, for example, of economic activity. Fieldwork was conducted
from mid-April 2011 to mid-April 2012.
7

8. Analysis presented here is based on the
2011/12 Annual Population Survey (APS)
The analysis presented here is based on the 2011/12 APS and corresponds with
the life satisfaction estimates published in July 2012, but uses unpublished data at
the more detailed geography of LA districts.
On 23 October 2013, ONS published estimates for LA districts for the first time. As
the estimates in both years are based on a sample survey, they will be subject to
some degree of random sampling error, reflected in the confidence intervals around
the estimates.
Estimates for each local authority area will differ between survey years
simply because they are based on different random samples of respondents.
Therefore, in presenting and exploring LA wellbeing levels based on ONS’s 201112 APS, DCLG intentionally does not name specific local authorities.
8

9. Key features of the DCLG analysis
Our analysis focuses on life satisfaction rather than other measures of personal wellbeing.
• Life satisfaction is measured by responses to the survey question: Overall, how
satisfied are you with your life nowadays? Respondents rate their satisfaction on a
scale from 0-10, where 10 is ‘completely satisfied’, and 0 is ‘not at all satisfied’.
The analysis is conducted at local authority level only
• This is not an analysis of individual responses to the Annual Population Survey.
• Rather, we summarise wellbeing levels in each local authority area by aggregating the
wellbeing ratings given by APS respondents living in each LA.
• There are 324 local authority districts in England (excluding City of London). These
include London Boroughs, metropolitan and non-metropolitan district councils, and
unitary councils.
There are three summary measures of life satisfaction used in our analysis to describe
the average level of wellbeing in the LA area and the extent to which people experience
extreme levels of wellbeing within the LA:
• mean rating of life satisfaction in the LA area. Nationally, this is 7.4.
• proportion of adults in the LA area rating their wellbeing as high (9 or 10 out of
10). Nationally, 26 per cent of adults rate their wellbeing at these levels.
• proportion of adults in the LA area rating their wellbeing as very low (0 to 4 out of
10). This corresponds to the long tail of low wellbeing, which contains 7 per cent of the
population nationally.
9

10. 3. Background
10

11. The national distribution of life satisfaction
The Annual Population Survey
estimates the average (mean) rating
for life satisfaction among adults in
England is 7.4 on a scale from 0 to 10,
where 10 is completely satisfied.
Distribution of Life Satisfaction (England)
Medium: 50%
35
30
25
High: 26%
20
%
Low: 18%
15
Very Low: 7%
10
5
0
0
Not at all
satisfied
1
2
3
4
5
6
Life Satisfaction
7
8
9
10
Completely
satisfied
This distribution is relevant when thinking about what,
if anything, government should do to raise levels of
wellbeing: should government and local authorities
focus on people in the low wellbeing tail? Are their
relatively cost-effective things that can be done to
maintain or improve everyone’s wellbeing?
The national distribution shows half of
adults rated their life satisfaction 7 or
8, with over a quarter giving a higher
rating of 9 or 10.
The distribution is highly skewed: a
quarter of adults rated their life
satisfaction less than 7, but there is a
notably long tail of low life satisfaction
with 7% of adults rating it between 0
(meaning not at all satisfied with their
life) to 4.
As the following slides show, the
average level of life satisfaction and
the distribution of life satisfaction
varies between local authority areas.
11

12. ONS regression analysis identified the key
drivers of life satisfaction
ONS have explored the variation in personal wellbeing among APS respondents using regression modelling.
Taking a range of characteristics into account, ONS find the strongest factors associated with the life
satisfaction of individuals are:
•
•
•
•
Health
Employment status: being employed or retired
Relationship status: being married or a couple
Being content with employment choice/situation
Age has a moderate effect:
There is a U shaped relationship between life satisfaction and age even after taking health into account, with the
highest satisfaction ratings reported by young adults and older people
There are smaller and negative effects associated with:
•
•
•
Living alone
Being Black African/Caribbean/Black British
Having no religious affiliation
Among employees:
Higher wages help life satisfaction (but not happiness, anxiety or feeling life is worthwhile).
Urban/rural: generally across regions, life satisfaction is higher in rural rather than urban areas, even when other
personal characteristics have been taken into account; and Londoners are found to have similar life satisfaction to
people in other urban areas in GB.
While there are differences in satisfaction levels between local authority areas, ONS find that most of the variation
in satisfaction ratings occurs between individuals, wherever they live, rather than between neighbourhoods
or local authorities. ONS plan further to explore the characteristics of places associated with wellbeing.
ONS publications link: http://www.ons.gov.uk/ons/guide-method/user-guidance/well-being/publications/index.html
What matters most to personal wellbeing: http://www.ons.gov.uk/ons/dcp171766_312125.pdf
12
12

13. Cabinet Office regression analysis identifies
differences in the drivers of low and high
satisfaction
Exploratory analysis by Cabinet Office builds on the ONS regression modelling by exploring the
factors which are associated with the likelihood of an individual rating their wellbeing as a) very low
(0 to 4), or b) high (9,10). Separate individual level models were constructed for these extremes of
the wellbeing scale.
This innovative analysis provides useful insight into differences in the characteristics of
people who are at the extremes of the wellbeing distribution.
The factors found to be most strongly associated with the likelihood of an individual reporting their life
satisfaction as 0 to 4 or 9 to 10 correspond well with those identified by ONS in relation to mean life
satisfaction. These were:
•
Unemployment – relative to being retired, this was a risk factor
for both very low and high satisfaction i.e. being unemployed is
associated with a higher likelihood of rating one’s satisfaction as
very low and a lower likelihood of rating one’s satisfaction as
high. Underemployment (wanting to work more hours) was a
stronger risk factor for high satisfaction.
•
Marital status – compared with being married, being divorced,
separated, widowed or single was a risk factor for levels of very
low and high life satisfaction. Being separated was a stronger
risk factor for low satisfaction.
•
Health – poor health was a risk factor for both levels of very low
and high life satisfaction, but it was a stronger risk factor for
rating satisfaction as very low.
The term risk factor is used
to describe a factor which
increases levels of low
wellbeing or decreases levels
of high wellbeing.
A protective factor is good for
levels of low wellbeing (ie it
reduces them), or good for
levels of high wellbeing (ie it
increases them).
13
13

14. Cabinet Office regression analysis identifies
differences in the drivers of low and high
satisfaction (continued)
There were other shared risk factors for both very low and high life satisfaction. Some
shared risk factors were stronger risks for low satisfaction than for high: having poor health
or a disability, being in social housing compared with owning outright, being separated, being a
smoker, being in receipt of benefits, or being in a Black or minority ethnic group (BME). In contrast,
being underemployed was a stronger risk factor for high satisfaction than for very low satisfaction.
Living with dependent children was a protective factor for both low and high satisfaction, but was a
stronger protective factor for low satisfaction.
The modelling identified protective place-related and community-based factors which were
associated with rating one’s satisfaction as high, but were not associated with rating one’s
satisfaction as very low. These were:
•
•
•
•
The proportion of people in the respondent’s local authority who feel a sense of
belonging to their area
The proportion of people in the respondent’s local authority who feel they can influence
local decisions
Rurality (living in a hamlet or village, rather than in an urban area)
Having moved to a new address in the past year
14
14

15. 4. Setting the scene: differences in life satisfaction
between local authority areas
15

16. 6.2
6.8
6.6
6.4
Source: ONS Annual Population Survey 2011-12, England
Kent
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on a
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Ham
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Bath
and
Average life satisfaction ratings vary
among unitary and county councils
Mean rating given by adults to the question:
'Overall, how satisfied are you with your life nowadays?' on a scale of 0-10 where 10 is completely satisfied
8.0
Average life satisfaction
England: 7.40
7.8
7.6
7.4
7.2
7.0
Points show the mean rating. The bars show the 95% confidence intervals
around this average, indicating a margin of error. The confidence intervals
are wider where survey sample sizes were smaller. Where the confidence
intervals do not overlap, the differences between areas are statistically
significant. (This is an approximate test: technically: even where the bars
overlap a little, the differences between places may also be statistically
significant).
Decreasing wellbeing
16

17. Local authority area distributions of life
satisfaction can differ markedly
The proportion of adults rating their life
satisfaction as very low (0-4) ranges across
LA districts from 1% to 16% (average =
6.3% ).
The proportion of adults rating their life
satisfaction as high (9,10) ranges across LA
districts from 11% to 43% (average = 27%)
On the following slide, we present examples of places with different distributions which
can broadly be segmented into the following groups based on their life satisfaction levels:
“Polarised”
“Satisfied”
Higher proportions of both
very low and high wellbeing
Large proportions of high
wellbeing, little low
“Struggling”
“Moderate”
Large proportions of low
wellbeing, little high
Mainly ‘middle’ wellbeing,
few at the extremes
17

18. Examples of LA areas with different life
satisfaction distributions
Polarised: Staffordshire, largely urban population Satisfied: West Sussex, largely rural population
45
45
40
40
35
35
30
30
25
Each chart
shows the
distribution of
life
satisfaction
ratings
among survey
respondents
in the LA.
25
%
%
20
20
15
15
10
10
5
5
0
0
0
1
2
3
4
5
6
7
8
9
0
10
1
2
3
4
5
6
7
8
9
10
Respondents
Struggling: West Midlands, entirely urban population Moderate: Warwickshire, largely urban population rated life
45
45
40
35
35
30
satisfaction
from 0 to 10
(0 = 'not at all
satisfied’; 10
= ‘completely
satisfied’).
40
30
25
25
%
%
20
20
15
15
10
10
5
5
18
0
0
0
1
2
3
4
5
6
7
8
9
10
0
1
2
3
4
5
6
7
8
9
10

19. Segmenting local authority areas according to
their levels of very low and high life
satisfaction
% of residents with
Polarised places: low very high life satisfaction
wellbeing and also high
(mean centred)
16
wellbeing
In this scatter diagram, we plot the
levels of high and very low life
satisfaction for each local authority
district.
Satisfied places: high
wellbeing with little low
wellbeing
•Districts higher up the vertical axis
have a greater proportion of their
residents rating life satisfaction as high
(9,10).
Increasing % of residents with high life satisfaction
Met district/London borough
Unitary council
Non-met district
% of residents with
very low life satisfaction
(mean centred; axis is reversed)
16
0
0
-16
•Districts to the right of the chart have a
smaller proportion of their residents
rating the life satisfaction as low (0-4).
(The horizontal axis, representing the
proportion of adults with very low life
satisfaction, is reversed.)
• Districts with average levels of
satisfaction on both dimensions are
plotted around the centre of the
diagram.
•All estimates are subject to fairly wide
confidence intervals on both
dimensions.
Observations:
Districts with the greatest proportions of
residents rating their satisfaction as
high are almost exclusively nonmetropolitan districts.
Struggling places:
low wellbeing,
little high wellbeing
Moderate places: neither
low nor high wellbeing
-16
Decreasing % of residents with very low life satisfaction
Few places have truly polarised
populations, with large proportions of
19
people giving ratings at both extremes
(0-4 and 9-10).
19

20. 5. Characteristics of places with low or high life
satisfaction: correlations
20

21. Introduction to this section
This section begins by identifying the factors which are most strongly correlated with life satisfaction at the LA
district level as measured by:
• the mean rating of life satisfaction in the LA area.
• proportion of adults in the LA rating their life satisfaction as high (9 or 10 out of 10).
• proportion of adults in the LA rating their life satisfaction as very low (0 to 4 out of 10)
These correlates can inform the potential levers for improving wellbeing.
A key statistic is the correlation coefficient, ρ (rho), which describes the degree to which two factors are associated.
Because the work presented here is area-based analysis, DCLG consider a correlation of 0.4 or higher as large,
and a correlation of 0.5 or higher as very large.
DCLG explored a wide range of factors as described in the following two slides.
We begin by examining the strongest correlates with average levels of life satisfaction at LA level. We then contrast
the factors most strongly correlated with levels of a) very low and with levels of b) high wellbeing among LAs.
All findings indicate association rather than causation. Causality is
particularly difficult to establish in relation to attributes of places: while
certain attributes such as the amount of green space or having
coastline may be conducive to high wellbeing, where family and other
ties permit, people who value quality of life/high wellbeing and have
the means to afford it, will elect to live in areas with particular
attributes. Further, places where wellbeing levels are high may meet
the needs of some segments of the population better than others,
and this may cause some people – with lower wellbeing – to leave
the area. For example, young people may move out of areas with
high average wellbeing for employment, better pay or career
The analysis in this section is descriptive, and
many of the factors are also correlated with
each other. For example, in areas where
unemployment rates are high, there will
generally also be higher levels of ill-health or
disability, and higher levels of crime. In the next
section, we present the findings of regression
modelling which asks: of the many
characteristics that are correlated with life
21
satisfaction at LA level, which ones – when
taken together – explain the differences
21
between LA areas?

22. The association between life satisfaction
levels across LA districts and a wide range of
compositional and contextual factors
The following slide presents the wide range of factors that have been considered in this work as potential
drivers of levels of life satisfaction in LA areas.
We have endeavoured to collect a range of both compositional and contextual indicators for each local
authority district, across a range of sources. Much of the data is aggregated up from individual respondents
to the Annual Population Survey within each LA. Other sources include Census 2011, NOMIS for
employment data, Place Survey 2008 for residents’ perceptions of their area, and a range of administrative
or GIS data.
A compositional indicator is one which
reflects the personal circumstances of
people living in each LA area. A contextual
factor reflects the characteristics or
attributes of the area itself.
The factors considered cover the domains of
wellbeing identified by ONS in their framework for
measuring national wellbeing (left), and incorporate
local authority measures from the Public Health
Outcomes Framework.
In gathering data for this analysis, particular
attention has been given to attributes of places, in
terms of people’s perceptions of the place and
social capital/behaviours, and also characteristics
of the living or natural environment. The most
notable omissions are local indicators of mental
health or the strength of social networks/quality of
22
relationships.
22

23. Potential drivers of life satisfaction in local
authority areas
Broad group
1 - Self reported health
2 - Economic activity
3 - Employment details
4 - Living
arrangements/relationships
5 - Age
6 - Ethnicity
7 - Tenure and dwelling
8 - Religion
9 - Socio-economic status
10 - Highest qualification
11 - Gender
12 - Disability
13 - Migration
14 - Unpaid carers
15 - Crime/anti-social
behaviour
Factor
Self-reported health (5 point scale)
% of 16-64 year olds that are economically active/inactive (NOMIS)
Economic activity by type: e.g. Retired, Employee, Unemployed
% of employed or self-employed that work part time, full time, long hours
% of employed or self-employed that work part time, full time, long hours split by male/female
% of employed or self-employed that are male/female
Want new, additional job or longer hours
Permanent/non-permanent job
Couple/not couple; with/without dependent children - split by age group
With dependent children - by number
Marital status
Lives alone
Age in bands, mean age, median age
Ethnicity split into 9 groups
Tenure (owned outright, with a mortgage, private renter, social renter)
% of dwellings that are flats
% of dwellings that are HMOs (Houses of Multiple Occupation)
% of households with overcrowding in rooms/bedrooms
Religion (8 groups)
NS-SEC Socio Economic Classification (8 levels - main or last main job)
Highest qualification achieved (7 levels)
Female
Day to day activities limited by health problem or disability
Legally and work limiting disabled
Disability Living Allowance claimants
% of households with no/some/all adults speaking English as main
language
Recent immigrant or born in UK
Resident in UK - by number of years (4 bands)
Volume of total migration per 1,000 population
Volume of international migration per 1,000 population
Informal carers by hours per week (4 bands)
Perceive that anti-social behaviour is a problem in their local area
Perceive that drug dealing is a problem in their local area
Perceive that drunk or rowdy behaviour is a problem in their local area
Perceive that respect is a problem in their local area
Robbery offences recorded per 1,000 population
Burglary dwelling offences recorded per 1,000 population
Violence against the person offences per 1,000 population
Broad group
Factor
School appeals heard as % of admissions - primary/secondary
Schools net imports as % of school population
16 - Area desirability/prices Housing rent - median, average, lower quartile
House prices - median, lower quartile, ratio to earnings
% of pupils achieving at 5 or more GCSEs at grades A*-C
17 - Time at same address Length of time at address - by number of years (6 bands)
Life expectancy and disability free life expectancy
18 - Health inequality and life
Slope Index of Inequality (SII) for life expectancy at birth
expectancy
Age standardised mortality rates
Average weekly net equivalised household income before/after housing
costs
% of pupils eligible for free school meals
Indices of Deprivation
19 - Area deprivation/income
Gross disposable household income per head (by counties/groups of
unitary authorities)
Gross Value Added per head (by counties/groups of unitary authorities)
% of 16-18 year olds Not in Education, Employment or Training
% household spaces with no usual resident
Area size
Dwelling density
20 - Natural environment
Population density
attributes
Area of domestic gardens - as a % of area of domestic buildings
Area of greenspace - as a % of total area
Area of water - as a % of total area
Authorities with coastline
% Population by urban/rural area type
21 - Natural environment
Population size
classifications
Urban-Rural classification (6 groups)
22 - Commuting distance
Commuting distance (9 bands) - for 16-74 year olds employed in the LA
23 - Governance
Type of Local Authority (4 types)
Civic participation
Perceive that parents take responsibility for behaviour of children
Regular formal volunteering
Participate in sport (1 x 30 mins per week; 3 x 30 mins per week)
24 - Area
Perceive that people from different backgrounds get on well (cohesion)
perceptions/behaviours
Feel that they belong to immediate neighbourhood
Satisfied with local area
Perceive fair treatment by local services
Perceive ability to influence local decisions
Unless stated otherwise factors are the proportion of adults in the LA with the particular characteristic
23

24. At local authority level, the factors most
strongly correlated with mean life satisfaction
are as follows
Very high correlations
(coefficient of 0.5 up to 0.6, positive or negative)
•
Unemployment (-0.50); and households with
dependent children and no adult in employment
(-0.57)
•
Indicators of income and area deprivation. IMD
crime deprivation (-0.53) and income
deprivation (-0.50)
•
Women working full time (-0.50) and people
working from home (0.50)
•
Couples with no dependent children (0.51) and
lone parent households (-0.55)
•
Perceptions of anti-social behaviour (-0.56) and
that respect is a problem (-0.54)
•
Satisfaction with local area (0.55), sense of
belonging to the neighbourhood (0.50), and
perceptions that local services are fair (0.54)
•
Population living in rural areas (0.51)
•
Mean and median age (both 0.51). (Also the
proportion of 60-64 year olds (0.51)
Highly correlated: correlation coefficient of 0.4 up to 0.5
• % retired (0.42) or working full time (-0.45), % women working
full-time (-0.48). % working as small employers (0.42)
• Living as a couple (0.46), single (-0.44), and % having 0-4
dependent children in household (-0.47)
• Age groups: correlations were positive for age-groups from 45
and over (ranging between 0.42 and 0.46) and
• negative for 25-29 (-0.47) and 30-34 (-0.40)
• Tenure/housing: people own outright (0.49), social tenants (0.43), overcrowded bedrooms (-0.46)
• Ethnicity/religion: % White (0.47), Mixed (-0.42), Asian (-0.42),
Black (-0.40); and Muslim (-0.43)
• Migration: % resident in UK 5-10 years (-0.41); having some
or no adults in household with English as their main language
(-0.41, -0.43)
• % providing 1 to 19 hours unpaid care per week (0.49). (Note:
correlation for % providing no weekly unpaid care is negative
(-0.38))
• % participating in regular formal volunteering (0.45)
• Crime/ASB: burglary, robbery, violent offences (ranging
between -0.47 and -0.43); perceptions of drunk/rowdy
behaviour or of drug use/dealing (both -0.49)
• Other perceptions: feeling that people from different
backgrounds get on well i.e. cohesion (0.42) and that parents
take enough responsibility for their children (0.42).
• Other indicators of income and area deprivation: proportion
receiving free school meals (-0.45). IMD employment
deprivation (-0.40)
• Natural environment: green space (0.49), population density
(-0.42)
• Urban/rural classifications: % of population in rural or24
large
market towns (0.47), in villages (0.48), in dispersed rural
24
areas (0.45), or major urban authorities (-0.40)

25. At LA level, unemployment is the factor most
strongly correlated with ratings of very low life
satisfaction – but is less strongly correlated with high
satisfaction
% in LA area rating life satisfaction as very low or high by LA-level unemployment rate
45%
40%
Percentage of adults rating life satisfaction
as very low or high
ρ = -0.25
35%
30%
25%
20%
15%
ρ = 0.44
10%
5%
For very low
satisfaction, other
explanatory factors
include: disability,
economic inactivity
due to ill
health/disability; poor
health; ASB.
ρ (rho) indicates the
correlation coefficient, a
statistic that describes the
degree to which two
factors are associated (in
this case it describes the
correlation between the
level of high – or very low
– life satisfaction with the
unemployment rate). In the
work presented here,
DCLG consider a
correlation of 0.4 or higher
as large, and a correlation
of 0.5 or higher as very
large.
0%
0%
2%
4%
6%
8%
10%
12%
Unemployment rate (NOMIS 2011-12, modelled)
% very low life satisfaction
% high life satisfaction
14%
16%
18%
20%
25
25

26. Poor health is also highly correlated with low
life satisfaction at LA level – but there is no apparent
relationship with high life satisfaction
% in LA area rating life satisfaction as very low or high by LA-level self reported poor health
45%
Percentage of adults rating life satisfaction
as very low or high
40%
ρ = -0.02
35%
30%
25%
20%
ρ = 0.41 For high satisfaction, the most
15%
strongly correlated factors
include: whether the area is
urban/rural; % retired; and the
extent of green space. But the
factor with the strongest
association with high wellbeing
levels is shown overleaf.
10%
5%
0%
0%
2%
4%
6%
8%
10%
% of adults rating their health as bad or very bad (Census 2011)
% very low life satisfaction
% high life satisfaction
12%
14%
26
26
ρ (rho) = correlation coefficient

27. The extent to which people feel they belong to
their neighbourhood is the factor most
strongly correlated with LA level ratings of
high life satisfaction
% in LA area rating life satisfaction as very low or high by LA-level sense of belonging
45%
Percentage of adults rating life satisfaction
as very low or high
40%
35%
30%
Sense of belonging was measured by the
Place Survey 2008. Citizenship Survey
analysis at the national level and qualitative
case studies show sense of belonging is
related to having strong social networks
(such as having 3 or more close friends, or
having family and friends in the area); being
older, having lived in the area for a long time;
a sense of community, cohesion, and local
pride; and – particularly in deprived areas –
feeling safe in the area.
25%
20%
ρ = 0.52
15%
10%
ρ = -0.19
5%
0%
20%
30%
40%
50%
60%
Sense of belonging (2008 Place Survey)
% very low life satisfaction
% high life satisfaction
70%
80%
27
27
ρ (rho) = correlation coefficient

28. Different factors are most strongly
correlated to very low and high life
satisfaction
For very low life satisfaction at LA level the
strongest correlates are:
For high life satisfaction at LA level the strongest
correlates are:
Large correlations:
• Unemployment (0.44); households with dependent
children and no adult in employment (0.41)
• Very bad or bad self reported health (0.42, 0.41)
• Disability: % who are DDA and work disabled (0.44)
• % giving 20 to 49 hours unpaid care per week (0.41).
• Income deprivation (0.43) and employment deprivation
(0.44)
• Perceptions of ASB (0.41), of drug use/dealing (0.42),
and that respect is a problem (0.40)
• Satisfaction with local area (-0.41)
Very large correlations:
• Sense of belonging to the immediate neighbourhood (0.52)
• % 60-64 year olds (0.52)
• Urban/rural classifications: proportion of population living in
rural areas (0.50)
Large correlations:
• Economic activity and hours worked: % Retired (0.47);
Women working full time (-0.47); Small employers (0.42)
• Relationships and living arrangements: couples with no
dependent children (0.47), singles (-0.42), 0-4 dependent
children in household (-0.43), widows (0.42), lone parents in
part time employment (0.42)
• Property owned outright (0.43)
• Mean/median age (0.49/0.50). Correlations were positive for
age-groups from age 45 to 84 (range: 0.43 and 0.49) but
negative for age-groups 25-29 (-0.43) and 30-44 (-0.44)
• Ethnicity: Mixed (-0.44), White (0.41)
• Resident in UK 5-10 years (-0.41)
• 1 to 19 hours unpaid caring per week (0.43).
• Burglary offences (-0.41) and IMD Crime deprivation (-0.40)
• Green space (0.45)
• Urban/rural classifications: proportion of population living in
rural towns or large market towns (0.48), village population
28
(0.45), dispersed rural population (0.46)
28

29. 6. Understanding differences in life satisfaction
between local authority areas: regression modelling
29

30. Introduction to this section
The previous section identified the factors which were most strongly correlated with life satisfaction at the LA
level.
In this section, we present the findings of regression modelling. These seek to answer the question:
Of the many characteristics that are correlated with life satisfaction at LA level, which ones – when
taken together – best explain differences between LA areas?
This work is undertaken as a series of three regression models looking at differences between LA areas
according to:
•
•
•
their mean life satisfaction,
the percentage of people rating life satisfaction as very low (0-4), and
the percentage of people rating life satisfaction as high (9,10).
DCLG tested a wide range of factors to explore the main compositional and place-related factors that are
associated with life satisfaction. In building these models, we were strongly guided by ONS findings on what
matters most to individual life satisfaction. This was to ensure the findings at local authority level were
grounded in evidence on what drives individual life satisfaction.
Our findings suggest that, at the LA level, there are different factors associated with the extremes of the life
satisfaction distribution. This is consistent with the findings from the Cabinet Office exploratory analysis
presented on slides 13 and 14.
These findings are summarised here, but detailed models are presented in the annex, with a fuller discussion
of the findings and the methods used.
As in the presentation of correlations,
these findings from regression models
indicate association rather than
causation.
See slide 32 for an explanation for notes on
the model and terms used in table, and slide
30
43 for a description of the method of
modelling.
30

31. Mean life satisfaction: regression results
DCLG conducted regression modelling, testing a wide range of factors, guided by ONS findings on what matters most to
individual life satisfaction. The regression model below shows that when the following factors about each LA area are
considered together, they explain almost half of the variation observed in mean life satisfaction between districts (R square
= 0.493). Factors with coefficients shown in red are negatively associated with mean life satisfaction i.e. they are
associated with reduced average wellbeing.
Most of the factors associated with mean
life satisfaction in LA areas are related to
the personal circumstances of people living
Regression model for mean satisfaction in each LA district
there, with the proportion of adults living in
Unstandardized
Standardized
couples without dependent children
Factor
Coefficients
Coefficients
being the most highly associated factor.
(Constant)
7.68 **
Self-reported health, age, employment
0.75 **
0.24
% Living as a couple with no dependent children
and hours worked, educational
0.93 **
0.20
% Aged 20-24
qualifications and ethnicity are also highly
-0.98 **
-0.19
% of female employees that work 31-48 hours per week
associated.
% Greenspace in area
% Mixed ethnic group
1
Gross Value Added per head
% Unemployed
% Aged 80 & over
% Self-reported health: bad
% No qualifications
% Semi-routine occupations
% Self-reported health: very bad
% would work more hours at same basic rate
(Model R-Squared = 0.493)
* significant at 5% level; ** significant at 1% level
0.17 **
-3.37 **
0.00 **
-1.08 *
0.94 *
-1.23 *
-0.57 *
-0.59 *
-2.04 *
-0.52 *
0.17
-0.14
0.14
-0.13
0.11
-0.11
-0.10
-0.10
-0.10
-0.10
We also find the proportion of land covered
by the LA district which is green space is a
positive contextual factor, although this is in
itself highly correlated with predominantly
rural populations.
Alternative models find the proportion of
people living in rural areas is a positive
and significant factor when other factors
are taken into account.
Economic activity as measured by Gross
Value Added per head for the county/group
of unitary councils is also positively
associated with mean life satisfaction.
31

32. Notes on the model and terms used in the
table
All findings indicate association rather than causation.
The model shows a high R-Sq (0.493) which indicates that almost half of the total variance in mean life satisfaction between
local authority areas can be explained by factors shown in the table. This is consistent with other research at this high
geographical level (e.g. US cities).
The factors identified as significant in the model are broadly consistent with factors identified by ONS in their modelling
at individual level.
Where population characteristics do not vary substantially at local authority level, they will not influence differences in
wellbeing at this level of analysis. This may explain why some factors which one might expect to be influential are not
identified in this modelling.
While the model has identified specific factors as significantly associated with life satisfaction, this does not mean
other factors or combinations of factors were not significant or that the factors selected are the underlying ones which
drive satisfaction. Many of the factors entered into the models are inter-correlated. The combination of factors identified by the
models, when taken together, offer the most explanatory power in understanding life satisfaction levels in places but they also
reflect the priority they were given when factors were entered in steps during model building. We therefore built models giving
priority to factors found to be important at the individual level, and entered place-level attributes only after compositional
characteristics were taken into account. A number of alternative models were also considered where the underlying drivers were
less clear.
Factors are ordered by their standardised coefficients to indicate the factors that are most strongly associated with life
satisfaction.
Unstandardised coefficient: the size of this coefficient indicates the extent to which mean life satisfaction is expected to
increase or decrease given a one-point increase in the independent factor, holding all other factors constant.
Standardised coefficient: this measure tells us the number of standard deviations that the outcome will change as a result of
one standard deviation change in the predictor. It takes into account that each factor is distributed differently between local
authority areas and may be measured on a different scale to other factors in the model. This is a useful statistic as the size of
these coefficients tells us which factors are most strongly associated with the outcome.
32

33. Discussion of factors associated with
average life satisfaction ratings
Age: Being younger (aged 20-24) or older (aged 80+) is positively associated with high average life satisfaction (after
controlling for other factors).
Unemployment is negatively associated with mean life satisfaction, as is the proportion of people who would work
more hours (at the same rate of pay) if they could. There are parallels with ONS findings on unemployment and
underemployment being negative drivers of life satisfaction at the individual level.
We also find a negative association with the proportion of working (or self-employed) women who work 31-48 hours per
week. This is consistent with findings which show that the proportion of people rating their satisfaction as high (9-10) in
each LA area is negatively associated with the proportion of the workforce made up of women working such hours.
These findings may indicate that satisfaction is highest in areas where women do not need to work full-time, for
example, because the cost of living is lower, or where household wages tend to be higher.
Self-reported health: As one might expect, the proportion of people with bad or very bad self-reported health is
negatively associated with mean life satisfaction.
•
•
The coefficients show that a ten point increase in the percentage of people in the LA who report very bad
health is associated with a reduction in mean life satisfaction of about 0.2, all other things being constant.
There is some debate over the relationship between self-reported health and personal wellbeing. As ONS
points out, Dolan et. al (2008)1 note some of the association may be caused by the impact that wellbeing has
on health. Other research has shown that subjective evaluations of health matter more than objective
measures in terms of the relationship with personal wellbeing (Brown et. al., 2010 2; Diener et al., 19993).
Ethnicity, qualifications and socio-economic status: Local authority composition in terms of the proportion in the
Mixed ethnic group was significantly and negatively associated with mean life satisfaction. LA areas with a higher
proportion of people without qualifications or working in semi-routine occupations also tend to have lower mean life
satisfaction.
1. Dolan, P., Peasgood, T. and White, M. (2008). ‘Do we really know what makes us happy/? A review of the economic literature on the
factors associated with subjective well-being’, Journal of Economic Psychology, 29, 94-122
2. Diener, E., Suh, E. M., Lucas, R. E. and Smith, H. L. (1999). ‘Subjective well-being: Three decades of progress’, Psychological
Review, 125, 276–302
3. Brown, D., Smith, C. and Woolf J. (2010), ‘
The Determinants of Subjective Wellbeing in New Zealand: An Empirical Look at New Zealand’s Social Welfare Function’, New Zealand
33

34. Discussion of factors associated with
average life satisfaction ratings (continued)
Contextual effects:
The proportion of green space is a contextual factor associated with higher average life satisfaction, but
alternative models find the proportion of people living in rural areas is also a positive and significant factor
when other factors are taken into account. This factor has a large association with the proportion of green space
in the LA, which was selected in the model shown above.
Economic activity as measured by Gross Value Added per head for the county/group of unitary councils is
positively associated with mean life satisfaction. This measure reflects the income generated by resident
individuals or corporations in the production of goods and services. The coefficient is very small (rounded to
zero) as the range is extremely wide.
34

35. Factors associated with very low and high life
satisfaction scores
Of the many characteristics that are correlated with the percentage of people rating life satisfaction as very low (0 to 4) in each
of the 324 LADs, which ones – when taken together – best explain differences between LA areas? And which ones best
explain differences in the percentage of people rating life satisfaction as high (9,10)? To answer this, DCLG conducted
regression modelling, testing a wide range of factors, guided by ONS findings on what matters most to individual life
satisfaction. Our findings suggest that, at the LA level, there are different factors associated with the extremes of the
distribution of life satisfaction. (Detailed findings are presented in the annex.)
35
30
Distribution of Life Satisfaction
25
%
20
15
10
5
0
0
1
2
3
4
5
6
7
8
9
10
LA levels of very low life satisfaction are, broadly speaking,
positively associated with compositional characteristics of:
-% with poor health or a limiting disability
-% unemployed
-% of working men who work 16 to 30 hours per week
-% separated or divorced/lone parents
LA levels of high life satisfaction are, broadly speaking,
positively associated with:
-a good sense of belonging
-% in rural areas or market-towns
-% retired, or younger couples without dependent
children
Very low satisfaction levels are negatively associated with:
-% living as a couple, or % retired
-% living in large market towns
-% living close to work.
High satisfaction levels are negatively associated with:
-% women working full-time in the workforce
35
-% unemployed
35

36. Implications for action to improve life
satisfaction
These regression findings, and the findings on correlations, suggest actions to improve life satisfaction
should be tailored to populations with very low and high life satisfaction.
35
30
Distribution of Life Satisfaction
25
%
20
15
10
5
0
0
1
2
3
4
To uplift very low life satisfaction, actions could
include:
-increasing employment;
-early health interventions
-mitigating the detrimental impacts of ill-health or
disability on wellbeing e.g. by building social
networks, or by supporting people in employment.
-Other actions include tackling ASB and crime.
This suggests a key role for Health & Wellbeing
Boards.
5
6
7
8
9
10
To maintain or increase life satisfaction from moderate
to high levels, actions could focus on:
-increasing ‘belonging’ e.g. through volunteering and
building community spirit, and reducing fear of crime.
-addressing issues for full-time women workers, or
other challenged groups.
Can some aspects of rural living be created in urban
36
36
areas for wellbeing benefits?
36

37. Annex: Underlying models and the method of
modelling
37

38. Modelling very low and high life
satisfaction
On slide 31 we identified the main factors associated with mean life satisfaction. We now seek to
understand the quite marked differences between local authorities in the proportions of their populations
experiencing very low or high life satisfaction.
The purpose of this is to see how factors associated with life satisfaction at the extremes of the
distribution differ. This may, in turn, suggest different sets of policy actions to improve wellbeing.
The research question is to identify the main factors associated with levels of very low life satisfaction
and levels of high satisfaction, and to observe differences between the two sets of factors. The
dependent variables used in regression modelling are:
•
•
the proportion of people with very low life satisfaction – defined as those rating their life
satisfaction from 0 to 4; and
the proportion of people with high satisfaction – defined as those rating their life
satisfaction as 9 or 10.
In some local authorities, our estimates for both of these dependent variables are based on small
numbers of survey respondents and are therefore subject to relatively high levels of uncertainty. We
have therefore adapted our approach to regression modelling from Ordinary Least Squares to Weighted
Least Squares regression. There is no definitive way to assign weights but we have sought to ensure all
types of council are included in our analysis while giving lower weight to those with less reliable
estimates on the dependent variable (see slide 44).
As such, we recognise that the ensuing models are less robust than the one presented earlier for mean
wellbeing. But in justification, the findings do indicate interesting differences in the main factors
associated with very low and high life satisfaction.
38

39. Very low life satisfaction: regression results
Regression model for the percentage reporting very low satisfaction (0-4) in each LA district
Unstandardized
Standardized
Factor
Coefficients
Coefficients
(Constant)
0.03 *
0.57 **
0.25
% Self-reported health: very bad
0.14 **
0.22
% Self-reported health: fair
0.19 **
0.21
% Unemployed
-0.09 **
-0.21
% Commuting distance 0-2km
0.23 **
0.19
% Self-reported health: bad
0.36 **
0.14
% Lone parents aged 45 or over
-0.07 *
-0.12
% Commuting distance 10-20km
0.12 *
0.10
% of male employees that work 16-30 hours per week
(Model R-Squared = 0.379)
* significant at 5% level; ** significant at 1% level
The model gives a good indication of the main factors associated with levels very low life
satisfaction at LA level. But other factors could also be important, as described on the
next slide.
39

40. Very low life satisfaction: findings from
alternative models
Health: very low life satisfaction is positively associated with the proportions of adults reporting their health as very
bad, bad or fair. Alternative models find that disability has an equally strong association with levels of very low
life satisfaction as self reported health. The proportion of people with disabilities has a large association with the
proportion reporting health as very bad, bad or fair. Self reported health factors were entered into the model before
disability so had a greater chance of being selected in the model. If disability factors had been entered into the
model before disability it is likely that they would have come out as significant factors themselves.
In addition to unemployment rates, we find the proportion of working men who work between 16 to 30 hours
per week to be positively associated with low satisfaction levels in the LA area. Both of these may be indications of
weak local employment conditions.
Areas with a high proportion of retired people are likely to have fewer people with low life satisfaction: the proportion
of people who are retired has come up as a significant negative factor in alternative models. (A negative factor
implies a reduction in the number of people with low life satisfaction.)
The proportion of people that are in couples and the proportion of people that are separated or divorced can also
be considered to be associated with very low life satisfaction. These were found to be significant factors in
alternative models.
Commuting distance and urban/rural indicators: The model finds that the proportions of people who have a
short commute (of under 2 km) or a moderate commute of 10 to 20 km are negatively associated with very low
life satisfaction (i.e. this factor reduces the number of people with low wellbeing). This finding may reflect the tradeoffs people make between being close to work, and also living in areas where work remains accessible but where
other amenities can be enjoyed such as larger houses and more green space.
Alternative models find that having a high proportion of residents living in large market towns is a significant
negative factor associated with the level of low wellbeing in an area. Had urban/rural factors been entered into the
above model before commuting factors, the proportion of people living in large market towns would have been
identified as a significant factor.
40

41. High life satisfaction: regression results
Regression model for the percentage reporting high satisfaction (9-10) in each LA district
Unstandardized
Standardized
Factor
Coefficients
Coefficients
(Constant)
0.20 **
0.19 **
0.24
% who feel they belong to their immediate neighbourhood
0.24 **
0.19
% Living as a couple, aged less than 45, with no dependent children
-0.21 *
-0.15
% Aged 30-34
0.02 **
0.14
At least 80% population in rural settlements and large market towns
0.10 *
0.14
% Retired
-0.33 *
-0.13
% of employees that are female and work full time 31 to 48 hours
(Model R-Squared = 0.351)
* significant at 5% level; ** significant at 1% level
The strongest factor – sense of belonging – relates to place attachment. This is the proportion of adults who
feel they belong to the immediate neighbourhood, as measured by the Place Survey 2008.
Citizenship Survey analysis and qualitative case studies show sense of belonging is related to having strong
social networks (such as having 3 or more close friends, and having family and friends in the area); being
older, having lived in the area for a long time; a sense of community, cohesion, and local pride; and –
particularly in deprived areas – feeling safe in the area.
The inclusion of ‘sense of belonging’ in the model did not add substantially to its explanatory power (adding
only one percentage point to the proportion of variation in levels of high life satisfaction that were explained by
the model). But the ‘sense of belonging’ factor was highly effective in drawing together a range of alternative
models containing disparate factors. This reflects that levels of high satisfaction are associated with a diverse
set of characteristics: some characteristics are influential in some LAs, but in other LAs, other characteristics
are influential. The overall sense that people belong to the area emerges as a unifying factor among these
areas.
41

42. High life satisfaction: further discussion of
regression results
High life satisfaction levels are also associated with the most rural local authorities, where at least 80% of their
population in rural settlements and large market towns (as opposed to living in more urban areas). This is the most
rural of a six-band typology, with ‘major urban’ areas at the opposite extreme.
As with the model for average life satisfaction, we find the proportion of people living as a couple without
dependent children in the household to be significant and positively associated with levels of high satisfaction – but
here, it is based on those aged under 45 years.
As one might expect, having a high proportion of retirees is positively associated with levels of high satisfaction.
The proportion of people who are unemployed does not come up as a factor in the final models, but it only becomes
non-significant when the proportion living in rural areas is taken into account. The proportion of people in the Mixed
ethnic group has come up as a significant negative factor in alternative models.
‘Sense of belonging’, along with most of the other perceptions measures, were entered during the last
step in modelling. This was to give compositional characteristics and attributes of areas priority in
explaining differences in levels of high life satisfaction between areas.
Department of Communities and Local Government (2009). 2007-08 Citizenship Survey, Community Cohesion Topic Report
http://resources.cohesioninstitute.org.uk/Publications/Documents/Document/DownloadDocumentsFile.aspx?recordId=149&file=PDFversion
Department of Communities and Local Government (2011) Community Spirit in England: A report on the 2009-10 Citizenship Survey
http://webarchive.nationalarchives.gov.uk/20120919132719/http:/www.communities.gov.uk/publications/corporate/statistics/citizenshipsurvey200910spirit
Livingston, M., Bailey, N., and Kearns, A. (2008) People’s attachment to place – the influence of neighbourhood deprivation, Joseph
Rowntree Foundation http://www.jrf.org.uk/system/files/2200-neighbourhoods-attachment-deprivation.pdf
42

43. Method of Modelling
We have used the Ordinary Least Squares (OLS) technique for modelling mean life satisfaction. A variation
(Weighted Least Squares) was used in modelling very low and high life satisfaction. This is to account for different
sample sizes between LAs.
Explanatory variables were entered in the models in groups according to the order specified in the list of potential
drivers given in slide 23). Each group was entered into the model using the stepwise regression procedure. The
significant variables are taken, and kept in subsequent models, where additional factor groups are added to the
model using the stepwise procedure. If variables become non-significant at any stage they are removed.
Factor groups were entered in the model in a specified order, according to their importance in influencing wellbeing
at the individual level. This order of importance is taken from the ONS publication ‘Measuring National Well-being –
What matters most to Personal Well-being?’ This gives the factors highlighted as having a large effect on individual
life satisfaction the best opportunity to emerge as LA level predictors. The approach also helps to separate individual
and ‘place’ effects, giving priority to the former.
A key reason for taking a stepwise approach was to avoid the problems arising from multi-collinearity among the
factors being considered. Where two or more predictor variables in the regression model are highly correlated with
each other, the individual regression coefficients cannot be estimated precisely. In this event, the model may not give
valid results about individual predictors or about which predictors are most powerful in explaining variance in the
outcome variable, and which are redundant.
In effect, if variables chosen in the stepwise procedures appeared to be multi-collinear (as indicated by the VIF,
below) then they were removed manually. Although this occurred minimally, it is possible that variables chosen by the
model might have been replaced by other very similar ones that had been removed from the procedure.
The VIF (Variance Inflation Factor) is an indication of multi-collinearity in the model. A value greater than 5 for a
single variable (or an average of the VIFs of all the factors in the model much higher than 1) would indicate that
multi-collinearity was causing problems in the model. In the models presented here, most factors were under 2, and
the highest VIF was 2.7 for ‘a sense of belonging’.
43

44. Weighting models where estimates of very
low and high life satisfaction are less robust
Because our estimates for both of these dependent variables are based on small numbers of survey respondents in
some local authorities, they can be subject to relatively high levels of uncertainty.
We therefore adapted our approach to regression modelling from OLS to weighted least squares regression. Most
local authorities are given equal weight (a weight of 1), but those with the greatest uncertainty around the estimates,
as measured by the Relative Standard Error (RSE), are given a reduced weight. RSE is the ratio of the standard
error around an estimate to the estimate itself. ONS have used RSE < 20% as their benchmark in assessing the
robustness of local authority estimates.
An alternative would have been to drop LAs from analysis where estimates were considered less robust. We did not
favour this option because LAs with the highest RSE tend to be non-metropolitan district councils (where sample
sizes are less than 300), and these tend to also have the highest levels of life satisfaction. Excluding these LAs from
analysis from modelling would be unacceptably biasing.
•
In modelling high life satisfaction, 29 LAs (9%) had estimates where RSE > 20%, and were given
weights in inverse proportion to their RSE. In effect the lowest weight given was 0.65.
If the same benchmark were used in weighting estimates of very low wellbeing, the majority (68%) of local authorities
would have been down-weighted. Rather than do this, we have taken a more relaxed threshold (of RSE > 40% in
determining robustness.
•
In modelling very low life satisfaction, 90 LAs (28%) had estimates where RSE > 40%, and were given
weights in inverse proportion to their RSE. In effect the lowest weight given was 0.38.
The method of correction is ad-hoc, and attempts to avoid introducing bias into modelling.
44

45. Sample sizes by type of LA district
Annual Population Survey sample sizes vary considerably according to the type of local authority with the largest
samples in unitary authorities and Metropolitan district councils. This reflects both the larger population sizes and
also any over-sampling conducted on behalf of the LA.
Sample sizes were lowest for non-metropolitan district councils (ranging from 56 to 268, average = 148).
Five unitary authorities had sample sizes under or around 500 – notably new unitaries: Bedford (272), Rutland
(276), Cheshire West and Chester (434), Central Bedfordshire(475) and Cheshire East (515).
Sample size by type of local authority district
1,200
1,100
1,000
900
800
700
600
500
400
300
200
100
0
Non-metropolitan Districts
London
Boroughs
Metropolitan
Districts
Unitary
Authorities
45

46. The reliability of local authority district level
estimates of very low life satisfaction
Increasing % of residents with high life satisfaction
% of residents with 16
Polarised places: low
high life satisfaction
wellbeing and also
(mean centred)
high wellbeing
Satisfied places: high wellbeing
with little low wellbeing
The scatter diagram shows the
levels of high and very low life
satisfaction for each LA district.
The colour of the data points
indicates the reliability (Relative
Standard Error) of the estimates of
very low satisfaction.
% of residents with
very low life satisfaction
(mean centred; axis is reversed)
16
0
Struggling places: low wellbeing,
little high wellbeing
0
-16
Blue: RSE < 40% (ie more reliable)
Pink: RSE >= 40%
-16
We have not depicted the reliability
of estimates of high life satisfaction,
as these are more reliable than
those for very low life satisfaction in
all but one of the 324 LA districts.
Moderate places: neither low
nor high wellbeing
Decreasing % of residents with very low life satisfaction
46