social mix, neighbourhood outcomes and housing policy sg ‘ firm analytical foundations’...
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Social Mix, Neighbourhood Outcomes and Housing Policy
SG ‘ Firm Analytical Foundations’ Conference
22 April 2008Prof Glen Bramley
School of the Built Environment
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
School of the Built Environment
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)
School of the Built Environment
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
Glen Bramley & David WatkinsHeriot-Watt University
Annette Hastings, Nick BaileyGlasgow University
Rosie DayBirmingham Univ
BACK TO BASICS: the Cost of Clean Streets in Different Physical and Social
Circumstances
Research supported by Joseph Rowntree Foundation
School of the Built Environment
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?
School of the Built Environment
S.H.S. Descriptive Analysis
Litter/Rubbish and Vandalism by Deprivation in Case Study 1 and Scotland (SHS 1999-2005)
0.000.100.200.300.400.500.60
Fife Scot
Area & Deprivation Band
Pro
port
ion
of a
dults
rep
ortin
g pr
oble
ms
litrub2
vandal2
School of the Built Environment
Table 1: OLS Regression model for composite environment score, England 2003/04 (SEH, individual data linked to COA and LA variables)
Coeff Std Err Std Coeff t-stat signif B Beta (Constant) 10.875 0.085 128.478 0.000 numadult -0.097 0.015 -0.045 -6.364 0.000 Number of Children -0.107 0.015 -0.054 -6.965 0.000 Age of household head -0.002 0.001 -0.021 -2.709 0.007 Difference from bedroom standard -0.084 0.014 -0.048 -6.184 0.000 incscr04 Low income score -2.560 0.211 -0.111 -12.122 0.000 Pvacoa % vacant dwellings -0.007 0.004 -0.014 -1.958 0.050 Psrentoa % social rent -0.012 0.001 -0.149 -15.614 0.000 Pchhhd household growth (ward) 0.001 0.000 0.019 2.626 0.009 Chdens child density -0.013 0.001 -0.082 -9.754 0.000 Pflatoa %flats 0.002 0.001 0.028 3.047 0.002 Lroadrat (log ratio roads:dwgs) 0.199 0.037 0.042 5.365 0.000 Parkadeq (adequacy of parking) 0.357 0.025 0.097 14.021 0.000 Geogbar (rural proxy) 0.258 0.021 0.115 12.507 0.000 envxc23 (expenditure) 0.003 0.001 0.028 3.483 0.000 Dependent Variable: envscore Weighted Least Squares Regression - Weighted by sehwgt Model Summary Model R R Square Adj R Square Std. Error of the Estimate
1 0.350 0.123 0.122 1.723 Sum of Squares df Mean Square F Sig. Regression 7781.1 14.0 555.796 187.140 0.000 Residual 55671.5 18745.0 2.970 Total 63452.7 18759.0
School of the Built Environment
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)
School of the Built Environment
School of the Built Environment
Cleanliness outcomes by street deprivation
School of the Built Environment
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?
Professor Glen Bramley With Dr Caroline Brown, Nicola Dempsey, Dr Sinéad Power &
David [email protected]
EPSRC GRANT No:GR/S20529/01www.city-form.com
Paper presented at EURA Vital City Conference, Glasgow, September 2007
School of the Built Environment
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
School of the Built Environment
Neighbourhood Pride & Attachment by Deprivation - Overall & Socio-Economic Effect
50.0
75.0
100.0
125.0
150.0
175.0
0.0 20.0 40.0 60.0 80.0
Deprivation Score (% Poor)
Ind
ex o
f P
rid
e/A
ttac
hm
ent
NhPride
SocEc
School of the Built Environment
Selected Social Sustainability Outcomes: Socio-Economic Effects by Deprivation Level
50.0
75.0
100.0
125.0
150.0
175.0
0.0 20.0 40.0 60.0 80.0
Deprivation Score (% Poor)
Ind
ex S
core
Pride_SE
Inter_SE
Safe_SE
Envir_SE
AllSoc_SE
School of the Built Environment
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
School of the Built Environment
Social Sustainability by Tenure & Class
Social Sustainability Socio-Economic Effect by Social Renting Share
100.0
110.0
120.0
130.0
0.0 20.0 40.0 60.0 80.0
Social Rent %
So
cial
In
dex
Sco
re
AllSocSE
Social Sustainability Socio-Economic Effect by Home Ownership Share
100.0
110.0
120.0
130.0
0.0 20.0 40.0 60.0 80.0 100.0
Owner Occupation %
So
cial
In
dex
Sco
re
AllSocSE
Social Sustainability Socio-Economic Effect by Neighbourhood Social Class (High)
100
110
120
130
0.0 10.0 20.0 30.0 40.0 50.0 60.0
Class AB %
Ind
ex S
core
AllSocSE
Social Sustainability Socio-Economic Effect by Neighbourhood Social Class (Low)
100
110
120
130
0.0 10.0 20.0 30.0 40.0 50.0 60.0
Class DE %
Ind
ex S
core
AllSocSE
School of the Built Environment
Social Outcomes by Deprivation & Ethnicity
Social Sustainability Indices by Ethnic Mix
100.0
110.0
120.0
130.0
0.0 20.0 40.0 60.0 80.0 100.0 120.0
White %
Ind
ex S
core
AllSocSE
AllSoc
Social Sustainability Socio-Economic Effect by Neighbourhood Deprivation
100.0
110.0
120.0
130.0
0.0 20.0 40.0 60.0 80.0
IMD Score
Ind
ex S
core
AllSocSE
School of the Built Environment
Some Simpl(istic) Simulations
Original Index (SE) Marginal Population Population New Pop New IMD Pred New Score xIMDScore AllSocSE Effect Share Shift Share Score Soc Score Pop Share
2.9 123.8 -0.36 18.1 18.1 2.9 123.8 22.48.3 121.9 -0.36 18.6 18.6 8.3 121.9 22.6
12.6 120.3 -0.36 10.4 10.4 12.6 120.3 12.518.0 115.9 -0.82 11.6 2.6 14.2 26.2 109.1 15.524.6 115.5 -0.06 15.5 2.6 18.2 30.1 115.2 20.936.0 110.8 -0.42 13.4 13.4 36.0 110.8 14.945.2 109.2 -0.16 7.2 7.2 45.2 109.2 7.862.8 104.0 -0.30 5.2 -5.2 0.0 0.0 0.0 0.024.7 115.7 100.0 100.0 116.6
Social Sust SimulationOriginal Index (SE) Marginal Original Population New New New Pred Score xOwned % AllSocSE Effect Pop Share Shift Pop Share Owned % Soc Score Pop Share
12.9 108.2 0.11 8.2 -8.2 0.0 0.0 0.0 0.029.4 110.1 0.11 10.5 10.5 29.4 110.1 11.649.4 114.3 0.21 33.6 8.2 41.9 42.2 112.8 47.266.2 118.3 0.24 14.4 14.4 66.2 118.3 17.191.9 122.0 0.15 33.2 33.2 91.9 122.0 40.555.9 115.7 100.0 100.0 116.4
Moving Households from Lowest Ownership Areas to Middle Areas
Moving Households from Highest Deprivation Areas to Middle Areas
School of the Built Environment
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
Research undertaken for Welsh Assembly Government
by Glen Bramley and David Watkins(CRSIS/SBE, Heriot-Watt University, Edinburgh)
HOME-OWNERSHIP, POVERTY AND EDUCATIONAL ACHIEVEMENT: INDIVIDUAL, SCHOOL AND NEIGHBOURHOOD EFFECTS
By
Glen Bramley and Noah Kofi Karley
REPORT TO SCOTTISH EXECUTIVE EDUCATION DEPARTMENT
(Centre for Research Into Socially Inclusive Services Heriot Watt University,
Edinburgh, UK [email protected])
School of the Built Environment
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
School of the Built Environment
Table 1: Summary comparison of models including different combinations of variables at different levels (adjusted r-squared
Level of social variables School Ward COA Variables Included Level Level Level Primary Indiv pupil only 0.317 Indiv & school structure 0.330 Social factors 0.349 0.347 0.348 Home ownership 0.345 0.335 0.339 Social + ownership 0.351 0.347 0.349 Including school poverty 0.351 0.352 0.353 Secondary Indiv pupil only 0.445 Indiv & school structure 0.482 Social factors 0.491 0.489 0.496 Home ownership 0.486 0.485 0.492 Social + ownership 0.494 0.489 0.498 Including school poverty 0.494 0.491 0.499
Piecewise Linear Effect of Poverty/Ownership Factor
on Primary Attainment Scores
10.000
11.000
12.000
13.000
-4.000 -2.000 0.000 2.000 4.000 6.000
School Social Variables Factor 1 (Poverty/Non-ow nership: S D units)
KS
2 A
ve S
core
200
1/02
KS2Scor
Table 4: Comparison of Impact of Owner Occupation variables in models for all cases and those from low ownership Output Areas (5 English localities 2001-02)
OLS Model for Score Primary All Cases
Primary Low Own
Second-ary All
Second-ary Low Own
Coefficient % owners (COA) 0.007 -0.006 0.077 0.028
t-statistic 7.7 -1.9 15.5 1.7
Significance 0.000 0.052 0.000 0.097
Coefficient % owners (School) 0.025 0.017 0.208 0.057
t-statistic 8.2 2.7 9.6 1.3
Significance 0.000 0.000 0.000 0.192
School of the Built Environment
Table 7: Selected Characteristics of Secondary Pupils or Local Populations by Levels of Home Ownership (3 areas in Scotland, Datazone Level)
Banded Home Ownership
Free Meals
Record of Needs
Indiv Educ Prog
Low Income N'hood
Employ-ment Depriv
% Flats Density
Under 20% own 0.40 0.006 0.047 42.45 32.73 60.63 0.13 20-40% own 0.31 0.013 0.032 31.99 26.07 45.60 0.05 40-60% own 0.20 0.012 0.025 20.75 19.49 39.08 0.16 60-80% own 0.10 0.010 0.021 11.15 11.77 29.92 0.40 Over 80% own 0.03 0.008 0.018 4.13 6.11 15.60 0.14 Total 0.14 0.010 0.024 15.67 14.83 31.23 0.20 Banded Home Ownership
% White % Students
% LT Illness
%Unemp-loyment
% High Qualif
% No Qualif
Under 20% own 98.24 0.34 25.38 8.42 6.92 46.68 20-40% own 98.32 0.34 25.27 6.71 7.61 44.05 40-60% own 98.47 0.43 23.87 4.81 10.45 38.79 60-80% own 98.14 0.80 19.13 3.04 18.59 28.97 Over 80% own 97.40 1.31 12.80 1.85 27.00 19.21 Total 98.04 0.77 19.59 3.84 16.98 31.39
School of the Built Environment
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
School of the Built Environment
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
School of the Built Environment
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)
School of the Built Environment
Outcome-based needs for primary schoolsLEA Name Actual Need CiiBlaenau Gwent 2913 3472Merthyr Tydfil 2857 3277Neath P T 3026 3209Rhondda C T 2654 3023Carmarthen 3181 2989Torfaen 2752 2928Caerphilly 2581 2917Newport 2790 2876Swansea 2693 2856Cardiff 2857 2852Pembroke 2941 2849
Ceredigion 3785 2838Powys 2924 2813Gwynnedd 2875 2717Bridgend 2578 2716Wrexham 2678 2696Anglesey 2822 2664Denbigh 2744 2563Monmouth 2572 2441Conwy 2781 2432Flint 2531 2367Vale of Glam 2719 2350
Wales Ave 2801 2812
Outcome-based expenditure need (partial convergence) by current expenditure per pupil
2000
2500
3000
3500
4000
2000 2500 3000 3500 4000
Actual Expenditure per pupil
Exp
end
itu
re N
eed
(c
rite
rio
n C
ii)
Need Cii
Outcome-based funding distribution (partial convergence)
0
1000
2000
3000
4000
1 3 5 7 9 11 13 15 17 19 21
LEA
£ p
er p
up
il
Series1
Note: needs formula based on standardized costs and compensating for 40% of social disadvantage
School of the Built Environment
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
School of the Built Environment
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
School of the Built Environment
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)
School of the Built Environment
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)
School of the Built Environment
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