Neighbourhood Effects and the Welfare State.

Towards a European research agenda?

Roger AnderssonInstitute for Housing and Urban Research

Uppsala university, Sweden

Paper for the conference “Neighbourhood Effects Studies on the Basis of European Micro-data”

at Humboldt University of Berlin on March 29 and 30, 2007

Contents

• Firstly, I introduce my current research programme, a programme I believe also guides many colleagues engaged in studying residential segregation and the issue of neighbourhood effects (NE).

• Secondly, I will deal with three particular aspects of NE that need further and elaborated studies.

• Thirdly, as I know this is a conference focusing on micro data, my presentation also contains information about the Swedish statistical registers and how such data can be used for extending our knowledge about segregation dynamics and effects of segregation on the social trajectories of individuals.

A research programme on neighbourhood mix and neighbourhood effects

The Micro Structure of the Housing Stock (neighbourhoods’

composition in terms of tenure and housing types)

Social and Ethnic composition of neighbourhoods

Social interaction Effects on attitudes

and behaviour

Social opportunities

(1)

(2) (3)

(4)

Global, National and Urban Contexts

Summary of questions• Is there really a strong relation between housing mix and

social mix? This is a fundamental issue since planning for social mix is based on the assumption that the micro structures of the housing stock in terms of tenure, housing types, size and cost of dwellings etc are thought to strongly influence the population composition of neighbourhoods. (Relation 1)

• How does population composition of neighbourhoods affects residents’ social interaction and behaviour? (Relation 2)

• Are social opportunities related to peoples’ neighbourhood context? (Relation 3)

• If there is such a relation, to what extent is this produced/mediated by local social interaction? (Relation 4) The idea is that social opportunities might be directly or indirectly affected by residency.

Three equally important questions arise if one wants to study these

relations:

• What population mix matters?

• What scale matters?

• What time matters?

Data sourcesSwedish social scientists, especially segregation

researchers, have access to internationally unique types of data. I will briefly describe the basic features of these data.

Four characteristics are of key importance:

• 1. A personal ID code (personnummer) is used in all official registers. A similar code is used for firms. The individual-specific ID code comprises 10 digits and is given to everyone upon birth or immigration (permanent residents). This code is used by Statistics Sweden in all individual registers, such as the employment, income, population, education, and the event registers (birth, death, immigration, emigration).

• 2. There are constantly updated address registers (Register över totalbefolkningen, RTB); linked to the ID code mentioned in (1).

• 3. A geo-coded real estate and property register exists, linked to the address register (fastighetsregistren). The geo-coding of all real estates took several decades to finish, and this crucial part of the registers was not completed until about 1990.

• 4. The law grants researchers a reasonable easy and inexpensive access to data on individuals.

• By merging (1), (2), and (3) all residents in Sweden can be localised both in terms of housing and work places. This allows for the study not only of static distributions at any point in time but also of longer-term developments. An individual’s housing and employment career can thus be studied both in its social and geographic context. Obviously, both migration and commuting can be studied using complete populations. If a person moves, this will show up in the address register and due to the fact that all addresses refer to specific and geo-coded buildings, the exact origin and destination location will be known.

• One obvious advantage is that data can be generated for researcher-defined geographical areas, escaping the sometimes not so relevant formal geographical, administrative divisions.

• It is not difficult to realise that these data are “sensitive”, and the use is restricted in several ways. However, there is an important paragraph in the Swedish data security legislation saying that access to the registers should be generously provided to researchers.

• Applications from researchers are scrutinised by a special committee at Statistics Sweden, and also by regional research ethics committees, who decide whether permission should be given and if certain restrictions should apply. Some restrictions are of a more general character, for instance that data on individuals or firms provided to researchers never contain the explicit ID code and that specific individuals should not be identifiable in publications.

• Furthermore, the most detailed geocodes (coordinates) are seldom provided, and researchers normally have to settle with 100m by 100m coordinates (which of course is still a very detailed level). There are often also restrictions on handing out specific codes for the country of birth information.

Back to the research questions:

Housing mix and social mix• In short, the idea is that housing mix (a mix of housing

types and tenure types) will create social mix (a mix of households according to their socio-economic position) and that this will create better social opportunities for individuals. In fact, this standpoint is based on two crucial assumptions. The first is that social mix really enhances the individual opportunities (i.e. relations 3 and/or 4 in figure 1 are true). The second is that there is a strong relation between social mix and housing mix (relation 1 is true).

• Although these assumptions may be realistic/plausible, they are empirical questions that social science research needs to address.

The recently out-voted Swedish Social Democratic government kept on reiterating the importance of housing and social mix, echoing

a position established in the mid 1970s: “…mixed tenure in each and every neighborhood in our country is something I aim for. No matter if I

discuss from the perspective of social (class) aspects or (ethnic) integration aspects, it is of vital importance that there is other than rental tenure in our large (suburban) housing estates and that the inner cities comprise not only private ownership of

apartments but also rental housing.” (Mona Sahlin, Minister of Housing and the Built

Environment, talking in the Swedish Parliament, November 15, 2004

The minister continued, now talking about areas having a concentration of low income households and a high proportion of unemployed people:

• “It should be good housing in safe and well serviced neighbourhoods like we find in other parts of the cities. These areas should be characterized by great variety, which implies that it should be possible but also interesting for households having an above average income level to live there.” (Mona Sahlin, Minister of Housing and the Built Environment, talking in the Swedish Parliament, November 15, 2004)

Policy program theory?

• The minister does not explicitly say why this is important but other documents support the conclusion that Swedish ideas about mixing are based (albeit not entirely) on the assumption that there are social (negative and positive) externalities of different types of population mix. (See also Galster 2007)

Housing mix and social mix Evidence

• Musterd & Andersson (2005) find that relation (1) (see figure 1) is rather weak in Sweden as a whole. Further study is needed, not least studies that analyse the relation more in detail for cities of different size. One may hypothesize that although the relation is quite weak at the national level it might very well be much stronger in the larger cities.

• Most socially homogenous neighbourhoods are middle class, home ownership areas. The rich are spatially more concentrated than are the poor. The debate on mix disregards this fact and focuses on concentrated poverty and immigrant dense areas and how more mix could be achieved in such areas.

Social and ethnic mix and neighbourhood effects

• Many researchers make use of Charles Manski´s (2000) distinction between three types of neighbourhood effects: endogenous, contextual (exogenous) and correlated. (See Galster, 2006). If we face endogenous interactions, the propensity of an agent to behave in some way varies with the behaviour of the group. In contextual interactions, the propensity of an agent to behave in some way varies with exogenous characteristics of the group members. Correlated effects concern situations when agents in the same group tend to behave similarly because they have similar individual characteristics or face similar institutional environments.

The Micro Structure of the Housing Stock (neighbourhoods’

composition in terms of tenure and housing types)

Social and Ethnic composition of neighbourhoods

Social interaction Effects on attitudes

and behaviour

Social opportunities

(1)

(2) (3)

(4)

Global, National and Urban Contexts

Endogenous & exogenous effects: relation 2 and 4; correlated effects: relation 3 plus the wider context.

Correlated effects and the Welfare State: a proposition

• In countries where resources are fairly well distributed (reallocated over the tax system) differences in economic standard between households are less pronounced. Less social polarisation normally means less socio-economic segregation and also less spatial differences with regard to institutional quality.

• In cities with a well developed and reasonably priced local transportation system, labour market spatial mismatch can be expected to be of relatively little importance for the prospects of gaining and keeping employment.

What population mix matters?• Might of course depend on what type of outcome we are studying.

• For individual income development: we employed a multivariate model on Swedish data to estimate the relative importance of 4 types of mix dimensions (income, education, ethnicity, housing characteristics) and for each we tested different operationalisations (such as the relative size of ”advantaged” and ”disadvantaged” groups, entropy values, ratios). We found the importance of the neighbourhood income structure to be bigger than the other three. Effects are bigger for males than for females and for metropolitan areas compared to non-metropolitan areas.

• Neighbourhood definition: SAMS (average pop. Size: 1000).

• Sample size: not a sample, but the entire adult Swedish population. Longitudinal 1991-1999 material.

• Control for: a range of time-invariant and –variant personal and household demographic and socioeconomic characteristics, and Labour Market characteristics.

• Andersson, Musterd, Galster, Kauppinen (fc)

What population mix matters? (2)

• We ran similar models for exploring the importance of ethnic clusters in metropolitan Sweden for the income development of individuals in seven larger immigrant groups. We found the effects of own group concentrations to be negatively correlated with earning prospects if unemployment levels in ethnic clusters were modest or high.

• Neighbourhoods: bespoke, 500m grid areas, 250m around each individual.

• Sample size: not a sample but all adults residing in metropolitan Sweden 1995-2002.

• Musterd, Andersson, Galster, Kauppinen (fc)

Municipal and SAMS divisions in the Stockholm region

Illustrating the 100m and 500m grid systems. Example:

Data from the Tensta housing estate, Stockholm city.

0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900

0 233 562 363 124 237 331

100m 156 246 290 352 193 164 166 390 Tot. 3,669

200m 182 169 157 172 226 432 213 304 560 697

300m 205 94 212 298 244 649 575 1098

400m 222 220 163 271 249 318 207 323 221 374 327

500m 264 198 258 21 17 2

600m 224 226 143 262 212 Tot. 5,037 47 21

700m Tot. Pop. 500mX500m: 2,000 244 553 158 160 72 14

800m 254 135 118 20 2

900m Total population in Tensta: 17,431 216 151

1000m 250

What scale matters?

• Research Q:

“to what extent individual social mobility of adults is influenced by individual and neighbourhood characteristics, with a special focus on various levels of scale and various definitions of area compositions.”

What scale matters? (2)

• Proposition: It is reasonable to assume that if endogenous neighbourhood effects are in operation, such effects would be greater in the immediate surrounding of an individual and they would decrease as the size of the unit increases. However, for correlated effects it is more difficult to hypothesize which level would be the most important and the spatiality can also be expected to vary according to which outcome we decide to study.

What scale matters? (3)

• In this study we (Andersson & Musterd 2006) operationalized ‘neighbourhood’ at four spatial scales, running from the municipality, over an officially existing neighbourhood definition (SAMS) to coordinate-based bespoke neighbourhoods (500m, 100m). Using multivariate statistical techniques on employment and income development 1995-2002 for all inhabitants residing in Sweden’s three largest urban regions, controlling for a wide variety of personal and household characteristics, we were able to confirm our basic hypothesis that contextual effects on labour market performance are strongest at the very local level and non-existent or weak at the municipal level.

What scale matters?Metro areaStockholm Gothenburg Malmö

Scale No of units Aver. Pop. No of units Aver. Pop. No of units Aver. Pop.100m* 20,889 39 8,761 32 7,170 30500m** 20,889 1,030 8,761 772 7,170 839Sams*** 1,311 844 1,199 357 765 408Municipality 35 31,599 15 29,559 15 20,829

*100m are the fixed 100m by 100m grids. All grids having less than 10 persons aged 24-61 in 1999 are excluded.

**500m are the person-specific 500m by 500m grids. Mathematically, the number of different units equals that of 100m.

All persons sharing a 100m by 100m pair of coordinates will have the same 500m by 500m context.

***Small Area Market Statistics defined by Statistics Sweden in cooperation with each municipality.

Distribution of the level of unemployment over the 100m by 100m units, SAMS, 500m by 500m units, and municipalities in

metropolitan Sweden 1999.

0

0,02

0,04

0,06

0,08

0,1

0,12

0,14

0,16

0,18

0,2

Perc. unemployed,100 meter

Perc. unemployed,SAMS

Perc. unemployed,500 meter

Perc. unemployed,munic.

percentile 10

percentile 25

percentile 50

percentile 75

percentile 90

What time matters?

• Duration of “exposure”

• Duration of effects

• Lagged effects

• Studies planned by not done so far.

Problematizing exposure time:Neighbourhood movers and stayers

• Examples from two adjacent Stockholm neighbourhoods/districts

• Analyses of the 1990 cohort of neighbourhood residents

• …and a note on selective migration in distressed urban neighbourhoods

TenstaA large housingEstate built around 1970Pop.: 15,600 in1990.

Spånga, an olderarea, dominated bysingle housing andhome ownership.Pop.: 6,600 in 1990

Neighbourhood staying frequencies 1990-2004 for the 1990 population of

Spånga in Stockholm city (cohort size: N=6617 in 1990; 2537 in 2004).

0

10

20

30

40

50

60

70

80

90

100

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

Total cohort

50-64

40-49

30-39

20-29

age 10-19

Neighbourhood staying frequencies 1990-2004 for the 1990 population of

Tensta in Stockholm city (N= 15567 in 1990; 4206 in 2004).

0

10

20

30

40

50

60

70

80

90

100

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

Total cohort

50-64

40-49

30-39

20-29

10--19

0--9

Neighbourhood staying frequencies 1990-2004 for the 1990

population of Tensta in Stockholm city (by one year age groups*)

0

10

20

30

40

50

60

70

80

90

100

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

55 y

45 y

35 y

25 y

15 y

5 y

*Cohort sizes in 1990: age 5: 271 persons, age 15: 212, age 25: 291, age 35: 285, age 45: 220, age 55: 102. Source: GeoSweden 2004, Institute for Housing & Urban Research, Uppsala university.

Selective migration reproduces the positionof poor and less attractive neighbourhoods

0

10

20

30

40

50

60

70

80

90

100

25 28 31 34 37 40 43 46 49 52 55 58 61 64 67

Age

% e

mpl

oyed

Stayers 1995

In-movers 1995

Out-movers 1995

Employment frequencies by age in 1995 for people staying in, moving into,

and out of distressed Stockholm neighbourhoods 1990-1995. (Age 20-64 in 1990)

Source: Andersson & Bråmå (2004)

Conclusions

• ”…common policy thrust toward neighbourhood social mixing must be seen as based more on faith than fact” (Galster 2007, Europ. J. of Housing Policy, 7:1, p. 35)

• If George Galster is correct we have a lot of work to do.• Not all countries have data that allow for large scale

longitudinal studies of neighbourhoods and neighbourhood effects but a wider European research agenda should also include systematic studies of the black box of neighbourhood effects, i.e. exploring: ”what sorts of social externality processes actually are occurring in their nation’s neighbourhoods” (ibid.) Studies of different kinds are needed to fill this research gap.

A research programme on neighbourhood mix and neighbourhood effects

The Micro Structure of the Housing Stock (neighbourhoods’

composition in terms of tenure and housing types)

Social and Ethnic composition of neighbourhoods

Social interaction Effects on attitudes

and behaviour

Social opportunities

(1)

(2) (3)

(4)

Global, National and Urban Contexts

Thanks for your attention.

End.