Relative needs methods Cipfa 17 Sept 2013

Principles The aim is to calculate the level of funding councils need to meet their social care obligations = net public expenditure requirement (NPER). Two tests: (1) the care and support test: – whether people are eligible on the basis of need and how much support a person should receive (2) funding test – whether people are eligible according to their means. NPER levels per capita will differ between LAs – mainly driven by differences in need and wealth How much of NPER is driven by factors beyond LAs control? … Set a relative needs funding formula to compensate for these external factors

Principles Total NPER = LA-supported NPER + Dilnot NPER. – The latter is the additional cost on LAs that arises from the Dilnot reforms and comprises: Capped phase care costs Assessment/metering costs in the pre-care phase Deferred payments Increasing the upper capital limit for those in residential care

Methods: LA-supported NPER LA NPER is estimated using current LA expenditure data A sample of LAs: asked to provide the total number of people using council-funded services in each small area they lived before getting care (pre-care LSOA) Applying unit costs, total expenditure is calculated = LA-supported NPER. Regression analysis is used to estimate the association between NPER and the need and wealth-related characteristics at the pre-care LSOA – Need and wealth characteristics mainly from Census and administrative datasets (e.g. benefits data)

Methods: Dilnot NPER Dilnot NPER needs to calculated directly. We estimate the additional costs that would have arisen if these reforms were already in place … and then use local area need and wealth-related characteristics to predict Dilnot NPER. We distinguish: – pre-cap metering costs – post-cap care costs (noting new draft regulations) – extra cost component of the Dilnot NPER relating to the extension of the means-test They are treated separately because they have slightly different drivers We estimate the system as though in steady-state

Methods: Dilnot NPER Due to the difficulties in estimating Dilnot NPER, we use three approaches and triangulate the results: – Using self-payer numbers – Removing the wealth test in LA-supported formula – Using needs profiling

Methods: Dilnot NPER Self-payer numbers Key indicator of Dilnot NPER Focus on residential care (as biggest driver) Aim is to predict this number of self-payers (i.e. Dilnot NPER) using need and wealth characteristics of a small area Number of self-payers = the number of places x % Self- funder x occ rates Data: – Places: CQC reg data – % Self-funder and occ rates: Surveys of care homes Use regression analysis as before with need and wealth factors Used mainly to predict post-cap care costs

Methods: Dilnot NPER Removing the wealth test in LA-supported formula The LA-supported NPER estimation uses the wealth characteristics of the people in an LSOA to reflect the implications for NPER of the means-test. Under Dilnot, this means-test is removed post-cap for care costs. Use the LA- supported formula for Dilnot NPER formula but remove the wealth effect variables. Appropriate for: – Dilnot metering costs, – the extended means-test costs and the – deferred payments cost … because the LA-supported formula approach uses pre-care addresses, whereas the self-payer approach has current addresses

Methods: Dilnot NPER Needs profiling Estimate the numbers of people with disability by wealth band directly Use existing datasets on need and wealth characteristics Apply scaling factors: Dilnot NPER = % of disabled people needing care and exceeding the cap (per year) x number disabled Or: estimate numbers of Self-payers directly = Total places x occ – LA supported places (including out-of-area places) Most relevant for estimating a formula for the Dilnot care costs component. We would use this method to help validate the results from the self-payer approach in this respect

Any questions? This research has been commissioned and funded by the Policy Research Programme in the Department of Health. The views expressed in this presentation are not necessarily those of the department.