the impact of budget cuts on social care services for ... · the simulation • compares o public...
TRANSCRIPT
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The impact of budget cuts on
social care services for older people
Jose-Luis Fernandez and Julien Forder
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Popu
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Older population
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s (1
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latio
n (1
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Older population
Older users of community-basedservices
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Older population
Older users of community-basedservicesSupported Residents
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What is the likely impact of budget cuts in social care?
• What will be the size of the cuts? o Some councils (e.g. Birmingham) have mentioned cuts of approximately
20% of budgets o We look at the effects of a 6.7% p.a. real terms reduction in the total
budget available for social care in the 2 years after 2010/11 o This figure is taken from the IFS projections in their January 2010 Green
Budget (Chote, Emmerson and Shaw, 2010).
• Impact of cuts relative to what? o Due to the ageing effect and the increase in unit costs, maintaining
current levels of public support requires funding increases of nearly 3.5% per annum in real terms, according to our central projections, over the period to 2025/6.
o Two scenarios: unconstrained, demand-led, and constrained scenarios
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Expenditure constraints
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10.0
£ bn
Gross social care expenditure
Net public spending
Constrained net expenditure
%17 shortfall
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Strategies for reducing expenditure
• How will those cuts be achieved? (efficiency savings, increase in copayment rates?)
• Changes in service levels o Efficiency
• Types of services: residential vs. community-based care vs. new technologies • Types of users (those that benefit most from the care)
o High need: because with greater capacity to benefit in the short-term o Lower need: because opportunities to prevent the need for services or for
more intensive services o Equity: need in a broad sense
• Need for services: o Physical needs o Informal support o Environmental factors (housing…)
• Need for financial support: income and assets
• The analysis uses composite need index (including ADLs, informal care, age) to change need eligibility criteria
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The simulation model • Dynamic microsimulation model: distributional
implications and longitudinal effects • Based on BHPS data from 11 waves (30,000 obs) • Calibrated to reflect current observed levels and
distributions of key factors o Socio-demographic patterns (income, wealth, age, gender) o Need levels o Social care system (services, charging system) o Elements of social security system
• Disability benefits • Pension credit
• Attempts to model impact on human behaviour of changes in funding rules (and in particular demand effects)
• Models yield projections based on assumptions, not forecasts
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The simulation • Compares
o public and private expenditure, o service utilisation rates and o outcomes
• associated with two scenarios o budget cut “constrained” scenario o demand-led system which provides current levels of support
• The budget constrained scenario increases needs eligibility criteria to ensure net public spending stays within the constrained levels (i.e. “removes” least dependent individuals first)
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Impact on overall expenditure
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2008/09 2009/10 2010/11 2011/12 2012/13
£ bn
Constrained Private
Constrained Charges
Constrained Public net
Demand-led Private
Demand-led Charges
Demand-led Public net
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Impact on supported service users
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2008/09 2009/10 2010/11 2011/12 2012/13
Mill
ions
of u
sers
Community Demand-ledCommunity ConstrainedResidential Demand-ledResidential Constrained
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Impact on service users
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1
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2008/09 2009/10 2010/11 2011/12 2012/13
Rec
ipie
nts
(M)
Constrained Private
Constrained Sup. res care
Constrained Sup. com
Demand-led Non-users
Demand-led Private
Demand-led Sup. res care
Demand-led Sup. com
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Proportion affected in 2011/12 by need and wealth group
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1 ADL 2 ADL 3 ADL 4 ADL 5 ADL
Inc quint 1
Inc quint 2
Inc quint 3
Inc quint 4
Inc quint 5
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Unmet social care need among older people
0
20
40
60
80
100
120
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160
2008/09 2009/10 2010/11 2011/12 2012/13
Mill
ions
of h
ours
p.a
.
Demand-led scenario
Constrained scenario
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Concluding remarks • Results conditional on key assumptions:
o Size of cuts o Strategies for dealing with them o Our understanding of behavioural effects (e.g. demand effects)
• Local impact will vary across local authorities • Results suggest a very significant impact on the
number of individuals supported, and particularly on the number supported in the community
• Although the withdrawal of state support leads to increases in private consumption, unmet need overall increases rapidly
• Range of other outcomes also important (e.g. health care, informal carers)
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Appendices
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