A Statistical Approach for Analyzing Residential Isolation and its Determinants for Immigrant Communities: an Application to the Montréal Metropolitan Region
A Statistical Approach for Analyzing Residential Isolation and its Determinants for Immigrant Communities: an Application to the Montréal Metropolitan Region
Guillaume Marois 0 1
0 École d'urbanisme et d'architecture de paysage, Université de Montréal, CP 6128 Succursale Centre-ville , Montréal, (Québec) H3C 3J7 , Canada
1 International Institute for Applied Systems Analysis (IIASA) , Schlossplatz 1, A-2361, Laxenburg , Austria
The aim of this paper is to measure the net propensity to live in isolation for Montréal's main immigrant communities and to identify specific profiles that are particularly isolated. For that purpose, a statistical approach is used based on individual determinants to compute standardized isolation indexes that take into account the socioeconomic composition of the different groups. The models we developed also reveal how individuals' characteristics, such as generational status, date of migration, education, language abilities or income, affect their residential isolation. Results reveal that many individual characteristics have strong impacts on residential isolation, and that those impacts are not always the same among immigrant communities. Also, the low propensity to live in isolation observed for all immigrant communities suggests that the place stratification model is probably not relevant to explain the residential dynamics of immigrant communities in Montréal. However, some vulnerable groups are much more likely to live in isolation: Haitian and South Asian with low education, low-income Maghrebis, and Filipinos who arrived via the Live-in Caregivers program. Some wealthy groups are also more isolated, such as Italians arrived before 1981. Therefore, considering this wide heterogeneity among immigrant communities, studies on their residential dynamic should not consider them as a whole.
Migration; Housing location; Segregation; Isolation; Montréal
International migration is a metropolitan phenomenon in some geographical areas that
affects a significant number of individuals (Faist 2000). We observe high
concentrations of people in specific places with highly differentiated social and economic capital
(Sassen 1991). In the Greater Montréal region (in the province of Québec, Canada),
immigration is obvious in central areas of the city, but also exists in certain first-ring
suburbs. Montréal is the destination of about 85% of those who immigrate to Québec
(Institut de la statistique du Québec 2015). As such, the Montréal region has high levels
of immigration and international mobility (Apparicio and Séguin 2002; Canada 2013)
and communities of foreign origin which have existed for generations (Germain et al.
2012). International immigration has influenced the demographic makeup of Montréal
for many decades. Montréal’s cultural, ethnic, and religious diversities are striking,
even though its concentrations of people with similar cultural backgrounds (which are
often stigmatized) are less than many other North American or Western European
metropolitan regions (Apparicio et al. 2006; Brown and Chung 2006).
Most researchers who study racial segregation and the spatial locations of immigrants
and/or ethnic groups focus on the United States of America. They propose exogenous
factors to explain their results, such as discrimination, housing markets, and group
preferences (Johnston et al. 2004). Canadian cities can hardly be compared to American ones; the
racial diversity in Canada is much more recent than in the United States and results from
Canada’s recent immigration policies rather than historical reasons. Unlike Canada’s other
metropolitan areas, Montréal has a unique ethnic context. Montréal is the only large North
American city in which the French language is predominant (though it has an important and
wealthy English-speaking minority, as well). The study of residential integration of
immigrants in that context would thus provide new empirical knowledge on the subject matter.
Apparicio et al. (2006) studied the segregation of Montréal’s immigrant and ethnic
communities by examining many segregation indicators to identify different dimensions of
segregation: equality, concentration, and isolation. Although these researchers did not
identify any hyperisolated immigrant communities in Montréal, their indicators are mainly
descriptive and may not be adequate for assessing the individual determinants of segregated
living. Furthermore, very isolated groups which combine many individual characteristics
that foster a propensity for segregation could exist, although diluted in the gross isolation
index of a larger group. In the context of residential segregation, we note the concurrent
presence of immigrants with few integration Bcapabilities^. This is connected to notions of
economic, social, and cultural capital (Bourdieu 1979; 2000). Thus, disadvantaged
immigrants must be distinguished from others who have more opportunity, especially in housing
integration and ownership accession (Borjas 2002). As Iceland and Scopilliti (2008) noted,
"it would be useful to look at patterns of specific groups in more detail, as there is much
intragroup variation in both the historical context of immigration and the characteristics of
the immigrants themselves" (p.93). This paper proposes an approach in response to this
The aim of this paper is to measure the net propensity to live in isolation for
Montréal’s main immigrant communities and to identify specific profiles that are
particularly isolated. Residential isolation is a key dimension of the segregation and
refers to the spatial cohabitation among members of a same group (Johnston et al.
2004). We examined how individuals’ characteristics of Montréal’s main immigrant
communities influence their segregation level. First, we discussed on methodological
issues in regard to the calculation of a standardized isolation index that takes into
account both size and composition of the communities. Following this discussion, we
made an empirical demonstration of the standardized isolation index to measure
immigrant communities’ net propensities to live in segregation by performing statistical
controls on sociodemographic characteristics, which may impact their residential
locations. Finally, we identified the individuals’ determinants of residential segregation,
which then allows to identify specific profiles which are particularly isolated.
Residential choices are complex. This research, therefore, contributes to the
understanding of residential choice by focusing on individuals’ determinants of isolation
using a statistical approach. We postulate that isolation can be explained by multiple
factors related to individual characteristics as well as the interactions of the person with
the city and communities. Montréal is a relevant study case, because, as explained
below, its context allows controlling many variables of interest. In this way, the
complexity and relativity of residential isolation are better approached.
While the integration of immigrants involves many factors, including language,
employment, and education, the issue of housing remains crucial for immigrants’ residential
trajectories and establishment (Séguin et al. 2003). For decades, urban segregation has
involved both housing availability and housing conditions. Developed from the BChicago
School^ and urban ecology approaches (Park and Burgess 1925), segregation and spatial
patterns of immigrants’ residential locations interested researchers for decades. Immigrants’
integration into residential environments is thus well documented, providing well-known,
rigorous, and validated interpretive frameworks at both Bmacro^ and Bmicro^ levels (Park
and Burgess 1925; Massey and Denton 1985; Fong and Gulia 1999; Logan et al. 2002;
Apparicio et al. 2006; Andersen 2010). According to the spatial assimilation model,
immigrants first settle into segregated ethnic enclaves with poor housing conditions. As
they ameliorated, over the years, their socioeconomic status and language abilities, they also
improve their spatial location and housing situations and move to less segregated sectors. We
should thus expect to observe a lower propensity to live in residential isolation for
established and wealthier immigrants.
However, this trend could be altered when discrimination in the housing market or
structural barriers interfere into the residential opportunities (Charles 2003), what scholars
call the ethnic disadvantage model or the place stratification model. This pattern have been
widely observed and documented for American metropolitan areas, where discriminatory
practices from real estate agents and bank institutions as well as neighborhood hostility lead
to a persistent segregation for Afro-Americans (Turner et al. 2002; Iceland and Scopilliti
2008; Iceland and Wilkes 2006). Although the racial segregation tended to decline over
the last decades, Logan (2013) suggests that the residential behaviors of Whites
could limit the progress toward multi-ethnic metropolis, because they tend to
leave mixed neighborhoods and avoid areas where minorities are predominant.
More recently, many scholars also found that middle and upper class immigrants
could have preference to live with their ethnic group, reflecting a natural ethnocentrism
rather than social constraints or hostility between groups (Chung and Brown 2007;
Brown and Chung 2008; Walton 2012; Wen et al. 2009; Charles 2003). This residential
dynamic called resurgent ethnicity could maintain segregation or create resegregation.
Similar to the ethnic disadvantage model regarding the spatial distribution of
populations, in the resurgent ethnicity model, ethnic concentration is not necessarily
associated with poor quality neighborhood, social disorganization and uneven access to
amenities, particularly when the presence of well-educated people is appreciable
(Galea and Ahern 2005).
Moreover, social relationships are not limited to the residential dimension, immigrants’
residential proximity to other groups promotes their acculturation, linguistic skills, and
exogenous unions (Apparicio 2000). Residential segregation has multiple meanings that
make it difficult to explore merely with spatial differentiation (Brun et al. 1994). Whether
segregation takes on the attributes of Bghettos^ (with negative attributes) or Benclaves^
(closer to the notion of withdrawal) these concentrations are similar geographical
phenomena. These concentrations (objectively the same) imply different subjective experiences by
immigrants (Balakrishnan and Hou 1999). On the one hand, spatial concentration can lead to
negative effects such as limited access to urban resources and educational facilities. It can
also imply exclusion from labor markets, higher dropout rates, higher levels of youth crime,
and higher risks of stigmatization (Shihadeh and Flynn 1996; van Kempen, and şule
Özüekren, A. 1998; Fitoussi et al. 2004; Karsten et al. 2006; Sélimanovski 2008). From
this perspective, Johnston et al. (Johnston et al. 2002; Johnston et al. 2005) defined
segregation as a pattern linked with social exclusion, unequal treatment and negative effects
on feelings of selfesteem and identity among the minority population. On the other hand, the
same residential segregation can be seen as a positive Bresource^ which allows immigrants
to preserve their identities, languages, religions, and cultures (Dunn 1998; Peleman 2002;
Simpson 2004). More broadly, segregation provides them with spatial differentiation that
may actually facilitate their further integration.
As observed by Musterd et al. (1998), residential segregation (from the actor’s
standpoint) may be a strategy that hides another: the expectation of integration. If
immigrants arrive into a well-established immigrant community where they can easily
access social networks and cultural resources, the integration process, in general, and
the housing market integration, in particular, can be much easier. However, less
qualified immigrant groups with lower levels of social capital do not experience all
the benefits of this concentration (Cutler et al. 2008). This perspective shows the limits
of the theoretical and empirical frameworks connected to residential segregation.
Although common spatial segregation indicators provide detailed geographical
descriptions of residential locations of the studied groups, they do not assess the individual
determinants of segregated living and how individual characteristics of the immigrants
affect their residential behavior (Spivak et al. 2011). As such, the segregation level might
be considered low for animmigrant community as a whole, while one of its
subpopulations is highly isolated. Moreover, owing to their descriptive nature, these indexes
do not take into account groups’ socioeconomic composition. For example, a community
A in which most of members are new immigrants will probably have a higher isolation
level than a community B in which most of members immigrated long time ago. This does
not mean that member of community A have a higher propensity to live segregated, rather
the difference between the observed segregation would be a group composition effect.
This issue has been highlighted by Simpson (2004), who showed that the observed
increase of the number of majority South Asian areas in UK is due the arrival of
newcomers and natural growth rather than by a movement of South Asians toward those
areas. Overall, using descriptive segregation indicators could lead to confound social
trends as racial phenomenons. A descriptive approach trying to account for a variable such
as socioeconomic status require the calculation of index for every category and for all
studied groups (Spivak et al. 2011; Apparicio et al. 2006). This approach, however,
become very complex when it comes to plenty of variables, particularly when there are
interactions between them, such as income, education and year of immigration. Evidence
of the effect of the groups’ socioeconomic composition on segregation patterns are also
exposed by macro-level regression models (Iceland and Scopilliti 2008; Iceland and
Wilkes 2006; Hall 2013) using average group characteristics as independent variables,
but none of these focused on the individual level.
A widely used indicator of residential isolation is Bell’s isolation index (Bell 1954;
Apparicio 2000). This indicator represents the average proportion of people from the
same group living in a given neighborhood. This refers to the exclusive presence of the
same group in a neighbourhood – one of the main aspects of segregation. For an
immigrant community c, the isolation index Ic is defined by Eq. 1:
I c ¼ ∑
xjc=X c xjc=t j
is the population of community c in neighborhood j;
is the population of community c in the metropolitan region;
is the total population of neighbhorhood j.
Thus, an index of 1 would indicate that all member of community c live a
neighborhood where 100% of the population belong to the same community (complete
However, the proportion of the immigrant community in the metropolitan area
affects this index, because the mean proportion of people of the same community
will be higher for largest communities. In fact, if there was no segregation at all, Ic
would be the same as the proportion of the group in the total population, and
largest communities would thus have highest indexes. To take into account the
relative proportion of differents groups in a metropolitan area, the isolation index
can be transformed to get the modified isolation index MIc (Bell 1954; Apparicio
2000), as defined by Eq. 2:
M I c ¼ ðI c−X c=T Þ=ð1−X c=T Þ
in which T refers to the total population of the metropolitan region.
A MIc score close to 0 indicates that the proportion of community c is the same
across all neighbourhoods in a metropolitan area. However, if the index is closer to 1,
the group is more isolated.
As explained above, the composition of immigrant communities could also have a
significant impact on the isolation index (both gross and modified). To take this into
account in order to get a standardized isolation index, we used an approach inspired by
the Blocational attainment model^ (Alba and Logan 1992) In this model, a
characteristic of neighborhoods is the dependent variable and individuals’ characteristics are
independent variables. Many studies use neighborhoods’ median incomes or their
proportion of BWhites^ as dependent variables to determine if minority groups such
as BBlacks^ or BHispanics^ have the same access to desirable neighborhoods as
BWhites^ with similar characteristics (Logan et al. 1996; Myles and Hou 2003; Cort
2011; Glikman and Semyonov 2012). Fong and Chan (2010) built similar models using
the proportion of the same community in the neighborhood as dependent variable to
assess the net effect of economic resources, co-ethnic preferences, and the use of
coethnic information sources on co-ethnic clustering.
Following this approach, in this study, we set as the dependent variable the
proportion of the population in the neighborhood that belongs to the same immigrant
community. For a community c, the model can be defined by Eq. 3:
Ycis the proportion of the population in the neighborhood that belongs to community
Xkicare a set of characteristics k for an individual i from community c;
βkcare the parameters of characteristics k for community c.
When there are no independent variables, because the dependant variable is the
proportion of the same community living in the neighborhood, the intercept αc
corresponds exactly to the isolation index described above, but calculated from
disaggregated data (individual level) (Alba and Logan 1992). Calculating isolation indexes
by regression methods furthermore provides confidence intervals that inform
whether or not values result from a random process. Therefore, in Eq. 3, the
parameters β indicate the impact of related variables on the propensity to live
in isolation. In other words, analysis of β assess how individual characteristics
affect communities’ propensities to live in isolation. When there are a large
number of characteristics, αc can be considered as a standardized isolation
index SIc (see Eq. 4), i.e. the isolation index for the specific profile of the
reference categories of Xk. We can then determine which community is the most
likely to live isolated for a standardized profile by comparing their respective αc. In our
models, reference categories have been chosen prioritizing that they all count significant
number of individuals for every community. Moreover, this statistical approach at the
individual level gives confidence intervals for indexes which determine whether or not
they differ significantly from each other.
As described in Eq. 5, by performing the same transformation to SIc as the one we
used to get MIc, we obtain a standardized modified isolation index SMIc which takes
into account both the population composition and the proportion of the group.
SM I c ¼ ðSI c−X c=T Þ=ð1−X c=T Þ
Locational attainment models may contain inconsistencies due to spatial
autocorrelation because multiple individuals live in the same sector and thus have the same value
on the dependent variable (Logan et al. 1996; Cort 2011; Pelletier 2012). This situation
underestimates the standard error of parameters, because errors are correlated. Thus, as
other authors making use of locational attainment models have suggested (Myles and
Hou 2003; Cort 2011), we estimate parameters using the feasible generalized least
squares method to generate standard errors that take into account the correlated error
terms within neighborhoods (Greene 1997).
The second part of the analysis identifies specific profiles that display a higher
propensity to live in isolated neighbourhoood. Using the results of multivariate
analyses completed in the first part and taking into consideration the descriptive statistics of
immigrant communities. The SMIc is for a specific profile that corresponds to the
reference categories of independent variables. Thus, by adding to the SMIc selected
specific characteristics’ parameters, we get alternative isolation index for specific
Our analysis requests a large individual database that contains residential location at a
small geographical level and exhaustive individual characteristics, including an ethnic
dimension. The most recent database that provides that kind of information is the 2011
National Household Survey (NHS) (Canada 2013). That database contains individual
characteristics such as gender, age, family structure, income, education, year of
immigration, language, etc., but also information on the place of birth of the respondent and
his parents, which allows constructing immigrant communities based on the country of
origin of the first and second generation. The NHS also divided the Montréal
metropolitan area in 921 in sectors (census tracts) that can be used to define the
neighborhood. However, the NHS replaced the long form census that is no longer mandatory.
The response rate is about 70%, but could vary widely between groups. Consequently,
the non-response bias is likely to be larger for specific groups such as recent immigrants
or those who don’t speak French or English, which could underestimate their isolation
rates. The NHS remains the most comprehensive Canadian data source on population
and housing conditions. The 2006 mandatory long form Census has less bias, but is
outdated; since then, more than 250,000 immigrants settled in the province of Québec,
mainly in Montréal. Using this former census would reduce the relevance of analysis
because the context has changed. However, with the return of the mandatory long form
census in 2016, further investigations could be made to evaluate the plausible bias for
The Montréal metropolitan area has dozens of immigrant communities, making it
impossible to analyze each individually. Thus our analyses focus on the main groups.
For our purposes, an immigrant community consists of individuals living in a
household where the breadwinner1 is either born abroad or has a parent who is born abroad.
The basic principle behind the construction of communities is based on the country of
birth of the breadwinner or his parents, depending of his generational status, either:
Country of birth if born outside Canada;
Country of birth of parents if both are born outside Canada, from the same country;
Country of birth of the mother if both parents are born outside Canada, but from
Country of birth of the foreign-born parent if only one parent is born outside
We then regrouped countries that have contextual or geographical similarities
and kept the 15 main immigrant communities, excluding those who come from
France, England and USA for historical reasons. The characteristics are thus
those of the breadwinner, which is the usual procedure for that kind of study
(Rosenbaum and Friedman 2001; Myles and Hou 2003). This procedure allows
a better accuracy on the residential location of the second generation. Otherwise
these behaviors could be biased by children who still live with their parents
Descriptive Statistics of Immigrant Communities
The 15 immigrant communities of the Montréal metropolitan region studied in
this article are the following: Italian, Maghrebi, Haitian, Lebanese, Greek,
Chinese, Vietnamese, Portuguese, Romanian, Filipino, South American,
Central American, Sub-Saharan African, people from other Arabic countries,
and South Asian. These communities represent 1.1 million people—about 30%
of the population of the Montréal metropolitan area (3,752,475). Each of these
communities numbers at least 30,000 people. The Italian community is the
largest (167,000), followed by the Maghrebi (140,000) and Haitian (117,000)
communities. In Appendix 1, we provide descriptive statistics of these
Only Haitian, Central American, and Filipino communicities have similar
proportions of males or females as breadwinner. Males are the predominant
breadwinner in all other groups—particularly in the South Asian, Maghrebi, and
Lebanese communities, and for people from other Arabic countries. The
Filipino case may be due to the fact that many Filipinos immigrated to
Montréal via the Live-In Caregivers program, which mainly recruits women.
The predominance of female breadwinners in the Central American and Haitian
groups may be due to their larger proportion of single-parent families (25.1%
and 29.5%, while the proportion varies between 6.2% and 16.9% for other
communities), which are typically led by women. On the other hand, the South
1 The breadwinner refers to the primary household maintainer.
Asian (75.1%), Maghrebis (72.8%), and Lebanese (69.5%) had the largest
proportions of couples with children, which can is probably related with their
higher proportion of men as breadwinner.
Most people in the Italian (90%) and Greek (80%) communities immigrated before
1971 or are members of a second generation. Conversely, a large proportion of
Montréal’s Maghrebis, Chinese, Romanians, and Sub-Saharan Africans are
recent immigrants. More than 40% of them arrived in the 2000s and very
few of them are born in Canada. Most Haitian and Vietnamese immigrants
arrived between 1971 and 1990, in the wake of political unrest in their home
countries. Many Lebanese and South Asian people came to Montréal in the 80s
and the 90s. These groups’ time of arrival is reflected in the age of the
breadwinner. The Italian and Greek communities are the oldest, with more than
a quarter aged 65 and over; the proportion of elderly people among Maghrebis
and Sub-Saharan Africans (more recent arrivals) is below 5%.
Human capital, measured by education level and knowledge of official languages
differs widely between immigrant communities. Romanian, Chinese, Maghrebi, and
people from other Arabic countries are the most educated; nearly 50% of these
people who live in households in which the breadwinner has at least a bachor’s
degree. As noted above, these people are more recent immigrants; they enter
Canada primarily as qualified workers. The oldest waves of immigration are the
less educated since they arrived before the implementation of Canada’s
economic immigration programs. Many Portuguese, Greek, and Italian people live
in households in which the breadwinner does not have a degree (38.0%, 31.7%,
and 25.1%, respectively); very few have university degrees (11.8%, 18.9%, and
18.7%). Some communities consist of refugees; a high proportion of these
people have little or no education, such as in the Vietnamese (25%), Central
American (22.8%), South Asians (20.6%), and Haitian (18.6%) communities.
Many groups have a large proportion of people who know both English and
French, specifically the Italian (81.3%), Romanian (77.6%), and Lebanese
(76.6%) communities. However, some groups are more likely to only know
English, such as the Filipino (76.7%), South Asian (66.6%), and Chinese
(52.9%) communities. The only group in which fewer than half of people knew
English was the Haitian community; however, 53% of Haitians understand
French only. In the Chinese (15.6%) and the Vietnamese (11%) communities,
a notable proportion of people live in a household where the breadwinner did
not understand either English or French; this number is marginal in the other
communities we studied.
These communities are very heterogeneous in terms of their social capital,
generational status, and time since arrival; we were not surprised to find that the
distribution of population according to household’ income quintile varied
widely among these communities. The groups with the highest proportion in the
poorest quintile are the Maghrebi (37.7%), Sub-Saharan African (37.2%), South
Asian (35.8%), and Central American communities (34.6%). The groups with
the highest proportions in the wealthiest quintile are the Italian (22.6%), people
from other Arabic countries (19.8%) and Romanian (19.5%) communities. The
Italian community is the only group with an overall wealth greater than the
Montréal Metropolitan average.
Statistical Analysis of Residential Isolation
Immigrant Communities’ Net Propensity to Live in Isolation
We compared the standardized modified isolation indexes SMIc, which are derived
from the intercept value (SIc,) of the models (Appendix 2), to measure the propensity to
live isolated of communities for a similar profile. That profile corresponds to the
reference categories of the independent variables, which are those living in a household
where the breadwinner is a male immigrant arrived in Canada between 1981 and 1990,
aged 30 to 34 years old, has a university degree, is bilingual and has an income in the
third quintile. The SMIc in relation to the gross modified isolation index MIc shows the
effect of the composition of the population on the isolation (Fig. 1).
These results show that no communities are hyperisolated in the Montréal
metropolitan area; this supports Apparicio’s observations (2006). Indeed, the most isolated
communities have modified isolation (MI) indices only slightly higher than 0.10; the
scale goes up to 1. In comparison, Johnston et al. (2004) showed that MI indices can
exceed 0.5 for BBlack^ communities in some American metropolitan areas; the mean
was 0.253 in 1980 and 0.208 in 2000. However, in contexts where many immigrant
communities coexist in the same neighborhoods, a non-isolated community could still
have little residential proximity to the majority group.
While none of the communities we studied are hyperisolated, their levels of isolation
nevertheless vary. MIc numbers equal to or greater than 0.1 for the Italian (0.124),
Filipino (0.121), Greek (0.100), and South Asian (0.100) communities, meaning
that regardless of their sociodemographic compositions, these communities are
the most isolated in Montréal. Conversely, the metropolitan region’s least
isolated communities are the South American, Romanian, and Portuguese,
which have MIc levels below 0.02.
Our results show that for most communities, the standardized modified index SMIc
differs widely from the MIc. Because MIc are generally out of range of the confidence
interval of SMIc, we can confirm that differences in residential isolation among
Standardized modified isola on index (SMIc)
Modified isola on index (MIc)
T Confidence interval (95%)
Fig. 1 Standardized modified isolation index compared to modified isolation index of immigrant
communities, Montréal metropolitan region, 2011. Source: 2011 National Household Survey; Author’s calculations
communities are partly explained by differences in their socioeconomic compositions
and/or the differential behaviors of some subgroups. For instance, the SMIc of Greeks
(0.045) and South Asians (0.023) are much smaller than their corresponding MIc; these
communities do not statistically differ anymore from others when wecompared the
isolation of the reference profile. The MIc of Central Americans (0.035) and
SubSaharan Africans (0.030) are low, and their SMIc even lower (0.006 and 0.008,
respectively). Considering the condifence interval, this suggests that segregation in
terms of residential isolation is almost nonexistent for the reference profile of these
communities. On the other hand, the SMIc for Haitians and Italians are quite similar to
their MIc and these communities remain more isolated than others. Indeed, the Italian
community is the only one with a standardized index above 0.10.
Individual Determinants for Immigrant Communities to Live in Isolation
The independent variables of these models have divergent effects on the propensities of
Greater Montréal’s immigrant communities to live in isolation. Detailed parameters
(estimated from models) are presented in Appendix 2.
Gender only significantly affecsd the propensity to live in isolation in the Central
American, Lebanese, and Maghrebi communities. For Central American Lebanese
communities, members living in households where the breadwinner is a women are
more likely to live in isolation than those with a male breadwinner, according to the
parameters associated with that category (0.006 and 0.010, respectively). This
relationship is reversed in the Maghrebi community, in which female breadwinners are a little
less likely to live in isolation (−0.004) than males. However, considering the weak
magnitude of these parameters, the effect of gender on the isolation index remained
low. In the other communities we studied, this variable is not statistically significant.
Breadwinner age has little effect in most communities, except for those from Bother
Arabic countries^, in which the parameters clearly show that the propensity to live in
isolation increases with age.
Our regression models show that time of arrival is an important factor in
communities’ levels of isolation. According to the spatial assimilation theory (Massey and
Denton 1985), immigrants tend to live in segregated neighborhoods for their first few
years in a new country and adopt the residential behaviors of natives after their
assimilation (and in later generations). Thus, we could expect to observe a negative
relation between people’s propensity to live in isolation and the number of years since
their arrival—and also a lower propensity for isolation in second generations. In Fig. 2,
we illustrate the parameters of this variable for communities in which this relationship
is both significant and conclusive.2 We used those who arrived between 1981 and 1990
as a reference category (parameter = 0). All other parameters must be analyzed in
relation to that category.
As illustrated in Fig. 2, we observ the expected pattern in Filipino, Lebanese, South
Asian, and Sub-Saharan African communities. However, the amplitude of this effect
differs; the difference between arrival periods is much more important for the South
Asian community than the Sub-Saharan African and Lebanese communities, for which
the statistical significance of the pattern is not concluent. We also observ the expected
2 p < 0.05 for most of categories
Fig. 2 Effect of immigrants’ arrival periods and generational status on the proportion of the same immigrant
community living in the neighborhood, Montréal metropolitan region, 2011. Source: 2011 National
Household Survey; Author’s calculations
pattern in Haitians, but only in the older waves of immigrants and second generations;
there is no significant difference of parameters for immigrants who arrived after 1981.
In Italians, we observe the opposite pattern of what expected by the spatial
assimilation theory. Those living in a household where the breadwinner arrived before 1981
have much higher propensities to live in isolation, with parameters above 0.03.
However, the low number of Italians arrived past 1990 reduces the statistical
significance for recent cohorts. There is also an significant gap in the second generation
between those with two parents born abroad and those with only one parent born
abroad. Those with only one foreign-born parent are much less likely to live in
isolation. However, this result doesn’t mean that this community tend to live more in
isolation following years spent in Canada. This is probably due to different behaviors
between cohorts. A large proportion of Italians immigrated before 1971, when the
social and political context in Montréal was very different. Since the 70s, several policy
changes have affected the behaviors of immigrants, in particular the provincial
repatriation of immigration selection, the selection of immigrants according to their economic
and social capital, and regulations on language and diversity. Thus, we cannot assume
that recent immigrants (very few in the Italian case) will have the same behaviors as
The Maghrebi community exhibits an intriguing dynamic. Although their trend in
residential isolation follows the spatial assimilation theory pattern for those who arrived
after 1991, we observed specific behaviors that increase isolation in older immigrants.
Moreover, second generation immigrants whose parents were both born abroad
demonstrated a higher propensity to live in isolation (0.01); this effect is much lower in
those who had only one foreign-born parent (−0.027).
In other communities, the relationship between their period of arrival and the
propensity to live in isolation is not significant. However, this doesn’t necessarily
contradict the spatial assimilation theory, which also takes into account the immigrants’
income; this tends to increase over people’s years of residence in the country. This
situation can be seen in the case of the Central American community. For them, the
relationship between their propensity to live in isolation and their period of
arrival is not significant. However, we observ a clearly significant and negative
relationship with income (Fig. 3). For these specific communities, income is
more important than period of arrival, although both are related. In
communities whose period was significant, income also had an effect. Thus, the
combined effect of isolation is very high for newcomers and the very poorest—
specifically South Asians and Filipinos.
Although income is related to education, our models distinguish the effects of these
two variables. The overall relation is the same: more educated people are less likely to
live in isolation than less educated people (as illustrated in Fig. 4). However,
the amplitude of this effect is not the same between communities (and is not
significant for some of them). This effect is much higher for Italians, Haitians,
Greeks, and South Asians, with parameters above 0.25 for less educated people
as compared to people with a bachelor’s degree level of education or higher.
This relationship is significant, but smaller, in the Lebanese, Portuguese,
Vietnamese, and Central American communities. It’s not relevant to the other
communities we studied.
The effect of family structure on the propensity to live in isolation is
relatively low. However, there is a significant effect in the category Bcouples
with children^, which has an increased propensity to live in isolation compared
to couples without children—especially in the Italian (0.02) and Greek
communities (0.015). This exists in lower proportions (but is still significant) in
people from Maghreb (0.008), Lebanon (0.009), Vietnam (0.006), Central
America (0.006), and Sub-Saharan Africa (0.005). Single family parents only
appear to be more likely to live in isolation in South (0.002) and Central
American (0.007), Sub-Saharan African (0.006), Lebanese (0.012), and
Vietnamese (0.008) communities, though these parameters are quite low.
Fig. 3 Effect of income quintiles on the proportion of the same immigrant community living in the
neighborhood, Montréal metropolitan region, 2011. Source: 2011 National Household Survey; Author’s
T Confidence interval (95%)
Fig. 4 Effect of education on the proportion of the same immigrant community living in the neighborhood,
Montréal metropolitan region, 2011. Source: 2011 National Household Survey; Author’s calculations
The last independent variable in our analysis, the knowledge of Canada’s
official languages, French and English, has an effect on most groups’
propensities to live in isolation, except for the Sub-Saharan African, South American, and
Vietnamese communities. The isolation is much higher in those who do not
understand either of Canada’s official languages than in those who know both of
them in the Italian (0.035) Greek (0.042), and South Asian (0.032) communities.
This effect is smaller, but still significant, in the Portuguese (0.009) and Chinese
Identification of Specific Isolated Profiles
Combining the different parameters of the previous statistical analysis in regard to the
crossed effectives of the categories, we can identify profiles that are more likely to live
in isolation and which could have policy-concerns.
The SMIc for Haitian community was not among the highest we observed, but
was still appreciable (0.050). The reference profile concerned with the SMIc are
those with bachelor degree levels of education or higher. Statistical analysis
revealed that the propensity to live in isolation is much lower for those who
live in households where breadwinner has secondary level educations (+0.034)
or less (0.042). This represents about the third of the Haitian community.
Furthermore, we noted that those who arrived before 1981 were much less
likely to live in isolation. Thus, a first profile with a high propensity to live in
isolation can be described: Haitians who arrived in 1981 or later and have a
secondary level of education or less. This profile probably represents people
living in households in which the breadwinner is a Haitian that immigrated as a
refugee or dependent, which corresponds to minimal education levels. This
profile represents about 25,000 people (21.5% of the Haitian community).
Italians with Low Education Levels who Arrived before 1981
Although the SMIc for the Italian community was the highest of all the
communities we studied, it corresponds to a reference profile in which period
of arrival is between 1981 and 1990. Statistical analysis reveals that the
propensity for living in isolation is much higher in those who arrived before
1971 (+0.026) and between 1971 and 1980 (+0.030), which represents 41.1%
of the Italian community. Another specific individual characteristic that
significantly increases their propensity for living in isolation is education; very high
parameters are observed for those with a secondary level of education (+0.033)
or less (+0.045). They represent an important part of the community (44.6%).
This second profile corresponds to Italians who arrived before changes in
selection process of immigrant workers based on social capital. This group
includes 46,000 people. Many of the Italians that arrived during this period
have sponsored a family member. These people are among the oldest still-alive
waves of immigration. Because they are the wealthiest immigrants group, the
residential dynamic of resurgent ethnicity could probably explain this higher
Greeks with Low Education Levels who Arrived before 1981
The SMIc of Greeks was quite high (0.058). Less-educated Greeks, who are
very numerous (53.2% have a secondary level or less), were much more likely
to living in isolation (+0.032 / +0.033). Though their period of arrival is not
significant, very few of them arrived after 1981 (3.7%). Thus, a third profile
can be identified: Greeks with low levels of education who arrived before 1981,
which represents 18,900 people (36.7% of the Greek community).
Many Maghrebis have recently arrived in Montréal (58.0% arrived after 2000).
Although they are educated (they were selected for immigration based on their
economic and social capital) many of them find difficulty in the labor market.
This occurs for many reasons, including literacy, numeracy skills, and
discrimination (Picot and Sweetman 2005; Ferrer et al. 2006; Eid 2012). Thus, a large
proportion of these people are in the poorest income quintile (37.7%); they also
present the highest propensity to live in isolation (as revealed in our statistical
analysis). Thus, the moderated SMIc for the Maghrebi community (0.034),
which concerns the third quintile, became much higher for the poorest
(+0.022) in this group. Because Maghrebis are a very important recent
immigrant groups and expected forthcoming ones, this specific profile’s isolation and
housing situations are policy-concerns. Our specific analysis of these
communities calls for a more detailed analysis of the Maghrebi community’s
level of integration and residential strategies.
Filipinos who Arrived via the Live-in Caregivers Program
Statistical analysis of their isolation levels reveals that Filipinos are the most
likely of the groups we studied to live in isolation, as illustrated by their SMIc
(0.079), the second highest we studied. Moreover, this propensity is much
higher in those living in households in which breadwinners had arrived within
the last 10 years (+0.039 for those arrived between 2001 and 2006 and +0.040
for those arrived after 2006) and for those in the two poorest income quintiles
(+0.035 and +0.017). We observed 2440 Filipinos who arrived in 2001 or later
and whose income was in the two poorest quintiles (8% of the community).
Although they are not very numerous, this group’s situation is of great policy
concern. Many Filipinos in this situation are immigrants who arrived via the
Live-In Caregivers program, which mainly recruits women from the Philippines
(Castonguay et al. 2009). This program gives permanent residence to people
who spend two years serving their employers as live-in caregivers. Following
this period, they can find their own housing and sponsor their families.
Although postsecondary degrees are required for acceptance into this program,
many studies show that the professional, social, and economic situations of
these people are quite poor (Rose and Ouellet 2000; Bilala 2013).
South Asian Refugees
South Asians have a very high MIc (0.100), but a much lower SMIc (0.023),
meaning that their high isolation is mainly explained by their composition.
Statistical analyses reveal a much higher propensity to live in isolation in
immigrants arrived in or after 1991 (up to +0.049 for those arrived between
2006 and 2011). The propensity to live isolated is also increased in those who
live in a household where the breadwinner has no bachelor degree (up to
+0.048 for those who don’t have secondary level), is in the two poorest income
quintiles (respectively +0.026 and +0.014), and does not speak an official
language (+0.032). These specific profile counts 28,020 individuals, a
significant part of the community (38%). This particular profile seems correspond to
refugees, since they do not have characteristics that are associated with
economic immigration programs.
For most communities, the results of this study are consistent with the classical spatial
assimilation model. As expected, residential isolation tended to decrease when the
socioeconomic status and the number of years lived in the country increase. However,
higher residential isolation levels are observed for Italians and Greeks who immigrated
long time ago. They are wealthier than the average and also have good housing
situations (Balakrishnan and Wu 1992), suggesting that a resurgent ethnic dynamic
has been in operation.
The low standardized isolation indexes for immigrant communities suggest
that discriminatory practices from real estate agents and bank institutions as
well as structural barriers in the accessibility to ownership are probably lower
in Montréal for immigrant communities than in American metropolitan areas. In
support of this hypothesis, Marois and Bélanger (2014) also found that there is
no BWhite Flight^ in Montréal, because the propensity to move from the inner
city to the suburb was the same for members of visible minority and Whites
once the income, the duration of residency in Canada and the language are
statistically controlled. Thus, the place stratification theory that is used to
explain the residential dynamic of Afro-Americans and Hispanics in many
American cities (Logan and Alba 1993; South and Crowder 1998; Pais et al.
2012) is probably not relevant for the residential dynamic of immigrant
communities in Montréal. Additional investigation should be made in regard to the
immigrants’ neighborhood and the quality of their housing situation to be more
In regard with the Maghrebis, a particular issue is revealed as the study did
not find lower isolation for the second generation, as expected. This result
shows the limits of the theoretical and empirical frameworks connected to
residential segregation and calls for a reinvestment in Bclassical^ experiential
approaches with rigorous mixed quantitative-qualitative methods. Does this
observation result from discrimination in the housing market or from persistent
preferences to live in co-ethnic sectors? Apparicio et al. (2006) found that
religion was a more important variable than ethnicity in the residential segregation in
Montréal. In addition, Tournier (2013) reported that the second generations of
Maghrebis in France were more religious than their parents and this return to religion
was not related to socioeconomic status or discrimination. Although the context is not
the same in Canada, the hypothesis of an increase of the religiosity in the second
generation of Maghrebis could maybe explain the particular pattern observed in our
study for this subpopulation. This being said, we suggest investigating residential
environments far beyond merely residence locations.
From a basic perspective, we found that many individual characteristics strongly
impact the residential isolation and that the effects are not always the same between
different immigrant communities. For instance, our models showed that the income
quintile is a very important determinant of the residential isolation level in the
Philippian and South Asian communities, while this variable was not significant for
some others such as the Chinese and Vietnamese communities. Similarly, the isolation
level differed widely when considering education in the Haitian and South Asian
communities, but the amplitude was much smaller in the Vietnamese and Portuguese
communities. Isolation and integration process are realities that vary according to
specific cultures of origin and possibilities for mobilizing social capital. When studying
residential trajectories and location, considering immigrant communities as a whole
could hide particular dynamics of subpopulations. This is particularly important to
consider when the immigrant population is very heterogeneous like in Montréal and
many other North-American metropolises. The individual determinants based approach
used in this study allowed identifying specific profiles, such as the low-income
Maghrebis and the Haitian refugees, for which the higher isolation would be diluted
in the low isolation index observed in their whole community.
The statistical approach used in this study can only measure the residential
segregation under the dimension of isolation. However, other dimensions also exist,
such as equality, concentration and spatial aggregation, which all have relevance
for measuring different spatial dynamics, those bring into play contrasted
integration processes. Although Apparicio et al. (2006) found that all these
dimensions have low segregation levels in Montréal for immigrant communities, further
works should investigate this kind of analysis for specific profiles, such as those
identified in our study.
Our analysis confirms that there are no hyperisolated immigrant communities in
Montréal. The statistical method used in this study however shows that
propensity to live in isolation varies between communities. The estimated
parameters also allow positing that people in certain profiles are much more likely to
live in isolation. Moreover, some of these people have vulnerable socioeconomic
characteristics. Among them, Filipinos who arrived via the Live-In Caregivers program
are of particular concern; they often face poverty and isolation. However, isolation is not
always correlated with poor housing conditions. Furthermore, the absence of isolated
communities does not mean that Montréal does not have neighborhoods with very high
levels of ethnic concentration. The question of the interaction with native communities
should also be investigated. These fingings led us to research perspectives on integration
trajectories for housing markets and residential conditions.
Lived and used environments (segregated or not) can be jointly studied to aid in the
understanding of the structures and mechanisms underlying segregated logics. Such
work facilitates advances in the analysis of residential concentration beyond the notions
of Bsocial belonging^ and Bsocial-spatial proximity^. The next step is to go beyond
spatial concentration and investigate behaviors. Consequently, immigrants’ spatial
concentrations do not explain their levels of integration. This allows for the prediction
of processes that lead to integration – both in terms of socio-spatial patterns and rules.
This being said, spatial concentration is nonetheless helpful in reading social and spatial
environments in which migrants’ experiences can be assessed and explored.
In the next steps of our research, we will contribute to the general hypothesis that
immigration is a dynamic urban development lever. If immigration changes a host
society, it also changes cities (Germain et al. 2012). Montréal provides a basic example
of this mechanism, due to its unique demographics. We will also explore issues of
Bmigrant home^ to increase the fundamental knowledge of the concept of Bhome^ and
its wider impacts on social and spatial integration.
Acknowledgements Open access funding provided by International Institute for Applied Systems Analysis
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