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Social Mix, Neighbourhood Outcomes and Housing Policy. SG ‘ Firm Analytical Foundations’ Conference 22 April 2008 Prof Glen Bramley. What’s this paper about?. Government policies and rhetoric have placed a new emphasis on social mix & balance in neighbourhoods

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social mix neighbourhood outcomes and housing policy

Social Mix, Neighbourhood Outcomes and Housing Policy

SG ‘ Firm Analytical Foundations’ Conference

22 April 2008

Prof Glen Bramley

what s this paper about
What’s this paper about?
  • Government policies and rhetoric have placed a new emphasis on social mix & balance in neighbourhoods
  • This raises questions about whether such policies are achieveable & sustainable, as well as whether they are desirable
  • This contribution focuses on aspects of ‘desirability’, in terms of social, economic and environmental outcomes
  • It draws on evidence from a number of studies
  • It discusses some of the analytical uncertainties
  • And draws out some pointers for policy
the research base
The Research Base
  • ESRC ‘Cities’ research in Edinburgh-Glasgow (Bramley & Morgan, Housing Studies, 2003 + others)
  • 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)
  • Welsh Assembly Government ‘Alternative Resource Allocation Methods for Local Government’ (outcome-based funding model for schools; Bramley & Watkins forthcoming)
  • EPSRC ‘CityForm’ Consortium, social sustainability & urban form (Bramley & Power, Environment & Planning B, 2008; Bramley et al, Planning Research Conference, HWU 2007)
  • J Rowntree ‘Cleansweep’ study of neighbhourhood environmental services with Glasgow Univ (Bramley/Bailey/Hastings/Day/Watkins, EURA Conference, Glasgow, Sept 2005)
how social mix affects outcomes
How Social Mix Affects Outcomes
  • Poor individuals will have poor outcomes anyway – simple composition effect
  • Housing market sorts poorest into intrinsically least desirable areas (selection effect)
  • Behaviour by poor people (reflecting culture, expectations) worsens problems (e.g. rubbish, litter)
  • Social interactions within neighbourhood reinforce negative patterns of behaviour (crime, ASB) – low collective efficacy in resisting
  • Social interactions and cultures within local institutions reinforce low outcomes (e.g. schools)
  • Increased workload on local services not recognised by resource allocation so performance suffers
  • Housing tenure may have some additional effects e.g.home ownership through stability & commitment
slide5

BACK TO BASICS: the Cost of Clean Streets in Different Physical and Social Circumstances

Glen Bramley & David WatkinsHeriot-Watt UniversityAnnette Hastings, Nick BaileyGlasgow UniversityRosie DayBirmingham Univ

Research supported by Joseph Rowntree Foundation

poor neighbourhoods and environmental problems
Poor neighbourhoods and environmental problems

Previous research suggests the risk factors associated with environmental problems

  • Physical features: open spaces, housing densities; built form (alleys, wind tunnels); street scape (unfenced gardens, on street parking)
  • Economic, social and demographic factors: economic inactivity, high child density, overcrowding, concentrations of vulnerable people
  • So can service provision predict and control for risk?
initial modelling results national
Initial Modelling Results (national)
  • Worse environmental scores associated with poverty, social renting, older people, families (esp lone parent), high child density, overcrowding, terraced housing, London
  • Better environmental scores in rural & suburban areas, areas with more flats (?), where adequate parking, ethnic minorities, higher occupations & growth areas
  • Modest positive association with service expenditure (in England, not Scotland)
initial findings from case 1
Initial Findings from Case 1
  • Deprived areas have a heavier workload (i.e. less resources) for routine sweeping, but attract more responsive resources
  • Deprived areas have more problem-generating factors: non-working population, density, overcrowding, flats & child density
  • Deprived areas have worse environmental outcomes
  • Regression model confirms relationships of context with outcomes; problems establishing relationship with resources
  • Work to be extended and refined
urban form and social sustainability planning for happy cohesive and vital communities

Urban Form and Social Sustainability: planning for happy, cohesive and ‘vital’ communities?

Professor Glen Bramley

With Dr Caroline Brown, Nicola Dempsey, Dr Sinéad Power & David Watkins

g.bramley@sbe.hw.ac.uk

EPSRC GRANT No:GR/S20529/01www.city-form.com

Paper presented at EURA Vital City Conference, Glasgow, September 2007

measuring social sustainability
Measuring Social Sustainability
  • 8 elements measured; all based on responses to multiple questionse.g. social interaction based on 13 questions, such as whether they have friends in neighbourhood, see them frequently, know neighbours by name, look out for each other, chat, borrow, etc.
  • Where possible, combined positives & negatives & scaled in natural way; (100 would be neutral; 0 would be worst possible scores; 200 best possible)
  • Factor analysis generally confirmed groupings

-‘Neighbourhood pride/attachment’ is best single representative measure- Closely related to environmental quality, home satisfaction, interaction

cityform findings
CityForm Findings
  • Most social sustainability outcomes (except service access & collective participation) are worse in more deprived /social rented etc. areas
  • Modelled effects of socio-economic variables also show this pattern, although sometimes muted after controlling for other factors, and sometimes non-linear/uneven
  • Socio-economic effects tend to be bigger than urban form effects although both are important (also have to allow for demography, accessibility)
  • ‘National’ (S.E.H.) results consistent with 5-city case study-based results
some simpl istic simulations
Some Simpl(istic) Simulations

Moving Households from Lowest Ownership Areas to Middle Areas

Moving Households from Highest Deprivation Areas to Middle Areas

comments on simulations
Comments on Simulations
  • Even these simple examples suggest that there can be modest gains in average scores, simply from ‘shuffling the pack’
  • ‘Worst’ areas are eliminated – former residents experience major improvement (Rawlsian principle)
  • Some (probably) middling areas see some worsening
  • However, this ignores (a) individual change effects e.g. individuals not only move area but some also change tenure, or get a job, etc.(b) interactive deprivation effect from deconcentration
  • Therefore overall impact likely to be significantly positive
alternative resource allocation models for local education services in wales

Alternative Resource Allocation Models for Local Education Services in Wales

Research undertaken for Welsh Assembly Government by Glen Bramley and David Watkins(CRSIS/SBE, Heriot-Watt University, Edinburgh)2007g.bramley@sbe.hw.ac.uk

work on school attainment
Work on School Attainment
  • Work grew out of interest in resource allocation for local services and ‘Where does public spending go?’ as well as interest in neighbourhoods & housing
  • Enabled by major advances in data availability associated with PLASC/ScotXEd, SATS, LMS,
  • Fairly standard modelling using data @ pupil, school, small & larger neighbourhood levels
  • Like other work, shows importance of poverty (FSM), special needs, parental educational background, etc.
  • Draws particular attention to effects of clustering of poverty etc. at school (and assoc neighbourhood) level
  • Explores particular role of home ownership
key findings
Key Findings
  • Poverty & deprivation are key drivers of attainment, at both individual and school (/=?neighbourhood) levels
  • Other significant factors including LAC, SEN, parental qualif’s, family background, mobility etc.
  • Evidence that home ownership may have an additional effect, at individual and school levels- but closely correlated with poverty in some cases
  • It is clearly better to go to a school with fewer poor kids, even if you are poor, and possibly to a school with more owner occupiers, even if parents are not owners.
  • Search for non-linearities a bit inconclusive, but sensitivity appears greater in middle range
what are we trying to achieve
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
outcome based funding model
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 factors (in terms of attainment)
  • Allocate enough extra money to bring predicted attainment x% closer to mean
  • Given minimum school allocation = lowest observed, feasible x=40% (primary)
outcome based needs for primary schools
Outcome-based needs for primary schools

Note: needs formula based on standardized costs and compensating for 40% of social disadvantage

changing schools funding
Changing Schools Funding
  • Wales model shows technical feasibility of outcome approach
  • But suggests that full equalization could not be achieved in short run, even if political will…
  • 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
reflections on resource allocation
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
other approaches to improving school outcomes
Other 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)
key analytical and policy challenges
Key analytical and policy challenges
  • How far is it a zero-sum game, how far positive for all?
  • This depends on significance of area effects, school effects, interaction effects, behavioural changes
  • Do middle classes have to suffer some discomfort to achieve a more Rawlsian outcome for worst off?
  • Non-linearities theoretically important, empirically elusive & not necessarily convenient
  • Possible to simulate both population change and system change (e.g. school reorganisation)
more reflections
More Reflections
  • If cost of good services to poor areas is so high, maybe other approaches should be tried (as well as redistribution) – prevention better than cure?
  • Changing neighbourhoods’ social mix should help, particularly if there are additional adverse ‘area concentration’ effects (as in the case of schools)
  • Mechanisms include planning for affordable housing, mix in new build, tenure diversification in regeneration, use of LCHO, sales of vacant SR stock
  • But this is only feasible in some areas in short term – very long term policy
  • Engagement, motivation, ‘social capital’ also important