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An Outcome-based Resource Allocation Model: Local Education Services in Wales

An Outcome-based Resource Allocation Model: Local Education Services in Wales

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An Outcome-based Resource Allocation Model: Local Education Services in Wales

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  1. An Outcome-based Resource Allocation Model: Local Education Services in Wales SG ‘ Tackling Multiple Deprivation’ Conference 2 June 2009 Prof Glen Bramley

  2. What’s this paper about? • ‘Outcome-based resource allocation’ appears to be in vogue – as in ‘Local Outcome Agreements’ • But such approaches, based on targeting outcomes, may only apply to marginal elements of total spending • Mainstream service funding streams still often governed by more traditional formulae based on demographics and ‘need indicators’ • These formulae tend to replicate past patterns of spending • They may give more to deprived areas, but do they give enough? And do they reflect different disadvantages evenly?

  3. A Fresh Approach • Paper looks at what a systematic ‘outcome-based’ approach to funding a mainstream service could look like • Takes case of local education in Wales • Asks ‘what are we trying to achieve?’ • Identifies key things to be modelled – attainment, costs, special needs, (provision structure) • Models whole national system at indiv pupil, neighbourhood, school and LEA levels • Highlights key decisions/options in process • Demonstrates how far you could go towards more desirable (equitable) outcomes, with given resources • Discusses issues arising, & linked policies

  4. The Research Base • Treasury/NRU/Scot Exec ‘Mainstream Services & Neighbourhood Deprivation’ (Bramley, Evans, Noble 2005) • Scot Exec Educ Dept ‘Home ownership and educational achievement’(Bramley & Karley, Housing Studies, 2007) • Fife Council ‘Developing a Social Justice Analysis System for Fife’, (Bramley & Watkins 2005-06) • Welsh Assembly Government ‘Alternative Resource Allocation Methods for Local Government’ (outcome-based funding model for schools; Bramley, Karley & Watkins forthcoming)

  5. The Literature • Large body of work on educational attainment and outcomes • Increasingly sophisticated methodologies used e.g. multi-level modelling • Much of this focussed on ‘school effectiveness’ • We are rather more interested in the contextual ‘control’ variables in these studies – the influence of family background, neighbourhood, peer group effects, etc. • Common interest in effect of school resources – some earlier studies rather negative, more recent work tending to find positive effects (but requiring more sophisticated modelling) • Also issues of structure of school provision, choice/selection • Dominant finding that poverty and social background strongest factors • Interesting sub-themes around area/school concentration effects and housing tenure effects

  6. What are we trying to achieve? • *Minimum standards approach - a ‘floor’ level of attainment for all areas/schools • *A convergence approach – a certain proportional reduction in the spread of attainment between most and least deprived areas/school • *Equal attainment for individual pupils with equivalent initial individual endowment/disadvantage (i.e. trying to neutralise the school or area effect of disadvantage) • Equal entitlement to (lifetime) educational resources– attainment is mainly relevant via progression, or later participation in adult, further or higher education • Maximise percentage attaining (say) 5+ A*-C at KS4 across Wales – implies allocating resources at margin where marginal productivity, in terms of this percentage, is highest– social efficiency vs equity • Incentives approach, whereby schools/LEAs get some bonus for attaining above a (need-related?) threshold level

  7. Operationalising the Concepts • In order to give effect to these we need a - robust model of determinants of attainment which can distinguish factors we want to take account ofb - robust model linking £ resources to outcomes taking account of environment, provision structure, etc. • a can be easier than b, and there are issues about getting the best fit-for-purpose model • These models have been enabled by massive development of administrative data systems linking pupil characteristics, including attainment, to their school and neighbourhood characteristics

  8. Data Sources • Attainment levels x indiv pupils at 3 key stages (11, 14, 16) • Pupil level census (PLASC) inc age, stage, gender, language, ethnicity, SEN, FSM + school and post codes • Distance home-school and pupil mobility derived from this using GIS • School budgets & spend; also size (pupils), type (e.g. denom) • School-level measures of teaching workforce number/qualifs/turnover/recruitment diffs • Measures of settlement pattern & characteristics based on 216 urban settlements & rural remainders • Census data on socio-demographic characteristics at COA level, some quite specific (e.g. children with low occup class parents; adults >35 no qualifs) • Wales Indices of Multiple Deprivation (WIMD) • (No data on school building stock, capacity or quality available)

  9. Key Findings - Attainment • Indiv pupil factors explain most, esp prior attainment (+ secondary),SEN -, FSM (- poverty), in care -, mobility +/- • Varying school size effects (small schools less effective) • Spending /pupil positive, (stronger /clearer in secondary) • Concentrations of FSM & SEN in schools –ve • Neighbourhood factors: housing depriv (-), lone or cohab parents (-), no qualifs -, low SEG –, children caring (-) • Owner occupation +, rural +, flats + (city centre?@)

  10. Cost Models • Models estimated for school costs per pupil at school level • Reasonable fit (r-sq 62%-73%) • Small primaries cost much more • Size, composition, quality of teaching force • Transport/distance effects (but note separate model developed for transport costs) • SEN factors • Pupil mobility • School level FSM x weighting in LEA’s funding formula for deprivation

  11. Special Needs • Tried also to model incidence of composite measure of special needs • Similar to attainment models • Fit generally poorer (random variations in assessment practice, or in LD incidence?) • Included LEA ‘dummy’ variables to proxy local policy variation • Some systematic variation with similar social disadvantage variables as in attainment models- in care, FSM, renters, no qualifs, low occups, lone or cohab parents, poor health, overcrowding

  12. Outcome –based funding model • Analysis at school (‘virtual catchment’) level • Standardize school size for settlement size • Standardize costs given size, spec needs, etc. • Measure relative disadvantage due to social/other (?) factors (in terms of attainment) • Allocate enough extra money to bring predicted attainment x% closer to mean (or to a minimum standard level y s.d. below mean) • Given minimum school £ allocation, >=: lowest observed, feasible degree of redistribution is determined

  13. Criterion B compensates for‘almost everything’, to a degree;Criterion C compensates for specified social disadvantages, to a (higher) degree;Criterion A brings up to minimumattainment standard, allowingfor more factors than C but lessthan B.

  14. Outcome based allocations are not overall (much) more variable than existing expenditure. The most redistributive is Criterion A ‘Minimum Standard’. Criterion B can only go 30% towards full equalization of outcomes Criterion B can go 40% of way to equalizing for social disadvantages

  15. Outcome-based needs for primary schools Note: needs formula based on standardized costs and compensating for 40% of social disadvantage

  16. Outcome based formulae generally redistribute from rural to urban and from affluent to deprived areas The main gaining group are deprived urban Rural losses are partly due to provision rationalisation and cost standardisation There is considerable variation in the impact among relatively deprived LEAs (some were already quite well funded) Patterns of Redistribution

  17. Secondary Schools Summary

  18. Secondary Schools - Comments • Range of existing spend and new formula allocations tend to be narrower • Similar high/gaining LA’s (Blaenau Gwent, Merthyr Tydfil) and low/losing Las (Monmouth, Vale of Glam, Ceredigion) • Criteria A and B achieve similar degree of levelling up /partial (30%) equalization, as in primary case; Criterion C achieves higher degree of equalization (60%) • Results not that dramatic due to- high floor of minimum spend per school- larger size of schools, less scope for polarisation- stronger expenditure->attainment effect- prior attainment excluded from factors allowed for in Crit C

  19. Conclusions 1 • Feasibility outcome-based approach demonstrated • Shows value of rich admin databases • Care needed over - specification of what reasons for difference are compensated- modelling of cost and of school provision structure- understanding and modelling special needs provision • Outcome-based resource allocation is not a utopian fantasy- even with minimum allocation floors, substantial progress could be made- variation in allocations not significantly greater than current actuals • But, you can’t fully equalize, or level up to mean, with current resources • Needs formulae could be simplified, with more current/updating indicators

  20. Conclusions 2 • Initial reaction to this report mixed – LA’s find it difficult to agree – zero sum game • Disparities between schools (& neighbourhoods) greater, but LEA formulae allocating to schools typically even less redistributive • Small rural schools get most funding per pupil, and are of dubious educational value, but this issue is sensitive • Would have been easier to apply progressive redistribution in 2001-07 with increasing real resources • Still possible to aim for gradualist move towards outcome-oriented spending targets – as in NHS

  21. Reflections on Resource Allocation • ‘Poor’ areas tend to get poorer service outcomes, across quite diverse kinds of service • Poverty/social deprivation makes the service provision task more difficult and potentially costly • Poor areas get more resources of some kinds but less or the same of others • They do not get enough extra resources to make a decisive difference to outcomes • Therefore it may appear that there is a perverse negative relationship of resources with outcomes • Local political resistance to re-allocation of resources likely to be formidable

  22. Complementary approaches to improving school outcomes • Reduction in poverty thru’ e.g. tax/benefits, labour market, minimum wage, etc. (poverty the strongest predictor of poor outcomes) • Reduction in concentrations of poverty, e.g. thru’ planning/regeneration including tenure diversification*(* Bramley & Karley article in Housing Studies 2007 argues that owner occupation at indiv/nhood/school levels raises attainment) • Focused use of ‘special needs’ resources e.g. special units for disturbed pupils • Close or amalgamate failing schools • Earlier intervention, preschool/nursery; after school clubs • Changing curriculum (addressing motivation, engagement)