Residential segregation and unemployment:
The case of Brussels
Claire DUJARDIN*, Harris SELOD**, and Isabelle THOMAS***
November 16, 2005
*
FNRS Research Fellow, Department of Geography and CORE, Université catholique de
Louvain, Place L. Pasteur, 3, B-1348 Louvain-la-Neuve, Belgium. Fax: +32-10-47-28-77.
E-mail: [email protected]
**
INRA Researcher (PARIS-Jourdan), CREST and CEPR, France. Fax: +33-1-43-13-6362. E-mail: [email protected]
***
FNRS Senior Research Associate, Department of Geography and CORE, Université
catholique de Louvain, Belgium. Fax: +32-10-47-28-77. E-mail: [email protected]
1
Acknowledgments
The authors would like to thank Patrick Deboosere and the Point d’Appui
Démographie (Vrije Universiteit van Brussel) for providing them with the data, as
well as four anonymous referees for useful comments. Harris Selod acknowledges
the support of the European Commission while he was visiting CORE as a Marie
Curie Fellow from the EC (Grant HMPF-CT-2000-00614).
2
Residential segregation and unemployment:
The case of Brussels
Summary
This paper investigates the causal effects of the spatial organization of
Brussels on unemployment propensities. Using 1991 Census data, we estimate the
unemployment probability of young adults while taking into account personal,
household and neighborhood characteristics. We solve the endogeneity of
residential locations by restricting our sample to young adults residing with their
parents, and evaluate the potential remaining bias by conducting a sensitivity
analysis. Our results suggest that the neighborhood of residence significantly
increases a youngster probability of being unemployed, a result which is quite
robust to the presence of both observed and unobserved parental covariates.
Keywords: Neighborhood effects, Residential segregation, Unemployment,
Endogeneity bias, Sensitivity analysis.
3
Introduction
For decades, sociologists, economists and geographers have written
extensively on how the spatial structure of cities reflects socioeconomic differences
in the population (see the seminal contributions of Burgess, 1925, Hoyt, 1939, or
Harris and Ullman, 1945, and their respective descriptions or “models” of urban
stratification). Strikingly, most cities today are characterized by stark disparities
opposing city centers and peripheries. In the U.S. for instance, inner cities are
usually poor and the catalyst of many social problems, whereas suburbs are more
well-off. Brussels exhibits a similar spatial structure since its inner city
concentrates unemployed workers and disadvantaged communities, including many
unskilled workers and ethnic minorities (Vandermotten et al, 1999; Thomas and
Zenou, 1999; Goffette-Nagot et al, 2000; Kesteloot et al, 2001).
To explain the general pattern of residential segregation in urban areas, the
economics literature has often stressed the role of location choices through which
individuals spontaneously sort themselves into a city according to their different
socioeconomic characteristics. In this respect, residential segregation is a standard
result in both public and urban economics (see Tiebout, 1956, for whom
individuals “vote with their feet” for the provision of local public goods, or Fujita,
1989, for a formalization of the standard monocentric model with heterogeneous
agents).
However, urban stratification should not just be considered as a process
whereby households with different socioeconomic backgrounds sort themselves in
the city. As a matter of fact, an abundant literature has investigated the reverse
4
causality, arguing that the spatial organization of cities could explain differences in
social and economic outcomes. In the U.S. context, it has been argued that the high
level of unemployment among inner-city minorities could be explained by
residential segregation and/or disconnection from job opportunities (see Kain,
1968; Kasarda, 1989; Jencks and Mayer, 1990; Wilson, 1987 and 1996, Cutler and
Glaeser, 1997). Even though the theories which link labor-market outcomes to the
spatial organization of cities have mainly inspired empirical papers on U.S.
metropolitan areas, the mechanisms put forward clearly have a general validity.
But only a few works have focused on European cities (see for instance Fieldhouse,
1999, on London, or Gobillon and Selod, 2006, on Paris). The objective of the
present paper is to test whether city structure can be a source of unemployment in
the Brussels metropolitan area.
Despite the huge interest on the topic, the bulk of empirical studies still has
not reached a consensus regarding the role of spatial factors in explaining
individual labor-market outcomes. As argued by Ginther et al (2000) or Dietz
(2002), this lack of consensus can probably be explained by the great diversity
regarding the methods used to test for the existence of spatial factors. In particular,
as individuals sort themselves into different parts of the urban space on the basis of
their personal characteristics (including their labor-market outcomes), studies
encounter an endogeneity bias, for which no perfect solution exists at present
(Glaeser, 1996). Among the few empirical studies that try to deal with the
endogeneity issue, most choose to restrict their sample to young adults residing
with their parents, arguing that the location choice of parents can be thought of as
fairly exogenous to the employment status of young adults (see e.g. O’Regan and
5
Quigley, 1996, 1998). However, it has been argued that this solution does not
completely eliminate endogeneity as there may exist unobserved parental
characteristics that influence both the residential choice and the employment status
of young adults (Glaeser, 1996). In the present paper, we nevertheless choose to
focus on young adults living with their parents. But in order to get better
confidence in our results, we resort to a strategy inspired by Ginther et al (2000)
and Harding (2003) which enables us to analyze the sensitivity of our estimated
spatial effects to the presence of both observed and unobserved parental covariates.
The paper is structured as follows. Section 1 presents a brief synthesis of the
economic literature that links the formation of unemployment to city structure.
Section 2 describes the database and presents the studied area. Section 3 describes
our methodological approach. Section 4 presents some stylized facts about the
Brussels metropolitan area. Section 5 presents and discusses the main results.
Section 6 concludes.
1.
Urban unemployment and city structure: a brief review of the literature
An abundant literature in sociology and urban economics suggests that the
spatial organization of cities can exacerbate unemployment among disadvantaged
communities. In this perspective, labor-market outcomes should depend on
individual characteristics (age, education, ethnicity,…) but also on the exact
location within the city. Two types of factors have been put forward to explain the
existence of such spatial effects, focusing either on the role played by the physical
6
disconnection from jobs or the harmful effects of residential segregation and the
social composition of neighborhoods.
The first works purporting to demonstrate the influence of space on
individual labor-market outcomes were based on the spatial mismatch hypothesis
put forward by Kain in 1968 (see Gobillon et al, 2005, for a mainly theoretical
survey of the literature, and Ihlanfeldt and Sjoquist, 1998, for an empirical one). In
theory, there are several mechanisms according to which distance to job
opportunities can be problematic. One important mechanism is that job-seekers
residing in areas disconnected from job opportunities are likely to refuse job offers
if commuting costs are too high in view of the offered wages (Brueckner and
Martin, 1997). Another mechanism is that workers’ job-search efficiency may
decrease with distance to jobs since it is obviously more difficult to search far away
from one’s place of residence (as modeled in Wasmer and Zenou, 2002). The
intensity of the search effort may also decrease with distance from job
opportunities, for instance if workers residing far away from job centers face lower
rents and thus feel less pressured to quickly find a job (as in Smith and Zenou,
2003). Similarly, high search costs may also deter workers from searching far
away (as in Ortega, 2000). Finally, firms could discriminate against distant
workers, for instance if distance makes them less productive because of long and
tiring commutes (as in Zenou, 2002). General empirical tests confirm that the
disconnection between places of residence and job locations exacerbates
unemployment (see Weinberg, 2004, or Martin, 2004), but few of the abovementioned mechanisms have been specifically tested (see Gobillon et al., 2005, for
more details on this issue).
7
Other works have focused on the role of residential segregation and more
generally the quality of the social environment on individual socioeconomic
outcomes. In this respect, several mechanisms can account for an adverse effect of
residential segregation, either directly on unemployment, or indirectly through low
employability. One mechanism is that residential segregation can be a hindrance to
human capital acquisition: in neighborhoods which concentrate low-ability
students, human capital externalities can deteriorate school achievements and
employability
(Benabou,
1993).
Social
problems
which
deteriorate
the
employability of workers can also spread through neighborhood interactions. For
instance, Crane (1991) develops an epidemic theory of ghettos in which the
propensity of youngsters to adopt a socially deviant behavior depends on the
proportion of same-behavior individuals in the neighborhood. This contagion is all
the more prevalent as adults are themselves unemployed and do not provide a
figure of social success to which youngsters could identify (Wilson, 1987).
Another mechanism whereby residential segregation can exacerbate unemployment
is that it can deteriorate social networks in disadvantaged communities. This is a
crucial point since a significant proportion of jobs are usually found through
personal contacts (Mortensen and Vishwanath, 1994) and since low-skilled
workers, young adults, and ethnic minorities often resort to such informal search
methods (Holzer, 1987 and 1988). In particular, in neighborhoods were local
unemployment rates are higher than average, local residents know fewer employed
workers that could refer them to their own employer or provide them with
professional contacts. In this respect, Reingold (1999) concludes that the poor
quality of social networks explains a significant portion of unemployment
8
problems in disadvantaged urban areas in the U.S. (see Selod and Zenou, 2001 and
2006, for formalizations). Another mechanism which links labor-market outcomes
to segregation involves the reluctance of employers to hire workers residing in
disadvantaged neighborhoods. The stigmatization of these neighborhoods is at the
root of redlining, a practice in which employers draw an imaginary red line around
a stigmatized neighborhood and beyond which they discriminate against residents
(see Zenou and Boccard, 2000, for a model). As for the spatial mismatch
hypothesis, there are general tests which show that segregation has an adverse
effect on the labor market (see Cutler and Glaeser, 1997), but distinguishing which
particular mechanisms explain this adverse effect remains on the research agenda.
2.
Data and studied area
2.1.
Studied area
This paper focuses on Brussels which, like Flanders and Wallonia, forms one
of the three institutional regions of Belgium: the Brussels-Capital Region
consisting of 19 municipalities (called communes in French) and hosting around 1
million residents on a 163 km² area. However, as in most cities, Brussels’
functional metropolitan area extends far beyond its institutional limits. Several
studies have tried to measure the spatial extension of its urban area applying
various methods (see Thomas et al, 2000 for a synthesis). In the present paper, we
chose to use the so-called Extended Urban Area (E.U.A., see Figure 1), which
9
perfectly reflects the social dualism between the city center of Brussels and its
close suburbs.1 It hosts 1.4 million inhabitants and extends over a 723 km² area.
[Insert Figure 1]
The smallest spatial unit for which census data are usually available is the
statistical ward, a subdivision of the municipality defined in 1971 according to
social, economic and architectural similarities (Brulard and Van der Hagen, 1972).
Statistical wards that present a common functional or structural character (for
example a common attraction pole like a school or a church) can further be
grouped into larger entities which we will refer to as neighborhoods, and which
constitute an intermediate level between the statistical ward and the municipality.
It is the spatial level of analysis chosen in this paper. There are 328 such
neighborhoods in the E.U.A., grouping on average 4,250 inhabitants. For statistical
reasons, neighborhoods of less than 200 inhabitants were not considered in the
subsequent analysis.
2.2
The data
The empirical analysis conducted in this paper is based on two databases
extracted from the 1991 Census of Population which differ according to their level
of aggregation (Institut National de Statistiques, 1991). In the first database, the
basic statistical unit is the individual. For all individuals aged 19 to 64 and residing
in the Extended Urban Area, the database provides the main personal
10
characteristics, including age, gender, education, citizenship, employment status,
statistical ward of residence, and kinship with household’s head. The database also
contains several household characteristics (for instance whether parents own a car)
as well as an identification number, which makes it possible to identify individuals
who belong to a same household. In the second database, the basic statistical unit
is the statistical ward.
It includes various indicators of the socioeconomic
composition and average housing characteristics of statistical wards. This database
was complemented with the average income of households in the statistical wards
computed from 1993 fiscal sources (Institut National de Statistiques, 1993). These
ward-level data were further aggregated to form neighborhood-level variables.
3.
Methodological approach
The objective of the present paper is to investigate whether the spatial
structure of Brussels may have an effect on unemployment. In that purpose, we
estimate unemployment probabilities at the individual level taking into account
personal, household, and neighborhood characteristics, using the following logistic
model:
⎛ P
Log ⎜⎜ i
⎝ 1 − Pi
⎞
⎟⎟ = α + βI i + γH i + δN i
⎠
[1]
where Pi is the unemployment probability of individual i, Ii is a vector of personal
characteristics, Hi is a vector of household characteristics, Ni is a vector of
neighborhood characteristics (social composition and physical access to jobs), and
α, β, γ and δ are parameters to be estimated. In particular, δ, when significantly
11
different from zero identifies an impact of spatial factors on unemployment, which
we will refer to as neighborhood effects in the rest of the paper. Using [1], the
individual probability of unemployment Pi is given by:
Pi =
3.1.
e
(α + βI i + γH i + δN i )
1+ e
(α + βI i + γH i + δN i )
[2]
Definition of neighborhood characteristics
Two types of indicators are used to account for the neighborhood
characteristics which may potentially influence individual unemployment
probabilities. The first type relates to the spatial mismatch theory and characterizes
the disconnection of neighborhoods from jobs, while the second type relates to the
social composition of neighborhoods.
Measuring spatial access to job opportunities is a difficult task as data on job
offers are generally not available. In the absence of data on job openings, we use
the census’ individual declarations of workplaces to compute the number of
occupied jobs in each neighborhood.2 Two indicators are then computed for each
neighborhood i: distance to jobs and job density. First, distance to jobs Di is
defined as the average distance from neighborhood i to each neighborhood j in the
E.U.A. (dij) weighted by the number of jobs located in each one of these
neighborhoods (Ej):
Di =
∑d
ij
Ej
j
∑E
[3]
j
j
12
with the intra-neighborhood distance dii being equal to 2/3 the radius of a disc of an
area equivalent to the area of neighborhood i, which comes down to assuming that
the population in each neighborhood is uniformly distributed around a central point
which concentrates all jobs. Second, we consider that the relevant job density for
residents of neighborhood i is the ratio of the number of jobs located in that
neighborhood and in the adjacent neighborhoods to the overall labor force residing
in the same areas. This definition has the advantage of smoothing job density over
space and attenuating extreme values. These two indicators were computed both
for all jobs and for low-skilled jobs (i.e. jobs occupied by workers having at most a
diploma of the lower secondary education segment –Ordinary Level equivalent in
the U.K. system).
Regarding the variables which characterize the social composition of
neighborhoods, researchers generally resort to one or several quantitative measures
of the aggregate characteristics of residents. However, even though it is likely that
individual outcomes are determined by a wide variety of neighborhood
characteristics (employment, education, racial composition,…), considering all
these characteristics together into a single regression may cause collinearity
problems (making parameter values and significance levels unstable) as many
indicators of neighborhood composition are highly inter-correlated (O’Regan and
Quigley, 1996; Johnston et al, 2004). To circumvent this problem, we use standard
Factorial Ecology methods (see e.g. Johnston, 1978) to summarize these multiple
characteristics into different types of social environment within Brussels (see
Section 4.2 below for the construction of the typology).
13
3.2.
The endogeneity of residential location
Linking individual labor-market outcomes to residential location raises the
issue of the endogeneity of location choices (Glaeser, 1996; Dietz, 2002). It is
indeed well-known that individuals with similar socioeconomic characteristics,
notably similar labor-market outcomes, tend to sort themselves in certain areas of
the urban space. For instance, individuals with well-paid jobs will choose to reside
in neighborhoods with a good social environment. There is thus a two-way
causality: on the one hand, residential location influences individual labor-market
outcomes, and on the other hand, individual outcomes influences the choice of a
residential location. Stated differently, we are allowed to think that individual
characteristics that influence labor-market outcomes may also influence residential
choices. Of course, standard models like equation [1] make it possible to control
for some individual and household characteristics which might influence both
neighborhood choice and individual outcomes. However, it is likely that some
individual and household characteristics which are unobserved to the researcher
(and therefore not included in Ii nor Hi) influence both the outcome of interest and
neighborhood choice. For example, individuals with a low labor-market attachment
(which directly influences the probability of unemployment) may choose to reside
in poor neighborhoods for some economic or social reason. As a consequence,
what the researcher perceives as a neighborhood effect through the estimated
parameter δ may simply stem from a correlated effect reflecting common
residential choice.
14
Various strategies have been developed to correct for the endogeneity of
neighborhood choice. For example, the existence of quasi-experimental situations
(such as government subsidized relocation programs in the U.S. like the Gautreaux
or the Moving To Opportunity programs) makes it possible to obtain more reliable
estimates of neighborhood effects (as households are moved from one
neighborhood to another through an exogenous intervention; see Oreopoulos, 2003,
for a review). However, due to the scarcity of such experiments, researchers are
often constrained to resort to more questionable strategies. Most studies restrict
their sample to young adults residing with their parents, arguing that location
choices were previously made by the parents and can thus be thought of as fairly
exogenous to the employment status of young adults (see e.g. O’Regan and
Quigley, 1996, 1998). This is also the strategy adopted in this paper, as our studied
sample is restricted to young labor-force participants aged 19 to 25 and residing
with their parents.
However, this approach does not completely eliminate the endogeneity bias.
Indeed, there may still exist parental unobserved characteristics which determine
their residential choice and also influence the employment outcomes of their adult
children (Glaeser, 1996). For example, lack of commitment to work may induce
parents to locate in high poverty neighborhoods but may also influence the
motivation of their children to search for a job as well as the intensity of their job
search effort. In this context, one cannot distinguish neighborhood effects from the
effect of those unobserved parental characteristics when estimating the
unemployment probability of young adults living with their parents.
15
3.3.
Sensitivity analysis
Following a suggestion made by Glaeser (1996, p. 62), we evaluate the
potential remaining endogeneity bias by conducting a sensitivity analysis in order
to assess the robustness of our estimated neighborhood effects. In that purpose, we
resort to a two-step strategy. In a first step, we follow the approach used by Ginther
et al (2000) and estimate several models of individual unemployment probability
which incorporate various sets of household characteristics Hi, moving away from
a model with no household variable towards a model including an extensive set of
parental controls (parental employment and professional status, parental education,
male or female household head, possession of an automobile). The comparison of
the estimated neighborhood effects in these models enables us to test the robustness
of our results to the presence of observed parental covariates.
In a second step, we test the sensitivity of our results to the endogeneity bias
which results from the omission of an unobserved covariate which is correlated to
both the probability of unemployment among young adults and parental residential
choice, following the approach developed by Rosenbaum and Rubin (1983) and
recently applied by Harding (2003) in the context of neighborhood effects. In order
to carry out the sensitivity analysis, we build a dummy variable which equals to
one if the individual resides in a deprived neighborhood and zero otherwise (see
Section 5.2 for the precise definition of deprived neighborhoods). The goal of our
analysis is to assess how an unobserved binary covariate which affects both the
probability of unemployment among young adults and the choice of parents to
16
reside in a deprived or a non-deprived neighborhood would alter our conclusions
about the magnitude and significance of neighborhood effects.
This is done by generating a series of unobserved binary variables U that
vary according to their degree of association with the neighborhood dummy
variable X and with the binary outcome measure Y. The degrees of association
between U and X and between U and Y are classically measured by odds ratios. In
practice, we use the Sensuc function in the Design S library written by Harrell
(2003) to generate a binary variable U sampled according to the following logistic
model:
⎛ Ui
Log ⎜⎜
⎝1−Ui
⎞
⎟⎟ = α + log(b)Yi + log(c) X i
⎠
[4]
where Yi is a binary variable indicating whether individual i is unemployed or not,
Xi is a binary variable indicating whether i resides in a deprived neighborhood or
not, and b and c are chosen odds ratios measuring the strength of the association
that we impose between U and Y and U and X respectively.3 α is determined so that
the overall prevalence of U=1 is 0.5 (or any other given value). Once a value for U
has been generated for each individual so that the distribution of U verifies the
three constraints (on the two sensitivity parameters b and c and on the overall
prevalence of U=1), new estimates of neighborhood effects are obtained by
including the U variable in the unemployment probability model given by [1]. This
is repeated for increasing values of the sensitivity parameters b and c in order to
investigate what level of endogeneity bias (i.e. the strength of the association
between U and Y and between U and X) would be needed to invalidate the results
and render our estimated neighborhood effects not significant.
17
4.
Statistical description of Brussels
4.1.
Stylized facts
The Brussels Extended Urban Area presents a well-marked spatial structure
characterized by important disparities opposing its city center to the periphery.
Figure 2 maps the percentage of unemployed workers among labor-force
participants, highlighting a zone of very high unemployment rates (above 20%) in
the central part of the urban area, along the former industrial corridor (the
Charleroi-Willebroek canal). On the contrary, unemployment is much lower
(below 10% or even 7%) in the suburbs (notably in the municipalities of Tervuren,
Overijse, Grimbergen, Dilbeek and Sint-Peeters-Leeuw).
[Insert Figure 2]
Figure 3 maps the percentage of North Africans (Moroccans, Algerians and
Tunisians) and Turks among the whole population. These nationalities correspond
to the latest wave of labor immigration in the second half of the sixties (Kesteloot
and Cortie, 1998). We consider them as a single group since they usually face the
same type of problems on the labor market.4 Figure 3 shows a high level of ethnic
residential segregation: North Africans and Turks are mainly located around the
center of Brussels, with a concentration above 10% or even 25% in some
neighborhoods. Moreover, as can be seen from the computation of dissimilarity
18
indices (Duncan and Duncan, 1955) 5, North Africans and Turks form the most
segregated group of foreigners in Brussels:
64% of them would have to be
relocated to obtain a uniform mix with the Belgian population. For non-Belgian
citizens of the European Economic Community (consisting of 12 countries in
1991), the dissimilarity index goes down to only 32%.
[Insert Figure 3]
It is widely acknowledged that the concentration of North Africans and
Turks in Brussels’ central neighborhoods is mainly due to the functioning of the
local housing market. Indeed, the promotion of home-ownership -an important
housing policy goal in Belgium- led to the quasi-absence of a social housing sector
and to the massive suburbanization of high- and middle-income households
towards the periphery. In this context, suburbanization accelerated the degradation
of central zones as housing units were rented to low-income households and
maintenance was neglected by owners mainly concerned with the additional profit
they could derive from their investments. This gave rise to a residual rental sector
which concentrates the oldest, most poorly equipped and cheapest housing units of
the city, which are the only dwellings poor foreigners can afford (Kesteloot and
Van der Haegen, 1997; Kesteloot and Cortie, 1998).
By comparing Figures 2 and 3, it can easily be seen that neighborhoods with
a high proportion of North Africans and Turks also exhibit high unemployment
rates, the correlation between the two variables being equal to 0.89. This high value
is partly explained by neighborhood composition (foreigners are usually more
19
likely to be unemployed because they have a lower education or because they are
discriminated against in the labor market). It could also be explained by the
exacerbating effect of residential segregation on labor-market outcomes.
Figure 4 maps the density of jobs for all jobs (on the left-hand side) and for
low-qualified jobs (on the right-hand side). It shows a zone of very high job
densities in the center of the E.U.A., where some neighborhoods have more than
five jobs per labor-force participant.
These neighborhoods constitute what is
usually called ‘The Pentagon’, where one can find the Administrative City as well
as many financial institutions and headquarters of national and international firms.
This zone of very high job densities is surrounded by a zone of high, though lower,
job densities (more than two jobs for one resident) comprising the European
institutions and extending towards the northeastern periphery where the national
airport as well as some industries are located. Note that the spatial distribution of
job densities is quite similar for all jobs and for low-qualified jobs. However, the
very high density zone of the center (more than five jobs per labor-force
participant) and the high density zone of the northeastern periphery are spatially
less extended when it comes to low-qualified jobs. By comparing Figures 2 and 4,
it can be seen that central zones with high unemployment rates also have relatively
high job densities, which seems in contradiction with the spatial mismatch theory.
Indeed, the correlation between unemployment rate and total job density is
significant and positive (0.35 for all jobs and 0.31 for low-qualified jobs) and the
correlation between unemployment rate and average distance to jobs is significant
and negative (-0.52 for both all and low-qualified jobs). In other words, in
20
Brussels, individuals living in high job density neighborhoods close to the city
center tend to be more unemployed.
[Insert Figure 4]
4.2.
Typology of neighborhoods
Standard Factorial Ecology methods are used to identify sociallyhomogeneous areas within Brussels, which will be subsequently used in the
regression analyses in Section 5. We first run a Principal Component Analysis
which defines a limited number of non-correlated factors summarizing the
information carried by a set of neighborhood variables (see Table A.1 in Appendix
1 for the list of variables and their contribution to the retained factors). Then,
neighborhoods are grouped according to their coordinates on the factorial axes,
using a hierarchical ascending classification (with the Ward method which
minimizes intra-group variance). We obtained five neighborhood types6 which are
presented on Figure 5 below (see Table A.2 in Appendix 1 for their mean
characteristics).
The first neighborhood type gathers very deprived areas in the center of
Brussels and corresponds to neighborhoods with high proportions of NorthAfricans, Turks, and female-headed households as well as with very high
unemployment rates. These neighborhoods are characterized by low educational
levels and have the lowest income levels of the whole agglomeration. They are
surrounded by a group of deprived neighborhoods presenting similar characteristics
21
but with a smaller proportion of North Africans and Turks and a less severe
situation in terms of education and unemployment. The third group of
neighborhoods extends to the Southwest and Northeast of the inner city, along the
former industrial axis. It groups neighborhoods that also have a lower
socioeconomic status, in particular an overrepresentation of blue collars and
individuals with a lower education. But the unemployment rates and income levels
are closer to the city’s average. The two remaining groups are characterized on
average by higher levels of education and professional statuses. The first, which we
labeled well-off, corresponds to the periphery of the Brussels’ E.U.A., while the
second, which we labeled very well-off, occupies the Southeast part of the Brussels
Capital Region as well as its continuation in the periphery (notably the
municipalities of Waterloo and Lasne). The latter differ from the former by its high
proportion of executives and its very high average income.
[Insert Figure 5]
5.
The effect of spatial structure on unemployment: main results
The previous section has shown that Brussels exhibits a high level of
residential segregation associated with high local unemployment rates but that
disadvantaged neighborhoods are usually close to job locations. However, a
statistical analysis is required before one can conclude on the role of spatial factors
on unemployment in Brussels. The aim of the present section is (i) to investigate
the role played by Brussels’ spatial structure on individual unemployment
22
probabilities and (ii) to test for the robustness of our results. As explained in
Section 3.2, in order to limit the endogeneity problems associated with residential
sorting, the sample is restricted to young labor-force participants living with their
parents, i.e. a sample of 34,250 individuals.7 In Section 5.1, we consider a model
including only individual characteristics and then progressively add other
neighborhood characteristics to the specification. Section 5.2 tests the robustness of
the results to the presence of both observed and unobserved parental
characteristics.
5.1.
The role of spatial factors in explaining unemployment probabilities
Table 1 presents the parameter estimates as well as the odds ratios for five
different models explaining the individual unemployment probability. Model Ia
considers only the role of individual characteristics (gender, age, education and
citizenship), while the four other models add various combinations of
neighborhood characteristics to the specification. In Model Ia and in all other
regressions, men or educated workers are less likely to be unemployed than women
or workers with a lower education. The probability of unemployment also
decreases with the age of the individual and when the individual is a foreigner.
North Africans and Turks are more disadvantaged than EEC citizens. Interestingly,
young Belgian adults born of foreign parents are also more likely to be
unemployed than young Belgian adults born of Belgian parents. This result
suggests that besides citizenship, the name or visible characteristics associated with
foreign origin are a handicap on the labor market. This is consistent with both the
23
existence of labor-market discrimination as well as social networks of lower
quality for individuals of foreign origin.
As can be seen from the likelihood ratios in Table 1, introducing
neighborhood characteristics significantly increases the fit of the regression. Model
Ib uses only the unemployment rate to represent neighborhood influences. It shows
that the local unemployment rate significantly increases the unemployment
likelihood of young adults. As suggested by the theory, this may hinge upon
deteriorated local social networks: the higher the proportion of unemployed
neighbors, the more difficult the insertion of a young adult in the labor market.
Model Ic substitutes the type of neighborhood for the unemployment rate, which
increases the fit of the model, thus confirming that the unemployment probability is
affected by a wide variety of neighborhood characteristics, instead of only by the
unemployment rate. All things else being equal, the unemployment probability is
the lowest for young adults residing in the industrial axis and in well-off areas. In
accordance with the theory, living in a central area with a socioeconomic
environment of lower quality (deprived or very deprived) significantly increases
the unemployment probability of young adults (with odds ratios of 1.740 and 1.873
respectively) in comparison with living in well-off areas. Surprisingly however,
living in a very well-off area also significantly increases the probability of
unemployment. However, we do not wish to conclude that living in a wealthy area
is detrimental to finding a job. A possible interpretation for this unexpected result
could be that unemployed youth in very well-off areas do not feel pressured to
intensively search for a job if they get enough financial support from their parents.
Another possible explanation could be the existence of a selection bias if living in a
24
wealthy area enhances the chances to find a well-paid job so that young adults that
originate from these areas are likely to quickly move out of their parents’ dwelling.
This would leave an over-representation of unemployed workers living with their
parents in very well-off areas. Investigating the veracity of this story would require
to model unemployment probabilities jointly with the process of leaving parental
home through a selection model. However, as the choice to leave home is probably
influenced by parental characteristics (such as household financial resources) as
well as neighborhood characteristics, this would require more specific data. In
particular, we would need to be able to identify the household and neighborhood of
origin of young adults who do not live with their parents. This is unfortunately not
feasible using the Belgian census data.
Model Id complements the specification by adding indicators of the physical
disconnection from jobs. As could easily be suspected from the statistical
description of Brussels (Section 4.1), the effect of distance to jobs is not
significant. As for job density, its impact is significant but plays in the wrong
direction: controlling for all other variables, young adults who reside in areas with
the highest job densities are less likely to hold a job. The same obtains when one
considers distance to low-skilled jobs and low-skilled job density. This last result is
not consistent with the spatial mismatch hypothesis and suggests that spatial
mismatch is not a problem in Brussels, a city in which the unemployed reside close
to the jobs they could occupy
25
5.2.
Sensitivity analysis
In order to test the robustness of our estimated neighborhood effects, in a
first step, we conduct a sensitivity analysis to the presence of parental observed
characteristics. In that purpose, we estimate different models which incorporate
various sets of parental and household characteristics. Table 2 presents the results
of our sensitivity analysis, moving away from a model with no household
characteristics (Model I) towards models including an increasingly comprehensive
set of parental controls (Models II to VI). Note that distance to jobs and job density
were not included as they played little role in the previous models. Models II to VI
show that the unemployment probability of a young adult is higher when the
household head (or spouse) is not participating to the labor-force or is unemployed
than when he is employed. This effect is highly significant and is consistent with
social network theories (at the household level, unemployed parents being little
able to help their job-seeking children) and socialization considerations
(unemployed parents failing to provide their children with an image of social
success to which they could identify). Living in a female-headed household (a
proxy for single mother households) also significantly increases the likelihood that
a young adult is unemployed, suggesting that these households are more frequently
prone to social problems detrimental to finding a job. Living in a household which
does not own a car (an indirect measure of lack of financial resources and low
mobility for those who do not) also significantly increases the unemployment
probability. The effects of parental professional status and educational level are not
always significant and, when significant, seems counterintuitive. Indeed, all other
26
things equal, having a parent with an executive profession or a higher level of
education increases the unemployment probability of young adults. This effect
mirrors our counterintuitive finding on residence in very well-off areas. As
mentioned previously this could be explained if rich kids are less pressured to
search intensively for a job (because of their parents’ financial support) or in
presence of a selection bias. With the present dataset, we are unfortunately not able
to distinguish between these two explanations.
By comparing the parameters and significance levels of the neighborhood
types across the different models, one can assess the sensitivity of our estimated
neighborhood effects to the inclusion of a more comprehensive set of parental
controls. Table 2 shows that although the inclusion of parental and household
characteristics significantly increases the fit of the model (see the likelihood ratios),
the estimated neighborhood effects change very little (for example, the odds ratio
associated with residence in very deprived areas varies from 1.903 to 1.707
between Model I and Model VI), and all parameters remains significant at a 1%
level.
In a second step, we complement the sensitivity analysis by testing the
robustness of our estimated neighborhood effects to the presence of unobserved
characteristics, using the methodology developed by Rosenbaum and Rubin
(1983). Since this methodology requires the use of a binary neighborhood variable
we redefined our five-modality variable by grouping together the two least favored
neighborhood types (very deprived and deprived) while considering all other
neighborhood types as another category. Doing so yields a dummy variable equal
to one if the individual resides in a deprived neighborhood and equal to zero
27
otherwise. Table 3 presents the estimated effects of living in such a deprived
neighborhood, for three combinations of parental and household characteristics: no
parental characteristics (as in Table 2’s Model I), only the parental employment
status (i.e. unemployed or not-participating to the labor-force; as in Table 2’s
Model II), and in the last case the full set of parental controls (as in Table 2’s
Model VI). For each one of these models, an artificially created binary variable U
is included and a “sensitivity matrix” is obtained by varying the sensitivity
parameters b and c, which measure the associations of U with the neighborhood
type and the employment status. As expected, Table 3 shows that in the three
model specifications, the estimated neighborhood effect decreases as the values of
b and c increase. This means that accounting for a previously omitted variable
correlated both to the neighborhood type and employment status does indeed
reduce the intensity of the neighborhood effect. However, the effect seems fairly
robust to the presence of unobserved covariates since the values of b and c would
have to be very high to make the neighborhood effect not significant. Indeed, for
the model including no parental or household characteristic (Model I), an
unobserved covariate which multiplies both the odds of living in a deprived
neighborhood and the odds of unemployment by 3.0 would be required to totally
erase the neighborhood effect. This is also true when observed parental
characteristics are included in the analysis. For example, when the full set of
parental characteristics are used (Model VI), the neighborhood effect becomes
insignificant when at least one of the two odds ratios reaches 3.0 and the other one
equals 2.5. These values are very high in comparison with the odds ratios estimated
on observed covariates. As can be seen from Table 2, none of the observed parental
28
and household characteristics produces odds ratios as high as 3.0 or even 2.5, the
highest odds ratio for a parental characteristics being 1.459 in Model VI (for
having an unemployed parent). In Table 2, even individual characteristics do not
produce odds ratios above 2.5: the highest odds ratios are 2.403 for North-African
or Turkish citizenship and 2.487 for Belgians born of foreign parents. In other
words, for the neighborhood effect to become insignificant when considering an
omitted variable, the effect of this omitted variable on unemployment would have
to be stronger than that of parental employment status and at least as strong as that
of citizenship. This provides some relatively strong evidence on the robustness of
neighborhood effects.
6.
Conclusion
The explanation of unemployment usually revolves around well-known
determinants (notably the lack of formal education or the skill mismatch between
supply and labor demand). Alternative theories suggest that, in predominantly
urban economies, unemployment may also be determined by the spatial
organization of cities which concentrates disadvantaged families in poverty zones
and/or distance them from job opportunities. This paper studies the particular case
of the Brussels metropolitan area and investigates the extent to which residential
location can affect the unemployment probability of young adults. As in many
other studies, we address the endogeneity of residential choices by restricting our
studied sample to the case of young adults residing with their parents. However, his
solution is imperfect as some unobserved parental characteristics may still
29
influence both the residential choice of parents and the employment status of young
adults. In this context, the originality of our work is to evaluate the potential
remaining endogeneity bias by conducting a sensitivity analysis to the presence of
both observed and unobserved parental characteristics.
Results mainly show that urban unemployment is exacerbated by residential
segregation but not by disconnection from jobs. Indeed, distance to jobs does not
exacerbate unemployment. This result is coherent with the spatial structure of
Brussels, which concentrates disadvantaged households in areas close to jobs. We
show however that urban unemployment is strongly exacerbated by social,
economic, and ethnic segregation at the place of residence. In particular, all things
else being equal living in a neighborhood with a socioeconomic environment of a
lower quality significantly increases the unemployment probability of young
adults. This result is quite robust to the presence of both observed and unobserved
covariates. Indeed, neighborhood effects remain statistically significant whatever
the combination of parental and household characteristics introduced in the
regression. Moreover, our sensitivity analysis to the presence of unobserved
covariate shows that the amount of selection on unobservables would have to be
unreasonably high to render the estimated neighborhood effect insignificant.
Our study is a first step that sheds light on the formation of unemployment in
Brussels. However, further research is needed to identify particular mechanisms
mediating the effect of segregation on unemployment.
30
Notes
1.
The Extended Urban Area of Brussels consists of the 36 municipalities of the
operational metropolitan area defined by Van der Haegen et al (1996) to
which 5 municipalities have been added (Lasne, La Hulpe, Rixensart,
Hoeilaart and Overijse) in conformity with Mérenne-Schoumaker et al (1998)
and Thomas and Zenou (1999).
2.
Raphael (1998) argues that employment growth measures (defined as the
difference in employment levels between two given years) should be used
instead of employment levels as the latter only reflect vacancies created by job
turnover and not vacancies created by job growth. However, the Belgian
Census of Population takes place only every ten years, a time period we
believe too important to adequately reflect the number of jobs openings that
may potentially matter for unemployed workers in a cross-section study.
3.
The odds ratio measures how much more likely it is for the outcome to occur
(i.e. to be equal to 1) for individuals whose independent variable X is equal to
1 compared to individuals with X equal to 0 (or for a marginal increase in the
case of a continuous independent variable). An odds ratio greater than 1
indicates that a value of 1 for X increases the odds of observing the outcome
in comparison with a value of 0. For example, the sensitivity parameter c
which
by:
measures
the
association
P(U = 1, Y = 1) P(U = 0, Y = 1)
P(U = 1, Y = 0) P(U = 0, Y = 0)
31
between
U
and
X
is
given
4.
Note that the nationality criterion underestimates the size of extended ethnic
group because many individuals have acquired the Belgian nationality or were
simply born Belgian from a mixed marriage (Kesteloot and Van der Haegen,
1997). However, since Belgian census data do not contain any information on
the ethnic origin of people, nationality is the only way to approach the issue of
ethnic segregation.
5.
Fori − Beli where Fori and Beli are
2∑
Bel
i For
The dissimilarity index is given by: 1
the respective numbers of foreigners and Belgians in neighborhood i and For
and Bel are the respective numbers of foreigners and Belgians in the whole
urban area.
6.
This was the optimal number of clusters according to several criteria,
including the Cubic Clustering Criterion, the pseudo-F and pseudo-t values.
7.
The number of observations indicated in the different tables might differ as
individuals for whom some individual or household characteristics were
missing had to be set aside. Individuals residing in neighborhoods of less than
200 inhabitants were also erased from the sample.
32
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38
Appendix 1: Building a typology of neighborhoods
Table A.1: Variables used in the principal component analysis and their
contributions to factors
Eigenvalue
Percent of variance explained
Loadings
% North-Africans and Turks
% female-headed households
Average household income
% students in technical classes
% individuals with lower education
% individuals with at least intermediate education
% individuals with a higher level of education
% blue-collars among employed workers
% executives among employed workers
Unemployment rate
Unemployment rate of the under 25
Factor 1
6.196
56.33%
Factor 2
3.104
28.219
-0.462
0.816
-0.055
-0.906
-0.943
0.874
0.958
-0.916
0.932
-0.448
0.108
0.723
-0.392
0.781
0.139
-0.077
-0.384
-0.138
0.278
-0.042
0.864
0.895
Only factors with eigenvalues superior or equal to one were retained. Figures
presented are those obtained after a Varimax rotation.
Table A.2: Mean characteristics of the five neighborhood types
Very
Deprived Indusdeprived
trial axis
% North-Africans and Turks
35.8
7.4
1.1
% female-headed households
22.9
23.7
13.3
Average household income
644
828
919
% students in technical classes
51.5
34.5
43.2
% with lower education
57.7
52.2
60.6
% with at least intermediate educ.
18.8
32.9
34.7
% with a higher level of education
7.2
15.6
12.7
% blue-collars
45.2
25.8
30.8
% executives
4.7
8.7
6.6
Unemployment rate
27.4
15.0
7.2
Unemployment rate of the under 25
33.2
23.6
9.0
Welloff
0.5
14.3
1074
29.4
47.9
44.2
20.8
18.0
11.5
6.6
10.9
Very
well-off
0.7
17.9
1212
16.1
33.3
51.8
30.3
9.6
18.6
8.0
20.6
Total
4.3
17.6
1033
28.8
45.7
42.1
21.3
19.8
12.4
10.0
17.7
The five neighborhood types were defined by a hierarchical ascending classification with
the Ward criterion.
39
Figure 1: The Brussels Extended Urban Area
40
Figure 2: Percentage of unemployed workers among labor-force participants
41
Figure 3: Percentage of North Africans and Turks
42
Figure 4: Density of total jobs and low-qualified jobs
43
Figure 5: Typology of neighborhoods
44
Table 1: A logistic regression of unemployment probability: comparison of different neighborhood
variables.
Model
Likelihood ratio
Ia
Ib
Ic
Id
Ie
1846.40
1993.80
2331.34
2343.80
2346.60
1.873***
1.740***
0.746***
Ref.
1.990***
1.737***
1.638***
0.742***
Ref.
1.970***
0.992NS
1.041***
1.741***
1.632***
0.737***
Ref.
1.962***
Neighborhood variables
Unemployment rate
Neighborhood type
Very deprived
Deprived
Industrial axis
Well-off
Very well-off
Distance to jobs
Job density
Distance to low-qualified jobs
Low-qualified job density
1.027***
0.992)NS
1.071***
Individual characteristics
Male
0.914***
0.921***
0.925**
0.926**
0.926***
***
***
***
***
Age
0.911
0.912
0.902
0.901
0.901***
Education
Lower
Ref.
Ref.
Ref.
Ref.
Ref.
Intermediate
0.600***
0.618***
0.635***
0.637***
0.637***
*
Higher
0.523***
0.554***
0.529***
0.530***
0.530***
Citizenship
Belgian (of Belgian parents)
Ref.
Ref.
Ref.
Ref.
Ref.
Belgian (of EEC parents)
1.460***
1.349***
1.311***
1.305***
1.303***
Belgian (of North-African or Turkish par.)
2.263***
1.665***
1.810***
1.785***
1.783***
***
***
***
***
Belgian (of parents of other citizenship)
3.272
2.735
2.628
2.600
2.600***
***
***
***
***
EEC
1.553
1.247
1.282
1.262
1.259***
***
***
***
***
North African and Turkish
2.897
2.039
2.271
2.238
2.234***
***
***
***
***
Other
3.204
2.680
2.656
2.634
2.631***
Number of observations: 32,421. Figures give the odds ratios.
***
significant at a 1% level; ** significant at a 5% level; * significant at a 10% level; NS not significant at a 10%
level.
45
Table 2: Sensitivity analysis to the presence of parental and household characteristics
I
II
III
IV
V
VI
Likelihood ratio
1867.61
1948.94
1976.94
2002.52
2089.50
2185.62
Neighborhood variables
Very deprived
Deprived
Industrial axis
Well-off
Very well-off
1.903***
1.724***
0.741***
Ref.
2.026***
1.817***
1.689***
0.732***
Ref.
2.026***
1.844***
1.699***
0.746***
Ref.
1.976***
1.863***
1.709***
0.756***
Ref.
1.927***
1.819***
1.649***
0.760***
Ref.
1.869***
1.707***
1.574***
0.760***
Ref.
1.853***
0.936**
0.898***
0.941*
0.893***
0.941*
0.891***
0.940*
0.889***
0.948NS
0.890***
0.961NS
0.893***
Ref.
0.641***
0.545***
Ref.
0.652***
0.562***
Ref.
0.648***
0.542***
Ref.
0.641***
0.517***
Ref.
0.650***
0.531***
Ref.
0.667***
0.548***
Ref.
1.391***
1.617***
2.464***
1.297***
2.350***
2.493***
Ref.
1.376***
1.472***
2.330***
1.227***
2.110***
2.248***
Ref.
1.414***
1.519***
2.326***
1.268***
2.161***
2.293***
Ref.
1.439***
1.563***
2.393***
1.305***
2.215***
2.393***
Ref.
1.540***
1.623***
2.516***
1.367***
2.386***
2.426***
Ref.
1.520***
1.617***
2.487***
1.361***
2.403***
2.348***
Model
Individual characteristics
Male
Age
Education
Lower
Intermediate
Higher
Citizenship
Belgian (of Belgian parents)
Belgian (of EEC parents)
Belgian (of North-African or Turkish par.)
Belgian (of parents of other citizenship)
EEC
North African and Turkish
Other
Parental and household characteristics1
Employment status and professional status
Not participating to labor force
1.281*** 1.314*** 1.315*** 1.226*** 1.191***
Unemployed
1.545*** 1.570*** 1.577*** 1.521*** 1.459***
Employed
Ref.
Executive
1.222*** 1.096NS
1.118*
1.128*
NS
NS
NS
1.061
1.017
1.020NS
Office worker
1.078
Intermediate profession
Ref.
Ref.
Ref.
Ref.
Farmer, trader
1.050NS 1.037NS 1.050NS 1.075NS
Blue-collar
0.893*
0.907NS 0.952NS 0.966NS
Education
Lower
Ref.
Ref.
Ref.
Intermediate
1.087*
1.090*
1.116**
Higher
1.330*** 1.343*** 1.390***
Male
0.670*** 0.758***
Possession of an automobile
0.670***
Number of observations: 27,635. Figures give the odds ratios.
***
significant at a 1% level; ** significant at a 5% level; * significant at a 10% level; NS not significant at a 10%
level.
1
Parental characteristics were built assigning to each young adult the characteristics of the household head when
present in the database (i.e. aged 64 or younger). If the household head was not in the database or if values were
missing, we used the characteristics of the household head's spouse. 4,786 cases had to be excluded, either
because values were missing for both the household head and his spouse or because none of them were present
in the database.
46
Table 3: Sensitivity analysis to the presence of an unobserved covariate
1.5
1.389***
1.341***
1.290***
1.275***
1.246***
1.239***
Sensitivity parameter c
2.0
2.5
1.405***
1.380***
1.300***
1.265***
***
1.237
1.215***
***
1.199
1.149***
***
1.176
1.112***
***
1.132
1.066*
3.0
1.384***
1.239***
1.178***
1.101**
1.039NS
1.026NS
3.5
1.397***
1.247***
1.148***
1.076*
1.018NS
0.991NS
1.5
1.357***
1.309***
1.260***
1.243***
1.215***
1.209***
Sensitivity parameter c
2.0
2.5
1.371***
1.346***
1.270***
1.237***
***
1.208
1.187***
***
1.171
1.119***
***
1.147
1.087**
***
1.103
1.041NS
3.0
1.352***
1.210***
1.152***
1.073*
1.016NS
1.002NS
3.5
1.361***
1.218***
1.121***
1.047NS
0.993NS
0.968NS
Model I
Sensitivity parameter b
1.0
1.5
2.0
2.5
3.0
3.5
1.0
1.392***
1.393***
1.391***
1.395***
1.377***
1.382***
Model II
Sensitivity parameter b
1.0
1.5
2.0
2.5
3.0
3.5
1.0
1.358***
1.359***
1.357***
1.361***
1.343***
1.348***
Sensitivity parameter c
Model VI
Sensitivity parameter b
1.0
1.5
2.0
2.5
3.0
3.5
1.0
1.316***
1.314***
1.328***
1.306***
1.311***
1.326***
1.5
1.317***
1.266***
1.230***
1.198***
1.167***
1.182***
***
***
***
***
***
2.0
1.313
1.221
1.171
1.151
1.118
1.083**
***
***
***
**
NS
2.5
1.317
1.206
1.133
1.085
1.039
1.013NS
***
***
***
NS
NS
3.0
1.299
1.174
1.109
1.056
0.982
0.967NS
***
***
*
NS
NS
3.5
1.304
1.172
1.071
1.007
0.972
0.938NS
Number of observations: 27,635. Figures give the odds ratios.
***
significant at a 1% level; ** significant at a 5% level; * significant at a 10% level; NS not significant at a
10% level.
Sensitivity parameters b and c measure the effect of an artificially created binary variable U on the
probability of living in a deprived neighborhood (expressed as the odds ratio for Y:U) and the probability
of unemployment (expressed as the odds ratio for X:U) respectively.
47