segregation, integration and neighbourhood effects debates and analyses sako musterd university of...
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![Page 1: Segregation, Integration and Neighbourhood Effects Debates and Analyses Sako Musterd University of Amsterdam University of Manchester CCSR Seminar June](https://reader035.vdocument.in/reader035/viewer/2022070306/5515fd2b550346a2308b4a38/html5/thumbnails/1.jpg)
Segregation, Integration and Neighbourhood Effects
Debates and Analyses
Sako Musterd
University of Amsterdam
University of Manchester
CCSR Seminar
June 3, 2008
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Central questions
A. Debates: what are the prevailing ideologies, perceptions, assumptions and policy responses regarding segregation and its potential (neighbourhood) effects?
B. Analyses: to what extent are these perceptions, etc. informed by theoretical knowledge and empirical evidence?
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Outline (debates and analyses)
1. Key concepts and prevailing ideologies and perceptions
2. Theories on segregation and concentration3. Theories on neighbourhood effects4. Segregation and concentration; levels and
dynamics5. Segregation and neighbourhood effects
6. Implications for urban policies
A
B
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1Key concepts and prevailing
ideologies and perceptions
Key concepts• Segregation• Concentration• Neighbourhood effects
– Individual opportunities– Livable neighbourhoods
• Integration (participation)• Assimilation
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Prevailing ideologies and perceptions • Vague use of key concepts (segregation, concentration,
integration, assimilation)• Segregation levels are regarded as high and increasing• Lack of integration is seen as key neighbourhood problem• Segregation is seen as the cause and thus as ‘bad’• Segregation would create negative neighbourhood effects• Fear for parallel societies and a strong call for assimilation• Neighbourhood restructuring and housing mix as panacea
1Key concepts and prevailing
ideologies and perceptions
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2 Theories of segregation and concentration
• Globalisation
• Economic restructuring• Welfare regime (special attention in next slide)
• Cultural (language, religion, discrimination, identity, level of acceptance of inequality, tolerance towards difference, eagerness to ‘enforce’ integration)
• Historic social, economic and cultural urban paths• Political attitudes towards diversity (ideas regarding
assimilation; multiculturalism and mix)
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2 Theories of segregation and concentration
• Welfare regime
– Benefit systems for unemployed, elderly and disabled
– Access to high quality education– Access to housing– Labour market access– Housing benefits– Health care systems access– Income redistribution
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2 Theories of segregation and concentration
• Segregation is a strong process, reflecting the relationship between spatial inequality and social inequality, lifestyle differences, and difference in terms of other resources
• Segregation is influenced by global, national, local and group level processes, structural and individual factors; and thus not simply to modify with single sector policies, such as housing policies
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3Theories on neighbourhood effects
(theories on segregation effects)
• Socialisation processes (role models)
• Social networks (communication)
• Stigmatisation
• Spatial mismatch
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4 empirical findings
1. Key concepts and prevailing ideologies and perceptions
2. Theories on segregation and concentration3. Theories on neighbourhood effects4. Segregation and concentration; levels
and dynamics5. Segregation and neighbourhood effects
6. Implications for urban policies
A
B
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4
Levels of ethnic segregation:
most segregated groups per city
12 EU countries 24 cities
0-100 : low-high segregation0 20 40 60 80 100
Munich Foreigners
Frankfurt Turks
Milan non-Italian
Milan Moroccans
Frankfurt Americans
Paris Algerians Dept. 75
Lille non-French
Madrid Moroccans
Oslo 3rd w orld immigrants
Vienna Foreigners
Düsseldorf Turks
Amsterdam Turks
Lisbon Cape Verdian
Rotterdam Turks
The Hague Turks
Stockholm Iranian 14 municip.
Brussels Moroccan
Bristol Pakistani
London Bangladeshi
Manchester Bangladeshi
Barcelona Filipinos
Birmingham Bangladeshi
Antw erp N. African, Bosnian
Bradford Bangladeshi
Leicester Bangladeshi
Oldham Bangladeshi
Oldham Pakistani
US 6 metrop. areas Blacks
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4 Levels of ethnic segregation; impact of
area-size (index 0-100 = low-high segregation)
0 20 40 60 80 100
Amsterdam Turks 1216 grids
Amsterdam Turks 369 neighbourhoods
Amsterdam Turks 93 neighbourhoods
Amsterdam Turks metro area 30 distr
Birmingham Bangladeshi ED
Birmingham Bangladeshi w ards
London Bangladeshi 15300 ED
London Bangladeshi 782 w ards
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Levels of ethnic segregation:
group comparison
index (0-100 = low-high segregation)
0 10 20 30 40 50 60 70 80
Amsterdam SurinameseAmsterdam Moroccans
Amsterdam Turks
Rotterdam SurinameseRotterdam Moroccans
Rotterdam Turks
The Hague SurinameseThe Hague Moroccan
The Hague Turks
London Black AfricanLondon Black Caribbean
London PakistaniLondon Bangladeshi
Manchester Black CaribbeanManchester Pakistani
Manchester Bangladeshi
Birmingham Black AfricanBirmingham Black Caribbean
Birmingham PakistaniBirmingham Bangladeshi
Leicester Black CaribbeanLeicester Pakistani
Leicester Bangladeshi
Bradford Black CaribbeanBradford Pakistani
Bradford Bangladeshi
Oldham Black CaribbeanOldham Pakistani
UK
Netherlands
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0 10 20 30 40 50 60 70
Barcelona Peruvians
Barcelona Moroccans
Barcelona Filipinos
Madrid Latins American
Madrid Peruvians
Madrid Moroccans
Milan Egyptians
Milan Filipinos
Milan Moroccans
Lisbon Brazilians
Lisbon Africans
Lisbon Cape Verdians
Levels of ethnic segregation:
group comparison
index (0-100 = low-high segregation)
Spain
Portugal
Italy
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4 Segregation levels and dynamics
in some Dutch cities (ethnic)
1980 1995 2000 2004 1980 1995 2000 2004 1980 1995 2000 2004
Turks 37.3 40.7 41.2 42.4 - 51.7 47.8 44.1 66.4 54.6 51.3 51.1
Moroccan 38.6 39.1 39.5 40.0 - 46.8 42.6 39.7 64.7 49.9 48.8 48.3
Surinamese 27.8 34.8 33.3 32.9 - 28.6 24.1 21.1 - 40.2 37.0 33.5
Antillean 26.2 34.9 37.1 33.3 - 28.5 30.2 29.7 - 25.5 27.3 28.1
Amsterdam Rotterdam The Hague
increasing/stable; decreasing; decreasing
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London
20,0
30,0
40,0
50,0
60,0
70,0
1991 2001
ID
Bangladeshi Indians Pakistani Carribean
birmingham
20,0
30,0
40,0
50,0
60,0
70,0
1991 2001
ID
Bangladeshi Indians Pakistani Carribean
Rotterdam
20,0
30,0
40,0
50,0
60,0
1995 2000 2004
ID
Turks Moroccan Surinamese Antillean
Amsterdam
20
30
40
50
1980 1995 2000 2004
ID
Turks Moroccan Surinamese Antillean
Segregation dynamics
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Concentrations in Amsterdam and the Amsterdam metropolitan region
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Concentrations of four population categoriesT M
S A
Amsterdam2004
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But don’t be mislead by graphs of spatial concentrations
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ConcentrationAmsterdam Surinamese 2004> 2sd above the mean> 19.8%In concentrations: 33%Of all Surinamese: 38%
These figures were the same in
1994!
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Strong concentrationAmsterdam Surinamese 2004> 4sd above the mean> 27.8%In concentrations: 38%Of all Surinamese: 31%
These figures were the same in
1994!
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Ethnic neighbourhoodAmsterdam Surinamese 2004 > 50%In concentrations: 57%Of all Surinamese: 2.9%
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‘Little Surinam’?2004> 60%In concentrations: 65%Of all Surinamese: 0.6%
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Dynamics: % that is Turkish [same for other categories] in so-called Turkish concentrations 1994-2007, Amsterdam
0
10
20
30
40
1993 1998 2003 2008
%
Turkish
Moroccan
Surinamese
Antillean
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Dynamics: % of all Turkish in the city [same for other categories] living in so-called Turkish concentrations 1994-2007, Amsterdam
10
20
30
40
50
1993 1998 2003 2008
%
Turkish
Moroccan
Surinamese
Antillean
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Dynamic Moroccans 2007
1973
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Amsterdam region, ‘non-western’, 2000> 4sd above the mean> 48%In concentrations: 63%Of all non-western: 50%
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Amsterdam region, ‘non-western’, 2004> 4sd above the mean> 51.5%In concentrations: 66%Of all non-western: 49%
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Ethnic concentrations are unstable 1994-2004 change relative to 1994; Turkish
concentrations in Amsterdam
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Ethnic concentrations are unstable 1994-2004 change relative to 1994; Moroccan
concentrations in Amsterdam
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Ethnic concentrations are unstable 1994-2004 growth rates in concentrations relative
to the expected growth on the basis of the development in Amsterdam as a whole
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4. Levels
Index of SegregationSocio-Economic Categories
0 10 20 30 40 50
Copenhagen 1st quintile
Amsterdam 1st quintile metro
Bern unemployed
Berlin hh income < € 900
Birmingham income support
Milan blue collar w orkers
Manchester income support
Manchester unemployed
Amsterdam 1st quintile
Berlin hh income > € 3500
Lille unemployed vs employed
Rotterdam 1st quintile
Amsterdam 5th quintile metro
Oslo social assistance
Birmingham unemployed
Leeds unemployed
Sheffield unemployed
Milan professionals
Copenhagen 10th decile
Amsterdam 5th quintile
Rotterdam 5th quintile
USA Portland OR MSA poor
Antw erp 'poor'
USA 100 largest cities poor
USA Rochester NY MSA poor
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4. Segregation levels (socio-economic) in some Dutch cities
Amsterdam Rotterdam
low incomes (1st quintile) vs rest
16.1 20.1 27.1
low income non Dutch vs rest
31.2 36.5 48.0
5th quintile vs rest
27.2 26.7 34.7
1st quintile vs 5th quintile
37.9 40.9 40.6
The Hague
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Social Mix is CommonIncome distribution of the richest (zuid, left) and poorest (westerpark, right) urban districts
of Amsterdam, quintiles, 1996
1st
15%
2nd
18%
3rd
18%
4th
21%
5th
28%
1st20%
2nd31%
3rd23%
4th17%
5th9%
richest poorest
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Social Mix is CommonIncome distribution of the poorest
neighbourhoods in the three largest Dutch cities, 2000
0
10
20
30
40
50
60
70
AmsterdamKolenkit
RotterdamSpangen
The HagueSchilderswijk
%
1st quintile
middle
5th quintile
Source: Pinkster 2006
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In short:
• Ethnic segregation levels are moderate and generally not increasing
• Ethnic concentrations are still limited (except in UK and B)• Ethnic concentrations are dynamic, due to housing careers• Segregation levels of lowest income categories are moderate• Segregation of low and high social strata is relatively high,
but segregation between low and the middle is low• Social mix is common already
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5 Segregation and neighbourhood
effects• Moderate segregation: few effects?
• Small-scale concentrations: few effects?
• Even the poorest areas are mixed: few effects?
• Some experiences in The Netherlands and Sweden: seven large-scale neighbourhood effect studies
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a. Longitudinal studies in The Netherlands: Does Neighbourhood Matter?
• Impact of social composition of 500 x 500 m environments on individual’s social mobility (2 mln. cases; 1989-1994; tax income data).
Musterd, S., W. Ostendorf & S. de Vos (2003) Environmental Effects and Social Mobility. Housing Studies. Vol. 18 6. pp. 877-892.
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Findings
• There are weak effects of social compositions on social mobility for people without a job
• There are fairly strong effects for people with a stronger position
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Neighbourhood effects on ‘socially weaker’ and ‘socially stronger’ individuals in The Netherlands; percentages relative to households not belonging to pensioners.
0
10
20
30
40
50
60
70
0 – 2
2 – 4
4 – 6
6 – 8
8 – 10
10 – 12
12 – 14
14 – 16
16 – 20
20 – 30
30 – 40
% on benefits in the environment 1989
%benefits in 1989 and1994
paid job in 1989, benefitsin 1994
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b. Longitudinal studies in Sweden: Does Neighbourhood Matter?
• Impact of social composition of 500 x 500m environments on individual’s employment careers (5.5 mln. cases; 1991-1999; GeoSweden; 16-65 year old).
Musterd, S. & R. Andersson (2006) Employment, Social Mobility and Neighbourhood Effects. International Journal of Urban and Regional Research 30 (1), pp. 120-140.
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Findings
• Neighbourhood effects exist also after controlling for a range of variables
• Those who were able to improve their educational level during recession were not affected by the environment
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Percentage of unemployed in 1991 staying unemployed in 1995 and 1999, per environment type 1991, per educational attainment category 1991-1995 and both years (1991, 1995) living in one of the three big cities in Sweden
0
20
40
60
80
0-2 2-4 4-6 6-8 8-10 10-12 12-14 14-16
% unemployed in the environment 1991
%
low stable (<=10yrs)
medium stable (11-12yrs)
medium high stable(13-14 yrs)
high (15+)
upward
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c. Longitudinal studies in Sweden: Does Neighbourhood Matter?
• Impact of social and physical composition of 9,200 SAMS environments on individual’s employment careers (5.5 mln. cases; 1991-1999; GeoSweden; 16-65 year old).
• Focus on housing mix, social mix and social opportunities.
Musterd, S. & R. Andersson (2005) Housing Mix, Social Mix and Social Opportunities. Urban Affairs Review, Vol. 40, No. 6, pp. 761-790.
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Key-concepts
• Housing mix: from absolutely homogeneous to highly heterogeneous (mixed) (9 types, entropy measures)
• Social mix: clusters on the basis of scores in classes of income deciles (low, mixed-low, mixed, mixed-high, high)
• Ethnic mix (based on nationalities and share of refugees)
• Socio-ethnic clusters (all combined)• Social mobility: change in employment position
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Findings on housing mix and social mix/ethnic mix
• Housing mix and social mix association is not very strong
• Same holds for housing mix and ethnic mix
• ~25% of homogeneous housing areas are relatively homogeneous low income areas
• ~20% of the most heterogeneous housing areas are homogeneous low income areas
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Findings regarding impact of mix on social opportunity (also next slide)
• There is limited difference in opportunities of low educated in homogenous low social status areas and mixed low and highly mixed areas.
• In these three types of area the lowest share of people that stays employed is found in both physically homogeneous and heterogeneous areas
• A shift to mixed high and homogenous high areas would help, but is difficult to realise
• There are clear effects of education and of country of origin of self and parents
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Perc. individual staying employed in 91,95,99 in various social and housing environments
per educational attainment level 91-95
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
90.0
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tero
ge
ne
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all low stable medium medium high high
Mixed lowHighly mixed Mixed high Homogeneous low Homogeneous high
social
physical
education
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0.0
20.0
40.0
60.0
80.0
100.0
low
sta
ble
med
ium
1
med
ium
2
high
(15+
)
low
sta
ble
med
ium
1
med
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(15+
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low
sta
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1
med
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high
(15+
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low
sta
ble
med
ium
1
med
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high
(15+
)
entirely swedish swedish one parentforeign
swedish bothparents foreign
not swedish bothparents foreign
% e
mpl
oyed
91,
95,
99
Perc. individuals staying employed in 91,95,99 living in a poor refugee area per country of origin, per
educational attainment level 91-95
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d. Longitudinal studies in Sweden: What Mix Matters?
• Neighbourhood incomes (lowest and highest 3 income deciles; and overall diversity, via entropy measure)
• Educational level (share of low and share of high educated and diversity based on 7 categories)
• Ethnic composition (similarly)• Housing tenure structure (similarly)
Andersson, R., Musterd, S., Galster, G. and Kauppinen, T. (2007) “What Mix Matters?”. Exploring the relationships between individual’s incomes and different measures of their neighbourhood contexts. Housing Studies 22 (5), pp. 637-660.
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FindingsThe share of adult males with earnings in the lowest 3 income deciles in 1995 holds greatest explanatory power for the later income earned, after controlling for:
• personal characteristics that can vary over time (e.g. marital or fertility status, educational attainment)
• personal characteristics that do not vary after 1995 (e.g., year and country of birth, experiences prior to 1995)
• municipality of residence in 1995 • characteristics of local labour market(s) in which individual resides
in 1995 and 1999 (e.g., mean earnings)
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e. Longitudinal studies in Sweden: Are ethnic enclaves good or bad?
• Multiple measures of immigrant environments• For 1995-2002, residing in one of the three big Swedish
metropolitan areas in at least one of the years 1995, 1999, 2002
• Seven immigrant ethnic groups
Musterd, S., R. Andersson, G. Galster & T. Kauppinen (2008) Are Immigrant’s Earnings Influenced by the Characteristics of their Neighbours? Environment and Planning A, pp. 785-805.
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Findings
• Own group ethnic concentrations can initially pay dividends for immigrants, but these benefits turn into disadvantages over time, after approx. two years
• The impact of other immigrants is positive only if unemployment levels are very low
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f. Longitudinal studies in Sweden: Does neighbourhood income mix affect earnings of adults?
• Controls for omitted variable bias
• Controls for selection bias
• Differences equations 1991-1995 and 1996-1999
Galster, G., R. Andersson, S. Musterd and T. Kauppinen (2008) Does neighbourhood income mix affect earnings of adults? Journal of Urban Economics 63, pp. 858-870
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Findings
• Males not employed full time benefit from middle-income neighbours and not from either high- or low-income neighbours.
• Full-time employed males benefit from high-income neighbours
• Even in comprehensive welfare states role models and interpersonal networks shape economic opportunities
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g. Longitudinal studies in Sweden: What Scale Matters?• Different spatial scales were compared• 100m x 100m• SAMS• Municipality• Metropolitan region• Multi-level modelling for 1995-2002
Andersson, R. & S. Musterd (fc) What Scale Matters? problematizing scale in neighbourhood effect studies. Under review.
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Findings
• Large impacts of environments on earnings at smallest scale (100x100m)
• Low income environments have largest impacts
• Share of unemployed in the neighbourhood has most impact at slightly higher level (SAMS)
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Conclusions• Segregation is a strong process driven by objectives to
translate social inequality and lifestyle differences into spatial inequality
• Segregation is influenced by global, national, local and group level processes
• Moderate effects for weakest households in the Netherlands, but stronger effects for stronger households
• Clear effects of neighbourhood compositions in Swedish contexts
• Many ‘problematic neighbourhoods’ are mixed already• Housing mix and social mix are not 1-1 related
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6 Implications for Urban Policy • Social mix (if carefully targeted) may contribute
to personal economic success
• But may be difficult to obtain.– Spatial dispersal: discrimination– Forced mix in meritocratic societies is difficult;
contra residential choice and lifestyle homogeneity preferences
– Mixed housing policies may be counter-productive when stronger households move away
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Other strategies to consider
• Combating stigmatisation through direct interventions in stigmatised areas
• Improve integration by rising the level of education of all individual residents (not via ABI’s)
• Improve integration by assisting all unemployed residents in getting a full-time job (not via ABI’s)
Finally• Judgment of integration requires process studies
and detailed analysis of the type of association (linear, non-linear relations; thresholds; multilevel)
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Sako Musterd
Department of Geography, Planning and International Development StudiesUniversity of Amsterdam