Policies for Mixed Communities: Still Looking for Evidence?

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

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…

6. And Real problem…

7. But ‘Evidence’ still just Circumstantial • Mixed Communities a ‘solution’ but not tested against the evidence • E.g. ODPM(20050 - 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: • Demonstration of direction of causation: concentrated poverty ‘worse’ than diffused – positive externalities for poor of living close to affluent • If causation – size of negative ‘neighbourhood effect’ • Evaluate potential foregone gains from ‘specialised’ neighbourhoods relative to any negative neighbourhood effects • 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 relativeto 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 • 10th to 90th decile => +10.4% • 90th 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 • Observe impact on moving individuals from deprived to affluent neighbourhoods [or now vice versa – Weinhardt 2009] • 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 1st 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 • Direct welfare: consumption benefits e.g. ethnic neighbourhoods, liberal professionals; young singles; young child raisers. Mutual support + demand generates appropriate local goods & services • 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, 2004 - 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 • 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