Embed Size (px)
Citation preview
Consumption Patterns among older consumers
Statistical analysis
Adele Atkinson and David Hayes
December 2010 www.ilcuk.org.uk
R
The International Longevity Centre - UK (ILC-UK) is an independent, non-partisan think-tank dedicated to addressing issues of longevity, ageing and population change. It develops ideas, undertakes research and creates a forum for debate.
The ILC-UK is a registered charity (no. 1080496) incorporated with limited liability in England and Wales (company no. 3798902).
ILC–UK 11 Tufton Street London SW1P 3QB Tel : +44 (0) 20 7340 0440 www.ilcuk.org.uk
This report was first published In December 2010 This report is published as part of a series of papers produced in conjunction with Age UK on the older consumer. The Golden Economy, The Consumer Marketplace in an Ageing Society was published in December 2010 and is available on the ILC-UK website © ILC-UK 2010
Contents
Contents, Figures and Tables 3
About the authors 6
Acknowledgements 6
1. Introduction 7
1.1 Aims and objectives 7
1.2 Limitations 7
1.3 Data 7
2. Wealth and assets 9
3. Income 12
4. Spending 15
4.1 Facing difficulties with shopping and communicating 15
4.2 Consumption patterns 15
4.3 Essential expenditure 19
4.3.1 Housing costs 19
4.3.2 Paying for utilities 23
4.3.3 Food 25
4.4 Durables bought and owned 27
4.5 Online shopping 28
4.6 Social participation 33
4.7 Satisfaction with participation levels 36
4.7.1 Demand for cinema visits 37
4.7.2 Desire to eat out more 37
4.7.3 Wanting to go to the art gallery or museum 37
4.7.4 Wanting to go to the theatre 38
4.8 Satisfaction with participation levels 39
4.9 Discrimination 39
5. References 41
Figures Figure 1 Distribution of household wealth: by age of household head, 2006/08 9 Figure 2 Distribution of household wealth excluding pensions: by age of
household head, 2006/08 10 Figure 3 Distribution of net household property wealth1: by age of household
head, 2006/08 10 Figure 4 Distribution of net household financial wealth1: by age of household
head, 2006/08 11 Figure 5 Source of income as a percentage of total income: by age of household
reference person, 2007 12 Figure 6 Average gross household weekly income: by age of household
reference person, 2007 13 Figure 7 Average gross weekly income per person: by age of household
reference person, 2007 14 Figure 8 Categories of household expenditure: by age of household reference
person, 2007 18 Figure 9 Average household weekly housing expenditure: by age of household
reference person, 2007 20 Figure 10 Average weekly expenditure on food items per person: by age of head
of household 26 Figure 11 Percentage of internet shoppers making online orders: by age (1) 29 Figure 12 Percentage of internet shoppers making online orders: by age (2) 30 Figure 13 Percentage of internet shoppers making online orders: by age (3) 31 Figure 14 Social participation: by age 34 Tables Table 1 How often respondent has too little money to spend on needs 14 Table 2 Difficulty with shopping, communicating and handling money 15 Table 3 Household expenditure: by age of household reference person (2007) 17 Table 4 EFS consumption expenditure quintile: by age - highest income quintile 19
Table 5 Adaptations made to current home: by age 22 Table 6 Adaptations made to current home (people with walking aids): by age 23 Table 7 Payment methods for utilities: by age 24 Table 8 Whether having too little money stops respondent buying first choice
food items 26 Table 9 Ownership of consumer durables (ELSA Wave 3) 27 Table 10 Has made any online purchase in the last 12 months 28 Table 11 Social participation: by age 33 Table 12 Club membership: by age 36 Table 13 Infrequent participation and respondent would like to participate more
often 36 Table 14 Percentage who believe that they know their rights 40
About the authors
Dr Adele Atkinson is an expert in the field of financial capability, and has a particular interest in understanding the financial decisions and behaviours of individuals.
David Hayes is an experienced quantitative researcher and a qualified accountant. His research interests include the study of happiness and the use of quantitative modelling techniques to explore issues of human geography.
Acknowledgements
This discussion paper draws on data from a number of sources, as acknowledged below and in the text that follows.
1. Published output from the Wealth and Assets Survey 2006/2008, as reported in
‘Wealth In Great Britain’ (Office for National Statistics, 2009).
2. Study Number 5050 - English Longitudinal Study of Ageing: Wave 0 (1998, 1999 and 2001) and Waves 1-3 (2002-2007) (Marmot et al., 2009).
The ELSA data were made available through the UK Data Archive. ELSA was developed by a team of researchers based at the National Centre for Social Research, University College London and the Institute for Fiscal Studies. The data were collected by the National Centre for Social Research. The funding is provided by the National Institute of Aging in the United States, and a consortium of UK government departments coordinated by the Office for National Statistics.
3. Expenditure and Food Survey 2004; 2007 (Office for National Statistics and
Department for Environment Food and Rural Affairs, 2009).
EFS is now part of the Living Costs and Food (LCF) module of the Integrated Household Survey (IHS), previously the Expenditure and Food Survey, and before that the Family Expenditure Survey). The Office for National Statistics (ONS) has overall project management and financial responsibility for the EFS, while the Department for Environment, Food and Rural Affairs (DEFRA) sponsors the food data. It is a multi-stage stratified random sample survey of households in the United Kingdom. The data are Crown copyright, accessed with permission from the UK Data Archive.
Crown Copyright material is reproduced with the permission of the Controller of HMSO and the Queen's Printer for Scotland.
This report has been made possible by the kind support of Age UK, ILC-UK is grateful to
Age UK for supporting ILC-UK’s work in this field.
Responsibility for the analyses or interpretations presented here rests entirely with the
authors of this report.
7
1. Introduction
1.1 Aims and objectives
The main aim of this report is to explore existing datasets to inform a new study of
consumption patterns among older consumers. This study will, where possible, compare
older consumers with younger cohorts and/or track individuals’ patterns of consumption
across time.
In particular the key objectives are to provide recent estimates of the following, where
practicable:
• Assets, wealth and income;
• Spending on various types of products;
• Types of payment;
• Use of different sales outlets (shop, catalogue, online, etc).
We have set out to describe the spending patterns and financial situation of different
types of household. We also aim to identify how many people might benefit from particular
products such as those designed for people with reduced mobility. We aim to check
whether it is possible to identify sectors that might see either a reduction or boost in
demand as consumption patterns among older consumers change. We also consider
whether there is any evidence that older people are affected by the increase in internet
shopping and whether this is likely to be occurring because of a lack of equipment or
payment methods, or for some other reason. Furthermore, we aim to uncover any
evidence to suggest that those who shop in more traditional ways pay more (or less) for
the goods they buy.
1.2 Limitations
This research has been undertaken on a limited budget and so focuses on what the data
tells us about the recent past, rather than predicting changes in the future. Such modelling
is labour and time intensive, and would need to be undertaken as part of a larger research
project.
While we are keen to explore the impact of recent financial upheaval, it is too soon to be
able to observe this period with the data currently available.
1.3 Data
We have used the English Longitudinal Study of Ageing (ELSA) to explore recent
quantitative indicators of consumption patterns among older people (Marmot et al., 2009).
This data first focuses on adults over the age of 50 (specifically people born on or before
29 February 1952). It collects a great deal of information about health, but also includes
questions about expenditure on certain key items and social participation. We have
8
focused on the second and third waves of ELSA. Wave 2 was undertaken between June
2004 - July 2005, while Wave 3 fieldwork took place between May 2006 - August 2007.
The main limitations of this data, from the perspectives of this particular study, are that the
coverage is not of the United Kingdom, but only England, and that the fieldwork for
available waves predates the widespread financial crisis. It should also be remembered
that this data does not allow for comparisons with younger cohorts, since it focuses
specifically on the over 50s.
In order to compare older people with their younger counterparts, we have turned to the
Expenditure and Food Survey (EFS), focusing primarily on data from 2007. EFS is a
continuous, cross sectional survey of household expenditure, food consumption and
income. This dataset is primarily used to provide information about spending patterns for
the Retail Price Index, and about food consumption and nutrition.
We have also drawn on published results from other surveys to supplement analysis of
archived datasets. These include results from the first wave of the Wealth and Assets
Survey (WAS)1 undertaken by the Office for National Statistics (ONS), the Family
Spending report which uses EFS data 2 and various results from Eurobarometer datasets
compiled on behalf of the European Commission34. The Eurobarometer data allows us to
consider wider influences and issues that face older people as consumers within Europe,
but the relatively small sample sizes (around 1300 interviews for the “Attitudes towards
the Environment” study, for example) mean that findings broken down by age should be
seen as indicative rather than conclusive.5
1 http://www.statistics.gov.uk/downloads/theme_economy/wealth-assets-2006-2008/Wealth_in_GB_2006_2008.pdf
2 http://www.statistics.gov.uk/downloads/theme_social/Family_Spending_2007/FamilySpending2008_web.pdf
3 http://ec.europa.eu/public_opinion/archives/ebs/ebs_295_sheet_uk.pdf
4 http://ec.europa.eu/public_opinion/flash/fl_247_en.pdf
5 Please note that throughout the report, some of the categories in tables have bases of less than 100. Please treat
these figures with caution.
9
2. Wealth and assets
In this chapter we report key findings of the first ever Wealth and Assets Survey across
Great Britain. This covers the period July 2006 to June 2008. Over the two-year period
interviews were held with adults in over 30,000 private households.
The recently published results of the first wave of the WAS gives us a comprehensive
indication of how household wealth in Great Britain varies by the age of the head of the
household6 (Office for National Statistics, 2009). The study looks at wealth held in
property, financial wealth, private pensions, physical wealth (typically household
contents), and the liabilities that offset these assets, such as mortgages or loans. The
graph below (Figure 1), for example, shows the average net wealth held by households
taking into account all physical, property and financial assets and liabilities, including
pensions.
We can see that average household wealth was at its peak among those aged 55 to 64
(with a median of £416,100) declining gradually among older cohorts to a median of less
than half that for those aged 85 and over (£171,800). However, as Figure 2 shows, much
of this decline can be accounted for by a reduction in pension wealth. Median non-
pension wealth peaked at £243,300 in the pre-retirement years (55 to 64), falling by
around £30,000 to £213,200 among those in the next age category. Those households
headed by someone aged 85+ had non-pension wealth of £156,300, around £15,000 less
than their total household wealth.
Figure 1 Distribution of household wealth: by age of household
head, 2006/08
Source: Wealth and Assets Survey, Office for National Statistics (Click-use graphs) © Crown copyright 2009
6 This is based on the household’s highest earner. If earnings are the same, or not applicable, the oldest household
member is taken as the head of the household. Please also note that, like most national household surveys, it does not include people living in residential homes, hospitals or other institutions.
10
Figure 2 Distribution of household wealth excluding pensions: by age
of household head, 2006/08
Source: Wealth and Assets Survey, Office for National Statistics (Click-use graphs) © Crown copyright 2009
By far the largest proportion of household wealth in the UK is held in housing or land
(‘property’ wealth). The WAS data indicates that households headed by 55-64 year olds
also had the highest average net property wealth, with a median of £200,000, but this is
only slightly higher than among those in the next oldest cohort (£195,000). On average,
households headed by someone in an older age group held slightly more wealth in
property than those with a household head under the age of 55 (almost certainly because
the younger cohorts have outstanding borrowing). The median amount held in property
among those aged 75 and over was £180,000.
Figure 3 Distribution of net household property wealth1: by age of
household head, 2006/08
1Results exclude households with zero net property wealth.
Source: Wealth and Assets Survey, Office for National Statistics (Click-use graphs) © Crown copyright 2009
11
The WAS asks detailed questions about the financial assets (and liabilities) held by
households. This includes all formal and most informal financial assets held by anyone in
the household, including, for example money held in a current account, savings held in a
Child Trust Fund, premium bonds, and informal savings held at home, or kept with a
family member. The graph below suggests that net financial wealth peaked among
households with a head aged 55 to 64 (median of £18,000), as did wealth in total, and
property wealth. Those households headed by someone aged 85+ had just over half the
financial wealth of the pre-retirement group (£10,000) but considerably more than younger
cohorts (35 to 44 year olds, for example had a median of just £2,500).
Figure 4 Distribution of net household financial wealth1: by age of
household head, 2006/08
1Results exclude households with zero net financial wealth.
Source: Wealth and Assets Survey, Office for National Statistics (Click-use graphs) © Crown copyright 2009
There is a noticeable difference between the mean and median amounts held in financial
wealth, indicating that small numbers of people had very large amounts of financial
wealth, a finding that persists across the age groups.
12
3. Income
We now turn to the EFS data from 2007 and the third wave of ELSA to explore how
income varies by age.
We have looked at the main sources of income by age, as reported in the EFS 2007. The
graph below (Figure 5) shows the differences in sources of income by the age of the head
of household. We see that wages made up over 80 per cent of income for households
with a head aged under 30, compared with less than five per cent for those headed by
adults aged 75 and over. Conversely, benefits made up around 55 per cent of income for
adults aged 75+ with pensions making up another 30 per cent.
Figure 5 Source of income as a percentage of total income: by age
of household reference person, 2007
Notes: Pensions other than social security benefits, benefits excluding housing benefit and council tax benefit Source: Expenditure and Food Survey, Office for National Statistics (created from data in click-use data table)
Taking all of these source of income into account, the gross household weekly average
income peaked for households headed by someone aged 30 to 49 (at almost £850 a
week, or £44,000 a year), and fell gradually among older cohorts (Figure 7). The average
weekly income of the oldest households in 2007 (75 or older) was around £300, or just
under £16,000 a year.
13
Figure 6 Average gross household weekly income: by age of
household reference person, 2007
Source:
Expenditure and Food Survey, Office for National Statistics (created from data in click-use data table) Averages are means across all households
However, it should be remembered that household sizes also change with age, and it is
very likely that these older households are supporting fewer people. Indeed the average
number of people in each household (according to the EFS 2007 data) varied from 2.5
among the youngest cohort (less than 30) to 1.4 among households headed by someone
aged 75 or over. Taking this into account, we can make an estimate of the income per
person. Figure 7 shows that, on average, income was actually greatest on a per person
basis for those in households headed by someone aged between 50 and 64 (£321). It
ranged between £212 and £287 among other cohorts and averaged over £100 per week
less among the oldest households compared with those who were headed by someone
aged 50 to 64.
Finally in this section, we look briefly at whether income is sufficient to meet the needs of
older people. ELSA respondents were asked how often they found they had too little
money to spend on their needs – ‘needs’ was not defined, but left up to the respondent to
define as they chose. There is a clear relationship between this variable and age, with
older cohorts being less likely to report that they had too little money. However, it is of
some concern that over a third (34 per cent) of those aged 55 to 59, and 15 per cent of
the oldest respondents reported that they had too little money at least some of the time.
14
Table 1 How often respondent has too little money to spend on
needs
Column percentages
Source: ELSA Wave 3 Core respondents. Weighted percentages (excluding missing data).
Figure 7 Average gross weekly income per person: by age of
household reference person, 2007
Note: this graph has been created by taking the average household income and dividing by the average number of people per household (regardless of age).
Source: Expenditure and Food Survey, Office for National Statistics (created from data in click-use data table) Averages are means across all households
Age
52 to
54
55 to
59
60 to
64
65 to
69
70 to
74
75 to
79
80 to
84
85 to
89
90+
All
Never 38 34 41 44 44 48 56 56 61 43
Rarely 29 32 30 28 28 24 23 26 24 28
Sometimes 24 24 22 22 21 22 17 15 10 21
Often 4 6 5 5 5 4 3 3 2 5
Most of the time
4 4 2 2 2 1 1 1 3 2
Unweighted Base
116 1,634 1,399 1,161 1,103 880 593 330 99 7,315
15
4. Spending
This chapter draws on data from the EFS and ELSA to explore expenditure and
consumption. It also summarises European data that may provide insights into two of the
potential barriers to consumption among older people: the extent to which people suffer
discrimination and environmental concerns.
4.1 Facing difficulties with shopping and
communicating
Before looking in detail at various types of spending, it is worth considering the extent to
which older consumers struggle with shopping, using the phone and managing money.
ELSA asks whether respondents have difficulty with these activities because of “a
physical, mental, emotional or memory problem”. We can see from Table 2 that shopping
is difficult for more people than using the telephone or managing money, with almost one
in ten 70 to 74 year olds facing difficulties shopping (nine per cent) – rising to 60 per cent
of those aged 90 or over. All the measures mentioned above increase with age.
Table 2 Difficulty with shopping, communicating and handling money
Percentages within age-group
52 to
54
55 to
59
60 to
64
65 to
69
70 to
74
75 to
79
80 to
84
85 to
89
90+
All
Difficulty shopping
for groceries
2 7 8 8 9 13 19 32 60 11
Difficulty making
telephone calls
1 1 1 1 3 3 6 7 24 3
Difficulty managing
money, e.g. paying bills,
keeping track of
expenses
- 3 2 2 3 4 7 14 34 4
Unweighted Base
119 1,656 1,419 1,176 1,129 907 622 372 135 7,535
Source: ELSA Wave 3 Core respondents. Weighted percentages
4.2 Consumption patterns
The Family Spending report includes information about the average weekly spend by age
of household person; the table is reproduced below. This average data spreads the total
expenditure across all households, irrespective of whether they ever purchase an item or
how many people are in the household, and so where average amounts are lower this
16
could be because fewer people are purchasing the item, or because less is being spent
(which in itself could be related to many things, from a change in preference, to a change
in the number of people in the household or the amount of disposable income available).
We can see from Table 3 that household expenditure on food was lower among
households headed by someone aged 75 or over (£33.40 per week) than all other
households. This pattern is repeated for alcoholic drinks (£4.90), clothing (£7.70), housing
and fuel (£35.50), transport (£19.50), communication (£5.80), education (almost nothing),
restaurants (£12.80) and miscellaneous expenditure (£12.80). Interestingly, the pattern of
expenditure for those aged 65 to 74 shows that while these households always spend
more on average than older households, they sometimes also spend more than
households headed by someone under the age of 30. Households headed by someone
aged 50 to 64 spend the most on both health and recreation.
The EFS is designed to provide very detailed information about consumption patterns. In
this section we use the data to explore consumption by age. We find that, in 2007, the
proportion of household expenditure going towards particular types of consumption varied
slightly by age. For example, as can be seen in Figure 8 below, food and health made up
larger parts of household expenditure in older households, while education expenditure
ceased to play a part among those over the age of 64. Recreation continued to make up
around 15 per cent of expenditure, on average, throughout retirement, although a lower
proportion of expenditure went on transport costs beyond retirement age. There was a
striking increase in the proportion of income spent on housing and fuel among those aged
75 and over to 16 per cent; up from 11 per cent among 65 to 74 year olds.
Given findings reported elsewhere in this document, it is interesting that the proportion of
expenditure spent on communications (which comprises postal services, and both the
purchase and use of landline and mobile telephones) did not vary much by age7. Across
the age bands, around three per cent of expenditure went on this category of expense.
This suggests that if older households were to increase their take up of internet or mobile
telecoms they would need to either a) be prepared to either increase the proportion of
their budget allocated to communications, b) reduce their use of other forms of
communication, most probably landline or c) make cuts elsewhere. It may be that a
package that combined several types of service into one contracted fee would appeal to
such households so that they could budget more easily while dealing with a single
provider.
7It is worth remembering here that as income decreases with age, this means that the absolute amount spent on
communication must also decrease with age. This is not to say that absolute expenditure on communications did not increase with age. In fact, given that total expenditure decreased with age, this must be the case, as is shown in Table 3.
17
Table 3 Household expenditure: by age of household reference person8
(2007)
Aged <30 30-49 50-64 65-74 75+ All
Weighted number of households (thousands)
2,620 9,760 6,450 3,130 3,390 25,350
Total number of households in sample 590 2,320 1,640 850 740 6,140
Total number of persons in sample 1,480 7,010 3,590 1,470 1,090 14,650
Total number of adults in sample 1,070 4,360 3,270 1,450 1,080 11,220
Weighted average number of persons per household
2.5 2.9 2.2 1.7 1.4 2.4
Commodity or service Average weekly household expenditure (£)
1 Food & non-alcoholic drinks 38.40 53.90 53.20 43.60 33.40 48.10
2 Alcoholic drinks, tobacco &
narcotics 10.70 12.60 13.40 9.40 4.90 11.20
3 Clothing & footwear 22.40 28.50 24.00 13.00 7.70 22.00
4 Housing(net)1, fuel & power 84.40 56.20 47.90 36.40 35.50 51.80
5 Household goods & services 23.30 36.70 34.60 25.40 16.90 30.70
6 Health 3.30 4.80 8.20 6.40 5.00 5.70
7 Transport 56.40 76.80 74.60 38.10 19.50 61.70
8 Communication 14.50 14.10 12.30 8.50 5.80 11.90
9 Recreation & culture 44.70 66.10 67.50 51.80 28.10 57.40
10 Education 12.00 9.90 6.20 [0.70] [0.70] 6.80
11 Restaurants & hotels 39.90 47.10 41.10 22.70 12.80 37.20
12 Miscellaneous goods & services 33.10 43.50 37.20 23.90 20.10 35.30
1-12
All expenditure groups 383.00 450.00 420.30 279.90 190.50 379.80
13 Other expenditure items 76.70 111.90 77.00 40.80 27.50 79.30
Total expenditure 459.70 561.90 497.30 320.80 218.00 459.20
Total expenditure 185.30 191.20 225.20 185.00 151.30 194.80
1 Excluding mortgage interest payments, council tax and Northern Ireland rates. ONS, Family Spending 2007 © Crown copyright 2008
8 A ‘household reference person’ (HRP) is the person who own or rents or is otherwise responsible for the
accommodation. In the case of joint householders, the person with the highest income takes precedence and becomes the HRP. Where incomes are equal, the older is taken as the HRP.
Average weekly expenditure per person (£)
18
Figure 8 Categories of household expenditure: by age of household
reference person, 2007
Notes: Other expenditure items includes mortgage costs whereas Housing (net) excludes mortgage interest payments, council tax/rates Source: Expenditure and Food Survey, Office for National Statistics (created from data in click-use data table)
When considering the proportion of expenditure in this way, it is worth remembering that
proportions could be changed through either a reduction in one (or more) expenses, or an
increase in a particular expense. So for example, if a household no longer has to find
money for transport when they receive free bus passes, they may choose to spend the
additional money on recreation. Conversely, if essential items such as healthcare costs or
heating increase, a household may be forced to reduce expenditure in other areas in
order to make ends meet. It is for this reason that we also report analysis of actual
19
expenditure on certain items. A further point to bear in mind is that analysis of
consumption does not take into account ways in which people may allocate their income,
such as putting money aside in savings or investments. It is generally assumed that the
proportion of income that is saved is highest in middle age (Berry & Williams, 2009;
Demery & Duck, 2003).
We can also use the EFS to explore the consumption of people with high incomes, to see
whether there is any evidence that older people with relatively high incomes are more
likely to consume less than their income would predict. Ideally we would look at assets,
particularly financial holdings, but the EFS does not measure this.
Table 4 looks only at those 20 per cent of people with the highest equivalised income. We
might expect that all of those people with the highest income would also fall into the
highest expenditure category, but that is not the case. In fact around five per cent of those
high income respondents aged 65 and over have expenditure that is among the lowest of
all EFS respondents.
Table 4 EFS consumption expenditure quintile: by age - highest
income quintile
Column percentages
30 a
nd
b
elo
w
30 to
49
50 to
64
65 to
74
75
+
All
Expenditure in Lowest quintile
1 3 2 2 5 6 3
2 14 10 7 12 16 10
3 21 15 15 28 23 16
4 24 25 29 25 29 26
Highest quintile 5
38 48 47 30 26 45
Unweighted base
92 602 376 57 31 1,158
Source: EFS 2007 (weighted percentages)
4.3 Essential expenditure
4.3.1 Housing costs
Data available from the Expenditure and Food Survey helps us to explore levels of
expenditure by age in more detail. Starting with essential housing costs, we can see from
the published results of the 2007 EFS that both rent and mortgage related costs were
lower among households with an older reference person9, but that beyond the age of 65,
households renting their home paid more, on average, than those who were buying a
home with a mortgage (presumably because they had paid off the vast majority of the
9 Such a person can be considered to be equivalent to the ‘head of household’ used in the WAS data described above
20
mortgage by retirement age) (Office for National Statistics, 2008). Average rental costs
did not decrease with age to the same extent as mortgage costs, so the data suggests
that while average weekly mortgage costs for mortgagors/homeowners were just over £50
for adults aged 65 to 74, rental costs were an average of £87 a week for renters within
this age group (although some of these will be covered by housing benefit10).
While the cost of paying rent or mortgage is the most obvious housing-related expense,
there are other expenses that households often need to meet. Evidence from a European
study suggests that a significant minority of adults who have retired feel it necessary to
adapt their house or move home to accommodate their changing needs, so while 14 per
cent of European retirees had adapted their home to help with reduced mobility, seven
per cent had moved since they retired (The Gallup Organization, 2008). However, these
proportions are considerably smaller than the proportions of younger respondents who
anticipated taking such decisions upon retirement (60 per cent and 41 per cent
respectively). This may indicate a lack of options for those retirees who anticipated
making such changes, or a misperception of their likely needs in retirement. In the UK, the
proportion of younger people anticipating the need to adapt their house was 79 per cent,
lower only than that of Sweden and Finland, while 66 per cent of non retired UK
respondents felt that they would consider moving after retirement – only the Swedes were
more likely to believe they would consider this option (69 per cent). Yet just nine per cent
of UK retirees had actually moved, and 18 per cent had adapted their home; higher than
the European average, but considerably less than might be expected given the view of
younger respondents.
Figure 9 Average household weekly housing expenditure: by age of
household reference person, 2007
Note that mortgage costs include both interest and capital repayment. Rental costs are gross (costs before Housing Benefit, rebates or allowances). Note that very few adults aged over 75 had a mortgage and so these findings should be seen as indicative only (this is why no value is marked on the above figure) Source ONS, Family Spending 2007 reanalysis of Table 2.4, 2.8 and 2.9, rebased using the number of people renting/buying in each age category by running frequencies on the household datafile.
10
Data on average allowances and rebates is available but we have not included this as it would only decrease the average amounts.
21
Data from ELSA give us far more detail about the adaptations present in older people’s
homes. However, it should be noted that all respondents are asked whether their
accommodation has been modified in any way to assist people with physical impairments,
even if the respondent does not use them personally. As we can see from Table 5 the
majority of homes have not been adapted, but of those that have, handrails and bathroom
modifications are most common. The proportion of properties with various adaptations
increases with the age of the respondent, suggesting that the demand for such facilities
typically increases with age, as might be expected. In particular, it is noteworthy that the
proportion of people with alerting devices, lifts, chairlifts, ramps and widened access
approximately doubles over the age of 85, compared with the 80 to 84 year old age
group.
Given the various modifications that have been undertaken to ease access (including
ramps, handrails and widening doors), it may be worth considering whether there is a
particular reason that so few people have kitchen modifications (just two per cent of all
respondents).
While some of the adaptations listed in ELSA will have cost the resident considerable
amounts of money, others may have been in the property before they moved in, or paid
for by social services. Unfortunately, we have been unable to find data that differentiates
between ordinary household maintenance costs, and the costs of essential modifications,
and so it is difficult to say more about this in terms of spending and consumption. This is
something that may benefit from additional research. It would be useful to understand the
extent to which people are able to undertake the modifications that they feel are
necessary, the cost of such modifications and, for those who manage to fund the
improvements, the impact of this expenditure on their overall budget.
22
Table 5 Adaptations made to current home: by age
Percentages within age-group
Age
52
to 5
4
55
to 5
9
60
to 6
4
65
to 6
9
70
to 7
4
75
to 7
9
80
to 8
4
85
to 8
9
90
+
All
Widened doorways/hallways
3 3 3 5 4 5 5 9 12 4
Ramps or street level entrances
3 3 3 4 5 5 5 12 10 4
Hand rails 5 8 10 13 18 24 29 39 40 16
Automatic or easy open doors
1 0 0 1 0 1 2 3 3 1
Accessible parking/drop-off
site 1 3 3 4 5 5 7 6 6 4
Bathroom modifications
6 7 9 13 17 20 27 33 41 15
Kitchen modifications
2 1 1 1 2 2 2 1 6 2
Lift 1 1 1 1 1 2 4 8 5 2
Chair lift or stair glide
0 1 1 2 2 3 5 9 9 2
Alerting devices (e.g. button
alarms) 2 2 3 4 6 8 11 23 24 6
Other special features
1 1 1 1 1 1 1 1 3 1
None of these 89 84 82 75 69 63 54 41 34 72
Unweighted Base 115 1,649 1,411 1,173 1,125 901 611 356 120 7,461 Source: ELSA Wave 3 Core respondents. Weighted percentages
We can explore this data further, by looking at the proportions of people who used
walking aids. The survey asks about the following aids:
• Cane or walking stick.
• Zimmer frame or walker.
• Manual wheelchair.
• Electric wheelchair.
• Buggy or scooter.
• Elbow crutches.
In total, 20 per cent of respondents had such an aid. However, among these an average
of 38 per cent had a home with no special adaptations, which may indicate an unmet
demand for ways of making life at home easier. Of course, many of these people may
have no difficulty in the home, or may not want their home modified.
23
Table 6 Adaptations made to current home (people with walking
aids): by age
Percentages within age-group
Age
52 to
54
55 to
59
60 to
64
65 to
69
70 to
74
75 to
79
80 to
84
85 to
89
90+
All
Widened doorways/hallways 10 8 8 7 10 9 11 11 9
Ramps or street level entrances 14 12 10 10 13 10 16 8 12
Hand rails 39 36 35 35 44 43 48 41 41
Automatic or easy open doors 2 2 3 1 3 2 4 2 2
Accessible parking/drop-off site
7 4 8 9 6 10 7 3 7
Bathroom modifications 40 36 37 36 40 40 44 44 39
Kitchen modifications
5 6 5 6 5 2 2 7 4
Lift 2 2 3 1 5 6 10 3 4
Chair lift or stair glide 6 6 5 6 9 9 14 8 8
Alerting devices (e.g. button alarms)
7 10 9 11 19 19 30 26 17
Other special features 6 8 2 2 2 1 1 3 3
None of these 38 48 45 44 37 34 29 31 38
Unweighted Base 3 132 156 162 235 250 244 221 92 1,495
Source: ELSA Wave 3 Core respondents. Weighted percentages
4.3.2 Paying for utilities
There are noticeable differences in the methods used to pay for utilities by age. We can
see from the third wave of ELSA data that while 64 per cent of adults over the aged of 52
were paying for utilities by direct debit, this ranged from 72 per cent among the younger
respondents through to 56 per cent of 85 to 89 year olds (and the indication is that this
dropped off further among the very oldest respondents, although the base is low, and so
the findings should be treated with caution).
A regression analysis to explore the propensity of paying by direct debit has identified
some interesting relationships. The likelihood11 of paying by direct debit were slightly
11
Note that while the terms ‘odds’ and ‘likelihood’ are similar concepts, they are not identical. Higher odds equal a higher likelihood, but they are not exactly equivalent.
24
lower for men, and lower for those in their 70s than either older, or younger generations
once other factors are taken into account. Respondents who described themselves as
white had almost twice the likelihood of using direct debit as others, and those with a
qualification also had twice the likelihood of those without, other things controlled for. As
the frequency with which respondents felt they had too little money increased so the
propensity to pay by direct debit decreased – so that those who were often in this
situation had only about half the likelihood of using direct debit as those who never found
themselves short of money. Couples who shared their finances had one and a half times
the likelihood of using direct debit of either couples with separate finances or single
adults.
Table 7 Payment methods for utilities by age
Cell percentages
Source: ELSA Wave 3 Core respondents. Weighted percentages; of those who answered that they were using this payment method for either gas or electricity payments (excluding missing data). Note that the columns do not necessarily add to 100% as different methods could be used for each of the two bills.
Interestingly, receipt of the winter fuel allowance, the expense of public transport and
health preventing the use of public transport were not significant predictors. Similarly
feeling worse off than friends was not significant.
These findings suggest that direct debit is not being used as much among people who
find it hard to make ends meet as it is by other households. There are several possible
explanations for this; for example it could be that people who are unable to make ends
meet are also the ones without a bank account, or that those with a tight budget prefer to
manage their money in a more visible way (such as paying by cheque). It is also possible
that these people are finding it difficult to make ends meet because they don’t use (or
don’t have access to) direct debits.
It might be assumed that direct debits would be preferred by people who cannot easily get
out of the house to post a cheque or make a payment. However, there is no evidence of
Age
52 to
54
55 to
59
60 to
64
65 to
69
70 to
74
75 to
79
80 to
84
85 to
89
90
+
All
Direct debit 72 67 67 63 67 57 58 56 42 64
Monthly or quarterly bill
21 26 26 29 28 36 35 33 42 29
Prepayment meter/card
15 8 7 6 4 5 3 2 6 6
Frequent cash
payments 1 2 1 2 3 2 3 2 2 2
Unweighted Base
101 1,456 1,260 1,067 1,001 789 510 256 66 6,506
25
this from the analysis; there is nothing to suggest that use of direct debit increases where
people find it difficult or unpleasant to travel. It may be worth exploring further the extent
to which those with limited mobility or unsafe neighbourhoods are aware of the benefits of
setting up direct debit payments.
The finding that ethnicity is a significant predictor suggests a more complex issue,
particular given that we have an indicator of disposable income (we know that people
from some ethnic minorities tend to be poorer, on average, but having controlled for this it
is interesting that the effect still persists). Further exploration of this finding may be
warranted.
4.3.3 Food
Turning now to food expenditure, the data indicates that the oldest households had lower
expenditure per person on nutritious items such as meat, fish, fruit and vegetables than
other households of retirement age, but slightly increased their expenditure on sweets
and treats. However, it appears that much of the decrease can be accounted for by the
fact that the slightly younger cohort (aged 65 to 74) spent more than other adults on these
healthier items, on average. In fact, households of retirement age spent more per person
on most food items than younger households, with the exception of staple carbohydrates
(bread, pasta etc), where expenditure did not particularly vary with age.
As we don’t know whether the quantity or quality of staple goods being bought varies by
age, we cannot know whether expenditure and demand for staple foods remain constant
across the age groups or whether some people have to buy less (or lower quality)
because they have to pay a higher price per unit.
ELSA can, however, help us to explore the extent to which people feel financially
constrained in their food choices (Table 8). It seems that a small proportion of
respondents to Wave 3 ELSA did find that their food choices were limited by a lack of
money. One in twenty respondents mentioned that this was an issue, although it
decreased with age.
26
Table 8 Whether having too little money stops respondent buying
first choice food items
Column percentages
Source: ELSA Wave 3 Core respondents. Weighted percentages
Figure 10 Average weekly expenditure on food items per person by age of head of household
Note: this graph has been created by taking the average household expenditure and dividing by the average number of people per household. Source: Expenditure and Food Survey 2007, Office for National Statistics (created from data in click-use data table) © Crown copyright 2009
Age
52 to
54
55 to
59
60 to
64
65 to
69
70 to
74
75 to
79
80 to
84
85 to
89
90+
All
Not mentioned
94 94 94 96 96 95 97 96 98 95
Mentioned 6 6 6 4 4 5 3 4 2 5
Unweighted Base
116 1,633 1,400 1,161 1,103 880 591 326 99 7,309
27
4.4 Durables bought and owned
We can see from the most recent wave of ELSA, that the vast majority of people over the
age of 50 in England owned (at least some of) the more common consumer durables,
such as DVD players and microwave ovens.
It seems that televisions are the only durable that are owned by a larger proportion of
older cohorts than younger ones. Almost everyone over the age of 80 reported owning a
TV (96 per cent) compared with around three quarters of those aged 52 to 54 (73 per
cent). In contrast, some six in ten (60 per cent) of those aged 55 to 59 had a computer,
but only one in ten (nine per cent) of the oldest cohort had computers.
The proportions owning particular durables peaked at different ages. So a greater
proportion of 55 to 59 year olds owned a DVD player than any other age group surveyed
(60 per cent), while microwave ownership and CD players peaked among the 70 to 74
year old cohort (86 per cent and 80 per cent). Video recorders were owned by a larger
proportion of 75 to 79 year olds than other cohorts, but across all the cohorts more people
had a video recorder than a DVD player.
Given the unusual pattern of television ownership, it seems worth exploring the
longitudinal element of ELSA to see what else we can find out about TV ownership.
Looking at respondents who were in both Wave 2 and Wave 3, we find that three per cent
of respondents didn’t have a TV at Wave 2 but acquired one in the following two years –
most of these were under the age of 60. A further 20 per cent replaced an existing
television in the following two years, and this varied little by age.
Table 9 Ownership of consumer durables (ELSA Wave 3)
Percentages within age-group
Source: ELSA Wave 3 Core respondents. Weighted percentages; of those who answered yes (excluding missing data).
Age
52 to
54
55 to
59
60 to
64
65 to
69
70 to
74
75 to
79
80 to
84
85 to
89
90+
All
Television 73 79 84 88 91 94 96 98 94 88
Video recorder 68 72 77 80 83 83 71 62 44 76
DVD player 61 70 69 67 65 54 44 30 18 61
CD player 69 76 78 78 80 77 65 52 30 74
Computer 56 60 58 51 42 29 22 14 9 45
Dishwasher 33 29 32 30 27 20 18 18 5 27
Microwave 68 74 78 80 86 84 79 77 61 79
Unweighted Base
117 1,646 1,411 1,174 1,127 904 618 369 132 7,498
28
4.5 Online shopping
Like direct debit payments discussed above, online shopping may be seen as a valuable
alternative approach for shoppers seeking easy access and convenience. In this section,
we return to the EFS. The EFS includes data relating to the extent to which people of all
ages make online purchases, and can help us to identify the changing pattern of use with
age.
We have created a variable that identifies whether people have used the internet for any
of 16 categories of goods/services (if any) they have ordered over the internet in the past
three months to a year (including travel, transport, season tickets and other purchases)12.
The questionnaire wording asks them to mention online orders, even if they did not pay
online; we are using the phrase ‘online purchases’ to describe this.
We can see from Table 10 that almost half of 30 to 34 year olds had made at least one of
these purchases online in the last 12 months (47 per cent). The probability declines
markedly with age, such that only half as many ( 23 per cent) 60-64 year olds surveyed
had made an online purchase in the last 12 months, and around half that in the 65 to 69
year old group (13 per cent).
We have undertaken some analysis of the separate items that went into this measure.
Because so few older people had made any online purchases, we have collapsed the age
bands. Even so, caution is necessary when considering the older categories, as only 36
households with a head aged 75 or over had bought anything online over the period
discussed.
Table 10 Has made any online purchase in the last 12 months
Age
19
an
d b
elo
w
20 to
24
25 to
29
30 to
34
35 to
39
40 to
44
45 to
49
50 to
54
55 to
59
60 to
64
65 to
69
70 to
74
75 to
79
80+
All
Yes 22 30 36 47 35 37 35 37 31 23 13 10 7 2 1,645
Unweighted Base
23 170 395 459 593 647 621 541 549 552 446 405 325 410 6,136
Source: Expenditure and Food Survey 2007 Weighted percentages
12
The questionnaire asks about some orders in the past 3 months, and others in the last year. Financial products and vehicles in the last 12 months: Mortgage payment, Endowment policy, Mortgage protection, Structural insurance Private pensions, Life insurance, Medical insurance, Season tickets, Vehicles (vehicle insurance was not recorded in the data); Household goods and travel in the last 3 months:, Furniture, Carpets, Package holidays, Hotel accommodation, Self catering, Travel in the last 12 months: Flights from UK; Final question for all those who shopped online in the last 12 months: Other
29
In the figure below (Figure 11) we see that across all age bands, internet shoppers were
far more likely to have bought house insurance than any other financial product listed in
the questionnaire (however, unfortunately, car insurance was not properly recorded in the
dataset).
Figure 11 Percentage of internet shoppers making online orders: by age (1)
Financial and other items bought in the last 12 months Unweighted base of people who had bought anything online: 30 and below=184, 30-49=838, 50 to 64=490, 65 to 74=97, 75+ 36 Source: Expenditure and Food Survey 2007 Weighted data
30
Turning now to the items asked about over a three month period, we see that internet
shoppers of all ages were most likely to have booked a hotel online, but that those
internet shoppers aged 65 to 74 were more likely than other age groups to have bought a
package holiday online.
Figure 12 Percentage of internet shoppers making online orders: by
age (2)
Online purchases bought in the last 3 months Unweighted base of people who had bought anything online: 30 and below=184, 30-49=838, 50 to 64=490, 65 to 74=97, 75+ =36 Source: Expenditure and Food Survey 2007 Weighted data
The final figure in this series (Figure 13) shows the responses to two further questions
that were asked over a 12 month period: ‘flights from the UK’ and all ‘other’. These two
questions were answered in the affirmative by the vast majority of people who had bought
anything online. So, we see that those aged 65 to 74 who had bought anything online
were more likely to book flights than younger online shoppers, and the data suggests that
they were more likely to do so than older generations. Conversely, the younger cohort
were most likely to have bought something else online in the last 12 months, although
over half of the internet shoppers interviewed said they had bought something online
other than the listed specific items (57 per cent of the 65 to 74 year old internet shoppers).
A brief look at the detailed spending records suggests that the other category is largely
made up of CDs and audio cassettes, DVDs and books. This is potentially an important
finding for libraries across the country as well as book shops and music outlets.
31
Figure 13 Percentage of internet shoppers making online orders: by
age (3)
Online purchases bought in the last 12 months Unweighted base of people who had bought anything online: 30 and below=184, 30-49=838, 50 to 64=490, 65 to 74=97, 75+ =36 Source: Expenditure and Food Survey 2007 Weighted data
It is clear that, across the board, internet shopping decreases with age. In order to gain a
better understanding of the relationship between age and internet purchases we have
employed a regression analysis technique to look at the predictors of internet shopping.
The analysis shows that once other factors are taken into account, 65 to 69 year olds had
around half the likelihood of having made an internet purchase in the last twelve months
as the comparison group (45-49). At the extremes we see that 45 to 49 year olds had
more than five times the likelihood of having made an internet purchase in the last twelve
months compared to those aged 80 or over13. Indeed, as the table above shows, only two
per cent of people aged 80+ had purchased goods or services on the internet within the
last twelve months.
The regression indicates that the most important factor for predicting shopping online is
having internet access within the home. We would expect that people who want to shop
online regularly would be connected, and that, conversely, those with a connection would
be more likely to shop. And this is borne out by the analysis; having an internet
connection in the house dramatically increases the chances of having made an online
purchase within the last 12 months. So, after controlling for age and other factors, those
with an internet connection had over eight times the likelihood of having made such a
13
As the regression take into account all respondents, there are 410 people in the oldest age category, making this a valid comparison
32
purchase compared to people without a connection. This may suggest that libraries and
other public access points could be doing more to help individuals feel secure about using
the internet to make purchases, particularly if this could help consumers save money, as
is often the case when people have the opportunity to compare prices. Of course, this
assumes that there is an appetite for shopping online, or indeed for the internet generally.
We have put equivalised income (a measure of income per person in the household) into
the regression by ranking all respondents according to their income, and then splitting
them into five equal sized groups, or quintiles. Those in quintile 1 have the lowest
incomes, while those in quintile 5 have the highest. The results indicate that income is
also strongly associated with online shopping (even after taking into account home
internet access). Those with income in the top 20 per cent had 3.3 times the likelihood of
shopping online as those in either of the two lowest quintiles.
Even after taking age and home access and other factors into account, car ownership is
related to greater likelihood of using the internet to shop. This shows that people with cars
were more likely than their carless peers to be shopping online regardless of age and
income, suggesting that the internet is not being used primarily by people with limited
mobility.
Additional analysis that excluded income showed notable differences in internet shopping
by region. However, once income was taken into account these differences became
insignificant, because of the regional differences in income levels.
Housing association tenants had about the same likelihood as those in Local Authority
housing of shopping online, while renters of private, furnished rental properties had 2.6
times the odds (those renting unfurnished private properties had 1.6 times the odds).
The analysis also shows that mortgagors were twice as likely as social tenants to have
made an internet purchase, and people who owned their house outright have more than
2.5 times the odds of having made an internet purchase as those in Local Authority rented
accommodation.
As having an internet connection at home is so highly correlated with internet shopping
we have rerun the analysis without controlling for a home connection. This indicates that
the highest income quintile has more than 4 times the odds of the lowest one of shopping
online, and that those aged 40 to 49 have over 10 times the odds of the oldest group.
Other variables discussed above are still significant, but region is also significant again,
with those in Northern Ireland being significantly less likely to shop online than those in
other regions. This is because the proportion of people in Northern Ireland with an internet
connection in the household was just 51 per cent, compared with a maximum of 68 per
cent in the South East.
33
4.6 Social participation
Much of the non-essential consumption of older people may be expected to be linked to
social participation. Through a paper-based self completion questionnaire, ELSA collects
details about a range of activities and media by which older people may participate in
society. There are fewer respondents in this section of the survey, and they may not
reflect those who were the most socially isolated in certain ways, but the figures give
some indication of the ways in which social participation decreases with age.
Table 11 Social participation: by age
Percentages within age-group Age
52 to
54
55 to
59
60 to
64
65 to
69
70 to
74
75 to
79
80 to
84
85 to
89
90
+
All
Daily newspaper 60 60 68 72 74 73 75 67 66 68 Hobby or pastime 75 81 83 79 78 73 65 69 64 78 Holiday in the UK in the last 12 months 62 59 64 60 58 52 43 37 25 57
Holiday abroad in the last 12 months
62 59 58 52 45 32 23 15 5 48
Daytrip or outing in the last 12 months
81 73 72 69 64 56 51 46 43 66
Use of internet and/or email 68 65 52 38 29 18 11 7 8 41
Owns a mobile phone 91 88 83 76 70 57 44 36 19 73 No participation 0 <1 <1 1 1 1 1 1 8 1
Unweighted Base 100 1,436 1,236 1,053 977 767 495 249 65 6,378 Source: ELSA Wave 3 Core respondents. Weighted percentages; of those who answered yes (excluding missing data).
34
Looking at this as a line graph helps us to see the dramatic variation by age:
Figure 14 Social participation: by age
Source: ELSA Wave 3 Core respondents. Weighted percentages It seems that the proportion enjoying hobbies and pastimes increased briefly around
retirement age, only to fall away again among older respondents. Similarly, retirement
appears to have triggered additional newspaper reading14, although this stayed high for
some time, before dropping gently beyond the age of 75.
Turning now to communication technology, we can see from Table 11 that on average,
just two in five people over the age of 52 used the internet or email while almost three
quarters had a mobile phone. However, Figure 14 illustrates that the use of
communications technology in general falls more quickly than other types of social
participation, despite the fact that it could potentially provide a means of social interaction
with likeminded people or regular contact with friends and family for those with reduced
mobility. This may indicate that providers could do more to explain the benefits of such
technology to older generations. It would also be useful to find out whether demand for
such services is lower among older people, or whether the products available do not meet
their needs.
We can’t answer all of these questions with the available data, but once again, we have
been able to look for associations between certain characteristics and mobile phone use.
This suggests that, other things being equal, age is a significant predictor of mobile phone
use, with those in their early fifties having over five times the odds of having a phone as
people in their 80s. Ethnicity is also an important predictor; white respondents have 1.8
14
Unfortunately the data does not tell us which type of newspaper people take.
35
times the odds of owning a mobile phone. Those people who reported that health
prevents them from using public transport had only around half the odds of having a
mobile, while, other things being constant, people with qualifications had twice the odds of
those without.
The regression analysis indicates that men were slightly less likely than women to have a
phone, but that people in couples were significantly more likely than single people. Those
who found that they often didn’t have money for the things they wanted had lower odds
than those who never found this to be the case (while this should not be read as wanting
a phone, it could indicate that they were unable to afford one).
These findings suggest that mobile phones are not being used as a security device for
people in less safe areas, or as a means of communication for people who cannot easily
travel. In fact all of the indications point to ownership being primarily linked to disposable
income and living as a couple.15
Table 12 shows that social clubs are important to a sizeable proportion of older people.
The use of such clubs actually increases with age, so that almost a quarter of 85 to 89
year olds were members. It is unclear from the data how active their membership was, but
we might assume that if membership was increasing then people must be joining up in
order to be active participants rather than simply continuing to pay membership fees for a
club they no longer attend. Such clubs may provide a useful way of accessing older
consumers keen to know more about the benefits and limitations of relatively new
technologies and shopping options such as online communications and shopping.
There is a notable difference between membership of social clubs and sports
membership. People of retirement age and above were less likely than their younger
counterparts to be members of sports clubs, despite the fact that we might assume that
they have more free time, and can access such facilities when the majority of members
are at work. It is plausible that such clubs are not actively encouraging older generations
to remain members, or that the services that they offer are less relevant to older people.
Alternatively, it could be that the cost of such membership becomes prohibitive once a
household is living off a retirement income.
15
Note that we also tested a second regression model with housing tenure, but this was not significant. We might have expected tenure to reflect the use of mobiles instead of landlines, but this does not seem to be the case.
36
Table 12 Club membership: by age
Percentages within age-group Age
52 to
54
55 to
59
60 to
64
65 to
69
70 to
74
75 to
79
80 to
84
85 to
89
90+
All
Member of social clubs
16 16 21 23 23 22 20 24 29 20
Member of sports clubs, gyms, exercise classes
25 23 24 20 19 13 9 5 4 19
Unweighted Base 95 1,405 1,200 997 938 715 463 232 55 6,100 Source: ELSA Wave 3 Core respondents. Weighted percentages; of those who answered yes (excluding missing data).
4.7 Satisfaction with participation levels
Elsa provides us with information about the extent to which older people are satisfied with
some aspects of their social life. As part of the self completion questionnaire, respondents
provide information about the frequency with which they undertake several activities, and
then answer a question about whether they are happy with the frequency with which they
are able to take part in these activities. We have used these questions to identify the
proportion of people answering the questions who undertook the activities less than once
a month and were unhappy with this situation. Of course, the reasons for being unable to
participate may relate to many things, such as transport, mobility, convenience and cost,
but it is clear from the table below that many older people would have liked to participate
more in these activities.
Table 13 Infrequent participation and respondent would like to
participate more often
Percentages within age-group Age
52 to
54
55 to
59
60 to
64
65 to
69
70 to
74
75 to
79
80 to
84
85 to
89
90+
All
Cinema 38 34 27 26 26 21 22 30 34 28
Eating out 28 24 22 21 22 23 24 24 29 23
Art gallery, museum 36 34 30 26 24 24 30 26 19 29
Theatre, concert, opera 45 48 44 43 41 39 44 42 39 44
Unweighted Base 92 1,307 1,079 879 825 625 372 188 47 5,405 Source: ELSA Wave 3 Core respondents. Weighted percentages; of those who answered yes (excluding missing data). Note that differing numbers of respondents gave yes/no responses (unweighted) – unweighted base represents maximum number responding to any of the questions.
Taken together, four in ten respondents were unhappy about the fact that they were not
doing at least one of the first three activities regularly, a similar proportion to those who
were unhappy about their participation in the final category – theatre, concerts and opera.
37
4.7.1 Demand for cinema visits
Regression analysis of the likelihood of being unhappy about the frequency with which the
respondent went to the cinema indicates that once other things are taken into account,
older respondents were less likely to be unhappy with their level of participation than
those aged under 60. But while age itself seemed to lessen the likelihood of being
unhappy, people were more likely to want to go to the cinema more if they:
• Were female (men had 0.6 times the odds of women);
• Felt they were worse off than their friends;
• Sometimes or often had too little money;16
• Held some form of qualification, or if
• Their health limited the extent to which they could use public transport.
In particular, those who felt they often had too little money had two and a half times the
odds of being dissatisfied as those who never had this problem. However, the model
tested explained just over seven per cent of the variation in being dissatisfied, suggesting
that a range of other factors were influencing this. Interestingly, those who felt that their
local area was the kind of place that people would not want to walk around alone at night
was not a strong predictor. There was also no significant difference between couples and
single adults in terms of their responses, even after taking into account whether they
pooled their finances or kept them separately.
4.7.2 Desire to eat out more
We have run similar regressions for each of the other forms of participation. It is
interesting how different each is. The main predictors of respondents being unhappy with
the frequency with which they eat out are:
• Their health limited the extent to which they could use public transport (twice the
odds).
• They were unqualified.
• Felt they were worse off than their friends.
• Sometimes or often had too little money.
• They had been incontinent in the last 12 months.
As we would expect from the table above, age was not a significant factor in this
regression. Neither was gender, the cost of public transport, ethnicity, or being single or in
a couple.
4.7.3 Wanting to go to the art gallery or museum
Those people who did not go to an art gallery or museum at least once a month, and
would like to go more often were more likely to be:
16
We were unable to access income data for wave 3, and so have used the question discussed above about how often people feel they have too little money to spend on their needs as an income-related measure. While this is not the same as income, in some ways it is arguably a more important indicator when considering why people don’t do something they would like to do.
38
• Aged under 70.
• Female.
• Incontinent in the last 12 months.
And say that they:
• Didn’t use public transport more often because it is too expensive.
• Felt they were worse off than their friends.
• Sometimes or often had too little money.
There was no significant relationship between wanting to go to the museum and having
health issues that limited the use of public transport. Neither were ethnicity, relationship
status or being afraid to walk alone at night significant predictors.
It is interesting that this particular aspect of social participation seems to be so acutely
tied to income and the ability to make ends meet, despite the fact that many museums
and art galleries are free or heavily subsidised. As with the other regressions, a large
amount of the variation is not accounted for by the factors that we have listed, but even
so, there is some indication from this that museums could do more to encourage older
consumers to find out about what was on offer and display the entrance fee more
prominently.
4.7.4 Wanting to go to the theatre
Finally, we find that the propensity to go to the theatre infrequently and be dissatisfied
with this was not related to age, but was twice as strong among women as men, and
among those who sometimes had too little money compared with those who rarely or
never found this to be a problem. Furthermore, people were more likely to be dissatisfied
if they:
• Felt they were worse off than their friends, or
• Had a qualification.
There does not seem to be any significant relationship between feeling dissatisfied with
the frequency of going to the theatre and being single. Furthermore, neither incontinence,
safety in the streets at night nor the cost of public transport were significant in this
regression. Ethnicity was not a significant factor either.
A note on couples
It is interesting that older people living as a couple do not appear to be more or less
satisfied with their levels of participation than single people. This may simply be because
disposable income is a far more important determinant than having a companion to go out
with, or it may be that there are differences in the expectations of singles and couples. It
would be interesting to explore these and other explanations in more detail.
39
4.8 Environmental impact on spending
We have been asked to consider whether there is any evidence to support the hypothesis
that environmental concerns impact on the consumption patterns of older consumers. To
do this, we have returned to European data.
Results from a Eurobarometer study looking at “Attitudes of European citizens towards
the environment” highlight several environmental concerns that may be expected to
impact on consumption within the UK (Eurobarometer, 2008). The results indicate that
more people in the UK than across the EU27 as a whole considered consumption habits
(14 per cent compared with 11 per cent) and growing levels of waste (36 per cent
compared with 24 per cent) to be among the top five most important environmental
concerns. It would be interesting to follow this up with a study to understand whether such
perceptions lead people to change their own behaviour.
The impact on our health of chemicals used in everyday products also ranked among the
greatest concerns for over a third of consumers in the UK (37 per cent c.f. 32 per cent for
EU27), perhaps pointing to an unmet demand for products made without the use of
dangerous chemicals. Unfortunately, these findings are not broken down by age, and so
we cannot use them to identify issues of particular concern to older consumers. However,
the European data does provide some indication of the proportion of older adults in the
UK who felt well informed about environmental issues. Seven in ten UK respondents aged
55+ agreed that they were well informed compared with 54 per cent of this age group
across the EU27. Interestingly, proportions feeling well informed varied little by age within
the UK, ranging from 67 per cent of those aged 15 to 24, through to 74 per cent of those
aged 40 to 54.
Perhaps of more interest in terms of consumer behaviour, the adults in the UK were more
likely than their mainland counterparts to agree that “current labels on products allow you
to identify those products that are genuinely environmentally friendly” and this did not vary
by education, suggesting that the labels are clear and straightforward. However, the
proportion is still not particularly large: just over half of adults aged 55+ (55 per cent)
agreed with the statement. (Of course, it could be that the rest of the respondents were
not aware that the labels were helpful because they did not have any particular interest in
finding out such information.)
4.9 Discrimination
One thing that we could not explore with the ELSA data was the extent to which people
may feel that age discrimination prevents them from participating in social activities.
However, data from Europe, collected in June 2009, suggested that three in five
respondents to a nationally representative survey (61%) reported that age discrimination
is widespread in the UK. This compared with 58% across the EU2717. Indeed, UK
17
http://ec.europa.eu/public_opinion/archives/ebs/ebs_317_fact_uk_en.pdf
40
respondents were more likely to report that age discrimination was more widespread than
discrimination in respect to gender, ethnicity, religion, disability or sexual orientation.
Of course, discrimination isn’t just present in consumer environments, but potentially cuts
across all elements of life, including employment, entertainment and the provision of
services. Given this, it is of concern that the same survey suggests that while UK citizens
are considerably more likely to believe that they know their rights than respondents
across the EU27 as a whole, people aged 55 and over in the UK are less likely to report
that they would know their rights if they were a victim of discrimination of any sort than
their younger counterparts.
Table 14 Percentage who believe that they know their rights
Age
15 to
24
25 to
39
40 to
54
55+
UK 54 61 53 35
EU27 34 37 36 27
Source: http://ec.europa.eu/public_opinion/archives/ebs/ebs_317_fact_uk_en.pdf
41
5. References
Berry, S., & Williams, R. (2009). Household saving (No. 2009 Q3). London: Bank of
England.
Demery, D., & Duck, N. W. (2003). Demographic Change and the UK Savings Rate (No.
No 03/550). Bristol: University of Bristol.
Eurobarometer. (2008). Attitudes of European citizens towards the environment. Results
for United Kingdom: European Commission.
Gallup. (2008). Family Life and the Needs of an Aging Population (Flash EB series 247)
Marmot, M., Nazroo, J., Banks, J., Blundell, R., Erens, B., Lessof, C., et al. (2009).
English Longitudinal Study of Ageing: Wave 0 (1998, 1999 and 2001) and Waves 1-3
(2002-2007) 12th Edition, SN: 5050. Colchester, Essex: UK Data Archive.
Office for National Statistics. (2008). Family Spending. A report on the 2007 Expenditure
and Food Survey. Basingstoke: Palgrave Macmillan.
Office for National Statistics. (2009). Wealth in Great Britain. Main Results from the
Wealth and Assets Survey 2006/08. Cardiff.
Office for National Statistics and Department for Environment Food and Rural Affairs.
(2009). Expenditure and Food Survey, 2007 2nd Edition: Colchester, Essex: UK Data
Archive.
ILC–UK 11 Tufton Street
London SW1P 3QB
Tel : +44 (0) 20 7340 0440 www.ilcuk.org.uk
R
