social mix, neighbourhood outcomes and housing policy sg ‘ firm analytical foundations’...

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


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

• 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


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?


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


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


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)


Cleanliness outcomes by street deprivation


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


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


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


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


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


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


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


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


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)

[email protected]

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])


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


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


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


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?

• *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

• 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 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


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

• ‘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

• 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

• 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

• 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

social mix, neighbourhood outcomes and housing policy sg ‘ firm analytical foundations’...

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