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Policies for Mixed Communities: Still Looking for Evidence? Paul Cheshire 5 th Feb 2010 Neighbourhood Effects: Theory & Evidence University of St Andrews.

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Presentation on theme: "Policies for Mixed Communities: Still Looking for Evidence? Paul Cheshire 5 th Feb 2010 Neighbourhood Effects: Theory & Evidence University of St Andrews."— Presentation transcript:

1 Policies for Mixed Communities: Still Looking for Evidence? Paul Cheshire 5 th Feb 2010 Neighbourhood Effects: Theory & Evidence University of St Andrews

2 Policies for Mixed Communities  Rather like IRSR Aug 2009 – sorry: about whether significance of any neighbourhood effects justifies policy  What do you do when you are confronted with new facts?  Here the reverse: new emerging evidence re-enforces the reality of capitalisation: nicer neighbourhoods really do cost more: and the poor can’t afford them  More emerging evidence about extent and persistence of inequalities: a real problem deserving effective action  Two rigorous new studies showing neighbourhood effects are minor or undectable  And stronger evidence that ‘mixed neighbourhoods’ cost: and cost the poor

3 Policies for Mixed Communities  ‘Mixed Communities’ an explicit aim of government policy in Britain – and in other OECD countries ;  In USA an aspiration of New Urbanism: (Imbroscio, 2008 – ‘the Dispersal Community’)  In fact aspiration far older: Howard & Garden City Movement: early examples e.g. Bedford Park, London, 1871; Hampstead Garden Suburb, 1910  May be suspiciously old? – retro-fitting the facts to justify the solution?  Costs real - if opaque resources – visible expenditures but in England mainly via ‘Section 106 Agreements’

4 And Real problem…  Inequalities deeply rooted and persistent (National Equality Panel 2010)

5 And Real problem…


7 But ‘Evidence’ still just Circumstantial  Mixed Communities a ‘solution’ but not tested against the evidence  E.g. ODPM( in poorest neighbourhoods (most deprived 10%)  Life expectancy 2 years less than mean  One third of inhabitants – no formal qualifications  Crime higher…etc  Not in dispute…But not evidence of causation  Evidence to support policy requires: 1.Demonstration of direction of causation: concentrated poverty ‘worse’ than diffused – positive externalities for poor of living close to affluent 2.If causation – size of negative ‘neighbourhood effect’ 3.Evaluate potential foregone gains from ‘specialised’ neighbourhoods relative to any negative neighbourhood effects 4.Then – is forcing communities to be ‘mixed’ a cost effective means of addressing problem?

8 Nice Neighbourhoods Cost More….  Because overwhelming evidence of causation from income to neighbourhood choice (subject to income constraint)  Nice neighbourhoods cost more & the nicest cost much more - hedonic studies on capitalisation  Green spaces, river frontage, less noise, less crime, better golf: Consumption/Quality of life factors….  Better schools, better access to jobs: Production/life chance factors  All capitalised into house prices

9 Nice Neighbourhoods Cost More…. Much More  Wide range of such ‘goods’ only consumable given housing location: and – in quasi-fixed supply  Hedonic analysis: real ‘scientific’ progress – theoretical understanding, data, statistical techniques, experience & computing power  My position shifted  housing markets pretty efficient, with complex and sophisticated search processes, reasonably modelled as in equilibrium  Attributes – if appropriately defined – have uniform prices within given housing market  Interaction structural attributes & value of expected school quality; interaction local densities, incomes, distance from edge of city & demographic structure for price of open space  Brett Day – noise: Gibbons – crime; Troy & Grove – open space  local crime rate; Hilber – social capital; & toxins

10 Nice Neighbourhoods Cost More…. Much More  And estimated price functions highly non-linear  ‘quantity’  Logic of hedonics is attributes have separate supply & demand characteristics  Not identify formally? but can usefully think about them  Supply of some – fixed e.g. frontage on Thames, Hampstead Heath, St Andrews golf…catchment area of best state school in community  Of others highly elastic – e.g. produced by industrial process  But UK planning renders supply of land almost fixed  So: ability to buy attributes in inelastic supply not mainly function of income level but of income relative to others in housing market competing to buy: truly ‘positional’ goods!  Implies - incomes rise - attribute prices rise differentially – income elasticity of demand + supply elasticity: & if  Income distribution more unequal - most desirable neighbourhoods become relatively more expensive  incidence of segregation more intense  In more unequal societies more neighbourhood segregation: Sweden - UK

11 Nice Neighbourhoods Cost More…. Much More  Much more…e.g.  Primary school quality  10 th to 90 th decile => +10.4%  90 th to Max observed => +16.9%  Similar patterns with access to CBD & space attributes (& access to Thames)  And high income neighbourhoods have facilities for the affluent  ….a short drive to an upmarket deli, gastropub….  Poor neighbourhoods have facilities for demand of low income households  ….walk to discount store, cheap take-out  So we really do know poor people live in poor neighbourhoods because they can’t afford rich ones 

12 Quantifying ‘Neighbourhood Effects’  The (partial) sorting of rich & poor into separate neighbourhoods – almost inevitable: and not necessarily a bad thing. Spatial articulation of societal income inequality.  Question: does living in a poor neighbourhood make the poor poorer - independently of factors making them poor in first place? Damage life chances?  Methodologically difficult problem – unobserved characteristics; self-selection of neighbourhoods  Two main approaches 1.Observe impact on moving individuals from deprived to affluent neighbourhoods [ or now vice versa – Weinhardt 2009] 2.Track individuals over time  Best – or still best known- example of 1. Moving to Opportunity Program (MTO) set up 1992

13 MTO Programme/Experiment  Quasi-experimental: offered chance to move from very poor neighbourhood(= Census Tract 40%+ below poverty line) to affluent one (<10% below poverty line)  5 cities: participants randomly allocated to 3 groups  Group 1 – financial & professional help to move to affluent neighbourhood  Group 2 – vouchers to get new housing of their choice  Group 3 – no help though can move if able  Self-selection – only 25% of eligible volunteered  13% of volunteers rejected as unsuitable (would not pass 1 st base for testing new drug…)

14 MTO Results: Long Term Follow-up  But Kling et al, 2005; 2007  4-7 years: focus on adolescents  Results complex & quite negative  No economic gains for adults in Gp 1  Adolescents Gp1 : Gp 2 – small non-significant behavioural improvements overall  Girls showed non-significant improvements  Boys showed significant deterioration especially - property crime, behaviour in school & relationships  Kling et al, 2007 – Confirmed no economic gains for adults: differential impacts girls – boys: some health improvements for adults (but may be other ways of achieving them…)  And out-movers replaced by in-movers into poorest neighbourhoods: so net effect?

15 Moving the other way? into poor neighbourhoods  Weinhardt 2009 – ingenious, opportunist method  Given difficulty of getting into social housing – move is exogenous in timing & non-self selecting re area  Identify most deprived neighbourhoods as those with 80% or more in social housing: highly correlated with deprivation  English kids tested at 10/11 & 13/14 – KS2 & KS3  Compare school performance at KS3 of kids moving in between 10 and 14 with those moving in after K3  As usual ‘apparent’ neighbourhood effects – kids moving to ‘worst’ neighbourhoods do worse at KS3  But - control for KS2 & range of other factors –  All sign of neighbourhood effects disappears  academic performance does not suffer from moving into deprived area

16 Cohort studies  Oreopoulos (2003) Canada, 30-year tracking – origin in range of social housing neighbourhoods  Neighbourhood of origin had NO significant effect on labour market success or earnings  Bolster et al (2007) Britain, 10-year tracking  Neighbourhood of origin had NO significant effect on labour market success or earnings (perverse sign)  van Ham & Manley (2009)  10 year tracking & labour market outcomes – test for tenure mix effects/social housing: for social housing concentrations – NO effects: weak effects for owner occupiers – but self-selection e.g. house prices?  Evidence is neighbourhood effects are at most very weak + not straightforward + positive as well as negative

17 Benefits of ‘Specialised’ Neighbourhoods?  People do choose the neighbourhoods in which they live  Observe persistence of sorting: bigger the city, more specialised its neighbourhoods: since ancient Rome  Early ‘designed’ mixed communities quickly re-sorted 1)Direct welfare: consumption benefits e.g. ethnic neighbourhoods, liberal professionals; young singles; young child raisers. Mutual support + demand generates appropriate local goods & services 2)Productivity gains: access to labour market Informal information & job matching: Most effective search method – esp. for non-local language groups More important the larger the city (Ioannides and Loury, evidence of positive agglomeration economies & neighbourhood sorting) Bayer et al (2009): good evidence neighbours play important role in job matching - more important for less skilled 3)And evidence (Luttmer, 2005) income relative to neighbours source of welfare  so more homogeneous neighbourhoods?

18 Dynamics of Neighbourhood Segregation  Neighbourhoods selected subject to constraints – income  But talk of ‘local community’ misleads: not a stable set of families. More like occupants of a bus…  Segregation by incomes, demographics, politics, ethnicity, religion etc –  But significant local income mixing  As houses become vacant, new occupants  Cities – subject to shocks (growth, change in income distribution…)  constant change

19 Effects of Neighbourhood Improvement  Harlesden City Challenge – W. London  10, 000 population: 5 year programme: £37.5m  Training (useful); crime reduction; job creation; physical upgrading….  At end of period unemployment in ‘local community’ risen & higher relative to other comparable areas  Sample survey of ‘Stayers’, ‘Inmovers’ & ‘Outmovers’  Participated in training?  Stayers 13%; Inmovers 6%; Outmovers 37%  Outmovers had more skilled, enjoyable and better paid jobs…& 6 times more likely to have full time job  Get on  get out!  And don’t judge a regeneration programme on basis current residents

20 Conclusions  Residential segregation very persistent over time: & self-selection given income – people choose & evidence suggests - carefully  Causation from income to neighbourhood sure – nice neighbourhoods cost more & the nicest cost a lot more  Plausible that increased income inequality  more intensive residential segregation via house price dispersion  ‘Mixed communities’ policy treats a symptom and not a cause: treating fever with leeches instead of looking for causes of poverty  What are the benefits?  Evidence so far shows ‘communities’ have little/no impact on Production/life chance outcomes but may help job matching  Benefits may come through redistribution of Consumption/Quality of life opportunities (if policy goal?), but inadequate evidence & generally likely inefficient mechanism compared to resource transfers  No obvious evidence that poor derive positive externalities from living together with nice, educated and affluent: if not - just well-meant paternalism?

21 Conclusions  What are the costs of mixed community policies ?  Direct welfare costs through de-specialisation for both consumption & production  Higher housing supply costs in high land price communities: Fewer units/lower quality  greater long run inequality  Diversion of resources away from direct interventions (e.g. schools, education, training, families, policing) or redistribution  Evidence costs of policy substantially outweigh any benefits  And a displacement activity  But concentrated poverty ugly?  Mixing makes well-intentioned affluent feel better: + costs disguised  But poverty & social immobility real - need to act on causes:  Including having area based policies e.g. schools or crime

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