06Mar14 - Balancing the books: financial wellbeing in later life

Balancing the books: financial wellbeing in later life

An ILC-UK/PFRC joint conference

Thursday 6th March 2014

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Welcome

Paul Lewis

Freelance Financial Journalistand presenter of Money Box

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David Sinclair

Assistant Director, Policy and Communications

International Longevity Centre-UK

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David Hayes

Research AssociatePersonal Finance Research Centre

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Balancing the Books: Financial wellbeing in later life

Understanding older consumers:Consumer segmentation using the Living Costs and Food Survey David Hayes

5www.pfrc.bris.ac.uk/esrc

Our Approach

• Using the Living Costs and Food (LCF) Survey, we:1. Describe average household expenditure by age

(using descriptive analysis); 2. Segment older households based on their patterns

of expenditure (using cluster analysis);3. Explore cluster membership

www.pfrc.bris.ac.uk/esrc 6

• Alcohol & tobacco• Clothing & footwear• Communication• Education• Food & non-alc. drinks• Health• Household goods &

services

• Housing, fuel & power• Recreation & culture• Restaurants & hotels• Transport • Miscellaneous goods &

services


Standard LCF expenditure categories

Data Considerations

• Good sample of household heads aged 50+• To cover transition into and beyond retirement• Total sample size of 2,769• Good distribution of age groups (even 80+ ~ 12%)

• Equivalised expenditure• To take account of household size


Absolute and equivalised expenditure by age


50 but under 55 yrs

55 but under 60 yrs

60 but under 65 yrs

65 but under 70 yrs

70 but under 75 yrs

75 but under 80 yrs

80 and above0

100

200

300

400

500

600

510

189

286

160

AbsoluteEquivalised

Poun

ds p

er w

eek

(£)

Proportion of total expenditure by age

↑ Food & non-alc. drink increases: 12% to 19%↑ Housing, fuel & power doubles: 12% to 24%↔ Communication constant: 3%↓ Clothing & footwear halves: 6% to 3%↓ Transport decreases: 18% to 7%↓ Recreation drops: 16% to 11%


The segmentation (clustering) process

• Exploring how types of expenditure co-vary• Identifies dominant patterns• Classifies people into segments based on these

• Clustered on the 12 expenditure categories• ...and the optimal solution contained six clusters


Drivers of cluster membership

• Highly statistically significant variations in expenditure for all 12 categories

• Three categories were particularly strong• Alcohol and tobacco• Clothing and footwear• Housing, fuel and power


The clusters


The average equivalised expenditure across the sample is £217.

Percentage in cluster (%) Mean weekly expenditure

‘Conservative consumers’ 46 138

‘Foodies’ 19 228

‘Burdened by bills’ 11 231

‘Smokers’ 9 245

‘Recreation and clothing’ 4 392

‘Socialites’ 12 405

Conservative Consumers

• Spent £138 on average• Transport (£18) much lower than average (£32)• Only 47% connected to the internet• More likely to be the oldest old (22% cf. 15%)• 38% in the lowest income quartile; 60% retired• 56% gave benefits as main source of income


Spend far below average on non-essentials (such

as recreation and hotels)

Foodies

• Spent £228 on average• Close to average expenditure in most categories • A half (54%) live in two-adult households• Very few households are renting (12%, cf.25%)• Only 18% in lowest income quartile• Larger houses (58% cf. 50% with 6+ rooms)


Very high expenditure on food (£58 compared to the average of £34)

Burdened by Bills

• Spent £231 on average• All other expenditure is relatively low • Low transport costs (lowest petrol expenditure)• 70% in rented accommodation (cf. 24%)

• Including 45% from a social landlord• More single households


Very high proportion of expenditure on housing costs (£4 in every £10,

twice the average)

Smokers

• Spent £245 on average• Very high spend on alcohol and tobacco (£36 per

week/15% of total expenditure, cf. 3%)• One of the ‘younger’ clusters (62% under 65) • Almost a third still in full-time employment• Home-ownership is relatively low (42% cf. 54%)


Spent £28 a week on tobacco products

Recreation and Clothing

• One of the two high-spending clusters (£392)• High spend on recreation (£65) & transport (£53)• Only 21 per cent of this cluster are 70 and above• Two-thirds in larger houses (6+ rooms)• 20% say benefits main income (cf. 10% socialites)• Half of the cluster in the highest income quartile


At £65 each week, these fashionistas spend more on clothing than all the other groups combined!

The Socialites

• One of the two high-spending clusters (£405)• Spent £96 on transport costs (24% cf.15%) • Three quarters under 65; 41% working full time • Income – 57% earnings; 33% investments• More than half in highest income quartile• 90% of households connected to the internet


Enjoy the finer things in life, spending £131 per

week on eating out, holidays and recreation

Important socio-demographic characteristics

• Tenure: 97% of Socialites were homeowners• Compared with 29% of Burdened by Bills

• Age: 40% of Smokers aged under 60• Compared with just 26% of Conservative Consumers

• Income: 7% of R&C in lowest income quartile• Compared with 39% of Burdened by Bills


Policy implications

• Expenditure poverty not atypical• Housing costs key in wellbeing • Smokers are young (stop/morbidity)• No such thing as ‘the’ older consumer?

• Depends on preferences/constraints, resources, mobility


What else would help inform policy?

• Conservative Consumers are a diverse group• Positive/negative constraints?

• Hostels, boarding houses, and institutions such as rest/care and nursing homes are excluded

• The true effect of ageing vs. generational effects remains unclear – further analysis needed


Sharon Collard

Senior Research FellowPersonal Finance Research Centre

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Understanding older households’ balance sheets using the Wealth and Assets SurveySharon Collard


Wealth and Assets Survey

• Provides survey-based estimates of households’ economic wellbeing

• Individuals and households in Britain (not NI)• Longitudinal • Focused on Wave 2, 2008-2010

• 12,357 households headed by someone aged 50+


Total household wealth


50 to 54 55 to 59 60 to 64 65 to 69 70 to 74 75 to 79 80 and over Total -

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

MeanMedian

Components of wealth (% of each component)


50 to 54 55 to 59 60 to 64 65 to 69 70 to 74 75 to 79 80 and over Total -

10

20

30

40

50

60

70

80

90

100

PensionPhysicalFinancialProperty

Mortgage borrowing

• 74% of households headed by someone 50+ owns their main home• 21% still have a mortgage, owe on average £62,000

• Mortgage borrowing and amounts fall with increasing age


Heavy mortgage borrowing

• Owe £50,000+: 9%• Loan-to-value ratio of 50%+: 17% • Strongly associated with owning other property

• Especially if other property also mortgaged• Lower levels of other assets


Mortgaged households aged 75+

• Only 2% of over-75s still have a mortgage• But half owe more than £21,000• A quarter owes 25%+ of the estimated house value

• Greater tendency to have non-repayment mortgages• Including interest-only with no linked investment


Non-mortgage borrowing

• One in four aged 50+ have consumer credit• Owe on average £4,500

• More common among people in their 50s• Declines steeply with increasing age

• Drivers: high fixed costs, low household income, drop in income, struggle to make income last


David Hayes

Research AssociatePersonal Finance Research Centre

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Understanding mental wellbeing and financial management in later life using Understanding Society Wave 3

David Hayes


Understanding Society

• Uses third wave of Understanding Society (2011)• Largest social survey ever undertaken in U.K.• Socio-economics/attitudes/health• Almost 20,000 people aged 50 and above • Weighted to be nationally representative


Measurement of mental wellbeing

• General Health Questionnaire (GHQ) • A common tool to assess those likely to have, or

be at risk of developing, psychiatric disorders• People rate themselves according to certain

criteria (next slide); a score is calculated• GHQ12: score of above 3 = psychiatric caseness


GHQ 12 Measures

Concentration Capable of making decisions

Enjoy day-to-day activities

Losing confidence

Loss of sleep Constantly under strain

Ability to face problems

Believe in self-worth

Playing a useful role Problems overcoming difficulties

Unhappy or depressed

General happiness


Logistic regression

• We predict poor mental wellbeing (GHQ12)• Controls for the relationships between

‘predictor’ characteristics, to identify those related to an outcome measure of interest (i.e. mental wellbeing)

• Predictors are demographic and socio-economic characteristics


Better mental wellbeing among the over 50s:

• 65-74s, compared to early 50s• Men, compared to women• Households in rural areas


Poorer mental wellbeing among the over 50s:

• Unemployed older people, compared to full-time workers

• People in the North East, compared with the South West

• Mortgagors, compared to home-owners


The effect of financial management on mental wellbeing

• Compared to those living comfortably:• Those getting by have twice the odds of poor mental

wellbeing• Those finding it very difficult have eight times the

odds of poor mental wellbeing• Remember this is controlling for all variables

mentioned previously


Brian Beach

Senior Research Fellow, International Longevity Centre-UK

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Financial Dimensions of Quality of Life at Older Ages

Brian BeachInternational Longevity Centre – UK

Presentation Outline

• Background• Quality of Life at Older Ages• Data & Methodology• Financial Indicators• Examining Quality of Life• Changes in Quality of Life• Implications of Findings

Quality of Life at Older Ages

• CASP-19: Quality of Life in Older People– 19 Items (Hyde et al. 2003)

– Control, Autonomy, Self-Realisation, Pleasure

– 4-point Likert scale (0-3)

– CASP-19 scale from 0 to 57 (increasing QoL)

Quality of Life at Older AgesFactors associated with QoL:• Age: increases from 40s to age 68, declines afterward• Social Relationships

– Marital status/Partnership (also if providing care)– Close Family, Relatives, Friends – and frequency of contact

• Health– Limiting long-standing illness– Limitations in Activities of Daily Living (ADLs)– Depression

• Housing Tenure• Socio-Economic Status (education, social class)

Data & Methodology

• English Longitudinal Study of Ageing (ELSA)– Nationally representative survey of population aged 50+

– Longitudinal Panel design: 5 Waves (2002-2010)

– 9,000+ core participants per wave

– 5,000+ in all five waves

– CASP-19 included in self-completion questionnaire

Financial Indicators

– Income

– Housing Wealth

– Financial Wealth

– Pensions (Private & State)

– Expectation of Future Financial Difficulty

Data & Methodology

• Other Variables:– Limiting illness– Depression– ADL limitations– Education– Subjective SES– Housing Tenure

– Age Group– Gender– Coupled (married or co-habiting)

– Close Family/Relatives– Close Friends

• Number + Frequency of Contact

Data & Methodology

• Multiple regression analysis– Cross-sectional by wave– Fixed-effects models for multiple waves– Multiple imputation for missing values & Weighting

• Longitudinal analysis– Modelling change in CASP across waves– Change in predictors

Examining Quality of Life

• Negative associations with QoL:– Limiting illness– ADLs– Depression– Providing care (Wave 5)– Age (85+ in Wave 5, all in FE)– [Having a degree-level education (Wave 5)]– Expected Future Financial Difficulty

Examining Quality of Life• Positive associations with QoL:

– [Being female (Wave 5)]– Home ownership v Mortgage (FE, males)– Subjective SES– Number of close family/relatives– Number of close friends (not FE, females)– Frequency of contact with friends– Higher Financial Wealth– Pensions: Private (Q4-5 in Wave 5) & State

Changes in Quality of Life• Positive: Subjective SES, Frequency of contact with friends• Negative: Expected future financial difficulty, Limiting illness,

ADLs, Depression, Age

Gender differences:• Males: Positive association with Financial Wealth (Q2, Q5)

– Age significant from age 80

• Females: Positive association with Private Pensions (Q4, Q5)– Age significant from age 70– Negative association of being in a couple

Changes in Other VariablesExamining Wave 5 compared to Wave 1:

– Wave 5 QoL strongly predicted by QoL at Wave 1

– Positive impact on QoL: • Less depression

– Negative impact on QoL: • Onset of limiting illness• Decline in subjective SES

Implications of Findings• Health factors play a crucial role in QoL at older ages• Social ties: importance of social inclusion

• Improving individuals’ sense of financial security may relate to improved quality of life– Importance of subjective perception: SES

• Significance of financial wealth– Implications for patterns of savings & debt

• State pension provision important– Private pensions could play a greater role if sufficiently substantial

Gemma Tetlow

Programme DirectorInstitute for Fiscal Studies

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Peter Tutton

Head of PolicyStepChange

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Dick Stroud

Managing Director - 20plus30Acting Managing Director - Digital Unite

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Lawrence Churchill

TrusteeInternational Longevity Centre-UK

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Balancing the books: financial wellbeing in later life

An ILC-UK/PFRC joint conference

Thursday 6th March 2014

#balancingthebooks

06Mar14 - Balancing the books: financial wellbeing in later life

Economy & Finance

Transcript of 06Mar14 - Balancing the books: financial wellbeing in later life